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.     

Sea water surface energy balance in the Arctic fjord (Hornsund,SW Spitsbergen) in May–November 2014

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.     

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO₂ fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO₂) for two time horizons, namely 2020 and 2050. The potential benefits from CO₂ fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from ?5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from ?5 to ?30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO₂ fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.     

Potential impact of climate change on carbon in agricultural soils in Canada 2000–2099

Under the threat of global warming it is important to determine the impact that future changes in climate may have on the environment and to what extent any adverse effects can be mitigated. In this study we assessed the impact that climate change scenarios may have on soil carbon stocks in Canada and examined the potential for agricultural management practices to improve or maintain soil quality. Historical weather data from 1951 to 2001 indicated that semi-arid soils in western Canada have become warmer and dryer and air temperatures have increased during the spring and winter months. Results from the Canadian Center for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model (CGCM1,2) under two climate change forcing scenarios also indicated that future temperatures would increase more in the spring and winter. Precipitation increased significantly under the IPCC IS92a scenario and agreed with historical trends in eastern Canada whereas the IPCC SRES B2 scenario indicated very little change in precipitation and better matched historical trends in western Canada. The Century model was used to examine the influence of climate change on agricultural soil carbon (C) stocks in Canada. Relative to simulations using historical weather data, model results under the SRES B2 climate scenario indicated that agricultural soils would lose 160 Tg of carbon by 2099 and under the IS92a scenario would lose 53 Tg C. Carbon was still lost from soils in humid climatic regions even though C inputs from crops increased by 10–13%. Carbon factors associated with changes in management practices were also estimated under both climate change scenarios. There was little difference in factors associated with conversion from conventional to no-till agriculture, while carbon factors associated with the conversion of annual crops to perennial grass were lower than for historical data in semi-arid soils because water stress hampered crop production but were higher in humid soils.     

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.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

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.     

Paper 1. the mink methodology: background and baseline

A four step methodology has been developed for study of the regional impacts of climate change and the possible responses thereto. First the region's climate sensitive sectors and total economy are described (Task A, current baseline). Next a scenario of climate change is imposed on the current baseline (Task B, current baseline with climate change). A new baseline describing the climate sensitive sectors and total regional economy is projected for some time in the future (Task C, future baseline, year 2030) in the absence of climate change. Finally, the climate change scenario is reimposed on the future baseline (Task D, future baseline with climate change). Impacts of the climate change scenario on the current and future regional economies are determined by means of simulation models and other appropriate techniques. These techniques are also used to assess the impacts of an elevated CO₂ concentration (450 ppm) and of various forms of adjustments and adaptations. The region chosen for the first test of the methodology is composed of the four U.S. states of Missouri, Iowa, Nebraska and Kansas. The climate change scenario is the actual weather of the 1930s decade in the MINK region. ‘Current’ climate is the actual weather of the period 1951–1980.     

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere–ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop–climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed `downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño–Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as `borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.     

Time of emergence of anthropogenic warming signals in the Northeast Asia assessed from multi-regional climate models

Time of Emergence (ToE) is the time at which the signal of climate change emerges from the background noise of natural climate variability, and can provide useful information for climate change impacts and adaptations. This study examines future ToEs for daily maximum and minimum temperatures over the Northeast Asia using five Regional Climate Models (RCMs) simulations driven by single Global Climate Model (GCM) under two Representative Concentration Pathways (RCP) emission scenarios. Noise is defined based on the interannual variability during the present-day period (1981-2010) and warming signals in the future years (2021-2100) are compared against the noise in order to identify ToEs. Results show that ToEs of annual mean temperatures occur between 2030s and 2040s in RCMs, which essentially follow those of the driving GCM. This represents the dominant influence of GCM boundary forcing on RCM results in this region. ToEs of seasonal temperatures exhibit larger ranges from 2030s to 2090s. The seasonality of ToE is found to be determined majorly by noise amplitudes. The earliest ToE appears in autumn when the noise is smallest while the latest ToE occurs in winter when the noise is largest. The RCP4.5 scenario exhibits later emergence years than the RCP8.5 scenario by 5-35 years. The significant delay in ToEs by taking the lower emission scenario provides an important implication for climate change mitigation. Daily minimum temperatures tend to have earlier emergence than daily maximum temperature but with low confidence. It is also found that noise thresholds can strongly affect ToE years, i.e. larger noise threshold induces later emergence, indicating the importance of noise estimation in the ToE assessment.     

