Downscaling ability of one-way nested regional climate models: the Big-Brother Experiment

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.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

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.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

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     

Present climate simulation over Korea with a regional climate model using a one-way double-nested system

Summary This study investigates the capability of the regional climate model RegCM3 to simulate surface air temperature and precipitation over the Korean Peninsula. The model is run in one-way double nested mode, with a 60 km grid point spacing “mother” domain encompassing the eastern regions of Asia and a 20 km grid point spacing nested domain covering the Korean Peninsula. The simulation spans the three-year period of 1 October 2000 through 30 September 2003 and the boundary conditions needed to run the mother domain experiment are provided from the NCEP reanalysis of observations. The model results are compared with a high density station observation dataset to examine the fine scale structure of the surface climate signal. The model shows a good performance in capturing both the sign and magnitude of the seasonal and inter-annual variations of the surface variables both over East Asia as a whole and over the Korean Peninsula in the nested system. Some persistent biases are however present. Surface temperature is systematically underestimated, especially over mountainous regions in the warm season. This feature may be due to the relatively coarse representation of the Korean topography. The simulated precipitation over the mother domain successfully reproduces the broad spatial pattern of observed precipitation over East Asia along with its seasonal evolution. On the other hand, fine scale details from the nested results show a varying level of quality for the different individual years. Because of the better resolved topographic forcing, the increased resolution of the nested model improves the spatial agreement with the fine scale observation fields for temperature and cold season precipitation. For summer monsoon precipitation the simulation of individual monsoon convective events and tropical storms is however more important than the topographic forcing, and therefore the performance of the nested system is more case-dependent.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

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.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields

To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.     

Evaluation of statistical downscaling in short range precipitation forecasting

The objective of this study is to compare several statistical downscaling methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical downscaling methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of “blind” criteria, such as “give the statistical downscaling methods all the information they can process”.Results show that the use of statistical downscaling methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical downscaling method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical downscaling method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.     

Assessing the effect of domain size over the Caribbean region using the PRECIS regional climate model

This study investigates the sensitivity of the one-way nested PRECIS regional climate model (RCM) to domain size for the Caribbean region. Simulated regional rainfall patterns from experiments using three domains with horizontal resolution of 50 km are compared with ERA reanalysis and observed datasets to determine if there is an optimal RCM configuration with respect to domain size and the ability to reproduce important observed climate features in the Caribbean. Results are presented for the early wet season (May–July) and late wet season (August–October). There is a relative insensitivity to domain size for simulating some important features of the regional circulation and key rainfall characteristics e.g. the Caribbean low level jet and the mid summer drought (MSD). The downscaled precipitation has a systematically negative precipitation bias, even when the domain was extended to the African coast to better represent circulation associated with easterly waves and tropical cyclones. The implications for optimizing modelling efforts within resource-limited regions like the Caribbean are discussed especially in the context of the region’s participation in global initiatives such as CORDEX.     

A non-parametric, statistical downscaling algorithm applied to the Rohini River Basin, Nepal

Climate change scenarios generated by general circulation models have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river basin scales. This study presents a new non-parametric statistical K-nearest neighbor algorithm for downscaling climate change scenarios for the Rohini River Basin in Nepal. The study is an introduction to the methodology and discusses its strengths and limitations within the context of hindcasting basin precipitation for the period of 1976?C2006. The actual downscaled climate change projections are not presented here. In general, we find that this method is quite robust and well suited to the data-poor situations common in developing countries. The method is able to replicate historical rainfall values in most months, except for January, September, and October. As with any downscaling technique, whether numerical or statistical, data limitations significantly constrain model ability. The method was able to confirm that the dataset available for the Rohini Basin does not capture long-term climatology. Yet, we do find that the hindcasts generated with this methodology do have enough skill to warrant pursuit of downscaling climate change scenarios for this particularly poor and vulnerable region of the world.     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region.     

Multi-decadal scenario simulation over Korea using a one-way double-nested regional climate model system. Part 1: recent climate simulation (1971–2000)

We present an analysis of a high resolution multi-decadal simulation of recent climate (1971–2000) over the Korean Peninsula with a regional climate model (RegCM3) using a one-way double-nested system. Mean climate state as well as frequency and intensity of extreme climate events are investigated at various temporal and spatial scales, with focus on surface air temperature and precipitation. The mother intermediate resolution model domain encompasses the eastern regions of Asia at 60 km grid spacing while the high resolution nested domain covers the Korean Peninsula at 20 km grid spacing. The simulation spans the 30-year period of January 1971 through December 2000, and initial and lateral boundary conditions for the mother domain are provided from ECHO-G fields based on the IPCC SRES B2 scenario. The model shows a good performance in reproducing the climatological and regional characteristics of surface variables, although some persistent biases are present. Main results are as follows: (1) The RegCM3 successfully simulates the fine-scale structure of the temperature field due to topographic forcing but it shows a systematic cold bias mostly due to an underestimate of maximum temperature. (2) The frequency distribution of simulated daily mean temperature agrees well with the observed seasonal and spatial patterns. In the summer season, however, daily variability is underestimated. (3) The RegCM3 simulation adequately captures the seasonal evolution of precipitation associated to the East Asia monsoon. In particular, the simulated winter precipitation is remarkably good, clearly showing typical precipitation patterns that occur on the northwestern areas of Japan during the winter monsoon. Although summer precipitation is underestimated, area-averaged time series of precipitation over Korea show that the RegCM3 agrees better with observations than ECHO-G both in terms of seasonal evolution and precipitation amounts. (4) Heavy rainfall phenomena exceeding 300 mm/day are simulated only at the high resolution of the double nested domain. (5) The model shows a tendency to overestimate the number of precipitation days and to underestimate the precipitation intensities. (6) A CSEOF analysis reveals that the model captures the strength of the annual cycle and the surface warming trend throughout the simulated period.     

