Mitigating the Effects of Climate Change on the Water Resources of the Columbia River Basin

The potential effects of climate change on the hydrology and water resources of the Columbia River Basin (CRB) were evaluated using simulations from the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). This study focuses on three climate projections for the 21st century based on a `business as usual' (BAU) global emissions scenario, evaluated with respect to a control climate scenario based on static 1995 emissions. Time-varying monthly PCM temperature and precipitation changes were statistically downscaled and temporally disaggregated to produce daily forcings that drove a macro-scale hydrologic simulation model of the Columbia River basin at 1/4-degree spatial resolution. For comparison with the direct statistical downscaling approach, a dynamical downscaling approach using a regional climate model (RCM) was also used to derive hydrologic model forcings for 20-year subsets from the PCM control climate (1995–2015) scenario and from the three BAU climate(2040–2060) projections. The statistically downscaled PCM scenario results were assessed for three analysis periods (denoted Periods 1–3: 2010–2039,2040–2069, 2070–2098) in which changes in annual average temperature were +0.5,+1.3 and +2.1 °C, respectively, while critical winter season precipitation changes were –3, +5 and +1 percent. For RCM, the predicted temperature change for the 2040–2060 period was +1.2 °C and the average winter precipitation change was –3 percent, relative to the RCM controlclimate. Due to the modest changes in winter precipitation, temperature changes dominated the simulated hydrologic effects by reducing winter snow accumulation, thus shifting summer streamflow to the winter. The hydrologic changes caused increased competition for reservoir storage between firm hydropower and instream flow targets developed pursuant to the Endangered Species Act listing of Columbia River salmonids. We examined several alternative reservoir operating policies designed to mitigate reservoir system performance losses. In general, the combination of earlier reservoir refill with greater storage allocations for instream flow targets mitigated some of the negative impacts to flow, but only with significant losses in firm hydropower production (ranging from –9 percent in Period1 to –35 percent for RCM). Simulated hydropower revenue changes were lessthan 5 percent for all scenarios, however, primarily due to small changes inannual runoff.     

Mediterranean climate extremes in synoptic downscaling assessments

The behaviour of precipitation and maximum temperature extremes in the Mediterranean area under climate change conditions is analysed in the present study. In this context, the ability of synoptic downscaling techniques in combination with extreme value statistics for dealing with extremes is investigated. Analyses are based upon a set of long-term station time series in the whole Mediterranean area. At first, a station-specific ensemble approach for model validation was developed which includes (1) the downscaling of daily precipitation and maximum temperature values from the large-scale atmospheric circulation via analogue method and (2) the fitting of extremes by generalized Pareto distribution (GPD). Model uncertainties are quantified as confidence intervals derived from the ensemble distributions of GPD-related return values and described by a new metric called “ratio of overlapping”. Model performance for extreme precipitation is highest in winter, whereas the best models for maximum temperature extremes are set up in autumn. Valid models are applied to a 30-year period at the end of the twenty-first century (2070–2099) by means of ECHAM5/MPI-OM general circulation model data for IPCC SRES B1 scenario. The most distinctive future changes are observed in autumn in terms of a strong reduction of precipitation extremes in Northwest Iberia and the Northern Central Mediterranean area as well as a simultaneous distinct increase of maximum temperature extremes in Southwestern Iberia and the Central and Southeastern Mediterranean regions. These signals are checked for changes in the underlying dynamical processes using extreme-related circulation classifications. The most important finding connected to future changes of precipitation extremes in the Northwestern Mediterranean area is a reduction of southerly displaced deep North Atlantic cyclones in 2070–2099 as associated with a strengthened North Atlantic Oscillation. Thus, the here estimated future changes of extreme precipitation are in line with the discourse about the influence of North Atlantic circulation variability on the changing climate in Europe.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

A new atmospheric proxy for sea level variability in the southeastern North Sea: observations and future ensemble projections

