Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.     

Sensitivity of Ceres-Maize Yields to Statistical Structure of Daily Weather Series

To study impacts of climate variations on cropproduction, the growth models are used to simulateyields in present vs. changed climate conditions.Met&Roll is a four-variate (precipitation amount,solar radiation, minimum and maximum temperatures) stochasticweather generator used to supply synthetic dailyweather series for the crop growth model CERES-Maize.Three groups of experiments were conducted in thisstudy: (1) Validation of Met&Roll reveals some discrepanciesin the statistical structure of synthetic weatherseries, e.g., (i) the frequency of occurrence of longdry spells, extreme values of daily precipitationamount and variability of monthly means areunderestimated by the generator; (ii) correlations andlag-1 correlations among weather characteristicsexhibit a significant annual cycle not assumed by themodel. On the whole, the best fit of the observed andsynthetic weather series is experienced in summermonths. (2) The Wilcoxon test was employed to comparedistributions of maize yields simulated with use ofobserved vs. synthetic weather series. As nostatistically significant differences were detected,it is assumed that the generator imperfections inreproducing the statistical structure of weatherseries negligibly affect the model yields. (3) Thesensitivity of model yields to selectedcharacteristics of the daily weather series wasexamined. Emphasis was placed on the characteristicsnot addressed by typical GCM-based climate changescenarios: daily amplitude of temperature, persistenceof the weather series, shape of the distribution ofdaily precipitation amount, and frequency ofoccurrence of wet days. The results indicate that someof these characteristics may significantly affect cropyields and should therefore be considered in thedevelopment of climate change scenarios.     

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.     

Southern Quebec (Canada) summer-season heat spells over the 1941–2000 period: an assessment of observed changes

Summary Summer-season (May–September) daily maximum temperature (T _max) and daily minimum temperature (T _min) observations and three types of heat spells obtained from these temperature observations at seven weather stations located in southern Quebec (Canada) for the 60-year period from 1941 to 2000 are studied to assess temporal changes in their characteristics (i.e. frequency of occurrence, seasonal hot days and extremal durations of heat spells). Type-A and Type-B heat spells are obtained respectively from T _max and T _min observations and Type-C heat spells from simultaneous joint observations of T _max and T _min using suitable thresholds and spells of duration ≥1-day and ≥3-day. The results of this investigation show that the majority of the selected percentiles (i.e. 5P, 10P, 25P, 50P, 75P, 80P, 90P, 92P, 95P, and 98P) of T _max observations show a negative time-trend with statistically significant decreases (at 10% level) in some of the higher percentiles and in the maximal values at four out of seven stations. Almost all of the selected percentiles (same as for the T _max) and the maximal and minimal values of T _min observations show a positive trend, with statistically significant increases for all seven stations. Examination of frequencies of occurrence of heat spells, seasonal hot days and annual extremes of heat spell durations indicate that many of these characteristics of heat spells have undergone statistically significant changes over time at some of the stations for Type-A and Type-B heat spells as compared to Type-C heat spells. The Type-C heat spells are generally small in number and are found to be relatively temporally stable. More severe Type-C heat spells, i.e. the ones having T _max and T _min values simultaneously above very high thresholds and with duration ≥3-day have been rarely observed in southern Quebec.     

A new framework for multi-site weather generator: a two-stage model combining a parametric method with a distribution-free shuffle procedure

Keeping the spatial correlation of synthetic precipitation data is of utmost importance for hydrological modeling; however, most present weather generators are single-site models and ignore the spatial dependence in daily weather data. Multi-site weather generator is an effective method to solve this problem. This study proposes a new framework for multi-site weather generator denoted as two-stage weather generator (TSWG), in which the first stage generates the single-site precipitation occurrence and amount with a parametric chain-dependent process, and the second stage rebuilds the spatial correlation of the synthetic data using a post-processing, distribution-free shuffle procedure. Results show that TSWG reproduces the statistical parameters of the parametric stage quite well, such as wet days and precipitation amount, and it almost perfectly preserves the inter-station correlations of precipitation occurrence and amount as well as their dependences. Most important, it matches the input requirement of hydrological model and gives satisfactory hydrological simulations. There are several advantages for this new framework: (1) only one correlation matrix and two simple steps, no more input variables or iterative optimizations, are needed to rebuild the spatial correlation; (2) the statistical parameters of the observed data can be easily preserved; (3) the inter-station correlations can be satisfactorily rebuilt. As a post-processing method, the shuffle procedure used to reconstruct the spatial correlation has some potential extensions, such as turning current single-site weather generator into multi-site models and generating future multi-site climate scenarios.     

