Application of statistical downscaling in GCMs at constructing the map of precipitation in the Mekong River basin

This study used the Statistical Downscaling Model (SDSM) to increase the resolution of the Global Circulation Model (GCM) at forecasting the amount of precipitation in the Mekong River basin. The model was initially calibrated using the reanalysis data by National Centers for Environmental Prediction (NCEP) and the data on observed precipitation. The results of comparison between the SDSM calculations and the observational data were used to generate the distribution of precipitation until 2099 using HadCM3, SRES A2 and B2 scenarios. After total annual precipitation had been downscaled, the percentage change in precipitation was interpolated among the selected stations in order to create precipitation maps. Both A2 and B2 scenario indicate the possibility of remarkable increase in annual precipitation in the Mekong basin, which may amount to 150 and 110%, respectively. The December–January–February precipitation is likely to increase significantly in the most part of the region, and in some areas, almost by three times. On the contrary, the June–July–August precipitation will remarkably decrease in the different parts of the territory under study. As the water resource sector is the backbone of the economics of this region including hydropower and agricultural sector, the changes in the amount of precipitation and its interannual variability can put the usual water business into stress. Thus, proper adaptive measures should be applied both at local and at regional levels for the benefit of all associated countries utilizing the resource of the Mekong River.     

Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India

The study evaluates statistical downscaling model (SDSM) developed by annual and monthly sub-models for downscaling maximum temperature, minimum temperature, and precipitation, and assesses future changes in climate in the Jhelum River basin, Pakistan and India. Additionally, bias correction is applied on downscaled climate variables. The mean explained variances of 66, 76, and 11 % for max temperature, min temperature, and precipitation, respectively, are obtained during calibration of SDSM with NCEP predictors, which are selected through a quantitative procedure. During validation, average R ² values by the annual sub-model (SDSM-A)—followed by bias correction using NCEP, H3A2, and H3B2—lie between 98.4 and 99.1 % for both max and min temperature, and 77 to 85 % for precipitation. As for the monthly sub-model (SDSM-M), followed by bias correction, average R ² values lie between 98.5 and 99.5 % for both max and min temperature and 75 to 83 % for precipitation. These results indicate a good applicability of SDSM-A and SDSM-M for downscaling max temperature, min temperature, and precipitation under H3A2 and H3B2 scenarios for future periods of the 2020s, 2050s, and 2080s in this basin. Both sub-models show a mean annual increase in max temperature, min temperature, and precipitation. Under H3A2, and according to both sub-models, changes in max temperature, min temperature, and precipitation are projected as 0.91–3.15 °C, 0.93–2.63 °C, and 6–12 %, and under H3B2, the values of change are 0.69–1.92 °C, 0.56–1.63 °C, and 8–14 % in 2020s, 2050s, and 2080s. These results show that the climate of the basin will be warmer and wetter relative to the baseline period. SDSM-A, most of the time, projects higher changes in climate than SDSM-M. It can also be concluded that although SDSM-A performed well in predicting mean annual values, it cannot be used with regard to monthly and seasonal variations, especially in the case of precipitation unless correction is applied.     

Factors favorable to frequent extreme precipitation in the upper Yangtze River Valley

Extreme precipitation events in the upper Yangtze River Valley (YRV) have recently become an increasingly important focus in China because they often cause droughts and floods. Unfortunately, little is known about the climate processes responsible for these events. This paper investigates factors favorable to frequent extreme precipitation events in the upper YRV. Our results reveal that a weakened South China Sea summer monsoon trough, intensified Eurasian-Pacific blocking highs, an intensified South Asian High, a southward subtropical westerly jet and an intensified Western North Pacific Subtropical High (WNPSH) increase atmospheric instability and enhance the convergence of moisture over the upper YRV, which result in more extreme precipitation events. The snow depth over the eastern Tibetan Plateau (TP) in winter and sea surface temperature anomalies (SSTAs) over three key regions in summer are important external forcing factors in the atmospheric circulation anomalies. Deep snow on the Tibetan Plateau in winter can weaken the subsequent East Asian summer monsoon circulation above by increasing the soil moisture content in summer and weakening the land–sea thermal contrast over East Asia. The positive SSTA in the western North Pacific may affect southwestward extension of the WNPSH and the blocking high over northeastern Asia by arousing the East Asian-Pacific pattern. The positive SSTA in the North Atlantic can affect extreme precipitation event frequency in the upper YRV via a wave train pattern along the westerly jet between the North Atlantic and East Asia. A tripolar pattern from west to east over the Indian Ocean can strengthen moisture transport by enhancing Somali cross-equatorial flow.     

Evaluation of statistical downscaling in short range precipitation forecasting

The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”.Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.     

Effect of spring soil moisture over the Indo-China Peninsula on the following summer extreme precipitation events over the Yangtze River basin

Climate Dynamics - Extreme precipitation events (EPEs) over the Yangtze River basin (YRB) exert widespread impacts on regional ecological environment and people’s life. Using observed...     

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.     

