Optimal filtering for Bayesian detection and attribution of climate change

In the conventional approach to the detection of an anthropogenic or other externally forced climate change signal, optimal filters (fingerprints) are used to maximize the ratio of the observed climate change signal to the natural variability noise. If detection is successful, attribution of the observed climate change to the hypothesized forcing mechanism is carried out in a second step by comparing the observed and predicted climate change signals. In contrast, the Bayesian approach to detection and attribution makes no distinction between detection and attribution. The purpose of filtering in this case is to maximize the impact of the evidence, the observed climate change, on the prior probability that the hypothesis of an anthropogenic origin of the observed signal is true. Whereas in the conventional approach model uncertainties have no direct impact on the definition of the optimal detection fingerprint, in optimal Bayesian filtering they play a central role. The number of patterns retained is governed by the magnitude of the predicted signal relative to the model uncertainties, defined in a pattern space normalized by the natural climate variability. Although this results in some reduction of the original phase space, this is not the primary objective of Bayesian filtering, in contrast to the conventional approach, in which dimensional reduction is a necessary prerequisite for enhancing the signal-to-noise ratio. The Bayesian filtering method is illustrated for two anthropogenic forcing hypotheses: greenhouse gases alone, and a combination of greenhouse gases plus sulfate aerosols. The hypotheses are tested against 31-year trends for near-surface temperature, summer and winter diurnal temperature range, and precipitation. Between six and thirteen response patterns can be retained, as compared with the one or two response patterns normally used in the conventional approach. Strong evidence is found for the detection of an anthropogenic climate change in temperature, with some preference given to the combined forcing hypothesis. Detection of recent anthropogenic trends in diurnal temperature range and precipitation is not successful, but there remains strong net evidence for anthropogenic climate change if all data are considered jointly.

---

Signal analysis of the atmospheric mean 500/1000 hPa temperature north of 55°N between 1949 and 1994

 The lower tropospheric mean temperature 500/1000 hPa is examined in the Northern Hemisphere high-latitude region north of 55°N with regard to a climate change signal due to anthropogenic climate forcing as a supplement to previous studies which concentrated on near surface temperatures. An observational data set of the German Weather Service is compared with several model simulations including different scenarios of greenhouse gas and sulfate aerosol forcing derived from the two recent versions of the coupled climate model in Hamburg, ECHAM-3/LSG and ECHAM-4/OPYC. The signal analysis is based on the optimal fingerprint method, which supplies a detection variable with optimal signal-to-noise ratio. The natural variability measures are derived from the corresponding long-term control experiments. From 1970 onward, we find high trend pattern analogies between the observational data and the greenhouse-gas induced model simulations. The fingerprint of this common temperature signal consists of a predominate warming with maximum over Siberia and a weak cooling over the North Atlantic reaching an estimated significance level of about 1%. A non-optimized approach has also been examined, leading to even closer trend pattern correlations. The additional forcing by sulfate aerosols decreases the correlation of this climate change simulation with the observations. The natural variability constitutes about 50% of the conforming trend patterns. The signal-to-noise ratio is best over the oceans while the tropospheric temperatures over the land masses are contaminated by strong noise. The trend pattern correlations look the same for both model versions and several ensemble members with different noise realizations. Received: 4 January 1999 / Accepted: 11 April 2001     

