呼和浩特市热度日和冷度日变化特征

利用1952—2006年呼和浩特市逐日平均温度统计了热度日(HDD)和冷度日(CDD)变化特征。表明,呼和浩特市HDD以1月最大(918度日),多年年均值为4527度日,55a间呈现比较明显的平稳降低态势,线性趋势率为-145.5度日/10a;CDD以7月最大(42度日);多年年均值为74度日,多年变化呈现波动上升的趋势,线性趋势率为16.5度日/10a。HDD和CDD的日数动态变化与二者多年变化趋势是一致的,分别呈现降低和上升的趋势。呼和浩特市理论供暖和制冷日数分别为271d和38d。     

北京地区热度日和冷度日的变化特征

应用1951—2004年北京逐日平均温度制作北京月、年的热度日 (HDD) 和冷度日 (CDD)。其中平均月HDD以1月 (687.9度日) 最大, 多年平均的年值为2922.6度日, 多年变化呈明显的下降趋势, 下降率为-99.5度日/10 a。平均月CDD以7月 (259.2度日) 最大。CDD多年平均的年值为826.7度日, 多年变化呈上升趋势, 上升率为39.0度日/10 a。1971—2004年HDD月城郊差值 (北京站-密云站) 冬季较大, 最大值为-73.8度日 (12月)。月CDD的城郊差值比HDD的差值小, 最大差值在8月 (34.0度日)。年HDD和CDD与年平均气温具有较高的相关性。年际、年代际HDD与年际、年代际平均气温具有反位相变化趋势, 随着气候增暖, 北京地区HDD将趋于减小, 冬季用于供暖的能源将减少; CDD将趋于增加, 夏季用于制冷降温的能源将增加。     

1961-2010年气候变化对天津市采暖期和夏季空调期的影响

根据1961－2010年天津市逐日气象要素,包括平均温度、最高温度、最低温度、相对湿度以及风速,采用统计学方法分析了近50 a天津市的冷暖度日变化以及冬季采暖期和夏季空调期的气候变化特征,探讨了冷暖度日、舒适日数与区域气候变化之间的关系。结果表明：近50 a天津市冷暖度日具有相反的变化趋势,暖度日（HDD）显著下降、冷度日（CDD）显著上升,且暖度日的变化幅度大于冷度日,变化主要集中于80年代之后。冷暖度日与极端温度之间具有显著相关性。采暖期低温日数呈下降趋势、空调期高温日数呈上升趋势。采暖期冷不舒适日数呈显著下降趋势,夏季空调期热不舒适日数呈显著上升趋势,与同期平均气温之间存在显著性相关,且不舒适气候的随机性呈增多趋势。说明在气候变暖背景下,区域气候变化对天津市采暖期和夏季空调期具有重要影响。     

Theoretical Risk Formulation for Degree-day Calculations

Summary Energy consumption, agricultural activities and comfort in building design are all related to temporal temperature variations. Truncation of the temperature series at a constant base temperature level leads to surpluses and deficits as deviations. Surpluses are instances for cooling and deficits for heating. In meteorology and heat engineering these are referred to as cooling and heating degree-days, respectively. Since the temperature records are random in character their future predictions are necessary through statistical and probabilistic methods. In this paper, the degree-days are assumed to have a normal probability distribution function and therefore, their averages and standard deviations are considered sufficient for modeling cooling and heating degree-day amounts. Theoretical derivations are presented for degree-day risk calculations in their general forms and a simple implementation is given for two cities in Turkey. Received January 23, 1998 Revised June 16, 1998     

华中区域取暖、降温度日的年代际及空间变化特征

选取华中区域（河南、湖北、湖南）通过均一化检验的53个气象站,利用1961—2007年逐日平均气温计算区域逐年取暖度日（HDD）、降温度日（CDD）,分析其趋势及年代际变化和空间分布特征。结果表明：47年来区域年HDD总体呈显著下降趋势;CDD呈微弱的下降,变化不显著,这与暖季较小的增温速率有关。HDD由河南向湖南基本上呈现递减的趋势,CDD与HDD呈相反的变化特征。HDD同冷季平均气温呈反位相变化,而CDD同暖季平均气温呈同位相变化。     

Changes in climate classification and extreme climate indices from a high-resolution future projection in Korea

