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1.
Future climate changes, as well as differences in climates from one location to another, may involve changes in climatic variability as well as changes in means. In this study, a synthetic weather generator is used to systematically change the within-year variability of temperature and precipitation (and therefore also the interannual variability), without altering long-term mean values. For precipitation, both the magnitude and the qualitative nature of the variability are manipulated. The synthetic daily weather series serve as input to four crop simulation models. Crop growth is simulated for two locations and three soil types. Results indicate that average predicted yield decreases with increasing temperature variability where growing-season temperatures are below the optimum specified in the crop model for photosynethsis or biomass accumulation. However, increasing within-year variability of temperature has little impact on year-to-year variability of yield. The influence of changed precipitation variability on yield was mediated by the nature of the soil. The response on a droughtier soil was greatest when precipitation amounts were altered while keeping occurrence patterns unchanged. When increasing variability of precipitation was achieved through fewer but larger rain events, average yield on a soil with a large plant-available water capacity was more affected. This second difference is attributed to the manner in which plant water uptake is simulated. Failure to account for within-season changes in temperature and precipitation variability may cause serious errors in predicting crop-yield responses to future climate change when air temperatures deviate from crop optima and when soil water is likely to be depleted at depth.  相似文献   

2.
Despite recent advances in supercomputing, current general circulation models (GCMs) have significant problems in representing the variability associated with organized tropical convection. Furthermore, due to high sensitivity of the simulations to the cloud radiation feedback, the tropical convection remains a major source of uncertainty in long-term weather and climate forecasts. In a series of recent studies, it has been shown, in paradigm two-baroclinic-mode systems and in aquaplanet GCMs, that a stochastic multicloud convective parameterization based on three cloud types (congestus, deep and stratiform) can be used to improve the variability and the dynamical structure of tropical convection, including intermittent coherent structures such as synoptic and mesoscale convective systems. Here, the stochastic multicloud model is modified with a parameterized cloud radiation feedback mechanism and atmosphere-ocean coupling. The radiative convective feedback mechanism is shown to increase the mean and variability of the Walker circulation. The corresponding intensification of the circulation is associated with propagating synoptic scale systems originating inside of the enhanced sea surface temperature area. In column simulations, the atmosphere ocean coupling introduces pronounced low frequency convective features on the time scale associated with the depth of the mixed ocean layer. However, in the presence of the gravity wave mixing of spatially extended simulations, these features are not as prominent. This highlights the deficiency of the column model approach at predicting the behavior of multiscale spatially extended systems. Overall, the study develops a systematic framework for incorporating parameterized radiative cloud feedback and ocean coupling which may be used to improve representation of intraseasonal and seasonal variability in GCMs.  相似文献   

3.
An attempt is made to integrate subgrid scale scheme on the work of Dimri and Ganju (Pure Appl Geophys 167:1–24, 2007) to understand the overall nature of surface heterogeneity and landuse variability along with resolvable finescale micro/meso scale circulation over the Himalayan region, which is having different altitudes and orientations causing prevailing weather conditions to be complex. This region receives large amount of precipitation due to eastward moving low-pressure synoptic weather systems, called western disturbances, during winter season (December, January, February—DJF). Surface heterogeneity and landuse variability of the Himalayan region gives rise to numerous micro/meso scale circulation along with prevailing weather. Therefore, in the present work, a mosaic type parameterization of subgrid scale topography and landuse within a framework of a regional climate model (RegCM3) is extended to study interseasonal variability of surface climate during a winter season (October 1999–March 2000) of the work of Dimri and Ganju (Pure Appl Geophys 167:1–24, 2007). In this scheme, meteorological variables are disaggregated from the coarse grid to the fine grid, land surface calculations are then performed separately for each subgrid cell, and surface fluxes are calculated and reaggregated onto the coarse grid cell for input to the atmospheric model. By doing so, resolvable finescale structures due to surface heterogeneity and landuse variability at coarse grid are subjected to parameterize at regular finescale surface subgrid. Model simulations show that implementation of subgrid scheme presents more realistic simulation of precipitation and surface air temperature. Influence of topographic elevation and valleys is better represented in the scheme. Overall, RegCM3 with subgrid scheme provides more accurate representation of resolvable finescale atmospheric/surface circulations that results in explaining mean variability in a better way.  相似文献   

