By Nic Lewis
An observational estimate of transient (multidecadal) warming relative to cumulative CO2 emissions is little over half that per IPCC AR5 projections.
AR5 claims that CO2-caused warming would be undiminished for 1000 years after emissions cease, but observations indicate that it would halve.
Introduction
In recent years climate scientists and policymakers have increasingly focused on the sensitivity of the climate system to cumulative carbon emissions, which provides a simple link between politically-chosen maximum global warming targets and implicit carbon emission budgets. Figure 1 below, a reproduction of IPCC AR5 Figure SPM.10, shows how warming projected by climate models progresses during the four AR5 emission scenarios, RCP2.6 to RCP8.5. This chart appears to be of key importance so far as policymaking is concerned.
The multimodel mean warming response to cumulative carbon emissions in Figure 1 is almost identical for all scenarios, and is approximately linear. These are only model projections, and there are wide differences between the responses of the various models (shown by the coloured plume). However, there are reasons to believe that there is a fundamental link between cumulative carbon emissions and warming, at least in equilibrium. In this article I shall attempt to quantify this relationship, using observationally-based estimates rather than complex climate-carbon cycle models. The results suggest that the AR5 projections are over pessimistic by a factor of almost two in this century, and by even more in the long run.
Figure 1. Reproduction of IPCC AR5 WG1 Figure SPM.10: Simulated global mean surface temperature increase as a function of cumulative total global CO2 emissions from various lines of evidence. Multi-model results from a hierarchy of climate-carbon cycle models for each RCP until 2100 are shown with coloured lines and decadal means (dots). The multi-model mean and range simulated by CMIP5 models, forced by a CO2 increase of 1% per year, is given by the thin black line and grey area. The 1% per year CO2 simulations exhibit lower warming than those driven by RCPs, which include additional non-CO2 forcings. Temperature values are given relative to the 1861−1880 base period, emissions are post 1870.[i]
There are two principal metrics for sensitivity to cumulative carbon emissions. The best known is the transient response to carbon emissions (TCRE).[ii] This measures the change in global mean surface temperature (GMST) at the end of a period, typically of the order of a century long, during which CO2 is emitted smoothly. TCRE is stated per 1000 GtC (≡ 1 TtC) emissions, and usually assumes a total of 1000 GtC is emitted. Note that 1000 GtC is the carbon content of 3667 GtCO2.
In CMIP5 earth system models (ESMs), which couple carbon cycle models with atmosphere-ocean global climate models, TCRE ranges from 0.8°C to 2.4°C, with a mean of 1.6°C.[iii] The assessment in AR5, which largely mirrors the CMIP5 ESM range, was that the TCRE is likely between 0.8°C to 2.5°C, for cumulative CO2 emissions less than about 2000 GtC, until the time at which temperatures peak. The multi-model mean TCRE under the middling RCP4.5 and RCP6.0 scenarios shown in Figure SPM.10 is 2.0°C, after deducting warming attributable to non-CO2 forcings,[iv] some 25% higher than the 1.6°C mid-point of the TCRE range for CMIP5 ESMs. The high 2.0°C mean TCRE implicit in AR5 Figure SPM.10 reflects the makeup of the ensemble of CMIP5 ESMs involved as well as the inclusion of projections by higher-TCRE Earth models of intermediate complexity (EMICs).
The second metric is the equilibrium response to carbon emissions (ERCE).[v] This measures the equilibrium change in GMST per 1000 GtC of CO2 emissions.[vi] It is reached long after the emissions have ceased, once the deep ocean has come into both thermal and carbon-concentration equilibrium with the atmosphere and the land ecosystem. AR5 did not give an explicit range for ECRE.
Transient and equilibrium response to carbon emissions in CMIP5 ESMs
Remarkably, in CMIP5 ESMs there is little change in GMST between the end of a century over which CO2 is emitted and equilibrium being reached between the atmosphere with the deep ocean, which here is treated as occurring after 1000 years. This is illustrated by the red line in Figure 2.[vii] That means that their equilibrium and transient responses to cumulative carbon emissions are almost identical.
Figure 2. Reproduction of Figure 2.a of Froelicher et al. (2015).3 GMST changes simulated (Phase I) and estimated (Phases II and III) by 12 CMIP5 ESMs.[viii] The multimodel mean is shown by the green/red/blue line. Dark coloured bands show ± 1 standard deviation uncertainty; light coloured bands extend to minimum and maximum model values, both estimated. CO2 is emitted during Phase 1 only, in the amount (varying between models, and averaging 1.9 TtC) needed to increase its atmospheric concentration to 2.7x its preindustrial level by year 100. (The black line, estimated after year 1000, is for the GFDL-ESM2M model, which has a TCRE at the bottom of the range.)
The remarkable stability of GMST in CMIP5 ESMs over a millennium following the cessation of CO2 emissions arises because, in these models, the countervailing effects of ocean absorption of atmospheric CO2 and of ocean heat uptake typically almost cancel out.[ix] The resulting, gradually slowing, decline over time in atmospheric CO2 concentration reduces radiative forcing. Against that, the gradual reduction in the rate of oceanic heat uptake leads to slowly increasing warming for a given level of radiative forcing, as the temperature response to the forcing moves from reflecting the transient climate response (TCR) towards reflecting the higher equilibrium climate sensitivity (ECS) level.[x]
Observationally-based estimates of the transient response to carbon emissions
So much for models and the IPCC’s assessment of TCRE; what do observations indicate? It is fairly straightforward to estimate TCRE from warming and cumulative CO2 emissions to date, but various adjustments need to be made.
According to the HadCRUT4.5 record the GMST increase to the recent decade, 2007-16, was 0.846°C relative to 1850-82, the period from the start of the observational record to just before a period of heavy volcanism. The HadCRUT4 record, however, is not globally complete and may not capture the full global mean increase in surface air temperature, at least in recent years when there was rapid Arctic warming. I therefore use instead the infilled, Cowtan and Way, version of HadCRUT4, which warmed by 0.931°C between the same periods. An alternative way to allow for HadCRUT4’s incomplete global coverage would be to scale 0.846°C up by the ratio of the GMST trend estimated by the globally-complete 1979-on ERA-interim 2 m air temperature measure over 1979-2016, being 0.178°C/decade, to the HadCRUT4.5 trend of 0.173°C/decade over the same period. Doing so would imply an 1850-82 to 2007-16 warming estimate of 0.875°C.
Not all of the observed warming to 2007-16 was due to CO2 emissions. I estimate that total forcing in 2007-16, relative to the mean over 1850-82, was 2.61 W/m2, of which CO2 accounted for 1.67 W/m2, or 64%.[xi] Scaling the observed warming estimate of 0.931°C by 0.64 implies warming in 2007-16 attributable to CO2 emissions averaged 0.596°C. It also implies a TCR estimate of ~1.35°C.
The AR5 estimate of cumulative CO2 emissions over 1750–2011 was 555 GtC. From this, cumulative emissions from 1750 to their 1850-1882 mean, being 31 GtC per the RCP scenarios, need to be deducted in order to compare warming and cumulative emissions over the same period. The difference between average cumulative emissions in 2007–16 and their 2011 value, estimated as 6 GtC,[xii] then needs to be added. That brings the cumulative 1850-82 to 2007-16 total to 530 GtC. However, there is reason to believe that historical CO2 emissions from land-use changes (LUC) are larger than assumed,[xiii] with a suggested underestimation by 35 GtC over 1901-2014.[xiv] Adding this amount would bring the cumulative 1850-82 mean to 2007-16 CO2 emissions to 565 GtC. The observationally-estimated historical TCRE is then simply 0.596 / (565/1000) = 1.05°C.
Observationally-based estimates of the equilibrium response to carbon emissions
Estimating the equilibrium climate response to cumulative emissions is more complicated than estimating the transient response. It requires a simple model that incorporates not only climate system physics but also represents ocean carbonate chemistry and the land biosphere carbon cycle.
The reason why equilibrium warming can in principle be related to cumulative carbon emissions is simple. Unlike other radiatively active-gases in the atmosphere, the timescale on which CO2 is broken down by inorganic chemical reactions (rock weathering) is extremely long – many millennia. On shorter timescales, emitted carbon is simply partitioned between the atmosphere, land ecosystem and ocean. The land ecosystem carbon reservoir will only increase if plant and tree growth is faster, which requires a higher atmospheric CO2 level. The amount of CO2 that can dissolve in the ocean is also directly linked to the atmospheric CO2 concentration. Therefore, although in combination the ocean and land carbon sinks can absorb most of the CO2 emitted into the atmosphere, some of that CO2 must remain in the atmosphere, leaving an elevated concentration. Provided the (temperature-dependent) equilibrium relationships between the increases in the land and ocean carbon reservoirs and the increase in atmospheric CO2 concentration can be satisfactorily approximated, and an estimate of ECS is available, then an estimate of ECRE can be derived.
The simple model I use, which reflects the relevant relationships, is represented by a small number of equations that relate the key variables. I give details of the model equations and the derivation of ECRE in the appendix. The model uses an observationally-based ECS estimate of 1.75°C. The equilibrium ocean and terrestrial ecosystem carbon cycle behaviours of the simple model are both within or at the edge of the EMIC range, which is what the Phase II behaviour in Figure 2 reflects.
The best estimate of ECRE derived from the simple model is 0.5°C.
Based on an ECRE of 0.5°C, if emissions over the 21st century match those in the moderate-mitigation RCP4.5 scenario, which reach 1281 GtC cumulatively over 1765-2100, the equilibrium GMST rise from preindustrial locked in by 2100 would be 0.65°C – lower than current warming, and only a quarter of AR5’s central projection of warming in 2100 under RCP4.5. The corresponding atmospheric CO2 concentration would be 341 ppm, 63 ppm above the preindustrial level, with only 13.5% of the emitted CO2 remaining in the atmosphere. Even assuming that the entire ocean warmed by the full 0.65°C, the ultimate thermosteric rise in sea level – taking a thousand years or more – would be under half a metre.[xv]
Comparing CMIP5 ESM based and observationally-based TCRE and ECRE estimates
The observationally-based TCRE estimate of 1.05°C, although within the AR5 range and the almost identical CMIP5 ESMs model range, is little more than half the level reflected in the central RCP scenario projections in the AR5 SPM.10 chart. Assuming that the 1.05°C estimate is realistic going forward, the IPCC’s chart overstates expected 21st century warming by a factor of approaching two, for all scenarios.
As regards equilibrium warming, while in CMIP5 ESMs the 1000-year ECRE estimate is typically close to the TCRE for the same model, the observationally-based ECRE estimate is only half the observational TCRE estimate.
AR5 Figure SPM.10 projects a broadly linear ratio of multi-model mean warming to cumulative CO2 emissions over the rest of this century, implying that future and historical TCRE are closely aligned. However, the fact that the observationally-based estimate of ECRE is much lower than that of TCRE implies that in the real climate system TCRE is likely to decline over time, so that TCRE estimated from historical observations is likely slightly to overestimate future TCRE. That is because the warming caused by earlier emissions will over time decline from that implied by TCRE towards that implied by the lower ECRE – unlike in the models used by AR5 where ECRE is close to TCRE – which should more than counter the gradual decrease in the ocean’s efficiency at absorbing CO2 as its level of dissolved inorganic carbon increases.
It is pertinent to ask why the ECRE to TCRE estimate ratio based on observational data and a simple model is a factor of two lower than it appears to be in CMIP5 ESMs (Figure 2). There are two reasons. First, the observationally-based estimate of 13.5% of emitted CO2 remaining in the atmosphere after 1000 years is lower than in EMICs (mean 25%, range 17–31%), the carbon-cycle behaviour of which CMIP5 ESMs are assumed to mirror. Secondly, ECS to TCR ratio is much lower for the observationally-based estimates used here: approximately 1.3 compared with an average of 1.8 for CMIP5 models.
Appendix
Estimating the equilibrium climate response to cumulative emissions using observations
I base the required simple model on an established formula that validly reflects ocean carbonate chemistry.[xvi]
ln[pCO2(equil) / pCO2(PI)] ≡ ln[1 + ΔIatmos(equil) / Iatmos(PI)]
= [ΔItotal − ΔIland(equil)] / IB (1)
Equation (1) states that the logarithm of the increase in atmospheric CO2 concentration pCO2 from its initial preindustrial (PI) level to its level in equilibrium after cumulative CO2 emissions of ΔItotal, which is the same as the logarithm of one plus the ratio of the equilibrium increase ΔIatmos in the atmospheric carbon inventory to the initial atmospheric carbon inventory Iatmos, equals the ratio of the excess of total emissions over that part absorbed by the terrestrial ecosystem to the “buffered” carbon inventory IB. AR5 provides a value for preindustrial Iatmos, of 589 GtC.
The buffered carbon inventory equals the carbon content of the atmosphere Iatmos plus the (dissolved) carbon content of ocean Iocean divided by the global mean Revelle or evasion factor R, which represents the buffering arising from ocean carbonate chemistry. AR5 estimates preindustrial IA at 38,000 GtC, while R can be taken from a formula that gives it in terms of the ratio of ΔIocean to Iocean(PI).[xvii] Equation (1) gives more accurate results if, when determining IB, both Iocean and R are taken at their equilibrium rather than their preindustrial values.
I take ECS, on a 1000 year time horizon, as 1.75°C, which is consistent with recent energy budget estimates.[xviii] I allow for climate-carbon feedback arising from the negative effect on the solubility of CO2 in seawater of the rise in ocean temperature (assumed to be 85% of the rise in GMST caused by rising atmospheric CO2 concentration), although it may at least partially be countervailed by increasing efficiency of the ocean biological pump.
It is common to assume that ΔIland is linearly related to ΔIatmos (positively) and to ΔT, the change in GMST (negatively).[xix] The positive influence of ΔIatmos is greatly dominant.[xx] One can therefore estimate the ratio of ΔIland to ΔIatmos for 1000 GtC of cumulative emissions from their relationship from preindustrial to date. AR5 estimated the land sink at 160 GtC over 1750-2011. As that is a balancing figure, the estimated missing LUC emissions of 35 GtC need to be added to it, giving 195 GtC. This is a transient figure, reflecting the effect of CO2 fertilisation on growth of vegetation and the resulting (incomplete) increase in vegetation and soil carbon to date. One can convert it to an equilibrium value by assuming that the terrestrial carbon inventory would decay exponentially in the absence of primary production by photosynthesis, with a fixed time constant corresponding to the average lifetime of carbon in the terrestrial pool. A period of 15-20 years is consistent with what has been assumed elsewhere.[xxi] Modelling, conservatively, on the basis of a 15 year time constant suggests that in 2011 the cumulative land carbon uptake had reached 81% of its eventual, equilibrium, value if atmospheric CO2 concentration remained unchanged thereafter. That implies ΔIland(equilib) = 240 GtC for ΔIatmos(equilib) equal to the increase in atmospheric carbon content up to 2011, of 240 GtC per AR5. That is, ΔIland(equilib) = 1.00 x ΔIatmos.[xxii]
The foregoing relationships, combined with the fact that ΔIocean = ΔItotal – (ΔIatmos + ΔIland), enable a solution that satisfies all the equations to be determined iteratively. The solution implies that 135 GtC out of 1000 GtC CO2 emissions will remain in the atmosphere in equilibrium, producing an increase in concentration of 63 ppm. The ocean absorbs 730 GtC, while the land ecosystem absorbs 135 GtC.
Without any carbon uptake by the land ecosystem and without climate feedback reducing carbon uptake by the ocean, just over 150 GtC would remain in the atmosphere. That is within the 150-210 GtC range projected on the same basis by an ensemble of EMICs.[xxiii] The unit ratio of carbon absorbed by the land ecosystem to that remaining in the atmosphere in equilibrium in the simple model is well within the range projected after 1000 years by an ensemble of EMICs and CMIP5 ESMs.[xxiv]
The radiative forcing resulting from the 63 ppm increase in atmospheric CO2 concentration is 1.1 W/m2. Based on the assumed 1.75°C ECS, the 1.1 W/m2 ultimate increase in CO2 forcing implies that the equilibrium rise in GMST – which by definition is the ECRE – will be 0.5°C.
Nicholas Lewis 11 December 2018
UPDATE 13 December 2018
The below version of Figure 2, produced in response to reader suggestions, marks (orange line) the approximate path of temperature change implied by my observationally-based TCRE and ECRE estimates, in response to the CMIP5 ESMs mean total emissions over years 1-99 of 1919 GtC. Note that the ensemble of CMIP5 ESMs involved (whose mean warming is shown by the green, red and blue lines) had a lower mean TCRE than the ensemble of CMIP5 ESMs and EMICs used for IPCC AR5 Figure SPM.10. Note also that the warming simulated by GFDL-ESM2M (black line), which has a TCRE of 1.03°C, is marginally lower than that per my observational TCRE estimate in year 100, but is much higher in year 1000 than that per the observational ECRE estimate. That reflects this model both having a higher ECS (~3.0°C), and simulating a higher proportion (~20%) of emitted CO2 remaining in the atmosphere in year 1000, than per my observational ECRE estimate.
[i] IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F.,et al (eds.)]. Cambridge University Press, pp. 1–30.
