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1.
The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution. 相似文献
2.
Andreas Haensler Stefan Hagemann Daniela Jacob 《Theoretical and Applied Climatology》2011,106(1-2):153-169
High-resolution regional climate change simulations have proven to offer an added value compared to available global climate model simulations. However, over many regions of the globe, long-term high-resolution climate change projections are rather sparse. We present a transient high-resolution climate change projection with the regional climate model with the regional climate model REMO over the southern African region, following the SRES A1B emission scenario. The simulation was conducted at 18?km grid spacing for the period from 1960 to 2100, making it to the longest available climate change projection at such a high resolution for the region. In the first part of the study, we focus on the impact of the model setup on the simulated rainfall over the southern African region. In the standard setup, we used the output of the global climate model ECHAM5/MPIOM directly to force REMO. This setup led to a very strong wet bias over the region. Changing it to the double-nesting setup significantly reduced this bias, but a substantial wet bias still persists. The remaining bias could partly be attributed to a warm bias in the SST forcing over the southern Atlantic Ocean. Thus, we applied an SST correction based on the anomaly approach to the data, which led to a further improvement of the rainfall simulation. As the SST bias in the southern Atlantic is a common feature of all global climate models assessed by the IPCC, we recommend the chosen model setup, including the SST correction, as general procedure for dynamical downscaling studies over the southern African region. In the second part, we present the projected spatial and temporal changes of temperature and precipitation, including several rainfall characteristics, over the southern African region. Herby we compare the projections of the high-resolution REMO simulation to those of the forcing regional and global models. We generally find that for temperature the magnitude of the projected changes of the regional model only slightly differs from the GCM projection; however, the spatial patterns are much better resolved in the RCM projections. For precipitation, REMO shows a more intense drying toward the end of the twenty-first century than it is simulated by the global model. This can have a major influence when investigating the impacts of future climate change on a regional or even local scale. In combination with the improved spatial patterns, the application of high-resolution climate change information could therefore improve the results of such applications. 相似文献
3.
The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface
forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency
of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the
Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference
climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution
of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation
analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller
sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment
has been carried out for four winter months over the west coast of North America. The results show that complex topography
and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings,
found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility.
Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique
displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between
the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing
the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of
spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better
downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found
by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution.
Figure legends were missing in original article. Climate Dynamics (2005) 23: 473-493. The complete article is given here.
DOI: 10.1007/s00382-004-0438-5 相似文献
4.
Downscaling ability of one-way nested regional climate models: the Big-Brother Experiment 总被引：8，自引：4，他引：4
A methodology is developed for testing the downscaling ability of nested regional climate models (RCMs). The proposed methodology, nick-named the Big-Brother Experiment (BBE), is based on a "perfect-prognosis" approach and hence does not suffer from model errors nor from limitations in observed climatologies. The BBE consists in first establishing a reference climate by performing a large-domain high-resolution RCM simulation: this simulation is called the Big Brother. This reference simulation is then degraded by filtering short scales that are unresolved in today's global objective analyses (OA) and/or global climate models (GCMs) when integrated for climate projections. This filtered reference is then used to drive the same nested RCM (called the Little Brother), integrated at the same high-resolution as the Big Brother, but over a smaller domain that is embedded in the Big-Brother domain. The climate statistics of the Little Brother are then compared with those of the Big Brother over the Little-Brother domain. Differences can thus be attributed unambiguously to errors associated with the nesting and downscaling technique, and not to model errors nor to observation limitations. The results of the BBE applied to a one-winter-month simulation over eastern North America at 45-km grid-spacing resolution show that the one-way nesting strategy has skill in downscaling large-scale information to the regional scales. The time mean and variability of fine-scale features in a number of fields, such as sea level pressure, 975-hPa temperature and precipitation are successfully reproduced, particularly over regions where small-scale surface forcings are strong. Over other regions such as the ocean and away from the surface, the small-scale reproducibility is more difficult to achieve. 相似文献
5.
