A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

A flexible climate model for use in integrated assessments

 Because of significant uncertainty in the behavior of the climate system, evaluations of the possible impact of an increase in greenhouse gas concentrations in the atmosphere require a large number of long-term climate simulations. Studies of this kind are impossible to carry out with coupled atmosphere ocean general circulation models (AOGCMs) because of their tremendous computer resource requirements. Here we describe a two dimensional (zonally averaged) atmospheric model coupled with a diffusive ocean model developed for use in the integrated framework of the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change. The 2-D model has been developed from the Goddard Institute for Space Studies (GISS) GCM and includes parametrizations of all the main physical processes. This allows it to reproduce many of the nonlinear interactions occurring in simulations with GCMs. Comparisons of the results of present-day climate simulations with observations show that the model reasonably reproduces the main features of the zonally averaged atmospheric structure and circulation. The model’s sensitivity can be varied by changing the magnitude of an inserted additional cloud feedback. Equilibrium responses of different versions of the 2-D model to an instantaneous doubling of atmospheric CO₂ are compared with results of similar simulations with different AGCMs. It is shown that the additional cloud feedback does not lead to any physically inconsistent results. On the contrary, changes in climate variables such as precipitation and evaporation, and their dependencies on surface warming produced by different versions of the MIT 2-D model are similar to those shown by GCMs. By choosing appropriate values of the deep ocean diffusion coefficients, the transient behavior of different AOGCMs can be matched in simulations with the 2-D model, with a unique choice of diffusion coefficients allowing one to match the performance of a given AOGCM for a variety of transient forcing scenarios. Both surface warming and sea level rise due to thermal expansion of the deep ocean in response to a gradually increasing forcing are reasonably reproduced on time scales of 100–150 y. However a wide range of diffusion coefficients is needed to match the behavior of different AOGCMs. We use results of simulations with the 2-D model to show that the impact on climate change of the implied uncertainty in the rate of heat penetration into the deep ocean is comparable with that of other significant uncertainties. Received: 10 March 1997 / Accepted: 20 October 1997     

Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations

Large ensembles of coupled atmosphere–ocean general circulation model (AOGCM) simulations are required to explore modelling uncertainty and make probabilistic predictions of future transient climate change at regional scales. These are not yet computationally feasible so we have developed a technique to emulate the response of such an ensemble by scaling equilibrium patterns of climate change derived from much cheaper “slab” model ensembles in which the atmospheric component of an AOGCM is coupled to a mixed-layer ocean. Climate feedback parameters are diagnosed for each member of a slab model ensemble and used to drive an energy balance model (EBM) to predict the time-dependent response of global surface temperature expected for different combinations of uncertain AOGCM parameters affecting atmospheric, land and sea-ice processes. The EBM projections are then used to scale normalised patterns of change derived for each slab member, and hence emulate the response of the relevant atmospheric model version when coupled to a dynamic ocean, in response to a 1% per annum increase in CO₂. The emulated responses are validated by comparison with predictions from a 17 member ensemble of AOGCM simulations, constructed from variants of HadCM3 using the same parameter combinations as 17 members of the slab model ensemble. Cross-validation permits estimation of the spatial and temporal dependence of emulation error, and also allows estimation of a correction field to correct discrepancies between the scaled equilibrium patterns and the transient response, reducing the emulation error. Emulated transient responses and their associated errors are obtained from the slab ensemble for 129 pseudo-HadCM3 versions containing multiple atmospheric parameter perturbations. These are combined to produce regional frequency distributions for the transient response of annual surface temperature change and boreal winter precipitation change. The technique can be extended to any surface climate variable demonstrating a scaleable, approximately linear response to forcing.     

Representing glaciers in a regional climate model

A glacier parameterization scheme has been developed and implemented into the regional climate model REMO. The new scheme interactively simulates the mass balance as well as changes of the areal extent of glaciers on a subgrid scale. The temporal evolution and the general magnitude of the simulated glacier mass balance in the European Alps are in good accordance with observations for the period 1958–1980, but the strong mass loss towards the end of the twentieth century is systematically underestimated. The simulated decrease of glacier area in the Alps between 1958 and 2003 ranges from −17.1 to −23.6%. The results indicate that observed glacier mass balances can be approximately reproduced within a regional climate model based on simplified concepts of glacier-climate interaction. However, realistic results can only be achieved by explicitly accounting for the subgrid variability of atmospheric parameters within a climate model grid box.     