[ii] Collins, M., and Coauthors, 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136. (Chapter 12 of IPCC AR5 WG1)
[iii] Gillett, N. P., V. K. Arora, D. Matthews, and M. R. Allen, 2013: Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Climate, 26, 6844–6858, doi:10.1175/JCLI-D-12-00476.1.
[iv] Approximately 14% of the increase in forcing from 1861-1880 until 1000 GtC emissions are reached arises from non-CO2 sources under RCP4.5 and RCP6.0.
[v] Frölicher, T. L., and D. J. Paynter, 2015: Extending the relationship between global warming and cumulative carbon emissions to multi-millennial timescales. Environ. Res. Lett., 10, 075002, doi:10.1088/1748-9326/10/7/075002.
[vi] ECRE values usually assume cumulative emissions of 1000 GtC, as for TCRE.
[vii] The subsequent slightdecline in GMST (blue line) arises from reactions with calcium carbonate in seafloor sediments removing oceanic dissolved carbon.
[viii] Estimated values for phases II and III use airborne fractions of cumulative carbon emissions simulated by eight EMICs, as hardly any CMIP5 ESMs have carried out actual 1000+ year simulations with a fully-coupled carbon cycle model.
[ix] This occurs despite ocean characteristics varying between models. Such insensitivity to ocean characteristics probably arises because there is much in common between the mechanisms that govern the rates of CO2 and heat transport out of the surface mixed layer into the deeper ocean
[x] TCR represents GMST warming at the end of a 70 year period over which CO2 concentration, rising by 1% p.a., doubles. ECS represents the ultimate GMST increase when CO2 concentration is doubled and then held constant until the atmosphere-ocean system reaches equilibrium. Changes in slow climate system components such as land ice sheets are disregarded.
[xi] These estimates are of effective radiative forcing and are based on IPCC AR5 best estimates up to 2011 save that revised greenhouse gas concentration- forcing relationships (Etminan et al 2016: doi:10.1002/2016GL071930) and post-1990 aerosol and ozone forcing change estimates (Myhre et al. 2017, doi:10.5194/acp-17-2709-2017) have been incorporated. The Etminan et al. CO2 concentration- forcing relationship is also used when calculating what warming a given increase from preindustrial CO2 concentration will produce.
[xii] Global carbon budget 2017. Boden, T. A., Marland, G., and Andres, R. J.: Global, Regional, and National Fossil-Fuel CO2 Emissions, Oak Ridge National Laboratory, U.S.A., 2017; http://cdiac.ess-dive.lbl.gov/trends/emis/overview_2014.html; and average of two bookkeeping models: Houghton, R. A. and Nassikas, A. A.: Global and regional fluxes of carbon from land use and land cover change 1850-2015, Global Biogeochemical Cycles, 31, 456-472, 2017; Hansis, E., Davis, S. J., and Pongratz, J.: Relevance of methodological choices for accounting of land use change carbon fluxes, Global Biogeochemical Cycles, 29, 1230-1246, 2015.
[xiii] Arneth, A., et al, 2017. Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nature Geoscience, 12, 79-84.
[xiv] It is possible that the underestimation of LUC emissions is overstated, but on the other hand it includes no allowance for possible underestimation prior to 1901. All the 1901-2014 additional LUC emissions are treated as included in the cumulative average for 2007-16. Note that when the additional LUC emissions are included, the (anthropogenic; post 1750) airborne fraction in 2011 is 41%.
[xv] This is based on heat input to the ocean equating to 0.5 W-yr/m2 over the Earth’s surface causing a sea level rise of approximately 1 mm.
[xvi] Goodwin, P. et al, 2007: Ocean-atmosphere partitioning of anthropogenic carbon dioxide on centennial timescales. Global Biogeochem. Cycles, 21, GB1014, doi:10.1029/2006GB002810.
[xvii] Backastow, R, 1981. Numerical evaluation of the evasion factor. In Carbon Cycle Modelling, Scope 16, Bolin, B, ed., John Wiley & Sons, p. 98. Employing instead the function used to relate the partial pressure of CO2 in seawater to its dissolved inorganic carbon content used in the HILDA model (Seigenthaler,U and Joos, F, 1992. Use of a simple model for studying oceanic tracer distributions and the global carbon cycle. Tellus 44B, 186-207) gives an almost identical result.
[xviii] Lewis, N., and J. Curry (2018): The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity. Journal of Climate 2018. doi.org/10.1175/JCLI-D-17-0667.1 PDF copy here.
[xix] E.g., Friedlingstein, P., 2015. Carbon cycle feedbacks and future climate change. Phil. Trans. R. Soc. A 373.2054: 20140421
[xx] The ratio of ΔT to ΔIatmos will moreover not differ greatly between that to date and that in equilibrium for 1000 GtC of cumulative emissions, on the basis of a TCR of 1.35°C and ECS of 1.75°C.
[xxi] For example, the land biosphere time constant with the largest coefficient in the HILDA model (Joos, F et al 1996. An efficient and accurate representation of complex oceanic and biospheric models of anthropogenic carbon uptake. Tellus 48B 397-417), ignoring two interannual terms, is 20 years.
[xxii] This estimate is consistent, given the adverse relation between ΔIland and ΔT of –28 GtC/°C estimated in Friedlingstein, P (2015) with the (transient) carbon-carbon feedback (CO2 fertilisation strength) estimated in that paper when it is adjusted for the ratio of transient to equilibrium land carbon uptake and for the estimated missing LUC emissions.
[xxiii] Archer et al, 2009. Atmospheric Lifetime of Fossil Fuel Carbon Dioxide. Annu. Rev. Earth Planet. Sci 3 7:117–34
[xxiv] For 100 GtC of emissions, starting from the 2010 CO2 level. Joos et al 2013. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. ACP, 13, 2793-2825.
The next generation of climate models CMIP6 are in development. Fair to say that the Russian INMCM5 improves upon its ability to replicate the historical record, much better than others in the ensemble. For example:
https://rclutz.files.wordpress.com/2018/10/volodin-fig5.png
Figure 1. The 5-year mean GMST (K) anomaly with respect to 1850–1899 for HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next figures numbers on the time axis indicate the first year of the 5-year mean.
Particulars on the design and parameters of INMCM5 are here:
https://rclutz.wordpress.com/2018/10/22/2018-update-best-climate-model-inmcm5/
“Atmospheric and oceanic computational simulation models often successfully depict chaotic space–time patterns, flow phenomena, dynamical balances, and equilibrium distributions that mimic nature. This success is accomplished through necessary but nonunique choices for discrete algorithms, parameterizations, and coupled contributing processes that introduce structural instability into the model. Therefore, we should expect a degree of irreducible imprecision in quantitative correspondences with nature, even with plausibly formulated models and careful calibration (tuning) to several empirical measures.” https://www.pnas.org/content/104/21/8709
Nonunique choices have implications for sensitively dependent evolution of nonunique solutions.
https://royalsocietypublishing.org/cms/attachment/2154c38c-a265-463a-9b2c-6599017362ee/rsta20110161f08.jpg
“Schematic of ensemble prediction system on seasonal to decadal time scales based on figure 1, showing (a) the impact of model biases and (b) a changing climate. The uncertainty in the model forecasts arises from both initial condition uncertainty and model uncertainty.” https://royalsocietypublishing.org/doi/full/10.1098/rsta.2011.0161
Robert, thanks for that reference to McWilliams’ 2007 article. I noticed this comment:
“More famously, the Intergovernmental Panel on Climate Change (IPCC) report (21) shows the spread among climate models for global warming predictions. One of its results is an ensemble-mean prediction of ≈3°C increase in global mean surface temperature for doubled atmospheric CO2 concentration with an ensemble spread of ≈50% on either side. The predicted value for the climate sensitivity and its intermodel spread have remained remarkably stable throughout the modern assessment era from the National Research Counsel (NRC) in 1979 (22) to the anticipated results in the IPCC Fourth Assessment Report (foreshadowed, e.g., in ref. 3) despite diligent tuning and after great research effort and progress in many aspects of simulation plausibility. An even broader distribution function for the increase in mean surface air temperature is the solution ensemble for a standard atmospheric climate model produced by Internet-shared computations (23), but there is a question about how carefully the former ensemble members were selected for their plausibility.”
He seems to think that an ensemble of related models would reduce the uncertainty of model forecasts, but I am doubtful that the results are better than crowd-guessing. Surely including models that hindcast poorly is not good practice.
There is distinction to be made between systematically designed perturbed physics ensembles and opportunistic ensembles of the CMIP type.
This is the former compared to the latter.
https://watertechbyrie.files.wordpress.com/2018/05/rowlands-2012.jpg
Any model of this type can give 1000’s of plausible, divergent solutions. How do they pick the right one? “The bases for judging are a priori formulation, representing the relevant natural processes and choosing the discrete algorithms, and a posteriori solution behavior.”
What was said is that only systematically designed perturbed physics ensembles can provide an estimate of irreducible imprecision. In the Rowland et al study – https://www.nature.com/articles/ngeo1430 – broader than the limits of ‘expert assessment’ of plausible computer models runs – each member chosen on the basis of a posteriori solution behavior – provided in CMIP opportunistic ensembles. The mean of CMIP ensembles is more like a Delphic method – an average of expert opinion – than crowd guessing.
Re: “I take ECS, on a 1000 year time horizon, as 1.75°C, which is consistent with recent energy budget estimates.”
Before people making the usual mistake of claiming this is observationally-based, not like those model-based estimates from the IPCC, then I’d like to remind them that:
1) This uses energy budget models.
2) One can object to energy-budget-model-based estimates, without violating basic physics and without objecting to analyses of historical surface / near-surface warming. For example, one can note that effective climate sensitivity for future CO2-induced warming is larger than effective climate sensitivity over the past century or so.
These points need repeating, given the frequency of people offering the “model-based vs. observationally-based” false dichotomy. For instance:
https://judithcurry.com/2018/09/05/warming-patterns-are-unlikely-to-explain-low-historical-estimates-of-climate-sensitivity/#comment-880108
And some background on this:
“Inference of climate sensitivity from analysis of Earth’s energy budget
[…]
Forster & Gregory (2006, p. 39) overstated the benefits of such an approach by claiming, “Importantly, the [ECS] estimate is completely independent of climate model results.” As Equation 1 derives directly from conservation of energy, the Forster & Gregory (2006) claim would appear valid. But it in fact makes the assumption that the α derived from a particular observational period is the same as the α applicable under long-term climate change. Another way of stating this assumption is saying that the effective climate sensitivity (the apparent ECS diagnosed from a specific α) is the same as the true ECS. Uncertainties around the derivation of ECS from an energy budget approach can be attributed to two causes: the model used to translate α into an ECS estimate and the quality of the observation-based data sets.”
“Beyond equilibrium climate sensitivity
[…]
Often labelled as ‘observational’, these methods do rely on models: both to provide forcing estimates, such as aerosol forcing, and to link forcing to climate response through energy balance models. Hence, observational estimates are complementary to methods using comprehensive models, but have their own uncertainties.”
Don’t panic. Climate models:
https://fractalfoundation.org/OFCA/eqn_lorenz.jpg
Are not zero dimensional EBM:
Changes in heat storage = absorbed solar radiation – emitted terrestrial radiation
Ed Lorenz’s “little model”!!! 😂🤣
The origin of sensitive dependence?
“Perhaps we can visualize the day when all of the relevant physical principles will be perfectly known. It may then still not be possible to express these principles as mathematical equations which can be solved by digital computers. We may believe, for example, that the motion of the unsaturated portion of the atmosphere is governed by the Navier–Stokes equations, but to use these equations properly we should have to describe each turbulent eddy—a task far beyond the capacity of the largest computer. We must therefore express the pertinent statistical properties of turbulent eddies as functions of the larger-scale motions. We do not yet know how to do this, nor have we proven that the desired functions exist.” Lorenz E. N.. 1969 Three approaches to atmospheric predictability. Bull. Am. Met. Soc. 50, 345-351.
50 years later – and the problems remain.
“Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable. They echo other famous limitations on scientist’s expectations, namely the undecidability of some propositions within axiomatic mathematical systems (Gödel’s theorem) and the uncomputability of some algorithms due to excessive size of the calculation.” James McWilliams
If you recognize the form – you should be able to recognize the substance.
Yes, chaos isn’t going away …
Atomsk: The idea that estimates of climate sensitivity from “energy balance models” should not be characterized as “observationally-base” is politically-motivated nonsense. TCR is defined as dT/dF, whether these values come from observations or AOGCMs. The technical definition defines dF as 1% per year, but everyone agrees that the slower observed rate of forcing increase is unimportant. Converting TCR into ECS is based on the law of conservation of energy, which is hardly a “model”.
The only assumption in energy balance models arises from internal (unforced) variability – chaos. In AOGCM experiments, trivially different initialization conditions result in different amounts of warming. The SPM for AR5 WG1 (p 17 Section D3) tells us:
“The best estimate of the human-induced contribution to warming is similar to the observed warming over this period.”
“The contribution [to observed warming] … from natural internal variability is likely to be in the range of −0.1°C to 0.1°C.”
If you believe the IPCC, observed warming differs little from forced warming. If AOGCMs are correct in their estimate of unforced variability, then their central estimates for climate sensitivity climate sensitivity are wrong. If AOGCMs are wrong about unforced variability, they are wrong.
All observationally-based experiments involve some sort of model. Observations of the rate of sea level rise are based a statistical linear AR1 model for the noise in the data. Do you call those “model-based estimates of SLR”?
Atomsk writes: “one can note that effective climate sensitivity for future CO2-induced warming is larger than effective climate sensitivity over the past century or so.”
Let’s state this more accurately: The central estimates for modeled climate sensitivity are larger than observed climate sensitivity over the past century. SOME models predict a significant increase in climate sensitivity over the next century and some do not.
Frank too, Good comment. I would just add that the increase in sensitivity with time in GCM’s seems to be related to the “pattern of warming” argument. Basically, GCM’s get the observed pattern of warming to date rather wrong. The reasoning goes that in the long term, the real world will return to a pattern of warming similar to GCM’s. This argument is rather backwards. If the to date pattern of warming is quite wrong in GCM’s, why would one have any expectation that their long term pattern was more correct? I can’t think of any good argument for that position. It seems like more a leap of faith than anything else.
There’s a physics-based argument that the apparent ECS increases with time. The big assumption in the use of EBMs for ECS is that once you add in the imbalance, you get the full ECS. That does not account for the reason for the imbalance being that the ocean is lagging the global average, and currently warming at only half the rate of the land. With the ocean lagging, so is the water vapor feedback. Projecting ECS has to allow for the extra water vapor feedback as the ocean catches up to the global mean, but while it lags, the sensitivity will be underestimated by simply adding in the imbalance the way they do. Assuming in EBMs that things are monolithic between different surface types has been criticized by Kyle Armour before. A better EBM would separate out at least ocean and land, and possibly polar areas and not assume that all these warm at the same rate all the time under a forcing change.
Jim D wrote: “The big assumption in the use of EBMs for ECS is that once you add in the imbalance, you get the full ECS.”
Given that most photons leaving the planet are emitted from the upper troposphere, I’d say that the temperature there is most relevant to ECS. However, lapse rate feedback is expected to make warming there greater than at the surface, so you would need to correct for that problem to get an ECS relevant to surface warming.
Re: “Atomsk: The idea that estimates of climate sensitivity from “energy balance models” should not be characterized as “observationally-base” is politically-motivated nonsense.”
Funny, since I have no political position on AGW.
It’s “observationally-based” *and* “model-based”, since it uses observational analyses, an EBM, and GCMs. I’ve explained that already multiple times (including to you). No politics is needed for making that point.
Re: “Converting TCR into ECS is based on the law of conservation of energy, which is hardly a “model”.”
EBMs are not just based on conservation of energy. How many times do people need to explain this to you?
EBMs go beyond conservation of energy since, for example, they need to use a model to translate their estimate of effective climate sensitivity into ECS. You can’t simply do that based on conservation of energy, since climate sensitivity can increase as warming occurs due to changing feedbacks, without violating conservation of energy. The paleoclimate evidence supports the idea that climate sensitivity increases with the initial warming, as gone over in papers such as:
“Climate sensitivity in the geologic past”
So go back, and re-read what I quoted from Forster:
“Inference of climate sensitivity from analysis of Earth’s energy budget
[…]
Forster & Gregory (2006, p. 39) overstated the benefits of such an approach by claiming, “Importantly, the [ECS] estimate is completely independent of climate model results.” As Equation 1 derives directly from conservation of energy, the Forster & Gregory (2006) claim would appear valid. But it in fact makes the assumption that the α derived from a particular observational period is the same as the α applicable under long-term climate change. Another way of stating this assumption is saying that the effective climate sensitivity (the apparent ECS diagnosed from a specific α) is the same as the true ECS. Uncertainties around the derivation of ECS from an energy budget approach can be attributed to two causes: the model used to translate α into an ECS estimate and the quality of the observation-based data sets.”
Re: “The only assumption in energy balance models arises from internal (unforced) variability – chaos”
Once again, it’s like you don’t even read what you’re responding to. That is not all EBMs assume. Even the people who use EBM-based estimates (ex: Forster) admit that.
Re: “If AOGCMs are correct in their estimate of unforced variability, then their central estimates for climate sensitivity climate sensitivity are wrong.”
What you just wrote makes no sense.
