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- Author or Editor: Zhenzhong Zeng x
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Abstract
Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008–15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%–95%, and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I = 0.47) and the cold frontal system in the Pearl River delta (Moran’s I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 25°C over Beijing and T > 28°C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.
Abstract
Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008–15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%–95%, and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I = 0.47) and the cold frontal system in the Pearl River delta (Moran’s I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 25°C over Beijing and T > 28°C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.
Abstract
Downward shortwave radiation R sd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale R sd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate R sd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in R sd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.
Abstract
Downward shortwave radiation R sd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale R sd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate R sd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in R sd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.
Abstract
Global reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.
Abstract
Global reanalysis products are important tools across disciplines to study past meteorological changes and are especially useful for wind energy resource evaluations. Studies of observed wind speed show that land surface wind speed declined globally since the 1960s (known as global terrestrial stilling) but reversed with a turning point around 2010. Whether the declining trend and the turning point have been captured by reanalysis products remains unknown so far. To fill this research gap, a systematic assessment of climatological winds and trends in five reanalysis products (ERA5, ERA-Interim, MERRA-2, JRA-55, and CFSv2) was conducted by comparing gridcell time series of 10-m wind speed with observational data from 1439 in situ meteorological stations for the period 1989–2018. Overall, ERA5 is the closest to the observations according to the evaluation of climatological winds. However, substantial discrepancies were found between observations and simulated wind speeds. No reanalysis product showed similar change to that of the global observations, although some showed regional agreement. This discrepancy between observed and reanalysis land surface wind speed indicates the need for prudence when using reanalysis products for the evaluation and prediction of winds. The possible reasons for the inconsistent wind speed trends between reanalysis products and observations are analyzed. The results show that wind energy production should select different products for different regions to minimize the discrepancy with observations.
Abstract
Better understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.
Abstract
Better understanding and quantification of river floods for very local and “flashy” events calls for modeling capability at fine spatial and temporal scales. However, long-term discharge records with a global coverage suitable for extreme events analysis are still lacking. Here, grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, we developed a 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) subdaily modeling. This data record, referred as Global Reach-Level Flood Reanalysis (GRFR), is publicly available at https://www.reachhydro.org/home/records/grfr.
Abstract
To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980–2018) for 4722 meteorological stations. Through examining monthly averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (−19%), South America (−16%), Australia (−14%), and Asia (−13%), with corresponding rate reductions of −0.13, −0.08, −0.09, and −0.06 m s−1 decade−1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean–atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including the Western Hemisphere warm pool, East Atlantic pattern, Pacific decadal oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind power generation, now and in the future.
Abstract
To investigate changes in global wind speed phenomena, we constructed homogenized monthly time series (1980–2018) for 4722 meteorological stations. Through examining monthly averaged wind speeds (MWS), we found that seasonal wind speed range (SWSR; calculated as the difference between maximum and minimum MWS) has declined significantly by 10% since 1980 (p < 0.001). This global SWSR reduction was primarily influenced by decreases in Europe (−19%), South America (−16%), Australia (−14%), and Asia (−13%), with corresponding rate reductions of −0.13, −0.08, −0.09, and −0.06 m s−1 decade−1, respectively (p < 0.01). In contrast, the SWSR in North America rose 3%. Important is that the decrease in SWSR occurred regardless of the stilling or reversal of annual wind speed. The shrinking SWSR in Australia and South America was characterized by continuous decreases in maximum MWS and increases in the minimum. For Europe and Asia, maximum and minimum MWS declined initially after 1980, followed by substantial increases in minimum MWS (about 2000 and 2012, respectively) that preserved the long-term reduction in the range. Most reanalysis products (ERA5, ERA-Interim, and MERRA-2) and climate model simulations (AMIP6 and CMIP6) fail to reproduce the observed trends. However, some ocean–atmosphere indices (seasonality characteristics) were correlated significantly with these trends, including the Western Hemisphere warm pool, East Atlantic pattern, Pacific decadal oscillation, and others. These findings are important for increasing the understanding of mechanisms behind wind speed variations that influence a multitude of other biogeophysical processes and the development of efficient wind power generation, now and in the future.
Abstract
Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.
Abstract
Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.