Multi-pattern fingerprint method for detection and attribution of climate change

 The multi-variate optimal fingerprint method for the detection of an externally forced climate change signal in the presence of natural internal variability is extended to the attribution problem. To determine whether a climate change signal which has been detected in observed climate data can be attributed to a particular climate forcing mechanism, or combination of mechanisms, the predicted space–time dependent climate change signal patterns for the candidate climate forcings must be specified. In addition to the signal patterns, the method requires input information on the space–time dependent covariance matrices of the natural climate variability and of the errors of the predicted signal patterns. The detection and attribution problem is treated as a sequence of individual consistency tests applied to all candidate forcing mechanisms, as well as to the null hypothesis that no climate change has taken place, within the phase space spanned by the predicted climate change patterns. As output the method yields a significance level for the detection of a climate change signal in the observed data and individual confidence levels for the consistency of the retrieved climate change signal with each of the forcing mechanisms. A statistically significant climate change signal is regarded as consistent with a given forcing mechanism if the statistical confidence level exceeds a given critical value, but is attributed to that forcing only if all other candidate climate change mechanisms (from a finite set of proposed mechanisms) are rejected at that confidence level. Although all relations can be readily expressed in standard matrix notation, the analysis is carried out using tensor notation, with a metric given by the natural-variability covariance matrix. This simplifies the derivations and clarifies the invariant relation between the covariant signal patterns and their contravariant fingerprint counterparts. The signal patterns define the reduced vector space in which the climate trajectories are analyzed, while the fingerprints are needed to project the climate trajectories onto this reduced space. Received: 19 April 1996/Accepted: 21 April 1997     

Climate variability and trends in downscaled high-resolution simulations and projections over Metropolitan France

In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15–20 km), followed with a statistical downscaling method in order to reach a target resolution of 8 km. The statistical downscaling technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected downscaled temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021–2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ.     

Detection of changes in the summer precipitation time series of the region Emilia-Romagna, Italy

Summary  The main characteristics of the spatial and temporal variability of summer precipitation observed in 40 rainfall stations of the Emilia-Romagna region in northern Italy, are analysed for the period 1922 to 1995. Non-parametric tests and Empirical Orthogonal Function (EOF) analysis were used as tools in order to achieve the paper’s objective. The Pettitt and Mann-Kendall tests detect shift points and trends in the precipitation time series, respectively, while the EOF analysis reveals the main characteristics of spatial variability. The Standard Normal Homogeneity Test (SNHT) was used to detect the inhomogeneity of the data set. Almost all stations exhibit an increasing trend with a systematic significant upward shift around 1962. The climate signal is more significant in the north-western, central and north-eastern part of the region, and the spatial extension strongly depends on the network density and the time period analysed. The change in summer precipitation is mainly due to a change during August and is confirmed by the SNHT test which does not reveal an inhomogeneity in the series. The first EOF pattern indicates that a common large-scale process could be responsible for summer precipitation variability in the Emilia-Romagna region. The second EOF pattern shows an opposite sign of climate variability between north-western and south-eastern areas. The Apennine mountains show the largest climate variability in the summer precipitation field. Received March 8, 2000 Revised July 17, 2000     

CMIP5全球气候模式对青藏高原地区气候模拟能力评估

青藏高原是气候变化的敏感和脆弱区,全球气候模式对于这一地区气候态的模拟能力如何尚不清楚。为此,本文使用国际耦合模式比较计划第五阶段（CMIP5）的历史模拟试验数据,评估了44 个全球气候模式对1986~2005 年青藏高原地区地表气温和降水两个基本气象要素的模拟能力。结果表明,CMIP5 模式低估了青藏高原地区年和季节平均地表气温,年均平均偏低2.3℃,秋季和冬季冷偏差相对更大;模式可较好地模拟年和季节平均地表气温分布型,但模拟的空间变率总体偏大;地形效应校正能够有效订正地表气温结果。CMIP5 模式对青藏高原地区降水模拟能力较差。尽管它们能够模拟出年均降水自西北向东南渐增的分布型,但模拟的年和季节降水量普遍偏大,年均降水平均偏多1.3 mm d-1,这主要是源于春季和夏季降水被高估。同时,模式模拟的年和季节降水空间变率也普遍大于观测值,尤其表现在春季和冬季。相比较而言,44 个模式集合平均性能总体上要优于大多数单个模式;等权重集合平均方案要优于中位数平均;对择优挑选的模式进行集合平均能够提高总体的模拟能力,其中对降水模拟的改进更为显著。     

Climate change in terms of modes of atmospheric variability and circulation regimes over southern South America