Atmosphere–ocean interactions are known to dominate seasonal to decadal sea level variability in the southeastern North Sea. In this study an atmospheric proxy for the observed sea level variability in the German Bight is introduced. Monthly mean sea level (MSL) time series from 13 tide gauges located in the German Bight and one virtual station record are evaluated in comparison to sea level pressure fields over the North Atlantic and Europe. A quasi-linear relationship between MSL in the German Bight and sea level pressure over Scandinavia and the Iberian Peninsula is found. This relationship is used (1) to evaluate the atmospheric contribution to MSL variability in hindcast experiments over the period from 1871–2008 with data from the twentieth century reanalysis v2 (20CRv2), (2) to isolate the high frequency meteorological variability of MSL from longer-term changes, (3) to derive ensemble projections of the atmospheric contribution to MSL until 2100 with eight different coupled global atmosphere–ocean models (AOGCM’s) under the A1B emission scenario and (4) two additional projections for one AOGCM (ECHAM5/MPI-OM) under the B1 and A2 emission scenarios. The hindcast produces a reasonable good reconstruction explaining approximately 80 % of the observed MSL variability over the period from 1871 to 2008. Observational features such as the divergent seasonal trend development in the second half of the twentieth century, i.e. larger trends from January to March compared to the rest of the year, and regional variations along the German North Sea coastline in trends and variability are well described. For the period from 1961 to 1990 the Kolmogorov-Smirnow test is used to evaluate the ability of the eight AOGCMs to reproduce the observed statistical properties of MSL variations. All models are able to reproduce the statistical distribution of atmospheric MSL. For the target year 2100 the models point to a slight increase in the atmospheric component of MSL with generally larger changes during winter months (October–March). Largest MSL changes in the order of ~5–6 cm are found for the high emission scenario A2, whereas the moderate B1 and intermediate A1B scenarios lead to moderate changes in the order of ~3 cm. All models point to an increasing atmospheric contribution to MSL in the German Bight, but the uncertainties are considerable, i.e. model and scenario uncertainties are in the same order of magnitude.     

Extremes of near-surface wind speed over Europe and their future changes as estimated from an ensemble of RCM simulations

In this study, we analyse the uncertainty of the effect of enhanced greenhouse gas conditions on windiness projected by an ensemble of regional model simulations driven by the same global control and climate change simulations. These global conditions, representative for 1961–1990 and 2071–2100, were prepared by the Hadley Centre based on the IPCC SRES/A2 scenario. The basic data sets consist of simulated daily maximum and daily mean wind speed fields (over land) from the PRUDENCE data archive at the Danish Meteorological Institute. The main focus is on the results from the standard 50 km-resolution runs of eight regional models. The best parameter for determining possible future changes in extreme wind speeds and possible change in the number of storm events is maximum daily wind speed. It turned out during this study that the method for calculating maximum daily wind speed differs among the regional models. A comparison of simulated winds with observations for the control period shows that models without gust parameterisation are not able to realistically capture high wind speeds. The two models with gust parametrization estimate an increase of up to 20% of the number of storm peak (defined as gusts?≥?8 Bft in this paper) events over Central Europe in the future. In order to use a larger ensemble of models than just the two with gust parameterisation, we also look at the 99th percentile of daily mean wind speed. We divide Europe into eight sub-regions (e.g., British Isles, Iberian Peninsula, NE Europe) and investigate the inter-monthly variation of wind over these regions as well as differences between today’s climate and a possible future climate. Results show differences and similarities between the sub-regions in magnitude, spread, and seasonal tendencies. The model ensemble indicates a possible increase in future mean daily wind speed during winter months, and a decrease during autumn in areas influenced by North Atlantic extra-tropical cyclones.     

Winter weather regimes over the Mediterranean region: their role for the regional climate and projected changes in the twenty-first century

