One more dot on the graphs for our annual model-observations comparisons updates. Given how extraordinary the last two years have been, there are a few highlights to note.
[Read more…] about Comparison Update 2024Model-Obs Comparisons
The AMOC is slowing, it’s stable, it’s slowing, no, yes, …
There’s been a bit of media whiplash on the issue of AMOC slowing lately – ranging from the AMOC being “on the brink of collapse” to it being “more stable than previously thought”. AMOC, of course, refers to the Atlantic Meridional Overturning Circulation, one of the worlds major ocean circulation systems which keeps the northern Atlantic region (including Europe) exceptionally warm for its latitude. So what is this whiplash about?
As is often the case with such media whiplash, there isn’t much scientific substance behind it, except for the usual small incremental steps in the search for improved understanding. It is rare that one single paper overthrows our thinking, though media reports unfortunately often give that impression. Real science is more like a huge jigsaw puzzle, where each new piece adds a little bit.
The latest new piece is a new reconstruction of how the AMOC has changed over the past 60 years, by Jens Terhaar and colleagues. The background to this discussion is familiar to our regular readers (else just enter ‘AMOC’ in the RealClimate search field): proper measurements of the AMOC flow are only available since 2004 in the RAPID project, thus for earlier times we need to use indirect clues. One of these is the sea surface temperature ‘finger print’ of AMOC changes as discussed in our paper Caesar et al. 2018 (Fig. 1). There we used the cold blob temperature anomaly (Nov-May) as an index for AMOC strength. Other studies have used other sea surface temperature or salinity patterns as well as paleoclimatic proxy data (e.g. sediment grain sizes), and generally found an AMOC decline since the 19th Century superimposed by some decadal variability. The new paper critices our (i.e. Caesar et al) reconstruction and suggests a new method using surface heat fluxes from reanalysis data as an indicator of AMOC strength.
Here’s three questions about it.
1. Does the ‘cold blob’ work well as AMOC indicator?
We had tested that in the historic runs of 15 different CMIP5 climate models in Caesar et al. 2018 (our Fig. 5) and found it works very well, except for two outlier models which were known to not produce a realistic AMOC. Now Terhaar et al. redid this test with the new CMIP6 model generation und found it works less well, i.e. the uncertainty is larger (although for future simulations where the AMOC shows a significant decline in the models, our AMOC index also works well in their analysis).
Which raises the question: which models are better for this purpose: CMIP5 or CMIP6? One might think that newer models are better – but this does not seem to be the case for CMIP6. Irrespective of the AMOC, the CMIP6 models created substantial controversy when their results came out: the climate sensitivity of a subset of ‘hot models’ was far too high, these models did not reproduce past temperature evolution well (compared to observed data), and IPCC made the unprecedented move of not presenting future projections as straightforward model average plus/minus model spread, but instead used the new concept of “assessed global warming” where models are weighted according to how well they reproduce observational data.
In the North Atlantic, the historic runs of CMIP6 models on average do not reproduce the ‘cold blob’ despite this being such a striking feature of the observational data, as shown clearly in the Summary for Policy Makers of the IPCC AR6 (see Fig. 2 below). Of the 24 CMIP6 models, a full 23 underestimate the sea surface cooling in the ‘cold blob’. And most of the CMIP6 models even show a strengthening of the AMOC in the historic period, which past studies have shown to be linked to strong aerosol forcing in many of these models (e.g. Menary et al. 2020, Robson et al. 2022). The historic Northern Hemisphere temperature evolution in the models with a strong aerosol effect “is not consistent with observations” and they “simulate the wrong sign of subpolar North Atlantic surface salinity trends”, as Robson et al. write. Thus I consider CMIP6 models as less suited to test how well the ‘cold blob’ works as AMOC indicator than the CMIP5 models.
2. Is the new AMOC reconstruction method, based on the surface heat loss, better?
In the CMIP6 models it looks like that, and the link between AMOC heat transport and surface heat loss to the north makes physical sense. However, in the models the surface heat loss is perfectly known. In the real ocean that is not an observed quantity. It has to be taken from model simulations, the so-called reanalysis. While these simulations assimilate observational data, over most of the ocean surface these are basically sea surface temperatures, but surface heat loss depends also on air temperature, wind speed, humidity, radiation and cloud cover in complex ways, all of which are not accurately known. Therefore these surface heat loss data are much less accurate than sea surface temperature data and in my view not well suited to reconstruct the AMOC time evolution.
That is supported by the fact that two different reanalysis data sets were used, leading to quite different AMOC reconstructions. Also the AMOC time evolution they found differs from other reconstruction methods for the same time period (see point 3 below).
