A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to cons...A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.展开更多
Tree species respond to climate change at multiple scales,such as species physiological response at fine scale and species distribution (quantified by percent area) at broader spatial scale.At a given spatial scale,sp...Tree species respond to climate change at multiple scales,such as species physiological response at fine scale and species distribution (quantified by percent area) at broader spatial scale.At a given spatial scale,species physiological response and distribution can be correlated positively or negatively.The consistency of such correlation relationships at different spatial scales determines whether species responses derived from local scales can be extrapo-lated to broader spatial scales.In this study,we used a coupled modeling approach that coupled a plot-level ecosystem process model (LINKAGES) with a spatially explicit landscape model (LANDIS).We investigated species physio-logical responses and distribution responses to climate warming at the local,zonal and landscape scales respectively,and examined how species physiological response and distribution correlated at each corresponding scale and whether the correlations were consistent among these scales.The results indicate that for zonal and warming-sensitive species,the correlations between species physiological response and distribution are consistent at these spatial scales,and therefore the research results of vegetation response to climate warming at the local scale can be extrapolated to the zonal and landscape scales.By contrast,for zonal and warming-insensitive species the correlations among different spatial scales are consistent at some spatial scales but at other scales.The results also suggest that the results of azonal species at the local scale near their distribution boundaries can not be extrapolated simply to broader scales due to stronger responses to climate warming in those boundary regions.展开更多
Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the ro...Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.展开更多
In the last 50 years,the methodology of large-eddy simulation(LES)has been greatly developed,while lots of different subgridscale(SGS)models have appeared.However,the understanding of the procedure of SGS modeling is ...In the last 50 years,the methodology of large-eddy simulation(LES)has been greatly developed,while lots of different subgridscale(SGS)models have appeared.However,the understanding of the procedure of SGS modeling is still not clear.The present contribution aims at reviewing the recent SGS models and,more importantly,expressing our recent understanding on the SGS modeling of LES in physical space.Taking the Kolmogorov equation for filtered quantities(KEF)as an example,it is argued that the KEF alone is not enough to be a closure method.Three physical laws are then introduced to complete this closure procedure and are expected to inspire the future researches of SGS modeling.展开更多
基金supported by China Meteorological Administration R & D Special Fund for Public Welfare (Meteorology) (Grant Nos. GYHY200906018 and GYHY200906015)the National Natural Science Foundation of China (Grant No.41005051)the National Key Technologies R & D Program of China (Grant No. 2009BAC51B05)
文摘A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.
基金Under the auspices of International Partnership Program of Chinese Academy of Sciences (No.KZCX2-YW-T06)Key Direction in Knowledge Innovation Programs of Chinese Academy of Sciences (No.KZCX2-YW-444)Major State Basic Research Development Program of China (No.2009CB421101)
文摘Tree species respond to climate change at multiple scales,such as species physiological response at fine scale and species distribution (quantified by percent area) at broader spatial scale.At a given spatial scale,species physiological response and distribution can be correlated positively or negatively.The consistency of such correlation relationships at different spatial scales determines whether species responses derived from local scales can be extrapo-lated to broader spatial scales.In this study,we used a coupled modeling approach that coupled a plot-level ecosystem process model (LINKAGES) with a spatially explicit landscape model (LANDIS).We investigated species physio-logical responses and distribution responses to climate warming at the local,zonal and landscape scales respectively,and examined how species physiological response and distribution correlated at each corresponding scale and whether the correlations were consistent among these scales.The results indicate that for zonal and warming-sensitive species,the correlations between species physiological response and distribution are consistent at these spatial scales,and therefore the research results of vegetation response to climate warming at the local scale can be extrapolated to the zonal and landscape scales.By contrast,for zonal and warming-insensitive species the correlations among different spatial scales are consistent at some spatial scales but at other scales.The results also suggest that the results of azonal species at the local scale near their distribution boundaries can not be extrapolated simply to broader scales due to stronger responses to climate warming in those boundary regions.
基金funded by National High Technology Research and Development Program of China (863 Program,2012AA092303)Project of Shanghai Science and Technology Innovation (12231203900)+2 种基金Industrialization Program of National Development and Reform Commission (2159999)National Science and Technology Support Program (2013BAD13B01)Shanghai Leading Academic Discipline Project
文摘Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.
基金supported by the National Natural Science Foundation of China(Grant Nos.11202013 and 51420105008)
文摘In the last 50 years,the methodology of large-eddy simulation(LES)has been greatly developed,while lots of different subgridscale(SGS)models have appeared.However,the understanding of the procedure of SGS modeling is still not clear.The present contribution aims at reviewing the recent SGS models and,more importantly,expressing our recent understanding on the SGS modeling of LES in physical space.Taking the Kolmogorov equation for filtered quantities(KEF)as an example,it is argued that the KEF alone is not enough to be a closure method.Three physical laws are then introduced to complete this closure procedure and are expected to inspire the future researches of SGS modeling.