The simulated low-frequency variability patterns of the atmospheric circulation, ranging from interannual to interdecadal timescales, are studied in an area encompassing southern South America. The experiment is a transient simulation performed with the IPSL CCM2 coupled global model, in which the greenhouse forcing is continuously increasing. The main modes of low-frequency variability are found to remain stationary throughout the simulation, suggesting they depend more on the internal dynamics of the atmospheric flow than on its external forcing. Inspection of the circulation regimes that represent the more recurrent patterns at interannual and interdecadal timescales showed that climate change manifests itself as a change in regime population, suggesting that the negative phase of the Antarctic Oscillation-like pattern becomes more frequented in a climate change scenario. Changes of regime occurrence are superimposed to a positive trend whose spatial pattern is reminiscent of the structure of the Antarctic Oscillation-mode of variability. Moreover, it resembles the spatial patterns of those regimes that show a significant change in population. The change in regime frequencies of the circulation patterns of low-frequency variability are in opposite phase with respect to the trend, thus, the behaviour of these patterns of variability, superimposed to a changing mean state, modulates the climate change signal. The analysis of the high frequencies, in terms of recurrent patterns representing intraseasonal and synoptic-scale of variability, shows no significant changes in regime characteristics, concerning both spatial and temporal behaviour.     

Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change

 A multi-fingerprint analysis is applied to the detection and attribution of anthropogenic climate change. While a single fingerprint is optimal for the detection of climate change, further tests of the statistical consistency of the detected climate change signal with model predictions for different candidate forcing mechanisms require the simultaneous application of several fingerprints. Model-predicted climate change signals are derived from three anthropogenic global warming simulations for the period 1880 to 2049 and two simulations forced by estimated changes in solar radiation from 1700 to 1992. In the first global warming simulation, the forcing is by greenhouse gas only, while in the remaining two simulations the direct influence of sulfate aerosols is also included. From the climate change signals of the greenhouse gas only and the average of the two greenhouse gas-plus-aerosol simulations, two optimized fingerprint patterns are derived by weighting the model-predicted climate change patterns towards low-noise directions. The optimized fingerprint patterns are then applied as a filter to the observed near-surface temperature trend patterns, yielding several detection variables. The space-time structure of natural climate variability needed to determine the optimal fingerprint pattern and the resultant signal-to-noise ratio of the detection variable is estimated from several multi-century control simulations with different CGCMs and from instrumental data over the last 136 y. Applying the combined greenhouse gas-plus-aerosol fingerprint in the same way as the greenhouse gas only fingerprint in a previous work, the recent 30-y trends (1966–1995) of annual mean near surface temperature are again found to represent a significant climate change at the 97.5% confidence level. However, using both the greenhouse gas and the combined forcing fingerprints in a two-pattern analysis, a substantially better agreement between observations and the climate model prediction is found for the combined forcing simulation. Anticipating that the influence of the aerosol forcing is strongest for longer term temperature trends in summer, application of the detection and attribution test to the latest observed 50-y trend pattern of summer temperature yielded statistical consistency with the greenhouse gas-plus-aerosol simulation with respect to both the pattern and amplitude of the signal. In contrast, the observations are inconsistent with the greenhouse-gas only climate change signal at a 95% confidence level for all estimates of climate variability. The observed trend 1943–1992 is furthermore inconsistent with a hypothesized solar radiation change alone at an estimated 90% confidence level. Thus, in contrast to the single pattern analysis, the two pattern analysis is able to discriminate between different forcing hypotheses in the observed climate change signal. The results are subject to uncertainties associated with the forcing history, which is poorly known for the solar and aerosol forcing, the possible omission of other important forcings, and inevitable model errors in the computation of the response to the forcing. Further uncertainties in the estimated significance levels arise from the use of model internal variability simulations and relatively short instrumental observations (after subtraction of an estimated greenhouse gas signal) to estimate the natural climate variability. The resulting confidence limits accordingly vary for different estimates using different variability data. Despite these uncertainties, however, we consider our results sufficiently robust to have some confidence in our finding that the observed climate change is consistent with a combined greenhouse gas and aerosol forcing, but inconsistent with greenhouse gas or solar forcing alone. Received: 28 April 1996 / Accepted: 27 January 1997     