And there is another issue: we’ve previously looked at ERA5 surface heat flux trend, as shown here from my article in Oceanography 2024:
You see in both figures (in temperature as well as surface heat flux) the AMOC slowdown ‘fingerprint’ which includes both the ‘cold blob’ and a warming along the American coast due to a northward Gulf Stream shift, which is also a symptom of AMOC weakening. However, Terhaar et al. integrate over the whole northern Atlantic north of 26 °N so that the red area of increasing heat loss largely compensates for the blue area of decreasing heat loss. So in their analysis these two things cancel, while in the established concept of the ‘fingerprint’ (see Zhang 2008: Coherent surface-subsurface fingerprint of the Atlantic meridional overturning circulation) these two things both reinforce the evidence for an AMOC weakening.
3. How do these new reconstructions compare to others?
Here is how the Terhaar reconstructions (bottom two) compare:
The reconstruction at the bottom using a reanalysis product from Japan doesn’t resemble anything, while the blue one using the European ERA5 reanalysis at least has the 1980s minimum and early 2000s maximum in common with other data, albeit with much smaller amplitude; it is a lot smoother. Thus it also misses the strong AMOC decline 2004-2010 and subsequent partial recovery seen in the RAPID measurements as well as the Caesar and Worthington reconstructions. A main reason for the lack of significant trend in the Terhaar reconstructions further is the time interval they used; for the same time span the Caesar reconstruction also does not show an even remotely significant trend (p-value is only 0.5), so in this respect our reconstructions actually agree for the period they overlap. The fact that ours shows a significant AMOC decline is because of the stable AMOC we find during 1900-1960, which is stronger than in the following sixty years. Here our reconstruction method shows its advantage in that reliable and accurate sea surface temperature data exist so far back in time.
Hence, I do not believe that the new attempt to reconstruct the AMOC is more reliable than earlier methods based on temperature or salinity patterns, on density changes in the ‘cold blob’ region, or on various paleoclimatic proxy data, which have concluded there is a weakening. But since we don’t have direct current measurements going far enough back in time, some uncertainty about that remains. The new study however does not change my assessment of AMOC weakening in any way.
And all agree that the AMOC will weaken in response to global warming in future and that this poses a serious risk, whether this weakening has already emerged from natural variability in the limited observational data we have, or not. Hence the open letter of 44 experts presented in October at the Arctic Circle Assembly (see video of my plenary presentation there), which says:
We, the undersigned, are scientists working in the field of climate research and feel it is urgent to draw the attention of the Nordic Council of Ministers to the serious risk of a major ocean circulation change in the Atlantic. A string of scientific studies in the past few years suggests that this risk has so far been greatly underestimated. Such an ocean circulation change would have devastating and irreversible impacts especially for Nordic countries, but also for other parts of the world.
Post script
Since I’m sometimes asked about that: last year a data study by Volkov et al. revised the slowing trend of the Florida current as well as the AMOC. Contrary to ‘climate skeptics’ claims, it has no impact on our long-term estimate of ~3 Sv slowing since 1950, i.e. -0.4 Sv/decade (Caesar et al. 2018). Both the original and the revised trend estimates for the RAPID section data (see Figure) suggest the recent AMOC weakening since 2004 is steeper than the long-term trend we estimated.
Not just another dot on the graph? Part II
Annual updates to the model-observation comparisons for 2023 are now complete. The comparisons encompass surface air temperatures, mid-troposphere temperatures (global and tropical, and ‘corrected’), sea surface temperatures, and stratospheric temperatures. In almost every case, the addition of the 2023 numbers was in line with the long term expectation from the models.
[Read more…] about Not just another dot on the graph? Part IIThe Scafetta Saga
It has taken 17 months to get a comment published pointing out the obvious errors in the Scafetta (2022) paper in GRL.
Back in March 2022, Nicola Scafetta published a short paper in Geophysical Research Letters (GRL) purporting to show through ‘advanced’ means that ‘all models with ECS > 3.0°C overestimate the observed global surface warming’ (as defined by ERA5). We (me, Gareth Jones and John Kennedy) wrote a note up within a couple of days pointing out how wrongheaded the reasoning was and how the results did not stand up to scrutiny.
[Read more…] about The Scafetta SagaReferences
- N. Scafetta, "Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5‐T2m", Geophysical Research Letters, vol. 49, 2022. http://dx.doi.org/10.1029/2022GL097716
Some new CMIP6 MSU comparisons
We add some of the CMIP6 models to the updateable MSU [and SST] comparisons.
