ALBERT

Description: Understanding changes in background dryness over land is key information for adapting to climate change because of its critical socioeconomic consequences. However, causes of continental dryness changes remain uncertain because various climate parameters control dryness. Here, we verify dominant climate variables determining dryness trends over continental eastern Asia, which is characterized by diverse hydroclimate regimes ranging from arid to humid, by quantifying the relative effects of changes in precipitation, solar radiation, wind speed, surface air temperature, and relative humidity on trends in the aridity index based on observed data from 189 weather stations for the period of 1961–2010. Before the early 1980s (1961–1983), change in precipitation is a primary condition for determining aridity trends. In the later period (1984–2010), the dominant climate parameter for aridity trends varies according to the hydroclimate regime. Drying trends in arid regions are mostly explained by reduced precipitation. In contrast, the increase in potential evapotranspiration due to increased atmospheric water-holding capacity, a secondary impact of warming, works to increase aridity over the humid monsoon region despite an enhanced water supply and relatively less warming. Our results show significant drying effects of warming over the humid monsoon region in recent decades; this also supports the drying trends over warm and water-sufficient regions in future climate.

Description: Understanding changes in continental surface dryness is key information for adapting to climate change because of the critical socioeconomic consequences. Recent studies reveal that spatial patterns of continental dryness trends are in contrast to the "dry gets drier, wet gets wetter" paradigm. Causes of the complexity in dryness trends remain uncertain because various climate parameters control continental dryness. Here, we quantify the relative effects of dominant climate drivers on dryness trends over continental East Asia, which is characterized by diverse hydro-climate regimes ranging from humid to arid, by analyzing observed data from 189 weather stations for the period of 1961–2010. Since the early 1980s, monsoon climate zones (east of 100° E) have been getting significantly drier, but the related mechanisms vary according to the hydro-climate regime. Drying trends in arid regions are mostly explained by reduced precipitation. In contrast, in humid areas, the increase in evapotranspiration due to increased atmospheric water-holding capacity, a secondary impact of warming, is the primary condition for the increase in dryness. This drying impact of atmospheric moisture deficiency is much stronger in humid areas than in arid areas. Our results suggest that enhanced atmospheric water demands caused by warming can threaten water resources in wet monsoon areas and possibly in other warm and water-sufficient regions.

Description: The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Description: The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.