We investigate the future changes in the climate zone and six extreme temperature indices in Korea, using the 20-km high-resolution atmospheric general circulation model (MRI-AGCM3.1S). The Trewartha and K?ppen climate classification schemes are applied, and four summer-based extreme temperature indices (i.e., summer days, tropical nights, growing degree days, and cooling degree days (CDD) and two winter-based indices (frost days and heating degree days (HDD) are analyzed. To represent significantly the change in threshold indices, the monthly mean bias is corrected in model. The model result reasonably captures the temporal and spatial distribution of the present-day extreme temperatures associated with topography. It was found that in the future climate, the area of the subtropical climate zone in Korea expands northward and increases by 21% under the Trewartha classification scheme and by 35% under the K?ppen classification scheme. The spatial change in extreme climate indices is significantly modulated by geographical characteristics in relation to land-ocean thermal inertia and topographical effects. The change is manifested more in coastal regions than in inland regions, except for that in summer days and HDD. Regions with higher indices in the present climate tend to reveal a larger increase in the future climate. The summer-based indices display an increasing trend, while the winter-based indices show a decreasing trend. The most significant increase is in tropical nights (+452%), whereas the most significant decrease is in HDD (?25%). As an important indicator of energy-saving applications, the changes in HDD and CDD are compared in terms of the frequency and intensity. The future changes in CDD reveal a higher frequency but a lower temperature than those in HDD. The more frequent changes in CDD may be due to a higher and less dispersed occurrence probability of extreme temperatures during the warm season. The greater increase in extreme temperature events during the summer season remains an important implication of projecting future changes in extreme climate events.     

黑龙江省50a来度日的时空分布特征

选用黑龙江省经过均一性检验的59个台站1961-2010年逐日最高、最低气温计算全省逐年热度日（HDD）、冷度日（CDD）,分析其趋势及年代际和空间分布特征。结果表明：50 a来HDD总体呈显著的下降趋势,CDD整体表现为显著上升趋势。HDD基本上呈现由北向南递减趋势,CDD与HDD呈相反的变化特征,二者纬向分布明显。HDD与CDD均与对应时段平均气温具有较高的相关性,HDD与冷季平均气温呈反位相变化,CDD同暖季平均气温呈同位相变化。随着气候变暖,冷季用于供暖的能源将减少,暖季制冷所需的能源消耗将不断增加。     

Regional Energy Demand Responses To Climate Change: Methodology And Application To The Commonwealth Of Massachusetts

Climate is a major determinant of energy demand. Changes in climate may alter energy demand as well as energy demand patterns. This study investigates the implications of climate change for energy demand under the hypothesis that impacts are scale dependent due to region-specific climatic variables, infrastructure, socioeconomic, and energy use profiles. In this analysis we explore regional energy demand responses to climate change by assessing temperature-sensitive energy demand in the Commonwealth of Massachusetts. The study employs a two-step estimation and modeling procedure. The first step evaluates the historic temperature sensitivity of residential and commercial demand for electricity and heating fuels, using a degree-day methodology. We find that when controlling for socioeconomic factors, degree-day variables have significant explanatory power in describing historic changes in residential and commercial energy demands. In the second step, we assess potential future energy demand responses to scenarios of climate change. Model results are based on alternative climate scenarios that were specifically derived for the region on the basis of local climatological data, coupled with regional information from available global climate models. We find notable changes with respect to overall energy consumption by, and energy mix of the residential and commercial sectors in the region. On the basis of our findings, we identify several methodological issues relevant to the development of climate change impact assessments of energy demand.     

气候变暖前后我国热度日和冷度日的特征分析

基于全国1960—2011年气象台站的逐日资料,运用度日分析法、Mann-Kendall检验法研究了气候变暖前后我国热度日(HDD)、冷度日(CDD)的变化情况。结果表明,我国北方及高原地区冬季气温在1987年前后发生突变,气候变暖后HDD有所下降;我国南方夏季气温在1997年前后发生突变,气候变暖后CDD有所上升。利用NCEP/NCAR再分析资料和环流指数资料进一步分析气候变暖前后的环流场特征,发现冬季AO指数与我国东北、华北、新疆西藏部分地区HDD具有显著的负相关关系,AO指数偏强(弱)时,强冷空气活动减少(增加),HDD减小(增大);夏季西太平洋副高面积指数与我国南方地区CDD具有显著的正相关关系,副高面积指数偏强(弱)时,晴热高温天气增加(减少),CDD增大(减小)。     