随机天气模型参数化方案的研究及其模拟能力评估

文中介绍了随机天气模型 WGEN的基本结构及其模拟原理 ,并针对其中随机过程的统计结构特征和 GCMs输出要素的不同时空尺度特点 ,利用动态数据的参数化分析方法等统计学技术 ,确定了该模型参数的估计方法。同时基于蒙特卡罗数值计算原理 ,给出了 WGEN的随机试验方法 ,并通过模拟基准气候 ,从时间分布和空间场两方面对模型在中国东北地区的模拟效果及其能力进行了评估。结果表明 ,模型对于最高气温、最低气温、降水和辐射等要素均具有较好的模拟效果 ,模拟序列与观测序列的取值分布有较一致的概率特性。由此可以结合 GCMs大尺度网格上输出的月和年要素值 ,通过调控随机过程的参数 ,生成具有不同气候变率的 2× CO2 逐日气候变化情景 ,实现气候预测模式与气候影响模式的嵌套 ,进一步研究气候变率变化的可能影响。     

在气候影响研究中引入随机天气发生器的方法和不确定性

通过采用不同的随机天气发生器生成一定气候背景下各种气候变率情景，许多学者在最近的研究中已经认识到气候变率对农作物生长发育影响的重要性。传统的气候影响评估方法直接以大气环流模式的模拟试验结果作为未来气候情景，这样不可能理解如上的重要性。本文着重评述将随机天气发生器应用于气候变化影响研究的一般方法框架，以及作者的具体个例研究方法。文中最后分析了目前该领域研究中还存在的一些不确定性。 在当前的气候变化影响研究中，有不同的方法用来研制一种称为WGEN的典型随机天气发生器的参数化方案及其随机试验方法。不同的研究者也有不同的参数调控方法。通常的思路是通过气候控制试验和2×CO2试验之间的气候变量平均值和方差的变化来扰动随机天气发生器的参数，以生成未来逐日气候变化情景。本文作者根据短期气候预测模式的输出产品建立了一套WGEN的参数化方案及其随机试验方法，并且在时间和空间两个尺度上检验和评估了此参数化方案下WGEN的模拟能力。另外，作者由未来降水的变化，调试随机天气发生器参数，生成了气候变率变化情景。这些参数调节可以产生各种不同类型和定性大小的气候变率变化，用于气候影响评估的敏感性分析。通过如上方法，作为一个个例，文中评估了未来气候变率变化     

A simple soil moisture scheme for regional climate simulations in the tropics

Summary A regional climate model (RCM) is described which incorporates an improved scheme for soil moisture availability (SMA) compared to an earlier version. The improvement introduces a sensitivity of SMA to soil type, vegetation cover and ground albedo, making the model more adaptable to divers regions. In addition, the interactive SMA depends on past precipitation, ground temperature and terrain relief. Six RCM simulations of the monthly mean climate over southern Africa are performed at 0.5° grid spacing. Improvements in the RCM climate simulations compared to control runs are attributed to the newer SMA scheme. Only a slight improvement in skill results from driving the RCM with observational analyses as opposed to GCM “predicted” lateral boundary conditions. The high spatial resolution of the RCM provides a distinct advantage in the simulated spatial distribution of precipitation compared with a global model run at an effective grid spacing of 2.8°. The mesoscale precipitation signal in the RCM simulations is more dominant during the rather dry December 1982 than during December 1988. The improved SMA scheme contributed to a realistic partition between latent and sensible heat fluxes at the ground-atmosphere boundary and consequently a realistic diurnal cycle of ground temperature. Simulated differences in the spatial distribution of rainfall between December 1982 and December 1988 are more realistic with the improved scheme. Received June 28, 2001 Revised August 27, 2001     

High-Frequency and Low-Frequency Variability in Stochastic Daily Weather Generator and Its Effect on Agricultural and Hydrologic Modelling

