Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a sig...Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.展开更多
In situ data in West Africa are scarce,and reanalysis datasets could be an alternative source to alleviate the problem of data availability.Nevertheless,because of uncertainties in numerical prediction models and assi...In situ data in West Africa are scarce,and reanalysis datasets could be an alternative source to alleviate the problem of data availability.Nevertheless,because of uncertainties in numerical prediction models and assimilation methods,among other things,existing reanalysis datasets can perform with various degrees of quality and accuracy.Therefore,a proper assessment of their shortcomings and strengths should be performed prior to their usage.In this study,we examine the performance of ERA5 and ERA-interim(ERAI)products in representing the mean and extreme climates over West Africa for the period 1981-2018 using observations from CRU and CHIRPS.The major conclusion is that ERA5 showed a considerable decrease in precipitation and temperature biases and an improved representation of inter-annual variability in much of western Africa.Also,the annual cycle is better captured by ERA5 in three of the region’s climatic zones;specifically,precipitation is well-reproduced in the Savannah and Guinea Coast,and temperature in the Sahel.In terms of extremes,the ERA5 performance is superior.Still,both reanalyses underestimate the intensity and frequency of heavy precipitations and overestimate the number of wet days,as the numerical models used in reanalyses tend to produce drizzle more often.While ERA5 performs better than ERAI,both datasets are less successful in capturing the observed long-term trends.Although ERA5 has achieved considerable progress compared to its predecessor,improved datasets with better resolution and accuracy continue to be needed in sectors like agriculture and water resources to enable climate impact assessment.展开更多
Based on the hourly observational data during 2007-2016 from surface meteorological stations in China,this paper compares the influence of 3-hourly precipitation data,mainly from the Chinese Reanalysis-Interim(CRA-Int...Based on the hourly observational data during 2007-2016 from surface meteorological stations in China,this paper compares the influence of 3-hourly precipitation data,mainly from the Chinese Reanalysis-Interim(CRA-Interim),ECMWF Reanalysis 5(ERA5)and Japanese Reanalysis-55(JRA-55),on the simulation of the spatial and temporal distribution of regional precipitation in China and the bias distribution of the simulation.The results show that:(1)The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China.The simulation of topographic forced precipitation in complex terrain by using CRA-interim is more detailed,while CRA-interim has larger negative bias in central and East China,and larger positive bias in southwest China.(2)In terms of seasonal precipitation,the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone in spring and summer,especially in southwest China.According to CRA-interim,location of the rain belt in the First Rainy Season in South China is west by south,and the summer precipitation has positive bias in southwest and South China.(3)All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood,but overall the CRA-Interim generally shows negative bias,while the ERA5 and JRA-55 exhibit positive bias.(4)For the diurnal variation of precipitation in summer,all the reanalysis datasets perform better in simulating the daytime precipitation than in the night,and the bias of CRA-interim is less in the Southeast and Northeast than elsewhere.(5)The ERA5 generally performs the best on the evaluation of quantitative precipitation forecast,the JRA-55 is the next,followed by the CRA-Interim.The CRA-Interim has higher missing rate and lower threat score for heavy rains;however,at the level of downpour,the CRA-Interim performs slightly better.展开更多
Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric ...Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.展开更多
Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relie...Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.展开更多
基金the Banaras Hindu University,Varanasi,Uttar Pradesh(India),for providing a seed grant(Letter No.R/Dev/D/IoE/Equipment/Seed Grant-II/2022-23/52078)under the Institute of Eminence(IoE)Jyotsna Singh(Ref.No.210510120701),Subhash Singh(Ref.No.220510022095),and Purushottam Tiwari(Ref.No.210510406257)are grateful to the University Grants Commission(UGC)of the Ministry of Education,Government of India(New Delhi)for providing financial support to the present study+2 种基金the Copernicus Climate Change Service(C3S)team at the European Centre for Medium-Range Weather Forecasts(ECMWF)for providing ERA5 reanalysis data in the public domainreceived a seed grant from the Banaras Hindu University,Varanasi,Uttar Pradesh(India)(Letter No.R/Dev/D/IoE/Equipment/Seed Grant-II/2022-23/52078)under the Institute of Eminence(IoE)Jyotsna Singh(Ref.No.210510120701),Subhash Singh(Ref.No.220510022095),and Purushottam Tiwari(Ref.No.210510406257)received a fellowship from the University Grants Commission(UGC)of the Ministry of Education,Government of India(New Delhi)。
文摘Precipitable Water Vapor(PWV)constitutes a pivotal parameter within the domains of atmospheric science,and remote sensing due to its profound influence on Earth’s climate dynamics and weather patterns.It exerts a significant impact on atmospheric stability absorption and emission of radiation,thus engendering alterations in the Earth’s radiative equilibrium.As such,precise quantification of PWV holds the potential to enhance weather prognostication and fortify preparedness against severe meteorological phenomena.This study aimed to elucidate the spatial and temporal changes in seasonal and annual PWV across the Indus River Basin and its sub-basins using ERA5 reanalysis datasets.The present study used ERA5 PWV(entire atmospheric column),air temperature at 2 m(t2m)and 500 hPa(T_500hPa),evapotranspiration,and total cloud cover data from 1960 to 2021.Theil Sen slope estimator and Mann-Kendall test were used for trend analysis.Correlation and multiple regression methods were used to understand the association of PWV with other factors.The findings have unveiled the highest increase in mean PWV during the monsoon(0.40 mm/decade),followed by premonsoon(0.37 mm/decade),post-monsoon(0.27 mm/decade),and winter(0.19 mm/decade)throughout the study period.Additionally,the mean PWV exhibited the most pronounced positive trend in the sub-basin Lower Indus(LI),followed by Panjnad(P),Kabul(K),and Upper Indus(UI)across all seasons,except winter.Annual PWV has also risen in the Indus basin and its sub-basins over the last six decades.PWV exhibits a consistent upward trend up to an elevation of 3500 m within the basin which is most pronounced during the monsoon season,followed by the pre-monsoon.The escalating PWV within the basin is reasonably ascribed to increasing air temperatures,augmented evapotranspiration,and heightened cloud cover.These findings hold potential utility for pertinent authorities engaged in water resource management and planning.
