Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses.     

Variation and uncertainty in the predicted flowering dates of cherry blossoms using the CMIP5 climate change scenario

In this study, we analyzed changes in the predicted flowering date (PFD) for cherry blossom trees under changing climate conditions by simulating the PFDs for six sites on the Korean Peninsula between 1981 and 2010. The spatial downscaled climate data from the Representative Concentration Pathways (RCP) 8.5 scenarios of 30 global climate models (GCMs) were used in the analysis. Here, we present the range of uncertainty in the PFDs, which were calculated by comparing the simulated PFDs to the observed flowering dates. We determined that the root-mean-square errors (RMSEs) of PFDs from individual GCMs, at 7-15 days, were greater in range than those of the mean PFDs from multiple GCMs, at 7-8 days. During three future periods of 2011-2040, 2041-2070, and 2071-2100, the standard deviations (SD), the interquartile ranges (IQRs), and the relative changes in the mean predicted flowering dates (MPFDs) were calculated to quantify the uncertainty levels inherent from the climate scenarios of multiple GCMs. Distinctive changes in the SDs and IQRs of MPFD were found among the analyzed sites. The SDs increased with time between each future period in Seoul, Incheon, and Jeonju, whereas those in Daegu, Busan, and Mokpo decreased with time. In addition, the IQRs increased with time at Seoul, Incheon, Jeonju, and Daegu but not at Busan and Mokpo. The relative changes in the MPFDs at all six sites became greater with time toward the year 2100. Therefore, combining multiple GCM scenarios may not contribute largely to reduce the uncertainty in the PFDs under changing climate conditions, although it may be useful in quantifying the uncertainty in order to make better decisions based on more accurate information.     

重庆高温热浪指数和暖夜指数变化及其情景预估

利用重庆地区1951—2008年逐日气温资料分析了近58年重庆高温热浪指数(HWDI)和暖夜指数(TN90)的年际、年代际变化及其与气温各要素的关系。利用用于IPCC-AR4的全球气候模式产品,在验证其对重庆地区热浪和暖夜指数的模拟能力的基础上,筛选模拟能力较好的模式进行合理组合,并考虑模式模拟气候参考时段的偏差,预估高(A2)、中(A1B)、低(B1)3种排放情景下21世纪重庆地区热浪和暖夜指数的变化。与目前气候(1980—1999年)相比,3种情景下21世纪重庆地区热浪和暖夜指数都将呈显著增加的趋势。2011—2100年,3种不同情景下热浪指数的平均增幅为7.7～12.3天,暖夜指数的平均增幅为12.2%～16.4%。分阶段来看,3种不同排放情景下,21世纪前期(2011—2040年)热浪指数的平均增幅为3.1～4.1天,暖夜指数的平均增幅为5.7%～7.3%;中期(2041—2070年)热浪指数的平均增幅为6.9～11.3天,暖夜指数的平均增幅为12.2%～17.5%;后期(2071—2100年)热浪指数的平均增幅为12.4～21.6天,暖夜指数的平均增幅为17.1%～27.8%。     

Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, S?o Francisco and the Paraná River basins

The objective of this study is to assess the climate projections over South America using the Eta-CPTEC regional model driven by four members of an ensemble of the Met Office Hadley Centre Global Coupled climate model HadCM3. The global model ensemble was run over the twenty-first century according to the SRES A1B emissions scenario, but with each member having a different climate sensitivity. The four members selected to drive the Eta-CPTEC model span the sensitivity range in the global model ensemble. The Eta-CPTEC model nested in these lateral boundary conditions was configured with a 40-km grid size and was run over 1961–1990 to represent baseline climate, and 2011–2100 to simulate possible future changes. Results presented here focus on austral summer and winter climate of 2011–2040, 2041–2070 and 2071–2100 periods, for South America and for three major river basins in Brazil. Projections of changes in upper and low-level circulation and the mean sea level pressure (SLP) fields simulate a pattern of weakening of the tropical circulation and strengthening of the subtropical circulation, marked by intensification at the surface of the Chaco Low and the subtropical highs. Strong warming (4–6°C) of continental South America increases the temperature gradient between continental South America and the South Atlantic. This leads to stronger SLP gradients between continent and oceans, and to changes in moisture transport and rainfall. Large rainfall reductions are simulated in Amazonia and Northeast Brazil (reaching up to 40%), and rainfall increases around the northern coast of Peru and Ecuador and in southeastern South America, reaching up to 30% in northern Argentina. All changes are more intense after 2040. The Precipitation–Evaporation (P–E) difference in the A1B downscaled scenario suggest water deficits and river runoff reductions in the eastern Amazon and S?o Francisco Basin, making these regions susceptible to drier conditions and droughts in the future.     

