By Nic Lewis
An important new paper by Thorsten Mauritsen, Associate Professor at Stockholm University[i] and myself has just been accepted for publication (Lewis and Mauritsen 2020)[ii]. Its abstract reads:
Recently it has been suggested that natural variability in sea surface temperature (SST) patterns over the historical period causes a low bias in estimates of climate sensitivity based on instrumental records, in addition to that suggested by time-variation of the climate feedback parameter in atmospheric general circulation models (GCMs) coupled to dynamic oceans. This excess, unforced, historical “pattern effect” (the effect of evolving surface temperature patterns on climate feedback strength) has been found in simulations performed using GCMs driven by AMIPII SST and sea ice changes (amipPiForcing). Here we show in both amipPiForcing experiments with one GCM and through using Green’s functions derived from another GCM, that whether such an unforced historical pattern effect is found depends on the underlying SST dataset used. When replacing the usual AMIPII SSTs with those from the HadISST1 dataset in amipPiForcing experiments, with sea ice changes unaltered, the first GCM indicates pattern effects that are indistinguishable from the forced pattern effect of the corresponding coupled GCM. Diagnosis of pattern effects using Green’s functions derived from the second GCM supports this result for five out of six non-AMIPII SST reconstruction datasets. Moreover, internal variability in coupled GCMs is rarely sufficient to account for an unforced historical pattern effect of even one-quarter the strength previously reported. The presented evidence indicates that, if unforced pattern effects have been as small over the historical record as our findings suggest, they are unlikely to significantly bias climate sensitivity estimates that are based on long-term instrumental observations and account for forced pattern effects obtained from GCMs.
In this article I explain in more detail what Lewis and Mauritsen (2020) is all about and what its main findings and conclusions are. For a full picture, please read the paper, which is open-access; it is available in a reformatted version here.
The back-story is concerns have been expressed that accounting for changing temperature patterns increases historical period energy budget based estimates of climate sensitivity.[iii] [iv] [v] This idea is now being used in assessments of climate sensitivity to increase significantly estimates based on historical period warming (e.g., Sherwood et al. 2020[vi]).
As I explained in a detailed 2018 article,[vii] the key paper making and quantifying this effect (Andrews et al 2018) was based on simulations driven by an observationally-based estimate of the evolution of SST and sea-ice over the historical period (amipPiForcing experiments, over 1871–2010), that showed, in six models, their climate feedback strength (λ, here λamip) on average to be substantially greater, and hence their effective climate sensitivity (EffCS[viii], here EffCSamip), substantially lower, than when responding to long-term CO2 forcing.
Only a relatively small part of the differences reported in Andrews et al. (2018) can be attributed to effective climate sensitivity to CO2 forcing increasing over time in most atmosphere-ocean global climate models (AOGCMs). Based on typical CMIP5 AOGCM behaviour, that factor, which is allowed for in some historical period energy budget based estimates, such as Lewis and Curry (2018)[ix], would only account for approximately 5% out of the 40% shortfall in effective climate sensitivity that Andrews et al. found,[x] with a further 7% due to their use of mismatching CO2 forcing values.7 This implies that the bulk of the average difference they found was instead attributable to unforced (internal) climate variability having affected SST patterns – a (positive) unforced historical pattern effect. Although I put forward arguments, both in Lewis and Curry 2018 and in my 2018 article, against claims of such an effect having occurred, such claims have become widely accepted by climate scientists.
There are other possible explanations for the differences reported in Andrews et al. (2018). One is that the AOGCMs’ simulated long-term SST and sea-ice patterns, and the resulting radiative responses, are unrealistic. Another is that the GCM radiative responses in amipPiForcing experiments are unrealistic. In this article I shall put those questions aside. A further possibility is that the forced response of the climate system to the historical mixture of forcings differs significantly from that to pure CO2 with the same time-profile of evolving effective radiative forcing. LC18 put forward evidence against that being the case. Moreover, I produced evidence in a subsequent article that, in the two models for which radiative response in standard CMIP5 climate model historical experiment was accurately known, there was no evidence that the response to the mix of anthropogenic forcings differed from that to pure CO2 forcing.[xi]
I wrote in my 2018 article that to justify the a existence of a dampening unforced historical pattern effect one would need – even assuming long-term SST and sea-ice patterns, and the radiative response to them, simulated by AOGCMs to be realistic – to establish:
- that correctly-calculated EffCSamip estimates are adequately robust to choice of historical SST and sea-ice observational dataset;
- that the differences between climate feedback strength over the historical period in amipPiForcing simulations (λamip) and when AOGCMs generate their own SST and sea-ice patterns in response to radiative forcing (λhist) could feasibly be due to natural internal climate system variability.
