Comment by Cowtan & Jacobs on Lewis & Curry 2018 and Reply: Part 2

By Nic Lewis

In an earlier article here I discussed a Comment on Lewis and Curry 2018 (LC18) by Kevin Cowtan and Peter Jacobs (CJ20), and a Reply from myself and Judith Curry recently published by Journal of Climate (copy available here). I wrote that I would defer dealing with the differences between observed and CMIP5 model-simulated historical warming, which formed the basis of CJ20’s numerical analysis, until a subsequent article. I now do so.

Differences between observed and CMIP5 model-simulated historical warming

CJ20 compared mean warming in CMIP5 model historical simulations with observed warming between varying early and late windows. They found that “Different window choices can lead to the conclusion that the model results show significantly faster warming than the observations do or that the observations warm slightly faster than the model results”. However, such a comparison is meaningless unless the evolution of forcing is the same in the model simulations as it is estimated to be in reality. We show in the Reply that this is not the case: forcing increases more slowly in the CMIP5 model mean than as estimated in LC18 based on IPCC AR5 best-estimate time series, updated to 2016 and revised where appropriate. To provide a better comparison, we remove the temperature changes caused by volcanic forcing (to which the climate system responds differently from other forcings), which are much larger in CMIP5 models than in observations – and compare total forcing with the volcanic component removed.

Figure 1 in the Reply, reproduced below, shows how observed and CMIP5 simulated historical global temperature evolution compares, before and after removal of volcanic influences.

Figure 1. CMIP5-mean and observed global mean warming before and after removing the response to volcanism: centered 9-year running means of anomalies relative to the 1869–82 mean. CMIP5 historical simulations have been extended using RCP4.5 simulation data. The averaging period is reduced at either end, to a minimum of 5 years.

The faster initial rise in observed than simulated temperature is likely due to the omission of mean volcanic forcing from most CMIP5 preindustrial control simulations. That omission reduces CMIP5-mean warming over the historical period by 0.1°C, mainly during the third quarter of the 19th century. But even from the adopted baseline of 1869-1882, the primary LC18 early window, CMIP5-mean warming eventually climbs clear of observed warming. What we said in the Reply was:

On decadal timescales, the mean evolution of warming of CMIP5 models over the historical period broadly matches that of observed warming until 2000, with some fluctuation (Figure 1, thick purple and cyan lines). When the fitted response to volcanic forcing is removed (Figure 1, black and orange-red lines), CMIP5-mean historical/RCP4.5 warming exceeds observed warming by the mid-1980s, with the gap widening from the mid-1990s.

The post-1900 cooling, and the lack of warming between the 1940s and the 1970s, in observed surface temperature with the response to volcanism removed, likely reflects cool phases of the Atlantic Multidecadal Oscillation (AMO).

Since the transient climate response (TCR) of CMIP5 models is 35% or so higher than the observationally-based best estimate in LC18, one might expect CMIP5-mean warming to exceed observed warming before then. The main reason it doesn’t is that historical forcing evolves more slowly in CMIP5 models than per the LC18 revised and updated AR5-based forcing time series. This is mainly, but not entirely, due to CMIP5-mean aerosol forcing, growing increasingly more negative than per LC18 up to the 1970s, since when it has not changed greatly.

Forcing evolution in CMIP5 historical simulations can only be derived approximately, since unfortunately it was not generally diagnosed. However, we show in the Supporting Information for the Reply that, for two models where evolving historical simulation forcing was diagnosed, it can be accurately derived as:

ΔF = ΔN + λ × ΔT

where Δ signifies a change or anomaly from a reference period mean, F is effective radiative forcing (ERF), N is global top-of-atmosphere radiative imbalance, T is global surface temperature and λ is climate feedback estimated by regression of N against T over the first 50 years after an abrupt step increase in CO2 concentration. See also my article here. This method was used in Forster et al. 2013, but with λ estimated over the full 150 years of abrupt4xCO2 simulations, a period with a much longer average age of forcing than the historical period and which generally gives lower climate feedback estimates for CMIP5 models. While use of this method involves partial circularity when going on to compare warming ratios and forcing ratios between models and observations, it appears to be quite accurate, and superior to cruder methods such as that used in Gregory et al. 2019

Figure 2 in the Reply, reproduced below, shows the estimated forcing evolution in CMIP5 historical simulations (red line) compared to the AR5-based/LC18 median estimate (black line), and how their ex-volcanic ratio (blue line) compares with the corresponding ratio of ex-volcanic warming relative to TCR (green line).

Figure 2. CMIP5-mean and AR5-based/LC18 ex-volcanic ERF relative to F2xCO2 (the ERF for a doubling of atmospheric CO2 concentration) over 1861–2016, their ratio and the corresponding ratio of CMIP5-mean and observational warming relative to respectively CMIP5-mean and observational TCR estimates, of 1.82 K and 1.33 K respectively. Based on an ensemble of 25 CMIP5 models with the requisite data and the LC18 preferred median TCR estimate when using globally-complete Had4_krig_v2 temperature data. Values are anomalies from the 1869–82 mean. Relative ERF and relative warming ratios are calculated model-by-model before computing CMIP5 means. Ratios are of centered 15-year running means (shortened to 5 years by the final year plotted, 2014).