The winter time weather variability over the Mediterranean is studied in relation to the prevailing weather regimes (WRs) over the region. Using daily geopotential heights at 700 hPa from the ECMWF ERA40 Reanalysis Project and Cluster Analysis, four WRs are identified, in increasing order of frequency of occurrence, as cyclonic (22.0 %), zonal (24.8 %), meridional (25.2 %) and anticyclonic (28.0 %). The surface climate, cloud distribution and radiation patterns associated with these winter WRs are deduced from satellite (ISCCP) and other observational (E-OBS, ERA40) datasets. The LMDz atmosphere–ocean regional climate model is able to simulate successfully the same four Mediterranean weather regimes and reproduce the associated surface and atmospheric conditions for the present climate (1961–1990). Both observational- and LMDz-based computations show that the four Mediterranean weather regimes control the region’s weather and climate conditions during winter, exhibiting significant differences between them as for temperature, precipitation, cloudiness and radiation distributions within the region. Projections (2021–2050) of the winter Mediterranean weather and climate are obtained using the LMDz model and analysed in relation to the simulated changes in the four WRs. According to the SRES A1B emission scenario, a significant warming (between 2 and 4 °C) is projected to occur in the region, along with a precipitation decrease by 10–20 % in southern Europe, Mediterranean Sea and North Africa, against a 10 % precipitation increase in northern European areas. The projected changes in temperature and precipitation in the Mediterranean are explained by the model-predicted changes in the frequency of occurrence as well as in the intra-seasonal variability of the regional weather regimes. The anticyclonic configuration is projected to become more recurrent, contributing to the decreased precipitation over most of the basin, while the cyclonic and zonal ones become more sporadic, resulting in more days with below normal precipitation over most of the basin, and on the eastern part of the region, respectively. The changes in frequency and intra-seasonal variability highlights the usefulness of dynamics versus statistical downscaling techniques for climate change studies.     

Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.     

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.     

Modelling European winter wind storm losses in current and future climate

Severe wind storms are one of the major natural hazards in the extratropics and inflict substantial economic damages and even casualties. Insured storm-related losses depend on (i) the frequency, nature and dynamics of storms, (ii) the vulnerability of the values at risk, (iii) the geographical distribution of these values, and (iv) the particular conditions of the risk transfer. It is thus of great importance to assess the impact of climate change on future storm losses. To this end, the current study employs—to our knowledge for the first time—a coupled approach, using output from high-resolution regional climate model scenarios for the European sector to drive an operational insurance loss model. An ensemble of coupled climate-damage scenarios is used to provide an estimate of the inherent uncertainties. Output of two state-of-the-art global climate models (HadAM3, ECHAM5) is used for present (1961–1990) and future climates (2071–2100, SRES A2 scenario). These serve as boundary data for two nested regional climate models with a sophisticated gust parametrizations (CLM, CHRM). For validation and calibration purposes, an additional simulation is undertaken with the CHRM driven by the ERA40 reanalysis. The operational insurance model (Swiss Re) uses a European-wide damage function, an average vulnerability curve for all risk types, and contains the actual value distribution of a complete European market portfolio. The coupling between climate and damage models is based on daily maxima of 10 m gust winds, and the strategy adopted consists of three main steps: (i) development and application of a pragmatic selection criterion to retrieve significant storm events, (ii) generation of a probabilistic event set using a Monte-Carlo approach in the hazard module of the insurance model, and (iii) calibration of the simulated annual expected losses with a historic loss data base. The climate models considered agree regarding an increase in the intensity of extreme storms in a band across central Europe (stretching from southern UK and northern France to Denmark, northern Germany into eastern Europe). This effect increases with event strength, and rare storms show the largest climate change sensitivity, but are also beset with the largest uncertainties. Wind gusts decrease over northern Scandinavia and Southern Europe. Highest intra-ensemble variability is simulated for Ireland, the UK, the Mediterranean, and parts of Eastern Europe. The resulting changes on European-wide losses over the 110-year period are positive for all layers and all model runs considered and amount to 44% (annual expected loss), 23% (10 years loss), 50% (30 years loss), and 104% (100 years loss). There is a disproportionate increase in losses for rare high-impact events. The changes result from increases in both severity and frequency of wind gusts. Considerable geographical variability of the expected losses exists, with Denmark and Germany experiencing the largest loss increases (116% and 114%, respectively). All countries considered except for Ireland (?22%) experience some loss increases. Some ramifications of these results for the socio-economic sector are discussed, and future avenues for research are highlighted. The technique introduced in this study and its application to realistic market portfolios offer exciting prospects for future research on the impact of climate change that is relevant for policy makers, scientists and economists.     