Analysis of the change in temperature trends in Subansiri River basin for RCP scenarios using CMIP5 datasets

This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011–2100) maximum temperature (T _max) and minimum temperature (T_min) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T _max, T _min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011–2100, 2011–2040, 2041–2070, 2070–2100) using Mann-Kendall non-parametric test and Sen’s slope estimator, respectively. The temperature data series for the observed duration (1981–2000) has been found to show increasing trends in T _max and T _min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011–2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T _max and T _min have been seen to have decreasing trends in some months.

     

21世纪黑河流域降水统计降尺度及预估北大核心CSCD

借助第五阶段国际耦合模式比较计划(CMIP5)多模式集合数据、欧洲中期预报中心再分析资料及黑河流域站点观测记录等,检验了模式降水估计偏差,设计了3种降尺度方法,对2011~2100年模式集合预估降水做了降尺度偏差订正。结果表明,即使去掉模式气候飘移,在黑河流域的模拟或估计降水偏差依然较大。本文选用15个CMIP5模式集合做降水预估。依据贝叶斯模式平均(BMA)和多元线性回归(MLR)构造降尺度模型,其因子有700 hPa位势高度场、经向风和比湿等。检验表明,两种降尺度模型各有优缺点,BMA降尺度降水平均值精度较高,但方差和相关系数较低;MLR的方差和相关系数均较高,但在黑河下游极端干旱区或少雨季节易出现“负降水”偏差。在降尺度模型中加入模式降水因子后,BMA的降水方差和相关系数均有明显提高,MLR的负降水问题得到一定程度抑制。BMA模型在黑河上游最优,MLR在中、下游及整个流域最优。因此,选用BMA和MLR对RCP4.5情景下2011~2100年的降水预估做降尺度偏差订正,结果表明,经BMA和MLR降尺度后预估的整个黑河流域降水呈下降趋势,相对于1971~2000年参考期,流域前期(2011~2040年)、中期(2041~2070年)、后期(2071~2100年)降水下降率依次为−9.7%、−12.5、−12.1%,即前、中期降水明显减少,后期变化不大。其中上游降水有一个弱的增加趋势,其变化率依次为1.4%、1.6%、2.3%;中游降水呈明显减少趋势,其变化率依次为−16.3%、−21.4%、−22.6%;下游降水前期减少,中、后期明显增加,其变化率依次为−13.0%、4.2%、21.4%。该预估结果表明,随着全球气候暖化,黑河上游祁连山区降水会缓慢增加,但中游农耕区降水明显减少,流域水资源供需矛盾可能会进一步加剧。因此,黑河流域未来的分水方案及相关的生态、农业、经济等发展规划需要据此做一些调整,以适应未来气候和黑河流域水资源的可能变化。     

Scenario Analysis on the Adaptation of Different Maize Varieties to Future Climate Change in Northeast China