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

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

A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China

This study evaluated the performance of three frequently applied statistical downscaling tools including SDSM, SVM, and LARS-WG, and their model-averaging ensembles under diverse moisture conditions with respect to the capability of reproducing the extremes as well as mean behaviors of precipitation. Daily observed precipitation and NCEP reanalysis data of 30 stations across China were collected for the period 1961–2000, and model parameters were calibrated for each season at individual site with 1961–1990 as the calibration period and 1991–2000 as the validation period. A flexible framework of multi-criteria model averaging was established in which model weights were optimized by the shuffled complex evolution algorithm. Model performance was compared for the optimal objective and nine more specific metrics. Results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various precipitation characteristics under different circumstances. SDSM showed more adaptability by acquiring better overall performance at a majority of the stations while LARS-WG revealed better accuracy in modeling most of the single metrics, especially extreme indices. SVM provided more usefulness under drier conditions, but it had less skill in capturing temporal patterns. Optimized model averaging, aiming at certain objective functions, can achieve a promising ensemble with increasing model complexity and computational cost. However, the variation of different methods' performances highlighted the tradeoff among different criteria, which compromised the ensemble forecast in terms of single metrics. As the superiority over single models cannot be guaranteed, model averaging technique should be used cautiously in precipitation downscaling.     

Prediction of spring precipitation in China using a downscaling approach

The aim of this paper is to use a statistical downscaling model to predict spring precipitation over China based on a large-scale circulation simulation using Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER) General Circulation Models (GCMs) from 1960 to 2001. A singular value decomposition regression analysis was performed to establish the link between the spring precipitation and the large-scale variables, particularly for the geopotential height at 500?hPa and the sea-level pressure. The DEMETER GCM predictors were determined on the basis of their agreement with the reanalysis data for specific domains. This downscaling scheme significantly improved the predictability compared with the raw DEMETER GCM output for both the independent hindcast test and the cross-validation test. For the independent hindcast test, multi-year average spatial correlation coefficients (CCs) increased by at least ~30?% compared with the DEMETER GCMs’ precipitation output. In addition, the root mean-square errors (RMSEs) decreased more than 35?% compared with the raw DEMETER GCM output. For the cross-validation test, the spatial CCs increased to greater than 0.9 for most of the individual years, and the temporal CCs increased to greater than 0.3 (95?% confidence level) for most regions in China from 1960 to 2001. The RMSEs decreased significantly compared with the raw output. Furthermore, the preceding predictor, the Arctic Oscillation, increased the predicted skill of the downscaling scheme during the spring of 1963.     

长江三角洲城市群对夏季日降水特征影响的模拟研究

利用耦合了单层城市冠层模型UCM的中尺度模式WRF,探讨了长江三角洲地区城市化对夏季日降水特征的影响。结果表明,WRF模式能较好地再现长三角地区2003—2007年夏季降水的空间分布,比较成功地模拟出了降水中心的位置及强度。城市化使得长三角地区夏季降水日数减少了1～5 d,这种降水日数的减少主要是由于城市化使小雨日数减少引起。城市化增强了长三角大部分地区的日降水强度。进一步对长三角地区4个典型城市群宁镇扬、苏锡常、上海和杭州湾城市群进行了夏季降水日变化分析,得出城市化对降水日变化的影响存在一定的区域差异。对于长三角整个大城市群,城市化对降水量、降水强度日峰值出现时刻以及降水强度日峰值大小无明显影响,而使得降水量日峰值减少。城市化使得苏锡常地区降水量日峰值略有增加,宁镇扬和上海地区降水量日峰值都减小,而杭州湾城市群区降水量日峰值出现时刻延后。城市化使得4个典型城市群降水强度日变化曲线形态发生改变,使得上海地区降水强度日峰值出现时刻延后,使得杭州湾城市群区夜雨增强。     

Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models

Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (T_min and T_max), 90^th percentile of maximum temperature (T_max90), 10^th percentile of minimum temperature (T_min10), number of frost days (T_nfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter T_max90 and autumn T_min10. Finally, MSLP is the best predictor for spring T_min while Z500 is the best predictor for spring T_max90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.     

Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling

The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied.     

High-resolution precipitation data derived from dynamical downscaling using the WRF model for the Heihe River Basin,northwest China

Theoretical and Applied Climatology - The community of climate change impact assessments and adaptations research needs regional high-resolution (spatial) meteorological data. This study produced...     

统计降尺度方法在天津小时降水和气温精细化预报中的应用

基于ECWMF模式预报数据对2018年3—11月降水和2 m温度进行统计降尺度,利用先频率匹配法、再阈值法对插值后的降水订正,利用Kalman滤波型的递减平均统计降尺度法对插值后的温度订正,最终获得逐小时降水量和温度的预报。结果表明：(1)对于晴雨预报准确率,绝大多数预报时效频率匹配法和阈值法均对其有明显提高,前者最大改进幅度可达20%以上。对于相对误差,阈值法对空报现象有较显著改进。对于1 h降雨量大于等于20 mm的短时强降水,频率匹配法订正后的TS评分有明显提高。对2018年“安比”台风事件,除具有以上改进效果外,频率匹配法提高了降水主体形态和量级的预报水平,阈值法对空报站订正正确。(2)对于温度的ECWMF模式预报检验,几乎在任何预报时效内都是3月的绝对误差最大。通过Kalman滤波型的递减平均统计降尺度法后,各月的绝对误差都有不同程度减小。总体上,订正后的绝对误差曲线仍具有订正前的周期性波动,波峰、波谷位置也与订正前基本一致,且绝对误差越大,订正幅度越大。个例分析也表明订正后保留了温度预报空间分布的准确性,且绝对误差有明显下降。