Evaluation of the twenty-first century RCM simulations driven by multiple GCMs over the Eastern Mediterranean–Black Sea region

In this study, human-induced climate change over the Eastern Mediterranean–Black Sea region has been analyzed for the twenty-first century by performing regional climate model simulations forced with large-scale fields from three different global circulation models (GCMs). Climate projections have been produced with Special Report on Emissions Scenarios A2, A1FI and B1 scenarios, which provide greater diversity in climate information for future period. The gradual increases for temperature are widely apparent during the twenty-first century for each scenario simulation, but ECHAM5-driven simulation generally has a weaker signal for all seasons compared to CCSM3 simulations except for the Fertile Crescent. The contrast in future temperature change between the winter and summer seasons is very strong for CCSM3-A2-driven and HadCM3-A2-driven simulations over Carpathians and Balkans, 4–5 °C. In addition, winter runoff over mountainous region of Turkey, which feeds many river systems including the Euphrates and Tigris, increases in second half of the century since the snowmelt process accelerates where the elevation is higher than 1,500 m. Moreover, analysis of daily temperature outputs reveals that the gradual decrease in daily minimum temperature variability for January during the twenty-first century is apparent over Carpathians and Balkans. Analysis of daily precipitation extremes shows that positive trend is clear during the last two decades of the twenty-first century over Carpathians for both CCSM3-driven and ECHAM5-driven simulations. Multiple-GCM driven regional climate simulations contribute to the quantification of the range of climate change over a region by performing detailed comparisons between the simulations.     

Trends in northern midlatitude atmospheric wave power from 1950 to 2099

In a warming climate, atmospheric wave activity and associated weather patterns may change, although conflicting results have been reported on this topic. Additionally, atmospheric wave changes in a future climate have mainly focused on waves of a specified spatial scale, rather than a particular spatiotemporal scale. Here, changes in the variability of Rossby waves of multiple spatiotemporal scales are analyzed using the wavenumber-frequency power spectrum, a tool commonly applied to analyze atmospheric equatorial waves. Daily 500 hPa geopotential height data over 40°–60°N from historical (1950–2005) and future (2006–2099) simulations from 20 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the RCP8.5 scenario were analyzed. When compared to the historical period, the late 21st century climate projections showed a decline in spectral power for both eastward and westward propagating waves with wavenumbers greater than 8 that spanned over all frequencies in all seasons, but an increase in mean power for eastward propagating waves with wavenumbers 1–7 over all frequencies was shown in winter and spring. This increase in power was accompanied by increased variance, i.e., an increased meridional extent of 500 hPa ridges and troughs, and was the result of increases in the mean number of high amplitude events and duration of activity within this wave band. These results indicate that large-scale (~ 104 km) eastward propagating weather systems may intensify with higher amplitudes for ridges and troughs, while short-scale (102–103 km) weather systems may decrease in their intensity due to reduced variability in the late 21st century under the high emissions scenario. Potential mechanisms for these changes are discussed, including enhanced Arctic warming and midlatitude-tropical interactions.     

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

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.     

A comparative assessment of the potential impact of climate change on the ski industry in New Zealand and Australia

In this paper we assess the impact of climate change, at a micro-scale for a selection of four sites in New Zealand and Australia. These sites are representative of the key destination ski regions. In contrast to previous work, our work will for the first time, allow for a direct comparison between these two countries and enable both an estimate of the absolute impacts at a given site, as well as the relative impacts between the two countries. This direct comparison is possible because we have used exactly the same snow model, the same 3 global climate models (GCMs) and the same techniques to calibrate the model for all locations. We consider the changes in natural snow at these locations for the 2030–2049 and 2080–2099 time periods, for one mid-range emissions scenario (A1B). This future scenario is compared to simulations of current, 1980–1999, snow at these locations. We did not consider the snowmaking or economic components of the ski industry vulnerability, only the modelled changes in the natural snow component. At our New Zealand sites, our model indicates that by the 2040s there will be on average between 90 % and 102 % of the current maximum snow depth (on 31 August) and by the 2090s this will be on average reduced to between 46 % and 74 %. In Australia, our models estimates that by the 2040s there will be on average between 57 % and 78 % of the current maximum snow depth and by the 2090s this will be on average further reduced to between 21 % and 29 %. In terms of days with snowdepths equal to or exceeding a ski industry useable levels of 0.30 m, at our lowest elevation, and most sensitive sites, we observe a change from 125 days (current) to 99–126 (2040s) and 52–110 (2090s) in New Zealand. In Australia, a reduction from 94 to 155 days (current) to 81–114 (2040s) and 0–75 (2090s) is observed. In each case the changes are highly depended on the GCM used to drive the climate change scenario. While the absolute changes will have direct impacts at each location, so too will the relative changes with respect to future potential Australia–New Zealand tourism flows, and beyond. Our study provides an approach by which other regions or countries with climate sensitive tourism enterprises could assess the relative impacts and therefore the potential wider ranging ramifications with respect to destination attractiveness.     