4.
This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.  相似文献   

5.
Climate variability and change in Bulgaria during the 20th century   总被引：1，自引：1，他引：1  
Summary Climate data used for climate variability and change analyses, must be homogeneous, to be accurate. The data currently used in the Météo-France homogenization procedure, which does not require computation of regional reference series, was applied to precipitation and average air temperature series in Bulgaria. The Caussinus-Mestre method, with a double-step procedure, was used to detect multiple breaks and outliers in the long-term series of precipitation and average air temperature. A two factor linear model was applied for break correction. The homogenization procedure was run till all or most break risk was gone. Analysis of climate variability and change in Bulgaria during the 20th century was done on already homogenized precipitation and average air temperature series. The statistical significance of the trends obtained was evaluated by the coefficient of Spearman rank correlation. The variations of annual precipitation in Bulgaria showed an overall decrease. The country has experienced several drought episodes during the 20th century, most notably in the 1940s and 1980s. Seasonal precipitation in spring shows a positive trend at most weather stations across the country. The trend for summer and autumn precipitation is negative. A statistically significant increasing trend of winter precipitation in north Bulgaria was detected. No significant warming trend in the country was found during the last century inspite of the warming observed during the last two decades. Summer in Bulgaria tends to be warmer from the beginning of the 1980s. There is a statistically significant increasing trend of average air temperature during the winter season at the weather stations near the Danube river (north Bulgaria) during the periods 1901–2000 and 1931–2000.  相似文献   

6.
Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.  相似文献   

7.
This paper addresses deficiencies of stochastic Weather Generators (WGs) in terms of reproduction of low-frequency variability and extremes, as well as the unanticipated effects of changes to precipitation occurrence under climate change scenarios on secondary variables. A new weather generator (named IWG) is developed in order to resolve such deficiencies and improve WGs performance. The proposed WG is composed of three major components, including a stochastic rainfall model able to reproduce realistic rainfall series containing extremes and inter-annual monthly variability, a multivariate daily temperature model conditioned to the rainfall occurrence, and a suitable multi-variate monthly generator to fit the low-frequency variability of daily maximum and minimum temperature series. The performance of IWG was tested by comparing statistical characteristics of the simulated and observed weather data, and by comparing statistical characteristics of the simulated runoff outputs by a daily rainfall-runoff model fed by the generated and observed weather data. Furthermore, IWG outputs are compared with those of the well-known LARS-WG weather generator. The tested characteristics are a variety of different daily statistics, low-frequency variability, and distribution of extremes. It is concluded that the performance of the IWG is acceptable, better than LARS-WG in the majority of tests, especially in reproduction of extremes and low-frequency variability of weather and runoff series.  相似文献   

8.
The results are presented of the estimation of surface air temperature variations in different climatically quasi-homogeneous regions of Russia using the nonparametric method of regression analysis (quantile regression). Daily observation records from 517 weather stations were used. The quantile regression technique used for analyzing the trends in long-term series allows obtaining information on trends for the whole range of quantile values from 0 to 1 of dependent variable distributions. Seasonal and regional features of daily minimum, mean, and maximum air temperature trends are considered in a wide range of quantile values. The proposed method that generalizes long-term trends obt ained for groups of stations by quantile regression, is applied to quasi-homogeneous climate regions identified on the territory of Russia.  相似文献   