费尔干纳盆地1950-2019年降水时空变化及其影响机制

基于1950-2019年费尔干纳盆地降水格点数据,利用线性回归法和经验正交函数（EOF）分析等方法,探究了费尔干纳盆地降水年际变化的影响因素及其空间分布模态,并研究了降水时空变化后的大尺度环流影响因素。结果显示：（1）1950-2019年,费尔干纳盆地降水总体呈下降趋势,为-2.20mm/10a,但并未通过显著性检验,同时对单个月份的降水进行检验也都未发现明显趋势。（2）费尔干纳盆地降水的主要模态有两个,解释了全区降水变化的70.52%,第一模态解释了全区降水变化的59.90%,空间向量场呈现全区一致型,表征研究区整体的降水变化情况,受到ENSO和西风带的影响;第二模态解释了全区降水变化的10.62%,空间向量场表现出从西北到东南的空间反相模态,表征研究区降水空间异质性,受到欧亚大陆北部输送的水汽影响。（3）厄尔尼诺/南方涛动（ENSO）事件通过调整水汽输送路径和季风环流模式影响了费尔干纳盆地的降水变化;来自欧亚大陆北部的气流是造成费尔干纳盆地降水空间格局差异的主要原因。     

Spatial Patterns of Precipitation Anomalies for 30-yr Warm Periods in China During the Past 2000 Years

The spatial patterns of precipitation anomalies during five 30-yr warm periods of 691-720, 1231-1260, 1741-1770, 1921-1950, and 1981-2000 were investigated using a dryness/wetness grading dataset covering 48 stations from Chinese historical documents and 22 precipitation proxy series from natural archives. It was found that the North China Plain (approximately 35 -40 N, east of 105 E) was dry in four warm periods within the centennial warm epochs of 600-750, the Medieval Warm Period (about 900-1300) and after 1900. A wet condition prevailed over most of China during 1741-1770, a 30-yr warm peak that occurred during the Little Ice Age (about 1650-1850). The spatial pattern of the precipitation anomaly in 1981-2000 over East China (25 -40 N, east of 105 E) is roughly consistent with that in 1231-1260, but a difference in the precipitation anomaly appeared over the Tibetan Plateau. The spatial patterns of the precipitation anomalies over China varied between all five 30-yr warm periods, which implies that the matching pattern between temperature and precipitation change is multiform, and the precipitation anomaly could be positive or negative when a decadal warm climate occurs in different climate epochs. This result may provide a primary reference for the mechanism detection and climate simulation of the precipitation anomaly of the future warm climate.     

A method to study the impact of climate change on variability of river flow: an example from the Guadalupe River in Texas

This work introduced a method to study river flow variability in response to climate change by using remote sensing precipitation data, downscaled climate model outputs with bias corrections, and a land surface model. A meteorological forcing dataset representing future climate was constructed via the delta change method in which the modeled change was added to the present-day conditions. The delta change was conducted at a fine spatial and temporal scale to contain the signals of weather events, which exhibit substantial responses to climate change. An empirical transformation technique was further applied to the constructed forcing to ensure a realistic range. The meteorological forcing was then used to drive the land surface model to simulate the future river flow. The results show that preserving fine-scale processes in response to climate change is a necessity to assess climatic impacts on the variability of river flow events.     

区域极端降水事件阈值计算方法比较分析

根据南京站1951—2010年逐日降水资料,采用2种传统的百分位法以及3种正态变换方法,探讨了确定区域极端降水事件阈值的最佳方法。结果表明,由于降水量的实际概率分布是一种明显的偏态分布,而传统的百分位法是在假设降水量遵从均匀分布条件下进行的,计算结果的稳定性较差。正态变换的3种方法是在降水量实际概率分布下采用百分位法计算阈值的,结果稳定性较好。其中以方法4效果最佳。为消除气候变化的影响,可以将研究时段按降水量变化的不同趋势分为几个气候阶段分别计算阈值。或者采用滑动气候阶段处理整个研究时段,并以各个滑动气候阶段阈值的平均值作为整个研究时段的阈值。     