After my annual update, I was pointed to some MSU-related diagnostics for many of the CMIP6 models (24 of them at least) from Po-Chedley et al. (2022) courtesy of Ben Santer. These are slightly different to what we have shown for CMIP5 in that the diagnostic is the tropical corrected-TMT (following Fu et al., 2004) which is a better representation of the mid-troposphere than the classic TMT diagnostic through an adjustment using the lower stratosphere record (i.e. ).
References
- S. Po-Chedley, J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer, "Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming", Proceedings of the National Academy of Sciences, vol. 119, 2022. http://dx.doi.org/10.1073/pnas.2209431119
- Q. Fu, C.M. Johanson, S.G. Warren, and D.J. Seidel, "Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends", Nature, vol. 429, pp. 55-58, 2004. http://dx.doi.org/10.1038/nature02524
2022 updates to model-observation comparisons
Our annual post related to the comparisons between long standing records and climate models.
As frequent readers will know, we maintain a page of comparisons between climate model projections and the relevant observational records, and since they are mostly for the global mean numbers, these get updated once the temperature products get updated for the prior full year. This has now been completed for 2022.
[Read more…] about 2022 updates to model-observation comparisonsIssues and Errors in a new Scafetta paper
Earlier this week, a new paper appeared in GRL by Nicola Scafetta (Scafetta, 2022) which purported to conclude that the CMIP6 models with medium or high climate sensitivity (higher than 3ºC) were not consistent with recent historical temperature changes. Since there have been a number of papers already on this topic, notably Tokarska et al (2020), which did not come to such a conclusion, it is worthwhile to investigate where Scafetta’s result comes from. Unfortunately, it appears to emerge from a mis-appreciation of what is in the CMIP6 archive, an inappropriate statistical test, and a total neglect of observational uncertainty and internal variability.
[Read more…] about Issues and Errors in a new Scafetta paperReferences
- N. Scafetta, "Advanced Testing of Low, Medium, and High ECS CMIP6 GCM Simulations Versus ERA5‐T2m", Geophysical Research Letters, vol. 49, 2022. http://dx.doi.org/10.1029/2022GL097716
- K.B. Tokarska, M.B. Stolpe, S. Sippel, E.M. Fischer, C.J. Smith, F. Lehner, and R. Knutti, "Past warming trend constrains future warming in CMIP6 models", Science Advances, vol. 6, 2020. http://dx.doi.org/10.1126/sciadv.aaz9549
Another dot on the graphs (Part II)
We have now updated the model-observations comparison page for the 2021 SAT and MSU TMT datasets. Mostly this is just ‘another dot on the graphs’ but we have made a couple of updates of note. First, we have updated the observational products to their latest versions (i.e. HadCRUT5, NOAA-STAR 4.1 etc.), though we are still using NOAA’s GlobalTemp v5 – the Interim version will be available later this year. Secondly, we have added a comparison of the observations to the new CMIP6 model ensemble.
[Read more…] about Another dot on the graphs (Part II)Update day 2021
As is now traditional, every year around this time we update the model-observation comparison page with an additional annual observational point, and upgrade any observational products to their latest versions.
A couple of notable issues this year. HadCRUT has now been updated to version 5 which includes polar infilling, making the Cowtan and Way dataset (which was designed to address that issue in HadCRUT4) a little superfluous. Going forward it is unlikely to be maintained so, in a couple of figures, I have replaced it with the new HadCRUT5. The GISTEMP version is now v4.
For the comparison with the Hansen et al. (1988), we only had the projected output up to 2019 (taken from fig 3a in the original paper). However, it turns out that fuller results were archived at NCAR, and now they have been added to our data file (and yes, I realise this is ironic). This extends Scenario B to 2030 and Scenario A to 2060.
Nothing substantive has changed with respect to the satellite data products, so the only change is the addition of 2020 in the figures and trends.
So what do we see? The early Hansen models have done very well considering the uncertainty in total forcings (as we’ve discussed (Hausfather et al., 2019)). The CMIP3 models estimates of SAT forecast from ~2000 continue to be astoundingly on point. This must be due (in part) to luck since the spread in forcings and sensitivity in the GCMs is somewhat ad hoc (given that the CMIP simulations are ensembles of opportunity), but is nonetheless impressive.
The forcings spread in CMIP5 was more constrained, but had some small systematic biases as we’ve discussed Schmidt et al., 2014. The systematic issue associated with the forcings and more general issue of the target diagnostic (whether we use SAT or a blended SST/SAT product from the models), give rise to small effects (roughly 0.1ºC and 0.05ºC respectively) but are independent and additive.