9.
CLIMATE CHANGE: LONG-TERM TRENDS AND SHORT-TERM OSCILLATIONS   总被引：2，自引：0，他引：2  
Identifying the Northern Hemisphere (NH) temperature reconstruction and instrumental data for the past 1000 years shows that climate change in the last millennium includes long-term trends and various oscillations. Two long-term trends and the quasi-70-year oscillation were detected in the global temperature series for the last 140 years and the NH millennium series. One important feature was emphasized that temperature decreases slowly but it increases rapidly based on the analysis of different series. Benefits can be obtained of climate change from understanding various long-term trends and oscillations. Millennial temperature proxies from the natural climate system and time series of nonlinear model system are used in understanding the natural climate change and recognizing potential benefits by using the method of wavelet transform analysis. The results from numerical modeling show that major oscillations contained in numerical solutions on the interdecadal timescale are consistent with that of natural proxies. It seems that these oscillations in the climate change are not directly linked with the solar radiation as an external forcing. This investigation may conclude that the climate variability at the interdecadal timescale strongly depends on the internal nonlinear effects in the climate system.  相似文献   

10.
东北地区近百年气候变化及突变检测   总被引：31，自引：0，他引：31  
利用沈阳、大连、营口、长春、哈尔滨、黑河6个代表站1905~2001年的月平均气温和月降水观测数据,建立了东北地区近百年来季、年的气温和降水序列。对所建温度和降水序列分别与同一区域内26个代表站的温度序列和51个代表站的降水序列近40年同期资料做了相关分析,检验了序列的代表性。在所建序列的基础上,分析了东北地区百年气温的年代、年和季节等不同时间尺度的变化特点和地域分布特征,以及百年降水量的变化规律,采用谱分析方法探讨了序列的周期性变化特征,并采用Mann-Kendall和Yamamoto方法对经过滑动平均的气温和降水序列进行了突变分析。  相似文献   

11.
Michael E. Mann 《Climatic change》2011,107(3-4):267-276
Long Range Dependence (LRD) scaling behavior has been argued to characterize long-term surface temperature time series. LRD is typically measured by the so-called “Hurst” coefficient, “H”. Using synthetic temperature time series generated by a simple climate model with known physics, I demonstrate that the values of H obtained for observational temperature time series can be understood in terms of the linear response to past estimated natural and anthropogenic external radiative forcing combined with the effects of random white noise weather forcing. The precise value of H is seen to depend on the particular noise realization. The overall distribution obtained over an ensemble of noise realizations is seen to be a function of the relative amplitude of external forcing and internal stochastic variability and additionally in climate “proxy” records, the amount of non-climatic noise present. There is no obvious reason to appeal to more exotic physics for an explanation of the apparent scaling behavior in observed temperature data.  相似文献   

12.
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.  相似文献   

13.
Climate variability parameters and air temperature trends in Russia, derived from observational data, are compared with those derived from climate modeling in the second half of the 20th-early 21st century, using the atmosphere-ocean general circulation model ensemble. The computation results from these models were used in the preparation of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. It is demonstrated that the ensemble averaging allowed us to efficiently filter the internal climate variability and get relatively stable estimates of trends. As a whole, for Russia, these estimates are in good agreement with the observational data, both for a year on average, and in individual seasons. The comparison of model and observed air temperature trends on a regional scale turns out to be irrelevant in a number of cases because of a high inadequacy of trend estimates derived from the observational data.  相似文献   

14.
The results are presented of the statistical analysis of correspondence between the model simulations and observations of temperature changes on the territory of Russia. Three model ensembles are considered, differing in the level of taking account of the impact of external forcings on the climate system of the Earth. For each of them, the statistical correspondence is estimated between the observed surface air temperature variations in the second half of the 20th century and at the beginning of the 21st century and model simulations taking account of the natural variability typical of the climate system. The analysis demonstrated that, in spite of the uncertainties associated with the differences in the representation of anthropogenic and natural external forcings on the climate in model simulations as well as with the imperfection of climate models and with internal variability of the climate system, the model experiments enable to obtain the relevant information both on the temporal evolution of temperature changes on the territory of Russia and on their spatial peculiarities.  相似文献   