It is standard to use the AMIPII SST and sea-ice dataset[xii] to drive GCMs in amipPiForcing experiments. The AMIPII sea-ice dataset is based closely on HadISST1 data throughout the historical period, with only minor modifications. However, the AMIPII SST dataset is only based on HadISST1 data until late 1981, after which it is based on OIv2 SST data[xiii]. The OIv2 post-1980 SST dataset is based largely on the same in situ and satellite data as HadISST1, but with different bias corrections and a different interpolation method for reconstructing SSTs in areas lacking in situ observations.
Andrews et al. (2108) showed, in their Supporting Information, that EffCSamip estimates are not robust to choice of historical sea-ice observational dataset. They found that climate feedback in amipPiForcing simulations by two UK Meteorological Office GCMs was much weaker – λamip was smaller, and hence EffCSamip was higher[xiv] – when the HadISST2 rather than the AMIPII sea-ice dataset was used, in conjunction with HadISST2 SST data. The difference was mainly due to the change in sea-ice data rather than in SST data, and was large enough to reverse the sign of the unforced historical pattern effect, making it negative.[xv] While sea-ice variation is thus an important contributor to differences in climate feedback, we do not explore sensitivity to sea-ice dataset in Lewis and Mauritsen (2020), caveating our results in that respect. Instead we use consistent sea-ice data, enabling isolation of the influence of SST dataset on historical climate feedback.
Thorsten and I show in our paper that λamip estimates are far from robust to choice of historical SST dataset, and that when the widely used HadISST1 dataset is used in place of the AMIPII SST dataset – with unchanged AMIPII sea-ice data – λamip and λhist are indistinguishable: no unforced historical pattern effect is found with the models we used. We also investigated the unforced historical pattern effect using five other SST datasets, finding a significant estimated effect only in one case.[xvi]
Although the Lewis and Mauritsen (2020) findings are based on simulations by only two GCMs, directly for ECHAM6.3 and indirectly (via “Green’s functions”) for CAM5.3,[xvii] they are consistent with historical warming in the Indo-Pacific warm pool, relative to that over the ice-free ocean as a whole, being much higher in the AMIPII than in the HadISST1 SST dataset.[xviii]
There is evidence, at least in CMIP5 models, that climate feedback strength is strongly positively related to relative warming in the Indo-Pacific warm pool.[xix] On physical grounds, warming in tropical ascent regions, of which the most important is the Indo-Pacific warm pool, relative to elsewhere is expected to produce a strong increase in outgoing radiation at the top of atmosphere.[xx] This effect, as estimated using the CAM5.3 Green’s functions, is illustrated in Figure 1 of our paper, reproduced below.
Figure 1. CAM5.3 Green’s functions: panels (a) and (b) show the change in respectively global mean Ts (K) and in global mean R (Wm−2) per 1K increase in local grid-cell SST, while panel (c) shows the global climate feedback parameter λ (Wm−2K−1) for a change in local grid-cell SST (the ratio of the values plotted in panel (b) to those plotted in panel (a)).
The Lewis and Mauritsen (2020) main results are set out in its Tables 1 and 2, reproduced below:
Table 1. Excess Indo-Pacific warm pool SST trends and climate feedback, in ECHAM6.3 amipPiForcing simulations and in MPI-ESM1.1 coupled 1pctCO2 and historical simulations. All values are based on ensemble mean Ts and R data (save for AMIPII and HadISST1 SST trends and standard deviations of individual run feedback estimates). Feedback estimates are from OLS regression, of pentadal mean data for amipPiForcing simulations. Values in brackets are standard errors of the OLS regression feedback estimates, which reflect underlying deviations from a linear relationship as well as internal variability.
Table 2. Excess Indo-Pacific warm pool SST trends and Green’s function derived estimates of climate feedback in CAM5.3 AMIPII-based amipPiForcing simulations, in CESM1-CAM5 coupled 1pctCO2 and historical/RCP8.5 simulations, and for warming in six observational SST datasets, along with feedback estimated from the actual CAM5.3 AMIPII-based amipPiForcing simulation data. Feedback estimates are from OLS regression of pentadal mean R and Ts values derived from the evolving SST warming patterns in the relevant simulation or observationally-based dataset. Data over 1871-2010, the amipPiForcing experiment period, is used, with data from the historical experiment extended using RCP8.5 experiment data, save in the 1pctCO2 simulation case where years 1–70 data is used.
It has been found in CMIP5 AOGCMs that on average approximately 60% of the change in feedback parameter over time during abrupt4×CO2 simulations comes from the tropics (30°N–30°S),[xxi] due in particular to the west tropical Pacific warming significantly less than the east tropical Pacific, with the tropical pattern becoming more El Niño like as simulations progress. However, the Green’s function feedback estimates for the seven observationally-based SST datasets are strongly correlated with warm pool SST trends relative to those over the tropics and mid-latitudes (r=0.90), but not relative to those over the tropics alone (r=−0.10) (Figure 2).
