Causes of model dry and warm bias over central U.S. and impact on climate projections

Abstract

Climate models show a conspicuous summer warm and dry bias over the central United States. Using results from 19 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), we report a persistent dependence of warm bias on dry bias with the precipitation deficit leading the warm bias over this region. The precipitation deficit is associated with the widespread failure of models in capturing strong rainfall events in summer over the central U.S. A robust linear relationship between the projected warming and the present-day warm bias enables us to empirically correct future temperature projections. By the end of the 21st century under the RCP8.5 scenario, the corrections substantially narrow the intermodel spread of the projections and reduce the projected temperature by 2.5 K, resulting mainly from the removal of the warm bias. Instead of a sharp decrease, after this correction the projected precipitation is nearly neutral for all scenarios.

The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 over the Central United States

“…The summertime precipitation bias over the Central United States has persisted in Atmospheric General Circulation Models (AGCMs) for several model generations (Dai et al, ; Dai, ; Sheffield et al, ; Lin et al, , and references therein). The model bias exists in both the seasonal mean and the diurnal cycle of precipitation.…”

“…First, the underestimated precipitation can cause a soil moisture deficit even if the model has a realistic representation of land evaporation process (Klein et al, ). Second, the cloud bias related to the precipitation bias can lead to a substantial radiation bias (Lin et al, ; Van Weverberg et al, ; Van Weverberg et al, ). Therefore, mitigating the precipitation bias over the Central United States is important to GCM development.…”

Evaluating Soil Moisture Feedback on Convective Triggering: Roles of Convective and Land‐Model Parameterizations

“…The parameterization of deep convection is a major source of uncertainty in climate model projections of future changes in the water cycle over land (Klein et al, ; Qian et al, ; Qiao & Liang, ; Van Weverberg et al, ; Wilcox & Donner, ; Yang et al, ). A related source of uncertainty is the parameterization of evapotranspiration (ET) and its relationship to soil moisture and vegetation state (Cheruy et al, ; Y. Lin et al, ; I. N. Williams et al, ). In particular, poor representation of the feedback between soil moisture and precipitation can contribute to the persistence and amplification of climate prediction biases (Bonan & Levis, ; Boussetta et al, ).…”

Coordination to Understand and Reduce Global Model Biases by U.S. and Chinese Institutions

“…Evidence has been presented that 64 these biases affect the magnitude of future change of precipitation and warming (Cheruy 2014; 65 Lin et al 2017). Reducing this bias is therefore of both scientific and practical importance.…”

“…The workshop was attended by representatives of six Chinese and three U.S. modeling 31 institutions that currently plan to submit simulation results to CMIP6 (Table 1- research suggests that this bias may be caused by the lack of heavy precipitation associated with 62 mesoscale convective systems in the models whose impact is amplified through land-atmosphere 63 interactions that are unique to this region (Lin et al 2017). Evidence has been presented that 64 these biases affect the magnitude of future change of precipitation and warming (Cheruy 2014; 65 Lin et al 2017).…”