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CMIP6

The need for pluralism in climate modelling

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How should we allocate resources for climate modelling if the goal is to improve climate-related decisions? Higher resolution, machine learning and/or storylines? A call for a deeper discussion on how we should develop the climate modelling toolbox.

Guest post by Marina Baldissera Pacchetti, Julie Jebeile and Erica Thompson

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Not just another dot on the graph? Part II

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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.

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The Scafetta Saga

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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.

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References

  1. 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

Evaluation of GCM simulations with a regional focus.

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Do the global climate models (GCMs) we use for describing future climate change really capture the change and variations in the region that we want to study? There are widely used tools for evaluating global climate models, such as the ESMValTool, but they don’t provide the answers that I seek.

I use GCMs to provide information about large-scale conditions, processes and phenomena in the atmosphere that I can use as predictors in downscaling future climate projections. I also want to know whether the ensemble of GCM simulations that I use provides representative statistics of the actual regional climate I’m interested in. 

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CMIP6: Not-so-sudden stratospheric cooling

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As predicted in 1967 by Manabe and Wetherald, the stratosphere has been cooling.

A new paper by Ben Santer and colleagues has appeared in PNAS where they extend their previous work on the detection and attribution of anthropogenic climate change to include the upper stratosphere, using observations from the Stratospheric Sounding Units (SSUs) (and their successors, the AMSU instruments) that have flown since 1979.

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References

  1. B.D. Santer, S. Po-Chedley, L. Zhao, C. Zou, Q. Fu, S. Solomon, D.W.J. Thompson, C. Mears, and K.E. Taylor, "Exceptional stratospheric contribution to human fingerprints on atmospheric temperature", Proceedings of the National Academy of Sciences, vol. 120, 2023. http://dx.doi.org/10.1073/pnas.2300758120

Some new CMIP6 MSU comparisons

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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. TMT_{corr} = 1.1 TMT - 0.1 TLS).

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References

  1. 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
  2. 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

Scafetta comes back for more

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A new paper from Scafetta and it’s almost as bad as the last one.

Back in March, we outlined how a model-observations comparison paper in GRL by Nicola Scafetta (Scafetta, 2022a) got wrong basically everything that one could get wrong (the uncertainty in the observations, the internal variability in the models, the statistical basis for comparisons – the lot!). Now he’s back with a new paper in a different journal (Scafetta, 2022b) that could be seen as trying to patch the holes in the first one, but while he makes some progress, he now adds some new errors while attempting CPR on his original conclusions.

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References

  1. 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
  2. N. Scafetta, "CMIP6 GCM ensemble members versus global surface temperatures", Climate Dynamics, vol. 60, pp. 3091-3120, 2022. http://dx.doi.org/10.1007/s00382-022-06493-w