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.     

基于CFS模式的中国站点夏季降水统计降尺度预测

本研究针对中国夏季站点降水,研制建立了基于Climate Forecast System(CFS)实时预测数值产品及观测资料的统计降尺度预测系统。此预测系统选取了CFS模式中当年夏季500 hPa高度场和观测资料中前一年秋、冬季海表面温度场作为预测因子,两因子的关键区分别为泛东亚地区和热带太平洋地区。统计降尺度模型对1982~2011年中国夏季降水的回报效果较CFS模式原始结果显著提高,空间距平相关系数由0.03提高到0.31,时间相关系数在中国大部分地区显著提高,最大可达0.6。均方根误差较CFS模式原始结果明显降低,同时,此降尺度模型较好的回报出2011年汛期降水的距平百分率的空间分布型。     

Evaluation of a WRF dynamical downscaling simulation over California

This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical downscaling experiment performed at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average precipitation, 2 m temperature, and snowpack are performed. The regional model reproduces the spatial distribution of precipitation quite well, but substantially overestimates rainfall along windward slopes. This is due to strong overprediction of precipitation intensity; precipitation frequency is actually underpredicted by the model. Moisture fluxes impinging on the coast seem to be well-represented over California, implying that precipitation bias is caused by processes internal to WRF. Positive-definite moisture advection and use of the Grell cumulus parameterization result in some decrease in precipitation bias, but other sources are needed to explain the full bias magnitude. Surface temperature is well simulated in all seasons except summer, when overly-dry soil moisture results in a several degree warm bias in both CCSM3 and WRF. Additionally, coastal temperatures appear to be too warm due to a coastal sea surface temperature bias inherited from CCSM3. Modeled snowfall/snowmelt agrees quite well with observations, but snow water equivalent is found to be much too low due to monthly reinitialization of all regional model fields from CCSM3 values.     

Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP

This study assesses future climate change over East Asia using the Global/Regional Integrated Model system—Regional Model Program (RMP). The RMP is forced by two types of future climate scenarios produced by the Hadley Center Global Environmental Model version 2 (HG2); the representative concentration pathways (RCP) 4.5 and 8.5 scenarios for the intergovernmental panel on climate change fifth assessment report (AR5). Analyses for the current (1980–2005) climate are performed to evaluate the RMP’s ability to reproduce precipitation and temperature. Two different future (2006–2050) simulations are compared with the current climatology to investigate the climatic change over East Asia centered in Korea. The RMP satisfactorily reproduces the observed seasonal mean and variation of precipitation and temperature. The spatial distribution of the simulated large-scale features and precipitation by the RMP is generally less reflective of current climatic conditions than that is given by the HG2, but their inter-annual variations in East Asia are better captured by the RMP. Furthermore, the RMP shows higher reproducibility of climate extremes including excessive heat wave and precipitation events over South Korea. In the future, strong warming is distinctly coupled with intensified monsoonal precipitation over East Asia. In particular, extreme weather conditions are increased and intensified over South Korea as follows: (1) The frequency of heat wave events with temperature greater than 30 °C is projected to increase by 131 and 111 % in the RCP 8.5 and 4.5 downscaling, relative to the current climate. (2) The RCP 8.5 downscaling shows the frequency and variability of heavy rainfall to increase by 24 and 31.5 %, respectively, while the statistics given by the RCP 4.5 downscaling are similar to those of the current climate.     

Projected climate change scenario over California by a regional ocean–atmosphere coupled model system

This study examines a future climate change scenario over California in a 10-km coupled regional downscaling system of the Regional Spectral Model for the atmosphere and the Regional Ocean Modeling System for the ocean forced by the global Community Climate System Model version 3.0 (CCSM3). In summer, the coupled and uncoupled downscaled experiments capture the warming trend of surface air temperature, consistent with the driving CCSM3 forcing. However, the surface warming change along the California coast is weaker in the coupled downscaled experiment than it is in the uncoupled downscaling. Atmospheric cooling due to upwelling along the coast commonly appears in both the present and future climates, but the effect of upwelling is not fully compensated for by the projected large-scale warming in the coupled downscaling experiment. The projected change of extreme warm events is quite different between the coupled and uncoupled downscaling experiments, with the former projecting a more moderate change. The projected future change in precipitation is not significantly different between coupled and uncoupled downscaling. Both the coupled and uncoupled downscaling integrations predict increased onshore sea breeze change in summer daytime and reduced offshore land breeze change in summer nighttime along the coast from the Bay area to Point Conception. Compared to the simulation of present climate, the coupled and uncoupled downscaling experiments predict 17.5 % and 27.5 % fewer Catalina eddy hours in future climate respectively.     

