Convective and stratiform precipitation characteristics in an ensemble of regional climate model simulations

We apply a recently proposed algorithm for disaggregating observed precipitation data into predominantly convective and stratiform, and evaluate biases in characteristics of parameterized convective (subgrid) and stratiform (large-scale) precipitation in an ensemble of 11 RCM simulations for recent climate in Central Europe. All RCMs have a resolution of 25 km and are driven by the ERA-40 reanalysis. We focus on mean annual cycle, proportion of convective precipitation, dependence on altitude, and extremes. The results show that characteristics of total precipitation are often better simulated than are those of convective and stratiform precipitation evaluated separately. While annual cycles of convective and stratiform precipitation are reproduced reasonably well in most RCMs, some of them consistently and substantially overestimate or underestimate the proportion of convective precipitation throughout the year. Intensity of convective precipitation is underestimated in all RCMs. Dependence on altitude is also simulated better for stratiform and total precipitation than for convective precipitation, for which several RCMs produce unrealistic slopes. Extremes are underestimated for convective precipitation while they tend to be slightly overestimated for stratiform precipitation, thus resulting in a relatively good reproduction of extremes in total precipitation amounts. The results suggest that the examined ensemble of RCMs suffers from substantial deficiencies in reproducing precipitation processes and support previous findings that climate models’ errors in precipitation characteristics are mainly related to deficiencies in the representation of convection.     

Evaluation of Mediterranean Sea water and heat budgets simulated by an ensemble of high resolution regional climate models

Air-sea heat and freshwater water fluxes in the Mediterranean Sea play a crucial role in dense water formation. Here, we compare estimates of Mediterranean Sea heat and water budgets from a range of observational datasets and discuss the main differences between them. Taking into account the closure hypothesis at the Gibraltar Strait, we have built several observational estimates of water and heat budgets by combination of their different observational components. We provide then three estimates for water budget and one for heat budget that satisfy the closure hypothesis. We then use these observational estimates to assess the ability of an ensemble of ERA40-driven high resolution (25 km) Regional Climate Models (RCMs) from the FP6-EU ENSEMBLES database, to simulate the various components, and net values, of the water and heat budgets. Most of the RCM Mediterranean basin means are within the range spanned by the observational estimates of the different budget components, though in some cases the RCMs have a tendency to overestimate the latent heat flux (or evaporation) with respect to observations. The RCMs do not show significant improvements of the total water budget estimates comparing to ERA40. Moreover, given the large spread found in observational estimates of precipitation over the sea, it is difficult to draw conclusions on the performance of RCM for the freshwater budget and this underlines the need for better precipitation observations. The original ERA40 value for the basin mean net heat flux is ?15 W/m² which is 10 W/m² less than the value of ?5 W/m² inferred from the transport measurements at Gibraltar Strait. The ensemble of heat budget values estimated from the models show that most of RCMs do not achieve heat budget closure. However, the ensemble mean value for the net heat flux is ?7 ± 21 W/m², which is close to the Gibraltar value, although the spread between the RCMs is large. Since the RCMs are forced by the same boundary conditions (ERA40 and sea surface temperatures) and have the same horizontal resolution and spatial domain, the reason for the large spread must reside in the physical parameterizations. To conclude, improvements are urgently required to physical parameterizations in state-of-the-art regional climate models, to reduce the large spread found in our analysis and to obtain better water and heat budget estimates over the Mediterranean Sea.     

The impact of deforestation on the hydrological cycle in the western Mediterranean: an ensemble study with two regional climate models

 A deforestation experiment is performed over the western Mediterranean, applying two different RCMs with differing domains and an ensemble technique to obtain a measure of their internal variability. The internal variability is used to assign statistical significance to the results, and also to discuss whether the models are sufficiently free to develop internal mesoscale processes. Considerable internal variability values found for hydrological variables even in autumn and winter seem to support the assumption that the models are free enough to be applied to such a sensitivity study. The combined use of two models, with strongly differing domains, and significance assigned through the use of internal variability should highlight responses to deforestation which are of physical origin and not a result dependent on one particular model. The overall significant response from both RCMs to deforestation is a reduction of evaporation (spring and summer, extending over the whole deforested zone) and a decrease in precipitation (late spring and summer, over some regions). A detailed analysis over subzones shows remarkable agreement between the two models over some of these subzones, showing non-local effects in precipitation response. Received: 8 February 2000 / Accepted: 12 January 2001     

