Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as ‘field’ or ‘global’ significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ^° grid resolution. Daily precipitation climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.     

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 Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Regional climate model projections for the State of Washington

Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.     

Future climate change scenarios over Korea using a multi-nested downscaling system: A pilot study

This study examines a scenario of future summer climate change for the Korean peninsula using a multi-nested regional climate system. The global-scale scenario from the ECHAM5, which has a 200 km grid, was downscaled to a 50 km grid over Asia using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). This allowed us to obtain large-scale forcing information for a one-way, double-nested Weather and Research Forecasting (WRF) model that consists of a 12 km grid over Korea and a 3 km grid near Seoul. As a pilot study prior to the multi-year simulation work the years 1995 and 2055 were selected for the present and future summers. This RSM-WRF multi-nested downscaling system was evaluated by examining a downscaled climatology in 1995 with the largescale forcing from the NCEP/Department of Energy (DOE) reanalysis. The changes in monsoonal flows over East Asia and the associated precipitation change scenario over Korea are highlighted. It is found that the RSM-WRF system is capable of reproducing large-scale features associated with the East-Asian summer monsoon (EASM) and its associated hydro-climate when it is nested by the NCEP/DOE reanalysis. The ECHAM5-based downscaled climate for the present (1995) summer is found to suffer from a weakening of the low-level jet and sub-tropical high when compared the reanalysis-based climate. Predicted changes in summer monsoon circulations between 1995 and 2055 include a strengthened subtropical high and an intensified mid-level trough. The resulting projected summer precipitation is doubled over much of South Korea, accompanied by a pronounced surface warming with a maximum of about 2 K. It is suggested that downscaling strategy of this study, with its cloud-resolving scale, makes it suitable for providing high-resolution meteorological data with which to derive hydrology or air pollution models.     

The WAMME regional model intercomparison study

Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.     

Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France

In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15–20 km), followed with a statistical downscaling method in order to reach a target resolution of 8 km. The statistical downscaling technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected downscaled temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021–2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ.     

Projected climate change scenario over California by a regional ocean–atmosphere coupled model system

This study examines a future climate change scenario over California in a 10-km coupled regional downscaling system of the Regional Spectral Model for the atmosphere and the Regional Ocean Modeling System for the ocean forced by the global Community Climate System Model version 3.0 (CCSM3). In summer, the coupled and uncoupled downscaled experiments capture the warming trend of surface air temperature, consistent with the driving CCSM3 forcing. However, the surface warming change along the California coast is weaker in the coupled downscaled experiment than it is in the uncoupled downscaling. Atmospheric cooling due to upwelling along the coast commonly appears in both the present and future climates, but the effect of upwelling is not fully compensated for by the projected large-scale warming in the coupled downscaling experiment. The projected change of extreme warm events is quite different between the coupled and uncoupled downscaling experiments, with the former projecting a more moderate change. The projected future change in precipitation is not significantly different between coupled and uncoupled downscaling. Both the coupled and uncoupled downscaling integrations predict increased onshore sea breeze change in summer daytime and reduced offshore land breeze change in summer nighttime along the coast from the Bay area to Point Conception. Compared to the simulation of present climate, the coupled and uncoupled downscaling experiments predict 17.5 % and 27.5 % fewer Catalina eddy hours in future climate respectively.     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

High-resolution simulations for Vietnam - methodology and evaluation of current climate

To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.     

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Evaluation of a climate simulation in Europe based on the WRF–NOAH model system: precipitation in Germany

The Weather Research and Forecast (WRF) model with its land surface model NOAH was set up and applied as regional climate model over Europe. It was forced with the latest ERA-interim reanalysis data from 1989 to 2008 and operated with 0.33° and 0.11° resolution. This study focuses on the verification of monthly and seasonal mean precipitation over Germany, where a high quality precipitation dataset of the German Weather Service is available. In particular, the precipitation is studied in the orographic terrain of southwestern Germany and the dry lowlands of northeastern Germany. In both regions precipitation data is very important for end users such as hydrologists and farmers. Both WRF simulations show a systematic positive precipitation bias not apparent in ERA-interim and an overestimation of wet day frequency. The downscaling experiment improved the annual cycle of the precipitation intensity, which is underestimated by ERA-interim. Normalized Taylor diagrams, i.e., those discarding the systematic bias by normalizing the quantities, demonstrate that downscaling with WRF provides a better spatial distribution than the ERA interim precipitation analyses in southwestern Germany and most of the whole of Germany but degrades the results for northeastern Germany. At the applied model resolution of 0.11°, WRF shows typical systematic errors of RCMs in orographic terrain such as the windward–lee effect. A convection permitting case study set up for summer 2007 improved the precipitation simulations with respect to the location of precipitation maxima in the mountainous regions and the spatial correlation of precipitation. This result indicates the high value of regional climate simulations on the convection-permitting scale.     

Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation

Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann?CWhitney and Brown?CForsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area.     

Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development. Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19^th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda, Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada.     

Can Artificial Climate Trends in Global Reanalysis be Reduced by Dynamical Downscaling:A Case Study of China

In this study,the ability of dynamical downscaling for reduction of artificial climate trends in global reanalysis is tested in China.Dynamical downscaling is performed using a 60-km horizontal resolution Regional Integrated Environmental Model System (RIEMS) forced by the NCEP-Department of Energy (DOE) reanalysis II (NCEP-2).The results show that this regional climate model (RCM) can not only produce dynamically consistent fine scale fields of atmosphere and land surface in the regional domain,but it also has the ability to minimize artificial climate trends existing in the global reanalysis to a certain extent.As compared to the observed 2-meter temperature anomaly averaged across China,our model can simulate the observed inter-annual variation and variability as well as reduce artificial climate trends in the reanalysis by approximately 0.10 C decade 1 from 1980 to 2007.The RIEMS can effectively reduce artificial trends in global reanalysis for areas in western China,especially for regions with high altitude mountains and deserts,as well as introduce some new spurious changes in other local regions.The model simulations overestimated observed winter trends for most areas in eastern China with the exception of the Tibetan Plateau,and it greatly overestimated observed summer trends in the Sichuan Basin located in southwest China.This implies that the dynamical downscaling of RCM for long-term trends has certain seasonal and regional dependencies due to imperfect physical processes and parameterizations.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.     

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

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

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

High-resolution precipitation and temperature downscaling for glacier models

The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate downscaling. For impact studies that involve glacier modelling, the sparsity or complete absence of climate monitoring activities within the regions of interest presents a substantial additional challenge. Downscaling methods for this application must be independent of climate observations and cannot rely on tuning to station data. We present new, computationally-efficient methods for downscaling precipitation and temperature to the high spatial resolutions required to force mountain glacier models. Our precipitation downscaling is based on an existing linear theory for orographic precipitation, which we modify for large study regions by including moist air tracking. Temperature is downscaled using an interpolation scheme that reconstructs the vertical temperature structure to estimate surface temperatures from upper air data. Both methods are able to produce output on km to sub-km spatial resolution, yet do not require tuning to station measurements. By comparing our downscaled precipitation (1 km resolution) and temperature (200 m resolution) fields to station measurements in southern British Columbia, we evaluate their performance regionally and through the annual cycle. Precipitation is improved by as much as 30% (median relative error) over the input reanalysis data and temperature is reconstructed with a mean bias of 0.5°C at locations with high vertical relief. Both methods perform best in mountainous terrain, where glaciers tend to be concentrated.