Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over Ea...Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over East Asia using the regional climate model version 4.4 （RegCM4.4）driven by the global models of CSIRO-Mk3-6-0, EC-EARTH, HadGEM2-ES, and MPI-ESM-MR. Under global warming of 1.5℃, 2℃, 3℃,and 4℃, significant decrease of HDD can be found over China without considering population factor, with greater decrease over high elevationand high latitude regions, including the Tibetan Plateau, the northern part of Northeast China, and Northwest China; while population-weightedHDD increased in areas where population will increase in the future, such as Beijing, Tianjin, parts of southern Hebei, northern Shandong andHenan provinces. Similarly, the CDD projections with and without considering population factor are largely different. Specifically, withoutconsidering population, increase of CDD were observed over most parts of China except the Tibetan Plateau where the CDD remained zerobecause of the cold climate even under global warming; while considering population factor, the future CDD decreases in South China andincreases in North China, the Sichuan Basin, and the southeastern coastal areas, which is directly related to the population changes. The differentfuture changes of HDD and CDD when considering and disregarding the effects of population show that population distribution plays animportant role in energy consumption, which should be considered in future research.展开更多
Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an ap...Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature （SAT）. Cooling degree days （CDD） are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network （GHCN-D） SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1~ x0.1~ latitude-longitude grid. The resulting comparison indicates that （a） the interpolated data can be used as reliable proxy to price the CDD and （b） a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks.展开更多
The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data colle...The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.展开更多
In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that...In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days(HDD) and cooling degree days(CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices.展开更多
文摘Future changes of heating degree days （HDD） and cooling degree days （CDD） in the 21st century with and without considering populationfactor are investigated based on four sets of climate change simulations over East Asia using the regional climate model version 4.4 （RegCM4.4）driven by the global models of CSIRO-Mk3-6-0, EC-EARTH, HadGEM2-ES, and MPI-ESM-MR. Under global warming of 1.5℃, 2℃, 3℃,and 4℃, significant decrease of HDD can be found over China without considering population factor, with greater decrease over high elevationand high latitude regions, including the Tibetan Plateau, the northern part of Northeast China, and Northwest China; while population-weightedHDD increased in areas where population will increase in the future, such as Beijing, Tianjin, parts of southern Hebei, northern Shandong andHenan provinces. Similarly, the CDD projections with and without considering population factor are largely different. Specifically, withoutconsidering population, increase of CDD were observed over most parts of China except the Tibetan Plateau where the CDD remained zerobecause of the cold climate even under global warming; while considering population factor, the future CDD decreases in South China andincreases in North China, the Sichuan Basin, and the southeastern coastal areas, which is directly related to the population changes. The differentfuture changes of HDD and CDD when considering and disregarding the effects of population show that population distribution plays animportant role in energy consumption, which should be considered in future research.
基金supportedin part by the US National Science Foundation (GrantNos. AGS-1015926 and AGS-1015957)supported in part by a U.S. National Oceanographic and Atmospheric Administration (NOAAGrantNo. EL133E09SE4048)
文摘Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature （SAT）. Cooling degree days （CDD） are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network （GHCN-D） SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1~ x0.1~ latitude-longitude grid. The resulting comparison indicates that （a） the interpolated data can be used as reliable proxy to price the CDD and （b） a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks.
基金Project supported by the National Key Technology R&D Program of China (No. 2012BAH29B02)the PhD Programs Foundation of Ministry of Education of China (No. 200100101110035)
文摘The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.
文摘In this paper we present a stochastic model for daily average temperature to calculate the temperature indices upon which temperature-based derivatives are written. We propose a seasonal mean and volatility model that describes the daily average temperature behavior using the mean-reverting Ornstein-Uhlenbeck process. We also use higher order continuous-time autoregressive process with lag 3 for modeling the time evolution of the temperatures after removing trend and seasonality. Our model is fitted to 11 years of data recorded, in the period 1 January 2005 to 31 December 2015, Bahir Dar, Ethiopia, obtained from Ethiopia National Meteorological Services Agency. The analytical approximation formulas are used to price heating degree days(HDD) and cooling degree days(CDD) futures. The suggested model is analytically tractable for derivation of explicit prices for CDD and HDD futures and option. The price of the CDD future is calculated, using analytical approximation formulas. Numerical examples are presented to indicate the accuracy of the method. The results show that our model performs better to predict CDD indices.
