Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to ...Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the展开更多
Considerable spring precipitation occurs over South China(SC),a region that is adjacent to large-scale Asian topography and oceans.Its reasonable simulation is crucial for improving regional climate predictability.Thi...Considerable spring precipitation occurs over South China(SC),a region that is adjacent to large-scale Asian topography and oceans.Its reasonable simulation is crucial for improving regional climate predictability.This study investigates spring precipitation biases over SC and their possible causes in atmosphere-only and coupled Flexible Global Ocean–Atmosphere–Land System finite-volume version 3(FGOALS-f3) models with different horizontal resolutions.The performance of spring precipitation simulation over SC varies across different FGOALS-f3 model versions,with the best reproducibility in the high-resolution coupled model(25 km).In the low-resolution atmosphere-only model(100–125 km),the precipitation dry bias over SC is closely linked to overestimated surface sensible forcing over the eastern Tibetan Plateau(TP),which weakens the subtropical anticyclone over the western Pacific(SAWP) through regional circulation responses.By contrast,the high-resolution atmosphere-only model further amplifies surface thermal forcing in the Asian continents,causing intensified land–sea thermal contrast between the Southeast Asian continents and western Pacific,enhanced southerly winds and SAWP,and increased water vapor transport into SC.Meanwhile,the reduced middle–high level cold bias over 10°–30°N in the high-resolution atmosphere-only model intensifies the East Asian westerly jet and ascent over SC,leading to enhanced spring precipitation there.The high-resolution coupled model simulation not only reduces sea surface cold bias over the Bay of Bengal,thus intensifying the Indian–Burma trough and strengthening low-level water vapor transport into SC,but also enhances ascent over SC.As a result,the high-resolution coupled model better reproduces the magnitude and pattern of spring precipitation over SC than its atmosphere-only model.Compared with low-resolution models,the domain-mean spring precipitation dry bias decreases by 11.2% over SC in the high-resolution atmosphere-only model and by 35.9% in the coupled m展开更多
To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in p...To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.展开更多
The western North Pacific subtropical high(WNPSH) dominates the summer climate over East Asia. The intensity,position, and shape of WNPSH influence the spatiotemporal distributions of precipitation, temperature, and t...The western North Pacific subtropical high(WNPSH) dominates the summer climate over East Asia. The intensity,position, and shape of WNPSH influence the spatiotemporal distributions of precipitation, temperature, and tropical cyclone activities in this region. This paper intends to investigate the performance of the UK Met Office Global Seasonal forecast system version 5(GloSea5) in simulation/prediction of the WNPSH based on a hindcast dataset. Analyses of the hindcast data show a systematic bias in the mean circulation over West Pacific, with negative geopotential height anomalies over the western North Pacific(WNP) and cyclonic anomalies in the 850-hPa winds and water vapor transport, indicating a weakening and eastward shift of the WNPSH. Despite the model’s bias in the climatology, it well captured the interannual variability of the monthly and seasonal-mean intensity of the WNPSH and the position of its ridge line in boreal summer from 1993 to 2015. The seasonal hindcasts indicate that there is significant prediction skill at up to three-month lead time for both the intensity and position of the WNPSH ridge line. The relationship between the WNPSH and different phases of the El Nino–Southern Oscillation(ENSO) in both the observational data and GloSea5 hindcasts was then investigated. The model captured the summer WNPSH anomalies well during most of the ENSO phases, except in the La Nina decaying and neutral summers. The intensity of the anticyclone in the WNP is weak in the decaying phase of El Nino in the GloSea5 hindcasts compared with the reanalysis data. GloSea5 is capable of representing the lagged teleconnection between El Nino events in the previous winter and the intensity of the WNPSH in the following summer. Regression analysis reveals weakened negative sea surface temperature anomalies(SSTAs) over the WNP in GloSea5, which reduced the gradient between the tropical western Pacific and the tropical Indian Ocean, resulting in a weaker easterly anomaly and stronger westerly anomaly, contributing t展开更多
The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global...The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.展开更多
基金Supported by the National Key Research and Development Program of China(2018YFC1506601)National Natural Science Foundation of China(91437220)+1 种基金China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002 and GYHY201206008)China Meteorological Administration“Meteorological Data Quality Control and Multi-source Data Fusion and Reanalysis”project。
文摘Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the
基金Supported by the National Key Research and Development Program of China (2022YFF0802003)National Natural Science Foundation of China (42288101,42275026,and 41975109)+2 种基金Natural Science Foundation of Yunnan Province (202301AV070001)Yunnan University Graduate Research and Innovation Fund (KC-22221894)National Key Scientific and Technological Infrastructure Project of China “Earth System Science Numerical Simulator Facility”(EarthLab)。
