Using Climate Model Simulations to Constrain Observations

Author:

Santer Benjamin D.1,Po-Chedley Stephen1,Mears Carl2,Fyfe John C.3,Gillett Nathan3,Fu Qiang4,Painter Jeffrey F.1,Solomon Susan5,Steiner Andrea K.6,Wentz Frank J.2,Zelinka Mark D.1,Zou Cheng-Zhi7

Affiliation:

1. a Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.

2. b Remote Sensing Systems, Santa Rosa, CA 95401, USA

3. c Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, V8W 2Y2, Canada

4. d Dept. of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA

5. e Massachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, MA 02139, USA

6. f Wegener Center for Climate and Global Change, University of Graz, A-8010 Graz, Austria

7. g Center for Satellite Applications and Research, NOAA/NESDIS, Camp Springs, Maryland 20746, USA

Abstract

AbstractWe compare atmospheric temperature changes in satellite data and in model ensembles performed under phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In the lower stratosphere, multi-decadal stratospheric cooling during the period of strong ozone depletion is smaller in newer CMIP6 simulations than in CMIP5 or satellite data. In the troposphere, however, despite forcing and climate sensitivity differences between the two CMIP ensembles, their ensemble-average global warming over 1979-2019 is very similar. We also examine four properties of tropical behavior governed by basic physical processes. The first three are ratios between trends inwater vapor (WV) and trends in sea surface temperature (SST), lower tropospheric temperature (TLT), and mid- to upper tropospheric temperature (TMT). The fourth property is the ratio between TMT and SST trends. All four ratios are tightly constrained in CMIP simulations but diverge markedly in observations. Model trend ratios between WV and temperature are closest to observed ratios when the latter are calculated with data sets exhibiting larger tropical warming of the ocean surface and troposphere. For the TMT/SST ratio, model-data consistency depends on the combination of observations used to estimate TMT and SST trends. If model expectations of these four covariance relationships are realistic, our findings reflect either a systematic low bias in satellite tropospheric temperature trends or an overestimate of the observed atmospheric moistening signal. It is currently difficult to determine which interpretation is more credible. Nevertheless, our analysis reveals anomalous covariance behavior in several observational data sets and illustrates the diagnostic power of simultaneously considering multiple complementary variables.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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