Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July–August–September 2003 and April–May–June 2004. The regional model is based on the Regional Spectral Model of the National Centers for Environmental Prediction of the United States. It is the first time that the model has been applied to a region dominated by the East Asian Monsoon. The article shows that the regional climate model, when being forced by reasonably good forecasts from a global model, can generate useful seasonal rainfall forecasts for the region, where it is dominated by the East Asia monsoon. The spatial details of the dry conditions obtained from the regional climate model forecast are also found to be comparable with the observed distribution.     

Calibrating a rainfall erosivity assessment model at regional scale in Mediterranean area

A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha⁻¹ h⁻¹) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk.     

Evaluation of present-day rainfall simulations over West Africa in CORDEX regional climate models

The objective of this study is to evaluate the ability of seven CORDEX regional climate models (RCMs), driven by ERA-Interim reanalysis dataset to simulate the observed rainfall characteristics over West Africa during the period of 1990–2008. The seasonal climatology, annual rainfall cycles, interannual variability, 850 hPa specific humidity, and wind fields of the RCMs outputs were assessed over a number of spatial scales covering three climatically homogenous subregions (Guinea Coast, Savannah, and Sahel) and the entire West Africa domain. The ability of the RCMs to simulate the response to El Nino and La Nina events were further assessed. Results indicate that the RCMs captured the spatial pattern of rainfall and the three distinctive phases of the West African monsoon reasonably. It is worth noting that RCA and CRCM5 failed to distinctively reproduce the monsoon jump while CCLM, HIRHAM, and REMO largely overestimated the amount of the pre- and the post-monsoon rainfall. The analysis also showed significant biases in individual models depending on the subregion and season under consideration. These biases appear to be linked to the model’s failure to resolve convective processes and topography accurately. The majority of the RCMs used were consistent with the ground observation in capturing the dry (wet) conditions associated with the El Nino (La Nina) events. Statistical analysis conclusively revealed that the RCMs performance varies over the subregions and seasons, implying that no single model is best at all time. In general, REGCM3 was found to be the most outstanding of all the RCMs and is therefore recommended for use in rainfall assessment over West Africa.     

区域气候模式分辨率对夏季降水模拟的影响

利用最新发布的区域气候模式RegCM4,选取3种水平分辨率,3种垂直分辨率,对2001～2003年6～8月气候状况进行模拟,将模拟降水与GPCP降水资料在季、月、日尺度进行对比分析,研究分辨率对夏季降水模拟的影响,得到主要结论:RegCM4模拟结果能较好地反映模拟区域降水由东南向西北递减的分布趋势,模拟降水量随水平、垂直分辨率的增加而减小,当水平分辨率为30 km,垂直分辨率为14层时,全区域模拟降水偏差最小;不同区域、不同月份区域平均降水对分辨率的敏感性存在明显差异;在高海拔区域,分辨率对降水模拟影响显著,随着水平分辨率的提高,模拟降水更接近观测值;模拟日降水强度分布的峰值随着垂直分辨率的提高,向弱降水方向移动显著;分辨率配置主要影响模拟降水偏差的低频分量及24 d周期分量,通过合理调整分辨率配置可以有效减小模拟系统误差。     

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km². The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.     

Magnetic susceptibility as a proxy for rainfall: Worldwide data from tropical and temperate climate

