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The Tibetan Plateau, as a major elevated heat source, plays a critical role in the Asian monsoon and global climate. Observational data revealed significant correlations between spring surface air temperature in the Tibetan Plateau and downstream summer precipitation interannual variations. Sensitivity experiments using the regional Climate-Weather Research and Forecasting model (CWRF) were conducted to understand the physical processes and mechanisms underlying such delayed teleconnections. A positive temperature forcing was imposed over the plateau on the surface and subsurface soil layers only at the initial conditions around May 1st, 2003. This regional forcing quickly induces positive perturbations in local air temperature and, more importantly, maintains its signal in local soil, especially deep layers, for several months. Consequently, the soil temperature serves as a charged capacitor to modulate the planetary atmospheric circulation and through Rossby wave chains to cause significant summer precipitation anomalies over broad regions. This relayed teleconnection pattern is consistent with that identified from observational data records and CWRF climate simulations during 1980–2015. Diagnostic analyses of observations and simulations suggest that the Tibetan Plateau heating significantly impacts summer East Asian monsoon climate through influencing the South Asian High and shifting the East Asian jet. [ABSTRACT FROM AUTHOR]

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]

Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms. [ABSTRACT FROM AUTHOR]

Results from eight regional climate models (RCMs) participating in the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) initiative of the Global Energy and Water Exchanges (GEWEX) are examined and compared with observations over the Tibetan Plateau (TP). The RCM common domain covers most areas of East Asia with a horizontal resolution of 20–30 km. The model simulation covers a period from April to September in each year between 1991 and 2015. This study explores the RCMs' ability for seasonal climate simulation over the TP, focusing on the summer monsoon climate as part of the LS4P initiative. An intercomparison is made among eight RCMs for precipitation, surface air temperature, mid-troposphere atmospheric circulation, moisture conditions, and surface energy fluxes. It shows that the downscaling characteristics differ significantly between two major RCM types. The RegCM4 models show positive precipitation biases over the entire TP, especially over the south and southeast TP, while the WRF models mostly show both positive and negative precipitation biases over the TP with relatively high spatial correlation between simulated and observed precipitation. The multi-model ensemble mean produces overall smaller precipitation biases than most individual RCMs, with the largest biases over the southeastern TP, and smaller surface air temperature biases over most areas of the TP, especially over the central and southwestern TP. Moreover, the ensemble mean can better reproduce the inter-annual variation of precipitation and surface air temperature than most RCMs with proper magnitude. Sensitivity analyses using RegCM4 with different physics parameterizations show that varying land and cumulus schemes may induce large precipitation differences over the TP by affecting moisture and atmospheric circulation conditions in the lower and upper troposphere, respectively. Moreover, turbulent heat and radiation fluxes differences are associated with the temperature differences between different RegCM4 models. [ABSTRACT FROM AUTHOR]

The regional Climate-Weather Research and Forecasting model (CWRF) was used to downscale the NCAR Community Climate System Model V4.0 (CCSM4) projection of China precipitation changes from the present (1974–2005) to future (2019–2050) under the high emission scenario RCP8.5. The CWRF downscaling at 30-km improved CCSM4 in capturing observed key precipitation spatiotemporal characteristics, correcting rainband dislocations, seasonal-mean biases, extreme-rainfall underestimates and rainy-day overestimates. For the future, CWRF generally reduced CCSM4 projected changes in magnitude, producing still significant increases mostly in summer for mean precipitation in the Northeast, North China and Southwest and for extreme precipitation in North China, South China and the Southwest. These regional precipitation increases were direct responses to enhanced ascending motions and moisture transports from adjacent oceans as the east Asian jet shrunk westward and the Hadley circulation widened northward under global warming. The identification of such robust physical mechanisms added confidence in the CWRF downscaled regional precipitation changes. Furthermore, the CWRF downscaling corrections were systematically carried from the present into future, accounting for projection uncertainties up to 40%. Regional biases, however, could not be simply removed from projected changes because their correspondences were strongly nonlinear, highlighting CWRF's ability to project more reliable changes by reducing model structural uncertainties. [ABSTRACT FROM AUTHOR]