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Climate model projections compared to observations

Since we have been periodically posting updates (e.g. 2009, 2010, 2011, 2012, 2015, 2016 etc.) of model output comparisons to observations across a range of variables, we have now set up this page as a permanent placeholder for the most up-to-date comparisons. We include surface temperature projections from 1981, 1988, CMIP3, CMIP5, and CMIP6, and MSU satellite products from CMIP5, and we will update this on an annual basis, or as new observational products become available. For each comparison, we note the last update date, and where the comparison was first discussed.

Note that Hausfather et al. (2020) made a more complete assessment of surface temperature projections from models from 1970 through to CMIP3 (discussed here).

Global mean surface temperature anomalies

Hansen et al (1981)

Hansen 1981 profjections overlain with actual temperatures to 2022 that lie above the projections.

Original discussion (figure originally courtesy of Geert Jan van Oldenborgh, Hansen et al. (1981)). Observations are the GISTEMP LOTI annual figures and 5 year mean. Last updated: 16 Jan 2023.

Hansen et al (1988)

Hansen et al 1988 projections overlain with surface temperature observations.

Original discussion (2007), Last discussion (2018). Scenarios from Hansen et al. (1988). Observations are the GISTEMP LOTI annual figures. Trends from 1984: GISTEMP: 0.20ºC/dec, Scenarios A, B, C: 0.34, 0.28, 0.15ºC/dec respectively (all 95% CI ~±0.02 or 0.03). Last updated: 16 Jan 2023.

CMIP3 (circa 2004)

Projections from he CMIP3 ensemble with actual observations plotted above.

Last discussion (2015). Model spread is the 95% envelope of global mean surface temperature anomalies from all individual CMIP3 simulations (using the SRES A1B projection post-2000). Observations are the standard quasi-global estimates of anomalies with no adjustment for spatial coverage or the use of SST instead of SAT over the open ocean. Last updated: 16 Jan 2023.

CMIP5 (circa 2011)

CMIP5 ensemble projections with observational record overlaid.

Last discussion (2015). Model spread is the 95% envelope of true global mean surface temperature anomalies from all CMIP5 historical simulations (using the RCP4.5 projection post-2005). Forcing adjustment is updated from Schmidt et al. (2014). Observations are the standard quasi-global estimates of anomalies with no adjustment for spatial coverage or the use of SST instead of SAT over the open ocean. Last updated: 16 Jan 2023.

As above, but using the blended SST/SAT product from the CMIP5 models produced by Cowtan et al (2015) instead of the pure SAT field. Note that this makes about a 0.05ºC difference in 2022 (compared to a 0.1ºC difference estimated from the forcings adjustment above). Last updated: 17 Jan 2023.

CMIP6 (circa 2021)

The latest phase of CMIP has been ongoing since around 2019, and now has sufficient models to provide a basis for projections going forward. These models used observed boundary conditions (GHG levels, deforestation, solar, volcanoes etc.) up to 2014, and projections based on the Shared Socioeconomic Pathways (SSPs) from 2015 onwards. The same caveat with respect to the comparison to the blended SAT/SST observations stands as with CMIP5, but this is a relatively small effect (i.e. it’s expected to be around 0.05ºC in 2022).  Note however, that some CMIP6 models have climate sensitivities outside both the CMIP5 range and the range constrained by observations.  Thus in these figures, we plot the full mean (1 ensemble member per model) and the mean of a subset of the models that have a transient climate response (TCR) within the likely constrained range [1.4,2.2]ºC as assessed by IPCC AR6 (Hausfather et al., 2022).

Surface temperature changes in CMIP6 models, using a screened ensemble and a fuller ensemble (37 models).

Model spread is the 95% envelope of surface air temperature anomalies using 37 model simulations from the historical runs and SSP2-4.5. TCR values used in the screen are Hausfather et al. (2022). Last updated: 20 Jan 2023.

Satellite-derived atmospheric temperatures

TMT (global and tropical means) (timeseries and trends)

Original discussion (Jan 2017). Mid-troposphere satellite products from UAH, RSS, NOAA STAR  (v3, v4.1 and v5) and UW (Po-Chedley et al, tropical values only). Model values use synthetic MSU/AMSU-TMT weightings, spread is 95% envelope of simulations. Last updated: 2 Feb 2023.

Corrected-TMT (Tropical means) (timeseries and trends)

Original discussion Mar 2023. The TMT-corrected diagnostic (where the TMT is adjusted by the lower stratospheric changes (TLS)) in a 24-model subsample of the CMIP6 database (Po-Chedley et al., 2021) compared to RSS v4, UAH v6 and NOAA STAR v5. Plots includes the full ensemble, and then a subset of models that have a TCR within the IPCC AR6 likely range. Model values use synthetic TMT-corrected weightings, spread is 95% envelope of simulations. Last updated: 19 Mar 2023.

Global SST changes

Sea surface temperature trends in CMIP6 models compared to observations from 1979 to 2022. The TCR screened simulations line up very nicely with the observations.
Histogram of 1979-2022 trends in global SST in CMIP6 models and observations. The TCR screened models line up nicely with observed trends, but some models (e.g. CanESM5) are warming too fast.

Original discussion 3/19/2023. Global mean SST trends in CMIP6 compared to HadSST4 and ERSSTv5. As above, models are screened using the TCR criterion. The models are a 15-model subset from the U. Melbourne collation. Last updated 19 Mar 2023.

SSU trends

Original discussion 5/21/23. Global mean SSU trends for three channels (NOAA-STAR v3.0) compared to a 9-member subset of the CMIP6 archive. Last updated 5/21/23.

If you have suggestions for additional comparisons, stylistic changes, clarifications etc., please leave a comment on the latest open thread. You can use these figures anywhere (with a citation and link back to RealClimate).

References

  1. 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
  2. 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
  3. 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, vol. 93, pp. 9341, 1988. http://dx.doi.org/10.1029/JD093iD08p09341
  4. 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
  5. K. Cowtan, Z. Hausfather, E. Hawkins, P. Jacobs, M.E. Mann, S.K. Miller, B.A. Steinman, M.B. Stolpe, and R.G. Way, "Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures", Geophysical Research Letters, vol. 42, pp. 6526-6534, 2015. http://dx.doi.org/10.1002/2015GL064888
  6. Z. Hausfather, K. Marvel, G.A. Schmidt, J.W. Nielsen-Gammon, and M. Zelinka, "Climate simulations: recognize the ‘hot model’ problem", Nature, vol. 605, pp. 26-29, 2022. http://dx.doi.org/10.1038/d41586-022-01192-2