Re: “All observationally-based experiments involve some sort of model. Observations of the rate of sea level rise are based a statistical linear AR1 model for the noise in the data. Do you call those “model-based estimates of SLR”?”
The difference is that I, unlike you, don’t go around falsely offering a false dichotomy between “observationally-based” estimates vs. “model-based” estimates. When EBM-based estimates are brought up, you always conveniently leave out the fact that they use models, so that you can saying they’re not like those IPCC estimates that use models. Sorry, but that’s not going to fly. Both approaches use models and both approaches use observations. Get over it.
Re: “Let’s state this more accurately: The central estimates for modeled climate sensitivity are larger than observed climate sensitivity over the past century. SOME models predict a significant increase in climate sensitivity over the next century and some do not.”
And you failed to state the relative proportion of models that do that, vs. those that don’t. We both know why.
You quote “Another way of stating this assumption is saying that the effective climate sensitivity (the apparent ECS diagnosed from a specific α) is the same as the true ECS.”
I don’t make that assumption. When I say ” “I take ECS, on a 1000 year time horizon, as 1.75°C, which is consistent with recent energy budget estimates”, I am referring in particular to the preferred best estimates given in Lewis and Curry (2018) Jnl. Climate, 31, 6051-6071 using a globally-complete temperature record. They are 1.66°C for effective climate sensitivity and 1.76°C for true ECS. ECS estimated as effective climate sensitivity on a 1000 year time horizon will be slightly below true ECS.
“Often labelled as ‘observational’, these methods do rely on models”
Yes, I’m well aware that observational estimates of climate sensitivity to some extent depend on GCMs. I have made that point more than once in the past. But as your quote says, it is normal to nevertheless refer to such estimates as ‘observational’. Moreover, the way in which GCMs are used to provide certain estimated values for ‘observational’ estimates of climate sensitivity is very different from the use of GCMs to themselves provide ‘model’ estimates of climate sensitivity. So the observational – model (GCM) sensitivity estimation dichotomy is real, albeit not absolute.
Re: “I don’t make that assumption. When I say ” “I take ECS, on a 1000 year time horizon, as 1.75°C, which is consistent with recent energy budget estimates”, I am referring in particular to the preferred best estimates given in Lewis and Curry (2018) Jnl. Climate, 31, 6051-6071 using a globally-complete temperature record. They are 1.66°C for effective climate sensitivity and 1.76°C for true ECS”
And here’s the other part of the quote:
“Uncertainties around the derivation of ECS from an energy budget approach can be attributed to two causes: the model used to translate α into an ECS estimate and the quality of the observation-based data sets.””
https://www.annualreviews.org/doi/full/10.1146/annurev-earth-060614-105156
So yes, the model you use to translate α into an ECS estimate, does not have to assume effective climate sensitivity for the historical period is identical to the true ECS. You can, for example, say that the true ECS is slightly higher. But given how you do that translation, one could still object the model you used to get your ECS estimate, will result in you under-estimating ECS. People have repeatedly made this criticism in the literature, including Dessler. One can also justify the criticism by citing larger estimates of ECS from paleoclimate analyses.
Re: “But as your quote says, it is normal to nevertheless refer to such estimates as ‘observational’. Moreover, the way in which GCMs are used to provide certain estimated values for ‘observational’ estimates of climate sensitivity is very different from the use of GCMs to themselves provide ‘model’ estimates of climate sensitivity. So the observational – model (GCM) sensitivity estimation dichotomy is real, albeit not absolute.”
I’m fine with them being called “observational”… right up until you make the dichotomy you just made. Yes, the estimates used observations, but they also use models, both an EBM and GCMs. Similarly, paleoloclimate ECS estimates use observational analyses and models.
Thus claiming an “observational – model” dichotomy creates the false impression that models are not being used on the model side. I know it creates this false impression, because I’ve had to repeatedly correct people who cite your work with the mistaken believe that you use just observational analyses, not models. I gave an example of this before.
It also doesn’t help that Curry has facilitated this sort of false impression in the past in other contexts. For instance:
“Gavin and I seem to live on different planets: I live on planet Earth observations, and Gavin lives on planet climate model”
https://judithcurry.com/2014/08/28/atlantic-vs-pacific-vs-agw/
And don’t even get me started on McKitrick, who creates the false impression that the paleoclimate estimates don’t exist:
https://business.financialpost.com/opinion/ross-mckitrick-all-those-warming-climate-predictions-suddenly-have-a-big-new-problem
In general usage, ‘models’ refers to GCMs/AOGCMs, but not to a single-equation zero-dimensional energy balance model. I made very clear in the text you quote that I was referring to GCMs.
“But given how you do that translation, one could still object the model you used to get your ECS estimate, will result in you under-estimating ECS.”
The ‘model’ I used to get from effective climate sensitivity to ECS is a statistical relationship derived from CMIP5 AOGCMs.
“People have repeatedly made this criticism in the literature, including Dessler.”
Lewis & Curry (2018) refutes all the main criticisms of global energy budget sensitivity estimates, save for possible bias from incomplete coverage of global surface temperature, which it addresses by use of a globally-complete record.
“One can also justify the criticism by citing larger estimates of ECS from paleoclimate analyses.”
By far the best understood and most studied paleoclimate analysis is of the LGM-preindustrial Holocene transition. Estimating ECS from that transition using an energy budget model is a well established approach. Lewis & Curry (2018) shows that, using modern estimates of the forcing and global temperature change, doing so gives and ECS estimate of 1.76 C, in line with the paper’s main ECS estimate.
Re: “By far the best understood and most studied paleoclimate analysis is of the LGM-preindustrial Holocene transition. Estimating ECS from that transition using an energy budget model is a well established approach. Lewis & Curry (2018) shows that, using modern estimates of the forcing and global temperature change, doing so gives and ECS estimate of 1.76 C, in line with the paper’s main ECS estimate.”
Sorry, but I’m not just going to throw the vast majority of the literature on paleoclimate ECS estimates, just to support the low estimate of ECS you defend. That would be cherry-picking. The paleoclimate evidence supports a higher ECS estimate than you advocate, and one cannot simply cherry-pick your view on the LCM in order to defend a low estimate. People interested in more thorough, comprehensives review of the paleoclimate literature, can go read papers such as:
“Climate sensitivity in the geologic past”
“Beyond equilibrium climate sensitivity”
“Palaeoclimate constraints on the impact of 2 °C anthropogenic warming and beyond”
I forgot to cover something else.
Re: “In general usage, ‘models’ refers to GCMs/AOGCMs, but not to a single-equation zero-dimensional energy balance model.”
It might have that usage in “skeptic” blogs and such circles. But when I read the literature, I see EBMs referred to as models. Hence the “M” in “EBMs”. But if you want to use the terms in a particular way, then that’s fine with me, as long as you’re clear that you’re doing this.
Re: “By far the best understood and most studied paleoclimate analysis is of the LGM-preindustrial Holocene transition. Estimating ECS from that transition using an energy budget model is a well established approach. Lewis & Curry (2018) shows that, using modern estimates of the forcing and global temperature change, doing so gives and ECS estimate of 1.76 C, in line with the paper’s main ECS estimate.”
This is what your paper says:
“The impact of recent forcing and ocean heat uptake data on estimates of climate sensitivity
[…]
Reasonably thorough proxy-based estimates of changes in surface temperature (Annan et al. 2013: 4.0 K; Friedrich et al. 2016: 5.0 K) and forcings (Kohler et al. 2010: total 9.5 Wm−2) are available for the LGM transition. These values imply, using (4), an ECS estimate of 1.76 K (averaging the two surface temperature increase estimates and taking F 2x CO2 per AR5, since the WMGG forcings were derived using AR5 formulae), in line with the median obtained by scaling this study’s ECShist estimate.”
Friedrich et al. 2016 is:
“Nonlinear climate sensitivity and its implications for future greenhouse warming”
It’s covered in figure 3 of:
“Beyond equilibrium climate sensitivity”
Friedrich et al. 2016 (page 3) gives an ECS estimate of ~1.76K for colder conditions, and ~4.88K under warmer conditions. Thus it shows ECS increasing with warming. When they extend their results to future greenhouse warming conditions (page 3 of the paper) they use the latter estimate, not the former one. Hence, they end up with a different conclusion that you do with respect to the models and future warming. They write:
“On the basis of temperature data from eight glacial cycles, our results provide an independent validation of the magnitude of current CMIP5 warming projections.”
http://advances.sciencemag.org/content/advances/2/11/e1501923.full.pdf
You also cite “Annan et al. 2013” (Annan and Hargreaves), which is this paper:
“A new global reconstruction of temperature changes at the Last Glacial Maximum”
That paper notes:
“Our new temperature anomaly of 4.0 ± 0.8 ◦C, combined with estimated forcing of 6–11 W m−2 (Annan et al., 2005; Jansen et al., 2007) would suggest a median estimate for the equilibrium climate sensitivity of around 1.7 ◦C, with a 95% range of 1.2–2.4 ◦C. However, such a simplistic estimate is far from robust, as it ignores any asymmetry or nonlinearity which is thought to exist in the response to different forcings (Hargreaves et al., 2007; Yoshimori et al., 2011). The ratio between temperature anomalies obtained under LGM and doubled CO2 conditions found in previous modelling studies varies from 1.3 (Schmittner et al., 2011) to over 2 (Schneider von Deimling et al., 2006a). More recently, Hargreaves et al. (2012) used the relationship found in the PMIP2 ensemble between the tropical temperature change at the LGM, and equilibrium climate sensitivity, to estimate the equilibrium climate sensitivity to be around 2.5 ◦C with a high probability of lying under 4 ◦C, although this result is subject to several important caveats.”
So I don’t see how that really supports your ECS estimate of 1.76K, when they say such as result is simplistic and far from robust because it doesn’t adequately address just the sort of issues Dessler and others bring up regarding non-linear responses. In a subsequent 2015 paper, Annan and Hargreaves put the ECS value from LGM at around 2.5K, though they note that there are remaining uncertainties:
“A perspective on model-data surface temperature comparison at the Last Glacial Maximum”
And you cite “Kohler et al. 2010”, which is this paper:
“What caused Earth’s temperature variations during the last 800,000 years? Data-based evidence on radiative forcing and constraints on climate sensitivity”
That paper places ECS at around 2.4K, when “excluding the ice sheet and vegetation components”.
So even if we grant your cherry-picking of the LGM out of the wealth of paleoclimate evidence, and even if we just look at the sources you used to provide values to support your calculated estimate of 1.76K, we still end up with an ECS value greater than the 1.76K value you gave in your paper. This makes me even more confident that climate scientists like Dessler are right in rejecting your estimate. Your estimate is looking more and more like a low outlier.
“The most commonly quoted climate sensitivity comes from the IPCC (2013) consensus: 3 ± 1.5 K at the 66% confidence level. The singular effect of CO2 contributes 1.2 K to this warming (Charney et al. 1979, Soden & Held 2006, Schmidt et al. 2010), meaning that other components in the Earth system provide a net amplification of 0.3–3.3 K.” https://www.annualreviews.org/doi/abs/10.1146/annurev-earth-100815-024150
Spot the missing positive feedback?
https://www.ipcc.ch/site/assets/uploads/2018/03/Fig7-09-1024×584.jpg
https://www.ipcc.ch/site/assets/uploads/2018/03/Fig7-10-1024×621.jpg
Ralph Knutti is involved in a typically thorough and meticulous review – but doesn’t get beyond the AR5 likely range of 1-2.5 degrees C for TCR that they argue is of most relevance for social cost estimates.
https://www.nature.com/articles/ngeo3017
The 3000 year estimate for ocean equilibrium I have in fact discussed recently at the urging of #jiminy.
https://judithcurry.com/2018/12/15/week-in-review-science-edition-91/#comment-886301
Here are the IPCC forcings.
https://www.ipcc.ch/site/assets/uploads/2018/02/Fig8-17-1-754×1024.jpg
That seem likely poorly estimated in the more poorly constrained parameters – especially black carbon and sulphate as I discuss elsewhere under this post.
https://judithcurry.com/2018/12/11/climate-sensitivity-to-cumulative-carbon-emissions/#comment-886025
The simplest, quickest and cheapest way to reduce global warming btw.
e.g. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/jgrd.50171
And as discussed elsewhere – internal variability seems radically underrepresented.
https://judithcurry.com/2018/12/15/week-in-review-science-edition-91/#comment-886344
And the third reference seems about speculative tipping points from AGW of from 1-2 degrees C that I didn’t read.
Now my reputation here as a climate catastrophist must be well established. But if the only recourse of massive AGW tools who argue that they have no politics is massive carbon taxes – then they are ineffectual wastrels as well. Almost always with scant scientific understanding to show for it.
Nic, one fundamentally disputed assumption in all RCP’s is the fast saturation of the CO2 sink in the coming century, so far there is no sign of sink saturation. The most extreme value of sink saturation in the Bern and ISAM models was taken as model value in the RCP’s.
(See IPCC TAR appendices page 807-809 for abundances of the ISAM and Bern models)
https://www.ipcc.ch/site/assets/uploads/2018/03/TAR-APPENDICES.pdf
Hans Erren,
I agree that most of the CMIP5 ESMs and EMICs used for the IPCC AR5 report have unjustifiably low future carbon sinks, primarily in relation to the land sink. There is good evidence that their land sinks have both too strong a cliamte-carbon feedback and too weak a carbon-carbon feedback (CO2 fertilisation effect).
Thanks Nic, as a lukewarmer I am eagerly waiting when the long promised rise in the airborne fraction finally will kick in, similar to the long awaited accelleration in temperature and sea level rise. Because without them we wil never reach two degrees at the end of this century.
If anything, sinks should accelerate. N and P in soil is constrained by CO2. More CO2 allows plants to feed N and P fixing organisms.
Nic Lewis, thank you for the essay.
“The best known is the transient response to carbon emissions (TCRE).[ii] This is measures the change”
Typo: “This is measures the change”?
Thanks; now fixed
This post is excellent, and I hope it leads to more research in the carbon cycle field.
Question: Does NOAA obtain seawater pH on a comprehensive basis? I have been studying carbon sink mechanisms in recent days, and cant find a reference to a comprehensive pH data base.
No. The two primary measurements that form the ocean pH basis are Station Aloha north of Hawaii and BATS east of Bermuda. Both are barren ocean and do not show the larger diurnal and seasonal biological variations.
For those for the Pacific, see Hoffman et al in PLoS ONE 6:e28983 (2011).
“The dissolution of sedimentary CaCO3 neutralizes excess CO2, thus preventing runaway acidification, and acts as a negative-feedback mechanism in regulating atmospheric CO2 levels over timescales of centuries to millennia.” https://www.pnas.org/content/115/46/11700
There is a stoichiometric equilibrium between carbon dioxide in the atmosphere and carbonic acid in oceans. Calcium carbonate is one of the most abundant substances on Earth – from hundreds of millions of years of biological deposition. It’s presence both in sediments and in open ocean organisms – in the presence of magnesium ions and other catalysts – maintains dissolved calcite and aragonite in a supersaturated state in the world’s ocean. It is this source of calcium that is utilized in shell and coral formation – and that complexes with carbonic acid in acid neutralization. The carbonic acid after a couple of deprotonation reactions will then be replaced as more carbon dioxide goes into solution. An ongoing process that it has been suggested will result in undersaturation of aragonite in surface waters of the Great Southern Ocean by the end of the century.
The study linked focuses on changes to the carbon compensation depth – but they do mention the ‘theoretical’ possibility of dissolution of calcium carbonate in surface waters in the presence of higher acidity – or less alkalinity if you prefer. Resulting in less ‘precipitation’ of CaCO3 to depth. Very likely given the efficiency of substance recycling in the photic zones of open ocean – and in shallow coastal zones.
There is a large daily and seasonal variation in pH due to respiration – and variability off the western margins of continents from upwelling. It is difficult to get a handle on changes in pH due to anthropogtenic CO2 for those reasons alone – but any change in pH from more CO2 going into solution is moderated by deprotonation reactions and the speed at which they proceed.
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This statement must be some subtle joke, right? Because everybody knows that the great majority of the 2007-16 observed warming, if not all, was due to ENSO. Modelers live in La-la land, not in planet Earth. One cannot take them seriously.
Excuse the comment. I misread the dates.
The satellites show no warming 2007-2016. There is obviously a natural oscillation in the system, so one cannot just take 2 points and call it warming if the later one happens to be warmer than the earlier one. You could do that with a sine wave.
Which satellites do not show warming between 2007 and 2016 David?
http://woodfortrees.org/plot/uah6/from:2007/to:2016/trend/plot/uah6/from:2007/to:2016
1st diagram is wrong. CO2 can’t rise much about 750 ppm. Consider Henry’s Law. As partial pressure of CO2 in atmosphere increases, so will it increase in the ocean. Oceans are both heat sinks and carbon sinks. Read: “The distribution of CO2 between atmosphere, hydrosphere, and lithosphere; minimal influence from anthropogenic CO2 on the global ‘Greenhouse Effect’“, by Tom Segalstad in “The Global Warming Debate“, ESEF. https://folk.uio.no/tomvs/esef/ESEFVO1.pdf Oldie but goodie.