Projected change in East Asian summer monsoon precipitation under RCP scenario

Future changes in East Asian summer monsoon precipitation climatology, frequency, and intensity are analyzed using historical climate simulations and future climate simulations under the RCP4.5 scenario using the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project 5 (CMIP5) multi-model dataset. The model reproducibility is evaluated, and well performance in the present-day climate simulation can be obtained by most of the studied models. However, underestimation is obvious over the East Asian region for precipitation climatology and precipitation intensity, and overestimation is observed for precipitation frequency. The overestimation of precipitation frequency is mainly due to the large positive bias of the light precipitation (precipitation <10 mm/day) days, and the underestimation of precipitation intensity is mainly caused by the negative bias of the intense precipitation (precipitation >10 mm/day) intensity. For the future climate simulations, simple multi-model ensemble (MME) averages using all of the models show increases in precipitation and its intensity over almost all of East Asia, while the precipitation frequency is projected to decrease over eastern China and around Japan and increase in other regions. When the weighted MME is considered, no large difference can be observed compared with the simple MME. For the MME using the six best models that have good performance in simulating the present-day climate, the future climate changes over East Asia are very similar to those predicted using all of the models. Further analysis shows that the frequency and intensity of intense precipitation events are also projected to significantly increase over East Asia. Increases in precipitation frequency and intensity are the main contributors to increases in precipitation, and the contribution of frequency increases (contributed by 40.8 % in the near future and by 58.9 % by the end of the twenty-first century) is much larger than that of intensity increases (contributed by 29.9 % in the near future and by 30.1 % by the end of the twenty-first century). This finding also implies an increased risk of intense precipitation events over the East Asian region under global warming scenario. These results regarding future climate simulations show much greater reliability than those using CMIP3 simulations.     

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: historical and future simulations of precipitation

This paper presents the first multi-model ensemble of 10-year, “convection-permitting” kilometer-scale regional climate model (RCM) scenario simulations downscaled from selected CMIP5 GCM projections for historical and end of century time slices. The technique is to first downscale the CMIP5 GCM projections to an intermediate 12–15 km resolution grid using RCMs, and then use these fields to downscale further to the kilometer scale. The aim of the paper is to provide an overview of the representation of the precipitation characteristics and their projected changes over the greater Alpine domain within a Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study and the European Climate Prediction system project, tasked with investigating convective processes at the kilometer scale. An ensemble of 12 simulations performed by different research groups around Europe is analyzed. The simulations are evaluated through comparison with high resolution observations while the complementary ensemble of 12 km resolution driving models is used as a benchmark to evaluate the added value of the convection-permitting ensemble. The results show that the kilometer-scale ensemble is able to improve the representation of fine scale details of mean daily, wet-day/hour frequency, wet-day/hour intensity and heavy precipitation on a seasonal scale, reducing uncertainty over some regions. It also improves the representation of the summer diurnal cycle, showing more realistic onset and peak of convection. The kilometer-scale ensemble refines and enhances the projected patterns of change from the coarser resolution simulations and even modifies the sign of the precipitation intensity change and heavy precipitation over some regions. The convection permitting simulations also show larger changes for all indices over the diurnal cycle, also suggesting a change in the duration of convection over some regions. A larger positive change of frequency of heavy to severe precipitation is found. The results are encouraging towards the use of convection-permitting model ensembles to produce robust assessments of the local impacts of future climate change.

     

基于广义线性模型和NCEP资料的降水随机发生器

天气发生器可以用来插补历史缺测气象数据或生成未来天气情境, 近年来被普遍应用于对气象变量的降尺度研究, 为陆面的水文、 生态模拟提供外强迫输入。广义线性模型 (GLM) 是近年来用于建立大尺度气象变量与地面气象因子之间的一种有效方法, 基于GLM的天气发生器具有一定的应用前景。本文以NCEP再分析资料中的单格点气温、 500 hPa位势高度、 位温、 相对湿度、 海平面气压等5个变量作为影响降水变化的大尺度因子建立模拟逐日降水量的广义线性模型。模型中对降水概率的描述采用Logistic模型模拟, 而对降水量则分别试用Gamma分布、 指数分布、 正态分布和对数正态分布来模拟, 试图比较和揭示这些基于不同理论分布的模型的能力。模型中待定参数的估计及对研究区逐日降水量的模拟采用了完全相同的实测逐日降水数据和同期NCEP再分析资料。参数的最大似然估计用遗传算法来实现, 对山东省临沂地区10个主要气象观测站降水资料的研究表明, Gamma分布模型的拟合效果最好, 对数正态分布次之, 指数分布再次, 正态分布最差; 参数估计分月获取的拟合效果略好于不分月的。模型逐日降水模拟表明, 对降水发生概率的模拟会低估各月的多年平均值, 基于指数分布的GLM会低估各月总降水量期望 (为月内每日降水量期望之和) 的多年平均值, 而基于对数正态分布的GLM则会在降水量较大时产生高估现象。由对应的天气发生器模型生成的随机模拟降水序列表明, 基于对数正态分布的模型会高估月降水量较大时的多年平均, 而基于指数分布及Gamma分布的模型则模拟效果较好。总体上看, 这种基于NCEP再分析资料和GLM的天气发生器对降水变率具有很强的解释和模拟能力。