An inter-comparison of regional climate models for Europe: model performance in present-day climate

The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.     

An empirical model for estimating stratospheric ozone vertical distributions over China

Based on the Stratospheric Aerosol and Gas Experiment (SAGE) II and the Halogen Occultation Experiment (HALOE) ozone profiles and the Total Ozone Mapping Spectrometer (TOMS) total ozone data sets, an empirical model for estimating the vertical distribution of stratospheric ozone over China is proposed. By using this model, the vertical distribution of stratospheric (16–50 km) ozone can be estimated according to latitude, month and total ozone. Comparisons are made between the modeled ozone profiles and the ...     

Significance of nitrogen dioxide absorption in estimating aerosol optical depth and size distributions

Abstract

Changes to the Beaufort Sea shoreline occur due to the impact of storms and rising relative sea level. During the open‐water season (June to October), storm winds predominantly from the north‐west generate waves and storm surges which are effective in eroding thawing ice‐rich cliffs and causing overwash of gravel beaches. Climate change is expected to be enhanced in Arctic regions relative to the global mean and include accelerated sea‐level rise, more frequent extreme storm winds, more frequent and extreme storm surge flooding, decreased sea‐ice extent, more frequent and higher waves, and increased temperatures. We investigate historical records of wind speeds and directions, water levels, sea‐ice extent and temperature to identify variability in past forcing and use the Canadian Global Coupled Model ensembles 1 and 2 (CGCM1 and CGCM2) climate modelling results to develop a scenario forcing future change of Beaufort Sea shorelines. This scenario and future return periods of peak storm wind speeds and water levels likely indicate increased forcing of coastal change during the next century resulting in increased rates of cliff erosion and beach migration, and more extreme flooding.     

Long-term projection of main parameters of regional climate till the year 3000

The methods are considered to solve the problem of secure isolation of radioactive waste containing long-lived products of nuclear fuel processing with a half-life of tens of thousands years (plutonium etc.). The methodology of long-term projection of average annual surface air temperature and total precipitation is proposed. Taking into account the possible scenarios of the development of the global socioeconomic system, variations due to the anthropogenic impact in average annual temperature at the Northern Hemisphere mid-latitudes for the period till 2300 are estimated. Based on paleoclimatic data, projections of natural trends in global average annual temperature and total precipitation till the year 3000 are developed. Taking into account the anthropogenic component, the projective ranges of these climatic parameters in the Vyatka River basin in 2100-3000 are evaluated.     

A regional climate model for the western United States

A numerical approach to modeling climate on a regional scale is developed whereby large-scale weather systems are simulated with a global climate model (GCM) and the GCM output is used to provide the boundary conditions needed for high-resolution mesoscale model simulations over the region of interest. In our example, we use the National Center for Atmospheric Research (NCAR) community climate model (CCM1) and the Pennsylvania State University (PSU)/NCAR Mesoscale Model version 4 (MM4) to apply this approach over the western United States (U.S.). The topography, as resolved by the 500-km mesh of the CCM1, is necessarily highly distorted, but with the 60-km mesh of the MM4 the major mountain ranges are distinguished. To obtain adequate and consistent representations of surface climate, we use the same radiation and land surface treatments in both models, the latter being the recently developed Biosphere-Atmosphere Transfer Scheme (BATS). Our analysis emphasizes the simulation at four CCM1 points surrounding Yucca Mountain, NV, because of the need to determine its climatology prior to certification as a high-level nuclear waste repository.We simulate global climate for three years with CCM1/BATS and describe the resulting January surface climatology over the western U.S. The details of the precipitation patterns are unrealistic because of the smooth topography. Selecting five January CCM1 storms that occur over the western U.S. with a total duration of 20 days for simulation with the MM4, we demonstrate that the mesoscale model provides much improved wintertime precipitation patterns. The storms in MM4 are individually much more realistic than those in CCM1. A simple averaging procedure that infers a mean January rainfall climatology calculated from the 20 days of MM4 simulation is much closer to the observed than is the CCM1 climatology. The soil moisture and subsurface drainage simulated over 3–5 day integration periods of MM4, however, remain strongly dependent on the initial CCM1 soil moisture and thus are less realistic than the rainfall. Adequate simulation of surface soil water may require integrations of the mesoscale model over time periods.The National Center for Atmospheric Research is sponsored by the National Science Foundation. of up to several months or longer.     