Figure 2: a reproduction of Figure 4 of Lewis and Mauritsen (2020). The relationship between climate feedback strength, estimated using the CAM5.3 Green’s functions and pentadal regression, and the warming trend in the Indo-Pacific Warm Pool relative to that over either 30°S–30°N (blue circles) or 50°S–50°N (red circles), both over 1871–2010, for SST per seven observational datasets (AMIPII, HadISST1, HadISST2, Had4_krig_v2, HadSST4_krig_v2, COBE-SST2, ERSSTv5). The red line shows a linear fit between the warming trend in the IPWP relative to that over 50°S–50°N and estimated climate feedback strength (r = 0.90). No equivalent fit is shown for the warming trend in the IPWP relative to that over 30°S–30°N, as the relationship is very weak (r = −0.10).
The Andrews et al. (2018) AMIPII-based λamip values, which are based on regressing annual ensemble mean simulated values of outgoing radiation R on surface air temperature T, exceed our estimates on the same basis of the corresponding λhist values for all six of the AGCMs involved. That implies a positive unforced historical pattern effect in all cases when using the AMIPII dataset. However, we found that regressing annual mean data, as is standard practice, non-negligibly biased λamip estimates (although not λhist estimates) in some cases. We therefore used instead estimates from regressing pentadal mean data. Using pentadal mean data substantially reduces noise in the regressor variable, which through regression dilution causes a downward bias in the slope coefficient, and also greatly diminishes the effect of responses to interannual fluctuations, thus providing more robust estimation.[xxii] As we show in our paper, the Andrews et al. λamip estimates are up to 9% too strong relative to those based on pentadal mean data, due to responses to interannual fluctuations.
We show in our paper that, for the five GCMs featured in Andrews et al. (2018) for which the estimated AMIPII-based unforced historical pattern effect derived from regressing pentadal data was positive, unforced variability in preindustrial control run segments from 43 CMIP5 AOGCMs is in all but 0.06% of cases inadequate to account for that unforced historical pattern effect. Moreover, in only 10% of cases is such variability sufficient to capture unforced pattern effects of one-quarter their strength. Of course, the realism of multidecadal internal variability in AOGCMs could be questioned. However, we concluded that if internal variability in at least some CMIP5 AOGCMs is realistic, it seems highly probable that either the AMIPII SST dataset is flawed or at least part of the historical pattern effect detected when using AMIPII SST data is forced.
Our principle conclusion is:
‘In this study we have found no evidence for a substantial unforced pattern effect over the historical period, arising from internal variability, in the available sea surface temperature datasets, save for when the AMIPII and ERSSTv5 datasets are used. Our results imply that the evidence suggesting existing constraints on EffCS from historical period energy budget considerations are biased low due to unusual internal variability in SST warming patterns is too weak to support such conclusion, and suggest that any such bias is likely to be small and of uncertain sign.’
We also say:
‘The various datasets try, in different ways, to take advantage of the satellite observations from when they become available around 1980. The post-1981 AMIPII dataset interpolation method, however does so in a way that emphasizes small scale features at the expense of the large scale patterns central to the study of pattern effects (Hurrell et al. 2008). Perhaps as a result, AMIPII warms more in the western tropical ocean basins and less in the eastern subsidence regions when compared to HadISST1. Earlier studies have in other contexts pointed to issues with the patterns of tropical warming in AMIPII’
‘It is unclear from our results to what extent there is a robust relationship between stronger climate feedback and higher SST trends in the Indo-Pacific warm pool compared with elsewhere, at least where the comparison is limited to the tropics.’
Originally posted here, where a pdf copy is also available
[i] Meteorology Department. Previously at the Max Planck Institute for Meteorology in Hamburg, where he worked closely with Bjorn Stevens.
[ii] Lewis, N. and Mauritsen, T., 2020: Negligible unforced historical pattern effect on climate feedback strength found in HadISST-based AMIP simulations. Journal of Climate, 1-52, https://doi.org/10.1175/JCLI-D-19-0941.1
[iii] Gregory , J. M. , and T. Andrews , 2016 : Variation in climate sensitivity and feedback parameters during the historical period . Geophys. Res. Lett., 43 , 3911 –3920 , https://doi.org/10.1002/2016GL068406
[iv] Andrews T. et al., 2018 Accounting for changing temperature patterns increases historical estimates of climate sensitivity. Geophys. Res. Lett. https://doi.org/10.1029/2018GL078887
[v] Gregory, J.M., Andrews, T., Ceppi, P., Mauritsen, T. and Webb, M.J., 2019. How accurately can the climate sensitivity to CO₂ be estimated from historical climate change? Climate Dynamics. https://doi.org/10.1007/s00382-019-04991-y
[vi] Sherwood, S., et al. “An assessment of Earth’s climate sensitivity using multiple lines of evidence.” Reviews of Geophysics (2020): e2019RG000678. https://doi.org/10.1029/2019RG000678
[vii] Warming patterns are unlikely to explain low historical estimates of climate sensitivity, 5 September 2018.