Comparison of two land surface schemes in week-long cloud-system-resolving simulations of warm season precipitation

This paper presents a case study of the impact of land surface treatment on warm season precipitation simulations at convection-permitting grid resolution. Two surface schemes are tested: Dudhia’s five-layer soil model (FLSM) and the Noah land-surface model (NLSM). The experimentation case involves a 1-week episode of active summertime convection over the central United States. The overall precipitation features, such as the diurnal regeneration of zonally propagating rainfall episodes and the spatial distribution of accumulative rainfall, are adequately replicated by the two parameterizations. In comparison, NLSM produces roughly 12% more and broader rainfall than FLSM. This differential rainfall amount is consistent with the differential surface moisture fluxes between the two schemes, whereas the precipitation feedback plays a negligible role. It is also found that FLSM generates comparatively stronger sensible heat transports from the land surface and thus a warmer temperature near the surface.     

多模式集合优选方案在淮河流域夏季降水预测中的应用

基于国家气候中心提供的1981—2010年4种季节气候预测模式的资料,将两种互为补充的降尺度因子挑选方案应用于淮河流域夏季降水预测,利用距平符号一致率ASCR、等级评定PG、距平相关系数ACC方法,评定了每种模式及其所采用的两种降尺度方法对淮河流域夏季降水的预测效果,并采用了一种优选方案进行多模式集合。结果表明,从4种模式的降水预测效果来看,NCEP_CFSv2和TCC_CPS1模式的评分较高,NCC_CGCM1和ECMWF_SYSTEM4模式相对较低;采用2种基于最优子集回归的降尺度方法后,NCC_CGCM1、TCC_CPS1和ECMWF_SYSTEM4模式的降尺度方法相对于模式降水预测为正订正,NCEP_CFSv2模式为负订正;将模式和降尺度预测方案进行优选,其集合平均的评分不仅高于模式降水预测的集合平均,也优于降尺度方法的集合平均,该方法发挥了不同模式的区域性优势,改进了原始集合平均的效果,为提高多模式解释应用水平提供了一种参考性方案。      

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

区域气候模式(RegCM2)与水文模式耦合的数值试验

建立不均匀的地表径流算法，修改RegCM2的径流方案，设计了一个适合与气候模式RegCM2耦合、能模拟水文站流量的汇流模式，模拟了1998年6、7、8月降水的空间分布，分析了该径流方案对降水、地表热量通量、地表径流、土壤湿度产生的影响。结果表明：（1）该方案在模拟1998年长江流域降水空间分布上有一定的合理性，在一定程度上改善了降水量的模拟，其影响大致是总降水量的10％；（2）地表径流方案改变了地面向大气输送的热量通量，这种作用随时间发生变化，这种变化与地表水分的再分配有关；（3）本方案计算的土壤渗透率较强，在暴雨初期，产生径流较少，而在暴雨后期土壤湿度增大，产生的地表径流较大，这一点更符合洪水形成的特点；（4）水文一气候耦合模式模拟了两个长江水文站的流量，模拟值基本反映了实测值的变化趋势，也表明耦合模式基本能反映1998年夏季长江流域大暴雨期间的地表水文过程。     

Evaluating a modified point-based method to downscale cell-based climate variable data to high-resolution grids

To address the demand for high spatial resolution gridded climate data, we have advanced the Daymet point-based interpolation algorithm for downscaling global, coarsely gridded data with additional output variables. The updated algorithm, High-Resolution Climate Downscaler (HRCD), performs very good downscaling of daily, global, historical reanalysis data from 1° input resolution to 2.5 arcmin output resolution for day length, downward longwave radiation, pressure, maximum and minimum temperature, and vapor pressure deficit. It gives good results for monthly and yearly cumulative precipitation and fair results for wind speed distributions and modeled downward shortwave radiation. Over complex terrain, 2.5 arcmin resolution is likely too low and aggregating it up to 15 arcmin preserves accuracy. HRCD performs comparably to existing daily and monthly US datasets but with a global extent for nine daily climate variables spanning 1948–2006. Furthermore, HRCD can readily be applied to other gridded climate datasets.     