The magnetic susceptibility of soils has been linked with climate, mainly through rainfall, by numerous investigators. For this study we assembled a worldwide dataset of 272 samples, both published and new, to determine the nature of the relationship between MS and rainfall. On a worldwide basis there is a crude relationship (r² = 0.265) between MS and rainfall. MS increases with increasing rainfall from about 200 mm/yr to 1000–1200 mm/yr. Above 1200 mm/yr, MS decreases as rainfall increases up to about 2000 mm/yr. Under arid and semi-arid conditions, below about 200 mm/yr of rainfall, MS and rainfall exhibit no relationship, likely the result of limited pedogenic activity.When tropical and temperate localities are analyzed separately, the correlation between MS and rainfall exhibits a dramatic improvement, r² = 0.568 and 0.520, respectively. For similar amounts of rainfall tropical localities generally exhibit lower MS values which may be related to differences in the seasonal variation of rainfall. Because the soil environment is highly variable, it is unlikely that a global or even regional equation could accurately estimate rainfall from MS. Using data from Morocco and Mali we demonstrate that local equations using only MS to estimate rainfall produce reasonable results, r² = 0.700 for Morocco and 0.611 for Mali. However, a single independent variable is unlikely to capture the variability of the complex soil system. To the local equations described above we experimented with adding several independent variables in addition to MS including diffuse reflectance spectral data (DRS), x-ray diffraction (XRD), and x-ray fluorescence (XRF). For DRS data, % reflectance and red, yellow, and near-infrared wavelengths appear important; for XRD illite and kaolinite counts are important whereas for XRF Fe₂O₃ is important. The addition of these other independent variables dramatically improves prediction quality. In the case of the Mali transect, estimating rainfall from these variables produces an r² that exceeds 0.9. Based on these data we conclude that MS has the potential to be a reasonable proxy of rainfall for mean annual precipitation values from 200 to 2000 mm/yr, especially if local equations are used. Predictions can be improved by including other independent variables that show sensitivity to climate and are related to MS through the chemistry of the weathering process; we included DRS, XRD, and XRF data with good results.     

气象产品的广义联合偏差修正方法及其应用

卫星气象产品、气候模式预测数据通常与地面观测数据存在偏差,为保证数据的可靠性和合理性,需要对其进行偏差校正,但偏差校正过程往往受具体区域气象特征、方法本身假定等因素的影响,导致修正效果不佳。为此,本文提出一种广义联合偏差修正方法,相较于现有研究最常用的单变量QM修正方法以及固定多变量修正顺序的JBC修正方法,该方法充分考虑到流域尺度降水和气温双变量的时空相关性,并结合其对径流的主导作用对变量修正顺序进行动态调整,实现了QM法和JBC法的优势互补。在澜沧江-湄公河流域的应用表明：考虑降水、气温相关性可显著改善降水和温度极值的修正效果,尤其是5、6月份,修正后气象与实测数据的纳什系数提升了0.5以上;考虑气象要素的修正次序显著降低了修正后的降水和温度频率分布及均值偏差;利用修正后的气象数据驱动分布式水文模型时,部分月份的径流模拟精度提升了54.3%。     

Impact of vegetation on the simulation of seasonal monsoon rainfall over the Indian subcontinent using a regional model

The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization) versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal) and 2002 (deficient). The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India, showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS.     

Cloud computing and virtualization within the regional climate model and evaluation system

The Regional Climate Model Evaluation System (RCMES) facilitates the rapid, flexible inclusion of NASA observations into climate model evaluations. RCMES provides two fundamental components. A database (RCMED) is a scalable point-oriented cloud database used to elastically store remote sensing observations and to make them available using a space time query interface. The analysis toolkit (RCMET) is a Python-based toolkit that can be delivered as a cloud virtual machine, or as an installer package deployed using Python Buildout to users in order to allow for temporal and spatial regridding, metrics calculation (RMSE, bias, PDFs, etc.) and end-user visualization. RCMET is available to users in an “offline”, lone scientist mode based on a virtual machine dynamically constructed with model outputs and observations to evaluate; or on an institution’s computational cluster seated close to the observations and model outputs. We have leveraged RCMES within the content of the Coordinated Regional Downscaling Experiment (CORDEX) project, working with the University of Cape Town and other institutions to compare the model output to NASA remote sensing data; in addition we are also working with the North American Regional Climate Change Assessment Program (NARCCAP). In this paper we explain the contribution of cloud computing to RCMES’s specifically describing studies of various cloud databases we evaluated for RCMED, and virtualization toolkits for RCMET, and their potential strengths in delivering user-created dynamic regional climate model evaluation virtual machines for our users.     

Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate

The Tropical Rainfall Measuring Mission (TRMM) is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. In this study, the daily rainfall from TRMM has been utilized to simulate the soil moisture content up to 30 cm vertical soil profile of at an interval depth of 15 cm by using the HYDRUS 1D numerical model for the three plots. The simulated soil moisture content using ground-based rainfall and TRMM-derived rainfall measurements indicate an agreeable goodness of fit between the both. The Nash–Sutcliffe efficiency using ground-based and TRMM-derived rainfall was found in the range of 0.90–0.68 and 0.70–0.40, respectively. The input data sensitivity analysis of precipitation combined with different irrigation treatment indicates a high dependency of soil moisture content with rainfall input. The overall analysis reveals that TRMM rainfall is promising for soil moisture prediction in absence of ground-based measurements of soil moisture.     

基于区域气候模式的流域农业干旱成因分析

以湄公河流域为研究区,采用区域气候模式RegCM3为模拟工具,以根系层土壤含水量为代表性指标,对A1B情景下未来研究区月尺度农业干旱进行了预估。基于地表能量平衡原理,系统分析了降水、蒸发、地表温度和根系层土壤含水量等农业干旱主要影响因素与区域气候模式模拟的大气环流、地表感热通量、地表潜热通量、地表净通量之间的联系和变化规律,从气陆间能量和水汽通量平衡角度,对农业干旱发生机理进行了识别。预估结果表明:从年内各月地表净通量和地表温度变化来看,未来春末(6月)和秋末(10月)湄公河流域温度增加明显,且土壤含水量减少也较为明显;同时,这两个时段蒸发旺盛和降水减少的趋势,有可能导致流域局部地区(尤其是非灌溉农业区)农业干旱的发生。     

Fingerprinting the 8.2 ka event climate response in a coupled climate model

Using results from coupled climate model simulations of the 8.2 ka climate event that produced a cold period over Greenland in agreement with the reconstructed cooling from ice cores, we investigate the typical pattern of climate anomalies (fingerprint) to provide a framework for the interpretation of global proxy data for the 8.2 ka climate event. For this purpose we developed an analysis method that isolates the forced temperature response and provides information on spatial variations in magnitude, timing and duration that characterise the detectable climate event in proxy archives. Our analysis shows that delays in the temperature response to the freshwater forcing are present, mostly in the order of decades (30 a over central Greenland). The North Atlantic Ocean initially cools in response to the freshwater perturbation, followed in certain parts by a warm response. This delay, occurring more than 200 a after the freshwater pulse, hints at an overshoot in the recovery from the freshwater perturbation. The South Atlantic and the Southern Ocean show a warm response reflecting the bipolar seesaw effect. The duration of the simulated event varies for different areas, and the highest probability of recording the event in proxy archives is in the North Atlantic Ocean area north of 40° N. Our results may facilitate the interpretation of proxy archives recording the 8.2 ka event, as they show that timing and duration cannot be assumed to correspond with the timing and duration of the event as recorded in Greenland ice cores. Copyright © 2011 John Wiley & Sons, Ltd.     

Hydrological response to radar rainfall maps through a distributed model

Weather radars in investigating physical characteristics of precipitation are becoming essential instruments in the field of short term meteorological investigation and forecasting. To analyze the radar signal impact in hydrological forecasting, precipitation input fields, generated through a statistical mathematical model, are supplied to a distributed hydrological model. Such a model would allow the control of the basin response to precipitation measurements obtained by a meteorological radar and, in the meanwhile, to evaluate the influence of distributed input. The distributed model describes the basin hydrological behavior, subdividing it into distinct geometrical cells and increasing the physical significance by reproducing the distributed hydrographic basins characteristics, such as infiltration capacity, runoff concentration time, network propagation speed, soil moisture influence. Each basin cell is characterized by its geological, pedological and morphological status, and may be considered a unitary hydrological system, linked to the others by geomorphological and hydraulic relationships. To evaluate the dynamics of the flood event a synthetic representation of the channel network is introduced, where each stream branch is modeled as a linear reservoir. Finally, the discharge in the outlet section is derived, taking into account the hydraulic characteristics of the upstream branches.     

