Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitati...Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.展开更多
It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLC...It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.展开更多
As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been s...As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.展开更多
Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observ...Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.展开更多
Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectb...Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.展开更多
Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observati...Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system(LDAS)are widely used in research over alpine regions such as the Tibetan Plateau(TP).It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013,which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature.By evaluating two global LDASs,five global atmospheric reanalysis datasets,and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis,we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86%during 1998–2013.This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP,respectively.Although GLDAS2.0,ERA5,and MERRA2 reduce biases of T2m simulation from their previous versions by 12%–94%,they do not show significant improvements in capturing the warming trend.The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement,as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP.Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period,and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.展开更多
积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的...积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。展开更多
A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weathe...A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.展开更多
The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)for...The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.展开更多
In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires ...In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.展开更多
Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and...Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and thematic requirements of specific users.This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets.We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends.We used a regression kriging method to integrate Globcover-2009,LC-CCI-2010,MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets.Overall correspondence of the integrated GLC map with reference LC was 80%based on 10-fold crossvalidation using 24,681 sample sites.This is globally 10%and regionally 6–13%higher than the input map correspondences.Based on LC class presence probability maps,expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments.Since more reference datasets are becoming freely accessible,GLC mapping can be further improved by using the pool of all available reference datasets.LC proportion information allow tuning LC products to specific user needs.展开更多
Two simulations of five years (2003-2007) were conducted with the Regional Climate models RegCM4, one coupled with Land surface models BATS and the other with CLM4.5 over West Africa, where simulated air temperature a...Two simulations of five years (2003-2007) were conducted with the Regional Climate models RegCM4, one coupled with Land surface models BATS and the other with CLM4.5 over West Africa, where simulated air temperature and precipitation were analyzed. The purpose of this study is to assess the performance of RegCM4 coupled with the new CLM4.5 Land</span><span style="font-family:""> </span><span style="font-family:Verdana;">surface scheme and the standard one named BATS in order to find the best configuration of RegCM4 over West African. This study could improve our understanding of the sensitivity of land surface model in West Africa climate simulation, and provide relevant information to RegCM4 users. The results show fairly realistic restitution of West Africa’s climatology and indicate correlations of 0.60 to 0.82 between the simulated fields (BATS and CLM4.5) for precipitation. The substitution of BATS surface scheme by CLM4.5 in the model configuration, leads mainly to an improvement of precipitation over the Atlantic Ocean, however, the impact is not sufficiently noticeable over the continent. While the CLM4.5 experiment restores the seasonal cycles and spatial distribution, the biases increase for precipitation and temperature. Positive biases already existing with BATS are amplified over some sub-regions. This study concludes that temporal localization (seasonal effect), spatial distribution (grid points) and magnitude of precipitation and temperature (bias) are not simultaneously improved by CLM4.5. The introduction of the new land surface scheme CLM4.5, therefore, leads to a performance of the same order as that of BATS, albeit with a more detailed formulation.展开更多
