Changes in extremes and hydroclimatic regimes in the CREMA ensemble projections

We analyze changes of four extreme hydroclimatic indices in the RCP8.5 projections of the Phase I CREMA experiment, which includes 21st century projections over 5 CORDEX domains (Africa, Central America, South America, South Asia, Mediterranean) with the ICTP regional model RegCM4 driven by three CMIP5 global models. The indices are: Heat Wave Day Index (HWD), Maximum Consecutive Dry Day index (CDD), fraction of precipitation above the 95th intensity percentile (R95) and Hydroclimatic Intensity index (HY-INT). Comparison with coarse (GPCP) and high (TRMM) resolution daily precipitation data for the present day conditions shows that the precipitation intensity distributions from the GCMs are close to the GPCP data, while the RegCM4 ones are closer to TRMM, illustrating the added value of the increased resolution of the regional model. All global and regional model simulations project predominant increases in HWD, CDD, R95 and HY-INT, implying a regime shift towards more intense, less frequent rain events and increasing risk of heat wave, drought and flood with global warming. However, the magnitudes of the changes are generally larger in the global than the regional models, likely because of the relatively low “climate sensitivity” of the RegCM4, especially when using the CLM land surface scheme. In addition, pronounced regional differences in the change signals are found. The data from these simulations are available for use in impact assessment studies.     

Probabilistic seasonal prediction of summer rainfall over East China based on multi-model ensemble schemes

The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.     

A regional climate change simulation over East Asia

In this study, regional climate changes for seventy years (1980–2049) over East Asia and the Korean Peninsula are investigated using the Special Reports on Emission Scenarios (SRES) B1 scenario via a high-resolution regional climate model, and the impact of global warming on extreme climate events over the study area is investigated. According to future climate predictions for East Asia, the annual mean surface air temperature increases by 1.8°C and precipitation decreases by 0.2 mm day^?1 (2030–2049). The maximum wind intensity of tropical cyclones increases in the high wind categories, and the intra-seasonal variation of tropical cyclone occurrence changes in the western North Pacific. The predicted increase in surface air temperature results from increased longwave radiations at the surface. The predicted decrease in precipitation is caused primarily by northward shift of the monsoon rain-band due to the intensified subtropical high. In the nested higher-resolution (20 km) simulation over the Korean Peninsula, annual mean surface air temperature increases by 1.5°C and annual mean precipitation decreases by 0.2 mm day^?1. Future surface air temperature over the Korean Peninsula increases in all seasons due to surface temperature warming, which leads to changes in the length of the four seasons. Future total precipitation over the Korean Peninsula is decreased, but the intensity and occurrence of heavy precipitation events increases. The regional climate changes information from this study can be used as a fruitful reference in climate change studies over East Asia and the Korean peninsula.     

基于多模式集合方案的中国东部夏季降水概率季度预测

借助ENSEMBLES计划提供的5个海-气耦合模式（CGCM）的多初值后报降水资料,采用常用的4种多模式集合方案,即等权集合（EE）、对单个集合成员先订正再等权集合（Cali-EE）、基于多元线性回归的集合方案（MLR）、基于贝叶斯统计学的集合方案（Bayes）,制作1960—2005年中国东部夏季降水概率密度函数（PDF）季度预测。在此基础上,比较最优（技巧最高）集合方案与气候学预测（衡量概率密度函数预测是否有技巧的基准）的技巧,初步评估目前基于多模式集合方案的、中国东部夏季降水的概率密度函数季度预测能力。结果表明,Bayes方案在华南最优,Cali-EE在长江流域、江淮流域以及中国北方的中部最优,MLR在中国北方的东部最优;基于这些最优集合方案的概率密度函数预测产品均具有高校准度,且其锐度高于或接近气候学预测;并且,对于所有区域,最优集合方案的预测技巧总是高于气候学预测,这暗示即使不提取模式其他变量中所包含的预测信息,对于中国东部夏季降水季度预测,常用的多模式集合方案也已具备制作有技巧的概率密度函数预测产品的能力。     

