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- Author or Editor: Ernesto Rodriguez x
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Abstract
Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies, due to its low computational expense compared to dynamical downscaling, which allows to explore uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 reanalysis at 0.25°) in two regions with very different climates: Spain (Mid-latitudes) and Central America (Tropics). Some key assumptions of SD have been tested: the strength of the predictors/predictand links, the skill of different approaches and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as well as the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where Transfer Function methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, Model Output Statistics (MOS) methods have achieved the best results for temperature. In Central America Transfer Function (TF) methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the machine learning method eXtreme Gradient Boost have achieved the best results in both regions. Additionally, it has been found that although the use of humidity indexes as predictors improve results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indexes have been compared: relative humidity, specific humidity and dew point depression. The use of the specific humidity has been found to seriously deviate trends given by the downscaled projections from those given by raw Global Climate Models in both regions.
Abstract
Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies, due to its low computational expense compared to dynamical downscaling, which allows to explore uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 reanalysis at 0.25°) in two regions with very different climates: Spain (Mid-latitudes) and Central America (Tropics). Some key assumptions of SD have been tested: the strength of the predictors/predictand links, the skill of different approaches and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as well as the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where Transfer Function methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, Model Output Statistics (MOS) methods have achieved the best results for temperature. In Central America Transfer Function (TF) methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the machine learning method eXtreme Gradient Boost have achieved the best results in both regions. Additionally, it has been found that although the use of humidity indexes as predictors improve results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indexes have been compared: relative humidity, specific humidity and dew point depression. The use of the specific humidity has been found to seriously deviate trends given by the downscaled projections from those given by raw Global Climate Models in both regions.
Abstract
Observations from the European Field Experiment in a Desertification-threatened Area (EFEDA) are used to evaluate the performance of the radiation, land surface, and boundary layer description of the numerical weather prediction (NWP) system High-Resolution Limited Area Model (HIRLAM) in semiarid conditions. Model analysis and 6-h forecast data of the fully coupled three-dimensional model are compared with the comprehensive dataset of a case study representing a sample of 22 days of anticyclonic conditions. Distributed micrometeorological surface stations, radiosondes, flux aircraft, and airborne lidar provide a unique validation dataset of the diurnal cycle of surface and boundary layer processes.
The model surface, soil, and boundary layer are found to be too moist and slightly too cold during most of the diurnal cycle. The model radiation and surface energy budgets are biased toward more humid conditions.
Model shortcomings are identified essentially in four areas. These are the moisture data assimilation, the land-use and soil classification with its associated physiographic database, the aerosol parameterization in the radiation code, and the boundary layer vertical resolution and entrainment description.
Practical steps for immediate improvement of the model performance are proposed. They focus on the use of a land-use and soil classification and physiographic database adapted to Mediterranean landscapes, in combination with the inclusion of aerosol parameters in the radiation scheme, that account for the typically higher aerosol load of arid and semiarid environments.
Abstract
Observations from the European Field Experiment in a Desertification-threatened Area (EFEDA) are used to evaluate the performance of the radiation, land surface, and boundary layer description of the numerical weather prediction (NWP) system High-Resolution Limited Area Model (HIRLAM) in semiarid conditions. Model analysis and 6-h forecast data of the fully coupled three-dimensional model are compared with the comprehensive dataset of a case study representing a sample of 22 days of anticyclonic conditions. Distributed micrometeorological surface stations, radiosondes, flux aircraft, and airborne lidar provide a unique validation dataset of the diurnal cycle of surface and boundary layer processes.
The model surface, soil, and boundary layer are found to be too moist and slightly too cold during most of the diurnal cycle. The model radiation and surface energy budgets are biased toward more humid conditions.
Model shortcomings are identified essentially in four areas. These are the moisture data assimilation, the land-use and soil classification with its associated physiographic database, the aerosol parameterization in the radiation code, and the boundary layer vertical resolution and entrainment description.
Practical steps for immediate improvement of the model performance are proposed. They focus on the use of a land-use and soil classification and physiographic database adapted to Mediterranean landscapes, in combination with the inclusion of aerosol parameters in the radiation scheme, that account for the typically higher aerosol load of arid and semiarid environments.