This is what we say about Figure 2 in the Reply:

When the green line is above the blue line, CMIP5-mean warming relative to that observed is greater than predicted by their respective TCR and [ex-volcanic ERF] estimates, and vice versa. The relative warming ratio starts off much higher than the relative forcing ratio, reflecting the unusually cold first quarter of the 20th century, before falling below the relative forcing ratio during the warm period centered around 1940, when the AMO was positive. From the late 1950s until circa 1990, the relative warming ratio largely tracks the rising relative forcing ratio, but generally exceeds it as the negative phase of the AMO, which reached its nadir in the 1970s, was associated with cooler global temperature. After 1990 the relative warming ratio remains close to the relative forcing ratio, as is to be expected if the LC18 TCR estimate is accurate.

From the late 1990s on, the ratio of estimated ERF in CMIP5 models to the updated and revised AR5-based ERF used in LC18 has been stable at around 0.85. That is very close to the 0.86 ratio in Otto et al. 2013 of estimated CMIP5-mean ERF in 2010 before and after adjusting for CMIP5 models’ stronger than AR5-based aerosol ERF.


The conclusion we drew in the Reply from this analysis sums up the results of our analysis:

 The differing evolution of temperature in observations versus models is consistent with the substantially different observationally-based and CMIP5-mean TCR estimates once differences in the evolution of estimated forcing and in the effects of volcanism and multidecadal internal variability are accounted for.

Nicholas Lewis                                                                      20 December 2019

67 responses to “Comment by Cowtan & Jacobs on Lewis & Curry 2018 and Reply: Part 2

  1. Reblogged this on Climate Collections.

  2. When I understood it correctly the “observed TCR” (GMST vs. ERFtotal) is infected not only by the forcing itself but also from the internal variability and to some extent by the (muted) influnence of ERF volcano on the GMST. If one doesn’t take account of this one gets biased TCR for different selected time spans. This was also the result of my article where I tried to account carefully for the “unforced parts” in the GMST development. After removing ENSO ( I didn’t use 9-year means but annual data, much more sensitive to ENSO) , the internal variability, deduced with another approach and volcano impacts gave a TCR=1.27, in 1950…2016, very similiar to LC18. In my experience too, one must take care not to conflate the active components on the GMST development, this would lead to the ( to some extent) imprecisely results as gotten by CJ20. Correct me if I’m wrong.

  3. There are unanswered questions ranging from the reliability of forcing estimates, to whether the mean of opportunistic ensembles is a best or even sufficient statistical analysis of model uncertainty and if enough is known about the dynamic spatio-temporal chaotic response of the planetary system. Anthropogenic forcing estimates range from 1.13 to 3.33 W/m2 – included within that are estimates of direct and indirect aerosol effects more likely than not to be wildly inaccurate. Model uncertainty is several times the change in observed quantities. Large variations in TOA radiant flux due to changes in ocean and atmospheric circulation patterns are seen but are difficult to account for.

    Ultimately – the system is so complex that observations (or reanalysis at any rate) are the best metric to compare to model output.

    “Indeed, the reconstruction of this pair of modes for regional climate indices (Fig. 3b, c) manifests as a multidecadal signal propagating across the climate index network (with certain time delays between different indices)—a so-called stadium wave (refs. 20,35,36,37)—which we will refer to as the global stadium wave (GSW) or, when referring to the global-mean temperature, Global Multidecadal Oscillation (GMO), although, once again, the oscillatory character of this phenomenon is impossible to establish due to shortness of the data record. The phasing of indices in the GSW is consistent with earlier work (ref. 20), which analysed a limited subset of the Northern Hemisphere climate indices (Supplementary Fig. 6). 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.”

    The ‘Nilometer’ record – analysed by Harold Hurst using a scaling methodology in the middle of the last century – shows that the GSW is not periodic but dynamical. Behaviour since observed in many climate data series. This implies that sensitivity is neither high not low – or indeed constant – but moderate while in an attractor state space and high at bifurcations.

  4. “The post-1900 cooling, and the lack of warming between the 1940s and the 1970s, in observed surface temperature with the response to volcanism removed, likely reflects cool phases of the Atlantic Multidecadal Oscillation (AMO)”

    Thank you.

    • Perhaps not. Decadal warming and cooling emerge in synchronous phase locking of Pacific and Atlantic signals. It’s a different paradigm first seen in the network model of Tsonis 2007. I think it is not exactly caused – but the evolution of the global stadium wave signal is biased to one state or another by the polar annular modes. These vary with both solar intensity and global warming. I think this is why the cumulative NAO vs AMO plotr of Smeed et al works.

      But the dominant changes in the energy dynamic of the planet are centered on the upwelling regions of the eastern Pacific. This is of course also a positive cloud feedback to global warming more generally.

      “We emphasize that the NE Pacific cloud changes described above are tied to cloud changes that span the Pacific basin. Despite much less surface sampling in the Southeast (SE) Pacific, cloud and meteorological changes in that region generally occur in parallel with those in the NE Pacific (Figs. 2 and 3). Also, we find that the leading mode in an empirical orthogonal function analysis (15% of the variance) of global cloud cover (fig. S3) has a spatial pattern similar to that in Fig. 3 and the time series shows the same decadal shifts as in Fig. 1, indicating that the changes in the NE Pacific are part of a dominant mode of global cloud variability.” Amy Clement et al. 2009, Observational and Model Evidence for Positive Low-Level Cloud Feedback

      This is Marcia Wyatt’s contribution to the development of this not so new paradigm.