The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate downscaling. For impact studies that involve glacier modelling, the sparsity or complete absence of climate monitoring activities within the regions of interest presents a substantial additional challenge. Downscaling methods for this application must be independent of climate observations and cannot rely on tuning to station data. We present new, computationally-efficient methods for downscaling precipitation and temperature to the high spatial resolutions required to force mountain glacier models. Our precipitation downscaling is based on an existing linear theory for orographic precipitation, which we modify for large study regions by including moist air tracking. Temperature is downscaled using an interpolation scheme that reconstructs the vertical temperature structure to estimate surface temperatures from upper air data. Both methods are able to produce output on km to sub-km spatial resolution, yet do not require tuning to station measurements. By comparing our downscaled precipitation (1 km resolution) and temperature (200 m resolution) fields to station measurements in southern British Columbia, we evaluate their performance regionally and through the annual cycle. Precipitation is improved by as much as 30% (median relative error) over the input reanalysis data and temperature is reconstructed with a mean bias of 0.5°C at locations with high vertical relief. Both methods perform best in mountainous terrain, where glaciers tend to be concentrated. 相似文献
6.
区域海气耦合模式是研究局地海气相互作用过程影响气候变率的重要平台,也是对全球气候模式进行"动力降尺度"的重要工具.本文介绍了LASG(State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics)/IAP(Institute of Atmospheric Physics)发展的区域海气耦合模式FROALS(Flexible Regional Ocean-Atmosphere-Land System model),并总结了过去五年围绕该区域海气耦合模式开展的研究工 作.FROALS的特点之一是有两个完全不同的大气模式分量和海洋模式分量选项,可以适应不同的模拟研究需 求.针对区域海气耦合模式在西北太平洋地区的模拟偏差,通过分步骤考察不同大气模式分量和不同海洋模式分量对模式模拟性能的影响,指出大气模式是导致区域海气耦合偏差的主要分量.通过改进对流触发的相对湿度阈值标准,有效地改善了此前区域海气耦合模式在亚洲季风区普遍出现的"模拟海温冷偏差".改进的FROALS对西北太平洋地区的大气和海洋环境有较好的模拟能力,合理地再现了西北太平洋地区表层洋流气候态和年际变率.较之非耦合模式,考虑区域海气耦合过程后,改进了东亚和南亚地区的降水和热带气旋潜势年际变率的模拟.最后,针对东亚—西北太平洋地区,利用FROALS对IAP/LASG全球气候模式模拟和预估的结果进行了动力降尺 度,得到了东亚区域50 km高分辨率区域气候变化信息.分析显示,FROALS模拟得到的东亚区域气候较之全球气候模式和非耦合区域气候模式结果具有明显的"增值",显示出区域海气耦合模式在该区域良好的应用前景. 相似文献
7.
Globally-coupled climate models are generally capable of reproducing the observed trends in the globally averaged atmospheric temperature or mean sea level. However, the global models do not perform as well on regional/local scales. Here, we present results from four 100-year ocean model experiments for the Western Baltic Sea. In order to simulate storm surges in this region, we have used the General Estuarine Transport Model (GETM) as a high-resolution local model (spatial resolution ≈ 1?km), nested into a regional atmospheric and regional oceanic model in a fully baroclinic downscaling approach. The downscaling is based on the global model ECHAM5/MPI-OM. The projections are imbedded into two greenhouse-gas emission scenarios, A1B and B1, for the period 2000–2100, each with two realisations. Two control runs from 1960 to 2000 are used for validation. We use this modelling system to statistically reproduce the present distribution of surge extremes. The usage of the high-resolution local model leads to an improvement in surge heights of at least 10% compared to the driving model. To quantify uncertainties associated with climate projections, we investigate the impact of enhanced wind velocities and changes in mean sea levels. The analysis revealed a linear dependence of surge height and mean sea level, although the slope parameter is spatially varying. Furthermore, the modelling system is used to project possible changes within the next century. The results show that the sea level rise has greater potential to increase surge levels than does increased wind speed. The simulations further indicate that the changes in storm surge height in the scenarios can be consistently explained by the increase in mean sea level and variation in wind speed. 相似文献
8.
A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as “field” or “global” significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ° grid resolution. Monthly temperature climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure. 相似文献
9.
Emmanouil Flaounas Philippe Drobinski Mathieu Vrac Sophie Bastin Cindy Lebeaupin-Brossier Marc Stéfanon Marco Borga Jean-Christophe Calvet 《Climate Dynamics》2013,40(11-12):2687-2705
This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales. 相似文献
10.
