Namaqualand's climate: Recent historical changes and future scenarios

A brief outline of some issues concerning global climate change research is presented before discussing local-scale changes in Namaqaland's rainfall. Using a gridded data set derived through interpolation of station records, trends in observed rainfall for the period 1950–1999 are discussed. To assess what changes may occur during the 21st century, the downscaled results of six different General Circulation Model projections are presented. The historical trends show some clear spatial patterns, which depict regions of wetting in the central coastal belt and the north-eastern part of the domain, and extensive drying along the escarpment. Reasonably good agreement is shown by the different downscaled projections. These suggest increased late summer convective precipitation in the north-east, but extensive drying along the coast in early and mid winter consistent with the poleward retreat of rain-bearing mid-latitude cyclones.     

MRI-CGCM模式气候预测回报试验在东亚夏季的检验和降尺度订正

应用1979—2010年MRI-CGCM模式回报、NCEP/NCAR再分析数据和中国东部降水观测资料检验了模式对东亚夏季风的模拟能力,并利用模式500 hPa高度场回报资料建立了中国东部夏季降水的奇异值分解（SVD）降尺度模型。模式较好地模拟了亚洲季风区夏季降水的气候态,但模拟的季风环流偏弱、偏南,导致降水偏弱。模拟降水的方差明显偏小,且模拟降水的外部、内部方差比值低,模拟降水受模式初值影响较大。模式对长江雨型的模拟能力最高,华南雨型次之,华北雨型最低。模式对东亚夏季风第1模态的模拟能力明显高于第2模态。对于东亚夏季风第1模态,模式模拟出了西太平洋异常反气旋,但强度偏弱,且未模拟出中高纬度的日本海气旋、鄂霍次克海反气旋,导致长江中下游至日本南部降水偏弱。各时次模拟环流均能反映但低估了ENSO衰减、印度洋偏暖对西太平洋反气旋的增强作用。对于东亚夏季风第2模态,模式对西太平洋的“气旋-反气旋”结构有一定的模拟能力,但未模拟出贝加尔湖异常反气旋和东亚沿海异常气旋,导致中国东部“北少南多”雨型在模拟中完全遗漏。仅超前时间小于4个月的模拟降水能够反映ENSO发展对降水分布的作用。通过交叉检验选取左场时间系数可以提高降尺度模型的预测技巧,SVD降尺度模型在华南、江南、淮河、华北4个区域平均距平相关系数分别为0.20、0.23、0.18、0.02,明显高于模式直接输出。      

A method for predicting the impact of climate change on slope stability

 A major effect of man-induced climate change could be a generally higher frequency and magnitude of extreme climatological events in Europe. Consequently, the frequency of rainfall-triggered landslides could increase. However, assessment of the impact of climate change on landsliding is difficult, because on a regional scale, climate change will vary strongly, and even the sign of change can be opposite. Furthermore, different types of landslides are triggered by different mechanisms. A potential method for predicting climate change impact on landsliding is to link slope models to climate scenarios obtained through downscaling General Circulation Models (GCM). Methodologies, possibilities and problems are discussed, as well as some tentative results for a test site in South-East France. Received: 25 October 1997 · Accepted: 25 June 1997     

A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling

Climate maps have been widely used for the construction of species distribution models. These maps derive from interpolation of data collected by meteorological stations. The sparse distribution of stations generates maps with coarse spatial resolution that are unable to detect microclimates or areas that can serve as plant or animal refuges. This work proposes a method for downscaling temperature maps using the solar radiation falling upon hillsides as predictor for the influence of relief on local variability. Solar irradiance is estimated from a digital elevation model of the study area using a routine based on analytical hillshading. Some examples of downscaling from 1 km to 25 m spatial resolution are shown. The results are compared with the surface temperature maps from Landsat 8 satellite imagery.     

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.     

