首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 140 毫秒
1.
Climate models are increasingly being used to force dynamical wind wave models in order to assess the potential climate change-driven variations in wave climate. In this study, an ensemble of wave model simulations have been used to assess the ability of climate model winds to reproduce the present-day (1981–2000) mean wave climate and its seasonal variability for the southeast coast of Australia. Surface wind forcing was obtained from three dynamically downscaled Coupled Model Intercomparison Project (CMIP-3) global climate model (GCM) simulations (CSIRO Mk3.5, GFDLcm2.0 and GFDLcm2.1). The downscaling was performed using CSIRO’s cubic conformal atmospheric model (CCAM) over the Australian region at approximately 60-km resolution. The wind climates derived from the CCAM downscaled GCMs were assessed against observations (QuikSCAT and NCEP Re-analysis 2 (NRA-2) reanalyses) over the 1981–2000 period and were found to exhibit both bias in mean wind conditions (climate bias) as well as bias in the variance of wind conditions (variability bias). Comparison of the modelled wave climate with over 20 years of wave data from six wave buoys in the study area indicates that direct forcing of the wave models with uncorrected CCAM winds result in suboptimal wave hindcast. CCAM winds were subsequently adjusted for climate and variability bias using a bivariate quantile adjustment which corrects both directional wind components to align in distribution to the NRA-2 winds. Forcing of the wave models with bias-adjusted winds leads to a significant improvement of the hindcast mean annual wave climate and its seasonal variability. However, bias adjustment of the CCAM winds does not improve the ability of the model to reproduce the storm wave climate. This is likely due to a combination of storm systems tracking too quickly through the wave generation zone and the performance of the NRA-2 winds used as a benchmark in this study.  相似文献   

2.
The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.  相似文献   

3.
Global climate change is one of the most serious issues we are facing today. While its exact impacts on our water resources are hard to predict, there is a general consensus among scientists that it will result in more frequent and more severe hydrologic extremes (e.g. floods, droughts). Since rainfall is the primary input for hydrologic and water resource studies, assessment of the effects of climate change on rainfall is essential for devising proper short-term emergency measures as well as long-term management strategies. This is particularly the case for a region like the Korean Peninsula, which is susceptible to both floods (because of its mountainous terrain and frequent intense rainfalls during the short rainy season) and droughts (because of its smaller area, long non-rainy season, and lack of storage facilities). In view of this, an attempt is made in the present study to investigate the potential impacts of climate change on rainfall in the Korean Peninsula. More specifically, the dynamics of ‘present rainfall’ and ‘future rainfall’ at the Seoul meteorological station in the Han River basin are examined and compared; monthly scale is considered in both cases. As for ‘present rainfall,’ two different data sets are used: (1) observed rainfall for the period 1971–1999; and (2) rainfall for the period 1951–1999 obtained through downscaling of coarse-scale climate outputs produced by the Bjerknes Center for Climate Research-Bergen Climate Model Version 2 (BCCR-BCM2.0) climate model with the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC SRES) 20th Century Climate in Coupled Models (20C3M) scenario. The ‘future rainfall’ (2000–2099) is obtained through downscaling of climate outputs projected by the BCCR-BCM2.0 with the A2 emission scenario. For downscaling of coarse-scale climate outputs to basin-scale rainfall, a K-nearest neighbor (K-NN) technique is used. Examination of the nature of rainfall dynamics is made through application of four methods: autocorrelation function, phase space reconstruction, correlation dimension, and close returns plot. The results are somewhat mixed, depending upon the method, as to whether the rainfall dynamics are chaotic or stochastic; however, the dynamics of the future rainfall seem more on the chaotic side than on the stochastic side, and more so when compared to that of the present rainfall.  相似文献   

4.
Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010–2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial–temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann–Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima.  相似文献   

5.
In this paper, we investigate changes in the wave climate of the west-European shelf seas under global warming scenarios. In particular, climate change wind fields corresponding to the present (control) time-slice 1961–2000 and the future (scenario) time-slice 2061–2100 are used to drive a wave generation model to produce equivalent control and scenario wave climate. Yearly and seasonal statistics of the scenario wave climates are compared individually to the corresponding control wave climate to identify relative changes of statistical significance between present and future extreme and prevailing wave heights. Using global, regional and linked global–regional wind forcing over a set of nested computational domains, this paper further demonstrates the sensitivity of the results to the resolution and coverage of the forcing. It suggests that the use of combined forcing from linked global and regional climate models of typical resolution and coverage is a good option for the investigation of relative wave changes in the region of interest of this study. Coarse resolution global forcing alone leads to very similar results over regions that are highly exposed to the Atlantic Ocean. In contrast, fine resolution regional forcing alone is shown to be insufficient for exploring wave climate changes over the western European waters because of its limited coverage. Results obtained with the combined global–regional wind forcing showed some consistency between scenarios. In general, it was shown that mean and extreme wave heights will increase in the future only in winter and only in the southwest of UK and west of France, north of about 44–45° N. Otherwise, wave heights are projected to decrease, especially in summer. Nevertheless, this decrease is dominated by local wind waves whilst swell is found to increase. Only in spring do both swell and local wind waves decrease in average height.  相似文献   

