Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.     

A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961–2006. The statistical downscaling models are established through multiple stepwise regressions of predictor principal components (PCs). The predictors in this study include temperature at 850 hPa (T850), and the combination of geopotential height and temperature at 850 hPa (H850+T850). For the combined predictors, Empirical Orthogonal Function (EOF) analysis of the two combined fields is conducted. The modeling results from HadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models (GCMs). The results show that (1) the trend of temperature in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming; (2) although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the trend of main PCs of the GCM predictors; (3) when the two-field combination and the projected EOF analysis are used, temperature change scenarios have a similar seasonal variation to the observed one; and (4) compared with the results of the common EOF analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

Comparison of Agricultural Impacts of Climate Change Calculated from High and Low Resolution Climate Change Scenarios: Part I. The Uncertainty Due to Spatial Scale

We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO₂ fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2 ×CO₂ runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis.     

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.     

Climate variability and projected change in the western United States: regional downscaling and drought statistics

Climate change in the twenty-first century, projected by a large ensemble average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is downscaled to Climate Divisions across the western United States. A simple empirical downscaling technique is employed, involving model-projected linear trends in temperature or precipitation superimposed onto a repetition of observed twentieth century interannual variability. This procedure allows the projected trends to be assessed in terms of historical climate variability. The linear trend assumption provides a very close approximation to the time evolution of the ensemble-average climate change, while the imposition of repeated interannual variability is probably conservative. These assumptions are very transparent, so the scenario is simple to understand and can provide a useful baseline assumption for other scenarios that may incorporate more sophisticated empirical or dynamical downscaling techniques. Projected temperature trends in some areas of the western US extend beyond the twentieth century historical range of variability (HRV) of seasonal averages, especially in summer, whereas precipitation trends are relatively much smaller, remaining within the HRV. Temperature and precipitation scenarios are used to generate Division-scale projections of the monthly palmer drought severity index (PDSI) across the western US through the twenty-first century, using the twentieth century as a baseline. The PDSI is a commonly used metric designed to describe drought in terms of the local surface water balance. Consistent with previous studies, the PDSI trends imply that the higher evaporation rates associated with positive temperature trends exacerbate the severity and extent of drought in the semi-arid West. Comparison of twentieth century historical droughts with projected twenty-first century droughts (based on the prescribed repetition of twentieth century interannual variability) shows that the projected trend toward warmer temperatures inhibits recovery from droughts caused by decade-scale precipitation deficits.     

First-order impacts on winter and summer crops assessed with various high-resolution climate models in the Iberian Peninsula

The first-order or initial agricultural impacts of climate change in the Iberian Peninsula were evaluated by linking crop simulation models to several high-resolution climate models (RCMs). The RCMs provided the daily weather data for control, and the A2 and B2 IPCC scenarios. All RCMs used boundary conditions from the atmospheric general circulation model (AGCM) HadAM3 while two were also bounded to two other AGCMs. The analyses were standardised to control the sources of variation and uncertainties that were added in the process. Climatic impacts on wheat and maize of climate were derived from the A2 scenario generated by RCMs bounded to HadAM3. Some results derived from B2 scenarios are included for comparisons together with impacts derived from RCMs using different boundary conditions. Crop models were used as impact models and yield was used as an indicator that summarised the effects of climate to quantify initial impacts and differentiate among regions. Comparison among RCMs was made through the choice of different crop management options. All RCM-crop model combinations detected crop failures for winter wheat in the South under control and future scenarios, and projected yield increases for spring wheat in northern and high altitude areas. Although projected impacts differed among RCMs, similar trends emerged for relative yields for some regions. RCM-crop model outputs compared favourably to others using European Re-Analysis data (ERA-15), establishing the feasibility of using direct daily outputs from RCM for impact analysis. Uncertainties were quantified as the standard deviation of the mean obtained for all RCMs in each location and differed greatly between winter (wheat) and summer (maize) seasons, being smaller in the latter.     

Modelling Hydrological Consequences of Climate Change—— Progress and Challenges

The simulation of hydrological consequences of climate change has received increasing attention from the hydrology and land-surface modelling communities. There have been many studies of climate-change effects on hydrology and water resources which usually consist of three steps： （1） use of general circulation models （GCMs） to provide future global climate scenarios under the effect of increasing greenhouse gases, （2） use of downscaling techniques （both nested regional climate models, RCMs, and statistical methods） for ＂downscaling＂ the GCM output to the scales compatible with hydrological models, and （3） use of hydrologic models to simulate the effects of climate change on hydrological regimes at various scales. Great progress has been achieved in all three steps during the past few years, however, large uncertainties still exist in every stage of such study. This paper first reviews the present achievements in this field and then discusses the challenges for future studies of the hydrological impacts of climate change.     