中国近50a来度日变化的研究

采用中国1951—2006年的气象观测资料,在运用度日分析法分析全国、各省及省会城市近50 a的平均温度变化的基础上,系统分析全国的度日变化情况;研究了中国六大区省会城市热度日（Heating Degree-day,HDD）和冷度日（Cooling Degree-day,CDD）的变化情况;分析了典型城市北京、上海、广州CDD的上升趋势和上升率,并求得相关方程。结果表明,北京、上海、广州CDD的长期变化都呈上升趋势,上升率分别为11.7℃/（10 a）、10.4℃/（10 a）、25.1℃/（10 a）。     

北京市气温对电力负荷影响的计量经济分析

以温度派生变量度日指数为解释变量构建了气温与电力负荷的计量经济模型。模型证明了天气对电力负荷的季节性影响, 且影响显著。通过引入序列相关AR结构和解释变量的动态结构, 模型得到逐步优化, 调整的拟合优度达95%。为了检验模型的预测能力, 利用历史数据对其进行了评估, 评估结果表明模型有较好的中期电力负荷预测能力。该模型对电力企业电力调度、电力建设有较大的参考价值。     

Historical Trend in River Ice Thickness and Coherence in Hydroclimatological Trends in Maine

We analyzed long-term records of ice thickness on the Piscataquis River in central Maine and air temperature in Maine to determine whether there were temporal trends that were associated with climate warming. The trend in ice thickness was compared and correlated with regional time series of winter air temperature, heating degree days (HDD), date of river ice-out, seasonal center-of-volume date (SCVD) (date on which half of the stream runoff volume during the period 1 Jan. to 31 May has occurred), water temperature, and lake ice-out date. All of these variables except lake ice-out date showed significant temporal trends during the 20th century. Average ice thickness around 28 February decreased by about 23 cm from 1912 to 2001. Over the period 1900 to 1999, winter air temperature increased by 1.7 °C and HDD decreased by about 7.5%.Final ice-out date on the Piscataquis River occurred earlier (advanced), by 0.21 days yr^–1 over the period 1931 to 2002, and the SCVD advancedby 0.11 days yr^–1 over the period 1903 to 2001. Ice thickness was significantly correlated (P-value <0.01) with winter air temperature, HDD, river ice-out, and SCVD. These systematic temporal trends in multiple hydrologic indicator variables indicate a coherent response to climate forcing.     

Present and projected degree days in China from observation,reanalysis and simulations

Degree days are usually defined as the accumulated daily mean temperature varying with the base temperature, and are one of the most important indicators of climate changes. In this study, the present-day and projected changes of four degree days indices from daily mean surface air temperature output simulated by Max Planck Institute, Earth Systems Model of low resolution (MPI-ESM-LR) model are evaluated with the high resolution gridded-observation dataset and two modern reanalyses in China. During 1979–2005, the heating degree days (HDD) and the numbers of HDD (NHDD) have decreased for observation, reanalyses (ERA-Interim and NCEP/NCAR) and model simulations (historical and decadal experiments), consistent with the increasing cooling degree days (CDD) and the numbers of CDD (NCDD). These changes reflect the general warming in China during the past decades. In most cases, ERA-Interim is closer to observation than NCEP/NCAR and model simulations. There are discrepancies between observation, reanalyses and model simulations in the spatial patterns and regional means. The decadal hindcast/forecast simulation performance of MPI-ESM-LR produce warmer than the observed mean temperature in China during the entire period, and the hindcasts forecast a trend lower than the observed. Under different representative concentration pathway (RCP) emissions scenarios, HDD and NHDD show significant decreases, and CDD and NCDD consistently increase during 2006–2100 under RCP8.5, RCP4.5 and RCP2.6, especially before the mid-21 century. More pronounced changes occur under RCP8.5, which is associated with a high rate of radiative forcing. The 20th century runs reflect the sensitivity to the initial conditions, and the uncertainties in terms of the inter-ensemble are small, whereas the long-term trend is well represented with no differences among ensembles.     

