Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level.     

Multivariate Spatial Analysis of Climate Change Projections

The goal of this work is to characterize the annual temperature for regional climate models. Of interest for impacts studies, these profiles and the potential change in these profiles are a new way to describe climate change and the inherent uncertainty. To that end, we propose a Bayesian hierarchical spatial model to simultaneously model the temperature profile for the four seasons of the year, current and future. These profiles are then analyzed focusing on understanding how they change over time, how they vary spatially, and how they vary between five different regional climate models. The results show that for temperature, the regional models have different profile shapes depending on a number of factors including spatial location, driving climate model, and regional climate model. This article has supplementary material online.     

Estimating the Health Impact of Climate Change With Calibrated Climate Model Output

Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using non-linear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of excess deaths attributed to future temperature for three cities in the US state of Alabama.     

淮北平原地下水埋深变化对气候变化的响应及预测

分析气候变化下的地下水埋深变化对区域未来的水资源利用和长期规划有着重要的意义。该研究利用五道沟水文实验站实测资料，分析淮北平原地下水埋深历史变化，在评估考虑了潜水蒸发的地下水埋深迭代算法在淮北平原的适用性的基础上，以CMIP5中三种气候模式的未来降水为主要影响因子，通过多模式集成方法，预估未来气候变化下淮北平原地下水埋深变化。结果表明：淮北平原地下水埋深年际变化波动较大，多年平均地下水埋深在季节上表现为春季（2.52 m）>冬季（2.49 m）>秋季（2.21 m）>夏季（2.17 m），空间上整体呈由北向南逐渐变浅的变化趋势；考虑了潜水蒸发的地下水埋深迭代算法对淮北平原地下水埋深变化趋势的模拟结果与实际变化趋势基本一致；在RCP4.5情景下，未来降水量将持续增加的影响，地下水埋深年内月变化先降低后增加，7月最浅为0.73 m，1月最深为4.22 m；季节变化夏季最浅0.94 m，冬季最深为3.98 m，但年变化整体呈下降趋势，起伏相对较小。研究结果对合理调控区域水资源及水环境保护具有重要的借鉴和指导意义。     

The Impact of Climate Change on Metal Transport in a Lowland Catchment

This study investigates the impact of future climate change on heavy metal (i.e., Cd and Zn) transport from soils to surface waters in a contaminated lowland catchment. The WALRUS hydrological model is employed in a semi-distributed manner to simulate current and future hydrological fluxes in the Dommel catchment in the Netherlands. The model is forced with climate change projections and the simulated fluxes are used as input to a metal transport model that simulates heavy metal concentrations and loads in quickflow and baseflow pathways. Metal transport is simulated under baseline climate (“2000–2010”) and future climate (“2090–2099”) conditions including scenarios for no climate change and climate change. The outcomes show an increase in Cd and Zn loads and the mean flux-weighted Cd and Zn concentrations in the discharged runoff, which is attributed to breakthrough of heavy metals from the soil system. Due to climate change, runoff enhances and leaching is accelerated, resulting in enhanced Cd and Zn loads. Mean flux-weighted concentrations in the discharged runoff increase during early summer and decrease during late summer and early autumn under the most extreme scenario of climate change. The results of this study provide improved understanding on the processes responsible for future changes in heavy metal contamination in lowland catchments.     

气候变化对河南省夏玉米主栽品种发育期的影响模拟

为模拟气候变化对夏玉米发育期影响,本文将河南省划分为4个夏玉米主栽区,分区进行主栽品种遗传参数调试验证,确定各区域品种平均遗传参数。将未来气候变化情景(A2和B2)下,2020s、2050s和2080s各时段的温度和降水增量加上基准值,模拟未来气候变化对河南省夏玉米发育期的影响。模型调参验证结果表明：各区域品种遗传参数存在一定差异,豫西地区当前种植品种播种-开花所需积温高于其它地区,而豫北和豫东当前种植品种开花-成熟所需积温高于其它地区;各区开花期调参和验证误差RMSE为2～4d,相对误差NRMSE均小于10%;各区域成熟期调参误差RMSE均小于4d,验证误差RMSE为3～7d,除豫西区外,各区域调参及验证期间的成熟期相对误差NRMSE均小于10%。表明CERES-Maize模型对河南省各区域夏玉米发育期模拟精度均较高。未来气候变化影响模拟结果表明：A2和B2情景下,夏玉米营养生长期平均缩短4.7d和3.1d,全生育期平均缩短12.9d和8.6d。夏玉米生育期缩短日数与各时段增温幅度趋势一致,全省4个区域中豫西区生育期日数缩短最多。     