Based on gridded meteorological data for the period 1981–2100 from the RegCM3 regional model, the changing trends of climatic resources in Northeast China are analyzed, and the distributions of maize varieties are accordingly adjusted. In order to explore the effects of different adaptation countermeasures on climatic productivity and meteorological suitability in the future, maize cultivars with resistance to high temperatures and/or drought are selected. The results show that, in the future, there is likely to be a significant increase in thermal resources, and potential atmospheric evaporation will increase correspondingly.Meanwhile, radiation is predicted to increase significantly during 2041–2070 in the growing season. However, changes in precipitation are unlikely to be sufficient enough to offset the intensification in atmospheric evaporation caused by the temperature increase. Water resources and high temperatures are found to be the two major factors constraining grain yield. The results also show that the warming climate will be favorable for maize production where thermal resources are already limited, such as in central and northern Heilongjiang Province and eastern Jilin Province; while in areas that are already relatively warm, such as Liaoning Province, climatic productivity will be reduced. The climatic productivity and the meteorological suitability of maize are found to improve when the planting of resistant varieties is modeled. The utilization of agricultural climatic resources through the adaptation countermeasures of maize varieties is to increase obviously with time. Specifically, maize with drought-resistant properties will have a marked influence on meteorological suitability during 2011–2070, with suitable areas expanding. During 2071–2100, those maize varieties with their upper limit of optimum temperature and maximum temperature increased by 2℃, or water requirement reduced to 94%, or upper limit of optimum temperature and maximum temperature increased by 1℃ and water requirement reduced to 98%, all exhibit significant differences in climatic potential productivity, compared to the present-day varieties. The meteorological suitability of maize is predicted to increase in some parts of Heilongjiang Provine, with the eastern boundary of the "unavailable" area shifting westward.     

21世纪重庆中雨以上天数的预估分析

利用用于IPCC-AR4的全球气候模式产品,验证其对重庆地区极端降水指数中雨以上天数(dR10)模拟能力的基础上,对模拟能力较好的模式进行组合,预估高(A2)、中(A1B)、低(B1)三种排放情景下未来21世纪重庆地区dR10的变化。不同排放情景下未来重庆dR10的变化不太一致。与目前气候(1980—1999年)相比,不同情景下未来21世纪重庆地区dR10在多数时期将可能减少。21世纪的后90a(2011—2100年),A2情景下重庆dR10减少最多,平均减少1.3d;3种情景平均将减少0.5d。21世纪初期(2011—2040年)、中期(2041—2070年)和后期(2071—2100年),A2情景下重庆dR10减少都最多,分别平均减少1.6d、1.6d和0.7d;3种情景平均分别减少0.8d、0.6d和0.1d。     

Modelling China’s potential maize production at regional scale under climate change

With the continuing warming due to greenhouse gases concentration, it is important to examine the potential impacts on regional crop production spatially and temporally. We assessed China’s potential maize production at 50 × 50 km grid scale under climate change scenarios using modelling approach. Two climate changes scenarios (A2 and B2) and three time slices (2011–2040, 2041–2070, 2071–2100) produced by the PRECIS Regional Climate Model were used. Rain-fed and irrigated maize yields were simulated with the CERES-Maize model, with present optimum management practices. The model was run for 30 years of baseline climate and three time slices for the two climate change scenarios, without and with simulation of direct CO₂ fertilization effects. Crop simulation results under climate change scenarios varied considerably between regions and years. Without the CO₂ fertilization effect, China’s maize production was predicted to suffer a negative effect under both A2 and B2 scenarios for all time slices, with greatest production decreases in today’s major maize planting areas. When the CO₂ fertilization effect is taken into account, production was predicted to increase for rain-fed maize but decrease for irrigated maize, under both A2 and B2 scenarios for most time periods.     