Charles L. Curry Bárbara Tencer Kirien Whan Andrew J. Weaver Michel Giguère Edward Wiebe 《大气与海洋》2016,54(4):364-384
We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10?km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX?>?25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45?km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability. 相似文献
11.
In atmospheric models, the partitioning of precipitation between infiltration and runoff has a major influence on the terrestrial water budget, and thereby on the simulated weather or climate. River routing models are now available to convert the simulated runoff into river discharge, offering a good opportunity to validate land surface models at the regional scale. However, given the low resolution of global atmospheric models, the quality of the hydrological simulations is much dependent on various processes occurring on unresolved spatial scales. This paper focuses on the parameterization of sub-grid hydrological processes within the ISBA land surface model. Five off-line simulations are performed over the French Rhône river basin, including various sets of parameterizations related to the sub-grid variability of topography, precipitation, maximum infiltration capacity and land surface properties. Parallel experiments are conducted at a high (8 km by 8 km) and low (1° by 1°) resolution, in order to test the robustness of the simulated water budget. Additional simulations are performed using the whole package of sub-grid parameterizations plus an exponential profile with depth of saturated hydraulic conductivity, in order to investigate the interaction between the vertical soil physics and the horizontal heterogeneities. All simulations are validated against a dense network of gauging measurements, after the simulated runoff is converted into discharge using the MODCOU river routing model. Generally speaking, the new version of ISBA, with both the sub-grid hydrology and the modified hydraulic conductivity, shows a better simulation of river discharge, as well as a weaker sensitivity to model resolution. The positive impact of each individual sub-grid parameterization on the simulated discharges is more obvious at the low resolution, whereas the high-resolution simulations are more sensitive to the exponential profile with depth of saturated hydraulic conductivity. 相似文献
12.
A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as ‘field’ or ‘global’ significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ° grid resolution. Daily precipitation climatology for the 1990–2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure. 相似文献
13.
Mid-Century Ensemble Regional Climate Change Scenarios for the Western United States 总被引：3，自引：0，他引：3
L. Ruby Leung Yun Qian Xindi Bian Warren M. Washington Jongil Han John O. Roads 《Climatic change》2004,62(1-3):75-113
To study the impacts of climate change on water resources in the western U.S., global climate simulations were produced using the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Parallel Climate Model (PCM). The Penn State/NCAR Mesoscale Model (MM5) was used to downscale the PCM control (20 years) and three future(2040–2060) climate simulations to yield ensemble regional climate simulations at 40 km spatial resolution for the western U.S. This paper describes the regional simulations and focuses on the hydroclimate conditions in the Columbia River Basin (CRB) and Sacramento-San Joaquin River (SSJ) Basin. Results based on global and regional simulations show that by mid-century, the average regional warming of 1 to 2.5 °C strongly affects snowpack in the western U.S. Along coastal mountains, reduction in annual snowpack was about70% as indicated by the regional simulations. Besides changes in mean temperature, precipitation, and snowpack, cold season extreme daily precipitation increased by 5 to 15 mm/day (15–20%) along theCascades and the Sierra. The warming resulted in increased rainfall at the expense of reduced snowfall, and reduced snow accumulation (or earlier snowmelt) during the cold season. In the CRB, these changes were accompanied by more frequent rain-on-snow events. Overall, they induced higher likelihood of wintertime flooding and reduced runoff and soil moisture in the summer. Changes in surface water and energy budgets in the CRB and SSJ basin were affected mainly by changes in surface temperature, which were statistically significant at the 0.95 confidence level. Changes in precipitation, while spatially incoherent, were not statistically significant except for the drying trend during summer. Because snow and runoff are highly sensitive tospatial distributions of temperature and precipitation, this study shows that (1) downscaling provides more realistic estimates of hydrologic impacts in mountainous regions such as the western U.S., and (2) despite relatively small changes in temperature and precipitation, changes in snowpack and runoff can be much larger on monthly to seasonal time scales because the effects of temperature and precipitation are integrated over time and space through various surface hydrological and land-atmosphere feedback processes. Although the results reported in this study were derived from an ensemble of regional climate simulations driven by a global climate model that displays low climate sensitivity compared with most other models, climate change was found to significantly affect water resources in the western U.S. by the mid twenty-first century. 相似文献
14.