气候模拟数据订正方法在作物气候生产潜力预估中的应用 ——以江苏冬小麦为例

利用全球气候模式BCC_CSM1.1（Beijing Climate Center Climate System Model version 1.1）,耦合区域气候模式RegCM4（Regional Climate Model version 4）输出的1961-1990年（基准时段）气候模拟数据,并根据同期实测资料,确定模拟值和实测值之间的非线性传递函数与方差订正参数,构建气候模拟数据的误差订正模型。利用1991-2005年（验证时段）模拟数据与实测资料验证该模型的有效性,并对RCP（Representative Concentration Pathway）情景下2021-2050年（未来时段）气候模拟数据进行订正,同时通过潜力衰减方法预估未来江苏冬小麦气候生产潜力格局。结果表明：将气候模拟数据订正方法应用到作物气候生产潜力预估是有效的。以均值传递函数和方差信息建立的模型可以较好订正江苏逐日气候模拟数据。订正后的秋冬季气温、辐射量、蒸散量和冬春季降水量模拟偏差明显减小。在此基础上研究发现,冬小麦的成熟期在RCP4.5和RCP8.5情景下介于153~175和153~174,较基准时段均明显提前。两种情景下冬小麦气候生产潜力分别介于10335~14368kg·hm^-2和9991~13708kg·hm^-2,较基准时段呈下降趋势。其变异系数分别介于7.6%~14.6%和7.5%~13.6%,较基准时段呈增大趋势,表明江苏冬小麦气候生产潜力总体趋于不稳定。未来时段,徐州中北部、连云港东北部、宿迁西部以及盐城东南部冬小麦在RCP4.5和RCP8.5情景下可以保持相对较高的生产潜力（≥12501kg·hm^-2）,该省应确保这些地区的冬小麦种植用地。研究建议,作物气候生产潜力预估应考虑利用研究区实测资料对气候模拟数据进行订正,以提高预估可信度。     

Geography and vintage predicted by a novel GIS model of wine delta18O

Wine hydrogen and oxygen stable isotopes record the climatic conditions experienced by the grape vine and the isotopic composition of the vine's source water during berry development. As such, stable isotopes have been explored extensively for use in detecting wine adulteration or for independently verifying claims of origin. We present the results of a study designed to evaluate the relationships between wine water delta18O and spatial climate and precipitation delta18O patterns across the winegrape-growing regions of Washington, Oregon, and California. Retail wines produced from typically small vineyards across these regions were obtained from the 2002 vintage, and the delta18O of wine water was analyzed using a CO2 equilibration method. Significant correlations were observed between the measured wine water delta18O from 2002 and the long-term average precipitation delta18O and late season 2002 climate, based on a spatial join with continuous geographic information system (GIS) maps of these drivers. We then developed a regression model that was implemented spatially in a GIS. The GIS model is the first of its kind and allows spatially explicit predictions of wine delta18O across the region. Because high spatial resolution monthly climate layers are now available for many years, wine delta18O could be modeled for previous years. We therefore tested the model by executing it for specific years and comparing the model predictions with previously published results for wine delta18O from seven vintages from Napa and Livermore Valleys, California. With the exception of one year, an anomaly potentially related to the effects of El Ni?o on precipitation isotopic composition, the model predicted well the wine delta18O for both locations for all vintages and generally reflected the consistent enrichment of wine from Napa relative to Livermore. Our results suggest that wine water delta18O records both source water delta18O and climate during the late stages of winegrape ripening and that GIS models of wine water delta18O are useful tools for independently verifying claims of regional origin and vintage.     

1960-2011年河西走廊地表干湿状况的时空变化及影响因素

[目的]为了探讨河西走廊地区地表干湿状况对气候变化的响应。[方法]利用河西走廊地区14个气象站1960—2011年逐日气象数据,基于FAO推荐的Penman—Monteith模型计算河西走廊各气象站的参考蒸散量和湿润指数,采用Sen’s斜率和Mann—Kendall趋势检验方法,分析河西地区地表干湿的时空变化特征,探讨影响湿润指数变化的主导因子。[结果]河西走廊地表多年平均干湿指数为0.00~0.56,且52a来河西走廊湿润指数整体呈增加趋势;湿润指数的变化具有明显地域性差异。各季节湿润指数亦整体呈增加趋势,以冬季增幅最大,秋、春次之,而夏季变化最小。[结论]河西走廊地区气候属于干旱,52a来湿润指数略有增大,其主要原因是降水的增加和风速的减小。     