新疆夏季降水和冬季气温预测方法及效果评估

本文利用4个国内外先进的气候模式（国家气候中心、ECMWF、NCEP和JMA）业务预测数据,采用2种多模式集合方法（等权平均和超级集合）、3种降尺度方法（BP-CCA、EOF迭代、高相关回归集成）和3种统计方法（CCA、最优气候值、高相关回归集成）以及降尺度集成和降尺度-统计方法集成,分析了目前季节模式、多模式集合、降尺度、统计方法、降尺度-统计集合等目前常用气候预测技术对新疆夏季降水和冬季气温的业务预测能力。 研究表明,以上技术方法对新疆夏季降水和冬季气温的预测预测能力有较大差别。目前先进的气候业务模式的预测技巧普遍很低,多模式超级集合和降尺度方法的技巧常高于单个模式,并且最佳的降尺度方法通常技巧高于最佳多模式集合方法。同时,统计方法和降尺度方法的预测技巧通常较为接近,而对二者进行超级集合可以具有相对很高的预测技巧。此外,现有常用气候预测技术方法对新疆夏季降水和冬季气温的趋势有一定的预测能力,但对气候异常的空间分布基本无预测能力。建议新疆气候预测技术围绕统计和降尺度方法集合发展。     

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.

     

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.     

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。     

Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases

A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future “observed” extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO₂. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9–42.1 °C for summer daily maximum temperature, 35.5–52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961–1990 of 35.7 °C, 42.1 and 78.4 mm respectively.     

Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods

This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales.     

Using urban effect corrected temperature data and a tree phenology model to project geographical shift of cherry flowering date in South Korea

Temperate zone deciduous tree phenology may be vulnerable to projected temperature change, and associated geographical impact is of concern to ecologists. Although many phenology models have been introduced to evaluate climate change impact, there has been little attempt to show the spatial variation across a geographical region due to contamination by the urban heat island (UHI) effect as well as the insufficient spatial resolution of temperature data. We present a practical method for assessing climate change impact on tree phenology at spatial scales sufficient to accommodate the UHI effect. A thermal time-based two-step phenological model was adapted to simulate and project flowering dates of Japanese cherry (Prunus serrulata var. spontanea) in South Korea under the changing climates. The model consists of two sequential periods: the rest period described by chilling requirements and the forcing period described by heating requirements. Daily maximum and minimum temperature are used to calculate daily chill units until a pre-determined chilling requirement for rest release is met. After the projected rest release date, daily heat units (growing degree days) are accumulated until a pre-determined heating requirement for flowering is achieved. Model parameters were derived from the observed bud-burst and flowering dates of cherry tree at the Seoul station of the Korea Meteorological Administration (KMA), along with daily temperature data for 1923–1948. The model was validated using the observed data at 18 locations across South Korea during 1955–2004 with a root mean square error of 5.1 days. This model was used to project flowering dates of Japanese cherry in South Korea from 1941 to 2100. Gridded data sets of daily maximum and minimum temperature with a 270 m grid spacing were prepared for the climatological normal years 1941–1970 and 1971–2000 based on observations at 56 KMA stations and a geospatial interpolation scheme for correcting urban heat island effect as well as elevation effect. We obtained a 25 km-resolution, 2011–2100 temperature projection data set covering peninsular Korea under the auspices of the Inter-governmental Panel on Climate Change—Special Report on Emission Scenarios A2 from the Meteorological Research Institute of KMA. The data set was converted to 270 m gridded data for the climatological years 2011–2040, 2041–2070 and 2071–2100. The phenology model was run by the gridded daily maximum and minimum temperature data sets, each representing climatological normal years for 1941–1970, 1971–2000, 2011–2040, 2041–2070, and 2071–2100. According to the model calculation, the spatially averaged flowering date for the 1971–2000 normal is earlier than that for 1941–1970 by 5.2 days. Compared with the current normal (1971–2000), flowering of Japanese cherry is expected to be earlier by 9, 21, and 29 days in the future normal years 2011–2040, 2041–2070, and 2071–2100, respectively. Southern coastal areas might experience springs with incomplete or even no flowering caused by insufficient chilling required for breaking bud dormancy.     