[viii] Effective climate sensitivity (EffCS) is an estimate of equilibrium climate sensitivity (ECS) derived by estimating climate feedback strength (λ) in a non-equilibrium situation and dividing it into an appropriately-derived estimate of the effective radiative forcing (ERF) from a doubling of preindustrial CO2 concentration. In an AOGCM experiment involving a step increase in CO2 concentration, this equates to linearly projecting warming to the point where the Earth’s radiation balance has been fully restored, and then scaling it appropriately if the increase in CO2 was not a doubling.
[ix] Lewis, N. and J. Curry, 2018: The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity. J. Climate, 31, 6051–6071, https://doi.org/10.1175/JCLI-D-17-0667.1; also Masters, T., 2014: Observational estimate of climate sensitivity from changes in the rate of ocean heat uptake and comparison to CMIP5 models. Climate Dyn., 42, 2173 –2181. https://doi.org/10.1007/s00382-013-1770-4
[x] Lewis and Curry (2018) estimated a 10% difference when using long term EffCS estimates based on regression over years 21–150 of CMIP5 abrupt4xCO2 simulations, but that would reduce to 5% if instead basing them on regression over years 1–150, the method used in Andrews et al. (2018).
[xi] Gregory et al 2019: Does climate feedback really vary in AOGCM historical simulations? 31 October 2019
[xii] Hurrell, J.W., Hack, J.J., Shea, D., Caron, J.M. and Rosinski, J., 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J. Climate, 21(19), 5145-5153. https://doi.org/10.1175/2008JCLI2292.1
[xiii] Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C. and Wang, W., 2002. An improved in situ and satellite SST analysis for climate. Journal of climate, 15(13), pp.1609-1625. https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2
[xiv] Climate feedback strength λ is reciprocally related to EffCS. Note that we use a positive sign convention for climate feedback, but Andrews et al. (2018) use a negative sign convention, so care is needed in interpreting their statements about it.
[xv] Using feedback estimated by regression over years 1-50 of the parent AOGCMs’ abrupt4xCO2 simulations as a proxy for their forced historical feedback over 1871-2010.
[xvi] We computed feedback using surface skin temperature (Ts) rather than near-surface air temperature (T), for the reasons set out in our paper, save when working with data from Andrews et al. (2018), who used T.
[xvii] Through amipPiForcing simulations by ECHAM6.3, and through applying Green’s functions derived from multiple patch-warming simulations by CAM5.3. The Green’s function approach exploits the apparent linear superpositionality in space of GCM responses to warming. Global changes in surface temperature Ts and outgoing radiation R resulting from imposed evolving historical SST patterns can thus easily be emulated by the sums of the global responses to SST changes in individual locations weighted by time-invariant Green’s function values for each location, and associated climate feedback estimates derived. Sea-ice is held constant in the CAM5.3 patch-warming simulations, which reduces changes in the emulated values of both Ts and R.
[xviii] We define the Indo-Pacific warm pool as the region 15°S–15°N, 45°E–195°E, and compare its warming trend with that for the ocean from 50°S–50°N as a whole, that area being essentially ice-free all year.
[xix] Dong, Y., Proistosescu, C., Armour, K.C. and Battisti, D.S., 2019: Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green’s Function Approach: The Pre-eminence of the Western Pacific. Journal of Climate, (2019).
[xx] That is because surface temperature in convective areas controls temperature in the tropical free troposphere, which spatially is fairly uniform, and influences temperature in the extratropics. An increase in free tropospheric temperature relative to surface temperature in descent regions strengthens the boundary layer temperature inversion, which is known to increase low cloud cover and hence reflected solar radiation.
[xxi] Andrews, T., Gregory, J. M., and Webb, M. J., 2015: The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Climate, 28(4), 1630-1648. https://doi.org/ 10.1175/JCLI-D-14-00545.1
[xxii] Using the ensemble mean from a number of amipPiForcing simulation runs data does not provide an adequate solution, because the noise in the SST data used to force the GCM is the same in all runs.
An imagined, overweening impact of past internal variability cannot only be an important factor if the hypothesized forcing due to parts per million increases in CO2 is admittedly, so very small, such admission undercuts alarmists’ claims of impending doomsday.