Complexity‐reduction modelling for assessing the macro‐scale patterns of historical soil moisture in the Euro‐Mediterranean region

Complexity‐reduction modelling can be useful for increasing the understanding of how the climate affects basin soil moisture response upon historical times not covered by detailed hydrological data. For this purpose, here is presented and assessed an empirical regression‐based model, the European Soil Moisture Empirical Downscaling (ESMED), in which different climatic variables, easily available on the web, are addressed for simplifying the inherent complexity in the long‐time studies. To accommodate this simplification, the Palmer Drought Severity Index, the precipitation, the elevation and the geographical location were used as input data in the ESMED model for predicting annual soil moisture budget. The test area was a large region including central Europe and Mediterranean countries, and the spatial resolution was initially set at 50 km. ESMED model calibration was made according to the soil moisture values retrieved from the Terrestrial Water Budget Data archive by selecting randomly 285 grid points (out of 2606). Once parameterized, ESMED model was performed at validation stage both spatially and temporally. The spatial validation was made for the grid points not selected in the calibration stage while the comparison with the soil moisture outputs of the Global Land Data Assimilation System–NOAH10 simulations upon the period 1950–2010 was carried out for the temporal validation. Moreover, ESMED results were found to be in good agreement with a root‐zone soil moisture product obtained from active and passive microwave sensors from various satellite missions. ESMED model was thus found to be reliable for both the temporal and spatial validations and, hence, it might represent a useful tool to characterize the long‐term dynamics of soil moisture–weather interaction. Copyright © 2013 John Wiley & Sons, Ltd.     

未来不同排放情景下气候变化预估研究进展

概述未来不同排放情景下气候变化预估研究的主要进展。首先,对用于开展气候变化预估研究的不同复杂程度的气候系统及地球系统模式及其模拟能力进行了简要的介绍,指出虽然目前气候系统模式在很多方面存在着较大的不确定性,但大体说来可提供当前气候状况的可信模拟结果;进而介绍了IPCC不同的排放情景,以及不同排放情景下全球与东亚区域气候变化预估的主要结果。研究表明,尽管不同模式对不同情景下未来气候变化预估的结果存有差异,但对未来50～100年全球气候变化的模拟大体一致,即全球将持续增温、降水出现区域性增加。在此基础上,概述了全球气候模式模拟结果的区域化技术,并重点介绍了降尺度方法的分类与应用。同时对气候变化预估的不确定性进行了讨论。最后,对气候变化预估的研究前景进行了展望,并讨论了未来我国气候变化预估研究的重点发展方向。     

基于BCC_CSM模式的中国东部夏季降水预测检验及订正

基于国家气候中心第二代季节预测模式的历史回报试验数据,检验了模式对我国东部夏季降水的预测能力,探讨了预测误差形成的可能原因,并应用降尺度方法提高了模式的降水预测技巧。分析表明:（1）模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态（偶极子型模态和全区一致型模态）,但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差;（2）模式能够合理预测大尺度环流和海表温度（SST）的变化特征,但是对中国东部夏季降水的总体预测技巧有限,这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关;（3）针对1991~2003年回报试验数据中的500 hPa位势高度、850 hPa纬向风和经向风、SST变量,在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子,进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验,结果表明:所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例,预测的第一模态（第二模态）与观测的空间相关系数由原始的0.12（0.48）提高到了0.58（0.80）,时间相关系数则从0.47（0.15）提高到0.80（0.67）;其它超前时间的预测试验中,降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。     

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r² = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.     

多模式集合预报及其降尺度技术在东亚夏季降水预测中的应用

利用动力季节模式输出的匹配域投影技术和多模式集合预报技术对多个国家和城市的站点月平均降水进行预报。预报变量是北京1个站、韩国60个站和曼谷地区8个站点的月平均降水,预报因子是从多个业务动力季节预报模式输出的多个大尺度变量。模式回报数据和站点观测降水数据时段是1983—2003年。降尺度预报降水的技巧是在交叉验证的框架下进行的。匹配域投影方法是设定一个可以活动的窗口在全球范围内大尺度场上进行扫描,寻求与目标站点降水最优化的因子和最相关的区域,目标站点的降水变率就是由该匹配域上大尺度环流场信息决定的。最终预报是用多个降尺度模式预报结果的集合预报(DMME)。多个降尺度模式预报结果的集合预报能显著地提高站点降水的预报技巧。北京站,多个降尺度模式预报结果的集合预报的预报和观测降水的相关系数可以提高到0.71;韩国地区,多个降尺度模式预报结果的集合预报平均技巧提高到0.75;泰国,多个降尺度模式预报结果的集合预报技巧是0.61。