两种统计降尺度方法在秦岭山地的适用性

基于ASD（automated statistical downscaling）统计降尺度模型提供的多元线性回归和岭回归两种统计降尺度方法,采用RCP4.5（representative concentration pathways 4.5）和RCP8.5情景下全球气候模式MPI-ESM-LR输出的预报因子数据、NCEP/NCAR再分析数据和秦岭山地周边10个气象站观测数据,评估两种统计降尺度方法在秦岭山地的适用性及预估秦岭山地未来3个时期（2006-2040年、2041-2070年和2071-2100年）的平均气温和降水。结果表明:率定期和验证期内,两种统计降尺度方法均可以较好地模拟研究区域的平均气温和降水的变化特征,且多元线性回归的模拟效果优于岭回归。在未来气候情景下,两种统计降尺度方法预估的研究区域平均气温均呈明显上升趋势,气温增幅随辐射强迫增加而增大。降水方面,21世纪未来3个时期降水均呈不明显减少趋势,但季节分配发生变化。综合考虑两种统计降尺度方法在秦岭山地对平均气温和降水的模拟效果和情景预估结果,认为多元线性回归降尺度方法更适用于秦岭山地气候变化的降尺度预估研究。     

Statistical downscaling of precipitation using local regression and high accuracy surface modeling method

Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976–2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976–2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011–2040) to T2 (2041–2070) and T2 to T3 (2071–2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.

     

Global river discharge and water temperature under climate change

Climate change will affect hydrologic and thermal regimes of rivers, having a direct impact on freshwater ecosystems and human water use. Here we assess the impact of climate change on global river flows and river water temperatures, and identify regions that might become more critical for freshwater ecosystems and water use sectors. We used a global physically based hydrological-water temperature modelling framework forced with an ensemble of bias-corrected general circulation model (GCM) output for both the SRES A2 and B1 emissions scenario. This resulted in global projections of daily river discharge and water temperature under future climate. Our results show an increase in the seasonality of river discharge (both increase in high flow and decrease in low flow) for about one-third of the global land surface area for 2071–2100 relative to 1971–2000. Global mean and high (95th percentile) river water temperatures are projected to increase on average by 0.8–1.6 (1.0–2.2) °C for the SRES B1–A2 scenario for 2071–2100 relative to 1971–2000. The largest water temperature increases are projected for the United States, Europe, eastern China, and parts of southern Africa and Australia. In these regions, the sensitivities are exacerbated by projected decreases in low flows (resulting in a reduced thermal capacity). For strongly seasonal rivers with highest water temperatures during the low flow period, up to 26% of the increases in high (95th percentile) water temperature can be attributed indirectly to low flow changes, and the largest fraction is attributable directly to increased atmospheric energy input. A combination of large increases in river temperature and decreases in low flows are projected for the southeastern United States, Europe, eastern China, southern Africa and southern Australia. These regions could potentially be affected by increased deterioration of water quality and freshwater habitats, and reduced water available for human uses such as thermoelectric power and drinking water production.     

Future changes in drought characteristics over South Korea using multi regional climate models with the standardized precipitation index

In this study, the projection of future drought conditions is estimated over South Korea based on the latest and most advanced sets of regional climate model simulations under the Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios, within the context of the national downscaling project of the Republic of Korea. The five Regional Climate Models (RCMs) are used to produce climate-change simulations around the Korean Peninsula and to estimate the uncertainty associated with these simulations. The horizontal resolution of each RCM is 12.5 km and model simulations are available for historical (1981-2010) and future (2021-2100) periods under forcing from the RCP4.5 and RCP8.5 scenarios. To assess the characteristics of drought on multiple time scales in the future, we use Standardized Precipitation Indices for 1-month (SPI- 1), 6-month (SPI-6) and 12-month (SPI-12). The number of drought months in the future is shown to be characterized by strong variability, with both increasing and decreasing trends among the scenarios. In particular, the number of drought months over South Korea is projected to increase (decrease) for the period 2041-2070 in the RCP8.5 (RCP4.5) scenario and increase (decrease) for the period 2071-2100 in the RCP4.5 (RCP8.5) scenario. In addition, the percentage area under any drought condition is overall projected to gradually decrease over South Korea during the entire future period, with the exception of SPI-1 in the RCP4.5 scenario. Particularly, the drought areas for SPI-1 in the RCP4.5 scenario show weakly positive long-term trend. Otherwise, future changes in drought areas for SPI-6 and SPI-12 have a marked downward trend under the two RCP scenarios.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Assessment of future changes in the maximum temperature at selected stations in Iran based on HADCM3 and CGCM3 models