Also this: http://www.co2science.org/articles/V12/N31/EDIT.php
mark4asp, I’m afraid you are wrong. CO2 has been far higher than 750 ppm earlier in the Earth’s history. The partial pressure of CO2 in the surface (mixed) layer of the ocean does increase fairly much in line with the atmospheric concentration over the years, but interchange between the surface layer and the deeper ocean is much slower. So while CO2 emissions have been increasing the ocean has only been absorbing ~25% of the emitted CO2, but in equilibrium (in, say 2000 years after emissions cease) the ocean will hold ~73% of the total emitted CO2 (see the Appendix).
This is a point of contention and an important point. As I understand it, it was declared, by the “consensus” that the interchange was only 2GT/y, about 20% of current emissions. They, basically, cherry pick every number which fits their catastrophe scenarios. Then assassinate the character of anyone who criticises them. Given the “consensus” don’t play by the rules, I wonder why I should trust them? I trust good observations; not “consensus” modeling.
I requesting people post good studies based only on observations.
So while CO2 emissions have been increasing the ocean has only been absorbing ~25% of the emitted CO2
The party line position is MM emissions (MME) are considered the largest portion of the growth in atm CO2.
If you would look at the fig. 12 I posted below, could you tell me at what point does the ocean absorb 25% of the emitted CO2 and how can you know that, considering the 12mo change in CO2 curve so closely follows SST? Further, since MME are supposed to be accelerating blah blah why isn’t there an increase in the 12 mo change trend over and above the ocean outgassing component? It should be flipping obvious, why isn’t it?
Its apparent to me the whole carbon budget charade is a ruse or self-deception that allows climate modellers tremendous latitude to massage the outcome in favor of their internal bias towards CO2 warming.
Who here knows the RCP8.5 has solar forcing fixed to the solar cycle 23 that had the highest TSI in the instrumental record, out until 2100? Does any of have the judgement to understand the fraud here? They use the highest TSI years as a basis and then claim CO2 will be doing all that warming, not the sun. Do they even know what they’re doing? Are they ignorant, stupid, or deliberately lying?
but in equilibrium (in, say 2000 years after emissions cease) the ocean will hold ~73% of the total emitted CO2
How do you know what is going to happen to that CO2? How do you know that much of it doesn’t get turned into rock chemically?
I seriously doubt the ocean is absorbing 25% of emissions. While ocean atmosphere Carbon flux is enormous, over a hundred Gt each way annually, the net absorption is thought to be only a Gt or two. That net absorption includes very substantial atmospheric Carbon produced by soils, termites, etc.
gymnosperm
“I seriously doubt the ocean is absorbing 25% of emissions.”
The IPCC AR5 WG1 report Table 6.1 shows, over 2002-11, net absorption of 2.4 +/- 0.7 GtC/yr by the ocean, out of 8.3 +/- 0.7 GtC fossit fuel & cement emissions and 0.9 +/- 0.8 GtC net land use change emissions. That gives a central estimate that the ocean is absorbing 26% of total emissions.
Segalstad was already wrong in 1998, he forgot that adding fossil CO2 in the atmosphere.causes a dynamic disequilibrium with the ocean that needs a relaxation time.
Regarding this: “However, there are reasons to believe that there is a fundamental link between cumulative carbon emissions and warming, at least in equilibrium.”
Given that the climate system is far-from-equilibrium this abstract statement may be true but it is irrelevant as far as reality (and hence policy-making) is concerned. Observation strongly suggests that in the real world there is no fundamental link between cumulative carbon dioxide emissions and warming. In fact there appears to have been no CO2 induced warming since satellite measurements began 40 years ago. The only warming to date has been a single step up of about 0,3 degrees C, coincident with the giant El Nino/La Nina cycle circa 1998-2000.
Note that I assume by “carbon emissions” you actually mean carbon dioxide emissions, even though CO2 is not carbon. Carbon emissions sounds like soot.
David W: ‘Note that I assume by “carbon emissions” you actually mean carbon dioxide emissions, even though CO2 is not carbon’
I mean the carbon content of CO2 emissions (and, in pronciple, that of CH4 and CO emissions). This is common usage. Hence the term “carbon budget” always being used rather than “CO2 budget”.
And equilibrium in this context is a radiative balance at toa – as transient as that is – rather than thermodynamic equilibrium.
How does this conform to the exponential decline in GHG effect of CO2? 50% of the effect is in the first 20 ppm. We are in the fifth half-life of that decline, so the next doubling to 800ppm should add another 1.3% to the effect.
And then there is the natural experiment of 1929-0931, when human global CO2 production declined 30% and atmospheric CO2 stabilized. Temperature kept rising to 1941. And then it declined during the WWII years, when a fair amount of CO2 was produced. Declined enough to produce alarms about the oncoming Ice Age – see the covers of Newsweek and Time in the early 70s.
“We are in the fifth half-life of that decline, so the next doubling to 800ppm should add another 1.3% to the effect.”
The climate state would be quite different when CO2 levels are very low, so climate feedbacks would be very different. E.g, there would be little water vapour feedback. It is however true that the radiative forcing from an 100 ppm increase in CO2 concentration from, say, 400 ppm to 500 ppm, will be lower than for the increase from 300 ppm to 400 ppm. That is partially counteracted by the ocean and land sinks weakening as temperature increases, and the ocean Revelle buffer factor increasing as it absorbs more CO2. In models this counteraction is likely excessive, at least in relation to the land sink; it appears to cancel out most of the benefits of the forcing increase being logarithmic. Accordingly, the AR5 warming – vs – cumulative CO2 emissions curve is only slightly concave downwards.
It would be nice if you would answer the question. Do the math.
Oh, I forgot. Math is hard.
Reblogged this on Quaerere Propter Vērum.
Estimates of climate sensitivity have been declining over the years https://landshape.files.wordpress.com/2015/06/climate_sensitivity.pdf Estimate using default data in MODTRAN6 is about 1 K° as shown in Section 14 of http://globalclimatedrivers2.blogspot.com
How does cumulative fossil fuel emissions effect temperature if it doesn’t control (in fact barely effects) atmospheric CO2? Have I missed some rebuttal of Harde 17, Salby, and Berry?
https://wattsupwiththat.com/2017/05/13/is-murry-salby-right/
Istvan explains it. I think your point is a dead end.
Ragnaar
As much as I appreciate Rud I can’t see that he did what he claims in that post. He interprets the data in a way that disagrees with Salby but doesn’t address the several first principal analyses that Salby uses to get to his conclusion. Salby’s interpretation of the data is as valid as Rud’s and if his physics is right his is correct. Rud’s insistence that the pause falsifies Salby’s case is in error as warming since the little ice age is likely the slow constant heat source driving the nearly constant increase in atmospheric CO2 that is not effected by changes in anthropogenic emissions rates.
At the same above link, he goes through the information supporting the oceans being a sink of CO2.
Nic,
Could you provide figures equivalent to the IPCC figures for your analysis, preferable with the same scale or with the IPCC lines on the same graph?
I think it would help me visualize the effects of your study versus the IPCC’s
atandb
The IPCC Figure SPM.10 scenario projections are not directly comparable to by analysis, as they include warming from all forcing agents not just CO2.
I have however now added an update with a version of Figure 2 that compares projected CO2-only warming with that per CMIP5 ESMs, albeit that set of models has a somewhat lower average TCRE than does the set of CMIP5 ESMs and EMICs used to produce Figure SPM.10.
Thanks Nic,
At least for me, the figure provided puts your paper into perspective.
When will people get it that it is the ocean outgassing via Henry’s Law that is responsible for driving the accumulation of CO2, via a solar-warmed ocean?
https://www.dropbox.com/s/74c6xxrxn1kjwqm/AGU%20Fig12.JPG?dl=0
Human emissions are a big fat nothingburger by comparison.
coolclimateinfo: When will people get it that it is the ocean outgassing via Henry’s Law that is responsible for driving the accumulation of CO2, via a solar-warmed ocean?
Cool graph. Has ocean pH been increasing (ocean CO2 concentration decreasing) during that half century?
thanx. Good question. CO2 is also used for plant food undersea right? So maybe the ocean florea greened from CO2 too as has land flora.
When will people get it that it is the ocean outgassing via Henry’s Law that is responsible for driving the accumulation of CO2, via a solar-warmed ocean?
Because it isn’t, as co2 is going into the oceans, temperature rise is only worth 10 ppm rise in the atmosphere.
What you see in the graph is inhibition of CO2 uptake during higher temperatures predominantly during el nino events. It’s land sink variation by the way. Ocean sink rises monotonally since 1960.
Hans, that is a ridiculous argument. It is a physical fact – law – that warmed water outgasses CO2.
From Columbia: “Because of the role of CO2 in climate, feedbacks in the carbon cycle act to maintain global temperatures within certain bounds so that the climate never gets too hot or too cold to support life on Earth. The process is a large-scale example of LeChatelier’s Principle.”
This is wrong. CO2 doesn’t carry heat back into the ocean. How does the tiny CO2 % do all the following simultaneously:
(1) be outgassed by the ocean and accumulate in the air
(2) be consumed as plant food
(3) and pump heat back into the whole ocean warming it to great depths
How can 0.04% of the air do all that together at the same time? It doesn’t.
It is absolutely obvious from my fig 12 that the CO2 in the atmosphere is accumulating from ocean warming making MME basically negligible.
Coolclimateinfo, I agree that a warner ocean outgasses CO2, however the are ice-age samples which limit the amount of outgassing.
In icecores we observe a co2 increase: per Henry’s law of about 16 ppmv/°C. That means that a 0.6°C increase is good for only10 ppmv CO2 increase in the atmosphere
http://www.ferdinand-engelbeen.be/klimaat/klim_img/Vostok_trends.gif
H/t Ferdinand Engelbeen
http://www.ferdinand-engelbeen.be/klimaat/co2_origin.html
coolclimateinfo: CO2 is also used for plant food undersea right? So maybe the ocean florea greened from CO2 too as has land flora.
maybe, but if there is net outgassing of CO2 from oceans, can pH be decreasing?
I think s fairly significant component of the additional global greening is ocean seagrass, seaweed, algae, etc.
coolclimateinfor: Hans, that is a ridiculous argument. It is a physical fact – law – that warmed water outgasses CO2.
With CO2 persistently being manufactured by human activity, then the rate at which it dissolves into the ocean (hence the rate at which atmospheric CO2 concentration increases) is modulated by ocean temperature, hence the curve that you display.
Henry’s law by itself is not sufficient to understand the dynamics when the source of new atmospheric CO2 is persistent.
This guy covers it:
http://euanmearns.com/is-ocean-acidification-a-threat/
I don’t think it’s in doubt that for some reason, the pH indicates the increased CO2 content of the oceans. We could argue about sampling being weak.
Oceans are a sink and not a source – because of the biological and physical CO2 pumps.
” the ocean outgassing via Henry’s Law that is responsible for driving the accumulation of CO2″
The usual basic question – if the ocean outgassed so much, and we emitted so much, each being comparable to the CO2 originally there, then where did it all go? Not into biomass; that would have to more than double.
then where did it all go?
Nick I have no more faith in carbon budgets than Trenberth’s energy budget, so my opinion is most likely CO2 is improperly accounted for.
Why shouldn’t we believe GISS CO2 carbon budget isn’t just tweaked like the GISS temperature series is tweaked?
Cool: Let me summarize your graph. Over the last century, rise in CO2 in the atmosphere per year has gradually climbed from about 1 to 2.5 ppm/yr +/- a variable factor X that ranges from about +1 to -1 ppm/yr. Ocean temperature has gradually risen about 0.1 K/yr, +/- a variable factor Y, that ranges from +0.1 to -0.1. There is a correlation between X land Y, with X lagging slightly behind Y. The ratio X/Y is about 10 ppm/K. In other words, each 0.1 K temperature variability results in outgassing of 1 ppm of CO2 from the mixed layer of the ocean – the only part of the ocean that can produce these swings in CO2. On this basis, one might expect outgassing to have added about 5 ppm of CO2 NET to the atmosphere.
As the ocean warmed about 5 K – at the surface – at the end of the last ice age, the entire ocean out-gassed about 100 ppm of CO2. That is about 20 ppm/K from the entire ocean. The entire depth of the ocean didn’t warm 5 K. It is fairly surprising to me that the entire ocean only out-gasses about twice as much per K of warming as the mixed layer.
During the LIA, CO2 in ice cores shows very little change, perhaps decreasing 5 ppm. If one estimates an 0.5 degK for cooling during the LIA, we are in the 10 ppm/K range.
These values suggest that attribution the bulk of the 125 ppm CO2 rise to less than 1 K of warming is absurd.
Too complicated.
Simply the IPCC prediction is 0.3 C per decade.
The observed rate by satellites is half that.
What you say.
Explained simply.
If they matched I would not be a skeptic.
Do most IPCC models assume positive water vapor feedback in the order of 2 times the temp. Increase due to CO2 forcing alone as opposed to Dr. Richard Lindzen’s assertion that the feedback may be negative?
Models don’t assume that. Models calculate the amount of water vapor in each grid cell and each time step – via x,y,z wind, evaporation, condensation, rain, etc. The effect of water vapor follows from these calculations and the radiative transfer equation.
Whether or not the models provide useful results from this finite element analysis is a question. But they do not have feedbacks built into them. The calculation of feedbacks comes from the results of the models.
Good to see you active here.
“But they do not have feedbacks built into them. The calculation of feedbacks comes from the results of the models.”
This is a bit of sophistry.
If feedbacks can be calculated from the results of the models then the feedbacks are derived from both the input data viz, x,y,z wind, evaporation, condensation, rain, etc. and the way of treating some of those data inputs as you well know.
Not just the radiative transfer equation.
A small change in the assumption of complicated water heat release at altitudes and pressures is assigned or assumed and can be changed by the modellers at will.
Hence assigning [or building positive water vapor feedback] happens by default in these models.
The proper approach here for someone of your status would be to acknowledge this fact and say that by using what we think are the best assumptions models give results with large feedback levels.
Sophistry – “the use of fallacious arguments, especially with the intention of deceiving”.
My response to such lines of discussion:
scienceofdoom
You said “Models don’t assume that”
Yet in Water vapor feedback and global warming 2000
Isaac M. Held and Brian J. Soden
Held says “There is no ambiguity as to how to compute the relative importance of different regions for water vapor feedback in a model that predicts changes in water vapor concentrations; the confusion only arises from differing presumptions as to a plausible model-independent starting point. Our justification for Equation 21 is only that it better resembles GCM predictions.”
-It seems GCM models assume [presume] water vapor feedbacks that are not realistic and knowingly use them because they inflate the amount of heat and hence temperature produced.
Contrary to what you said.
You know this yet chose to ignore or dismiss it.
Why?
Sorry about the sophistry barb but then…
angech, a prediction is different from a presumption.
Feedbacks are calculated on the basis of fundamental physics.
http://www.climatechange2013.org/images/figures/WGI_AR5_Fig7-9.jpg
And net feedback is negative even allowing for the vagaries of cloud and opportunistic ensembles.
http://www.climatechange2013.org/images/figures/WGI_AR5_Fig7-10.jpg
Jim D angech, a prediction is different from a presumption.
? A presumption though is an assumption.
Robert I. Ellison Feedbacks are calculated on the basis of fundamental physics.
But scienceofdoom assumes that they are using basic physics whereas he knows they are using assumptions with wide error ranges on the fundamental physics to end up with no physics at all.
He is usually extremely good with his fundamental physics so it is excellent to see him commentating, I just wish I could stop my snark.
angech, you own quote said it was a prediction from GCMs. Did you quote Held by mistake?
SOD and Angech: The moisture carrying capacity of the atmosphere at equilibrium rises about 7%/K. Observations of rising absolute humidity are similar. If I increase the water vapor scale factor in Modtran by 7% (which means 7% more at all altitudes), OLR drops 1.6 W/m2. So this crude rational give a reasonable value for water vapor feedback. The difficult problem is explaining why equilibrium considerations give a reasonable answer for a non-equilibrium environment – ie where relative humidity is less than 100%. For example, water vapor averages about 80% RH in the boundary layer over the oceans. Why? It is less than 100% because cooler and drier air is constantly being turbulently mixed from above. If that turbulent mixing remains about the same, relative humidity over the ocean will remain about the same. Relative humidity is closely linked to cloud formation and climate models are tuned to match the observed albedo of the planet. So a tuned model must get many aspects of water vapor transport about right – even though this may be accomplished through offsetting errors.
Climate models contain several dozen parameters to summarize the results of processes that occur on too small a scale to be properly described by grid cell. In theory, condensation begins at 100% relative humidity, but a grid cell that averages 99.5% relative humidity will have some clouds. The relative humidity at which clouds first start appearing is an adjustable parameter that is critical to getting the correct albedo. So is the size fine droplets of water need to reach before they start falling. Evaporation is proportional to wind speed and undersaturation, but the constant of proportionality (coefficient) arises from turbulence and is an adjustable parameter. The reflectivity of water droplets varies in a known way with their radius and the initial number of droplets may be limited by the number of cloud condensation nuclei. However, smaller droplets evaporate and the released water vapor condenses into thermodynamically more stable larger droplets. A parameter controls the rate of this process. None of these parameters controls the strength of one particular feedback – so feedbacks are not directly produced by tuning.
