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
Energy analysis plays an important role in developing an optimum and cost effective design of HVAC （heating, ventilating and air conditioning） system for an architecture. Although there are different energy analysis...Energy analysis plays an important role in developing an optimum and cost effective design of HVAC （heating, ventilating and air conditioning） system for an architecture. Although there are different energy analysis methods, which vary in complexity, the degree-day methods are the simplest methods and well-established tools. Energy consumption increases as the number of heating and cooling degree days increases and falls as the number of heating and cooling degree days falls. The value of degree days is a measure which can be used to indicate the demand for energy to heat or cool buildings and spaces. The monthly or annual cooling and heating requirements of specific buildings in different locations can be estimated by means of the degree-day concept. The base temperature is the outdoor temperature below or above which heating or cooling is needed. In this study, the degree days for the period of 2008-2012 were calculated for Turkey （10 cities） and also to develop new software for easy analysis about cooling degree days. This paper can be helpful for designing facade and also contribute to degree-day analyses.展开更多
Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consum...Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consumption and 26 ℃ as the basic temperature of CDD. The results indicated that the changing trends of CDD in Xi＇an and Chang＇an were basically identical within a year,and the demand for cooling refrigeration was large mainly from June to August,especially in July. The maximum of urban-rural difference of CDD between Xi＇an and Chang＇an appeared in June.In order to achieve the same temperature,energy needed by the urban area was 5-7 ℃·d more than the suburb from June to August. Temperature and the cooling energy consumption were closely related,and the correlation degree increased with the rise of temperature. The effects of temperature increase of 1 ℃ on cooling energy consumption rate in Xi＇an were more obvious than that in Chang＇an. In both Xi＇an and Chang＇an,the effects of temperature increase of 1 ℃ on cooling energy consumption rate in July and August were greater than that in May,June and September.Evaluation models of cooling energy consumption in summer in Xi＇an and Chang＇an were built using temperature anomaly and CDD variability and can be applied to business systems.展开更多
The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of...The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of amount of Clothing insulation to maintain comfort, and residential electricity consumption was also examined. Utilizing monthly data and multiple regression analyses, it is discovered vapor pressure was not significantly related to electricity consumption while Cloud cover was negatively associated with electricity use. Climatic variables, CDD and Clo provided highly comparable results in modeling summer residential electricity consumption. Mean temperature and Cloud gave the best result. Clo yielded a slightly higher R2 value (0.867) than that of CDD (0.865) in the models. These results indicated that Clo could replace the weather variables and CDD to model electricity consumption.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
Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields.Timely and accurate measures are desirable for assessing associated large-scale impacts and...Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields.Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction.Therefore,we propose a novel means to efficiently assess the impacts of chilling injury on soybean.Specific chilling injury events were diagnosed in 1989,1995,2003,2009,and 2018 in Oroqen community.In total,512 combinations scenarios were established using the localized CROPGRO-Soybean model.Furthermore,we determined the maximum wide dynamic vegetation index(WDRVI)and corresponding date of critical windows of the early and late growing seasons using the GEE(Google Earth Engine)platform,then constructed 1600 cold vulnerability models on CDD(Cold Degree Days),the simulated LAI(Leaf Area Index)and yields from the CROPGRO-Soybean model.Finally,we calculated pixel yields losses according to the corresponding vulnerability models.The findings show that simulated historical yield losses in 1989,1995,2003 and 2009 were measured at 9.6%,29.8%,50.5%,and 15.7%,respectively,closely(all errors are within one standard deviation)reflecting actual losses(6.4%,39.2%,47.7%,and 13.2%,respectively).The above proposed method was applied to evaluate the yield loss for 2018 at the pixel scale.Specifically,a sentinel-2A image was used for 10-m high precision yield mapping,and the estimated losses were found to characterize the actual yield losses from 2018 cold events.The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.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.