极端降水特性分析研究进展

极端降水是极端天气气候变化的重要指标,研究其时空分布特征对于正确认识全球气候变暖背景下的极端天气气候过程具有重要意义。就近年来国内外极端降水的特征及其与大气、海洋异常的关系研究进行简要的回顾,最好提出了其存在的不足方面。     

环太湖地区气候变化特征研究

根据1971—2010年环太湖地区苏州、常州、长兴等9个气象台站日平均气温和日降水量资料,采用EOF正交经验分析法、线性倾向率法、小波分析法和Mann-Kendall检验法研究了环太湖地区近40 a来的气候变化特征。结果表明:1) 1971—2010年间,环太湖地区整体上呈增暖趋势,环太湖地区的季节性增暖存在空间差异,西北部的气温在春、夏季明显升高,而东南部则在秋、冬季明显增暖,1990年前后该地区的增暖率存在完全相反的空间分布。2)该40 a中,降水表现为北部增加,南部减少。整个环太湖地区的降水在冬季普遍呈现增加趋势,春、夏季降水的空间分布差异性大于秋、冬季。3) M orlet小波分析结果表明,环太湖地区年平均温度存在16～17 a和6 a、26 a左右的变化周期;年降水量存在15～16 a和24 a的强显著性变化周期,各地区在年均温、年降水量周期振荡的强度上存在一定的差异。4) Mann-Kendall突变检验显示,1971—2010年环太湖地区各站点均表现为气温由低向高的突变,突变发生在1992—1993年。     

2006-2015年上海嘉定区气温和降水非均匀分布特征分析

利用上海嘉定区2006-2015年9个自动气象观测站的逐小时观测资料,通过对气温和降水日变化差异、年变化特征、空间分布状况的分析,研究了该地区气温和降水时空非均匀性分布特征。研究结果表明：嘉定区气温和降水日变化存在局地差异和季节差异,日最高气温出现时间在四季中最为集中,而日最低气温出现时间则比较分散;年平均气温呈走低趋势,而年降水量从整体上来说却呈增加趋势;上海嘉定区东南角为气温高值区且降水量相对偏大,中部地区为降水量高值区且气温也相对偏高,而北部降水量偏少且气温偏低,表明嘉定区东南部和中部相对暖湿,而北部偏干冷;气温和降水的标准差空间分布差异大,表明上海嘉定区气温和降水的空间非均匀性特征显著。通过嘉定气温和降水的时空演变规律的揭示,有助于更好地认识小尺度局部区域气候特征,也能为准确认识该地区的极端事件、精细化天气预报及区域气象灾害风险评估提供更加科学的气候变化背景。     

Comparing the spatial structure of variability in two datasets against each other on the basis of EOF-modes

In analysis of climate variability or change it is often of interest how the spatial structure in modes of variability in two datasets differ from each other, e.g. between past and future climate or between models and observations. Often such analysis is based on Empirical Orthogonal Function (EOF) analysis or other simple indices of large-scale spatial structures. The present analysis lays out a concept on how two datasets of multivariate climate variability can be compared against each other on basis of EOF analysis and how the differences in the multivariate spatial structure between the two datasets can be quantified in terms of explained variance in the leading spatial patterns. It is also illustrated how the patterns of largest differences between the two datasets can be defined and interpreted. We illustrate this method on the basis of several well-defined artificial examples and by comparing our approach with examples of climate change studies from the literature. These literature examples include analysis of changes in the modes of variability under climate change for the sea level pressure (SLP) of the North Atlantic and Europe, the SLP of the Southern Hemisphere, the surface temperature of the Northern Hemisphere, the sea surface temperature of the North Pacific and for precipitation in the tropical Indo-Pacific.     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