文摘In situ data in West Africa are scarce,and reanalysis datasets could be an alternative source to alleviate the problem of data availability.Nevertheless,because of uncertainties in numerical prediction models and assimilation methods,among other things,existing reanalysis datasets can perform with various degrees of quality and accuracy.Therefore,a proper assessment of their shortcomings and strengths should be performed prior to their usage.In this study,we examine the performance of ERA5 and ERA-interim(ERAI)products in representing the mean and extreme climates over West Africa for the period 1981-2018 using observations from CRU and CHIRPS.The major conclusion is that ERA5 showed a considerable decrease in precipitation and temperature biases and an improved representation of inter-annual variability in much of western Africa.Also,the annual cycle is better captured by ERA5 in three of the region’s climatic zones;specifically,precipitation is well-reproduced in the Savannah and Guinea Coast,and temperature in the Sahel.In terms of extremes,the ERA5 performance is superior.Still,both reanalyses underestimate the intensity and frequency of heavy precipitations and overestimate the number of wet days,as the numerical models used in reanalyses tend to produce drizzle more often.While ERA5 performs better than ERAI,both datasets are less successful in capturing the observed long-term trends.Although ERA5 has achieved considerable progress compared to its predecessor,improved datasets with better resolution and accuracy continue to be needed in sectors like agriculture and water resources to enable climate impact assessment.
基金National Natural Science Foundation of China(42030611,91937301)Second Tibetan Plateau Scientific Expedition and Research(STEP)Program(2019QZKK0105)。
文摘Based on the hourly observational data during 2007-2016 from surface meteorological stations in China,this paper compares the influence of 3-hourly precipitation data,mainly from the Chinese Reanalysis-Interim(CRA-Interim),ECMWF Reanalysis 5(ERA5)and Japanese Reanalysis-55(JRA-55),on the simulation of the spatial and temporal distribution of regional precipitation in China and the bias distribution of the simulation.The results show that:(1)The three sets of reanalysis datasets can all reflect the basic spatial distribution characteristics of annual average precipitation in China.The simulation of topographic forced precipitation in complex terrain by using CRA-interim is more detailed,while CRA-interim has larger negative bias in central and East China,and larger positive bias in southwest China.(2)In terms of seasonal precipitation,the three sets of reanalysis datasets overestimate the precipitation in the heavy rainfall zone in spring and summer,especially in southwest China.According to CRA-interim,location of the rain belt in the First Rainy Season in South China is west by south,and the summer precipitation has positive bias in southwest and South China.(3)All of the reanalysis datasets can basically reflect the distribution difference of inter-annual variation of drought and flood,but overall the CRA-Interim generally shows negative bias,while the ERA5 and JRA-55 exhibit positive bias.(4)For the diurnal variation of precipitation in summer,all the reanalysis datasets perform better in simulating the daytime precipitation than in the night,and the bias of CRA-interim is less in the Southeast and Northeast than elsewhere.(5)The ERA5 generally performs the best on the evaluation of quantitative precipitation forecast,the JRA-55 is the next,followed by the CRA-Interim.The CRA-Interim has higher missing rate and lower threat score for heavy rains;however,at the level of downpour,the CRA-Interim performs slightly better.
基金This research was supported by the National Natural Science Foundation of China(42161058).
文摘Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.
基金supported by the National Key Research and Development Program of China(2023YFC3206300)the National Natural Science Foundation of China(42477529,42371145,42261026)+2 种基金the China-Pakistan Joint Program of the Chinese Academy of Sciences(046GJHZ2023069MI)the Gansu Provincial Science and Technology Program(22ZD6FA005)the National Cryosphere Desert Data Center(E01Z790201).
文摘Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.