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Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases. [ABSTRACT FROM AUTHOR]

A consistent set of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation parameters, including leaf and stem area index (LAI and SAI, respectively), land-cover category (LCC), fractional vegetation cover (FVC), and albedo parameterization are developed, and their impacts on North American regional climate are evaluated based on 10-yr Climate-Weather and Research Forecasting Model (CWRF) simulations. As compared with the previous Advanced Very High Resolution Radiometer (AVHRR) set, MODIS LCC increases grassland and cropland fractions in the central Great Plains and Midwest, respectively. Evergreen needleleaf forest converts to mixed forest in the Southeast, and mixed forest converts to evergreen needleleaf in Canada. FVC decreases by 0.05-0.3 over the central Great Plains but increases by 0.1-0.35 over the northern Rocky Mountains, Canada, and the U.S. Southeast. MODIS LAI is less than AVHRR by 2-6, except in the central Great Plains, eastern Rocky Mountains, and central Mexico. LCC and FVC changes over the central Great Plains reduce CWRF warm biases by 0.71°C and wet biases by 0.36 mm day−1. Large LAI reductions cause latent and sensible heat fluxes to decrease by 0.78-5.81 and 0.91-6.54 W m−2, respectively. They also lessen cold biases over the Gulf States and Southeast and wet biases over the North American monsoon region and Canada during summer. In densely vegetated regions including eastern Canada, the Ohio Valley, and the mid-Atlantic region, spring and summer precipitation decreases and temperature increases result from LAI reductions that cause positive evapotranspiration-precipitation-soil moisture feedbacks. Conversely, precipitation and temperature decreases in sparely vegetated regions, such as the Great Plains, result from FVC reductions that cause negative albedo-evapotranspiration-precipitation-soil moisture feedbacks. [ABSTRACT FROM AUTHOR]