      • Robert I. Ellison:
        1. I’m not quite sure what you mean by “But the dominant changes in the energy dynamic of the planet are centered on the upwelling regions of the eastern Pacific. This is of course also a positive cloud feedback to global warming more generally.”. The E Pacific does appear to play a major role on timescales up to a few decades, but I don’t see how that can be a “positive cloud feedback to global warming”. (a) the E Pacific can play its own tune so is not a feedback, and (b) examination of the cloud activity in the region shows that clouds increase follows temperature increase and that cloud feedback (as used by the IPCC) is therefore negative. See also 3 below.
        2. Please can you provide a link to Marcia Wyatt’s contribution.
        3. Amy Clement’s paper says that it used observations and models, that observations “indicated that clouds act as a positive feedback in this region on decadal time scales” and that when the models were tested against this only one model could simulate this cloud action. On this I would say that (i) the paper is not always getting cause and effect the right way round, (ii) the different timescales are not being identified correctly, and (ii) the number of models is not specified (not that I could see) but only one model passing the test is not exactly impressive.

      • It is a feedback to SST. So as much as AGW warms the oceans – we might expect a positive feedback rather than not.

        I expect that there is variability in upwelling in the eastern Pacific over decades to millennia.

        As for cloud feedback. It is a mechanism involving Bénard–Rayleigh convection and closed and open cell marine boundary layer stratocumulus.

        I briefly discuss it here – – and here -
        You may find more than you want on Marcia Wyatt here – – try this as an introduction –

        The models are not hugely relevant but the observations are. Cause and effect are inferred from data – not the other way around.

    • “The post-1900 cooling, and the lack of warming between the 1940s and the 1970s, in observed surface temperature with the response to volcanism removed, likely reflects cool phases of the Atlantic Multidecadal Oscillation (AMO)”


      • JCH – I enjoyed your analysis but I doubt it would pass peer-review. But the original claim can be tested easily in the climate models. The models just apply the laws of physics (so we are told) so they must be able to model past cycles and predict future cycles of the AMO. So the relationship between the AMO and the cooling periods must be completely visible in the climate models. Mustn’t it?????

      • From what I’ve read, you would make a great AMO believer.

      • JCH:

        Can I figure out what you’re saying? The Pacific is big and the PDO can deliver. The Atlantic is smaller. It does have a significant Northern influence. It doesn’t have something comparable to the ENSO region. ENSO does its thing in the middle of an ocean in a narrow band. The Atlantic can impact land more because there’s so much of it compared to the Atlantic’s size around it. Is it a size thing? A past history thing?

        The Pacific has circular motions of it gyres but where’s the sinking? The Atlantic has sinking suggesting a surface conveyor belt. ENSO seems to transport warm West to East at the equator for its big show. The Atlantic may just vary its Equator to Northern regions transportation speed.

        So we have two different mechanisms and two different sizes.

      • The Pacific is big and the PDO can deliver. The Atlantic is smaller.
        There is more ocean and more stored energy in the Pacific.
        The Pacific is blocked from causing major change in the Arctic. The Pacific and the Antarctic work more together on the Southern Hemisphere climate cycles. The Atlantic and the Arctic work more together on the Northern Hemisphere climate cycles. Antarctic ice core records show a more tightly regulated temperature cycles than Greenland ice core records for the Arctic. The Pacific delivers warm water for evaporation and snowfall and ice sequestering on Antarctic land. The Atlantic, and the Gulf of Mexico delivers warm water for evaporation and snowfall and ice sequestering on Greenland and other cold places where much ice is sequestered in the Northern Hemisphere.

      • I believe there is an Atlantic version of ENSO, but it’s so small nobody notices it.

      • It straddles such a huge part of the high energy global tropics’

        Let me quote Koren (2017) yet again – although someone is bound to complain.

        “Marine stratocumulus cloud decks forming over dark, subtropical oceans are regarded as the reflectors of the atmosphere.1 The decks of low clouds 1000s of km in scale reflect back to space a significant portion of the direct solar radiation and therefore dramatically increase the local albedo of areas otherwise characterized by dark oceans below.2,3 This cloud system has been shown to have two stable states: open and closed cells. Closed cell cloud systems have high cloud fraction and are usually shallower, while open cells have low cloud fraction and form thicker clouds mostly over active cell walls and therefore have a smaller domain average albedo.4–6 Closed cells tend to be associated with the eastern part of the subtropical oceans, forming over cold water (upwelling areas) and within a low, stable atmospheric marine boundary layer (MBL), while open cells tend to form over warmer water with a deeper MBL. Nevertheless, both states can coexist for a wide range of environmental conditions.5,7”

        It’s a bistable chaotic behaviour (whoops there’s that word again) in which closed cells tend to persist for longer over cooler water before raining out from the center leaving open cells.

      • Here’s where I am going with this. The Pacific obviously has more area. But the shape of the Atlantic basin and its currents may equalize things with the Pacific’s size. Two areas of study are what happens to the ENSO region and what happens to the Gulf Stream and its smaller children? I am going to guess more studies are done on the Atlantic. We worry more about Greenland more than Antarctica. The Atlantic punches into the Arctic. The Arctic responded more than Antarctica. Because of the Atlantic, so I say. With the Pacific being the biggest influence of all the oceans on Antarctica, nothing happened except some dire warnings most of us just write off. Yes, I am aware of JCH’s ranking of the Atlantic or more like the AMO or something, and I am not arguing that as much as looking for an explanation of how the Atlantic seems to punch up? I believe in mass. And that the oceans mass will make this whole thing boring by absorbing and moderating and turning it all into only sea level rise. So I am looking beyond mass and volume while recognizing it’s also important.