The radiation budget in a regional climate model

The long- and short-wave components of the radiation budget are among the most important quantities in climate modelling. In this study, we evaluated the radiation budget at the earth??s surface and at the top of atmosphere over Europe as simulated by the regional climate model CLM. This was done by comparisons with radiation budgets as computed by the GEWEX/SRB satellite-based product and as realised in the ECMWF re-analysis ERA40. Our comparisons show that CLM has a tendency to underestimate solar radiation at the surface and the energy loss by thermal emission. We found a clear statistical dependence of radiation budget imprecision on cloud cover and surface albedo uncertainties in the solar spectrum. In contrast to cloud fraction errors, surface temperature errors have a minor impact on radiation budget uncertainties in the long-wave spectrum. We also evaluated the impact of the number of atmospheric layers used in CLM simulations. CLM simulations with 32 layers perform better than do those with 20 layers in terms of the surface radiation budget components but not in terms of the outgoing long-wave radiation and of radiation divergence. Application of the evaluation approach to similar simulations with two additional regional climate models confirmed the results and showed the usefulness of the approach.     

Elevation gradients of European climate change in the regional climate model COSMO-CLM

A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2 m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as well.     

Extremes and predictability in the European pre-industrial climate of a regional climate model

A high-resolution pre-industrial control simulation with the regional climate model REMO is analyzed in detail for different European subregions. To our knowledge, this is the first long pre-industrial control simulation by a regional climate model as well as at comparable resolution. We assess the ability of the climate model to reproduce the observed climate variability in various parts of the continent. In order to investigate the representation of extreme events in the model under pre-industrial greenhouse gas concentrations, selected seasons are examined with regard to the atmospheric circulation and other climatic characteristics that have contributed to the occurrences. A special focus is dedicated to land-atmosphere interactions. Extreme seasons are simulated by the model under various circumstances, some of them strongly resemble observed periods of extraordinary conditions like the summer 2003 or autumn 2006 in parts of Europe. The regional perspective turns out to be of importance when analyzing events that are constituted by meso-scale atmospheric dynamics. Moreover, the predictability of the European climate on seasonal to decadal time scales is examined by relating the statistics of surface variables to large-scale modes of variability impacting the North Atlantic sector like the Meridional Overturning Circulation, the El Niño Southern Oscillation, and the North Atlantic Oscillation. For this purpose, we introduce a measure of tail dependence that quantifies the correlation between extreme values in two variables that describe the state of the climate system. Significant dependence of extreme events can be detected in various situations.     

Alpine snow cover in a changing climate: a regional climate model perspective

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.     

Spectral nudging sensitivity experiments in a regional climate model

In this study, the scale selective bias correction (SSBC) method described by Kanamitsu et al. (2010) is further examined by considering the full wind nudging and the vertically weighted damping coefficient. The 2001 June?CJuly?CAugust RSM simulation over a relatively large domain covering much of the Asian continent, the northern part of Australia, and the Indian and western Pacific oceans was the main focus. The full wind nudging shows wind fields closer to the driving global analysis. However, it leads to significantly distorted fields (e.g., temperature and geopotential height) aloft, accompanying excessive precipitation over the western Pacific. The gradual reduction of vorticity nudging from the model top to the ground surface improves rainfall patterns without a discernible distortion of large-scale fields. Further evaluation of a 10-year-summer simulation over East Asia confirmed that this revised SSBC method improves the monsoonal rainfall against the method of Kanamitsu et al. It is therefore concluded that vorticity nudging alleviates largescale errors by maintaining the near geostrophic balance between mass and winds. The reduction of this nudging factor in the lower troposphere allows the ageostrophic component of wind to develop as in nature, which leads to the improvement of precipitation.     