PRECIS模拟区域的选择对宁夏区域气候模拟效果的敏感性分析

选定4种大小不同的模拟区域设置25km水平分辨率的PRECIS系统于宁夏地区,利用欧洲中期天气预报中心(ECWMF)1978年12月-1979年9月的再分析数据作为准观测边界条件驱动PRECIS,通过模拟的宁夏地区冬夏两季日平均气温和降水与观测资料的对比,分析25km水平分辨率气候模拟结果对模式区域选择的敏感性。结果表明:25km水平分辨率的PRECIS在4种不同模拟区域下都能较好地模拟宁夏地区典型年的日平均温度和降水量,尤其是对降水的模拟效果更好;总体上,PRECIS的模拟效果随着模拟区域的缩小而下降,但PRECIS在中小模拟区域下仍然能够获得较好的模拟效果。考虑到计算成本,本研究认为在中小模拟区域下25km水平分辨率的PRECIS能够较为高效地进行宁夏地区的气候模拟。     

Evaluating population persistence of Delmarva fox squirrels and potential impacts of climate change

In addition to the combined effects of forest fragmentation, habitat loss, and population isolation on the long-term persistence of many species including the endangered Delmarva fox squirrel (Sciurus niger cinereus), future changes in climate may make existing habitats less productive and more variable. The Palmer Drought Severity Index (PDSI) for the Delmarva Peninsula of the mid-Atlantic USA, reveals a trend for longer durations of potentially unfavorable conditions for fox squirrel population growth. We used a stochastic population matrix model and available life history information to assess population extinction risk for the Delmarva fox squirrel under a number of scenarios of landscape change and environmental variation, including uncertainties in the future range of climate patterns. Patch size (carrying capacity) was the most important factor influencing persistence of isolated populations. Extinction risk increased markedly across all patch sizes when year to year patterns in environmental variability were autocorrelated to match regional patterns in the PDSI. Increased autocorrelation matching the regional PSDI increased extinction risk, ranging from a factor of 5 to a factor of over 100 in some scenarios when compared to uncorrelated patterns in environmental variability. Increasing the range of variation in survival probabilities was less important to persistence, but its effect also increased when year-to-year changes were autocorrelated in time. Comparing model results with the size and landscape configuration of currently occupied patches on the Delmarva Peninsula showed that many existing populations are above the size threshold identified by these simulations for long-term persistence under current conditions, but these may become vulnerable should climate variability increase and adverse conditions persist for several years at a time.     

作物生长模型的应用研究进展

作物生长模型不仅能够进行单点尺度上作物生长发育的动态模拟,而且能够从系统角度评价作物生长状态与环境要素的关系。本文通过梳理当前作物生长模型应用的诸多研究成果,剖析模型在气候变化对农业生产影响研究、作物生长模型区域应用中的关键问题,总结了当前以作物生长模型为核心的农业决策支持系统开发的研究情况,意在促进作物生长模型在生态、农业、区域气候资源和气候变化等研究中更广泛地应用。结果表明,作物生长模型在国内外的研究与应用广泛而深入,在气候变化背景下,应用作物生长模型进行历史时期气候条件和农业气象灾害对作物生产状况和产量的影响研究已相当广泛且相对成熟。利用全球气候模式（GCM）或区域气候模式（RCM）构建未来气候变化情景,再与作物生长模型耦合已发展成为评估未来气候变化对农业生产影响的重要手段。通过集成与整合多作物生长模型、多气候模式集合模拟、优化气候模拟数据订正方法可有效降低气候变化对农业生产影响评估的不确定性。遥感数据同化技术能够将站点模型运用到区域尺度上评价不同环境因子对农业生产的影响,拓宽了作物生长模型的应用尺度范围并有效提高作物产量估算的精度。以作物生长模型为核心的农业决策支持系统的研究与应用越来越多元化,是辅助农业生产管理和决策的重要工具。然而,由于作物生态系统的复杂性,作物生长模型模拟结果仍存在很大的不确定性,今后对作物生长机理及过程间耦合机制的探索还需加强,以便进一步完善和改进模型,促进作物生长模型更广泛地应用。     

Tropical cyclones and coastal inundation under enhanced greenhouse conditions

The behavior of tropical cyclones under enhanced greenhouse conditions has been the subject of considerable speculation. Typical spatial scales of these cyclones are on the order of tens to hundreds of kilometers. Therefore they cannot be simulated in global climate models with resolutions of several hundred kilometers. Thus speculation has been largely based on extrapolation from their present observed distribution, and on simple parametric relationships. However, the conditions under which tropical cyclones form, the intensities they reach, and their usual paths depend on a number of dynamic and thermodynamic factors that may change in complex ways with changing climate. Recent studies using finer resolution global and regional climate models, sensitivity studies that model individual cyclones, and parametric studies have been reviewed. These suggest that the future behavior of tropical cyclones remains an open question, with changes of either sign possible in numbers and intensities. The paper also describes the combined effect on coastal inundation of mean sea level rise and changes in storm surges due to tropical and extratropical cyclones. Impact studies highlight the importance of taking both these factors into account and the highly site-specific nature of the problem.     