A multi-physics ensemble of regional climate change projections over the Iberian Peninsula

This study illustrates the sensitivity of regional climate change projections to the model physics. A single-model (MM5) multi-physics ensemble of regional climate simulations over the Iberian Peninsula for present (1970–1999) and future (2070–2099 under the A2 scenario) periods is assessed. The ensemble comprises eight members resulting from the combination of two options of parameterization schemes for the planetary boundary layer, cumulus and microphysics. All the considered combinations were previously evaluated by comparing hindcasted simulations to observations, none of them providing clearly outlying climates. Thus, the differences among the various ensemble members (spread) in the future projections could be considered as a matter of uncertainty in the change signals (as similarly assumed in multi-model studies). The results highlight the great dependence of the spread on the synoptic conditions driving the regional model. In particular, the spread generally amplifies under the future scenario leading to a large spread accompanying the mean change signals, as large as the magnitude of the mean projected changes and analogous to the spread obtained in multi-model ensembles. Moreover, the sign of the projected change varies depending on the choice of the model physics in many cases. This, together with the fact that the key mechanisms identified for the simulation of the climatology of a given period (either present or future) and those introducing the largest spread in the projected changes differ significantly, make further claims for efforts to better understand and model the parameterized subgrid processes.     

Comparison of four ensemble methods combining regional climate simulations over Asia

A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.     

Calibrated multi-model ensemble summer temperature predictions over Italy

A statistical calibration scheme is applied to multi-model global seasonal ensemble reforecasts in order to predict the interannual variability of summer averaged surface maximum temperature over Italy. In some cases, this technique is shown to be able to improve the skill scores of the seasonal predictions during the last 35 years, with respect to the direct model output (DMO), using seasonal predictions initialised 1 month before the beginning of the season. It is shown that the presence of some skill in the DMO multi-model predictions is mostly due to the correct prediction of the observed secular trends in maximum temperature, and, partly, to the correct prediction of outliers, in particular, of the summer of 2003. At the same time, while the removal of trends produces a small reduction of skill in both the raw and calibrated predictions, the removal of outliers improves the performance of the calibration scheme. Once all trends and outliers are removed, the DMO predictions have no skill, while the calibrated predictions still present a detectable skill. The improvement introduced by the calibration are shown to be statistically significant by applying resampling techniques. It is shown that the reason of this partial success is linked to the fact that although the models present several shortcomings, some models can capture the existence of a weak large-scale signal, possibly linked with the presence of a summer teleconnection between the equatorial Pacific and Europe, with a spatial pattern substantially different from that associated with the temperature secular trend. The teleconnection is associated with a modulation of the quasi-stationary barotropic eddies in the Northern Hemisphere extra-tropics.     

Forecasting quantitative rainfall over India using multi-model ensemble technique

A new approach to ensemble forecasting of rainfall over India based on daily outputs of four operational numerical weather prediction (NWP) models in the medium-range timescale (up to 5 days) is proposed in this study. Four global models, namely ECMWF, JMA, GFS and UKMO available on real-time basis at India Meteorological Department, New Delhi, are used simultaneously with adequate weights to obtain a multi-model ensemble (MME) technique. In this technique, weights for each NWP model at each grid point are assigned on the basis of unbiased mean absolute error between the bias-corrected forecast and observed rainfall time series of 366 daily data of 3 consecutive southwest monsoon periods (JJAS) of 2008, 2009 and 2010. Apart from MME, a simple ensemble mean (ENSM) forecast is also generated and experimented. The prediction skill of MME is examined against observed and corresponding outputs of each constituent model during monsoon 2011. The inter-comparison reveals that MME is able to provide more realistic forecast of rainfall over Indian monsoon region by taking the strength of each constituent model. It has been further found that the weighted MME technique has higher skill in predicting daily rainfall compared to ENSM and individual member models. RMSE is found to be lowest in MME forecasts both in magnitude and area coverage. This indicates that fluctuations of day-to-day errors are relatively less in the MME forecast. The inter-comparison of domain-averaged skill scores for different rainfall thresholds further clearly demonstrates that the MME algorithm improves slightly above the ENSM and member models.     

