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.     

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.     

Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM

We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961–1990 and the future climate period of 2046–2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We find that the uncertainty in regional climate change predictions associated with the spread of different realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for different realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not differ from each other at the 5% confidence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sufficient to characterize an AOGCM transient climate change prediction at the regional scale. Received: 12 January 1998 / Accepted: 7 July 1999     

A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale

Climate Dynamics - Seasonal precipitation forecasting is valuable for regional water management and agricultural food security. Current numerical models have large uncertainty in model structure,...     

Comparison of four ensemble methods combining regional climate simulations over Asia

A number of uncertainties exist in climate simulation because the results of climate models are influenced by factors such as their dynamic framework, physical processes, initial and driving fields, and horizontal and vertical resolution. The uncertainties of the model results may be reduced, and the credibility can be improved by employing multi-model ensembles. In this paper, multi-model ensemble results using 10-year simulations of five regional climate models (RCMs) from December 1988 to November 1998 over Asia are presented and compared. The simulation results are derived from phase II of the Regional Climate Model Inter-comparison Project (RMIP) for Asia. Using the methods of the arithmetic mean, the weighted mean, multivariate linear regression, and singular value decomposition, the ensembles for temperature, precipitation, and sea level pressure are carried out. The results show that the multi-RCM ensembles outperform the single RCMs in many aspects. Among the four ensemble methods used, the multivariate linear regression, based on the minimization of the root mean square errors, significantly improved the ensemble results. With regard to the spatial distribution of the mean climate, the ensemble result for temperature was better than that for precipitation. With an increasing number of models used in the ensembles, the ensemble results were more accurate. Therefore, a multi-model ensemble is an efficient approach to improve the results of regional climate simulations.     

The roles of bias-correction and resolution in regional climate simulations of summer extremes

Climate Dynamics - The suitability of dynamical downscaling in producing high-resolution climate scenarios for impact assessments is limited by the quality of the driving data and regional climate...     

A multi-physics ensemble of present-day climate regional simulations over the Iberian Peninsula

This work assesses the influence of the model physics in present-day regional climate simulations. It is based on a multi-phyiscs ensemble of 30-year long MM5 hindcasted simulations performed over a complex and climatically heterogeneous domain as the Iberian Peninsula. The ensemble consists of eight members that results from combining different parametrization schemes for modeling the Planetary Boundary Layer, the cumulus and the microphysics processes. The analysis is made at the seasonal time scale and focuses on mean values and interannual variability of temperature and precipitation. The objectives are (1) to evaluate and characterize differences among the simulations attributable to changes in the physical options of the regional model, and (2) to identify the most suitable parametrization schemes and understand the underlying mechanisms causing that some schemes perform better than others. The results confirm the paramount importance of the model physics, showing that the spread among the various simulations is of comparable magnitude to the spread obtained in similar multi-model ensembles. This suggests that most of the spread obtained in multi-model ensembles could be attributable to the different physical configurations employed in the various models. Second, we obtain that no single ensemble member outperforms the others in every situation. Nevertheless, some particular schemes display a better performance. On the one hand, the non-local MRF PBL scheme reduces the cold bias of the simulations throughout the year compared to the local Eta model. The reason is that the former simulates deeper mixing layers. On the other hand, the Grell parametrization scheme for cumulus produces smaller amount of precipitation in the summer season compared to the more complex Kain-Fritsch scheme by reducing the overestimation in the simulated frequency of the convective precipitation events. Consequently, the interannual variability of precipitation (temperature) diminishes (increases), which implies a better agreement with the observations in both cases. Although these features improve in general the accuracy of the simulations, controversial nuances are also highlighted.     

The South Pacific Convergence Zone in CMIP5 simulations of historical and future climate

The South Pacific Convergence Zone (SPCZ) is evaluated in historical simulations from 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and compared with previous generation CMIP3 models. A subset of 24 CMIP5 models are able to simulate a distinct SPCZ in the December to February (DJF) austral summer, although the position of the SPCZ in these models is too zonal compared with observations. The spatial pattern of SPCZ precipitation is improved in CMIP5 models relative to CMIP3 models, although the spurious double ITCZ precipitation band in the eastern Pacific is intensified in many CMIP5 models. All CMIP5 models examined capture some interannual variability of SPCZ latitude, and 19 models simulate a realistic correlation with El Niño–Southern Oscillation. In simulations of the twenty-first century under the RCP8.5 emission scenario, no consistent shift in the mean position of the DJF SPCZ is identified. Several models simulate significant shifts northward, and a similar number of models simulate significant southward shifts. The majority of CMIP5 models simulate an increase in mean DJF SPCZ precipitation, and there is an intensification of the eastern Pacific double ITCZ precipitation band in many models. Most models simulate regions of increased precipitation in the western part of the SPCZ and near the equator, and regions of decreased precipitation at the eastern edge of the SPCZ. Decomposition of SPCZ precipitation changes into dynamic and thermodynamic components reveals predominantly increased precipitation due to thermodynamic changes, while dynamic changes lead to regions of both positive and negative precipitation anomalies.     