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.     

High-resolution precipitation and temperature downscaling for glacier models

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.     

CSIRO Mk3.6.0模式及其驱动下RegCM4.4模式对中国气候变化的预估

使用区域气候模式RegCM4.4,对全球模式CSIRO-Mk3.6.0在RCP4.5情景下的气候变化试验结果（1950-2100年）在东亚地区进行25 km动力降尺度试验,比较了CSIRO-Mk3.6.0和RegCM4.4预估中国地区的21世纪气候变化。结果表明,两个模式预估未来中国地区气温持续升高,升温幅度具有区域性特征,RegCM4.4预估区域平均升温幅度低于CSIRO-Mk3.6.0,但二者年际波动基本一致。两个模式预估未来降水在中国西部以持续增加为主,东部则表现出较大的不一致性,预估区域平均年降水量变化不大,呈现冬季明显增加,夏季微弱减少的特点。此外,为了解区域气候模式对中国降水预估的不确定性,对本研究和以往RegCM3使用相同分辨率模拟得到的未来降水预估进行了对比,两个区域模式预估中国西部大部分地区未来降水一致性增加,东部存在明显不一致（冬季中、高纬除外）。     

Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis

The Mediterranean region is identified as one of the two main hot-spots of climate change and also known to have the highest concentration of cyclones in the world. These atmospheric features contribute significantly to the regional climate but they are not reproduced by the Atmosphere–Ocean General Circulation Models (AOGCM), due to their coarse horizontal resolution, which have recently been run in the frame of the 5th Climate Model Intercomparison Project. This article investigates the benefit of dynamically downscaling the Institut Pierre Simon Laplace (IPSL) AOGCM (IPSL-CM5) historical simulation by the weather and research forecasting (WRF) for the representation of the Mediterranean surface winds and cyclonic activity. Indeed, when considering IPSL-CM5 atmospheric fields, the dramatic underestimation of the cyclonic activity in the most cyclogenetic region of the world jeopardizes our ability to investigate in-depth the Mediterranean regional climate and trend in the context of global change. The WRF model shows remarkable skill to reproduce regional cyclogenesis. Indeed, cyclones occurrence is quasi-absent in IPSL-CM5 data but when applying dynamical downscaling their spatial–temporal variability is very close to the re-analysis. This is a clear benefit of dynamical downscaling in regions of strong topographic forcing. This “steady” source of forcing allows the production of lee cyclogenesis and the development of strong cyclones, whatever the quality of the large-scale circulation provided at the WRF’s boundaries by IPSL-CM5. However, dynamical downscaling still presents disadvantages as for instance the fact that large-scale inaccurate features of the IPSL-CM5 regional circulation are replicated by WRF due to the boundary controlled (small domain) simulation. The advantages and disadvantages of dynamical downscaling are thoroughly discussed in this paper revealing its importance for climate research, especially in the context of future scenarios and wind impacts.     

Sensitivity of Precipitation in Aqua-Planet Experiments with an AGCM

The sensitivity of precipitation was studied by conducting control aqua-planet experiments(APEs) with a model to determine atmospheric general circulation.The model includes two versions: that with a spectral dynamical core(SAMIL) and that with a finite-volume dynamical core(FAMIL).Three factors were investigated including dynamical core,time-step length,and horizontal resolution.Numerical results show that the dynamical core significantly affects the structure of zonal averaged precipitation.FAMIL exhibited an equatorial precipitation belt with a single narrow peak,and SAMIL showed a broader belt with double peaks.Moreover,the time step of the model physics is shown to affect the zonal-averaged tropical convective precipitation ratio such that a longer time step leads to more production and consumption of convective available potential energy and convection initiated away from the equator,which corresponds to equatorial double peaks of precipitation.Further,precipitation is determined to be sensitive to horizontal resolution such that higher horizontal resolution allows for more small-scale kinetic energy to be resolved and leads to a broader probability distribution of low-level vertical velocity.This process results in heavier rainfall and convective precipitation extremes in the tropics.Abstract The sensitivity of precipitation was studied by conducting control aqua-planet experiments(APEs)with a model to determine atmospheric general circulation.The model includes two versions:that with a spectral dynamical core(SAMIL)and that with a finite-volume dynamical core(FAMIL).Three factors were investigated including dynamical core,time-step length,and horizontal resolution.Numerical results show that the dynamical core significantly affects the structure of zonal averaged precipitation.FAMIL exhibited an equatorial precipitation belt with a single narrow peak,and SAMIL showed a broader belt with double peaks.Moreover,the time step of the model physics is shown to affect the zonal-averaged tropical convective precipitation ratio such that a longer time step leads to more production and consumption of convective available potential energy and convection initiated away from the equator,which corresponds to equatorial double peaks of precipitation.Further,precipitation is determined to be sensitive to horizontal resolution such that higher horizontal resolution allows for more small-scale kinetic energy to be resolved and leads to a broader probability distribution of low-level vertical velocity.This process results in heavier rainfall and convective precipitation extremes in the tropics.