The parameter that has the largest effect on ECS is the entrainment parameter. This parameter controls the amount of mixing of drier subsiding air into saturated rising masses of air. It can change SWR cloud feedback from strongly positive (0.5 W/m2/K) to strongly negative (-0.5 W/m2/K). Zhao (2016) doi: 10.1175/JCLI-D-15-0191.1
Heat can only escape the upper troposphere by radiative cooling to space. The rate of radiative cooling to space limits the amount of heat that can be transported upward as latent heat. If LWR radiative cooling to space increases at 2 W/m2/K (ECS 1.8 K in the absence of SWR feedbacks), then the increased flux from the surface can only be 2 W/m2/K. Net LWR from the surface doesn’t change much with warming at constant relative humidity, because DLR increases as much as OLR. A 7%/K increase in 80 W/m2 of latent heat flux is 5.6 W/m2/K, far more power than can escape if ECS is 1.8 K/doubling. (If sensible heat is co-transported with latent heat, make that 7 W/m2/K.) So convection must slow. So the LWR response appears to be limited by powerful energetic constraints. During seasonal warming (3.5 K), LWR rises about 8 W/m2/K or 2.2 W/m2/K and is highly linear. There are no obvious energetic constraints on SWR. An SWR feedback of +/-0.5 W/m2/K is an 0.5%/K change in 100 W/m2 of reflected SWR.
franktoo
Thank you for your well argued and informative comment about LW radiative response and the energetic constraints on it (which are also linked to the hydrological cycle response). You say:
“So the LWR response appears to be limited by powerful energetic constraints. During seasonal warming (3.5 K), LWR rises about 8 W/m2/K or 2.2 W/m2/K and is highly linear.”
However, it is not clear that the energetic constraint is at this level when warming is non-uniform. For the tropics (20S-20N), where solar energy input and ocean temperatures are highest, regressing TOA outgoing LW radiation per CERES data (2001-13) on surface temperature, using detrended and deseasonalised data, gave an increase of 4.05 W/m2/K (Mauritsen and Stevens 2015 DOI: 10.1038/NGEO2414, Table S2). An Iris effect resulting from convective aggregation appears to be the explanation. The average for CMIP5 models Historical simulations that they found (Table S3) is close to your 2.2 W/m2/K figure.
So I think that, while energetic constraints are important, they are not absolute – global mean feedbacks depend also on the spatial pattern of warming.
Nick,
“With more CO2, the emission reaching the ground comes from lower, warmer levels.”
How? If the radiation came directly back to the surface, it would be at the temperature it was emitted, but it doesn’t come directly back. The back radiation must run the same gauntlet of absorbing gasses in reverse.
The emissivity of CO2 is low, ~.2. Mostly it absorbs, gets jiggy, and transmits energy kinetically to surrounding gasses.
“The parameter that has the largest effect on ECS is the entrainment parameter. This parameter controls the amount of mixing of drier subsiding air into saturated rising masses of air. It can change SWR cloud feedback from strongly positive (0.5 W/m2/K) to strongly negative (-0.5 W/m2/K). Zhao (2016) doi: 10.1175/JCLI-D-15-0191.1”
Yes Franktoo, that’s where turbulence comes in and also convective aggregation. I doubt also the adiabatic lapse rate theory based on these issues.
Nic wrote: “For the tropics (20S-20N), where solar energy input and ocean temperatures are highest, regressing TOA outgoing LW radiation per CERES data (2001-13) on surface temperature, using detrended and deseasonalised data, gave an increase of 4.05 W/m2/K”.
Thanks for the reply. My problem with detrended and deseasonalized data is that the changes are so small making the confidence interval wide (and potentially subject to systematic error). -4.05 W/m2/K is +/-0.82 for CERES (smaller than I expected), -3.0+/-3.3 for ERBE and -4.0+/-1.5 when analyzed by Lindzen and Choi. When I look at the scatter in the data in M&S(2015) Figure 2a, my training in hard sciences with definitive well-controlled experiments rightly or wrongly makes it hard for me to take the slope seriously. There is much less uncertainty in the seasonal change, because the changes are huge and clearly linear.
It is clear that we want to know about feedbacks during global warming, not during warming in the NH and cooling in the ocean-dominated SH (seasonal warming). It is also clear that the tropical response is underweighted in seasonal warming. However, we need more than 20S to 20N also. The greatest value in looking at the feedbacks in response to seasonal warming is that they prove AOGCMs don’t get these feedbacks right and AOGCMs are mutually inconsistent with each other. In particular, LWR feedback from all and clear skies is equal in observations, but there is positive LWR cloud feedback in models. If I had 1/100 of your data analysis skills, I would look at the feedbacks to seasonal warming in various regions: latitude, ocean vs land, areas where boundary layer clouds are important, and areas with extremely high clouds that cause the most warming. With tight confidence intervals, one might have strong evidence about what processes AOGCMs are getting wrong, and which way they are biased. The scientific method is to attempt to invalidate hypotheses and an AOGCM is a sophisticated hypothesis about how our climate system behaves. Rather than declare AOGCMs invalid, Tsushima and Manabe merely say the information can be used to improve AOGCMs, but I think they need a better idea of what needs improving. Perhaps they have surveyed these possibilities and not published.
The SWR response to seasonal warming is non-linear with temperature and therefore isn’t really a feedback. But it is still diagnostic from how well AOGCMs reproduce observed seasonal changes in SWR. Many others report lagged SWR response, but the surface albedo and cloud albedo response aren’t likely to have the same lag. If about half of the sky is cloudy due to rising air masses and about half clear due to subsiding air masses (and there is no reason for the ratio to change as temperature rises), won’t cloud SWR feedback be near zero? (IIRC, boundary layer clouds are not due to rising air, so they would be in a separate category.) When I look at M&S(2015) Figure 2b, I translate +1, +2, +3 and +4 W/m2/K in SWR feedback into 1%, 2%, 3% and 4%/K, and then extrapolate to 3 degK of AGW or -6 degK at the LGM. Those are massive changes.
A question for people more scientifically literate than me. I recently read somewhere that at some point increased CO2 is similar to applying multiple layers of paint on metal — the additional [amount] of CO2 doesn’t have much of an effect–for example, similar to the 6th layer of paint on metal. Is what I read, correct or not?
JD
No. The evidence comes from the radiative transfer equation using the spectroscopic properties of CO2.
At around 15um 95% of radiation is absorbed within 1m (at surface pressure and temperature). So more CO2 has no effect around this wavelength.
Around 13um the absorption through a few km of the atmosphere is of the order of 50%. So more CO2 has a significant effect around these wavelengths.
You can see the details in Visualizing Atmospheric Radiation – Part Seven – CO2 increases – https://scienceofdoom.com/2013/01/13/visualizing-atmospheric-radiation-part-seven-co2-increases/
“So more CO2 has a significant effect around these wavelengths.”
And even where little radiation goes through to space directly, it still matters where it is absorbed, or more importantly emitted. With more CO2, the emission reaching the ground comes from lower, warmer levels. So even in bands where almost all IR is absorbed within a km, more CO2 makes a difference.
scienceof doom, thanks for your comments.
I do hope that you will write and post some more of your informative articles at your excellent blog before long.
I’m making the observation that Nic Lewis has a habit of not answering tough questions.
It’s more that I tend not to answer questions while I’m asleep. Nor ones that make no sense to me. I’ve no idea what the ‘MM emissions’ you refer to are. But ocean physical chemistry and gas exchange characteristics are well established and the principles are not worth arguing about, although things like ocean circulation and depth penetration of CO2 and heat have an appreciable influence on the outcome.
“How do you know what is going to happen to that CO2? How do you know that much of it doesn’t get turned into rock chemically?”
I believe it is well established that all or most of the emitted CO2 will eventually be permanently absorbed by silicate rock weathering. The problem is that this is an extremely slow process, taking tens to hundreds of throusands of years. On a shorter but still long timescale, ocean sediments will absorb some CO2 as CaCO3. May I suggest you read Box 6.1 in Ch.5 of IPCC AR5 WG1? The percentages may be somewhat too high and/or timescales over-long, but the general picture it presents seems sound to me.
Even though one may not agree with all of it, there is a lot of useful scientific information in the AR5 WG1 report.
Then I apologize Nic. Thank you for answering. MME=man-made emissions.
IPCC 1990 FIRST REPORT
“We calculate with confidence that: …CO2 has been responsible for over half the enhanced greenhouse effect; long-lived gases would require immediate reductions in emissions from human activities of over 60% to stabilise their concentrations at today’s levels…
Based on current models, we predict increase of global mean temperature during the [21st] century of about 0.3 o C per decade (with an uncertainty range of 0.2 to 0.5 o C per decade); this is greater than that seen over the past 10,000 years;”
–
My better half asked what AGW meant and what it would take for me to believe. I said according to the science above 0.3C per decade.
Not happening.
Only half.
“It is fairly straightforward to estimate TCRE from warming and cumulative CO2 emissions to date”
But I think you are low-balling the estimate. The AR5 itself gives CO2 forcing as 1.68 W/m2 out of 2.21, or 76%. And the upward revisions of land use emissions seems speculative; without it, emissions are 530 Gtons. That gives a TCRE of 1.33, considerably more than your 1.05.
And the 76% has wide error bars.
Hi Nick, thanks for your comment.
“But I think you are low-balling the estimate.”
Not at all. In fact, I have not adjusted up my estimate of total forcing to reflect the AR5 estimate of the very high (2x to 4x) efficacy of Black carbon on snow forcing.
“The AR5 itself gives CO2 forcing as 1.68 W/m2 out of 2.21, or 76%.”
AR5 only gave values to 2011, but they can be extended on a similar basis. My 1.67 W/m2 CO2 forcing is almost identical to the AR5 basis, as I extend it to 2016, which gives 1.66 W/m2, close to your 1.68 W/m2. However my 2.6 W/m2 total forcing – which reflects the forcing basis used in Lewis & Curry (2018) is 0.26 W/m2 higher than the 2.34 W/m2 I estimate on the AR5 bases for 2007-16. The difference in total forcing is explained as follows:
Other GHG (Methane & N2O) +0.11 : new (Etminan et al 2016) GHG forcing-concentration bases
Aerosol & Ozone +0.14 : new (Myhre et al 2017) post-1990, using updated emissions
Miscellaneous +0.01 : roundings etc.
I don’t know how you arrived at your 2.21 W/m2 total forcing change – perhaps that is anthropogenic only, which is almost 0.1 W/m2 lower than for total forcing.
“And the upward revisions of land use emissions seems speculative; without it, emissions are 530 Gtons.”
The IPCC AR5 based estimated 1850-82 mean to 2007-16 mean land use change (LUC) emissions part of the 530 GtC is (180 – 28 + 1) = 153 GtC. The Global Carbon Budget 2018 estimate is 181 GtC, 28 GtC higher. Their LUC estimates have been increasing significantly over the last few years; the Global Carbon Budget 2015 estimate of total 1850-2010 LUC emissions (the range of years it gave them for) was 153 GtC; in the 2018 budget it was 38 GtC higher at 191 GtC.
Moreover, there is another recent paper, Sanderman et al (2017) PNAS, which estimates that anthropogenic soil carbon emissions, which were not focussed on in the Arneth paper, have been underestimated by approaching 100 GtC, although much of this relates to before 1850.
So I think my addition of the Arneth et al (2017) estimated upwards adjustment of 35 GtC LUC emissions to adjust the AR5 estimate is most probably conservative.
“And the 76% has wide error bars”
This post focusses on the difference in central estimates, not their uncertainty ranges. But I agree that there is significant undertainty in my TCRE value, which represents a median estimate.
Nic,
“I don’t know how you arrived at your 2.29 W/m2 total forcing change”
It comes from AR5, fig SPM 5. Top Bar CO2 1.68; Bottom red bar 2011 2.29 (sorry, misread as 2.21) , yes total anthropogenic, so 2.34 including solar. That brings it down to 72%, and the TCRE 1.26.
Nick,
Noted, thanks. But, as I set out, I use newer, more accurate, estimates of GHG forcing-concentration relationships, and of revised estimates based on updated emissions data for post-1990 aerosol and ozone forcing changes, which have been published since AR5.
Nick, I think your question is answerd after reading the linked footnote XI?
Nick Stokes | December 12, 2018 at 12:34 am | Reply
“And the 76% has wide error bars.”
After Resplany this is small fry Nick.
This is Nic’s claim that observed is less than CMIP5. If CMIP5 is 1.6 and obs is 1.33 with a big error range, it doesn’t cast much doubt on CMIP5.
But the observational estimate is not 1.33 C. See my reply to your earlier comment.
Nic, to compare with AR5 Figure SPM.10 you need to divide by that 0.64 you assumed because their temperature change is the whole temperature change including all other proportionate forcing factors, not just the CO2 part. 1.05/0.64=1.64, which is more in line with other observationally based estimates.
Jim D, over the historical period to date CO2 forcing is a much higher proportion of total forcing in climate models than per observational estimates, largely because aerosol forcing is more strongly negative in models.
It is more relevant to compare CO2 with total forcing over the entire period until approximately 1000 GtC emissions have been reached; TCRE is usually estimated for 1000 GtC emissions. Over that period, only 14% of the total model forcing change was accounted for by non-CO2 forcing, based on the forcing dataset for the middling RCP scenarios. My 2.0 C estimate of the TCRE implicit in AR5 Fig. SPM.10 allows for that. See my note [iv].
Your assumption was 64% for the historical period, so your BAU would also be 64% through to 1000 GtC. That’s why showing the 1.05 C for just CO2 is not the full change, and you need to divide that by 0.64 to get the full change at 64% CO2.
“Your assumption was 64% for the historical period, so your BAU would also be 64% through to 1000 GtC.”
Not so. Under RCP6.0, which is the closest scenario to BAU (RCP8.5 is clearly unrealistic, and never was a BAU scenario in the first place), 88% of total forcing (and hence total warming) is projected to be from CO2 between 1861-80 and 2090-99. The position is similar under RCP4.5.
But in any case TCRE has no meaning for non-CO2 forcing. Unlike for CO2, for other emissions the forcing at the end of a multidecadal or multicentennial period has no direct link to the cumulative emissions over that period. For CH4, forcing is primarily determined by emissions in the previous decade or so, while for aerosols only emissions in the last week or so really matter.
If you plot what Fig SPM.10 does to get TCRE from its gradient, it doesn’t care about what fraction is CO2. The vertical axis is total temperature change regardless, and the horizontal axis is CO2 emissions. The TCRE derived from that gradient is independent of how much warming is from CO2 which is more uncertain than the total anyway. It is defined as the total temperature change per CO2 emissions. This is why your number has to be divided by 0.64 to match the temperature change axis leaving a gradient of 1.64 C per 1000 GtC.
“This is why your number has to be divided by 0.64 to match the temperature change axis leaving a gradient of 1.64 C per 1000 GtC.”
No, the slope of the RCP6.0 line, from the origin to between the last two dots, has to be divided by the ratio of CO2 forcing to total forcing over the same period, which as I wrote is 0.88. I did so. That gives a CO2-only TCRE of marginally under 2 C, and which is 2.0 C to 1 d.p.
TCRE does not depend on the CO2 fraction of forcing. It is just global temperatures and CO2 emissions.
“The transient climate response to cumulative carbon emissions (TCRE) is the ratio of the globally averaged surface temperature change per unit carbon dioxide (CO2) emitted”.
One thing that’s been a huge problem with these climate models is their tight fit with the historical record. If you have a model that isn’t taking into account some level of climate variability (several of these models have acknowledged error of that sort), then you should see the error in the past as well as in the future – that climate variability didn’t start tomorrow!
A common perverse result is that tightening the fit to existing data (by adjusting the loose parameters of the model) can then worsen the extrapolations to the future.
Global-scale multidecadal variability missing in state-of-the-art climate models – https://www.nature.com/articles/s41612-018-0044-6
Missing in both EBM and GCM it appears.
“A common perverse result is that tightening the fit to existing data (by adjusting the loose parameters of the model) can then worsen the extrapolations to the future.”
Indeed so. Over-fitting is a common problem and often a serious one.
Missing internal variability might be the bigger problem.
Thank you, Nic Lewis, for another excellent analysis and CE post.
The Left and other fraudsters call it Climate Change or sometimes just Climate but they really mean ‘Global Warming caused by burning fossil fuels’. Climate has always been changing. Compelling evidence as follows is that CO2 , in spite of being a ghg, has little to no effect on climate.
1. In the late Ordovician Period, the planet plunged into and warmed up from the Andean/Saharan ice age, all at about 10 times the current CO2 level [3].
2. Over the Phanerozoic eon (last 542 million years) there is no correlation between CO2 level and AGT [3].
3. During the last and previous glaciations AGT trend changed directions before CO2 trend [2].
4. Since AGT has been directly and accurately measured world wide (about 1895), AGT has exhibited up and down trends while CO2 trend has been only up. [2]
5. Since 2001, average temperature uptrend calculated by Global Climate Models (GCMs, aka General Circulation Models) which assume CO2 causes AGW, is about twice measured. [13]
6. Analysis of CO2 and Temperature data 2002-2008 shows a close correlation between dCO2/dT and lower tropospheric temperature. This demonstrates that CO2 follows temperature and not the reverse. [30]
7. Average global temperature is increasing about twice as fast as it should be calculated on the basis of increased vapor pressure of water resulting from temperature increase of the water. (Section 8 of my blog/analysis)
The references [ ] are given as live links in my blog/analysis at http://globalclimatedrivers2.blogspot.com
Dan P – Splendid!