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.     

Present and future near-surface wind climate of Greenland from high resolution regional climate modelling

The present and twenty-first century near-surface wind climate of Greenland is presented using output from the regional atmospheric climate model RACMO2. The modelled wind variability and wind distribution compare favourably to observations from three automatic weather stations in the ablation zone of southwest Greenland. The Weibull shape parameter is used to classify the wind climate. High values (κ > 4) are found in northern Greenland, indicative of uniform winds and a dominant katabatic forcing, while lower values (κ < 3) are found over the ocean and southern Greenland, where the synoptic forcing dominates. Very high values of the shape parameter are found over concave topography where confluence strengthens the katabatic circulation, while very low values are found in a narrow band along the coast due to barrier winds. To simulate the future (2081–2098) wind climate RACMO2 was forced with the HadGEM2-ES general circulation model using a scenario of mid-range radiative forcing of +4.5 W m^?2 by 2100. For the future simulated climate, the near-surface potential temperature deficit reduces in all seasons in regions where the surface temperature is below the freezing point, indicating a reduction in strength of the near-surface temperature inversion layer. This leads to a wind speed reduction over the central ice sheet where katabatic forcing dominates, and a wind speed increase over steep coastal topography due to counteracting effects of thermal and katabatic forcing. Thermally forced winds over the seasonally sea ice covered region of the Greenland Sea are reduced by up to 2.5 m s^?1.     

Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations

Theoretical and Applied Climatology - The study compares characteristics of observed sub-daily precipitation extremes in the Czech Republic with those simulated by Hadley Centre Regional Model...     

Resolution effects on regional climate model simulations of seasonal precipitation over Europe

We analyze a set of nine regional climate model simulations for the period 1961–2000 performed at 25 and 50 km horizontal grid spacing over a European domain in order to determine the effects of horizontal resolution on the simulation of precipitation. All of the models represent the seasonal mean spatial patterns and amount of precipitation fairly well. Most models exhibit a tendency to over-predict precipitation, resulting in a domain-average total bias for the ensemble mean of about 20% in winter (DJF) and less than 10% in summer (JJA) at both resolutions, although this bias could be artificially enhanced by the lack of a gauge correction in the observations. A majority of the models show increased precipitation at 25 km relative to 50 km over the oceans and inland seas in DJF, JJA, and ANN (annual average), although the response is strongest during JJA. The ratio of convective precipitation to total precipitation decreases over land for most models at 25 km. In addition, there is an increase in interannual variability in many of the models at 25 km grid spacing. Comparison with gridded observations indicates that a majority of models show improved skill in simulating both the spatial pattern and temporal evolution of precipitation at 25 km compared to 50 km during the summer months, but not in winter or on an annual mean basis. Model skill at higher resolution in simulating the spatial and temporal character of seasonal precipitation is found especially for Great Britain. This geographic dependence of the increased skill suggests that observed data of sufficient density are necessary to capture fine-scale climate signals. As climate models increase their horizontal resolution, it is thus a key priority to produce high quality fine scale observations for model evaluation.     

Rain-on-Snow events in Japan as projected by a large ensemble of regional climate simulations

Climate Dynamics - Rain-on-Snow (ROS) events can cause severe snowmelt hazards such as river flooding, avalanches, and landslides that have significant impacts on various sectors. The influence of...     

Decadal long convection-permitting regional climate simulations over eastern China: evaluation of diurnal cycle of precipitation

With a decadal long period (1998–2010) climate simulation using the Weather Research and Forecasting model at convection-permitting resolution (4 km) (WRF_CPM), the diurnal cycles of precipitation amount (PA), frequency (PF) and intensity (PI) and their related large-scale atmospheric circulations over eastern China are analyzed. The simulations are further compared against the CN05.1, CMORPH v1.0 and the ECMWF Re-Analysis Interim (ERAIN). Results show that WRF_CPM can reasonably represent the observed seasonal rainfall and the atmospheric circulations. As for the features at a sub-daily scale, WRF_CPM is superior at reproducing the diurnal amplitude of PF that is similar to PA in terms of the spatial distribution. Moreover, the diurnal peak timing of summer PF and PA over the three sub-regions, i.e., North China (NC), Yangtze-Huaihe River basin (YHR) and South China (SC), can be properly reproduced by WRF_CPM. The observed precipitation systems exhibit obvious eastward propagation from the Plateau to its downstream, which may be due to the solenoid circulations associated with the low-level anomalous wind and moisture convergence. However, they are almost overestimated by WRF_CPM and in turn causing overestimated precipitation along YHR. The early morning precipitation in WRF_CPM has a larger fraction than CMORPH, which is related to the overestimated nocturnal low-level jet. Whereas, due to the solar heating and the land-sea breezes, the late-afternoon precipitation peak is mainly located along the coasts of eastern China, which matches well with the vertical motion in WRF_CPM.     