Vulnerability and adaptation assessments of agriculturalcrops under climate change in the Southeastern USA

Summary  It is expected that a change in climatic conditions due to global warming will directly impact agricultural production. Most climate change studies have been applied at very large scales, in which regions were represented by only one or two weather stations, which were mainly located at airports of major cities. The objective of this study was to determine the potential impact of climate change at a local level, taking into account weather data recorded at remote locations. Daily weather data for a 30-year period were obtained for more than 500 sites, representing the southeastern region of the USA. Climate change scenarios, using transient and equilibrium global circulation models (GCM), were defined, created and applied to the daily historical weather data. The modified temperature, precipitation and solar radiation databases corresponding to each of the climate change scenarios were used to run the CERES v.3.5 simulation model for maize and winter wheat and the CROPGRO v.3.5 model for soybean and peanut. The GCM scenarios projected a shorter duration of the crop-growing season. Under the current level of CO₂, the GCM scenarios projected a decrease of crop yields in the 2020s. When the direct effects of CO₂ were assumed in the study, the scenarios resulted in an increase in soybean and peanut yield. Under equilibrium , the GCM climate change scenarios projected a decrease of maize and winter wheat yield. The indirect effects of climate change also tended to decrease soybean and peanut yield. However, when the direct effects of CO₂ were included, most of the scenarios resulted in an increase in legume yields. Possible changes in sowing data, hybrids and cultivar selection, and fertilization were considered as adaptation options to mitigate the potential negative impact of potential warming. Received July 20, 1999/Revised April 18, 2000     

Risk analysis of possible impacts of climate change on South Australian wheat production

This study presents a model-based risk assessment of wheat production under projected climate change by 2080 in eight locations of South Australia. The vulnerability of wheat production under future climate change was quantitatively evaluated via a risk analysis in which the identification of critical yield thresholds applies. Research results show that risk (conditional probability of not exceeding the critical yield thresholds) increased more or less across all locations under the most likely climate change. Wheat production in drier areas such as Minnipa, Orroroo and Wanbi will not be economically viable under the most likely climate change. Intensive studies on adaptation are now required.     

Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO₂ concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO₂ concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios.     

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。     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

Improved point scale climate projections using a block bootstrap simulation and quantile matching method

Statistical downscaling methods are commonly used to address the scale mismatch between coarse resolution Global Climate Model output and the regional or local scales required for climate change impact assessments. The effectiveness of a downscaling method can be measured against four broad criteria: consistency with the existing baseline data in terms of means, trends and distributional characteristics; consistency with the broader scale climate data used to generate the projections; the degree of transparency and repeatability; and the plausibility of results produced. Many existing downscaling methods fail to fulfil all of these criteria. In this paper we examine a block bootstrap simulation technique combined with a quantile prediction and matching method for simulating future daily climate data. By utilising this method the distributional properties of the projected data will be influenced by the distribution of the observed data, the trends in predictors derived from the Global Climate Models and the relationship of these predictors to the observed data. Using observed data from several climate stations in Vanuatu and Fiji and out-of-sample validation techniques, we show that the method is successful at projecting various climate characteristics including the variability and auto-correlation of daily temperature and rainfall, the correlations between these variables and between spatial locations. This paper also illustrates how this novel method can produce more effective point scale projections and a more credible alternative to other approaches in the Pacific region.     

CSIRO Mk3.6.0模式及其驱动下RegCM4.4模式对中国气候变化的预估

使用区域气候模式RegCM4.4,对全球模式CSIRO-Mk3.6.0在RCP4.5情景下的气候变化试验结果（1950-2100年）在东亚地区进行25 km动力降尺度试验,比较了CSIRO-Mk3.6.0和RegCM4.4预估中国地区的21世纪气候变化。结果表明,两个模式预估未来中国地区气温持续升高,升温幅度具有区域性特征,RegCM4.4预估区域平均升温幅度低于CSIRO-Mk3.6.0,但二者年际波动基本一致。两个模式预估未来降水在中国西部以持续增加为主,东部则表现出较大的不一致性,预估区域平均年降水量变化不大,呈现冬季明显增加,夏季微弱减少的特点。此外,为了解区域气候模式对中国降水预估的不确定性,对本研究和以往RegCM3使用相同分辨率模拟得到的未来降水预估进行了对比,两个区域模式预估中国西部大部分地区未来降水一致性增加,东部存在明显不一致（冬季中、高纬除外）。     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Assessing the impact of climate change on representative field crops in Israeli agriculture: a case study of wheat and cotton

Climate changes, associated with accumulation of greenhouse gases, are expected to have a profound influence on agricultural sustainability in Israel, a semi-arid area characterized by a cold wet winter and a dry warm summer. Accordingly this study explored economic aspects of agricultural production under projected climate-change scenarios by the “production function” approach, as applied to two representative crops: wheat, as the major crop grown in Israel’s dry southern region, and cotton, representing the more humid climate in the north. Adjusting outputs of the global climate model HadCM3 to the specific research locations, we generated projections for 2070–2100 temperatures and precipitations for two climate change scenarios. Results for wheat vary among climate scenarios; net revenues become negative under the severe scenario (change from −145 to −273%), but may increase under the moderate one (−43 to +35%), depending on nitrogen applied to the crop. Distribution of rain events was found to play a major role in determining yields. By contrast, under both scenarios cotton experiences a considerable decrease in yield with significant economic losses (−240 and −173% in A2 and B2 scenarios, respectively). Additional irrigation and nitrogen may reduce farming losses, unlike changes in seeding dates.