Reminds me that engineers think that theory approximates reality, physicists think that realty approximates theory, and mathematicians don’t see the difference.
1. There is NO EVIDENCE for a temperature reversal in the last 500 million years preceded by a CO2 change. Nor, more recently, did CO2 change precede the emergence from the Last Glacial Maximum, the temperature plunge into the Younger Dryas, the rapid rise to the Holocene Optimum (280 ppm CO2) and the gradual decline since then, punctuated by Minoan, Roman, Medieval and current Warmings, not to ignore the Little Ice Age. None of them preceded by CO2 change.
2. Even Arrhenius recognized the exponential decline of the CO2 GHG effect. The first 20 ppm of CO2 produces 50% of the GHG effect and it declines exponentially after that. We are in the fifth half-life of that decline. So if CO2 doubles to 800ppm, its GHG effect will increase by 1.3%, totally submerged in the other eight influences on global climate.
3. The experiment has already been done: In the years 1929-1931, global human CO2 emissions declined by 30%. Atmospheric CO2 stabilized. Temperature kept rising to 1941. And then it declined during the WWII years, when a fair amount of CO2 was produced. Declined enough to produce alarms about the oncoming Ice Age – see Newsweek and Time and Science News in the early 70s.
The earth has spent about half the last 500 million years close to 22C, with CO2 levels much higher but not preventing Ice Ages like the one we’re in now. The rapid rise out of the Hirnantian Ice Age is especially interesting because it occurred rapidly, at lower CO2 levels than when it started, under the Cool Young Sun (70% as luminous as now).
Most of the CO2 that we see is due to the temperature recovery from the Little Ice Age (LIA) with a lag of 300 years . Coincidentally, a much smaller amount is being added by humans. It’s an accident that warmists, the IPCC and their much-amplified propaganda machine have taken advantage of.
Niclewis:
You correctly stated that “Not all of the warming to 2007-2016 was due to CO2 emissions”, and then estimated that it was 0.596 deg C.(down from the Hadcrut4.5 level of 0.846 Deg. C.)
However, the warming 2007-2016 was actually due to the massive decrease in global atmospheric SO2 aerosol emissions, from 123 Megatons in 2007, to ~ 85 Megatons in 2016, for an expected temp. increase of ~ 0.76 deg. C., due to their reduced level in the atmosphere
Warming ALWAYS occurs after cooling volcanic SO2 aerosol emissions settle out of the atmosphere, simply because of the reduced air pollution, and similar warming HAS to occur whenever anthropogenic SO2 levels are also reduced.
For your analysis to have any credibility, you need to include the warming effects of reduced SO2 aerosol emissions in the atmosphere!
Burl Henry
Where do you get your figures for the reduction of global SO2 emissions from? They don’t agree with data I’ve seen.
Moreover, based on my forcing values and TCR estimate of 1.35 C the total cooling due to anthropogenic aerosols in 2007-16, relative to 1850-82, is less than a third of your 0.76 C figure, so the effect of a 123 to 85 Megaton SO2 emission reduction would be much lower than your estimate.
Niclewis\
Annual Global Anthropogenic SO2 aerosol emissions are currently available for each country for the years 1750-2014, and are currently being tracked by the CEDS team at the University of Maryland : http://www.globalchange.umd.edu/CEDS/ , and at https://doi.org/10.5194/gmd-11-369-2018-supplement [for the actual SO2 data which I have cited (except for the 2015-2016 data gleaned from other reports]
Reductions in SO2 aerosol emissions cause WARMING, not cooling as you have stated.
The “Climate Sensitivity Factor” for changes in SO2 aerosol emissions is ~.02 deg. of change for each net Megaton of change in annual global SO2 . aerosol emissions, either volcanic or anthropogenic.
Thus, the ~38 Megaton reduction in emissions x .02 gives an expected temperature rise of 0.76 deg. C (+ ~.05 deg. C. of natural recovery, for the decade, from the LIA cooling).
Burl Henry
Thanks for the links. The CEDS SO2 global emissions decline from, as you say, ~123 Mt in 2007, but only to 114 Mt in 2010 and then ~112 Mt in 2014, with most of the decline (Figure S40 and Table S3). Where exactly does your 2015/2016 figure of 85 Mt SO2 come from, and how do that source’s emission estimates for previous years compare with the CEDS estimates?
I am well aware that a reduction of SO2 emissions causes warming. My reference to cooling related to the increase in aerosols from 1850-82 to 2007-16. And my point was that if the 100+ Mt increase in SO2 emissions only produced cooling of ~0.25 C, a 38 Mt reduction of SO2 emissions, even if it had occurred, would not produce warming of anything like 0.76 C. The warming would be only ~0.1 C.
A problem is the ‘lensing effect’ in mixed aerosols causing enhanced BC warming.
https://www.pnas.org/content/pnas/113/16/4243/F2.medium.gif
“Time-course evolution of BC aerosol composition, light absorption (where EMAC-BC is the enhancement because of coatings), and associated climate effects (as DRF).”
https://www.pnas.org/content/113/16/4243
“Here it is shown that hindcast experiments can successfully capture many features associated with the 1976/77 and 1998/99 climate shifts. For instance, hindcast experiments started from the beginning of 1976 can capture sea surface temperature (SST) warming in the central-eastern equatorial Pacific and the positive phase of the Pacific decadal oscillation (PDO) throughout the 9 years following the 1976/77 climate shift, including the deepening of the Aleutian low pressure system. Hindcast experiments started from the beginning of 1998 can also capture part of the anomalous conditions during the 4 years after the 1998/99 climate. The authors argue that the dynamical adjustment of heat content anomalies that are present in the initial conditions in the tropics is important for the successful hindcast of the two climate shifts.” https://journals.ametsoc.org/doi/full/10.1175/JCLI-D-12-00626.1
The rate of global heat content change shifted 4 times last century. The next one is due within a decade – if it is not happening now. The nature of dynamical shifts involve slow changes in control variables initiating rapid internal adjustments in the system. Sensitivity is low between climate shifts and arbitrarily high at shifts.
https://watertechbyrie.files.wordpress.com/2014/06/unstable-ebm-fig-2-jpg1.jpg
Solutions of an energy-balance model (EBM), showing the global-mean temperature (T) vs. the fractional change of insolation (μ) at the top of the atmosphere. (Source: Ghil, 2013)
The EBM has two stable states with two points of abrupt climate change – the latter at the transitions from the blue lines to the red from above and below. The two axes are normalized solar energy inputs μ (insolation) to the climate system and a global mean temperature. The current day energy input is μ = 1 with a global mean temperature of 287.7 degrees Kelvin. This is a relatively balmy 58.2 degrees Fahrenheit.
The EBM uses physically based equations to determine chaotic responses in the climate system as a result of changes in solar intensity, ice reflectance and greenhouse gases. With a small decrease in radiation from the Sun – or an increase in ice cover – the system becomes unstable with runaway ice feedbacks. Runaway ice feedbacks drive the transitions between glacial and interglacial states seen repeatedly over the past 2.58 million years. These are warm interludes – such as the present time – of relatively short duration and longer duration cold states. The transition between climate states is characterised by a series of step changes between the limits.
Ghil’s model shows that climate sensitivity (γ) is variable. It is the change in temperature (ΔT) divided by the change in the control variable (Δμ) – the tangent to the curve as shown above. Sensitivity increases moving down the upper curve to the left towards the bifurcation and becomes arbitrarily large at the instability.
The problem in a chaotic climate – at any scale – then becomes not one of quantifying climate sensitivity in a smoothly evolving climate but of predicting the onset of abrupt climate shifts and their implications for climate and society.
Nic is there a chance of you/anyone producing your alternate version of Table 2 CMIP5- ESM above?
It would be very helpful for we uneducated types to stare at and try and make sense of and hopefully become better informed. Just asking?
Noted, thank you. I have now added a new version of Figure 2. See update at the bottom of the article, before the endnotes.
Sorry I meant Figure 2 NOT Table 2 in above comment.
Reblogged this on Climate Collections.
NIc: I was surprised that you didn’t need to get into the airborne fraction of emitted CO2 and more surprised by Goodwin (2007):
“To achieve PCO2 stabilization at present day levels for the MIT GCM requires limiting future CO emissions to 700 GtC.”
Current cumulative emissions are 565 GtC and CO2 is 410 ppm. We can burn another 700 GtC, and when the ocean CO2 sink reaches equilibrium with the atmosphere, we will back at 410 ppm. Now I see why TRCE is larger than ECRE. (Since both processes occur mostly by convection, the rate of heat and CO2 uptake by the deep ocean are linked to each other.
franktoo
“Current cumulative emissions are 565 GtC and CO2 is 410 ppm”.
The 565 GtC emissions estimate was between means for 1850-82 and 2007-16. The corresponding mean atmospheric concentrations were ~288 and ~393 ppm, or and increase of 105 ppm. That equates to 223 GtC, so the airborne fraction is 40% of total emissions to date.
“We can burn another 700 GtC, and when the ocean CO2 sink reaches equilibrium with the atmosphere, we will back at 410 ppm.”
The Goodwin et al 2007 figure of 700 GtC was for stabilising CO2 concentration to 388 ppm, which was reached in the MIT GCM, starting from 278 ppm, after emissions of only 300 GtC. That implies an airborne fraction of 78% after emissions of 300 GtC – an unrealistically high level. I think that the MIT GCM they used had no land carbon sink at all.
So I don’t think you can deduce much from Goodwin’s GCM simulations. But his formula for ocean equilibrium behaviour does seem to be a valid, quite good, approximation.
Nic: Thanks for the reply. I sometimes have difficulty seeing the concepts and big picture through the forest of numbers in your posts. (I actually moved some of your info to a table in Excel: concept, units, values, because things are much clearer to me in that format.) When I saw that your ECRE was half of your TCRE, I had no conceptual framework that let me assimilate this startling conclusion until I skimmed Goodwin. (I included the above quote for those who were having as much difficult as I was.) I gather you think Goodwin is overly pessimistic.
After some thought, I find equilibrium far easier to understand than transient. At equilibrium pre-industrially, we had something like 0.6, 2.1 and 3.9 TtC – about 1.4% in the air. What happens when we add another 2 TtC to the system. To a first approximation, it will distribute at equilibrium in the same ratio as it did pre-industrially. Then we can correct for rising pH and temperature in the ocean and perhaps for rising temperature on land. I gather that the Revelle factor means that new CO2 is being distributed about 1:10 between air and ocean (rather than at the current ratio of 0.15:10) due to rising pH. In principle, I should be able to express the distribution between ocean and air (and pH) as a function of total alkalinity, total carbon and some equilibrium constants.
You and others are interested in both transient and equilibrium responses, so your explanations are more complicated. The same is true for ECS and TCR: If our planet emits and reflects 2 W/m2 more LWR and SWR after warming 1 degK (2 W/m2/K), finding ECS is a simple problem. TCR is more complicated.
This post describes a very different future than the vague concepts about rising airborne fraction due to “saturating sinks” I had previously acquired. Thank you again.
I wrote: “At equilibrium pre-industrially, we had something like 0.6, 2.1 and 3.9 TtC – about 1.4% in the air.
I meant: At equilibrium pre-industrially, we had something like 0.6, 2.1 and 39 TtC in the air, land and ocean respectively – about 1.4% in the air.
Eddies transport heat and CO2 both vertically and horizontally in oceans constantly in turbulent motion.
e.g. https://www.gfdl.noaa.gov/ocean-mesoscale-eddies/
And I love this idea of ocean eddies being mathematically equivalent to black holes.
“The challenge in finding such eddies is to pinpoint coherent water islands in a turbulent ocean. The rotating and drifting fluid motion appears chaotic to the observer both inside and outside an eddy. Haller and Beron-Vera were able to restore order in this chaos by isolating coherent water islands from a sequence of satellite observations.”
http://www.ethlife.ethz.ch/archive_articles/130923_black_holes_ocean_aj/index_EN.html
And to think that this can be approximated with a one line formula – and that oceans take hundreds of years to equilibriate with either atmospheric heat or CO2 – is at odds with ocean heat data.
http://www.climate4you.com/images/ArgoWorldOceanSince200401%2065N-65S.gif
But turbulent chaos – as wee willie mocked.
Niclewis:
In response to your 6:10 am post:
The ~38 Megaton reduction in SO2 aerosol emissions, from 2014 to 2016, was primarily due to a totally unexpected 29.1 Megaton decrease in Chinese SO2 emissions: https://www.ecns.cn/2017/11-10/28048.shtml
(This was in response to a directive to lower atmospheric pollution levels, beginning in 2014).
In addition, the EPA reported a 2 Megaton decrease, 2014-2016, The European Gothenburg target was a 2.5 Megaton reduction 2014-2016 (no longer being tracked), giving a total of 33.6 Megatons. In addition to the U.S. and Europe, emissions from other countries have been trending downward, and this was estimated as 4 Megatons for the period, for a total of 37.6 Megatons.
With respect to the warming resulting from the reduction in SO2 aerosol emissions, I used empirical data from the 1991 Pinatubo and Hudson eruptions, which injected ~23 Megatons of SO2 aerosols into the stratosphere. This resulted in an 0.5 deg.C,.reduction in average anomalous global temperatures.
When they eventually settled out, temperatures rose to pre-eruption levels, a rise of 0.5 deg. C. resulting from the removal of 23 Megatons of SO2 aerosols from the atmosphere, or ~ .02 deg. C. of .temp. rise for each Megaton of reduction in SO2 emissions. (0.5./23 = .0217)
To test this simplistic hypothesis, I used the GISS 1978 anomalous J-D temp. (0.06), and the 2011 temp (0.58), a rise of 0.52 deg. C. The SO2 level in 1978 was 134 Megatons, and in 2011, it was 115 Megatons, a decrease of 19 Megatons. 0.52/19 = .0273. a difference of only .0056, indicating that the hypothesis is valid.
(The years 1978 and 2011 were chosen since there were no temporary La Ninas or El Ninos those years to affect temperatures)
I have found that .02 x the change in SO2 emission levels in Megatons will normally match the resulting temperature to within 0.1 deg. C, or less, as for the cited 2016 temperature increase, even over a span of nearly 40 years.
Burl Henry
Thanks for the link to https://www.ecns.cn/2017/11-10/28048.shtml, but it gives a 404 error. Can you give a working link that supports your 29.1 Megaton 2014-2016 decrease in Chinese SO2 emissions? I am certainly interested to see that. The ECLIPSEv5 data I have seen extends to 2015 and shows global emissions of ~99 Mt then, however that is only a small drop from its 2010 estimate of 102 Mt, which is well below the CEDs 2010 estimate.
Unfortunately one can’t simply compare the effects of anthropogenic SO2 emissions, which stay in the troposphere, with those from explosive volcanos, like Pinatuba, with reach the stratosphere. Aerosols only last in the troposphere for a few days; they quickly get cleared out by rain and other processes. But volcanic aerosols stay in the stratosphere for the order of a year. So 23 Mt of volcanic sulphate aerosols produce much more cooling than 23 Mt per annum, or 0.5 Mt per week, of anthropogenic sulphate aerosols, which will produce an average atmospheric sulphate burden of under 0.5 Mt.
niclewis:
I see that I entered the link incorrectly: It should be https://www.ecns.cn/2017/11-10/280408.shtml
With respect to your comments about the lifetimes of stratospheric and tropospheric, SO2 aerosol emissions, the short lifetimes of tropospheric emissions applies ONLY to intermittent sources, where they have time to wash out of the air.
The reality, however, is that almost all .anthropogenic SO2 emitters, such as power plants, factories, foundries, home eating units, shipping, internal combustion engines, etc., etc. are relatively continuous sources, so that they have essentially infinite lifetimes, ending only when they are modified to reduce emissions, or are shut down. As fast as they are washed out of the air, they are replaced.
Burl Henry
Thanks for the corrected link. Table S1 of the Supplementary information for the underlying paper (Li et al 2017: https://www.nature.com/articles/s41598-017-14639-8) appears to show a decline in China’s SO2 emissions of 10.4 Mt between 2014 and 2016 (from 18.8 to 8.4), not 29.1 Mt. Figure 1 shows a slightly larger reduction, but its 2015 and 2016 values are projected emissions, not actual measured values. A subsequent paper (Karplus et al 2018: https://www.pnas.org/content/115/27/7004) has cast doubt on the accuracy of the emissions data used in the Li et al paper, suggesting that many emitters may not actually have achieved the reductions that they reported.
You say that antropogenic SO2 emissions are continuous and that “As fast as they are washed out of the air, they are replaced.” That is true. But if they take a week to be washed out and 0.5 Mt is emitted per week, then the amount in the atmosphere at any time is only 0.5 Mt but the annual emissions are 52 times as much, or 26 Mt.
niclewis:
According to the CEDS data, Chinese SO2 aerosol emissions in 2014 were 37,503 Megatons, so that the 2016 total of 8.4 Megatons would represent a decrease of 29.1 Megatons. 2014-2016. Other world-wide decreases were `about 8 Megatons, for a total of ~37 Megatons.
Using the .02 deg. C of warming factor for each net Megaton of reduction in global SO2 aerosol emissions, a climatic response of 0,74 deg. C. would be expected.