      • You’re looking at a prize fight. Early 20th-century warming is cruising. He’s looking like a fearsome foe. Unstoppable. Suddenly, ETCW gets knocked flat (the flat red arrow is the KO.)

        The punches are so fast nobody sees them.

        Howard Cosell draws two possibilities, a blue 1-2 combo and a green 1-2 combo.

        Be honest rags, is what did it the blue 1-2 punch or the green 1-2 punch?

      • Ragnaar

        Is that handle of Viking origin? Funny story. I’m having some minor surgery on my left hand. The surgeon told me that the problem only occurs in people with Viking ancestors. So I am exploring my cultural heritage. Rape and pillage. It’s all making sense now.

        At this late stage I am inclined to think that synchronous chaos and the global stadium wave govern the evolution of global climate. Anastasios Tsonis used a mathematical network approach to analyse abrupt climate change on decadal timescales. Ocean and atmospheric indices selected – in this case the El Niño Southern Oscillation, the Pacific Decadal Oscillation, the North Atlantic Oscillation and the North Pacific Oscillation – can be thought of as chaotic oscillators that capture the major modes of northern hemisphere climate variability – at least on decadal to millennial scales. The network model treated these indices as nodes on a network. Tsonis and colleagues calculated the ‘distance’ between the indices. It was found that they would synchronise at certain times and then shift into a new state.

        I will link to this Tim Palmer video again – the relevant bit starts at the 11 minute mark. Although we may have to complex it back up a bit. There are dozens at least these of coupled chaotic oscillators on the spatio-temporal complexity of the Earth system. As such – I have given up having favorites. Sometimes their motions will cohere as a result of coupling and then diverge urged by humanity and the invisible and seemingly random hand of God.

        The Arctic vortex is a great see-sawing of masses of air pushed deep into lower latitudes or constrained to high latitudes as polar surface pressure dictates. Surface pressure respond to solar activity, AGW and seemingly random shifts in atmospheric circulation. At this juncture they are all leaning in the direction of reduced AMOC, a col north Pacific and cold and stormy weather in North America and Eurasia. It will all work out in the end. Unless a runaway ice sheet feedback covers large parts of the hemisphere in 3 mile deep ice.

        A couple of references on the solar connection –

        The Arctic Oscillation (AO) – or the North Atlantic Oscillation (NAO) in that region – not so much forces as biases the system to one state or another. I think that’s why the AMOC and AMO overlain with cumulative NAO chart of Smeed et al works so well.

        “Recent scientific evidence shows that major and widespread climate changes have occurred with startling speed. For example, roughly half the north Atlantic warming since the last ice age was achieved in only a decade, and it was accompanied by significant climatic changes across most of the globe. Similar events, including local warmings as large as 16°C, occurred repeatedly during the slide into and climb out of the last ice age. Human civilizations arose after those extreme, global ice-age climate jumps. Severe droughts and other regional climate events during the current warm period have shown similar tendencies of abrupt onset and great persistence, often with adverse effects on societies.”

        It all seems a matter of mechanisms at different spatial and temporal scales. In the words of Michael Ghil (2013) the ‘global climate system is composed of a number of subsystems – atmosphere, biosphere, cryosphere, hydrosphere and lithosphere – each of which has distinct characteristic times, from days and weeks to centuries and millennia. Each subsystem, moreover, has its own internal variability, all other things being constant, over a fairly broad range of time scales. These ranges overlap between one subsystem and another. The interactions between the subsystems thus give rise to climate variability on all time scales.’

      • You’re theory has the same fate as Frazier.

      • JCH:

        Your above plot is pretty convincing.

        If the Atlantic does have an oversized influence, what is the cause of that?

  5. I have some quibbles about how to handle the CMIP5 model forcing/temperature issue in the historical period. Why not use the EBM method to determine a GMST change using the AR5 observed forcing instead of the reverse determination? Forcing is an external variable when using the EBM and it seems backwards to me that forcing would be derived from internally generated model properties and variables of temperature and TOA net radiative changes in conjunction with the climate sensitivity related feedback parameter. Forcing in my view should be applied at the same values for all individual models. That it is not becomes more problematic when considering that in the future period most of the individual models respond as would be expected given the individual model feedback parameters and F2XCO2 values and an applied forcing that is the same value for all models.
    Doing this in my view would not change the results of what Nic has presented here, but with me it is more an issue of consistency. Also since model temperature change is used in the historical period more than forcing as a descriptive element of climate change what I propose could be called an adjusted GMST change for the individual model much the same as Forster et al. (2013) refer to an adjusted forcing for the individual model that is derived in part from GMST change.

    • “Forcing is an external variable when using the EBM and it seems backwards to me that forcing would be derived from internally generated model properties and variables of temperature and TOA net radiative changes in conjunction with the climate sensitivity related feedback parameter. Forcing in my view should be applied at the same values for all individual models.”

      ‘An Energy Balance Model or ‘EBM’ does not attempt to resolve the dynamics of the climate system”
      “Why not use the EBM method to determine a GMST change using the AR5 observed forcing instead of the reverse determination?It may not give the right answer.
      Observations tend to vary with natural variability rather than following the known rules of CO2 enhanced feedback.

    • ken,
      The warming in each model over the historical period if forcing equal to its best-estimate actual evolution could be and were applied in it would almost entirely reflect each model’s TCR, which is already known.