气候变化情景下海河流域水文循环变化模拟

Climate change scenarios, predicted using the regional climate modeling system of PRECIS (providing regional climates for impacts studies), were used to derive three-layer variable infiltration capacity (VIC-3L) land surface model for the simulation of hydrologic processes at a spatial resolution of 0.25° × 0.25° in the Haihe River Basin. Three climate scenarios were considered in this study: recent climate (1961-1990), future climate A2 (1991-2100) and future climate B2 (1991-2100) with A2 and B2 being two storylines of future emissions developed with the Intergovernmental Panel on Climate Change (IPCC) special report on emissions scenarios. Overall, under future climate scenarios A2 and B2, the Haihe River Basin would experience warmer climate with increased precipitation, evaporation and runoff production as compared with recent climate, but would be still likely prone to water shortages in the period of 2031-2070. In addition, under future climate A2 and B2, an increase in runoff during the wet season was noticed, indicating a future rise in the flood occurrence possibility in the Haihe River Basin.     

农林复合系统植物竞争研究进展

植物竞争是农林复合系统成功和可持续性的主要决定因素.使资源竞争最小化,资源利用最大化,是农林复合系统提高产量和总生产力的核心.本文对农林复合系统中林木一作物间对光、水分和养分的竞争作用、化感作用及与竞争相关的植物特性进行了综述.一般认为,在湿润地区,农林复合系统地上部分对光的竞争可能是系统生产力的主要决定因素;而在温带地区和半干旱热带地区,地下部分对水分的竞争可能限制着系统的生产力.在农林复合系统中,植物对光的竞争能力由叶面积、冠层结构等决定,对土壤水分和养分的竞争能力则取决于植物根系所占据的土壤空间、根系的形态和生理塑性、植物根系在土壤中的时空分布格局等.今后应加强不同区域农林复合系统中不同组分间的竞争作用的比较、竞争过程及生理机理、化感作用、菌根影响植物竞争的作用和机理、地上部分竞争和地下部分竞争的相互作用、全球变化与植物竞争等方面的研究.     

水稻格点作物模型在中国区域的不确定性评估

作物模型是评估气候变化对农业生产影响的主要手段之一,但中国对格点作物模型间的比较研究尚处于初始阶段。为全面评估不同作物模型在中国不同区域对水稻产量模拟的有效性,利用联合国粮农组织（FAO）和中国农业农村部种植业管理司（SYB）水稻年平均产量统计资料,对由2种气候资料（AgMERRA和WFDEI-GPCC）和 3种不同种植管理情景（Default、Fullharm和Harmnon情景）驱动的7种全球格点作物模型（CGMS-WOFOST、CLM-CROP、EPIC-BOKU、GEPIC、LPJML、PDSSAT和PEPIC模型）模拟的中国水稻产量进行了对比分析。结果表明：不同格点作物模型之间的模拟结果差异较大,在不同区域不同格点作物模型的模拟效果差异显著,不同格点作物模型对气候变化和种植管理情景的响应和敏感性不同,大部分模拟结果低估了水稻产量。使用不同水稻统计产量数据会对评估结果产生一定的影响。格点作物模型能够一定程度上模拟出水稻产量的年际变化和气候变化对产量的影响,但对于统计水稻产量上升的趋势较难模拟。通过综合分析产量在时间和空间上的波动情况,并利用2种评分方法对模拟表现打分,发现LPJML和PDSSAT在7种格点作物模型中模拟效果最好,同时也对不同气候数据和种植管理情景的变化最敏感,CLM-CROP的模拟效果最差。对不同种植管理情景,Default情景下的模拟效果显著高于Fullharm和Harmnon情景。多种格点作物模型集合平均（MME）可以降低单个格点作物模型模拟的误差,但需对MME中的集合模型进行挑选。     

Impact of expected increase in precipitation intensities on soil loss—results of comparative model simulations