Identification and assessment of climate change in the next decades with the aim of appropriate environmental planning in order to adapt and mitigate its effects are quite necessary. In this study, maximum temperature changes of Iran were comparatively examined in two future periods (2041-2070 and 2071-2099) and based on the two general circulation model outputs (CGCM3 and HADCM3) and under existing emission scenarios (A2, A1B, B1 and B2). For this purpose, after examining the ability of statistical downscaling method of SDSM in simulation of the observational period (1981-2010), the daily maximum temperature of future decades was downscaled by considering the uncertainty in seven synoptic stations as representatives of climate in Iran. In uncertainty analysis related to model-scenarios, it was found that CGCM3 model under scenario B1 had the best performance about the simulation of future maximum temperature among all of the examined scenario-models. The findings also showed that the maximum temperature at study stations will be increased between 1°C and 2°C in the middle and the end of 21st century. Also this maximum temperature changes is more severe in the HADCM3 model than the CGCM3 model.     

Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model

Climate change caused by anthropogenic activities has generated a variety of research focusing on investigating the past climate, predicting the future climate and quantifying the change in climate extreme events by using different climate models. Climate extreme events are valuable to evaluate the potential impact of climate change on human activities, agriculture and economy and are also useful to monitor the climate change on global scale. Here, a Regional Climate Model (RCM) simulation is used to study the future variations in the temperature extreme indices, particularly change in frequency of warm and cold spells duration over Pakistan. The analyses are done on the basis of simulating two 30 years simulations with the Hadley Center’s RCM PRECIS, at a horizontal resolution of 50 km. Simulation for the period 1961–1990 represents the recent climate and simulation for the period 2071–2100 represents the future climate. These simulations are driven by lateral boundary conditions from HadAM3P GCM of Hadley centre UK. For the validation of model, observed mean, maximum and minimum temperatures for the period 1961–1990 at all the available stations in Pakistan are first averaged and are then compared with the PRECIS averaged grid-box data. Also the observed monthly gridded data set of Climate Research Unit (UK) data is used to validate the model. Temperature indices in the base period as well as in future are then calculated and the corresponding change is observed. Percentile based spatial change of temperature shows that in summer, increase in daily minimum temperature is more as compared to the increase of daily maximum temperature whereas in winter, the change in maximum temperature is high. The occurrence of annual cold spells shows significantly decreasing trend while for warm spells there is slight increasing trend over Pakistan.     

Changes in the onset and length of seasons from an ensemble of regional climate models over Spain for future climate conditions

This study analyses the length and onset of the four seasons based on the annual climatic cycle of maximum and minimum temperatures. Previous studies focused over climatically homogeneous mid-high latitude areas, employing fixed temperature thresholds (related to climatic features such as freezing point) that can be inadequate when different climate conditions are present. We propose a method related to the daily minimum and maximum temperature 25th and 75th point-dependent climatic percentiles. It is applied to an ensemble of regional climate models (RCMs) of 25-km horizontal resolution over the peninsular Spain and Balearic Islands, where a large variety of climatic regimes, from alpine to semi-desertic conditions, are present. First, baseline climate (1961–2000) ERA40-forced RCM simulations are successfully compared with the Spain02 daily observational database, following astronomical season length (around 90 days). This result confirms the validity of the proposed method and capability of the RCMs to describe the seasonal features. Future climate global climate model-forced RCMs (2071–2100) compared with present climate (1961–1990) simulations indicate the disappearance of winter season, a summer enlargement (onset and end) and a slight spring and autumn increase.     

