Global surface air temperatures in CMIP6: historical performance and future changes

Abstract

Surface air temperature outputs from 16 global climate models participating in the sixth phase of the coupled model intercomparison project (CMIP6) were used to evaluate agreement with observations over the global land surface for the period 1901–2014. Projections of multi-model mean under four different shared socioeconomic pathways were also examined. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature with a pattern correlation typically greater than 0.98, but with large variability across models and regions. In addition, the CMIP6 mean can capture the trends of global surface temperatures shown by the observational data during 1901–1940 (warming), 1941–1970 (cooling) and 1971–2014 (rapid warming). By the end of the 21st century, the global temperature under different scenarios is projected to increase by 1.18 °C/100 yr (SSP1-2.6), 3.22 °C/100 yr (SSP2-4.5), 5.50 °C/100 yr (SSP3-7.0) and 7.20 °C/100 yr (SSP5-8.5), with greater warming projected over the high latitudes of the northern hemisphere and weaker warming over the tropics and the southern hemisphere. Results of probability density distributions further indicate that large increases in the frequency and magnitude of warm extremes over the global land may occur in the future.

Earth System Models Are Not Capturing Present‐Day Tropical Forest Carbon Dynamics

“…Ultimately, two aspects determine how well ESMs reproduce the natural intacttropical forest carbon sink in live biomass; (i) the simulated climate, its seasonality and multiannual trends, and (ii) the simulated response of the vegetation to these environmental drivers. CMIP6 models generally agree with observations on MAT and its historical trends in the tropics (Fan et al., 2020). CMIP6 ESMs are able to reproduce total annual precipitation in the African tropics, but show a dry bias in Amazonia and a wet bias in SE Asia.…”

Mean States and Future Projections of Precipitation Over the Monsoon Transitional Zone in China in CMIP5 and CMIP6 Models

“…In CMIP5, 13d-f). The significant warming in the mid-high latitudes has already been mentioned in previous studies (Feng et al 2014;Fan et al 2020), and the suggested reason for its increase in CMIP6 is the higher climate sensitivity (Fan et al 2020;Flynn and Mauritsen 2020;Zelinka et al 2020). The stronger warming gradient in CMIP6 induces a larger sea level pressure gradient between the tropics and mid-high latitudes, which favors a strengthening of the prevailing southerly winds over the eastern coast of East China and a weakening of the mid-high-latitude westerly winds (Figs.…”

Evaluation of CMIP6 Global Climate Models for Simulating Land Surface Energy and Water Fluxes During 1979–2014

“…According to their findings, to a large extent, the overestimation of LH and SH over land by the CMIP6 models may be attributed to overestimated surface humidity (partly due to overestimated PR) and underestimated air temperature [found by Fan et al. (2020) for CMIP6], respectively. It should also be noticed that the sensitivity of model behavior to forcing uncertainty varied significantly among models and climate conditions (Kato et al., 2007; Nasonova et al., 2011).…”

“…Zhou et al (2020) systematically analyzed the causes of overestimated LH and SH over tropical oceans for the CMIP5 models, and found that the biases in LH and SH were strongly related to sea-air humidity and temperature differences, respectively. According to their findings, to a large extent, the overestimation of LH and SH over land by the CMIP6 models may be attributed to overestimated surface humidity (partly due to overestimated PR) and underestimated air temperature [found by Fan et al (2020) for CMIP6], respectively. It should also be noticed that the sensitivity of model behavior to forcing uncertainty varied significantly among models and climate conditions (Kato et al, 2007;Nasonova et al, 2011).…”