文章利用重力恢复与气候实验卫星(Gravity Recovery and Climate Experiment,GRACE)时变重力场球谐系数文件,联合全球陆面数据同化系统(Global Land Data Assimilation System,GLDAS)水文模型反演安徽省2003—2016年地下水储量的时空变...文章利用重力恢复与气候实验卫星(Gravity Recovery and Climate Experiment,GRACE)时变重力场球谐系数文件,联合全球陆面数据同化系统(Global Land Data Assimilation System,GLDAS)水文模型反演安徽省2003—2016年地下水储量的时空变化。通过奇异谱分析(Singular Spectrum Analysis,SSA)地下水时间序列,结合热带降雨测量任务(Tropical Rainfall Measuring Mission,TRMM)降雨数据对地下水储量变化规律进行分析。结果表明,安徽省地下水储量在2011年和2014年前后发生较大变化,在2003—2011年的变化率为0.37 cm/a,2011—2014年的下降速率为-0.2 cm/a,2014—2016年的增长速率为1.9 cm/a;进一步与降雨数据关联,发现降雨量是影响安徽省地下水储量年际变化和季节性变化的主要因素。在空间上,安徽省呈现自东北向西南逐渐缓和的趋势,最大亏损出现在皖北地区,为-7.52 mm/a,在西南地区的最大盈余达到8.38 mm/a。展开更多
文摘Basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The purpose of this study was to examine the utility of reanalysis and global precipitation datasets in the river discharge simulation for a data-scarce basin. The White Volta basin of Ghana which is one of international rivers was selected as a study basin. NCEP1, NCEP2, ERA-Interim, and GPCP datasets were compared with corresponding observed precipitation data. Annual variations were not reproduced in NCEP1, NCEP2, and ERA-Interim. However, GPCP data, which is based on satellite and observed data, had good seasonal accuracy and reproduced annual variations well. Moreover, five datasets were used as input data to a hydrologic model with HYMOD, which is a water balance model, and with WTM, which is a river model;thereafter, the hydrologic model was calibrated for each datum set by a global optimization method, and river discharge were simulated. The results were evaluated by the root mean square error, relative error, and water balance error. As a result, the combination of GPCP precipitation and ERA-Interim evaporation data was the best in terms of most evaluations. The relative errors in the calibration and validation periods were 43.1% and 46.6%, respectively. Moreover, the results for the GPCP precipitation and ERA-Interim evaporation were better than those for the combination of observed precipitation and ERA-Interim evaporation. In conclusion, GPCP precipitation data and ERA-Interim evaporation data are very useful in a data-scarce basin water balance analysis.
文摘It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.
基金the auspices of the National Key R&D Program of China(No.2016YFA0602301)National Natural Science Foundation of China(No.41971287,41601349)+1 种基金Science and Technology Development Program of Jilin Province(No.20180520220JH,20180623058TC)Fundamental Research Funds for the Central Universities(No.2412019FZ003)。
文摘As a key parameter for indicating the fraction of surface-reflected solar incident radiation, land surface albedo plays an important role in the Earth’s surface energy budget(SEB). Since the Sanjiang Plain has been severely affected by human activities(e.g., reclamation and shrinking of wetlands), it is important to assess the spatiotemporal variations of surface albedo in this region using a long-term remote sensing dataset. In order to investigate the surface albedo climatology, trends, and mechanisms of change, we evaluated the surface albedo variations in the Sanjiang Plain, China from 1982 to 2015 using the Global LAnd Surface Satellite(GLASS) broadband surface albedo product. The results showed that: 1) an increasing annual trend(+0.000 58/yr) of surface albedo was discovered in the Sanjiang Plain based on the GLASS albedo dataset, with a much stronger increasing trend(+0.001 26/yr) occurring during the winter. Most of the increasing trends occurred over the cultivated land, unused land, and land use conversion types located in the northeastern Sanjiang Plain. 2) The increasing trend of land surface albedo in Sanjiang Plain can be largely explained by the changes of both snow cover extent and land use. The surface albedo in winter is highly correlated with the snow cover extent in the Sanjiang Plain, and the increasing trend of surface albedo can be further enhanced by the land use changes.
基金supported by Space Core Technology Development Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICTFuture Planning(NRF-2014M1A3A3A02034789)+1 种基金Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2013R1A1A2A10004743)the Korea Meteorological Administration Research and Development Program under Grant Weather Information Service Engine(WISE)project,KMA-2012-0001-A
文摘Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.
基金Supported by the US Environmental Modeling Center(EMC)Land Surface Modeling Project(granted to Youlong Xia)National Natural Science Foundation of China(51609111,granted to Baoqing Zhang)
文摘Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.
基金Supported by the National Key Research and Development Program of China(2018YFA0606002)National Natural Science Foundation of China(41875105 and 91547103)Startup Fund for Introduced Talents of Nanjing University of Information Science&Technology.