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.     

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).     

Effects of drought on winter wheat yield in north China during 2012�C2100

Winter wheat is one of China’s most important staple food crops, and its production is strongly influenced by weather, especially droughts. As a result, the impact of drought on the production of winter wheat is associated with the food security of China. Simulations of future climate for scenarios A2 and A1B provided by GFDL-CM2, MPI_ECHAM5, MRI_CGCM2, NCAR_CCSM3, and UKMO_HADCM3 during 2001-2100 are used to project the influence of drought on winter wheat yields in North China. Winter wheat yields are simulated using the crop model WOFOST (WOrld FOod STudies). Future changes in temperature and precipitation are analyzed. Temperature is projected to increase by 3.9-5.5 for scenario A2 and by 2.9-5.1 for scenario A1B, with fairly large interannual variability. Mean precipitation during the growing season is projected to increase by 16.7 and 8.6 mm (10 yr)-1 , with spring precipitation increasing by 9.3 and 4.8 mm (10 yr)-1 from 2012-2100 for scenarios A2 and A1B, respectively. For the next 10-30 years (2012-2040), neither the growing season precipitation nor the spring precipitation over North China is projected to increase by either scenario. Assuming constant winter wheat varieties and agricultural practices, the influence of drought induced by short rain on winter wheat yields in North China is simulated using the WOFOST crop model. The drought index is projected to decrease by 9.7% according to scenario A2 and by 10.3% according to scenario A1B during 2012-2100. This indicates that the drought influence on winter wheat yields may be relieved over that period by projected increases in rain and temperature as well as changes in the growth stage of winter wheat. However, drought may be more severe in the near future, as indicated by the results for the next 10-30 years.     

Characteristics of UV radiation at Tazhong of the Tarim Basin,west China

Tazhong station, located at the hinterland of the Taklimakan Desert in northwest China, experiences frequent dusty weather events during spring and summer seasons (its dusty season) caused by unstable stratified atmosphere, abundant sand source and strong low-level wind. On average, it has 246.2 dusty days each year, of which 16.2 days are classified as sand and dust storm days. To better understand the characteristic of solar ultraviolet (UV) radiation and factors influencing its variations under such an extreme environment, UV radiation data were collected continuously from 2007 to 2011 at Tazhong station using UVS-AB-T radiometer by Kipp and Zonen. This study documents observational characteristics of the UV radiation variations observed during the five-year period. Monthly UV radiation in this region varied in the range of 14.1–37.8 MJ m^?2 and the average annual amount was 320.7 MJ m^?2. The highest value of UV radiation occurred in June (62.5 W m^?2) while the lowest one in December (29.3 W m^?2). It showed a notable diurnal cycle, with peak value at 12:00–13:00 LST. Furthermore, its seasonal variation exhibited some unique features, with averaged UV magnitude showing an order of summer > spring > autumn > winter. The seasonal values were 37.0, 29.1, 24.9 and 15.9 MJ m^?2, respectively. In autumn and winter, its daily variations were relatively weak. However, significant daily variations were observed during spring and summer associated with frequent dust weather events occurring in the region. Further analysis showed that there was a significant correlation between the UV radiation and solar zenith angle under different weather conditions. Under the same solar zenith angle, UV radiation was higher during clear days while it was lower in sand and dust storm days. Our observations showed that there was a negative correlation between UV radiation and ozone, but such a relationship became absent in dusty days. The UV radiation was reduced by 6 % when cloud amount was 1–4 oktas, by 12 % when the cloud amount was 5–7 oktas, and by 24 % when the cloud amount was greater than 8 oktas. The relative reduction of UV radiation reached 26, 38, and 45 % in dust day, blowing sand day and sand and dust storm day, respectively. The results revealed that decrease in UV radiation can be attributed to cloud coverage and dust aerosols. Moreover, the reduction of UV radiation caused by dust aerosols was about 2–4 times greater than that caused by cloud coverage. These observational results are of value for improving our understanding of processes controlling UV radiation over sand desert and developing methods for its estimation and prediction.