Don’t know if I understood it correctly. Your starting point is the insufficently skill of the GCM to replicate the observed forced spatial warming over the time:
You studied the possibility that this is the result of the internal variability which is not shown in the forced part of the warming ( the GCM model mean). Your result is, that this is very unlikely (“negligible”).
The observed pattern produces a small sensitivity due to the fact that the IPWP warms faster than i.e. the tropical east pacific. This implies that in this area with strong convection the heat can be released much more efficient to the space than in areas with strong stratification. The pattern of warming has a big impact on the sensitivity and your paper shows, that this “low sensitivity pattern” is not a randomly product of the internal variability but a product of the forcing, which is not replicated by the GCM. In the end the pattern is a product of the forcing itself and it won’t change due to the internal variabilty, producing much higher sensitivity in the future. In other words: Some part of the reasons of the higher estimates for the sensitivity in GCM (or ESM) is their lack to produce the observed pattern and therefore the estimates for the sensitivity from observations are not biased low but the GCM are biased high. Did I get it right?
Thanks for your comment. There are in fact two separate lines of argument and evidence in the paper against the existence of a non-negligible unforced historical period pattern effect – that is, the unforced (i.e., due to internal climate system variability) evolution of a spatial pattern of differential SST warming over the full historical period that temporarily increased outgoing radiation above what it would otherwise have been, thus increasing estimates of climate feedback strength, and depressing estimates of climate sensitivity, based on global mean warming over the historical period
Both lines of argument relate to the fact that all the existing evidence supporting the existence of a positive historical period pattern effect comes from simulations by atmosphere-only GCMs (AGCMs) driven by evolving SST patterns from the AMIPII dataset.
The first argument is that when a different dataset, HadISST1, is used to drive these simulations, a negligible unforced historical period pattern effect arises, for two models in which this was directly or indirectly tested. And the same appears to occur with other SST datasets except ERSSTv5.
The second argument is that internal variability in CMIP5 coupled atmosphere-ocean GCMs (AOGCMs) is unable to produce a unforced historical period pattern effect of anything like the strength of that arising when the five AGCMs for which a significant such effect was found when they were driven by the AMIPII SST dataset.
As shown in the two results tables, over the historical period (to 2005 and 2010) the IPWP did not actually warm more that the ocean over 50S-50N as a whole, and warms less than over the tropics as a whole, in the HadISST1 dataset (and several other SST datasets), notwithstanding that it may have warmed faster than the tropical east Pacific.
Our results don’t directly imply that part of the reasons for the higher than observational estimates for climate sensitivity in AOGCMs is that they generate the wrong forced SST warming pattern. However, that could be part of the explanation, even though in the two models we used warming in the IPWP relative to elsewhere was higher in their historical and 1pctCO2 forced simulations seems to be higher, not lower, than per HadISST1.
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“It has been found in CMIP5 AOGCMs that on average approximately 60% of the change in feedback parameter over time during abrupt4×CO2 simulations comes from the tropics (30°N–30°S),[xxi] due in particular to the west tropical Pacific warming significantly less than the east tropical Pacific, with the tropical pattern becoming more El Niño like as simulations progress.”
Well El Nino conditions normally increase during centennial solar minima because of an increase in negative NAO/AO conditions, while rising CO2 forcing according to the consensus of IPCC circulation models should increase positive NOA/AO.
None of this can make any sense while ocean phases are assumed to be unforced internal variability, they act as negative feedbacks (with overshoot) to indirect solar variability. AMO and Arctic warming is normal during each centennial solar minimum.
“After writing the above in relative ignorance, I carefully reviewed your paper to see what information it did provide. Figure 1a shows the distribution of relative warming produced by CAM5.3 I was seeking in disguised format. Unfortunately, I’m still confused as to whether the dark red region warms the most or the least per degK of surface warming.”
Fig. 1a shows how much the mean global temperature in CAM5.3 changes in response to a 1K change in the local grid cell SST – hence the small range that the scale covers. Dark red shows the most warming fpr degK of local warming. Tropical grid cells have the largest area and would naturally have the greatest influence on global temperature, but that influence is much enhanced in areas of deep convection (mainly, where SST is highest – mainly the IPWP). That is because deep convection sets the temperature of the tropical free troposphere (i.e.,above the boundary layer) – which is fairly horizontally uniform over the tropics – over a wide area.
Fig 1b shows the same but for global outgoing radiation not global surface temperature. Warming in the IPWP causes a large increase in outgoing radiation, while warming in the east Pacific causes a reduction (because it reduces local low cloud cover).
“If this comment isn’t total nonsense, I hope it conveys the difficulty many readers might be having with the concept of pattern effects?”
Your comment makes good sense. I agree that getting to grips with pattern effects involves quite a steep learning curve. But they are fairly central to quite a lot of the discussion in the climate science community of climate feedback and how it varies.