文摘Accurate surface air temperature(T2m)data are key to investigating eco-hydrological responses to global warming.Because of sparse in-situ observations,T2m datasets from atmospheric reanalysis or multi-source observation-based land data assimilation system(LDAS)are widely used in research over alpine regions such as the Tibetan Plateau(TP).It has been found that the warming rate of T2m over the TP accelerates during the global warming slowdown period of 1998–2013,which raises the question of whether the reanalysis or LDAS datasets can capture the warming feature.By evaluating two global LDASs,five global atmospheric reanalysis datasets,and a high-resolution dynamical downscaling simulation driven by one of the global reanalysis,we demonstrate that the LDASs and reanalysis datasets underestimate the warming trend over the TP by 27%–86%during 1998–2013.This is mainly caused by the underestimations of the increasing trends of surface downward radiation and nighttime total cloud amount over the southern and northern TP,respectively.Although GLDAS2.0,ERA5,and MERRA2 reduce biases of T2m simulation from their previous versions by 12%–94%,they do not show significant improvements in capturing the warming trend.The WRF dynamical downscaling dataset driven by ERA-Interim shows a great improvement,as it corrects the cooling trend in ERA-Interim to an observation-like warming trend over the southern TP.Our results indicate that more efforts are needed to reasonably simulate the warming features over the TP during the global warming slowdown period,and the WRF dynamical downscaling dataset provides more accurate T2m estimations than its driven global reanalysis dataset ERA-Interim for producing LDAS products over the TP.
文摘积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201506002)National Key Research and Development Program of China(2018YFC1506601)+1 种基金National Natural Science Foundation of China(91437220)National Innovation Project for Meteorological Science and Technology(CMAGGTD003-5).
文摘A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.
基金Supported by the National Natural Science Foundation of China(91437220 and 41405083)Project Fund from the Education Department of Hunan Province(14C0897)Huaihua University Double First-Class Initiative in Applied Characteristic Discipline of Control Science and Engineering.
文摘The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.
基金Funding support for this work were provided by the following programs:the Strategic Priority Research Program of the Chinese Academy of Sciences[Grant No.XDA20100104]the Basic Resources Investigation of Science and Technology[Grant No.2017FY100900]and the National Earth System Science Data Sharing Infrastructure,National Science&Technology Infrastructure of China[Grant No.2005DKA32300].
文摘In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets.
基金This study was supported by the ESA Land Cover CCI[4000109875/14/I-NB]JRC CGLOPS1[199494]projects.
文摘Global scale land cover(LC)mapping has interested many researchers over the last two decades as it is an input data source for various applications.Current global land cover(GLC)maps often do not meet the accuracy and thematic requirements of specific users.This study aimed to create an improved GLC map by integrating available GLC maps and reference datasets.We also address the thematic requirements of multiple users by demonstrating a concept of producing GLC maps with user-specific legends.We used a regression kriging method to integrate Globcover-2009,LC-CCI-2010,MODIS-2010 and Globeland30 maps and several publicly available GLC reference datasets.Overall correspondence of the integrated GLC map with reference LC was 80%based on 10-fold crossvalidation using 24,681 sample sites.This is globally 10%and regionally 6–13%higher than the input map correspondences.Based on LC class presence probability maps,expected LC proportion maps at coarser resolution were created and used for characterizing mosaic classes for land system modelling and biodiversity assessments.Since more reference datasets are becoming freely accessible,GLC mapping can be further improved by using the pool of all available reference datasets.LC proportion information allow tuning LC products to specific user needs.
文摘Two simulations of five years (2003-2007) were conducted with the Regional Climate models RegCM4, one coupled with Land surface models BATS and the other with CLM4.5 over West Africa, where simulated air temperature and precipitation were analyzed. The purpose of this study is to assess the performance of RegCM4 coupled with the new CLM4.5 Land</span><span style="font-family:""> </span><span style="font-family:Verdana;">surface scheme and the standard one named BATS in order to find the best configuration of RegCM4 over West African. This study could improve our understanding of the sensitivity of land surface model in West Africa climate simulation, and provide relevant information to RegCM4 users. The results show fairly realistic restitution of West Africa’s climatology and indicate correlations of 0.60 to 0.82 between the simulated fields (BATS and CLM4.5) for precipitation. The substitution of BATS surface scheme by CLM4.5 in the model configuration, leads mainly to an improvement of precipitation over the Atlantic Ocean, however, the impact is not sufficiently noticeable over the continent. While the CLM4.5 experiment restores the seasonal cycles and spatial distribution, the biases increase for precipitation and temperature. Positive biases already existing with BATS are amplified over some sub-regions. This study concludes that temporal localization (seasonal effect), spatial distribution (grid points) and magnitude of precipitation and temperature (bias) are not simultaneously improved by CLM4.5. The introduction of the new land surface scheme CLM4.5, therefore, leads to a performance of the same order as that of BATS, albeit with a more detailed formulation.