6.
This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.  相似文献   

7.
Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall–runoff model is employed to simulate the rainfall–runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (Tmax) is between 3.7 and 4.7 °C until the time period of 2070–2099 (2080s). While, the increases range of minimum temperature (Tmin) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season.  相似文献   

8.
This paper assesses linear regression‐based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45‐ and 15‐km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger‐scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher‐resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation‐generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression‐based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

9.
Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.  相似文献   

10.
In this study, the applicability of the statistical downscaling model (SDSM) in downscaling precipitation in the Yangtze River basin, China was investigated. The investigation includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP/NCAR reanalysis data, the validation of the model using independent period of the NCEP/NCAR reanalysis data and the general circulation model (GCM) outputs of scenarios A2 and B2 of the HadCM3 model, and the prediction of the future regional precipitation scenarios. Selected as climate variables for downscaling were measured daily precipitation data (1961–2000) from 136 weather stations in the Yangtze River basin. The results showed that: (1) there existed good relationship between the observed and simulated precipitation during the calibration period of 1961–1990 as well as the validation period of 1991–2000. And the results of simulated monthly and seasonal precipitation were better than that of daily. The average R 2 values between the simulated and observed monthly and seasonal precipitation for the validation period were 0.78 and 0.91 respectively for the whole basin, which showed that the SDSM had a good applicability on simulating precipitation in the Yangtze River basin. (2) Under both scenarios A2 and B2, during the prediction period of 2010–2099, the change of annual mean precipitation in the Yangtze River basin would present a trend of deficit precipitation in 2020s; insignificant changes in the 2050s; and a surplus of precipitation in the 2080s as compared to the mean values of the base period. The annual mean precipitation would increase by about 15.29% under scenario A2 and increase by about 5.33% under scenario B2 in the 2080s. The winter and autumn might be the more distinct seasons with more predicted changes of precipitation than in other seasons. And (3) there would be distinctive spatial distribution differences for the change of annual mean precipitation in the river basin, but the most of Yangtze River basin would be dominated by the increasing trend.  相似文献   

11.
《水文研究》2017,31(1):35-50
A methodology based on long‐term dynamical downscaling to analyse climate change effects on watershed‐scale precipitation during a historical period is proposed in this study. The reliability and applicability of the methodology were investigated based on the long‐term dynamical downscaling results. For an application of the proposed methodology, two study watersheds in Northern California were selected: the Upper Feather River watershed and the Yuba River watershed. Then, precipitation was reconstructed at 3‐km spatial resolution and hourly intervals over the study watersheds for 141 water years from 1 October 1871 to 30 September 2012 by dynamically downscaling a long‐term atmospheric reanalysis dataset, 20th century global reanalysis version 2 by means of a regional climate model. The reconstructed precipitation was compared against observed precipitation, in order to assess the applicability of the proposed methodology for the reconstruction of watershed‐scale precipitation and to validate this methodology. The validation shows that the reconstructed precipitation is in good agreement with observation data. Moreover, the differences between the reconstructed precipitation and the corresponding observations do not significantly change through the historical period. After the validation, climate change analysis was conducted based on the reconstructed precipitation. Through this analysis, it was found that basin‐average precipitation has increased significantly over both of the study watersheds during the historical period. An upward trend in monthly basin‐average precipitation is not significant in wet months except February while it is significant in dry months of the year. Furthermore, peak values of basin‐average precipitation are also on an upward trend over the study watersheds. The upward trend in peak basin‐average precipitation is more significant during a shorter duration. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

12.
The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.  相似文献   

13.
Simulation of future climate scenarios with a weather generator   总被引：4，自引：0，他引：4  
Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.  相似文献   

14.
The response of the water level in wells to seismic impacts from remote earthquakes and explosions are analyzed. It is shown that in most cases the magnitude of the postseismic change in the water level scales as a square root of the amplitude of the deformation wave. The intensity of persistent changes averages a dynamical deformation of 1–5 cm/μstrain. Noticeable deviations from the mentioned range are possible, depending on the particular structural features of the layer.  相似文献   

15.
Many downscaling techniques have been developed in the past few years for projection of station‐scale hydrological variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K‐nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue‐type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