Regional climate model projections for the State of Washington 总被引：3，自引：1，他引：2
Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations. 相似文献
15.
Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods 总被引：2，自引：2，他引：0
Yurong Hu Shreedhar Maskey Stefan Uhlenbrook 《Theoretical and Applied Climatology》2013,112(3-4):447-460
Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability. 相似文献
16.
Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle 总被引：1，自引：1，他引：0
The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations. 相似文献
17.
There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least. 相似文献
18.
基于区域气候模式RegCM4对4个全球气候模式动力降尺度模拟（分别记为CdR、EdR、HdR、MdR）以及高分辨率格点观测数据CN05.1的日降水数据,利用“追踪式”客观识别方法,对1981—2005年中国区域性暴雨事件进行了识别,并评估了模式对其气候特征的模拟性能。结果表明：4个动力降尺度模拟以及多模式集合能较好地模拟区域性暴雨事件发生频次、平均持续时间、平均降水量、平均影响范围和综合强度的年内分布特征以及气候平均值。观测的区域性暴雨事件持续时间、平均降水量、平均影响范围和综合强度在不同区间的频率分布特征以及区域性暴雨事件的累计频次、累计持续时间和累计降水量的空间分布特征也能得到很好地再现。模拟值与观测的空间相关系数都在0.9以上,且均方根误差不超过0.4。不过,相对而言,模式模拟的区域性暴雨事件频次略少,主要由对中度区域性暴雨事件低估所致;模拟的平均持续时间和平均降水量略偏高,而平均影响范围略偏小。综合强度方面,除HdR外,其余模拟均有所高估,尤其是MdR。在频率分布特征和空间分布方面,CdR的模拟性能低于其他模拟。多模式集合模拟的平均持续时间、平均降水量、平均影响范围和综合强度的相对误差分别为13%、2%、-11%和3%。 相似文献
19.
Charles L. Curry Bárbara Tencer Kirien Whan Andrew J. Weaver Michel Giguère Edward Wiebe 《大气与海洋》2016,54(4):385-402
We evaluate the capacity of a regional climate model to simulate the statistics of extreme events, and also examine the effect of differing horizontal resolution, at the scale of individual hydrological basins in the topographically complex province of British Columbia, Canada. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by global reanalysis over the period 1973–1995. The simulations were evaluated with ANUSPLIN, a daily observational gridded surface temperature and precipitation product and with meteorological data recorded at 28 stations within the upper Peace, Nechako, and upper Columbia River basins. In this work, we focus largely on a comparison of the skill of each model configuration in simulating the 90th percentile of daily precipitation (PR90). The companion paper describes the results for a wider range of temperature and precipitation extremes over the entire WCan domain.Over all three watersheds, both simulations exhibit cold biases compared with observations, with the bias exacerbated at higher resolution. Although both simulations generally display wet biases in median precipitation, CRCM15 features a reduced bias in PR90 in all three basins in summer and throughout the year in the upper Columbia River basin. However, the higher resolution model is inferior to CRCM45 with respect to rarer heavy precipitation events and also displays high spatial variability and lower spatial correlations with ANUSPLIN compared with the coarser resolution model. A reduction in the range of PR90 biases over the upper Columbia basin is noted when the 15?km results are averaged to the 45?km grid. This improvement is partly attributable to the averaging of errors between different elevation data used in the gridded observations and CRCM, but the sensitivity of CRCM15 to resolved topography is also clear from spatial maps of seasonal extremes. At the station scale, modest but systematic reductions in the bias of PR90 relative to ANUSPLIN are again found when the CRCM15 results are averaged to the 45?km grid. Furthermore, the annual cycle of inter-station spatial variance in the upper Columbia River basin is well reproduced by CRCM15 but not by ANUSPLIN or CRCM45. The former result highlights the beneficial effect of spatial averaging of small-scale climate variability, whereas the latter is evidently a demonstration of the added value at high resolution vis-à-vis the improved simulation of precipitation at the resolution limit of the model. 相似文献
20.
USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS 总被引：14，自引：0，他引：14
Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments. 相似文献