Nic: I’ve been struggling to comprehend “pattern effects” because I haven’t really seen patterns of warming. I have seen patterns of feedbacks produced by AOGCMs for each surface grid cell or ocean grid cell. Here is what I haven’t seen so far.
First, all temperature changes could be scaled so one is looking at a 1 degK increase in GMST. For ocean, a 1 K increase in GMST might be an average increase in SST of 0.8 K. I want to plot temperature change above and below that mean change, 0.8 K So, the map is colored white for 0.7-0.9 degK of total warming and -0.1 to +0.1 K relative warming, redder going above +0.1 K and bluer going below -0.1 K. With a few contour lines (perhaps at 0, +/- 0.4, and +/-0.8 etc.) since color scales are hard to quantify. Now I could see a pattern of relative warming. Now I could compare the pattern of how 1 degK of GMST warming was distributed over the ocean by a model and by nature. And I could see if the feedback parameter in the regions with the larger relative warming were located in the regions that had a stronger or weaker radiative response to warming. Over the top of the map, the mean radiative response for this 1 K change would be listed. Pattern A of relative warming sends an additional 2 W/m2 of combined OLR and OSR to space from 1 K of warming. Pattern B of relative warming sends only 1 W/m2 more. Then I would have seen a “pattern effect”. Has anyone every displayed patterns this way?
Then I’d want to see a scatter plot with relative warming on the x-axis and relative feedback on the y-axis. Does one pattern of warming put most of the points in the first and third quadrants and another pattern put most of the points in the second and fourth quadrants. One might summarize such a plot with a correlation coefficient. Positive (negative) correlation produces higher (lower) climate sensitivity.
After writing the above in relative ignorance, I carefully reviewed your paper to see what information it did provide. Figure 1a shows the distribution of relative warming produced by CAM5.3 I was seeking in disguised format. Unfortunately, I’m still confused as to whether the dark red region warms the most or the least per degK of surface warming.
If this comment isn’t total nonsense, I hope it conveys the difficulty many readers might be having with the concept of pattern effects?
Figure 1a of Andrews & Webb (2018) shows part of what I’m looking for, though for some reason they have 4K in the title of everything and then report in units of K/K, which I think scales to 1K. amip-obs-4K shows relative warming from 1900-2012 and almost no warming in many regions of the Pacific. Here is a pattern, an eleven-decade observed pattern of all anthropogenic warming. So where are the patterns of warming produced by climate models using historic forcing? Patterns from climate models that we know (but often fail to mention) produce too few marine boundary layer clouds over the Eastern Pacific and possibly too much warming there. The difference between these two patterns of warming is poorly described by the terms “unforced” or internal variability, since it has lasted a century. And this mode of “variability” presumably was not observed in 100 historical runs of the MPI model.
When any difference between observation and model projection can always be ignored as “unforced variability”, climate science is no longer applying the scientific method – testing hypothesis against observations. AOGCMs have become a form of “revealed truth” or a religion.
In Figure1b, the authors show feedback grid cell by grid cell, but not the radiative response, lambda*dT, grid cell by grid cell. It is the sum of all radiative responses that restores equilibrium after a forcing. In the upper left corner of Figure 1b, there are regions of strongly negative feedback west of Panama and Chile, but there is little or no warming at these locations and therefore no radiative response to restore balance at the TOA.
Note there are at least two Andrews (2018) articles. The one cited above by Nic and Sherwood (2020) and Andrews and Webb atFigure 1a of Andrews & Webb (2018) shows part of what I’m looking for, though for some reason they have 4K in the title of everything and then report in units of K/K, which I think scales to 1K. amip-obs-4K shows relative warming from 1900-2012 and almost no warming in many regions of the Pacific. Here is a pattern, an eleven-decade observed pattern of all anthropogenic warming. So where are the patterns of warming produced by climate models using historic forcing? Patterns from climate models that we know (but often fail to mention) produce too few marine boundary layer clouds over the Eastern Pacific and possibly too much warming there. The difference between these two patterns of warming is poorly described by the term unforced or internal variability, since it has lasted a century. And this mode of “variability” presumably was not observed in 100 historical runs of the MPI model.
Note: There are at least two Andrews (2018) articles. The multi-author paper cited by Nic and Sherwood (2020) and Andrews&Webb at:
J. Climate (2018) 31 (2): 641–654.
I think that the ANdrews & Webb (2018) simulations used a 4K global mean temperature change, rather than 1K, to improve the ratio of signal to noise (GCM internal atmospheric variability).
The “observed” 1900-2012 SST trends in their Fig 1(a) reflect the AMIPII dataset. Warming in the IPWP is considerably lower in HadISST1 and most other SST datasets.