Hadcrut4 reported an increase of 0.79 deg. C., which is essentially identical.
This, I believe, validates both the CEDS data, and the .02 deg. C. factor.
Regarding the continuous anthripogenic SO2 aerosol emissions, they are never washed out, since they are constantly being replaced. The atmospheric loading is the TOTAL of the amount being emitted.
This can be proven from the observation that whenever there is a business recession, average global temperatures increase, because of fewer SO2 emissions due to the reduced industrial activity. The few exceptions are due to volcanic eruptions that put more SO2 aerosols into the atmosphere.
Burl Henry
The CEDS and the Li et al 2017 data for Chinese SO2 emissions appear to have very different values in the years leading up to 2014, so I don’t think one can reliably derive the 2014-2016 decline by comparing the 2014 CEDS figure with the 2016 figure in Li et al., even if the Li et al post start of 2014 data doesn’t turn out to be unrealistic. The jury is out on this.
“The atmospheric loading is the TOTAL of the amount being emitted.”
The atmospheric loading is the total amount of aerosols emitted during a period equal to the average atmospheric lifetime of aerosols, not the total amount emitted in a year. Surely this is obvious.
Co-emitted species in anthropogenic emissions – sulfate included – more than more than doublr the warming potential of black carbon.
https://www.pnas.org/content/pnas/113/16/4243/F2.medium.gif
“These results were shown to provide insights into naturally occurring closed and open cells in marine stratocumulus cloud systems,12 and the processes underlying transitions between these states. The oscillating branch of the solutions represents open cell clouds that typically produce stronger rain,1,13,14 and as the rain depletes the cloud water and suppresses the updraft by evaporating below cloud base, the average cloud tends to last for shorter durations.6 The closed cells tend to produce very little drizzle and their morphology remains stable for more than 10 h despite the fact that their characteristic scale suggests a theoretical lifetime of only ∼1 h.” Exploring the nonlinear rain and cloud equation – https://aip.scitation.org/doi/10.1063/1.4973593
Closed cells persist for longer over cooler ocean surfaces.
“The northeast Pacific Ocean experienced a “marine heatwave” between 2013 and 2015. This was characterized by the highest surface temperatures ever recorded in a vast swath of the ocean from near the Gulf of Alaska to off the coast of Baja California. The unprecedented warming event was linked to significant impacts on marine life and a severe drought in western North America. We analyze satellite data to show that the heatwave was associated with a record decrease in the typically high cloudiness over an area of the Pacific off Baja California that is roughly half the size of the contiguous United States. Such a deficit in cloud cover coincided with a large increase in the amount of sunlight absorbed by the ocean surface, resulting in extremely warm temperatures. Our findings suggest that a reinforcing interaction (or positive feedback) between clouds and ocean surface temperature can strongly contribute to significant and difficult‐to‐predict changes in marine climate.” https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL078242
Climate sensitivity to cumulative emissions suffers from a fatal statistics flaw.
https://tambonthongchai.com/2018/05/06/tcre/
https://tambonthongchai.com/2018/12/03/tcruparody/
When that error is corrected, no sensitivity remains
https://tambonthongchai.com/2018/12/14/climateaction/
To suggest that there is no causal relation between greenhouse gases and atmospheric warming is fooling yourself in the Feynman sense. Something to be guarded against.
Robert I. Ellison:
You said “To suggest that there is no casual relation between greenhouse gasses and atmospheric warming is fooling yourself in the Feynman sense”
No, you are the one fooling yourself. Unless I am mistaken, you can cite no falsifiable evidence that there is any casual relationship between greenhouse gasses and atmospheric warming.
“In experimental philosophy we are to look upon propositions inferred by general
induction from phenomena as accurately or very nearly true, not withstanding
any contrary hypothesis that may be imagined, till such time as other phenomena
occur, by which they may either be made more accurate, or liable to exceptions.” Isaac Newton
https://www.nature.com/articles/35066553
Nic Lewis’ conceptual Earth system science framework – that lends itself to simple statistical procedures – is too narrow to be credible. At the paradigm level he neglects dynamical complexity that is the dominant science paradigm – as well the emergent catastrophic climate meme.
e.g. https://www.youtube.com/watch?v=G25dGJ3yUYk
Neglect of multidecadal modes of deterministically chaotic climate change – and the assumption that only sustained forcing can change climate – lead to unrealistic correlations between global surface temperature and greenhouse gases.
“Since secular signals based on CMIP5 simulations are dominated by the forced response (section Forced vs. internal secular variability in climate models), their (scaled) subtraction from the observed secular temperature signal represents an estimate of the internal secular variability in the observed climate, with the total of 111 such estimates obtained using different historical CMIP5 simulations considered… The global-mean temperature trends associated with GSW are as large as 0.3 °C per 40 years, and so are capable of doubling, nullifying or even reversing the forced global warming trends on that timescale.”
https://www.nature.com/articles/s41612-018-0044-6
Not to mention – well mentioned twice before in this post – the ‘lensing effect’ of black carbon and sulfate that more than doubles the warming potential of black carbon. Something that requires regional scale atmospheric chemistry models – and data at the correct scale – to get right.
https://www.pnas.org/content/pnas/113/16/4243/F2.large.jpg
The simplest and cheapest way – using existing technology – to reduce warming is to reduce both black carbon and sulfate emissions.
And the land sink in restoring soils, forest, rangelands, wetlands and reclaiming desert is in the hands of people.
The data says CO2 has little if any effect on climate. Temperature is now about what it was in 2002. CO2 has increased since 2002 by 40% of the increase 1800 to 2002. By similarity, none of the other ghg (except water vapor) have any significant effect on climate either. https://pbs.twimg.com/media/DuaFJw7VYAEz-mU.jpg
We might have to rejig CERES and Argo for you.
https://watertechbyrie.files.wordpress.com/2018/12/loeb-2018-figure-1.png
https://www.mdpi.com/2225-1154/6/3/62
Mind you – it looks almost entirely like low level cloud cloud – more IR up and SW down – but I couldn’t discount greenhouse gases entirely.
Am I missing something? This all seems to be about noise level perturbations of a few watts per m^2 or so form CO2 if real, plus aerosols by emissions, cosmic rays, etc. All are small compared to far greater and overtly dominant natural effects. Nowhere do I see the negative feedback of oceanic evaporation that currently provides 90W/m^2 of cooling and another 50W/m^2 of control by insolation reduction from cloud albedo being added to the thermal balance. Both feedbacks self evidently vary directly with SST to maintain our ocean dependent land surface temperature equilibrium, as demonstrated so well at peak interglacial, an ultimately dominant response that flat lines 7Ka of relentless ocean warming while atmospheric CO2 is still rising in response to that ocean warming, ineffectually. as far as maintaining interglacial warming to any thermal runaway goes. I never see this discussed as a significant, never mind dominant control of our planetary equilibrium, overtly variable.. What is the sensitivity of this natural feedback to temperature in terms of W/m^2 per degree STT anomaly. Why don’t I see it? Is NASA et al wrong about the scale of this overtly largest of all control effects? Or does it magically defy the laws of physics and stay the same as SST varies. Did I read this wrong? etc.
CAVEAT: I am not very good at “climate science”, as I was raised as a physicist ratherthan epidemiologist, to believe in provable deterministic laws, distrust extrapolations of complex multi factor non linear data weighted by the guesswork of computer model programmers in quite simple control systems, and to prefer joined up rather than partial approaches to understanding systems such as the atmosphere, that must include dynamically connected environments such as the lithosphere and oceans, as mentioned above.
Brian asks: “What is the sensitivity of this natural feedback to temperature in terms of W/m^2 per degree STT anomaly. Why don’t I see it? Am I missing something?”
Nope. The increased emission of OLR and reflection of SWR per degK of warming – the overall climate feedback parameter (W/m2/K) – is the fundamental unanswered question in climate science. Only the concept is usual expressed as the reciprocal (K/(W/m2)) and then W/m2 is converted to doublings of CO2 (3.6? W/m2 = 1 doubling). That gives equilibrium climate sensitivity (K/doubling). The climate feedback parameter is the sum of Planck feedback and all of the other feedbacks written with a negative sign for power lost by the planet. A climate feedback parameter of -1, -2 or -3 W/m2/K is an ECS of 3.6, 1.8 or 1.2 K/doubling. One might say that a graybody with a temperature of 288 K and emissivity of 0.61 has a “climate feedback parameter” of -3.3 W/m2/K, which is very near the -3.2 W/m2/K obtained from climate models when no other feedbacks are allowed.
niclewis:
Regarding your post of 4:12 pm>
Yes, China ‘s SO2 emissions had very different annual totals in the run-up to 2014, but they had no bearing on those of 2014-2016. In 2014, a REQUIREMENT to reduce air pollution .was put into effect, with the result that they had fallen to 8.4 Megatons by 2016 (probably by turning on previously installed SO2 scrubbers).
The decrease was confirmed by both instrumentation results and a NASA satellite. I cannot agree that the jury is still out on this, And there was clearly a strong climatic response to this massive reduction in SO2 aerosol emissions!.
“The atmospheric loading is the total amount of aerosols emitted during a period equal to the average atmospheric lifetime of aerosols, not the total amount emitted in a year”
As I pointed out earlier, emissions from continuous sources have effectively
“infinite” lifetimes.
For example,If a given source has an emitting capacity of 1,000 tons per week, and as they are being washed out, they are also being replaced, so that its atmospheric loading remains constant at 1,000 tons per week, or 52,000 tons per year..
Thus, the atmospheric loading is the total of the emissions being emitted from all of the sources, as I had maintained.,
A bit of a stretch for someone who requires proof – provided to no response – of greenhouse gas forcing I know.
https://judithcurry.com/2018/12/15/week-in-review-science-edition-91/#comment-886161
Here’s a study that looks at sulfate reduction in China and the US in terms of SW clear sky power flux at TOA.
https://www.mdpi.com/2225-1154/6/3/62
I tried to check CERES clear sky data this morning but they are down for maintenance.
Robert I. Ellison:
With respect to your Dec.15 1:42 am post:
You need to read my earlier Dec. 12, 12:05 pm post to Niclews, since it also applies directly to you.
Especially “For your analysis to have any credibility, you need to include the warming effects of reduced SO2 aerosol emissions,in the atmosphere”..
This warming is a fact which you ignore, and your earlier Dec. 14 4:07 mention that “The simplest and cheapest way–using existing technology–to reduce warming is to reduce both black carbon and sulfate emissions” is, with respect to sulfate emissions, exactly what should NOT be done to reduce warming. Only increasing them will reduce warming.
Again – obviously a bit of a stretch.
https://www.mdpi.com/2225-1154/6/3/62
Robert I. Ellison:
No, not a stretch. Your cited paper regarding the pause failed to identify the actual cause of the “pause”.
It was caused by Western reductions in SO2 aerosol emissions being largely offset by Eastern increases in emissions, so that atmospheric SO2 levels were relatively constant.
Total anthropogenic SO2 aerosol emissions in 2001 were 110 Megatons, and in 2013 only 113 Megatons (with some bouncing around in the interim)
When they dropped to about 85 Megatons in 2016, it caused the very strong 2015-2016 El Nino. (The very strong 1997-1998 El Nino was also caused by a reduction in SO2 aerosol emissions (7.7 Megatons).
Earth’s Climate is extremely sensitive to changing levels of SO2 aerosols in the atmosphere, and cannot be ignored in any study of climate change… .
There are two papers. The one you didn’t read with any attention to detail discusses cloud change over the Pacific as the major cause with a minor contribution to SW power flux at TOA more broadly in the Atlantic and Pacific as a result of sulfate reductions in the US and China. The other discusses intensification of BC warming through coating with sulfate along with other compounds – the lensing effect. Neither of these studies are outliers.
Re BH: The aerosol forcing in the real world is smaller then estimated in the AR5 ERF. The “indirect effect” ( aka “ACI”) is about 50% ( at least) smaller, see https://judithcurry.com/2017/07/29/update-on-the-strength-of-aerosol-forcing/ and the direct (ARF) effect also by about 30% , see https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.121.218701 . The backround: the “black carbon” ( BC) forcing is much stronger than thought due to a “coating effect” of SO2 particles. The BC forcing is positive ( it warms the planet) , the SO2 forcing is (more) negative. Result: the net forcing ( with a negative sign) is much smaller than thought. Or the other way around: The GHG forcing is stronger, when calculating the given T-increase this will reduce the sensitivity.
Conflating volcanic emissions of SO2 with mixed species emissions from incompletely combusted fossil fuels seems to be an error.
https://so2.gsfc.nasa.gov/imgs/toms_omi_inventory_data_alt_mar18.png
As someone who never gives numbers without overwhelming support in the literature – models under predict the warming potential of black carbon by a factor of three.
“The best estimate of industrial‐era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m−2 with 90% uncertainty bounds of +0.17 to +2.1 W m−2. ” https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/jgrd.50171
With a corresponding decrease in SW reflection as SO2 coats aging BC in the ‘lensing effect’.
https://www.pnas.org/content/pnas/113/16/4243/F2.medium.gif
“Time-course evolution of BC aerosol composition, light absorption (where EMAC-BC is the enhancement because of coatings), and associated climate effects (as DRF).”
https://www.pnas.org/content/113/16/4243
I am wondering if the reduction in volcanic emissions is sufficient to account for the post hiatus decrease in clear sky reflected SW noted in Loeb et al 2018.
The major cause of post hiatus warming remains as less cloud cover over warmer ocean surfaces in the Pacific – a counter intuitive result related the persistence of convection cells in the presence of nucleating particles – from a number of sources – prior to raining out from the center and putting into train processes involving the suppression of convection.
… um… https://judithcurry.com/2018/12/15/week-in-review-science-edition-91/#comment-886161
Anybody know where to find current values for anthropogenic aerosol forcing.
Land surfaces will always be apparently warmer – as measured by thermometers – than ocean surfaces given physics of sensible and latent heat.
https://watertechbyrie.files.wordpress.com/2014/06/dietmardommenget_zps939fe12e-e1540922749884.png
But the atmosphere is well mixed with indeed most water vapor originating in oceans. And the rate of warming of oceans and land is the same – some 0.8 W/m2 most recently.
Land warms slowly to depth by conduction leading to hotter drier soils. CO2 fertilization leads to less surface moisture flux and higher surface sensible heat. As does drought.
Oceans warm to depth by turbulent mixing and a temperature gradient is established by convection as the warmest waters surface.
So how would you explain a land surface temperature and ocean surface temperature divergence?
https://watertechbyrie.files.wordpress.com/2018/12/sst-v-atmospheric-temp.png
Same reason land warms faster in the summer – thermal inertia. This data shows it better. Up to 1980, the ocean was keeping up, but when the CO2 forcing change accelerated to 0.3 W/m2/decade, it couldn’t anymore.
http://woodfortrees.org/plot/crutem4vgl/mean:120/mean:240/plot/crutem4vgl/from:1988/trend:-3.25/plot/hadsst3gl/mean:120/mean:240/plot/hadsst3gl/from:1988/trend
I presume it is the surface data yet again – and a progressive liberal sprinkling of narrative.
There are of course a number of physical mechanisms in play in the complex dynamical Earth system – but a label doesn’t do as a coherent theory.
Land cools and warms daily and seasonally – and oceans in local thermodynamic equilibrium with the atmosphere stay warmer because that’s where the energy store is.
One of the mechanisms is terrestrial aridity – it changes the balance between sensible heat – measured by thermometers – and latent heat. While total energy remains the same – surface temperature is biased hot.
https://watertechbyrie.files.wordpress.com/2017/06/spei-e1527825601176.jpg
Last time I showed this to #jiminy there was some post hoc rationalization or other. It doesn’t change the narrative of course.
You were mystified by the differential heating. I am not. It’s just physics. Same as happens in the summer, same as happens after sunrise. The land surface warms faster in response. Thermal inertia. It’s that easy.
Thermal inertia simply means that it takes time to heat water in a pot. Oceans are warmed from above to depths of a 100 meters or so by incoming SW that varies annually by +/-10 W/m2. It varies with sea surface temperature and cloud and with aerosol emissions. Heat is carried down to depth by turbulent mixing and to the warm surface layer by convection. The land surface temperature is biased not by aridity.
There are physical realities that go far beyond pots on a stove. An inability to acknowledge any of them is #jiminy’s brand of cultural ignorance.
So you also don’t understand why the oceans warm more slowly in the summer.
It is easy. The ocean warms and cools more slowly and lags as the seasons change. Thermal inertia does that. We see the warming side of this in the temperature record. You haven’t figured it out yet, maybe not with the seasonal cycles either. Common knowledge.
https://www.climate4you.com/images/ArgoWorldOceanSince200401%2065N-65S.gif
He gets me every time with snide digs about my ignorance that are central to the persona.
But the ocean surfaces remain in local thermodynamic equilibrium with the atmosphere and are the heat engine of the planet. Land surfaces are both cooler and warmer – as heat transport processes are so much faster in oceans than slow conduction in soil and rock – and as oceans are the global energy store. Substantial warming and cooling happens on an annual basis from orbital eccentricity – making #jiminy’s thermal inertia meaningless.
But the divergence in average warming rates is an aridity artifact of the surface temperature record. It doesn’t exist in the troposphere. It is to do with physics of latent and sensible heat.