东亚区域极端气候事件变化的数值模拟试验

使用ResCM2区域气候模式,嵌套澳大利亚CSIRO R21L9全球海气耦合模式,进行了温室效应（二氧化碳加倍）对东亚（主要是中国区域）极端气候事件影响的数值试验。控制试验的结果表明,区域模式能够较好地模拟中国区域的极端气候事件。对温室效应引起的它们的变化进行了信度检验,分析结果表明,温室效应将引起日最高和最低气温增加,日较差减小;使得高温天气增多,低温日数减少。降水日数和大雨日数在一些地区将增加。同时还会引起影响中国的台风活动的变化。     

Dust storm ensemble forecast experiments in East Asia

The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me...     

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.     

东亚与北美大气环流区域性特征的比较研究

本文以30年500hPa和海平面气压月平均资料为依据,对比分析了东亚和北美大气环流区域性特征的差异,以及不同的气候特征。如美国龙卷风灾害频繁。而中国季风雨带明显等。     

Intercomparison of precipitation simulated by regional climate models over East Asia in 1997 and 1998

Regional climate simulations in Asia from May 1997 to August 1998 were performed using the Seoul National University regional climate model （SNURCM） and Iowa State University regional climate model （ALT.MM5/LSM）, which were developed by coupling the NCAR/Land Surface Model （LSM） and the Mesoscale Model （MM5）. However, for physical processes of precipitation, the SNURCM used the Grell scheme for the convective parameterization scheme （CPS） and the simple ice scheme for the explicit moisture scheme （EMS）, while the ALT.MM5/LSM used the Betts-Miller scheme for CPS and the mixed phase scheme for EMS.
The simulated precipitation patterns and amounts over East Asia for the extreme climatic summer in 1997 （relative drought conditions） and 1998 （relative flood conditions） were especially focused upon. The ALT.MM5/LSM simulated more precipitation than was observed in 1997 due to more moisture and cloud water in the lower levels, despite weak upward motion. In the SNURCM, strong upward motion resulted in more precipitation than that was observed in 1998, with more moisture and cloud water in the middle levels. In the ALT.MM5/LSM, weak upward motion, unchanged moisture in the lower troposphere, and the decrease in latent heat flux at the surface increased convective precipitation only by 3% for the 1998 summer event. In the SNURCM, strong upward motion, the increase in moisture in the lower troposphere, and the increase in latent heat flux at the surface increased convective precipitation by 48% for the summer of 1998. The main differences between both simulations were moisture availability and horizontal momentum transport in the lower troposphere, which were also strongly influenced by large-scale forcing.     

The regional distribution of tropospheric ozone in East Asia from satellite-based measurements

Estimates of tropospheric ozone in the East Asian region were obtained using the TOMS and SAGE II satellite data sets through the application of residual analysis on a regional scale. The resulting tropospheric residual ozone shows seasonal variability with highest values in spring and summer. Latitudinal variations give indications of possible input to the tropospheric ozone column from anthropogenic activity. A strong correlation between residual and TOMS total ozone data during summer time suggests a significant level of photochemical ozone production in this region during this period. Comparisons are made with surface ozone measurements from remotely located sites in Japan and show a similar overall pattern.     