The simulation of medicanes in a high-resolution regional climate model

Medicanes, strong mesoscale cyclones with tropical-like features, develop occasionally over the Mediterranean Sea. Due to the scarcity of observations over sea and the coarse resolution of the long-term reanalysis datasets, it is difficult to study systematically the multidecadal statistics of sub-synoptic medicanes. Our goal is to assess the long-term variability and trends of medicanes, obtaining a long-term climatology through dynamical downscaling of the NCEP/NCAR reanalysis data. In this paper, we examine the robustness of this method and investigate the value added for the study of medicanes. To do so, we performed several climate mode simulations with a high resolution regional atmospheric model (CCLM) for a number of test cases described in the literature. We find that the medicanes are formed in the simulations, with deeper pressures and stronger winds than in the driving global NCEP reanalysis. The tracks are adequately reproduced. We conclude that our methodology is suitable for constructing multi-decadal statistics and scenarios of current and possible future medicane activities.     

区域气候模式RegCM_NCC在东北地区的应用研究

引进国家气候中心业务化的区域气候模式RegCM_NCC,通过操作系统调试、模拟区域确定、模式分辨率调整等本地化工作,初步建立了针对东北地区的区域气候模式系统,并应用该模式以夏季为例,对东北地区的气候进行了15 a（1991—2005年）时间长度的数值积分试验。结果表明：模式对环流的特征和东北地区地面气温具有一定的模拟能力,对气温模拟存在系统性的暖偏差,对降水模拟能力较差。     

Future extreme events in European climate: an exploration of regional climate model projections

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.     

Extreme climate events in China: IPCC-AR4 model evaluation and projection

Observations from 550 surface stations in China during 1961–2000 are used to evaluate the skill of seven global coupled climate models in simulating extreme temperature and precipitation indices. It is found that the models have certain abilities to simulate both the spatial distributions of extreme climate indices and their trends in the observed period. The models’ abilities are higher overall for extreme temperature indices than for extreme precipitation indices. The well-simulated temperature indices are frost days (Fd), heat wave duration index (HWDI) and annual extreme temperature range (ETR). The well-simulated precipitation indices are the fraction of annual precipitation total due to events exceeding the 95th percentile (R95T) and simple daily intensity index (SDII). In a general manner, the multi-model ensemble has the best skill. For the projections of the extreme temperature indices, trends over the twenty-first century and changes at the end of the twenty-first century go into the same direction. Both frost days and annual extreme temperature range show decreasing trends, while growing season length, heat wave duration and warm nights show increasing trends. The increases are especially manifested in the Tibetan Plateau and in Southwest China. For extreme precipitation indices, the end of the twenty-first century is expected to have more frequent and more intense extreme precipitation. This is particularly visible in the middle and lower reaches of the Yangtze River, in the Southeast coastal region, in the west part of Northwest China, and in the Tibetan Plateau. In the meanwhile, accompanying the decrease in the maximum number of consecutive dry days in Northeast and Northwest, drought situation will reduce in these regions.     

Internal variability in a regional climate model over West Africa

Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales.     

An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections

Ten regional climate models (RCM) have been integrated with the standard forcings of the PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre boundary conditions. The response over Europe, calculated as the difference between the 2071–2100 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance in eight sub-European boxes. Four sources of uncertainty can be evaluated with the material provided by the PRUDENCE project. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30). Model uncertainty is due to the fact that the models use different techniques to discretize the equations and to represent sub-grid effects. Radiative uncertainty is due to the fact that IPCC-SRES A2 is merely one hypothesis. Some RCMs have been run with another scenario of greenhouse gas concentration (IPCC-SRES B2). Boundary uncertainty is due to the fact that the regional models have been run under the constraint of the same global model. Some RCMs have been run with other boundary forcings. The contribution of the different sources varies according to the field, the region and the season, but the role of boundary forcing is generally greater than the role of the RCM, in particular for temperature. Maps of minimum expected 2m temperature and precipitation responses for the IPCC-A2 scenario show that, despite the above mentioned uncertainties, the signal from the PRUDENCE ensemble is significant.