文摘Considerable spring precipitation occurs over South China(SC),a region that is adjacent to large-scale Asian topography and oceans.Its reasonable simulation is crucial for improving regional climate predictability.This study investigates spring precipitation biases over SC and their possible causes in atmosphere-only and coupled Flexible Global Ocean–Atmosphere–Land System finite-volume version 3(FGOALS-f3) models with different horizontal resolutions.The performance of spring precipitation simulation over SC varies across different FGOALS-f3 model versions,with the best reproducibility in the high-resolution coupled model(25 km).In the low-resolution atmosphere-only model(100–125 km),the precipitation dry bias over SC is closely linked to overestimated surface sensible forcing over the eastern Tibetan Plateau(TP),which weakens the subtropical anticyclone over the western Pacific(SAWP) through regional circulation responses.By contrast,the high-resolution atmosphere-only model further amplifies surface thermal forcing in the Asian continents,causing intensified land–sea thermal contrast between the Southeast Asian continents and western Pacific,enhanced southerly winds and SAWP,and increased water vapor transport into SC.Meanwhile,the reduced middle–high level cold bias over 10°–30°N in the high-resolution atmosphere-only model intensifies the East Asian westerly jet and ascent over SC,leading to enhanced spring precipitation there.The high-resolution coupled model simulation not only reduces sea surface cold bias over the Bay of Bengal,thus intensifying the Indian–Burma trough and strengthening low-level water vapor transport into SC,but also enhances ascent over SC.As a result,the high-resolution coupled model better reproduces the magnitude and pattern of spring precipitation over SC than its atmosphere-only model.Compared with low-resolution models,the domain-mean spring precipitation dry bias decreases by 11.2% over SC in the high-resolution atmosphere-only model and by 35.9% in the coupled m
基金Supported by the National Key Research and Development Program of China(2022YFE0106800)National Natural Science Foundation of China(42230603)Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021001)。
文摘To further understand the prediction skill for the interannual variability of the sea ice concentration(SIC)in specific regions of the Arctic,this paper evaluates the NCEP Climate Forecast System version 2(CFSv2),in predicting the autumn SIC and its interannual variability over the Barents–East Siberian Seas(BES).It is found that CFSv2 presents much better prediction skill for the September SIC over BES than the Arctic as a whole at 1–6-month leads,and high prediction skill for the interannual variability of the SIC over BES is displayed at 1–2-month leads after removing the linear trend.CFSv2 can reasonably reproduce the relationship between the SIC over BES in September and such factors as the surface air temperature(SAT),200-hPa geopotential height,sea surface temperature(SST),and North Atlantic Oscillation.In addition,it is found that the prescribed SIC initial condition in August as an input to CFSv2 is also essential.Therefore,the above atmospheric and oceanic factors,as well as an accurate initial condition of SIC,all contribute to a high prediction skill for SIC over BES in September.Based on a statistical prediction method,the contributions from individual predictability sources are further identified.The high prediction skill of CFSv2 for the interannual variability of SIC over BES is largely attributable to its accurate predictions of the SAT and SST,as well as a better initial condition of SIC.
基金Supported by the National Key Research and Development Program of China(2017YFC1502303)National Natural Science Fundation of China(41730964,41975091,and 41605078)UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund。
文摘The western North Pacific subtropical high(WNPSH) dominates the summer climate over East Asia. The intensity,position, and shape of WNPSH influence the spatiotemporal distributions of precipitation, temperature, and tropical cyclone activities in this region. This paper intends to investigate the performance of the UK Met Office Global Seasonal forecast system version 5(GloSea5) in simulation/prediction of the WNPSH based on a hindcast dataset. Analyses of the hindcast data show a systematic bias in the mean circulation over West Pacific, with negative geopotential height anomalies over the western North Pacific(WNP) and cyclonic anomalies in the 850-hPa winds and water vapor transport, indicating a weakening and eastward shift of the WNPSH. Despite the model’s bias in the climatology, it well captured the interannual variability of the monthly and seasonal-mean intensity of the WNPSH and the position of its ridge line in boreal summer from 1993 to 2015. The seasonal hindcasts indicate that there is significant prediction skill at up to three-month lead time for both the intensity and position of the WNPSH ridge line. The relationship between the WNPSH and different phases of the El Nino–Southern Oscillation(ENSO) in both the observational data and GloSea5 hindcasts was then investigated. The model captured the summer WNPSH anomalies well during most of the ENSO phases, except in the La Nina decaying and neutral summers. The intensity of the anticyclone in the WNP is weak in the decaying phase of El Nino in the GloSea5 hindcasts compared with the reanalysis data. GloSea5 is capable of representing the lagged teleconnection between El Nino events in the previous winter and the intensity of the WNPSH in the following summer. Regression analysis reveals weakened negative sea surface temperature anomalies(SSTAs) over the WNP in GloSea5, which reduced the gradient between the tropical western Pacific and the tropical Indian Ocean, resulting in a weaker easterly anomaly and stronger westerly anomaly, contributing t
基金supported by the State Key Program of the National Natural Science of China(Grant No.41730964)the National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disaster(2018YFC1506000)+2 种基金the National Natural Science Foundation of China(Grant Nos.41975091 and 42175047)National Basic Research Program of China(2015CB453203)UK-China Research&Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP)China as part of the Newton Fund.
文摘The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.