Spatiotemporal characteristics of regional drought occurrence in East Asia

Droughts in the East Asian region (105–150°E, 20–50°N) are quantified using the effective drought index (EDI) over a period of 43 years, from 1962 to 2004, and the East Asian region was classified into six subregions on the basis of similarity in drought climate: (D1) South China; (D2) lower region of the Yangtze River, South Korea, and Central/South Japan; (D3) Central China and North Korea; (D4) Northwest China and middle region of the Yangtze River; (D5) North China; and (D6) Northeast China and North Japan. The EDI time series was then summarized for the different drought subregions and a drought map was created that shows the spatiotemporal characteristics of regional drought occurrence in East Asia. The map shows that in subregions, D1, D2, D3, D4, D5, and D6, there were 50 (11.63 per decade), 36 (8.37 per decade), 30 (6.98 per decade), 28 (6.51 per decade), 29 (6.74 per decade), and 33 (7.67 per decade) drought occurrences, respectively. The most common characteristic of droughts in the subregions is that short-term droughts (<200 days) which mainly occur in spring and summer, whereas long-term droughts (≥200 days) mainly occur in autumn and winter. D1 shows the highest frequency of short-term droughts. Short-term droughts occur more frequently than long-term droughts in D2 and D3, but D4 and D6 showed a higher frequency of long-term droughts than short-term droughts. D5 showed a similar frequency of short- and long-term droughts. Drought onset dates are evenly distributed throughout the year for D1, D2, and D3, but distributed mostly in spring and summer in D4, D5, and D6. All the differences are linked to variations in the precipitation cycle of each subregion. In terms of annual variations in drought occurrence, D2 showed weakening droughts (the annual lowest EDI shows a positive trend), whereas the other subregions showed intensifying droughts (the annual lowest EDI shows a negative trend). The greatest intensifying trend was observed in D5, followed by D3, D6, D4, and D1.     

Onset of the regional monsoon over Southeast Asia

Summary ?This is an observational study in which regional features of the different summer monsoon components over Asia especially the South China Sea (SCS) are examined. The authors use various data sets including satellite measurements to understand the onset, maintenance, and retreat of monsoon and explain the connection and independence among the variabilities in the monsoon components. It is shown that while outgoing longwave radiation (OLR) data can only measure tropical convection, upper-tropospheric water vapor band brightness temperature (BT) represents appropriately convective precipitation in both the tropics and the extratropics. The authors define criteria for measuring the SCS monsoon using precipitation, BT, OLR, and lower-tropospheric winds and suggest that multi-variables should be considered to depict regional monsoon features adequately. Under the criteria defined in this study, the SCS summer monsoon is considered as an expansion of deep convection from the tropics. The onset of the monsoon occurs in mid-May, with its precursory signal found over the Indochina peninsula. It is characterized by an abrupt establishment, especially over the central SCS. Although the role of convection over the southern SCS in the monsoon onset is unclear, the early precipitation over the northern SCS and South China, resulted from the effect of subtropical fronts, is separated from the tropical monsoon rainfall. The relative independence from one monsoon component to another is explained by the effects from local topography and land-sea thermal contrast. Received November 5, 1999/Revised April 13, 2000     

Higher precision estimates of regional polar warming by ensemble regression of climate model projections

This study presents projections of twenty-first century wintertime surface temperature changes over the high-latitude regions based on the third Coupled Model Inter-comparison Project (CMIP3) multi-model ensemble. The state-dependence of the climate change response on the present day mean state is captured using a simple yet robust ensemble linear regression model. The ensemble regression approach gives different and more precise estimated mean responses compared to the ensemble mean approach. Over the Arctic in January, ensemble regression gives less warming than the ensemble mean along the boundary between sea ice and open ocean (sea ice edge). Most notably, the results show 3?°C less warming over the Barents Sea (~7?°C compared to ~10?°C). In addition, the ensemble regression method gives projections that are 30?% more precise over the Sea of Okhostk, Bering Sea and Labrador Sea. For the Antarctic in winter (July) the ensemble regression method gives 2?°C more warming over the Southern Ocean close to the Greenwich Meridian (~7?°C compared to ~5?°C). Projection uncertainty was almost half that of the ensemble mean uncertainty over the Southern Ocean between 30° W to 90° E and 30?% less over the northern Antarctic Peninsula. The ensemble regression model avoids the need for explicit ad hoc weighting of models and exploits the whole ensemble to objectively identify overly influential outlier models. Bootstrap resampling shows that maximum precision over the Southern Ocean can be obtained with ensembles having as few as only six climate models.