湖泊沉积有机质的地球化学记录与古气候古环境重建

与深海沉积与冰芯记录相比,湖泊沉积主要反映区域气候变迁史,可以揭示百年、甚至十年尺度的古气候事件,是高分辨率古环境、古气候重建的理想场所.传统的地质地球化学方法主要侧重于宏观物理 /化学特性描述和孢粉学的研究,近 10年来,沉积有机质分子碳、氢同位素地球化学技术的渗入,使研究工作从传统的宏观、微观层次向分子级水平发展,对诸如古生产率估算、C3/C4植被演替史、古二氧化碳分压及古温度计算等深层次问题解决提供了强有力支持.本文评述了湖泊沉积有机质分子与碳、氢同位素地球化学记录及其在区域古环境、古气候研究中的应用前景.     

Study of seasonal climatology and interannual variability over India and its subregions using a regional climate model (RegCM3)

The temporal and spatial variability of the various meteorological parameters over India and its different subregions is high. The Indian subcontinent is surrounded by the complex Himalayan topography in north and the vast oceans in the east, west and south. Such distributions have dominant influence over its climate and thus make the study more complex and challenging. In the present study, the climatology and interannual variability of basic meteorological fields over India and its six homogeneous monsoon subregions (as defined by Indian Institute of Tropical Meteorology (IITM) for all the four meteorological seasons) are analysed using the Regional Climate Model Version 3 (RegCM3). A 22-year (1980–2001) simulation with RegCM3 is carried out to develop such understanding. The National Centre for Environmental Prediction/National Centre for Atmospheric Research, US (NCEP-NCAR) reanalysis 2 (NNRP2) is used as the initial and lateral boundary conditions. The main seasonal features and their variability are represented in model simulation. The temporal variation of precipitation, i.e., the mean annual cycle, is captured over complete India and its homogenous monsoon subregions. The model captured the contribution of seasonal precipitation to the total annual precipitation over India. The model showed variation in the precipitation contribution for some subregions to the total and seasonal precipitation over India. The correlation coefficient (CC) and difference between the coefficient of variation between model fields and the corresponding observations in percentage (COV) is calculated and compared. In most of the cases, the model could represent the magnitude but not the variability. The model processes are found to be more important than in the corresponding observations defining the variability. The model performs quite well over India in capturing the climatology and the meteorological process. The model shows good skills over the relevant subregions during a season.     

Orbital forcing of climate over South Africa: A 200,000-year rainfall record from the pretoria saltpan

Late Pleistocene variations in rainfall in subtropical southern African are estimated from sediments preserved in the Pretoria Saltpan, a 200000 year-old closed-basin crater lake on the interior plateau of South Africa. We show that South African summer rainfall covaried with changes in southern hemisphere summer insolation resulting from orbital precession. As predicted by orbital precession geometry (Berger, 1978), this South African record is out of phase with North African palaeomonsoon indices (Street and Grove, 1979; Rossignol-Strick, 1983; McIntyre et al., 1989); the amplitude of the rainfall response to insolation forcing agrees with climate model estimates (Prell and Kutzbach, 1987). These results document the importance of direct orbital insolation forcing on both subtropical North and South African climate as well as the predicted antiphase sensitivity to precessional insolation forcing.     

Aquifer response to regional climate variability in a part of Kashmir Himalaya in India

Forty major perennial springs, under different lithological controls, in a part of Kashmir Himalaya in India were studied to understand the response of spring discharges to regional climate variability. The average monthly spring discharge is high in Triassic Limestone-controlled springs (karst springs) and low in alluvium- and Karewa-controlled springs. In general, the measured monthly spring discharges show an inverse relation with the monthly precipitation data. However, a direct correlation exists between the spring discharges and the degree of snow/ice melt. The results suggest that the creation of a low and continuous (but stable) recharge from the Triassic Limestone and Panjal Trap aquifers, due to blockage of groundwater flow between strata with contrasting hydraulic conductivity, attenuates the discharge and gives rise to small fluctuations in the alluvium- and Karewa-controlled springs. The average monthly discharge of the karst and alluvial springs showed an overall decreasing trend for two and a half decades, with the lowest discharge recorded in 2001. The study revealed that the regional/global warming and below-normal precipitation in the period of snow accumulation (PSA) has triggered the receding of glaciers and attenuation of spring discharges.