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Biases in land‐atmosphere interaction simulations may originate from different model components or parameterizations and are transferred to the land‐atmosphere system, which causes biases in weather and climate predictions. With a focus on East Asia, we analyze the relationships between land surface sensible and latent heat fluxes (SH and LH), evaporative fraction (EF), soil moisture, and lifting condensation level (LCL) height in ERA5 and MERRA‐2 reanalyzes and simulations from a regional climate model (CWRF) with multiple parameterizations. The results show that SH (soil moisture and EF) is generally positively (negatively) correlated with LCL height because of their mutual positive (negative) influence and the positive feedback loops. The LH‐LCL relationship is sensitive to the local climate and generally positive in humid climates and negative in the semiarid climate; however, only negative correlations can be indicative of a possible impact of LH on LCL. MERRA‐2 shows an overall stronger influence of land on LCL height than ERA5 and CWRF. Different CWRF parameterizations produce different and sometimes contrasting LH‐LCL relationships, which is strongly related to the simulated climate and can be validated. This highlights the need for a deep process‐level understanding of land‐atmosphere interactions in coupled model simulations. Plain Language Summary: Numerical weather and climate models include many complex physical processes and their descriptions are often biased to some extent. Biases in the simulation of land‐atmosphere coupling may limit the use of land surface data for weather and climate predictions. In this study, we analyze the relationships between land surface sensible and latent heat fluxes (SH and LH) and lifting condensation level (LCL) height, whose couplings are important for the land‐atmosphere system and understanding them may help improve the accuracy of land‐atmosphere interaction simulations. We find that SH is almost always positively correlated with LCL height because of their mutual positive influence and the positive feedback loops. The LH‐LCL relationship changes with the local climate and is generally positive in humid climates and negative in semiarid climates, but only negative correlations can indicate a possible impact of LH on LCL. Different descriptions of the physical processes in the CWRF model produce different and sometimes contrasting LH‐LCL relationships, which is strongly related to their simulated climate and can be validated. This illustrates the need for more process‐level understanding of land‐atmosphere interactions in coupled model simulations. Key Points: LH‐LCL correlations can be positive or negative but only negative correlations can indicate the impact of land on LCLMERRA‐2 shows an overall stronger impact of land on LCL than ERA5 and a regional climate modelLH‐LCL correlations from a model with different physics parameterizations strongly depends on the simulated climate [ABSTRACT FROM AUTHOR]

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities. [ABSTRACT FROM AUTHOR]

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration-response functions. Using the U.S. Environmental Protection Agency's (EPA's) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration-response functions were chosen to match those used in the U.S. EPA's 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change. [ABSTRACT FROM AUTHOR]

This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (landcover category, and static fractional vegetation cover and varying leaf-plusstem- area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorporated into any RCM suitable for U.S. climate and hydrology modeling studies, while the data processing and validation procedures can be more generally applied to construct SBCs for any specific domain over the globe. [ABSTRACT FROM AUTHOR]

A regional climate model simulation of the period of 1979–88 over the contiguous United States, driven by lateral boundary conditions from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis, was analyzed to assess the ability of the model to simulate heavy precipitation events and seasonal precipitation anomalies. Heavy events were defined by precipitation totals that exceed the threshold value for a specified return period and duration. The model magnitudes of the thresholds for 1-day heavy precipitation events were in good agreement with observed thresholds for much of the central United States. Model thresholds were greater than observed for the eastern and intermountain western portions of the region and were smaller than observed for the lower Mississippi River basin. For 7-day events, model thresholds were in good agreement with observed thresholds for the eastern United States and Great Plains, were less than observed for the most of the Mississippi River valley, and were greater than observed for the intermountain western region. The interannual variability in frequency of heavy events in the model simulation exhibited similar behavior to that of the observed variability in the South, Southwest, West, and North-Central study regions. The agreement was poorer for the Midwest and Northeast, although the magnitude of variability was similar for both model and observations. There was good agreement between the model and observational data in the seasonal distribution of extreme events for the West and North-Central study regions; in the Southwest, Midwest, and Northeast, there were general similarities but some differences in the details of the distributions. The most notable differences occurred for the southern Gulf Coast region, for which the model produced a summer peak that is not present in the observational data. There was not a very high correlation in the timing of individual heavy... [ABSTRACT FROM AUTHOR]