An approximate energy cycle for inter-member variability in ensemble simulations of a regional climate model

The presence of internal variability (IV) in ensembles of nested regional climate model (RCM) simulations is now widely acknowledged in the community working on dynamical downscaling. IV is defined as the inter-member spread between members in an ensemble of simulations performed by a given RCM driven by identical lateral boundary conditions (LBC), where different members are being initialised at different times. The physical mechanisms responsible for the time variations and structure of such IV have only recently begun to receive attention. Recent studies have shown empirical evidence of a close parallel between the energy conversions associated with the time fluctuations of IV in ensemble simulations of RCM and the energy conversions taking place in weather systems. Inspired by the classical work on global energetics of weather systems, we sought a formulation of an energy cycle for IV that would be applicable for limited-area domain. We develop here a novel formalism based on local energetics that can be applied to further our understanding IV. Prognostic equations for ensemble-mean kinetic energy and available enthalpy are decomposed into contributions due to ensemble-mean variables (EM) and those due to deviations from the ensemble mean (IV). Together these equations constitute an energy cycle for IV in ensemble simulations of RCM. Although the energy cycle for IV was developed in a context entirely different from that of energetics of weather systems, the exchange terms between the various reservoirs have a rather similar mathematical form, which facilitates some interpretations of their physical meaning.     

Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.     

High-resolution simulations of global climate, part 1: present climate

We examine simulations of today's climate performed with a global atmospheric general circulation model run at spectral truncations of T42, T170, and T239, corresponding to grid cell sizes of roughly 310 km, 75 km, and 55 km, respectively. The simulations were forced with observed sea-surface temperatures and sea-ice concentrations. The T42 simulations and initial simulations at T170 and T239 were performed using a model version that was carefully "tuned" to optimize results at T42; subsequent simulations at T170 and T239 used a model version that was partly re-tuned to improve results at T170. On the scales of a T42 grid cell and larger, nearly all quantities we examined in all the T170 and T239 simulations agree better with observations, at least in terms of spatial patterns, than in the T42 simulations. In some cases the improvements are very substantial. Improvements are seen in all-season, global domain results, and in results pertaining to most seasons and latitude bands. Increasing the model resolution from T42 introduces biases (errors in the mean) into some simulated quantities; the worst of these were removed by the partial retuning we performed at T170. This retuning has little effect on the spatial patterns of results, except in Northern Hemisphere winter at T170, where it tends to bring improvements. We discuss aspects of simulated regional climates, and their dependence on model resolution.     

Analyses of extreme climate events over china based on CMIP5 historical and future simulations

Based on observations and 12 simulations from Coupled Model Intercomparison Project Phase 5 （CMIP5） models, cli- matic extremes and their changes over China in the past and under the future scenarios of three Representative Concentration Pathways （RCPs） are analyzed. In observations, frost days （FD） and low-temperature threshold days （TN10P） show a de- creasing trend, and summer days （SU）, high-temperature threshold days （TX90P）, heavy precipitation days （R20）, and the contribution of heavy precipitation days （P95T） show an increasing trend. Most models are able to simulate the main char- acteristics of most extreme indices. In particular, the mean FD and TX90P are reproduced the best, and the basic trends of FD, TN10P, SU and TX90P are represented. For the FD and SU indexes, most models show good ability in capturing the spatial differences between the mean state of the periods 1986--2005 and 1961-80; however, for other indices, the simulation abilities for spatial disparity are less satisfactory and need to be improved. Under the high emissions scenario of RCP8.5, the century-scale linear changes of the multi-model ensemble （MME） for FD, SU, TN10P, TX90P, R20 and P95T are -46.9, 46.0, -27.1, 175.4, and 2.9 days, and 9.9%, respectively; and the spatial change scope for each index is consistent with the emissions intensity. Due to the complexities of physical process pararneterizations and the limitation of forcing data, great uncertainty still exists with respect to the simulation of climatic extremes.