      Unfortunately forcing varies hugely between CMIP5 models for the same changes in atmospheric concentrations, so forcing evolution in their historical simulations will also differ greatly, particularly as some forcings are negative. One might expect that the instantaneous radiative forcings at the top-of-atmosphere (IRF) would be broadly similar, since these are not dependent on atmospheric adjustments, but they also differ greatly. These are ranges (in W/m2) of diagnosed IRF for the smallish subset of CMIP5 models used in the PDRMIP study:

      2xCO2 2.1 to 3.1

      3xCH4 0.8 to 1.8 (many models ignore SW absorption by methane)

      10xBlackCarbon 1.0 to 4.3

      5xSulphate -2.4 to -6.1

      • Nic, I agree with what you say here, but it does not address the differences I see between the historical period (1861-2005) and the future period (2006-2100) with regards to the individual models response to forcing and the models internal climate sensitivity.

        I went back to the Forster et al. (2013) data for the historical and future periods and compared the regression fits of individual model GMST changes versus F2XCO2/ρ and 1/ρ values for the individual models between periods. The historical period had no significant correlation while the future period had a very high correlation. The Forster paper has a plot for the historical period showing no correlation there, but is silent about the future period. Their data held all the information and differences I found but did not make the historical to future period comparison.

      • Ken
        The future period will have a high intermodel correlation between GMST change and F2XCO2/ρ + 1/ρ since the change in CO2 (+ other GHG) forcing dominates that in aerosol forcing in the future. But in high aerosol forcing models (the majority) that was not true over the historical period.

      • Nic, I have looked at what you point to here whereby an equal forcing differential in the historical period between the individual models carrying over into the future period will have a lesser effect on degrading the correlation of the change in GMST to F2XCO2/ρ in the future period because the total forcing is greater in that period.

        This effect to some degree has to be present, but when I looked at the effect I have found it not able to account for the difference in correlation between periods. A carry-over effect should show up as a blend from the historical to future period on increasingly higher correlations. That does, however, not seem to be an obvious effect when looking at the progression of increasing forcing (and temperature) on going from RCP 4.5 through RCP 6.0 to RCP 8.5 where the correlations stay much the same.

        I have done some simulations in order to show this in better form and I should post those results here for you to see.

    • In order for the differential forcing between individual models that appears in the historical period (and that degrades the correlation of the change in GMST (ΔT) to F2XCO2/ρ to insignificance) to change temperature trends and changes for an individual model it must be applied in an overall increasing or decreasing fashion. If that differential forcing continued into the future period that period would also show a degradation of the aforementioned correlation but tempered by the fact that the differential forcing would become smaller relative to the total forcing – which is increasing. Alternatively the differential forcing could plateau going into the future period and the result would be that what occurred in the historical period with regards to differential forcing would have no effect on the correlation of ΔT to F2XCO2/ρ. The alternative view would be in line with an aerosol atmospheric level increasing in the historical period and then leveling off in the future period.

      The results of my analysis to this time favor the alternative view but do not preclude some carryover effect. I doubt there is much interest here in the results of my analysis or discussing those findings, and thus I will merely post here a brief summary of some of that analysis.
      I looked for a breakpoint near the transition from the historical period into the future period for individual model changes in GMST versus forcing for the RCP 4.5 scenario for 81 model runs from 1950-2100. Plotting makes visible a break for most of the model runs, but here I wanted a more objective measure of the break. Sixty of the runs had a breakpoint with mean year being 2000 with a 5.2 year standard deviation, the mean/standard deviations of the first segment (earliest) and second segment slopes were 0.48/0.20 and 0.61/0.10, respectively. The slopes of the second segment were very significant with an r square value of 0.99 while the first segment slopes were mostly significant with an average r square of 0.37. Five of thirty nine models had no runs with significant breakpoints, while five models with multiple runs had some runs with significant breakpoints runs and some runs without significant breakpoints. Breakpoints are an indication of an abrupt change at that point in time in the correlation of the change in GMST versus a single applied forcing*.

      Another measure of the change between the historical and future time periods for the change in GMST versus F2XCO2/ρ was determined using a calculated GMST change (calcΔT). The calcΔT values for individual models for the RCP 4.5 scenario in the 1950-2005 historical and 2006-2053 future periods were determined by the equation ΔT=(F2XCO2i/F2XCO2m)*ΔF/ρ where F2XCO2i is the individual model value and F2XCO2m is the mean of all the models F2XCO2 values used in the analysis, ΔF is the change in forcing* in the two time periods and is a specific forcing series for RCP 4.5 and ρ is the climate resistance. For the historical period the derived ΔT (derΔT) is subtracted from the calcΔT to yield a difference ΔT (difΔT). The historical calcΔT for the individual models is then multiplied by the ratio of the means of the calcΔT values for individual models for the 1950-2005 and 2006-2053 periods giving 2XcalcΔT. The individual model difΔT values for the 1950-2005 period are subtracted from the individual model values of the 2XcalcΔT.

      What all these machinations accomplish is to simulate carrying all of the individual model forcing differentials from the 1950-2005 time period over into the future period of 2006-2053. The end results of this simulation is that instead of the actual regressions results for RCP 4.5 in the 2006-2053 period with slope, t.value slope and r squared value of 0.73, 6.3 and 0.67, respectively we obtain for the simulation the values of 0.42, 3.6. 0.27. This simulation shows that at most only part of the differential forcing is carried over to the future period from the historical period.

