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
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.