Have you noticed how the seasons are opposite in each hemisphere, but the eccentricity effect isn’t? Making stuff up as you go along is not helping you. Fast heat transport is what gives you low thermal inertia. Water has a faster vertical heat transport than land. Heat is distributed over a deeper layer, which leads to more heat capacity and lower thermal inertia. You won’t understand, but there it is in terms of physics.
…and when I said low thermal inertia, I meant high thermal inertia. That’s the oceans.
There is a lot of variability – but can you see any lag? CERES is red and Argo blue.
https://watertechbyrie.files.wordpress.com/2018/05/ocean-heat-and-power-flux.jpg
Why would you expect a lag when you’re accumulating the flux? The lag is between the instantanous flux and temperature. What you’re showing is energy conservation and the units of accumulated flux should be J/m2 not W/m2.
Oh for god’s sake. All I did was accumulate the average monthly energy imbalance – that changes mostly in outgoing energy – on the same time scale as Argo. Easy enough to put it in zeta Joules.
https://watertechbyrie.files.wordpress.com/2018/12/loeb-2018-figure-1.png
Although this one shows energy accumulating in the system but not at the surface. Hmmmm… so it must be in the oceans. That’s conservation of energy.
If #jiminy ever got past reiterating and misrepresenting basic physics and neglecting the rest it would be a Christmas miracle.
Do you understand the difference between accumulating a flux and the flux itself. It’s the difference between a sine and a cosine. Integration is the key word you need to look up. Plot the unintegrated flux against the temperature to see what I mean about a lag.
Do you understand the difference between energy and power? 1 W over one second is one Joule. Multiplying by a constant too difficult a concept? You need to mess about with trivialities instead?
If you understand that why are you comparing a temperature trend to an energy, not a heating rate?
The change in energy content of the oceans is measured as changes in temperature. What causes ocean heat to change is the power flux imbalance.
I think I know what the problem is. jiminy’s wild, post hoc cognitively dissonant, motivated reasoning.
The average is 0.8 W/m2 – and frankly I don’t know what he is whining about.
https://watertechbyrie.files.wordpress.com/2018/05/power-flux1.jpg
I think you now realize that accumulating energy causes warming.
And when you think about sin and cos waves being exactly the same just out of phase – and that what is happening is differentiation rather than integration – you realize that either he is treating the readership here as mathematically dismal or is so himself.
d(H&W)/dt = energy in – energy out
Where H&W in the first differential global energy equation is heat and work.
Do you still think the ocean temperature in J does not lag the forcing in W/m2?
A Drier Future?
Steven Sherwood has been around Australian hydrology for a while – but this is a puff piece not aimed at serious hydrologists.
This is where I sourced my graph. You might find that it goes well beyond the simple conceptual relationship between precipitation and evaporation discussed by Sherwood.
http://spei.csic.es/
Nor is it likely that models can predict regional rainfall in 2100 – or that we have much of a handle on the limits of natural variability.
https://watertechbyrie.files.wordpress.com/2018/05/nile-e1527722953992.jpg
Well obviously he needs to let us know he going to ignore it. 😂
Don’t show me Nilometers.
And I am not having a go at Steven Sherwood. It is a so-called science communication piece and not actually hydrological science. That JCH and #jiminy can’t tell the difference is not a surprise.
Wonder if Sherwood thinks you’re serious? Lol.
For people whose motivated insults keep disappearing – seriousness is moot.
“The heat content of surface air (i.e.,z right above ground level, so that z = 0 can be assumed)
can be expressed as:
H = CpT + L q
where Cp is the specific heat of air at constant pressure,T is the air temperature, L is the latent heat of vaporization, and q is the specific humidity [Haltiner and Williams, 1980]. The quantity, H, is called moist static energy and can be expressed in units of Joules kg’. The
surface dry static energy can be written as:
S = CpT
Surface air temperature trends that have been reported monitor S.The monitoring of
H, however, is the more appropriate metric to assess surface global warming.” https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004EO210004
Terrestrial aridity biases the surface temperature hot. Using the Standardised Precipitation-Evapotranspiration Index (SPEI) – global terrestrial aridity has increased since the 1980’s – coinciding with the period of land/ocean surface temperature divergence. Something in fact consistent with the Sherwood science communication piece.
And whether I am a serious hydrologist or not is not something that JCH is equipped to judge.
frankclimate:
Regarding the two papers that you cited:
In the first paper sulfate aerosols are considered to have only a cooling effect. However, when they are removed from the atmosphere, WARMING will naturally occur, because of the cleaner, more transparent air.
With respect to warming due to black carbon, I have seen no evidence that it actually causes any changes in average global temperatures. Possibly some minor local effects.
However, all temporary changes in average global temperatures can be associated with increasing or decreasing levels of SO2 aerosols in the atmosphere.
The gradual rise in temperatures since circa 1975 has been due to natural recovery from the LIA (.05 deg C./decade), and reductions in Anthropogenic SO2 aerosol emissions due to Clean Air efforts ,with emissions falling from 139 Megatons in 1979, to ~80 Megatons today. because of the cleansing of the air.
Robert I. Ellison:
You wrote “There are two papers. The one you didn’t read with any attention to detail discusses cloud change over the Pacific as the major cause…”
For cloud changes to occur, there first has to be a driving force, which is increased warming of the Earth’s surface due to greater intensity of the sun’s radiation. This increased intensity is the result of reduced atmospheric SO2 levels (cleaner air), the cause of all El Ninos.
ENSO is a stochastically forced charge/recharge oscillator – La Nina cause El Nino. And ENSO causes cloud changes anti-correlated with SST.
e.g. https://www.mdpi.com/2225-1154/6/3/62
sorry… charge/discharge…
Robert I.Ellison:
What a piece of garbage!
The authors are clueless as to what actually causes El Ninos and La Ninas, which is simply changing levels of SO2 aerosols in the atmosphere, primarily due to the random occurrence of VEI4,or higher volcanic eruptions.
“La Nina cause El Nino”.
NO, La Ninas are normally caused by large Volcanic eruptions, and appear on average about 15 months after the date of the eruption, after its SO2 aerosols have circulated around the globe. A few have also been man-made.
El Ninos form after the volcanoe’s SO2 aerosols have settled out of the atmosphere (after ~ 18-24 months after the eruption) , cleansing the air, and often resulting in the formation of a volcanic-induced El Nino. A few El Ninos, especially the 2014-2016 El Nino, have also been man-made.
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“The most energetic patterns of extratropical variability in the lower atmosphere are the annular modes—the Northern Hemisphere annular mode (NAM), or Arctic Oscillation, and its counterpart, the Southern Hemisphere annular mode (SAM), or Antarctic Oscillation.
Positive values of these indices correspond to strengthened subpolar westerly winds and weakened subtropical westerlies and, therefore, to increased wind stress curl over the midlatitude ocean gyres. Positive trends in
the amplitude of both annular modes over two or more decades have been described, beginning about 1970 in the case of the NAM (Thompson et al. 2000) and 1979 or earlier in the case of the SAM (Thompson et al. 2000; Marshall 2003; Renwick 2004). The decadal trends, plus large interannual variability represented in the northern and southern annular modes, have provided a substantial fraction of the low-frequency wind forcing for subtropical and subpolar ocean circulation variability.” https://journals.ametsoc.org/doi/abs/10.1175/JPO3004.1
Polar annular modes are modulated by – inter alia – solar intensity. Low solar intensity increases wind stress and spin up sub-polar gyres enhancing upwelling on the eastern margins of north and south America in the origins of cool Pacific surface conditions. All the rest is feedbacks in resonant coupled ocean and atmosphere dynamics. Including cloud in the dominant global source of cloud cover variability (Clement et al 2009).
There is a playlist – be patient – it is worth it.
Robert I.Ellison:
A very slick presentation of completely erroneous conclusions, with respect to the cause of El Nino formation!
Essentially all El Ninos can be clearly associated with random, decreased SO2 aerosol emissions into the atmosphere, which causes increased surface warming, leading to their formation.
See: https://www.Osf.io/bycj4/
.
..
BH:It would be nice if you would refer ( only one time is enough) to peer reviewed papers instead of your own ( erroneous) wild guesses!
😉
frankclimate:
1 .My “wild guesses” pass Karl Popper’s dictum that “scientific theories must be falsifiable (that is, empirically testable), and that prediction was the gold standard for their validation”. Unlike the “greenhouse gas” hypothesis, this “model”–which simply points out that changes in SO2 aerosol emissions explain the cause of the formation of all La Ninas and El Ninos, and the anomalous global warming that has occurred since circa 1975 –meets both criteria.
It is empirically tested whenever there is a large volcanic eruption’, and also when there are man-made changes in global SO2 aerosol emissions. Its predictions as to the amount of temperature change to be expected as a result of changes in SO2 aerosol emissions are accurate to within a tenth of a degree Centigrade, or, usually, less.
As a result of this precision, there is simply no room for any additional warming due to greenhouse gasses!
2. This has been submitted to a number of Journals, but is editorially rejected because “it differs too much from what others have written” “We have read your manuscript but will not be printing it” etc. The editors refuse to print anything that questions the “greenhouse gas” hypothesis, or even send it out for review.
Because of this, there are no “peer reviewed” papers with supportive data to cite, although there are a few speculative papers. For example, “Could cuts in sulfur from coal and ships explain the 2015 spurt in Northern Hemisphere temperatures?’, and “Why Reducing Sulfate Aerosol Emissions Complicates Efforts to Moderate Climate Change”.
It’s not that your theory is horrible. I mean your math is horrible, but the existence of aerosol forcing should not be controversial. However, I find it odd that you don’t like CO2 radiative forcing, and yet you do like aerosol forcing…at best we have to study aerosol forcing a bit more to make the claims you are making. Having said that, I don’t think anybody will argue that if we decrease aerosols to which a negative feedback is attributable, then we should see more warming.
Can we assume El Nino and La Nina are a direct result of changes in aerosols? Consider the signal. It’s clear you lack the math or the conceptual framework to properly check your assumptions. And so you should first try to falsify your own claims, before wrongly claiming a better theory.
I’ll plow this plowed ground and beat this dead horse yet some more. Maybe somebody will step up and ‘splain scientifically how/why I’ve got it wrong – or not.
Radiative Green House Effect theory (TFK_bams09):
1) 288 K – 255 K = 33 C warmer with atmosphere, RGHE’s only reason to even exist – rubbish. (simple observation & Nikolov & Kramm)
https://www.linkedin.com/feed/update/urn:li:activity:6465958633963347968
But how, exactly is that supposed to work?
2) There is a 100% efficient 333 W/m^2 up/down/”back” perpetual energy loop consisting of the 0.04% GHG’s that absorbs/”traps”/re-emits per QED simultaneously warming BOTH the atmosphere and the surface. – Good trick, too bad it’s not real, thermodynamic nonsense.
And where does this magical GHG energy loop first get that energy?
3) From the 16 C/289 K/396 W/m^2 S-B 1.0 ε ideal theoretical BB radiation upwelling from the surface. – which due to the non-radiative heat transfer participation of the atmospheric molecules is simply not possible.
No BB upwelling & no GHG energy loop & no 33 C warmer means no RGHE theory & no CO2 warming & no man caused climate change.
Got science? Bring it!!
Nick Schroeder, BSME CU ‘78, CO PE 22774
Experiments in the classical style:
https://principia-scientific.org/debunking-the-greenhouse-gas-theory-with-a-boiling-water-pot/
No 33 C and K-T
https://www.linkedin.com/feed/update/urn:li:activity:6466699347852611584
Nickreality65:
Yes, I am awaiting someone who can prove that I am wrong. So far, after many posts, no one has done so.
Yes, after so many posts with more or less the same content you think it’s right? Really? You don’t worry that there is no reviewed article, bolstering your guesses?? Or could it be that everyone here is tired to contradict anymore? Just like me?
Photons are literally packets of energy that in specific frequencies interact with greenhouse gases.
‘The energy of a photon is directly proportional to its frequency. This gives rise to the equation E = hf. E is the energy of the photon. h is Planck’s constant (6.63 × 10-34 Js) f is the frequency of the radiation.”
The interactions in various ways with more greenhouse gases keep photons in the atmosphere for nanoseconds longer than otherwise – and where there is more energy there is more heat. You only have to look at the oceans.
https://watertechbyrie.files.wordpress.com/2014/06/gw-photons-animated.gif
You can see the result of interactions of photons and greenhouse gas molecules in energy snapshots taken in space through narrow apertures at different times. QED.
https://watertechbyrie.files.wordpress.com/2014/06/harries-2001.png
You see here examples of what I call skeptical curmudgeons with crude and eccentric theories. Still they are better than pissant progressive urban doofus hipster believers with memes and otherwise scant science.
frankclimate (and Richard Ellison):
According to the NASA fact sheet on atmospheric aerosols “Stratospheric aerosols reflect sunlight, reducing the amount of energy reaching the lower atmosphere and the Earth’s surface, cooling them. Human-made sulfate aerosols “absorb no sunlight but they reflect it, thereby reducing the amount of sunlight reaching the Earth’s surface”
I am not guessing, just going where the facts lead me: varying amounts.of atmospheric aerosols WILL affect the amount of the sun’s energy reaching the Earth’s surface,changing its temperature, and this simple fact cannot be ignored (but is, by all “greenhouse gas” adherents). Perhaps you can scientifically explain why it is ignored, since you appear to be in that camp.
But, of course, you won’t, because it doesn’t fit your agenda.
,
It is not ignored – wrong perhaps in the light of other work I have cited.
https://data.giss.nasa.gov/modelforce/Miller_et_2014/Miller_et_al14_fig2_upd.png
https://data.giss.nasa.gov/modelforce/
But now I am bored with your foolishness.
Robert I. Ellison:
You will note in Figure (a) that all aerosol forcings are negative, which is correct only for when they are first introduced into the atmosphere. However, they eventually settle out, and temperatures return to pre-eruption levels, and for most stratovolcanoe eruptions, actually cause a volcanic-induced El Nino.
Further, anthropogenic SO2 aerosol emissions peaked at ~135 Megatons in the early 1970’s and the warming due to their subsequent reduction due to clean air efforts is huge, but is not shown in Fig (a), as a positive forcing, as it should be. Otherwise, the diagram is useless!
“But now I am bored with your foolishness”
NOT foolish, but apparently beyond your ability to understand. A pity.
So none of the other ‘forcings’ sink in? Sorry about that.
And while volcanic emissions are cooling,
https://so2.gsfc.nasa.gov/imgs/toms_omi_inventory_data_alt_mar18.png
Mixed black carbon and sulfate emissions from incompletely combusted fossil fuels is nowhere near that simple.
https://www.pnas.org/content/pnas/113/16/4243/F2.medium.gif
“Time-course evolution of BC aerosol composition, light absorption (where EMAC-BC is the enhancement because of coatings), and associated climate effects (as DRF).”
https://www.pnas.org/content/113/16/4243
Richard I. Ellison:
“So none of the other forcings sink in. Sorry about that”
If they have any effect, the effect is so miniscule that it doesn’t show up in any Hadcrut 4 or GISS plots of anomalous average global temperatures.
What DOES show up is essentially complete correlation with changes in atmospheric SO2 aerosol emissions, of either volcanic or anthropogenic origin.(~.02 deg.C of change for each Megaton of change in global SO2 aerosol emissions).
Plus, or course, some natural warming as the Earth’ recovers from the effects of the LIA cooling (~.05 deg. C per decade, since 1900).
https://www.Osf.io/bycj4/ .
.
For your kindly attention:
https://i.imgur.com/J0Zpx2b.jpg
Source: https://niclewis.files.wordpress.com/2018/04/lewis_and_curry_jcli-d-17-0667_accepted.pdf
(It’s a peer reviewed paper)
frankclimate:
Yes, I am familiar with the graph..
It is essentially the same as the Fig (a) plot that Richard Ellison had posted on Dec. 20.
The problem with it is that it shows only negative forcings for aerosol emissions.. When they settle out, their forcings effectively become positive, since their removal causes temperatures to increase.
This warming can be substantial, and is actually shown on the graph as the “total anthropogenic emissions” line, there being no demonstrable warming from greenhouse gasses (entirely hypothetical)..
Any “peer reviewed” paper that does not consider the climatic effects of SO2 aerosols, and supports only the greenhouse gas warming hypothesis should be discarded.
Harsh words, but provable.
The atmosphere does not “trap” energy and warm the earth like a greenhouse, it reflects away 30% of the ISR “cooling” the earth like one of those reflective panels placed behind a car’s windshield.
That’s why all of your animated pretentious handwavium arguments explaining the “warming” mechanism behind a non-existent RGHE are such a load of pseudo-scientific BS.
Earth warms as albedo decreases:
With 30 % albedo: 957.6 W/m^2, 360.5 K, 87.5 C, 189.5 F
With 14% albedo: 1,176.5 W/m^2 (22.9%), 379.5 K, 106.5 C, 223.8 F
With 0% albedo: 1,367.5 W/m^2 (42.8%), 394.0 K, 121.0 C, 250.0 F
The TCRE contains a statistics bug
https://tambonthongchai.com/2018/05/06/tcre/
https://tambonthongchai.com/2018/12/03/tcruparody/
When the bug is fixed the proportionality is not seen
https://tambonthongchai.com/2018/12/14/climateaction/