15.
A change in a sea-ice parameter in a global coupled climate model results in a reduction in amplitude (of about 60%) and a shortening of the predominant period of decadal low frequency variability in the time series of globally averaged surface air temperature. These changes are global in extent and also are reflected in time series of area-averaged SSTs in the equatorial eastern Pacific Ocean, the principal components of the first EOFs of global surface air temperature and sea level pressure, Asian monsoon precipitations and other quantities. Coupled ocean-atmosphere-sea ice processes acting on a global scale are modified to produce these changes. Global climate sensitivity is reduced when ice albedo feedback is weakened due to the change in sea ice that makes it more difficult to melt. The changes in the amplitude and time scale of the low frequency variability in the model are traced to changes in the base state of the climate simulations as affected by modifications associated with the changes in sea ice. Making sea ice more difficult to melt results in increased sea-ice area, colder high latitudes, increased meridional surface temperature gradients, and, to a first order, stronger surface winds in most regions which strengthen near-surface currents, particularly in the Northern Hemisphere, and decreases the advection time scale in the upper ocean gyres. Additionally, in the North Atlantic there is enhanced meridional overturning due to increased density mainly in the Greenland Sea region. This also contributes to an intensified North Atlantic gyre. The changes in base state due to the sea ice change result in a more predominant decadal time scale of near 14 years and significantly reduced contributions from lower frequencies in the range of 15–40 year periods. Received: 11 December 1998 / Accepted: 4 October 1999  相似文献   

16.
Two European temperature reconstructions for the past half-millennium, January-to-April air temperature for Stockholm (Sweden) and seasonal temperature for a Central European region, both derived from the analysis of documentary sources and long instrumental records, are compared with the output of climate simulations with the model ECHO-G. The analysis is complemented by comparisons with the long (early)-instrumental record of Central England Temperature (CET). Both approaches to study past climates (simulations and reconstructions) are burdened with uncertainties. The main objective of this comparative analysis is to identify robust features and weaknesses in each method which may help to improve models and reconstruction methods. The results indicate a general agreement between simulations obtained with temporally changing external forcings and the reconstructed Stockholm and CET records for the multi-centennial temperature trend over the recent centuries, which is not reproduced in a control simulation. This trend is likely due to the long-term change in external forcing. Additionally, the Stockholm reconstruction and the CET record also show a clear multi-decadal warm episode peaking around AD 1730, which is absent in the simulations. Neither the reconstruction uncertainties nor the model internal climate variability can easily explain this difference. Regarding the interannual variability, the Stockholm series displays, in some periods, higher amplitudes than the simulations but these differences are within the statistical uncertainty and further decrease if output from a regional model driven by the global model is used. The long-term trend of the CET series agrees less well with the simulations. The reconstructed temperature displays, for all seasons, a smaller difference between the present climate and past centuries than is seen in the simulations. Possible reasons for these differences may be related to a limitation of the traditional ‘indexing’ technique for converting documentary evidence to temperature values to capture long-term climate changes, because the documents often reflect temperatures relative to the contemporary authors’ own perception of what constituted ‘normal’ conditions. By contrast, the amplitude of the simulated and reconstructed inter-annual variability agrees rather well.  相似文献   

17.
Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.  相似文献   

18.
Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.  相似文献   

19.
Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere–ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop–climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed `downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño–Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as `borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.  相似文献   

20.
气象部门馆藏的西部最早的器测气象资料始于20世纪30年代,不能满足建立20世纪以来中国气候变化序列的需求,而古气候重建或气候模拟资料则可以延伸到器测时代以前。为了探讨长序列多源气候资料序列融合方法,采用贝叶斯方法对中国北疆地区8条树轮气温重建资料、器测资料与国际耦合模式比较计划第5阶段（CMIP5）模式模拟资料进行了融合试验。首先利用器测资料对气温代用资料进行校验与网格重建,并以此作为贝叶斯模型的先验分布,然后,用泰勒图选出了该区域气候模拟效果最佳的几个模式;最后将网格重建和气候模拟序列用贝叶斯模型进行了融合试验。结果表明,贝叶斯融合模型能有效提取各种数据来源的有用信息进行融合,融合结果的长期变化（线性）趋势更接近器测气候序列,并在一定程度上提高了序列的精度,减小了结果的不确定性;并且,融合结果能够纠正先验分布及气候模拟数据的明显偏差,为长年代气候序列重建提供了一个可行的思路。   相似文献   

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