Simulation of surface energy fluxes using high-resolution non-hydrostatic simulations and comparisons with measurements for the LITFASS-2003 experiment

Land-surface heterogeneity effects on the subgrid scale of regional climate and numerical weather prediction models are of vital interest for the energy and mass exchange between the surface and the atmospheric boundary layer. High-resolution numerical model simulations can be used to quantify these effects, and are a tool used to obtain area-averaged surface fluxes over heterogeneous land surfaces. We present high-resolution model simulations for the LITFASS area near Berlin during the LITFASS-2003 experiment, which were carried out using the non-hydrostatic model FOOT3DK of the University of Köln with horizontal resolutions of 1 km and 250 m. The LITFASS-2003 experimental dataset is used for comparison. The screen level quantities show good quality for the simulated pressure, temperature, humidity and wind speed and direction. Averaged over the four week experimental period, simulated surface energy fluxes at land stations show a small bias for the turbulent heat fluxes and an underestimation of the net radiation caused by excessive cloudiness in the simulations. For eight selected days with low cloud amounts, the net radiation bias is close to zero, but the sensible heat flux shows a strong positive bias. Large differences are found for latent heat fluxes over a lake, which are partly due to local effects on the measurements, but an additional problem seems to be the overestimation of the turbulent exchange under stable conditions in the daytime internal boundary layer over the lake. In the area average over the LITFASS area of 20 ×  20 km², again a strong positive bias of 70 W m^?2 for the sensible heat is present. For the low soil moisture conditions during June 2003, the simulation of the turbulent heat fluxes is sensitive to variations in the soil type and its hydrological properties. Under these conditions, the supply of ground water to the lowest soil layer should be accounted for. Different area-averaging methods are tested. The experimental set-up of the LITFASS-2003 experiment is found to be well suited for the computation of area-averaged turbulent heat fluxes.     

Relative impacts of increased greenhouse gas concentrations and land cover change on the surface climate in arid and semi-arid regions of China

Four dynamical downscaling simulations are performed with different combinations of land cover maps and greenhouse gas (GHG) levels using the Weather Research and Forecasting (WRF) model nested in the Community Earth System (CESM) model. A pseudo-global warming downscaling method is used to effectively separate the anthropogenic signals from the internal noises of climate models. Based on these simulations, we investigate the impacts of anthropogenic increase in GHG concentrations and land use and land cover change (LULCC) on mean climate and extreme events in the arid and semi-arid regions of China. The results suggest that increased GHG concentrations lead to significant increases in the surface air temperature at 2 m height (T2m) by 1–1.5 °C and greater increase in the warm day temperature (TX90p) than the cold day temperature (TX10p) in the arid and semi-arid regions. Moreover, precipitation increases by 30–50% in the arid region in cold season (November to March) due to the GHG-induced increase in moisture recycling rate and precipitation efficiency. LULCC leads to significant decreases in the T2m, TX90p, and TX10p by approximately 0.3 °C. The regional LULCC accounts for 66 and 68% decrease in T2m in warm and cold seasons, respectively. The rest changes in T2m results from the changes in lateral boundary condition induced by the global LULCC. In response to LULCC, both the warm and cold day temperatures show a significant decrease in cold seasons, which primarily results from the regional LULCC. LULCC-induced changes in precipitation are generally weak in the arid and semi-arid regions of China.     

