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1.
Joint variable spatial downscaling   总被引:1,自引:0,他引:1  
Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.  相似文献   

2.
This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.  相似文献   

3.
This study uses empirical agricultural impact models to compare the U.S. climate change predictions of 16 General Circulation Models (GCMs). The impact analysis provides a policy-relevant index by which to judge complex climate predictions. National aggregate impacts vary widely across the 16 GCMs because of varying regional and seasonal patterns of predicted climate change. Examining the predicted impacts from the full set of GCMs reveals that the seasonal detail in the GCM predictions is so noisy that it is not significantly different from a constant annual change. However, a consistent regional pattern does emerge across the set of models. Nonetheless, aggregating climate change across seasons and regions within the United States, using a national-annual climate change provides a reasonable and efficient approximation to the expected impact predicted by the 16 GCM models.  相似文献   

4.
Going to the Extremes   总被引:8,自引:1,他引:8  
Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments. An erratum to this article is available at . An erratum to this article can be found at  相似文献   

5.
General circulation models (GCMs) have demonstrated success in simulating global climate, and they are critical tools for producing regional climate projections consistent with global changes in radiative forcing. GCM output is currently being used in a variety of ways for regional impacts projection. However, more work is required to assess model bias and evaluate whether assumptions about the independence of model projections and error are valid. This is particularly important where models do not display offsetting errors. Comparing simulated 300-hPa zonal winds and precipitation for the late 20th century with reanalysis and gridded precipitation data shows statistically significant and physically plausible associations between positive precipitation biases across all models and a marked increase in zonal wind speed around 30°N, as well as distortions in rain shadow patterns. Over the western United States, GCMs project drier conditions to the south and increasing precipitation to the north. There is a high degree of agreement between models, and many studies have made strong statements about implications for water resources and about ecosystem change on that basis. However, since one of the mechanisms driving changes in winter precipitation patterns appears to be associated with a source of error in simulating mean precipitation in the present, it suggests that greater caution should be used in interpreting impacts related to precipitation projections in this region and that standard assumptions underlying bias correction methods should be scrutinized.  相似文献   

6.
This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO2 conditions and for 2 × CO2 conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.  相似文献   

7.
Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO2 and 1 × CO2 GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO2 concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.  相似文献   

8.
To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.  相似文献   

9.
利用基于张弛逼近的四维数据同化技术,构建了广东深圳的千米格距网格化气象数据集,由于同化了深圳及周边可获得的高频次观测数据,气象数据集基本准确表现出几种关键气象要素的年际变化和月变化特征。在网格化气象数据集基础上开发了“深圳市细网格气候信息平台”,并通过平台推出了若干精细气候数据产品:精细风玫瑰、逐网格风能等。这些数据产品已经在格点气温预报、风能示范项目选址以及详细规划的自然通风评估中发挥了实际作用。这些探索表明,网格化气象数据集的建立,有望为城市的网格化精细管理和建设提供气象科技支撑。   相似文献   

10.
The outputs of three GCMs, ECHAM5, CCSM3 and HadCM3, are downscaled for the eastern Mediterranean–Black Sea region for the period 1961–1990 using a regional climate model, RegCM3, to assess the capability of these models in simulating the climatology of the region. In addition, the NCEP/NCAR Reanalysis data are also downscaled for the same period to display the performance of the regional climate model for the same region, which constitutes a relatively complex terrain and rich variety of climates. The gridded observational dataset of CRU is primarily used in the evaluation of the models, however, a regional dataset, which is based on a relatively dense gauging network, is also used to see how it affects the performance measures of the models. The reanalysis simulation indicates that RegCM3 is able to simulate the precipitation and surface temperature as well as the upper level fields reasonably well. However, it tends to overestimate the precipitation over the mountainous areas. All three GCM models are found to be highly skilled in simulating the winter precipitation and temperature in the region. The two models, ECHAM5 and HadCM3, are also good at simulating the summer precipitation and temperature, but the CCSM3 simulation generates dryer and warmer conditions than the observations for the whole region, which are most likely a result of the dryness in the upper levels of the original outputs. The use of the regional observational dataset does not necessarily improve the pattern correlations, but it yields better match between the modeled and observed precipitation in terms of variability and root-mean-square difference. It could be said that the outputs of these GCMs can be used in the climate change downscaling and impact assessment studies for the region, given that their strengths and weaknesses that are displayed in the present study are considered.  相似文献   

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