      These results in my view mean that the best comparison we have for the observed and modeled climate with regards to temperature is the comparison of sensitivity measures such as ρ, λ and F2XCO2 and almost certainly not GMST changes in the historical period. The sensitivity measures are relatively well established in the models compared to the observed and that is why ongoing analyses and investigations carried out by scientists such as Lewis and Curry are critically important in determining the skill of models in replicating the observed climate.

      * Forcing series data was taken from Meinshausen et al., 2011, Climatic Change (Special Issue), DOI: 10.1007/s10584-011-0156-z, The RCP Greenhouse Gas Concentrations and their Extension from 1765 to 2300

  6. What is the effect on the match between the observed temperature profile of the atmosphere vs the modeled if one would run the CMIP5 models with your more accurate estimates on TCR and aerosol forcing? Isn’t it so that satellite measurements as well as weather balloons have shown that the warming profile of the atmosphere is not consistent with model results? Would
    the match be better with the new estimates? And furthermore, can one draw any conclusions regarding the size of the water vapour feedback and feedback due to cloud formation from these new results?

    • Fredrik: “if one would run the CMIP5 models with your more accurate estimates on TCR and aerosol forcing? ” It’s impossible because the sensitivity is an outcome of a GCM, you can’t give it as an input. The TCR (ECS) is the result mostly of the parametrisation ( e.g. clouds microphysics) of the GCM, deep inside of the model.

      • Ok I see. But can one turn the question around then; is it possible to get warming/temperature profile that agrees with observations with other parametrizations, that result in a TCR in good agreement with that estimated by Lewis and Curry?

      • Frederik, this is possible but only with the estimation of the used forcing agents. If the CMIP5’s make some reliable estimations about the GMST development 1976…2005 ( which is the case) and their sensitivity is too high (TCR=1.3 from LC18 and 1.85 is the TCR of the model mean= factor 1.42) to some degree than there is included, that some forcing agent ( in paricular ERF aerosols ) has some masking effect on the outcome. I tried to figure this out some time ago:

      • Interesting, thanks Frank! I read your previous post and the Sato paper. So the conventional GCMs overestimate TCR because (at least partly) of too much cooling due to aresosol-cloud interactions, when in reality (based on observations) the global mean ACI is leading to a slight warming effect, i.e., the same point Nic Lewis is making in the present post and their Reply.

        Then it should be interesting to run a GCM with the new mechanism for API (in essence what Sato et al did) and see if the temperature curve over time can be reproduced, and on the same time giving a temperature profile of the atmosphere that agrees with observations, and giving TCR in agreement with the present Lewis & Curry estimate. This is important, I think, because it is not enough that a model can reproduce the temperature history, but it must also be consistent with observations higher up in the atmosphere. Otherwise, we know there is some error with the model, and the match with temperature record is just due to some combination of parametrization (but wrong).

      • Thanks Fredrik for the reading of my humble contribution. The model simulations were uptdated indeed, in the last few month the new model familiy for AR6, called CMIP6, was released . In mean the sensitivity increased , not decreased.
        Some scientists lost the trust in those models, e.g. here:
        and here: .
        Some commenters here hyped the last Hausfather paper ( ) to show, that older models from the 70s and 80s reproduced the GMST with good performance to the 2010s. However, when you look at figure 2 ( bottom) of this paper you find the estimated TCR-values of the used models. The authors compare those vs. the “observed” TCR in short time spans accepting a high uncertainty. We know from LC18 and other papers the observed TCR much better constrained: about 1.3 K/ 2*CO2. When comparing this with the used models in the Hausfather paper one gets this: .
        The TCR mean is about factor 1.5 too high also in the “older models”. It’s the real message of this paper. The replication of the GMST up to the 2010s is not a proof that the estimated TCR is well in the ballpark, the differences to the real world would be amplified into the future. And models are made mostly for the estimation of the future.
        In the end: The beat goes on.

      • Ok thanks Frank for the good links! I agree they are a bit sceptical to the models, but especially the Nature article seems a bit odd to me. Where is the logic in first saying that it is biased errounously high, and then in the next breath say that the results still gives more support to fast mitigations? I think instead that all new results suggesting low climate sensitivity tells us that we should take it easy and not make fast and costly changes to society.

      • Frdedrik: The title of the article is a donation to “Nature” IMO to get it published. It has nothing to do with the content, which is scientificaly. The title itself isn’t.

  7. The model of climate control we are now obliged to believe is that just two “control knobs” control the climate. CO2, and volcanoes. The oceans don’t exist as a climate factor.

    Both these “control knobs” are illusions of smoke and mirrors, relying primarily on the inversion of cause and effect.

    CO2 is simple a proxy of global temperature. As oceans warm their gas solubility declines and CO2 is released into the atmosphere. Vice versa during cooling.

    Likewise, warming climate decreases ice cover which increases volcanism:

    Conversely, during glaciation, the pressure of ice and increased viscosity of the crustal rock caused by ice cooling decrease volcanoes.

    Thus both CO2 and volcanoes follow, rather than lead, global climatic temperature changes.

    Looking at climate proxy data from deep time, the time resolution is low enough to allow inversion of cause and effect without difficulty. More recent and high resolution ice core data however clearly show temperature change preceding CO2 change. But inversion of cause and effect is such an exquisitely developed art now in the climate community that explaining away this “inconvenient truth” is also considered trivial.

    FWIW, the reality is that neither CO2 nor volcanoes are all that irrelevant to long term climate. Ocean circulation and continental configuration are the real control knobs. A billion cubic kilometers of water with its exceptionally high heat capacity can’t be so easily dismissed as a climate factor.