The impact of the expected climate change on the frequency and extent of soil erosion processes is hardly assessable so far. This is mainly because available models of climate change reliably produce at best mean daily precipitation data, whereas erosion is the result of extreme but short time rainfall and runoff events, normally lasting no longer than a few hours. The frequency and intensity of these extreme rainfall events are expected to increase in some regions, which could lead to increased erosion rates. Mathematical models are able to describe erosion rates under conditions of these extreme events, however, so far prognostic meteorological data necessary for the application of these models are not available.The use of a new method for the projection of meteorological time series and their extremes using global climate simulations [Enke and Spekat, 1997, Enke, 2000, Enke, 2003, Enke et al., 2005 and Enke et al., in press] permits for the first time an approximation of future soil loss.This research is based on simulated, high resolution data for extreme rainfall events in the period of 2031–2050, which reproduces the mean frequency, intensity and duration of future events with high precipitation intensities relevant to erosion within the investigated seasonal period from June to August. The simulations are performed for two exemplary sites in Saxony, based on the EROSION 2D model (Schmidt, J., 1990. A mathematical model to simulate rainfall erosion, Catena, Suppl. 19), which is a process-based soil erosion model for simulating soil erosion and deposition by water on single slopes. Simulated precipitation for the 2031–2050 time period is used to model soil loss, and results are compared to soil loss based on 20 years of measured precipitation from 1981 to 2000.The simulation results allow the impacts of climate change on erosion rates to be quantified by comparing current climate with predicted, future climate. However, expected changes in land use due to changed economic conditions are not taken into account in this analysis.     

多模式集合模拟气候变化对玉米产量的影响

气候模式驱动作物模型是气候变化影响评估的主要手段。但是,单一气候模式输出和作物模型的结构差异使得研究结果存在不确定性。多模式集合的概率预估可以有效减少研究结果的不确定性。为此,本文利用1981—2009年东北地区海伦、长岭、本溪3地区农业气象站的历史气象资料和玉米作物数据,分别建立了作物统计模型并验证了APSIM机理模型在研究区域的适用性。在此基础上,与CMIP5在RCP4.5情景下的8个全球模式结合,尝试基于多模式集合评估了未来2010—2039年时段和2040—2069年时段气候变化对玉米产量的可能影响(相对于1976—2005年基准时段)。研究结果表明,APSIM模型对玉米生长发育和产量形成有很好的模拟能力。玉米生育期的模拟误差(RMSE)为3~4 d,产量的RMSE为0.6~0.8 t?hm~(-2)。建立的产量统计模型表明,玉米出苗阶段(5月中旬)的温度增加对产量增加有积极作用,而开花到成熟阶段(7月中旬到9月上旬)的温度和降水的增加、光照的不足均不利于产量增加。与1976—2005年基准时段相比,气候因素影响下2010—2039年玉米产量减少3.8%(海伦)~7.4%(本溪),减产的概率为64%(长岭)~73%(本溪);2040—2069年时段减产6.4%(海伦)~10.5%(本溪),减产的概率为74%(海伦)~83%(本溪)。未来2010—2039年时段和2040—2069年时段基于机理模型模拟的产量降低分别为6.6%(海伦)~8.9%(本溪)和9.7%(海伦)~13.7%(本溪),均高于相应时段基于统计模型得到的0.9%(海伦)~6.0%(本溪)和2.0%(长岭)~7.3%(本溪)减产结果。     

气候和土壤质地对中国东北地区旱地土壤有机碳影响的尺度效应

Understanding how spatial scale inffuences commonly-observed effiects of climate and soil texture on soil organic carbon (SOC) storage is important for accurately estimating the SOC pool at different scales. The relationships among climate factors, soil texture and SOC density at the regional, provincial, city, and county scales were evaluated at both the soil surface (0-20 cm) and throughout the soil profile (0-100 cm) in the Northeast China uplands. We examined 1022 profiles obtained from the Second National Soil Survey of China. The results indicated that the relationships between climate factors and SOC density generally weakened with decreasing spatial scale. The provincial scale was optimal to assess the relationship between climate factors and SOC density because regional differences among provinces were covered up at the regional scale. However, the relationship between soil texture and SOC density had no obvious trend with increasing scale and changed with temperature. There were great differences in the impacts of climate factors and soil texture on SOC density at different scales. Climate factors had a larger effect on SOC density than soil texture at the regional scale. Similar trends were seen in Heilongjiang and eastern Inner Mongolia at the provincial scale. But, soil texture had a greater effect on SOC density compared with climate factors in Jilin and Liaoning. At the city and county scales, the inffuence of soil texture on SOC density was more important than climate factors.