Burgundy regional climate change and its potential impact on grapevines

ARPEGE general circulation model simulations were dynamically downscaled by The Weather Research and Forecasting Model (WRF) for the study of climate change and its impact on grapevine growth in Burgundy region in France by the mid twenty-first century. Two time periods were selected: 1970–1979 and 2031–2040. The WRF model driven by ERA-INTERIM reanalysis data was validated against in situ surface temperature observations. The daily maximum and minimum surface temperature (T_max and T_min) were simulated by the WRF model at 8?×?8?km horizontal resolution. The averaged daily T_max for each month during 1970–1979 have good agreement with observations, the averaged daily T_min have a warm bias about 1–2?K. The daily T_max and T_min for each month (domain averaged) during 2031–2040 show a general increase. The largest increment (~3?K) was found in summer. The smallest increments (<1?K) were found in spring and fall. The spatial distribution of temperature increment shows a strong meridional gradient, high in south in summer, reversing in winter. The resulting potential warming rate in summer is equivalent to 4.7?K/century under the IPCC A2 emission scenario. The dynamically downscaled T_max and T_min were used to simulate the grape (Pinot noir grape variety) flowering and véraison dates. For 2031–2040, the projected dates are 8 and 12?days earlier than those during 1970–1979, respectively. The simulated hot days increase more than 50% in the two principal grapevine regions. They show strong impact on Pinot noir development.     

Seasonal variability of the observed and the projected daily temperatures in northern Saudi Arabia

Variability in the observed daily temperature for the 31-year period (1978–2008) is studied for northern Saudi Arabia (nSA) by computing the probability distribution functions (PDFs) on a seasonal basis. The 31-year base period is divided into three decades and the results for the first (1978–1987) and the last decade (1999–2008) are presented. When averaged over all seasons, mean values of the observed decadal PDFs depict a positive shift from the first to last decade in the minimum, mean, and maximum temperature of 0.81 °C, 1.03 °C, and 1.25 °C, respectively. The daily temperature datasets from a regional climate model (RCM) and two versions of a coupled atmosphere-ocean general circulation model (AOGCM) are compared with the observed daily temperature datasets. The RCM is driven by re-analysis data for the historical period and by the HadCM3 model for the future, while the AOGCMs used are the GFDL CM2.0 and 2.1 models, with both HadCM3 and the GFDL simulations corresponding to the SRES A1B scenario. The average shifts from 1978–1987 to 1999–2008 in the mean value of the PDFs for the minimum, mean and maximum temperature are 0.63 °C, 0.54 °C and 0.45 °C, respectively, for the RCM, and 0.97 °C, 0.97 °C and 0.96 °C, respectively, for the AOGCM. Thus, the RCM shows a smaller shift in the mean of PDF for maximum temperature than for mean or minimum temperature, the AOGCM shows a comparable shift for all three, and the observations show a greater shift in the PDF for maximum temperature. For the period 2070–2099 relative to 1978–2008, the three average shifts are 4.11 °C, 3.87 °C and 3.44 °C for the RCM and 3.63 °C, 3.74 °C and 3.84 °C for the AOGCM.     

Predicting the time of green up in temperate and boreal biomes

Direct observations as well as Normalized Difference Vegetation Index (NDVI) data from satellites have shown earlier leaf appearance in the northern hemisphere, which is believed to result from climate warming. The advance of leaf out to earlier times in the year could be limited or even reversed, however, as temperate and boreal trees require a certain amount of chilling in winter for rapid leaf out in spring. If this chilling requirement is not fulfilled, an increasing amount of warming is required. Implications of these chilling requirements at the biome level are not clear. One approach to estimate their importance is to generalize the exponential relationships between chilling and warming established for single species. Previous work using NDVI data suggests that this is indeed feasible but much has been limited to specific biomes or a very few years of data for the modelling. We find chilling requirements for northern temperate and boreal biomes by fitting various phenology models to green-up dates determined from NDVI using various methods and 12 years of data. The models predict that in northern middle and high latitudes the advance of green-up will be limited to a total of 4 to 5 days on average (but up to 15 days regionally) over the time period 2000–2060 as estimated using two contrasting climate simulations. This results from the exponentially increasing warming requirements for leaf out when winter chilling falls below a threshold as shown by a comparison with models that consider only spring warming. The model evaluation suggests an element of regional adaptation of the warming required for leaf out in large biomes.