    • phil salmon: CO2 is simple a proxy of global temperature. As oceans warm their gas solubility declines and CO2 is released into the atmosphere. Vice versa during cooling.

      It is not that simple: global temperature controls how rapidly CO2 is absorbed into the ocean, as well as how rapidly CO2 is released from the ocean. With humans generating large and increasing amounts of CO2, temperature mainly controls the net transfer from atmosphere to ocean (and other sinks). The rate of dissolution of CO2 from atmosphere to ocean depends also on the difference in concentration: it is unlikely that a tiny change in ocean temp (ca 0.4% on the absolute scale) could force an increase in the atmospheric concentration from ca 280 ppm to ca 405 ppm, especially in the face of increasing anthropogenic production of CO2.

    • Matthew
      I should have mentioned that I don’t doubt that the recent rise in CO2 from 280 to 400 ppm is in large part anthropogenic. Although a significant part could be natural associated with warming recovery from the LIA. I was mainly addressing historical climate data.

      If fossil plant stomatal data are to be believed, there has been appreciable CO2 variantion in the atmosphere in the past, including recent centuries.

  8. Bottom of the seventh paragraph: since when it has not changed greatly. Typo? when instead of then

  9. JCH, I usually laugh when you go off on your anti-AMO bias or your anti-LaNina bias. Is global warming so important to you that you simply dismiss any counterintuitive information? It’s not as if warming doesn’t exist it’s simply that there are other factors as well. Perhaps your just exaggerating to make a point, but I think it just reflects poorly on you however humorous.

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  11. “Total global emissions of black carbon using bottom‐up inventory methods are 7500 Gg yr−1 in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. ” Bond et al 2013 –

    A factor of three includes the black carbon lensing effect in mixed species emissions. Indirect cloud effects are an order of magnitude more uncertain.

    Uncertainty in initial conditions drives ‘irreducible imprecision’ in models. To quickly review – this perturbed physics ensemble constrained to surface temperature observations is compared to CMIP opportunistic ensembles. 66% confidence intervals are shown for the former and an ‘expert likely range’ for the latter. Only vastly improved observations of state variables – and much finer grids – can reduce uncertainty in models.

    We may note that internal variability is missing from state of the art models. Further divergence of real world observations from the mean of either approaches is inevitable. Decadal in the Kravtsov et al analysis and centennial to millennial in the real world. Dynamical systems theory says that divergence could be abrupt.


    The source of aerosol emissions is instructive. Emissions can be mitigated using existing clean and efficient technology. Including for cooking – saving millions of lives a year. An in transitioning from slash and burn agriculture to 21st century land and soil management. Carbon in grasslands will cycle through atmosphere, soils and biology. Intense burning releases more carbon dioxide to the atmosphere that well managed grazing.

    I note that research priorities in Joe Biden’s proposed DARPA-C include downstream processing of captured CO2 to produce ‘other products’. What to do with captured CO2 is the last remaining technological obstacle to ‘clean coal’.

    “Diesel engine sources of BC appear to offer the best mitigation potential to reduce near-term climate forcing. In developed countries, retrofitting older diesel vehicles and engines is a key mitigation strategy; in developing countries, transitioning a growing vehicle fleet to a cleaner fleet will be important.’ Bond et al 2013

    The construction industry is a significant source – where economics drives efficiency and innovation.

    The choice is there. We may count unicorns on pinheads with simple math and simpler assumptions. Or we can halt the madness and achieve different outcomes with different methods.

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  13. Sure hope the 97% worked out those theories for the problems with basic physics, but, it appears not …

    Australia, 2017: “Last year, there were just three for the entire season, itself a record for the quietest cyclone count in the country’s history.

    This year, on the other hand, the low numbers have blindsided them.
    Dr Andrew Watkins the manager of climate prediction services at the bureau, said scientists are, at present, trying to get to the bottom of exactly what happened.

    They have a few theories, Dr Watkins said, and are presently crunching the data to get to the bottom of it.
    “There are several theories and at the moment the data is pouring in from satellites and everywhere,” he said.

    “Many people just think tropical cyclones form over warm water, which is true, but there are other factors, a few things need to come together to form a cyclone.
    “They are quite amazing things that don’t form that easily.

    Maybe something strange is happening with one of these factors.

    But he said “basic physics” governed that [global warming] would increase the intensity of cyclones in the future.

    “For each degree of warming there is about seven per cent more moisture in the atmosphere, so there is more moisture now than, say, 50 years ago,” he said.

    It does not, however, explain this season’s anomaly.

    “Being perfectly honest, [global warming] is a factor in most of our climate science these days but in terms of tropical cyclones you couldn’t put this season down to [global warming],” he said.”

  14. Measured water vapor trend has increased faster than possible from feedback.

  15. So, we can confirm now with great confidence, that a Planet or Moon Without-Atmosphere Effective Temperature Complete Formula, according to the Stefan-Boltzmann Law, is:

    Te.planet = [ Φ (1-a) So (1/R²) (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (1)

    We have collected the results now:

    Comparison of results the planet Te calculated by the Incomplete Formula, the planet Te calculated by the Complete Formula, and the planet Te (Tsat.mean) measured by satellites:

    Planet or…..Te. incomplete……Te.complete……Te (sat.mean)
    Mercury………….437 K………….346,11 K…………..340 K
    Earth…………….255 K………….288,36 K…………..288 K
    Moon…………….271 Κ………….221,74 Κ…………..220 Κ
    Mars…………….211,52 K………215,23 K……………210 K

    These data, the calculated with a Planet Without-Atmosphere Effective Temperature Complete Formula and the measured by satellites are almost the same, very much alike.
    They are almost identical, within limits, which makes us conclude that the Planet-Without-Atmosphere Effective Temperature Complete Formula

    Te.planet = [ Φ (1-a) So (1/R²) (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (1)

    can calculate planet mean temperatures.