半干旱区不同下垫面近地层湍流通量特征分析

本文分析了2003～2005年半干旱区退化草地和农田下垫面近地层湍流通量日、季、年的变化特征, 探讨了不同年份的气候背景和下垫面土地覆被对地气交换过程的影响。结果表明: 半干旱区退化草地和农田近地层湍流通量具有明显的日变化、季变化周期; 地气间通量交换年际间的差异主要受当年的气候背景影响, 尤其是降水的影响; 同时还受到下垫面覆被的影响。潜热通量和感热通量的能量分配比率呈反位相变化, 且农田和退化草地的变化趋势相似, 在夏季潜热通量所占比例均超过感热通量; 两种下垫面的波恩比月均值变化趋势十分相似, 量级接近, 夏季低, 春、秋季高; 夏季退化草地和农田下垫面波恩比均小于或等于1。     

A COMPARATIVE STUDY OF TWO LAND SURFACE SCHEMES IN WRF MODEL OVER EASTERN CHINA

Two land surface models, Community Land Model (CLM3.5) and NOAH model, have been coupled to the Weather Research and Forecasting (WRF) model and been used to simulate the precipitation, temperature, and circulation fields, respectively, over eastern China in a typical flood year (1998). The purpose of this study is to reveal the effects of land surface changes on regional climate modeling. Comparisons of simulated results and observation data indicate that changes in land surface processes have significant impact on spatial and temporal distribution of precipitation and temperature patterns in eastern China. Coupling of the CLM3.5 to the WRF model (experiment WRF-C) substantially improves the simulation results over eastern China relative to an older version of WRF coupled to the NOAH-LSM (experiment WRF-N). It is found that the simulation of the spatial pattern of summer precipitation in WRF-C is better than in WRF-N. WRF-C also significantly reduces the summer positive bias of surface air temperature, and its simulated surface air temperature matches more closely to observations than WRF-N does, which is associated with lower sensible heat fluxes and higher latent heat fluxes in WRF-C.     

Statistical Downscaling Based on Dynamically DownscaledPredictors： Application to Monthly Precipitation in Sweden

A prerequisite of a successful statistical downscaling is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical downscaling. The method uses predictors that are upscaled from a dynamical downscaling instead of predictors taken directly from a GCM simulation. The method is applied to downscaling of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the downscaled precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of downscaled precipitation. Due to the cost of the method and the limited improvements in the downscaling results, the three-step method is not justified to replace the one-step method for downscaling of Swedish precipitation.     

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.     

Climate variability and projected change in the western United States: regional downscaling and drought statistics

Climate change in the twenty-first century, projected by a large ensemble average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is downscaled to Climate Divisions across the western United States. A simple empirical downscaling technique is employed, involving model-projected linear trends in temperature or precipitation superimposed onto a repetition of observed twentieth century interannual variability. This procedure allows the projected trends to be assessed in terms of historical climate variability. The linear trend assumption provides a very close approximation to the time evolution of the ensemble-average climate change, while the imposition of repeated interannual variability is probably conservative. These assumptions are very transparent, so the scenario is simple to understand and can provide a useful baseline assumption for other scenarios that may incorporate more sophisticated empirical or dynamical downscaling techniques. Projected temperature trends in some areas of the western US extend beyond the twentieth century historical range of variability (HRV) of seasonal averages, especially in summer, whereas precipitation trends are relatively much smaller, remaining within the HRV. Temperature and precipitation scenarios are used to generate Division-scale projections of the monthly palmer drought severity index (PDSI) across the western US through the twenty-first century, using the twentieth century as a baseline. The PDSI is a commonly used metric designed to describe drought in terms of the local surface water balance. Consistent with previous studies, the PDSI trends imply that the higher evaporation rates associated with positive temperature trends exacerbate the severity and extent of drought in the semi-arid West. Comparison of twentieth century historical droughts with projected twenty-first century droughts (based on the prescribed repetition of twentieth century interannual variability) shows that the projected trend toward warmer temperatures inhibits recovery from droughts caused by decade-scale precipitation deficits.     

Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project

The West African monsoon (WAM) circulation and intensity have been shown to be influenced by the land surface in numerous numerical studies using regional scale and global scale atmospheric climate models (RCMs and GCMs, respectively) over the last several decades. The atmosphere–land surface interactions are modulated by the magnitude of the north–south gradient of the low level moist static energy, which is highly correlated with the steep latitudinal gradients of the vegetation characteristics and coverage, land use, and soil properties over this zone. The African Multidisciplinary Monsoon Analysis (AMMA) has organised comprehensive activities in data collection and modelling to further investigate the significance land–atmosphere feedbacks. Surface energy fluxes simulated by an ensemble of land surface models from AMMA Land-surface Model Intercomparison Project (ALMIP) have been used as a proxy for the best estimate of the “real world” values in order to evaluate GCM and RCM simulations under the auspices of the West African Monsoon Modelling Experiment (WAMME) project, since such large-scale observations do not exist. The ALMIP models have been forced in off-line mode using forcing based on a mixture of satellite, observational, and numerical weather prediction data. The ALMIP models were found to agree well over the region where land–atmosphere coupling is deemed to be most important (notably the Sahel), with a high signal to noise ratio (generally from 0.7 to 0.9) in the ensemble and a inter-model coefficient of variation between 5 and 15%. Most of the WAMME models simulated spatially averaged net radiation values over West Africa which were consistent with the ALMIP estimates, however, the partitioning of this energy between sensible and latent heat fluxes was significantly different: WAMME models tended to simulate larger (by nearly a factor of two) monthly latent heat fluxes than ALMIP. This results due to a positive precipitation bias in the WAMME models and a northward displacement of the monsoon in most of the GCMs and RCMs. Another key feature not found in the WAMME models is peak seasonal latent heat fluxes during the monsoon retreat (approximately a month after the peak precipitation rates) from soil water stores. This is likely related to the WAMME northward bias of the latent heat flux gradient during the WAM onset.     

陆面过程对气候影响的数值模拟:SSiB与IAP/LASG L9R15 AGCM耦合及其模式性能

利用陆面过程模式 SSi B与 IAP/LASG发展的 L9R1 5AGCM的耦合 1 0 a积分试验 ,研究了全球尺度大气与地表的水分和能量交换以及陆地与大气环流和气候的相互作用。模拟表明 :SSi B模式可模拟出陆地上较为真实的表面通量及其日变化 ,较好地定量描述土壤 -植被 -大气连续体系 ( SPAC)中能量和水分的传输过程。因此 ,将其引入气候模式中能够模拟出比 CTL- AGCM更合理的气候平均状态、水汽分布以及水汽输送的气候特征 ,特别是亚洲夏季风水汽输送独特的地域性 ,再现了大气环流 ,尤其是陆面气候的基本特征。并指出 ,陆面过程参数化的引进及其陆面状况的变化显著地改善了全球陆地上的水分平衡状况。利用改进的再循环降水模式 ,进一步研究了陆面过程参数化明显改进降水模拟的物理机制。指出全球陆地 ,特别是盛夏北半球干旱、半干旱地区的再循环降水率明显减小 ,与陆面上表面潜热通量的显著减小区一致 ,从而克服了许多未耦合陆面过程的 AGCMs因对地表水过程非常简单地参数化导致的普遍存在着整个陆地降水偏高 ,改善了全球陆地上的水分平衡状况。因此 ,在充分耦合的陆气环流模式中模拟的降水分布与实况接近。     

国家气候中心多模式解释应用集成预测

多模式集合和降尺度技术是提升模式预测能力的有效工具。该文对国家气候中心多模式解释应用集成预测 (MODES) 技术与业务应用现状进行了综合介绍。MODES采用欧洲中期天气预报中心、东京气候中心、美国国家环境预报中心和中国气象局国家气候中心4个气候业务季节预测模式输出场,利用EOF迭代、变形的典型相关分析、最优子集回归和高相关回归集成4种统计降尺度方法以及等权平均、经典超级集合等集成方法进行全国月及季节降水和气温预测。目前对MODES进行了夏季回报检验和约1年的实时业务应用。回报检验和业务应用表明,MODES对气温有较好的预测能力 (月预测平均PS评分为76),对降水有一定预测技巧 (月预测平均PS评分为68),具有短期气候预测业务应用价值。