Abstract
The diurnal cycle of coastal rainfall over western Puerto Rico was studied with high-frequency radiosondes launched by undergraduate students at the University of Puerto Rico at Mayagüez (UPRM). Thirty radiosondes were launched during a 3-week period as part of NASA’s Convective Processes Experiment—Aerosols and Winds (CPEX-AW) field project. The objective of the radiosonde launches over Puerto Rico was to understand the evolution of coastal convective systems that are often challenging to predict. Four different events were sampled: 1) a short-lived rainfall event during a Saharan air dust outbreak, 2) a 2-day period of limited rainfall activity under northeasterly wind conditions, 3) a 2-day period of heavy rainfall over land, and 4) a 2-day period of long-lived rainfall events that initiated over land and propagated offshore during the evening hours. The radiosondes captured the sea-breeze onset during the midmorning hours, an erosion of lower-tropospheric inversions, and substantial differences in column humidity between the four events. All radiosondes were launched by volunteer undergraduate students who were able to participate in person, while the coordination was done virtually with lead scientists located in Puerto Rico, Oklahoma, and Saint Croix. Overall, this initiative highlighted the importance of student–scientist collaboration in collecting critical observations to better understand complex atmospheric processes.
Abstract
The diurnal cycle of coastal rainfall over western Puerto Rico was studied with high-frequency radiosondes launched by undergraduate students at the University of Puerto Rico at Mayagüez (UPRM). Thirty radiosondes were launched during a 3-week period as part of NASA’s Convective Processes Experiment—Aerosols and Winds (CPEX-AW) field project. The objective of the radiosonde launches over Puerto Rico was to understand the evolution of coastal convective systems that are often challenging to predict. Four different events were sampled: 1) a short-lived rainfall event during a Saharan air dust outbreak, 2) a 2-day period of limited rainfall activity under northeasterly wind conditions, 3) a 2-day period of heavy rainfall over land, and 4) a 2-day period of long-lived rainfall events that initiated over land and propagated offshore during the evening hours. The radiosondes captured the sea-breeze onset during the midmorning hours, an erosion of lower-tropospheric inversions, and substantial differences in column humidity between the four events. All radiosondes were launched by volunteer undergraduate students who were able to participate in person, while the coordination was done virtually with lead scientists located in Puerto Rico, Oklahoma, and Saint Croix. Overall, this initiative highlighted the importance of student–scientist collaboration in collecting critical observations to better understand complex atmospheric processes.
Abstract
Drought affects virtually every region of the world, and potential shifts in its character in a changing climate are a major concern. This article presents a synthesis of current understanding of meteorological drought, with a focus on the large-scale controls on precipitation afforded by sea surface temperature (SST) anomalies, land surface feedbacks, and radiative forcings. The synthesis is primarily based on regionally focused articles submitted to the Global Drought Information System (GDIS) collection together with new results from a suite of atmospheric general circulation model experiments intended to integrate those studies into a coherent view of drought worldwide. On interannual time scales, the preeminence of ENSO as a driver of meteorological drought throughout much of the Americas, eastern Asia, Australia, and the Maritime Continent is now well established, whereas in other regions (e.g., Europe, Africa, and India), the response to ENSO is more ephemeral or nonexistent. Northern Eurasia, central Europe, and central and eastern Canada stand out as regions with few SST-forced impacts on precipitation on interannual time scales. Decadal changes in SST appear to be a major factor in the occurrence of long-term drought, as highlighted by apparent impacts on precipitation of the late 1990s “climate shifts” in the Pacific and Atlantic SST. Key remaining research challenges include (i) better quantification of unforced and forced atmospheric variability as well as land–atmosphere feedbacks, (ii) better understanding of the physical basis for the leading modes of climate variability and their predictability, and (iii) quantification of the relative contributions of internal decadal SST variability and forced climate change to long-term drought.
Abstract
Drought affects virtually every region of the world, and potential shifts in its character in a changing climate are a major concern. This article presents a synthesis of current understanding of meteorological drought, with a focus on the large-scale controls on precipitation afforded by sea surface temperature (SST) anomalies, land surface feedbacks, and radiative forcings. The synthesis is primarily based on regionally focused articles submitted to the Global Drought Information System (GDIS) collection together with new results from a suite of atmospheric general circulation model experiments intended to integrate those studies into a coherent view of drought worldwide. On interannual time scales, the preeminence of ENSO as a driver of meteorological drought throughout much of the Americas, eastern Asia, Australia, and the Maritime Continent is now well established, whereas in other regions (e.g., Europe, Africa, and India), the response to ENSO is more ephemeral or nonexistent. Northern Eurasia, central Europe, and central and eastern Canada stand out as regions with few SST-forced impacts on precipitation on interannual time scales. Decadal changes in SST appear to be a major factor in the occurrence of long-term drought, as highlighted by apparent impacts on precipitation of the late 1990s “climate shifts” in the Pacific and Atlantic SST. Key remaining research challenges include (i) better quantification of unforced and forced atmospheric variability as well as land–atmosphere feedbacks, (ii) better understanding of the physical basis for the leading modes of climate variability and their predictability, and (iii) quantification of the relative contributions of internal decadal SST variability and forced climate change to long-term drought.