The high-frequency and low-frequency variabilities, which are often misreproduced by the daily weather generators, have a significant effect on modelling weather-dependent processes. Three modifications are suggested to improve the reproduction of the both variabilities in a four-variate daily weather generator Met&Roll: (i) inclusion of the annual cycle of lag-0 and lag-1 correlations among solar radiation, maximum temperature and minimum temperature, (ii) use of the 3rd order Markov chain to model precipitation occurrence, (iii) applying the monthly generator (based on a first-order autoregressive model) to fit the low-frequency variability. The tests are made to examine the effects of the three new features on (i) a stochastic structure of the synthetic series, and on (ii) outputs from CERES-Wheat crop model (crop yields) and SAC-SMA rainfall-runoff model (monthly streamflow characteristics, distribution of 5-day streamflow) fed by the synthetic weather series. The results are compared with those obtained with the observed weather series.Results: (i) The inclusion of the annual cycle of the correlations has rather ambiguous effect on the temporal structure of the weather characteristics simulated by the generator and only insignificant effect on the output from either simulation model. (ii) Increased order of the Markov chain improves modelling of precipitation occurrence series (especially long dry spells), and correspondingly improves reliability of the output from either simulation model. (iii) Conditioning the daily generator on monthly generator has the most positive effect, especially on the output from the hydrological model: Variability of the monthly streamflow characteristics and the frequency of extreme streamflows are better simulated. (iv) Of the two simulation models, the improvements related to the three modifications are more pronounced in the hydrological simulations. This may be also due to the fact that the crop growth simulations were less affected by the imperfections of the unmodified version of Met&Roll.     

A new daily weather generator to preserve extremes and low-frequency variability

This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.     

Statistical multisite simulations of summertime precipitation over South Korea and its future change based on observational data

In this study, a weather generator for summer (May 19 – September 15) precipitation over South Korea is developed. Precipitation data for 33 years (1979–2011) observed at 57 stations of Korea Meteorological Administration (KMA) are used to develop a new weather generator. Using the cyclostationary empirical orthogonal function (CSEOF) technique, the observed precipitation data is described as a linear combination of deterministic evolution patterns and corresponding stochastic amplitude (principal component) time series. An autoregressive-moving average (ARMA) model is used to generate one hundred sets of synthetic amplitude time series for the period of 1979–2061 (83 years) with similar statistical properties of the original amplitude time series. Based on these synthetic time series and the annually repeating evolution patterns, one hundred sets of synthetic summer precipitation were generated. Statistical characteristics of the synthetic datasets are examined in comparison with those of the KMA observational record for the period of the observational record. Characteristic changes of synthetic precipitations for a future period are also examined. The seasonal cycle in the synthetic precipitation is reproduced faithfully with typical bimodal peaks of summer precipitation. The spatial correlation patterns of the synthetic precipitation are fairly similar to that of the observational data. The frequency-intensity relationship of the synthetic precipitation also looks similar to that of the observational data. In the future period, precipitation amount increases except in the precipitation range of (0,10) mm day^?1 with nearly no change in the frequency of no-rain days; frequency increase is particularly conspicuous in the range of (100,500) mm day^?1.     

Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model

This study estimates the potential for added value in dynamical downscaling by increasing the spatial resolution of the regional climate model (RCM) over Korea. The Global/Regional Integrated Model System—Regional Model Program with two different resolutions is employed as the RCM. Large-scale forcing is given by a historical simulation of a global climate model, namely the Hadley Center Global Environmental Model version 2. As a standard procedure, the reproducibility of the RCM results for the present climate is evaluated against the reanalysis and observation datasets. It is confirmed that the RCM adequately reproduces the major characteristics of the observed atmospheric conditions and the increased resolution of the RCM contributes to the improvement of simulated surface variables including precipitation and temperature. For the added-value assessment, the interannual and daily variabilities of precipitation, temperature are compared between the different resolution RCM experiments. It is distinctly shown that variabilities are additionally described as the spatial resolution becomes higher. The increased resolution also contributes to capture the extreme weather conditions, such as heavy rainfall events and sweltering days. The enhanced added value is more evident for the precipitation than for the temperature, which stands for a usefulness of the high-resolution RCM especially for diagnosing potential hazard related to heavy rainfall. The results of this study assure the effectiveness of increasing spatial resolution of the RCM for detecting climate extremes and also provide credibility to the current climate simulation for future projection studies.     

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.     