    It is a situation that happens once in a lifetime in science. Although the evidences existed, were measured and remained isolated information so far.
    It was not obvious one could combine the evidences in order to calculate the planet’s temperature.
    A planet-without-atmosphere effective temperature calculating formula
    Te = [ (1-a) S / 4 σ ]¹∕ ⁴
    is incomplete because it is based only on two parameters:

    1. On the average solar flux S W/m² on the top of a planet’s atmosphere and
    2. The planet’s average albedo a.
    Those two parameters are not enough to calculate a planet effective temperature. Planet is a celestial body with more major features when calculating planet effective temperature to consider.
    The planet-without-atmosphere effective temperature calculating formula has to include all the planet’s major properties and all the characteristic parameters.
    3. The sidereal rotation period N rotations/day
    4. The thermal property of the surface (the specific heat cp)
    5. The planet surface solar irradiation accepting factor Φ ( the spherical surface’s primer solar irradiation absorbing property ).

    Altogether these parameters are combined in a Planet-Without-Atmosphere Effective Temperature Complete Formula:

    Te.planet = [ Φ (1-a) So (1/R²) (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (1)

    A Planet-Without-Atmosphere Effective Temperature Complete Formula produces very reasonable results: = 288,36 K, calculated with the Complete Formula, which is identical with the = 288 K, measured by satellites.

    Te.moon = 221,74 K, calculated with the Complete Formula, which is almost the same with the
    Tsat.mean.moon = 220 K, measured by satellites.

    A Planet-Without-Atmosphere Effective Temperature Complete Formula gives us planet effective temperature values very close to the satellite measured planet mean temperatures.

    Thus we have to conclude here that the satellites measured planet mean temperatures should be considered as the satellite measured Planet Effective Temperatures.

    All these new discoveries were possible only due to NASA satellites planet temperatures precise measurements!

  16. Ireneusz Palmowski

    How will we know that the AMO cycle has already reached its maximum? After growth of sea ice in the eastern Arctic.

    • The AMO is going to go right up the rising GMST ladder for the rest of this century: a 60-year cycle that goes flat or up, but never down, for 100 years.

    • I seem to recall that the AMO is detrended. At any rate – it’s unclear what readings of the woodfordimwits – get it – auguries are meant to mean for the future. Climate science may be uncertain – but it’s better than nothing. And it is suggesting that the world is complex and dynamic with multiple climate risks.

      “Any reduction in global mean near-surface temperature due to a future decline in solar activity is likely to be a small fraction of projected anthropogenic warming. However, variability in ultraviolet solar irradiance is linked to modulation of the Arctic and North Atlantic Oscillations, suggesting the potential for larger regional surface climate effects…

      For a high-end decline in solar ultraviolet irradiance, the impact on winter northern European surface temperatures over the late twenty-first century could be a significant fraction of the difference in climate change between plausible AR5 scenarios of greenhouse gas concentrations.”

      Although one could quibble about both projections and plausible emission scenarios.

      • His comment should be deleted for castigating Wood for Trees, which is merely data. If somebody thinks there is something wrong with the way it processes data, they should take it up with the site owner.

        Wood for Trees – wish it was better kept up; some data no longer updates – has democratized climate data. So he hates that, which is to be expected.

        By the way, whatever happen to the habitual 20 to 40 more years of La Niña dominance?

      • JCH: It seems to be that you off the rails. Of course the wft data are okay, however they use an AMO index with a linear detrending of the NA-SST. This is questionable, better tu use modern AMO measures: v. Oldenborgh ( regression on the GMST) or Trenberth ( regressing on the glob. SST).
        Read more, write less ( especially when it comes to the AMO(C) ) . The comment should not be deleted, btw.

      • Try this –

        Theory explains observations – otherwise it is simply empty graphology.

      • Better because it gets the result you want?

        It doesn’t matter what data you use; the AMO is a pretender ocean cycle.

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  18. Planet or Moon Without-Atmosphere Effective Temperature Complete Formula, according to the Stefan-Boltzmann Law, is:

    Te.planet = [ Φ (1-a) So (1/R²) (β*N*cp)¹∕ ⁴ /4σ ]¹∕ ⁴ (1)

    We have collected the results now:

    Comparison of results the planet Te calculated by the Incomplete Formula, the planet Te calculated by the Complete Formula, and the planet Te (Tsat.mean) measured by satellites:

    Planet or…..Te. incomplete……Te.complete……Te (sat.mean)
    Mercury………….437 K………….346,11 K…………..340 K
    Earth…………….255 K………….288,36 K…………..288 K
    Moon…………….271 Κ………….221,74 Κ…………..220 Κ
    Mars…………….211,52 K………215,23 K……………210 K

    The 288 K – 255 K = 33 oC difference doesn’t exist in the real world.

    There are only traces of greenhouse gasses
    The Earth’s atmosphere is very thin. There is not any measurable Greenhouse Gasses Warming effect on the Earth’s surface.

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