To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia(CA),six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of...To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia(CA),six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project(CMIP5),and their changes in the near-term future(2031-50)are assessed relative to the reference period(1986-2005).The quantile mapping(QM)method is applied to correct the model data before calculating the indicators.Results show the QM method largely reduces the biases in all the indicators.Growing season length(GSL,day),summer days(SU,day),warm spell duration index(WSDI,day),and tropical nights(TR,day)are projected to significantly increase over CA,and frost days(FD,day)are projected to decrease.However,changes in biologically effective degree days(BEDD,°C)are spatially heterogeneous.The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.展开更多
Climate change presents a major threat to the built environment and therefore requires reliable future climate data for building performance simulation(BPS).The implementation of advanced statistical downscaling metho...Climate change presents a major threat to the built environment and therefore requires reliable future climate data for building performance simulation(BPS).The implementation of advanced statistical downscaling methods remains difficult in BPS studies because specific historical weather data and complex implementation procedures are usually requested.The current statistical downscaling methods that are frequently used in BPS analysis were rarely validated against measurements to see if ongoing climate change process and weather extremes can be represented.This paper presents a new Distribution Adjusted Temporal Mapping(DATM)technique for downscaling future hourly weather data from the monthly GCM(Global Climate Model)data with Typical Meteorological Year(TMY)data being the baseline.The proposed method involves fitting probability distributions to TMY data for each climate variable,modifying these distributions according to the projected monthly changes from GCMs,and then mapping the future hourly weather data from the adjusted distributions.DATM is compared with the“morphing”technique for various climate variables and locations,and is validated against ten years onsite measured hourly weather data from 2015 to 2024.The outcomes reveal that DATM outperforms the morphing method in temperature downscaling in terms of reproducing climate variabilities and extreme events.For relative humidity and wind speed,DATM is slightly better in capturing the full range of variables even though both methods have their limitations.For solar radiation,DATM can reflect realistic peak solar radiation prediction in future climate downscaling.It also shows better performance in capturing the changes in temperature variability and extremes that are essential for the overall building resilience analysis.The results of both methods depend on climate zones and variables,which underlines the necessity of considering regional factors in climate data preprocessing.With climate change affecting the built environment,the proposed method in this research offers BPS researchers a more reliable way of evaluating future building performance under future emission scenarios.展开更多
Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long...Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long-term training samples not easily available,and(iii)local features are insufficiently reflected.In this work,a multi-model blending(MMB)algorithm with supplemental grid points(SGPs)is experimented to overcome these shortcomings.The MMB algorithm includes three steps:(1)single-model bias-correction,(2)dynamic weight MMB,and(3)light-precipitation elimination.In step 1,quantile mapping(QM)is used and SGPs are configured to expand the sample size.The SGPs are chosen based on similarity of topography,spatial distance,and climatic characteristics of local precipitation.In step 2,the dynamic weight MMB uses the idea of ensemble forecasting:a precipitation process can be forecast if more than 40% of the models predict such a case;moreover,threat score(TS)is used to update the weights of ensemble members.Finally,in step 3,the number of false alarms of light precipitation is reduced,thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.Verification results show that using the MMB algorithm has effectively improved the TS and bias score(BS)for blended 6-h QPF.The rate of increase in TS for heavy rainfall(25-mm threshold)reaches 20%-40%;in particular,the improvement has reached 47.6% for forecast lead time of 24 h,compared with the ECMWF model.Meanwhile,the BS is closer to 1,which is better than any single-model forecast.In sum,the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.展开更多
基金supported by the Strate-gic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020201)the General Project of the National Natural Science Foundation of China(Grant No.41875134).
文摘To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia(CA),six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project(CMIP5),and their changes in the near-term future(2031-50)are assessed relative to the reference period(1986-2005).The quantile mapping(QM)method is applied to correct the model data before calculating the indicators.Results show the QM method largely reduces the biases in all the indicators.Growing season length(GSL,day),summer days(SU,day),warm spell duration index(WSDI,day),and tropical nights(TR,day)are projected to significantly increase over CA,and frost days(FD,day)are projected to decrease.However,changes in biologically effective degree days(BEDD,°C)are spatially heterogeneous.The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.
文摘Climate change presents a major threat to the built environment and therefore requires reliable future climate data for building performance simulation(BPS).The implementation of advanced statistical downscaling methods remains difficult in BPS studies because specific historical weather data and complex implementation procedures are usually requested.The current statistical downscaling methods that are frequently used in BPS analysis were rarely validated against measurements to see if ongoing climate change process and weather extremes can be represented.This paper presents a new Distribution Adjusted Temporal Mapping(DATM)technique for downscaling future hourly weather data from the monthly GCM(Global Climate Model)data with Typical Meteorological Year(TMY)data being the baseline.The proposed method involves fitting probability distributions to TMY data for each climate variable,modifying these distributions according to the projected monthly changes from GCMs,and then mapping the future hourly weather data from the adjusted distributions.DATM is compared with the“morphing”technique for various climate variables and locations,and is validated against ten years onsite measured hourly weather data from 2015 to 2024.The outcomes reveal that DATM outperforms the morphing method in temperature downscaling in terms of reproducing climate variabilities and extreme events.For relative humidity and wind speed,DATM is slightly better in capturing the full range of variables even though both methods have their limitations.For solar radiation,DATM can reflect realistic peak solar radiation prediction in future climate downscaling.It also shows better performance in capturing the changes in temperature variability and extremes that are essential for the overall building resilience analysis.The results of both methods depend on climate zones and variables,which underlines the necessity of considering regional factors in climate data preprocessing.With climate change affecting the built environment,the proposed method in this research offers BPS researchers a more reliable way of evaluating future building performance under future emission scenarios.
基金Supported by the National Key Research and Development Program of China(2017YFC1502004)Special Project for Forecasters of China Meteorological Administration(CMAYBY2020-162)Special Project for Forecasters of National Meteorological Center(Y202135)。
文摘Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long-term training samples not easily available,and(iii)local features are insufficiently reflected.In this work,a multi-model blending(MMB)algorithm with supplemental grid points(SGPs)is experimented to overcome these shortcomings.The MMB algorithm includes three steps:(1)single-model bias-correction,(2)dynamic weight MMB,and(3)light-precipitation elimination.In step 1,quantile mapping(QM)is used and SGPs are configured to expand the sample size.The SGPs are chosen based on similarity of topography,spatial distance,and climatic characteristics of local precipitation.In step 2,the dynamic weight MMB uses the idea of ensemble forecasting:a precipitation process can be forecast if more than 40% of the models predict such a case;moreover,threat score(TS)is used to update the weights of ensemble members.Finally,in step 3,the number of false alarms of light precipitation is reduced,thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.Verification results show that using the MMB algorithm has effectively improved the TS and bias score(BS)for blended 6-h QPF.The rate of increase in TS for heavy rainfall(25-mm threshold)reaches 20%-40%;in particular,the improvement has reached 47.6% for forecast lead time of 24 h,compared with the ECMWF model.Meanwhile,the BS is closer to 1,which is better than any single-model forecast.In sum,the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.