鲁中地区极端高温变化特征分析

基于1966—2015年鲁中地区8个气象站日最高气温、日最低气温和相对湿度气象观测资料,利用年最高气温、高温日数、极端高温日数、暖夜日数、炎热日数和高温热浪日数等指数,研究其极端高温天气变化特征。结果表明:鲁中地区近50 a年最高气温随时间变化呈增加趋势,1990年后增加趋势明显,有6 a左右的主要变化周期,主要空间变化规律一致;极端高温天气呈增强趋势,尤其在1990年以后,年炎热日数和轻度高温热浪日数最多出现在1994年,年高温日数最多出现在1997年,年极端高温、暖夜日数和重度高温热浪日数均出现在2005年;中部地区发生极端高温天气频率高,南部山区发生频率低。     

Evaluating regional climate model estimates against site-specific observed data in the UK

This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre’s HadRM3 regional climate model (RCM), (50 × 50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960–90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over- or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data.     

Determining optimum tilt angles of solar surfaces in Sakarya,Turkey

Climate change constitutes a major challenge for high productivity in wheat, the most widely grown crop in Germany. Extreme weather events including dry spells and heat waves, which negatively affect wheat yields, are expected to aggravate in the future. It is crucial to improve the understanding of the spatiotemporal development of such extreme weather events and the respective crop-climate relationships in Germany. Thus, the present study is a first attempt to evaluate the historic development of relevant drought and heat-related extreme weather events from 1901 to 2010 on county level (NUTS-3) in Germany. Three simple drought indices and two simple heat stress indices were used in the analysis. A continuous increase in dry spells over time was observed over the investigated periods from 1901–1930, 1931–1960, 1961–1990 to 2001–2010. Short and medium dry spells, i.e., precipitation-free periods longer than 5 and 8 days, respectively, increased more strongly compared to longer dry spells (longer than 11 days). The heat-related stress indices with maximum temperatures above 25 and 28 °C during critical wheat growth phases showed no significant increase over the first three periods but an especially sharp increase in the final 1991–2010 period with the increases being particularly pronounced in parts of Southwestern Germany. Trend analysis over the entire 110-year period using Mann-Kendall test revealed a significant positive trend for all investigated indices except for heat stress above 25 °C during flowering period. The analysis of county-level yield data from 1981 to 2010 revealed declining spatial yield variability and rather constant temporal yield variability over the three investigated (1981–1990, 1991–2000, and 2001–2010) decades. A clear spatial gradient manifested over time with variability in the West being much smaller than in the east of Germany. Correlating yield variability with the previously analyzed extreme weather indices revealed strong spatiotemporal fluctuations in explanatory power of the different indices over all German counties and the three time periods. Over the 30 years, yield deviations were increasingly well correlated with heat and drought-related indices, with the number of days with maximum temperature above 25 °C during anthesis showing a sharp increase in explanatory power over entire Germany in the final 2001–2010 period.     

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

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

Trends in dry spells across Catalonia (NE Spain) during the second half of the 20th century

Summary A spatiotemporal trend analysis of different magnitudes related to the number and length of the dry spells in Catalonia (NE Spain) has been conducted based on daily rainfall records taken from 40 rain gauges during the second half of the 20^th century. Dry spells have been computed for threshold levels of 0.1, 1, 5 and 10 mm/day at annual and semi-annual scales. The winter half-year is defined from October to March and the summer half-year from April to September. The magnitudes considered are the number, the maximum length and the mean length of the dry spells for every year and half-year. The spatial patterns of the average values of these magnitudes at the annual scale show a greater similarity with those of the summer half-year than with those of the winter half-year. A S–N or SW–NE gradient of the number of dry spells appears during the summer half-year for every threshold level. Trends of the analysed magnitudes are derived from linear regression and local statistical significances at the 95% confidence level are established using the Mann-Kendall test. Field significant trends are investigated by means of Monte Carlo simulations. The most relevant finding is that the number of dry spells per year depicts significant trends for the annual and winter-half series, with an overall decreasing trend for 5 and 10 mm/day thresholds. These observed trends are in agreement with changes in North Atlantic cyclone tracks and in Mediterranean Low dynamics, due to increasing greenhouse gas concentrations.     

1957-2005年河南省降水和温度极端事件变化

利用河南省1957-2005年逐日降水,最高、最低和日平均气温资料,分析了近50 a河南省极端天气事件的变化趋势。结果表明：1957-2005年河南省暴雨日数、极端降水事件和严重干燥事件发生频率都在增加,但其线性趋势并不显著;暴雨和极端降水的变化趋势呈明显的南北差异;异常高温事件增加而异常低温事件减少,暖冬的趋势比较显著;高温日数和低温日数都显著减少,其变化趋势的空间分布具有很好的一致性。     

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2–4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.