Future change of extreme temperature climate indices over East Asia with uncertainties estimation in the CMIP5

How well the climate models simulate extreme temperature over East Asia and how the extreme indices would change under anthropogenic global warming are investigated. The indices studied include hot days (HD), tropical nights (TN), growing degree days (GDD), and cooling degree days (CDD) in summer and heating degree days (HDD) and frost days (FD) in winter. The representative concentration pathway 4.5 (RCP 4.5) experiments for the period of 2075–2099 are compared with historical simulations for the period of 1979–2005 from 15 coupled models that are participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). To optimally estimate future change and its uncertainty, groups of best models are selected based on Taylor diagrams, relative entropy, and probability density function (PDF) methods previously suggested. Overall, the best models’ multi-model ensemble based on Taylor diagrams has the lowest errors in reproducing temperature extremes in the present climate among three methods. Selected best models in three methods tend to project considerably different changes in the extreme indices from each other, indicating that the selection of reliable models are of critical importance to reduce uncertainties. Three groups of best models show significant increase of summerbased indices but decrease of the winter-based indices. Over East Asia, the most significant increase is seen in the HD (336 ± 23.4% of current climate) and the most significant decrease is appeared in the HDD (82 ± 4.2%). It is suggested that the larger future change in the HD is found over in the Southeastern China region, probably due to a higher local maximum temperature in the present climate. All of the indices show the largest uncertainty over Southeastern China, particularly in the TN (~3.9 times as large as uncertainty over East Asia) and in the HD (~2.4). It is further noted that the TN reveals the largest uncertainty over three East Asian countries (~1.7 and 1.4 over Korea and Japan, respectively). These future changes in extreme temperature events have an important implication for energy-saving applications and human molarity in the future.     

Spatial Heating Monthly Degree-Day Features and ClimatologicPatterns in Turkey

Summary  Degree-days as a measure of accumulated temperature deviations from a base temperature have many practical applications in various human related activities such as home cooling, heating, plant growth in agriculture and power generation in addition to energy requirement. Long temperature records are necessary for their reliable estimations at given stations. In this paper, degree-day measure has been applied to monthly temperature records for systematically changed base temperature values from − 25 °C to + 35 °C with 5 °C increments at 255 meteorology stations in Turkey. The results are represented in the form of spatial degree-day distribution maps, which are then related to various climatic, meteorological and topographic features of Turkey. For instance, free surface water bodies in forms of surrounding seas, lakes and rivers insert retardation in the expansion of heating degree-days over large regions. On the other hand, cold air penetration from polar regions in the northeastern Turkey originating from Siberia appears at moderate base temperature heating degree-days. Received August 20, 1998 Revised June 21, 1999     

Spatial distributions of heating, cooling, and industrial degree-days in Turkey

Summary The degree-day method is commonly used to estimate energy consumption for heating and cooling in residential, commercial and industrial buildings, as well as in greenhouses, livestock facilities, storage facilities and warehouses. This article presents monthly and yearly averages and spatial distributions of heating, cooling, and industrial degree-days at the base temperatures of 18 °C and 20 °C, 18 °C and 24 °C, and 7 °C and 13 °C, respectively; as well as the corresponding number of days in Turkey. The findings presented here will facilitate the estimation of heating and cooling energy consumption for any residential, commercial and industrial buildings in Turkey, for any period of time (monthly, seasonal, etc.). From this analysis it will also be possible to compare and design alternative building systems in terms of energy efficiencies. If one prefers to use set point temperatures to indicate the resumption of the heating season would also be possible using the provided information in this article. In addition, utility companies and manufacturing/marketing companies of HVAC systems would be able to easily determine the demand, marketing strategies and policies based on the findings in this study.     