The discrepancies between the CMIP5 ensemble and the lower atmospheric MSU/AMSU products are still noticeable, but remember that we still do not have a ‘forcings-adjusted’ estimate of the CMIP5 simulations for TMT, though work with the CMIP6 models and forcings to address this is ongoing. Nonetheless, the observed TMT trends are very much on the low side of what the models projected, even while stratospheric and surface trends are much closer to the ensemble mean. There is still more to be done here. Stay tuned!
The results from CMIP6 (which are still being rolled out) are too recent to be usefully added to this assessment of forecasts right now, though some compilations have now appeared:
The issues in CMIP6 related to the excessive spread in climate sensitivity will need to be looked at in more detail moving forward. In my opinion ‘official’ projections will need to weight the models to screen out those ECS values outside of the constrained range. We’ll see if other’s agree when the IPCC report is released later this year.
Please let us know in the comments if you have suggestions for improvements to these figures/analyses, or suggestions for additions.
References
- Z. Hausfather, H.F. Drake, T. Abbott, and G.A. Schmidt, "Evaluating the Performance of Past Climate Model Projections", Geophysical Research Letters, vol. 47, 2020. http://dx.doi.org/10.1029/2019GL085378
- G.A. Schmidt, D.T. Shindell, and K. Tsigaridis, "Reconciling warming trends", Nature Geoscience, vol. 7, pp. 158-160, 2014. http://dx.doi.org/10.1038/ngeo2105
Update day 2020!
Following more than a decade of tradition (at least), I’ve now updated the model-observation comparison page to include observed data through to the end of 2019.
As we discussed a couple of weeks ago, 2019 was the second warmest year in the surface datasets (with the exception of HadCRUT4), and 1st, 2nd or 3rd in satellite datasets (depending on which one). Since this year was slightly above the linear trends up to 2018, it slightly increases the trends up to 2019. There is an increasing difference in trend among the surface datasets because of the polar region treatment. A slightly longer trend period additionally reduces the uncertainty in the linear trend in the climate models.
To summarize, the 1981 prediction from Hansen et al (1981) continues to underpredict the temperature trends due to an underestimate of the transient climate response. The projections in Hansen et al. (1988) bracket the actual changes, with the slight overestimate in scenario B due to the excessive anticipated growth rate of CFCs and CH4 which did not materialize. The CMIP3 simulations continue to be spot on (remarkably), with the trend in the multi-model ensemble mean effectively indistinguishable from the trends in the observations. Note that this doesn’t mean that CMIP3 ensemble means are perfect – far from it. For Arctic trends (incl. sea ice) they grossly underestimated the changes, and overestimated them in the tropics.
The CMIP5 ensemble mean global surface temperature trends slightly overestimate the observed trend, mainly because of a short-term overestimate of solar and volcanic forcings that was built into the design of the simulations around 2009/2010 (see Schmidt et al (2014). This is also apparent in the MSU TMT trends, where the observed trends (which themselves have a large spread) are at the edge of the modeled histogram.
A number of people have remarked over time on the reduction of the spread in the model projections in CMIP5 compared to CMIP3 (by about 20%). This is due to a wider spread in forcings used in CMIP3 – models varied enormously on whether they included aerosol indirect effects, ozone depletion and what kind of land surface forcing they had. In CMIP5, most of these elements had been standardized. This reduced the spread, but at the cost of underestimating the uncertainty in the forcings. In CMIP6, there will be a more controlled exploration of the forcing uncertainty (but given the greater spread of the climate sensitivities, it might be a minor issue).
Over the years, the model-observations comparison page is regularly in the top ten of viewed pages on RealClimate, and so obviously fills a need. And so we’ll continue to keep it updated, and perhaps expand it over time. Please leave suggestions for changes in the comments below.
References
- J. Hansen, D. Johnson, A. Lacis, S. Lebedeff, P. Lee, D. Rind, and G. Russell, "Climate Impact of Increasing Atmospheric Carbon Dioxide", Science, vol. 213, pp. 957-966, 1981. http://dx.doi.org/10.1126/science.213.4511.957
- J. Hansen, I. Fung, A. Lacis, D. Rind, S. Lebedeff, R. Ruedy, G. Russell, and P. Stone, "Global climate changes as forecast by Goddard Institute for Space Studies three‐dimensional model", Journal of Geophysical Research: Atmospheres, vol. 93, pp. 9341-9364, 1988. http://dx.doi.org/10.1029/JD093iD08p09341
- G.A. Schmidt, D.T. Shindell, and K. Tsigaridis, "Reconciling warming trends", Nature Geoscience, vol. 7, pp. 158-160, 2014. http://dx.doi.org/10.1038/ngeo2105