1980-2020年山西省极端降水的研究

基于1980-2020年山西省109个气象观测站点的逐日降水资料,选取10个极端降水指数,采用气候倾向率、相关分析、因子分析、R/S预测方法等方法,对山西省极端降水进行了时空分布的研究,以期为山西省的气候变化、生态环境保护、防灾减灾、气象服务工作提供参考依据,结果表明：(1)从时间尺度来看,1980-2020年期间,山西省极端降水的强度和极值都有明显增加,连续干旱日数和连续湿日日数呈下降趋势,其余均表现出不同程度的增加,其中年总降水量增加幅度最明显;从空间尺度来看,年总降水量、降水强度、降水频率、极值均为从西北向东南逐渐增多,空间差异较明显;从各站点的空间分布来看,北部和中部地区的极端事件增加最显著,北部地区的干旱日数仍以增加趋势为主,连续湿日日数气候倾向率的空间差异较大,中部地区站点显著增加,南北部以减少趋势为主;(2)基于相关分析方法表明各极端降水指数(除干旱日数外)与年总降水量都有很好的相关关系,强降水量和极强降水量对年总降水量的贡献值呈现出增加趋势;采用因子分析方法提取了3个公共因子,方差贡献率累计达到了87％,可以看出极端降水强度和降水量指数在对极端降水方面影响较大;利用R/S分析法可以得到年总降水量、中雨日数、大雨日数、最大5日降水量这几个指数未来呈现弱减少趋势,而干旱日数仍为减少趋势,连续湿日日数为持续弱增加趋势。总体看来,山西省极端降水近年来呈现出增加趋势,在空间分布有明显差异。     

A European pattern climatology 1766–2000

Using monthly independently reconstructed gridded European fields for the 500 hPa geopotential height, temperature, and precipitation covering the last 235 years we investigate the temporal and spatial evolution of these key climate variables and assess the leading combined patterns of climate variability. Seasonal European temperatures show a positive trend mainly over the last 40 years with absolute highest values since 1766. Precipitation indicates no clear trend. Spatial correlation technique reveals that winter, spring, and autumn covariability between European temperature and precipitation is mainly influenced by advective processes, whereas during summer convection plays the dominant role. Empirical Orthogonal Function analysis is applied to the combined fields of pressure, temperature, and precipitation. The dominant patterns of climate variability for winter, spring, and autumn resemble the North Atlantic Oscillation and show a distinct positive trend during the past 40 years for winter and spring. A positive trend is also detected for summer pattern 2, which reflects an increased influence of the Azores High towards central Europe and the Mediterranean coinciding with warm and dry conditions. The question to which extent these recent trends in European climate patterns can be explained by internal variability or are a result of radiative forcing is answered using cross wavelets on an annual basis. Natural radiative forcing (solar and volcanic) has no imprint on annual European climate patterns. Connections to CO₂ forcing are only detected at the margins of the wavelets where edge effects are apparent and hence one has to be cautious in a further interpretation. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

近44a毛乌素沙地西缘气候特征及其未来可能变化趋势

利用1971—2014年毛乌素沙地西缘气象观测资料,分析了该地区的气温、降水、相对湿度的时空变化特征,运用R/S分析法预测未来毛乌素沙地西缘气候要素可能变化趋势。结果表明:(1)近44 a毛乌素沙地西缘气温呈显著上升趋势,降水量呈弱的增加趋势,相对湿度呈弱的下降趋势,但是二者趋势并不显著。(2)近44 a毛乌素沙地西缘气温空间分布呈南高北低,降水和相对湿度均呈东多西少,各地气候倾向率存在明显的差异性,中部气温增幅较大,东部降水增加较多,南部相对湿度减少较多。(3)近44 a来毛乌素沙地西缘各气象要素变化均表现出一定的周期性,年平均气温主要存在4~5、8~10 a的振荡周期,降水量主要存在3~4、6~8 a振荡周期,相对湿度主要存在2~4、6~8 a的振荡周期。(4)毛乌素沙地西缘未来气温仍呈上升趋势可能性较大,未来降水量可能变为减少趋势,未来相对湿度变化不稳定。