Analysis of precipitation extremes in an ensemble of transient regional climate model simulations for the Rhine basin

A non-stationary index-flood model was used to analyse the 1-day summer and 5-day winter precipitation maxima in the Rhine basin in an ensemble of 15 transient regional climate model (RCM) simulations. It is assumed that the seasonal precipitation maxima follow a generalized extreme value (GEV) distribution with time varying parameters. The index-flood assumption implies that the dispersion coefficient (the ratio of the scale and the location parameters) and the shape parameter are constant over predefined regions, while the location parameter varies within these regions. A comparison with the estimates from gridded observations shows that these GEV parameters are too large in the summer season, while there is a large overestimation of the location parameter and underestimation of the dispersion coefficient in winter. However, a large part of the biases in the summer season might be due to the low number of stations used for gridding the observations. Though there is considerable variation in the changes of the extreme value distributions among the RCM simulations, common tendencies can be identified. In summer, large quantiles increase as a consequence of an increase of the dispersion coefficient, while there is almost no change of low quantiles. In winter, low quantiles increase because of an increase of the location parameter. This effect is, however, counterbalanced by a decrease of the shape parameter in most RCM simulations, resulting in only a slight increase of large quantiles. Departures from the assumed index-flood model were observed in the Alpine region in the south of the basin. This is due to the strong spatial heterogeneity in the dispersion coefficient in a number of RCM simulations and a significant altitude dependence of the trend in the location parameter in winter in five RCM simulations.     

Changes in the onset and length of seasons from an ensemble of regional climate models over Spain for future climate conditions

This study analyses the length and onset of the four seasons based on the annual climatic cycle of maximum and minimum temperatures. Previous studies focused over climatically homogeneous mid-high latitude areas, employing fixed temperature thresholds (related to climatic features such as freezing point) that can be inadequate when different climate conditions are present. We propose a method related to the daily minimum and maximum temperature 25th and 75th point-dependent climatic percentiles. It is applied to an ensemble of regional climate models (RCMs) of 25-km horizontal resolution over the peninsular Spain and Balearic Islands, where a large variety of climatic regimes, from alpine to semi-desertic conditions, are present. First, baseline climate (1961–2000) ERA40-forced RCM simulations are successfully compared with the Spain02 daily observational database, following astronomical season length (around 90 days). This result confirms the validity of the proposed method and capability of the RCMs to describe the seasonal features. Future climate global climate model-forced RCMs (2071–2100) compared with present climate (1961–1990) simulations indicate the disappearance of winter season, a summer enlargement (onset and end) and a slight spring and autumn increase.     

Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year

Simulations of eight different regional climate models (RCMs) have been performed for the period September 1997–September 1998, which coincides with the Surface Heat Budget of the Arctic Ocean (SHEBA) project period. Each of the models employed approximately the same domain covering the western Arctic, the same horizontal resolution of 50 km, and the same boundary forcing. The models differ in their vertical resolution as well as in the treatments of dynamics and physical parameterizations. Both the common features and differences of the simulated spatiotemporal patterns of geopotential, temperature, cloud cover, and long-/shortwave downward radiation between the individual model simulations are investigated. With this work, we quantify the scatter among the models and therefore the magnitude of disagreement and unreliability of current Arctic RCM simulations. Even with the relatively constrained experimental design we notice a considerable scatter among the different RCMs. We found the largest across-model scatter in the 2 m temperature over land, in the surface radiation fluxes, and in the cloud cover which implies a reduced confidence level for these variables. An erratum to this article can be found at     

The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly.     

Biases in the diurnal temperature range in Central Europe in an ensemble of regional climate models and their possible causes