吉林省城市住宅采暖气候耗能距平序列的建立方法

 由于中国社会经济统计资料中均缺乏历年采暖能源消费数据,在分析气候变化对采暖能耗的影响时,始终存在着一个瓶颈。为此,提出了一种城市住宅采暖气候耗能距平序列的建立方法,这种方法把气候变化因素与相关的社会经济因素（如人口、人均居住面积等）结合在一起,较直观地反映了气候变化对城市住宅采暖耗能的影响。以地处高纬的吉林省为例,利用吉林省历年城镇人口、城镇人均居住面积、采暖度日数以及单位面积采暖耗煤量指标,建立了吉林省历年采暖气候耗能距平序列。结果表明：相对于1971-2000年30 a平均气候状况,吉林省2001/2002年的采暖期理论上因气候偏暖可以节约35.98万t标准煤,而2000/2001年采暖期因气候偏冷需要增加15.5万t标准煤。     

吉林省城市住宅采暖气候耗能距平序列的建立方法

由于中国社会经济统计资料中均缺乏历年采暖能源消费数据,在分析气候变化对采暖能耗的影响时,始终存在着一个瓶颈。为此,提出了一种城市住宅采暖气候耗能距平序列的建立方法,这种方法把气候变化因素与相关的社会经济因素（如人口、人均居住面积等）结合在一起,较直观地反映了气候变化对城市住宅采暖耗能的影响。以地处高纬的吉林省为例,利用吉林省历年城镇人口、城镇人均居住面积、采暖度日数以及单位面积采暖耗煤量指标,建立了吉林省历年采暖气候耗能距平序列。结果表明：相对于1971-2000年30 a平均气候状况,吉林省2001/2002年的采暖期理论上因气候偏暖可以节约35.98万t标准煤,而2000/2001年采暖期因气候偏冷需要增加15.5万t标准煤。     

The effect of global climate change, population distribution, and climate mitigation on building energy use in the U.S. and China

Climate change will affect the energy system in a number of ways, one of which is through changes in demands for heating and cooling in buildings. Understanding the potential effect of climate change on heating and cooling demands requires taking into account not only the manner in which the building sector might evolve over time, but also important uncertainty about the nature of climate change itself. In this study, we explore the uncertainty in climate change impacts on heating and cooling requirement by constructing estimates of heating and cooling degree days (HDD/CDDs) for both reference (no-policy) and 550 ppmv CO₂ concentration pathways built from three different Global Climate Models (GCMs) output and three scenarios of gridded population distribution. The implications that changing climate and population distribution might have for building energy consumption in the U.S. and China are then explored by using the results of HDD/CDDs as inputs to a detailed, building energy model, nested in the long-term global integrated assessment framework, Global Change Assessment Model (GCAM). The results across the modeled changes in climate and population distributions indicate that unabated climate change would cause building sector’s final energy consumption to decrease modestly (6 % decrease or less depending on climate models) in both the U.S. and China by the end of the century as decreased heating consumption more than offsets increased cooling using primarily electricity. However, global climate change virtually has negligible effect on total CO₂ emissions in the buildings sector in both countries. The results also indicate more substantial implications for the fuel mix with increases in electricity and decreases in other fuels, which may be consistent with climate mitigation goals. The variation in results across all scenarios due to variation of population distribution is smaller than variation due to the use of different climate models.     

Trend analysis of long-term temperature time series in the Greater Toronto Area (GTA)

As the majority of the world’s population is living in urban environments, there is growing interest in studying local urban climates. In this paper, for the first time, the long-term trends (31–162 years) of temperature change have been analyzed for the Greater Toronto Area (GTA). Annual and seasonal time series for a number of urban, suburban, and rural weather stations are considered. Non-parametric statistical techniques such as Mann–Kendall test and Theil-Sen slope estimation are used primarily for the assessing of the significance and detection of trends, and the sequential Mann test is used to detect any abrupt climate change. Statistically significant trends for annual mean and minimum temperatures are detected for almost all stations in the GTA. Winter is found to be the most coherent season contributing substantially to the increase in annual minimum temperature. The analyses of the abrupt changes in temperature suggest that the beginning of the increasing trend in Toronto started after the 1920s and then continued to increase to the 1960s. For all stations, there is a significant increase of annual and seasonal (particularly winter) temperatures after the 1980s. In terms of the linkage between urbanization and spatiotemporal thermal patterns, significant linear trends in annual mean and minimum temperature are detected for the period of 1878–1978 for the urban station, Toronto, while for the rural counterparts, the trends are not significant. Also, for all stations in the GTA that are situated in all directions except south of Toronto, substantial temperature change is detected for the periods of 1970–2000 and 1989–2000. It is concluded that the urbanization in the GTA has significantly contributed to the increase of the annual mean temperatures during the past three decades. In addition to urbanization, the influence of local climate, topography, and larger scale warming are incorporated in the analysis of the trends.