“So where are the patterns of warming produced by climate models using historic forcing?”
It’s surprisingly difficult to find any – maybe because they don’t tend to reflect well on the models?
Miller et al. (2014: doi.org/10.1002/2013MS000266 ) Figure 10 shows it for GISS-E2-R (NINT is the standard model variant).
Another paper (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.4644) shows CMIP5 mean spatial warming but only over 1955-2004.
“Pattern A of relative warming sends an additional 2 W/m2 of combined OLR and OSR to space from 1 K of warming. Pattern B of relative warming sends only 1 W/m2 more. Then I would have seen a “pattern effect”. Has anyone every displayed patterns this way?”
Try Andrews et al 2015 (doi.org/10.1175/JCLI-D-14-00545.1). Figure 5 (a) and (b) show the spatial changes in local surface temperature regressed on global surface temperatureover years 1-20 and 21-150 of abrupt4xCO2 simulations (in the CMIP5 mean) , while the top row of Fig. 4 shows the spatial changes in local TOA radiation regressed on global surface temperature. So these reflect a forced, time-related, pattern effect (plus the effect of non-temperature factors on outgoing radiation, such as changing snow and ice cover).
Note that by contrast Figure 1 of my article (and paper) shows changes (as emulated using Green’s functions) in global surface temperature and in outgoing LW + SW radiation resulting from local changes in surface temperature, which is quite different from what Andrews et al (2015) shows.
Nic: Thanks for the reply. I’ve known about and understood the local variations in feedback (that can their change with time in some models) in Andrews (2015) Figure 4 for a long time, but I never retained the warming pattern in Figure 5. I’m less interested in the warming pattern from 4X experiments than the warming patterns in amip experiments and historic runs that are being used to ignore ECS_hist.
On a global scale at equilibrium after a forcing:
dF + dR = 0
dF + lambda*dT = 0
Each grid cell has its own lambda (lambda_i) controlled by the nature of its climate. Let’s define lambda_bar as the average of all lambda_i and q_i as lambda_bar minus lambda_i. According to Andrews and the CMIP5 MM, the Equatorial Pacific initially radiates and reflects no addition power to space as that portion of the Pacific warms and later less as temperature rises (a localized runaway GHE). Ignoring the complications of inhomogeneous forcing such as aerosols, each ocean grid cell gets it own unique fraction (p_i) of GMST warming, dT. dT_i = p_i*dT. The top of the ocean and the atmosphere is always moving, so local temperature change in a grid cell is not the cumulative result of heat fluxes over decades. So the radiative response dR needed to restore equilibrium at the TOA is the sum of all local dR_i. Your Figure 1c shows a map of all dR_i.
dR_i = (lambda_bar + q_i) * (p_i * dT)
“global” lambda = (lambda_bar*dT) * (sum of all q_i*p_i)
If there were uniform warming, all p_i would be 1. The sum of all q_i is zero. Global lambda would be the average of all lambda, lambda_bar. However, when p_i vary, global lambda changes. Thus I intuitively want to see a scatter plot of q_i vs p_i. IMO, this plot summarizes a pattern effect – how inhomogeneity in warming makes global lambda differ from lambda_bar. The points in the first and third quadrants increase global lambda (below lambda_bar) and decrease climate sensitivity and the points in the second and fourth quadrants do the opposite. The further the points are from the axes, the bigger the effect.
One could color code the points from various locations on the planet, say the equatorial Pacific. Their location on the scatter plot simultaneously informs about the magnitude of the inhomogeneity in the temperature pattern p_i and in the local feedback parameter q_i, something that is missing from maps of dR_i.
I’ve probably restated my earlier comment in more mathematical terms. Hopefully I’m not wasting your time. Am digesting your other references and the patterns of warming are surprising. The multi-model mean for CMIP5 shows a very weak pattern except in the Arctic, but unforced variability has been averaged out.
A key point that I’m not sure your equations allow for: local warming can affect R elsewhere. These remote effects mainly occur in the IPWP and other tropical ascent areas, and there they dwarf the local effects. This graphic (from a piece by Thorsten Mauritsen in Nature) illustrates them:
The vertical black and red lines show atmospheric temperature profiles before and after warming in the IPWP (left) and elsewhere in the tropics (right). The key point is that over the tropics temperature in the free troposphere (above the inversion associated with the top of the planetary boundary layer) is almost uniform horizontally, and is normally set by convection in the IPWM.
BTW, these maps compare SST warming over 1870-2016/2017 in a observational dataset (I can’t recall which one) with the CMIP5 historical+RCP simulations mean:
Nic: Thanks for the Mauritsen figure illustrating horizontal convection of heat over the Pacific and how the lapse rate in ascending regions appears to control the lapse rate everywhere (at the same latitude?).