The study examines how regional climate models (RCMs) reproduce the diurnal temperature range (DTR) in their control simulations over Central Europe. We evaluate 30-year runs driven by perfect boundary conditions (the ERA40 reanalysis, 1961–1990) and a global climate model (ECHAM5) of an ensemble of RCMs with 25-km resolution from the ENSEMBLES project. The RCMs’ performance is compared against the dataset gridded from a high-density stations network. We find that all RCMs underestimate DTR in all seasons, notwithstanding whether driven by ERA40 or ECHAM5. Underestimation is largest in summer and smallest in winter in most RCMs. The relationship of the models’ errors to indices of atmospheric circulation and cloud cover is discussed to reveal possible causes of the biases. In all seasons and all simulations driven by ERA40 and ECHAM5, underestimation of DTR is larger under anticyclonic circulation and becomes smaller or negligible for cyclonic circulation. In summer and transition seasons, underestimation tends to be largest for the southeast to south flow associated with warm advection, while in winter it does not depend on flow direction. We show that the biases in DTR, which seem common to all examined RCMs, are also related to cloud cover simulation. However, there is no general tendency to overestimate total cloud amount under anticyclonic conditions in the RCMs, which suggests the large negative bias in DTR for anticyclonic circulation cannot be explained by a bias in cloudiness. Errors in simulating heat and moisture fluxes between land surface and atmosphere probably contribute to the biases in DTR as well.     

Annual precipitation cycle in regional climate models: the influence of horizontal resolution

The influence of increased horizontal resolution on regional climate models simulations of 1961–1990 period was investigated with a focus on precipitation. The main attention was paid to the annual cycle of precipitation described by a special characteristic, precipitation half-time. Two models (RegCM3 and ALADIN-CLIMATE/CZ), both of them in two horizontal resolutions (25 and 10 km), were used. An evaluation of model simulations with 25 km resolution on the European domain is presented as well as a more detailed evaluation of both 25 and 10 km versions on the area of the Czech Republic. Generally, the effects of increased horizontal resolution vary with climate model and evaluated characteristic. For the precipitation amount and the dependence of precipitation amount on altitude, the increase in horizontal resolution decreases the accuracy of results in both models. For the simulation of annual precipitation cycle and the precipitation half-time, RegCM3 results improved with the increased horizontal resolution, whereas ALADIN-CLIMATE/CZ results worsened.     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models

Making use of the Köppen–Trewartha (K–T) climate classification, we have found that a set of nine high-resolution regional climate models (RCM) are fairly capable of reproducing the current climate in Europe. The percentage of grid-point to grid-point coincidences between climate subtypes based on the control simulations and those of the Climate Research Unit (CRU) climatology varied between 73 and 82%. The best agreement with the CRU climatology corresponds to the RCM “ensemble mean”. The K–T classification was then used to elucidate scenarios of climate change for 2071–2100 under the SRES A2 emission scenario. The percentage of land grid-points with unchanged K–T subtypes ranged from 41 to 49%, while those with a shift from the current climate subtypes towards warmer or drier ones ranged from 51 to 59%. As a first approximation, one may assume that in regions with a shift of two or more climate subtypes, ecosystems might be at risk. Excluding northern Scandinavia, such regions were projected to cover about 12% of the European land area.     

Statistical bias correction for daily precipitation in regional climate models over Europe

We design, apply, and validate a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations. We refer to this as a statistical bias correction. Validation of the methodology is carried out using daily precipitation fields, defined over Europe, from the ENSEMBLES climate model dataset. The bias correction is calculated using data from 1961 to 1970, without distinguishing between seasons, and applied to seasonal data from 1991 to 2000. This choice of time periods is made to maximize the lag between calibration and validation within the ERA40 reanalysis period. Results show that the method performs unexpectedly well. Not only are the mean and other moments of the intensity distribution improved, as expected, but so are a drought and a heavy precipitation index, which depend on the autocorrelation spectra. Given that the corrections were derived without seasonal distinction and are based solely on intensity distributions, a statistical quantity oblivious of temporal correlations, it is encouraging to find that the improvements are present even when seasons and temporal statistics are considered. This encourages the application of this method to multi-decadal climate projections.     

Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

区域气候模式对研究地形复杂的青藏高原地区气候具有高分辨率的优势。以前的相关研究主要基于单个区域模式,我们评估了CORDEX多区域气候模式对青藏高原气候的模拟能力。结果显示:(1)5个区域气候模式一致模拟出了相似的气温、降水空间模态,但产生了冷偏差和湿偏差。所有区域气候模式未能再现观测的气温、降水趋势空间模态,并且平均高估了气温趋势、低估了降水趋势。综合考虑模拟的气温、降水趋势,多模式集合的结果最优。就单个模式而言,Reg CM4所得趋势最为合理。(2)各区域气候模式结果之间的差异十分显著,表明青藏高原气候模拟具有很大的模式依赖性。这一结果建议当利用单个区域气候模式开展青藏高原气候变化研究时需要谨慎。(3)多区域模式集合预估显示,相对1986–2005年,到2016–2035年气温(降水)将增加1.38±0.09°C(0.8%±4.0%)(RCP4.5)和1.77±0.28°C(7.3%±2.5%)(RCP8.5)。这些结果从多模式角度提高了我们对运用区域气候模式研究青藏高原气候的认识。     