文摘Energy analysis plays an important role in developing an optimum and cost effective design of HVAC （heating, ventilating and air conditioning） system for an architecture. Although there are different energy analysis methods, which vary in complexity, the degree-day methods are the simplest methods and well-established tools. Energy consumption increases as the number of heating and cooling degree days increases and falls as the number of heating and cooling degree days falls. The value of degree days is a measure which can be used to indicate the demand for energy to heat or cool buildings and spaces. The monthly or annual cooling and heating requirements of specific buildings in different locations can be estimated by means of the degree-day concept. The base temperature is the outdoor temperature below or above which heating or cooling is needed. In this study, the degree days for the period of 2008-2012 were calculated for Turkey （10 cities） and also to develop new software for easy analysis about cooling degree days. This paper can be helpful for designing facade and also contribute to degree-day analyses.
基金Supported by Foundation for Young Scholars of Shaanxi Meteorological Bureau in 2016 and 2017(2016Y-7,2017Y-11)
文摘Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consumption and 26 ℃ as the basic temperature of CDD. The results indicated that the changing trends of CDD in Xi＇an and Chang＇an were basically identical within a year,and the demand for cooling refrigeration was large mainly from June to August,especially in July. The maximum of urban-rural difference of CDD between Xi＇an and Chang＇an appeared in June.In order to achieve the same temperature,energy needed by the urban area was 5-7 ℃·d more than the suburb from June to August. Temperature and the cooling energy consumption were closely related,and the correlation degree increased with the rise of temperature. The effects of temperature increase of 1 ℃ on cooling energy consumption rate in Xi＇an were more obvious than that in Chang＇an. In both Xi＇an and Chang＇an,the effects of temperature increase of 1 ℃ on cooling energy consumption rate in July and August were greater than that in May,June and September.Evaluation models of cooling energy consumption in summer in Xi＇an and Chang＇an were built using temperature anomaly and CDD variability and can be applied to business systems.
文摘The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of amount of Clothing insulation to maintain comfort, and residential electricity consumption was also examined. Utilizing monthly data and multiple regression analyses, it is discovered vapor pressure was not significantly related to electricity consumption while Cloud cover was negatively associated with electricity use. Climatic variables, CDD and Clo provided highly comparable results in modeling summer residential electricity consumption. Mean temperature and Cloud gave the best result. Clo yielded a slightly higher R2 value (0.867) than that of CDD (0.865) in the models. These results indicated that Clo could replace the weather variables and CDD to model electricity consumption.
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National Natural Science Foundation of China,No.41977405,No.41571493,No.31561143003No.31761143006National Key Research&Development Program of China,No.2017YFA0604703,No.2019YFA0607401。
文摘Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields.Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction.Therefore,we propose a novel means to efficiently assess the impacts of chilling injury on soybean.Specific chilling injury events were diagnosed in 1989,1995,2003,2009,and 2018 in Oroqen community.In total,512 combinations scenarios were established using the localized CROPGRO-Soybean model.Furthermore,we determined the maximum wide dynamic vegetation index(WDRVI)and corresponding date of critical windows of the early and late growing seasons using the GEE(Google Earth Engine)platform,then constructed 1600 cold vulnerability models on CDD(Cold Degree Days),the simulated LAI(Leaf Area Index)and yields from the CROPGRO-Soybean model.Finally,we calculated pixel yields losses according to the corresponding vulnerability models.The findings show that simulated historical yield losses in 1989,1995,2003 and 2009 were measured at 9.6%,29.8%,50.5%,and 15.7%,respectively,closely(all errors are within one standard deviation)reflecting actual losses(6.4%,39.2%,47.7%,and 13.2%,respectively).The above proposed method was applied to evaluate the yield loss for 2018 at the pixel scale.Specifically,a sentinel-2A image was used for 10-m high precision yield mapping,and the estimated losses were found to characterize the actual yield losses from 2018 cold events.The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.
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.