16.
Eutrophic depletion of dissolved oxygen (DO) and its consequences for ecosystem dynamics have been a central theme of research, assessment and management policies for several decades in the Chesapeake Bay. Ongoing forecast efforts predict the extent of the summer hypoxic/anoxic area due to nutrient loads from the watershed. However, these models neither predict DO levels nor address the intricate interactions among various ecological processes. The prediction of spatially explicit DO levels in the Chesapeake Bay can eventually lead to a reliable depiction of the comprehensive ecological structure and functioning, and can also allow the quantification of the role of nutrient reduction strategies in water quality management. In this paper, we describe a three dimensional empirical model to predict DO levels in the Chesapeake Bay as a function of water temperature, salinity and dissolved nutrient concentrations (TDN and TDP). The residual analysis shows that predicted DO values compare well with observations. Nash–Sutcliffe efficiency (NSE) and root mean square error-observations standard deviation ratio (RSR) are used to evaluate the performance of the empirical model; the scores demonstrate the usability of model predictions (NSE, surface layer = 0.82–0.86; middle layer = 0.65–0.82; bottom layer = 0.70–0.82; RSR surface layer = 0.37–0.44; middle layer = 0.43–0.58 and bottom layer = 0.43–0.54). The predicted DO values and other physical outputs from downscaling of regional weather and climate predictions, or forecasts from hydrodynamic models, can be used to forecast various ecological components. Such forecasts would be useful for both recreational and commercial users of the Chesapeake Bay.  相似文献   

17.
Long-term time-dependent stochastic modelling of extreme waves   总被引：4，自引：3，他引：1  
This paper presents a literature survey on time-dependent statistical modelling of extreme waves and sea states. The focus is twofold: on statistical modelling of extreme waves and space- and time-dependent statistical modelling. The first part will consist of a literature review of statistical modelling of extreme waves and wave parameters, most notably on the modelling of extreme significant wave height. The second part will focus on statistical modelling of time- and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. In particular, Bayesian hierarchical space–time models were identified as promising tools for spatio-temporal modelling of extreme waves. Finally, a review of projections of future extreme wave climate is presented.  相似文献   

18.
The spatial resolution of wind forcing fields is critical for modeling ocean surface waves. We analyze here the performance of the non-hydrostatic numerical weather prediction system WRF-ARW (Weather Research and Forecasting) run with a 14-km resolution for hindcasting wind waves in the North Atlantic. The regional atmospheric model was run in the domain from 20° N to 70° N in the North Atlantic and was forced with ERA-Interim reanalysis as initial and boundary conditions in a spectral nudging mode. Here, we present the analysis of the impact of spectral nudging formulation (cutoff wavelengths and depth through which full weighting from reanalysis data is applied) onto the performance of the modeled 10-m wind speed and wind wave fields for 1 year (2010). For modeling waves, we use the third-generation spectral wave model WAVEWATCH III. The sensitivity of the atmospheric and wave models to the spectral nudging formulation is investigated via the comparison with reanalysis and observational data. The results reveal strong and persistent agreement with reanalysis data during all seasons within the year with well-simulated annual cycle and regional patterns independently of the nudging parameters that were tested. Thus, the proposed formulation of the nudging provides a reliable framework for future long-term experiments aiming at hindcasting climate variability in the North Atlantic wave field. At the same time, dynamical downscaling allows for simulation of higher waves in coastal regions, specifically near the Greenland east coast likely due to a better representation of the mesoscale atmospheric dynamics in this area.  相似文献   

19.
This paper presents the development of a probabilistic multi‐model ensemble of statistically downscaled future projections of precipitation of a watershed in New Zealand. Climate change research based on the point estimates of a single model is considered less reliable for decision making, and multiple realizations of a single model or outputs from multiple models are often preferred for such purposes. Similarly, a probabilistic approach is preferable over deterministic point estimates. In the area of statistical downscaling, no single technique is considered a universal solution. This is due to the fact that each of these techniques has some weaknesses, owing to its basic working principles. Moreover, watershed scale precipitation downscaling is quite challenging and is more prone to uncertainty issues than downscaling of other climatological variables. So, multi‐model statistical downscaling studies based on a probabilistic approach are required. In the current paper, results from the three well‐reputed statistical downscaling methods are used to develop a Bayesian weighted multi‐model ensemble. The three members of the downscaling ensemble of this study belong to the following three broad categories of statistical downscaling methods: (1) multiple linear regression, (2) multiple non‐linear regression, and (3) stochastic weather generator. The results obtained in this study show that the new strategy adopted here is promising because of many advantages it offers, e.g. it combines the outputs of multiple statistical downscaling methods, provides probabilistic downscaled climate change projections and enables the quantification of uncertainty in these projections. This will encourage any future attempts for combining the results of multiple statistical downscaling methods. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

20.
The Antarctic ice sheet surface mass balance shows high spatial variability over the coastal area. As state-of-the-art climate models usually require coarse resolutions to keep computational costs to a moderate level, they miss some local features that can be captured by field measurements. The downscaling approach adopted here consists of using a cascade of atmospheric models from large scale to meso-?? scale. A regional climate model (Modèle Atmosphérique Régional) forced by meteorological reanalyses provides a diagnostic physically-based rain- and snowfall downscaling model with meteorological fields at the regional scale. Although the parameterizations invoked by the downscaling model are fairly simple, the knowledge of small-scale topography significantly improves the representation of spatial variability of precipitation and therefore that of the surface mass balance. Model evaluation is carried out with the help of shallow firn cores and snow height measurements provided by automatic weather stations. Although downscaling of blowing snow still needs to be implemented in the model, the net accumulation gradient across Law Dome summit is shown to be induced mostly by orographic effects on precipitation.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号