Long time-scale potential predictability in an ensemble of coupled climate models

A diagnostic multi-model ensemble potential predictability study of surface air temperature is performed using data from nine models participating in the Coupled Model Intercomparison Project (CMIP1). The data are considered to be a sample of results from the population of models embodying current abilities to simulate the climate system and represent a range of numerics, resolution and of physical parametrizations. The potential predictability of pentadal, decadal, and 25-year means is analyzed. The multi-model ensemble provides a statistically stable estimate of the potential predictability variance fraction (ppvf) with a narrow confidence interval. This is not the case for individual models with modest lengths of simulation data nor, by implication, for the instrument-based observational record. Potential predictability is found predominately over the high-latitude oceans. There is evidence also for potential predictability at tropical latitudes in the Pacific and Atlantic, but not the Indian oceans, on the shorter of the time scales. The potential predictability variance fraction decreases with increasing time scale but appreciable values exist at all of the time scales considered, especially for the Southern Ocean and for the North Atlantic. Values over land, while statistically non-zero, are small. The autocorrelation structure of the data is investigated to account for its effect on the statistical estimation of the ppvf and to indicate the extent to which the data reflect simple oceanic damping of white noise atmospheric forcing. Ensemble autocorrelation structures differ between tropical and extra-tropical latitudes (at least on the time scales considered) with more oscillatory behaviour implied in tropical regions compared to high latitudes. It appears that the results are inconsistent with simple ocean damping and that higher order autocorrelation structures of temperature cannot be neglected generally or in the determination of the potential predictability. The statistical results suggest that predictability in the extratropics is associated with long ocean time scales while in the tropics it is associated with the coupled atmosphere-ocean system. Physically based analyses are required to understand this long time scale behaviour and an ensemble view is also needed in order to determine the behaviour that is robust across models and the real system.     

The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021–2050 and the 1961–1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM?×?GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021–2050 response which shows a similar pattern to the one obtained for 2071–2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021–2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe.     

基于全球及区域气候模式的江苏省降水变化趋势预估

利用气象观测资料,8个全球耦合气候系统模式的集合平均以及区域气候模式(RegCM4)的结果,通过方差分析、相关分析、趋势分析、扰动法等方法对模式性能进行了评估,并对江苏省在未来RCP8.5高端排放情景下降水的变化趋势进行了预估。结果表明,在RCP8.5情景下,至2020、2030和2050年,全球模式模拟的江苏省年平均降水在未来有逐渐增加的趋势,线性增加率约为7 mm/(10 a)。至2050年,江苏省年平均降水量将增加2%左右;区域模式模拟的年平均降水在未来线性增加率为1.5 mm/(10 a),变化不显著。区域模式模拟的夏季降水在未来有所增加,最多可增加20%～30%,但增幅随时间逐渐减小;全球模式模拟的夏季降水比现在有所减少,至2050年,减少了大约10%。区域模式模拟的冬季降水在未来不同时间段均比现在有所减少,同现在相比,最多可减少30%～40%;而全球模式模拟的冬季降水在未来则是先减少,后增加,至2050年,比现在大约增加10%。对于不同季节,总体而言,南部地区降水量的变化较北部地区显著。对于极端降水事件来说,江苏省未来小雨日数将减少,而暴雨日数则微弱增加。但由于全球模式本身的性能、区域模式对全球模式的依赖性以及温室气体排放的不确定性使上述预估结果仍具有不确定性。     

Linear methods for the statistical transformation of daily precipitation sums from regional climate models

This paper presents two new methods for the linear transformation of climate model precipitation data. For verification of the developed methods, daily precipitation sums from the REMO climate model were transformed to observed data from the meteorological stations in the Malse River basin in the Czech Republic. Both methods are based on an analysis of the gamma distribution. The comparison between the observed and transformed data indicates their suitable applicability. The precipitation amounts of transformed data comply with an equivalent probability distribution as the observed data, a distribution of dry days in time drifts towards realistic values. In addition, the efficiency of the formerly published method for nonlinear transformation and newly introduced linear methods is compared.