Expressing this situation in terms of a single column of grid cells with a dTi at the surface, a dRi at the TOA, and mediated by a lambda_i was clearly wrong. The dRi term must refer to the contribution to global dR from dT_i, which is, of course, what you did. However, as best I can tell, the rest of the analysis I proposed is still valid. The “pattern effect” can be seen in a scatter plot of p_i (dT_i = p_i * dGMST) and q_i (lambda_i – lambda_bar).
Since I had some questions about the Mauritsen figure you posted, I tried and failed to find the source. What are the squiggly lines of heat flow supposed to represent? There is horizontal turbulent mixing on the sides of convective towers (entrainment) that is not resolved in AOGCMs. Otherwise, there are winds that can be seen in the Held figure below. On the AVERAGE in the tropics, they are less than 5 m/s (18 km/h, 400 km/d) in much of the tropics and perhaps not fully capable, given the rate of subsidence, of evenly distributing latent heat released in ascending regions with heat from compression in subsiding regions on the other side of the Pacific. To add to my confusion, average winds are blowing in the opposite direction at the equator from expected from pictures of the Walker circulation. (If you go high enough, winds 10 or 20 degrees away from the equator blow from west to east in agreement with the Walker circulation.)
All past generations of AOGCMs have produced far too few marine boundary layer clouds. Even weather forecasting models had trouble until recently predicting the altitude of marine boundary layer clouds and when they start forming or breaking up. Factors controlling the formation and stability of clouds at the top of the boundary layer in subsiding regions have been studied in cloud-resolving models and used to re-parameterize clouds in many CMIP6 models in ways expected to increase climate sensitivity, but I haven’t heard whether those changes produced more or more realistic MBL clouds. CRMs are not large enough to include all of the long-range flows that produce MBL clouds: import of cold dry air ready to subside (sometimes from the other side of the Pacific), and an ocean surface cooled by upwelling and meridional currents from high latitudes and heated by the poorly-modeled SWR that gets through. So CRMs of MBL clouds are driven by idealized scenarios like the one in Mauritsen’s figure. (IIRC, some CRMs don’t even include a diurnal cycle.)
“Thanks for the Mauritsen figure illustrating horizontal convection of heat over the Pacific and how the lapse rate in ascending regions appears to control the lapse rate everywhere (at the same latitude?)”
Throughout the tropics (within the Hadley cells, I think), roughly speaking. What is controlled is the temperature near the top of the troposphere.
The Mauritsen figure is from this short piece: https://www.nature.com/articles/ngeo2838/. Let me know if you have difficulty obtaining a copy. The figure caption reads:
“Figure 1 | Schematic of the proposed mechanism for increasing low-level cloudiness with observed non-uniform sea surface temperature pattern.
a–c, In areas of sea surface warming, strong updrafts in convectively precipitating areas lead to a fairly homogeneous vertical temperature profile warming (west Pacific, the black and red curves are idealized vertical temperature profiles before and after warming, respectively). Where sea surface temperatures are relatively cool, the lowermost mixed-layer of the atmosphere is also cool, but a sharp rise in temperature (inversion) occurs at 1–2 km height and stratocumulus clouds form below this temperature inversion. In the mid- and upper parts of the tropical troposphere the horizontal temperature gradients are kept weak by various atmospheric motions (red curly symbols). Therefore, when the warmest regions warm more than the colder regions (a), then the inversion becomes stronger (b), and more low-level clouds form (c). Zhou and colleagues 5 show that this mechanism is responsible for the influence of sea surface temperature patterns on climate sensitivity. The map shows the trend in annual mean sea surface temperature for the period 1980–2005 from the HadSST3 dataset.”
Cool Paper. It makes it difficult to reconcile this with the results of Sherwood et al. 2020. I wonder if there will be a response at real climate.
I was unaware that 10 ensemble members of HadISST2 SST data were available. Is there even a link on their website that links to it, or is it just well hidden?
Thanks! I doubt that there will be any response at Realclimate. We’ll see.
The HadISST2 data is here: https://www.metoffice.gov.uk/hadobs/hadisst2/data/HadISST.18.104.22.168/index.html. I don’t think there is any link to it, because the datasetis not yet supported by a peer reviewed paper. Unfortunately it has not yet been updated beyond 2010. I was told that the Met Office has updated verions but they are daily and at higher resolution so not suitable for making available on their website. There is a techincal note testing how the HadISST2 analysis works in the real world, here: wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/Documents/Posters/P3-36_ea.pdf or here in poster form, in colour: https://icoads.noaa.gov/climar3/c3poster-pdfs/S3P1-Rayner.pdf
Thanks for the link the the technical note. It is appreciated.