A simple soil moisture scheme for regional climate simulations in the tropics

Summary A regional climate model (RCM) is described which incorporates an improved scheme for soil moisture availability (SMA) compared to an earlier version. The improvement introduces a sensitivity of SMA to soil type, vegetation cover and ground albedo, making the model more adaptable to divers regions. In addition, the interactive SMA depends on past precipitation, ground temperature and terrain relief. Six RCM simulations of the monthly mean climate over southern Africa are performed at 0.5° grid spacing. Improvements in the RCM climate simulations compared to control runs are attributed to the newer SMA scheme. Only a slight improvement in skill results from driving the RCM with observational analyses as opposed to GCM “predicted” lateral boundary conditions. The high spatial resolution of the RCM provides a distinct advantage in the simulated spatial distribution of precipitation compared with a global model run at an effective grid spacing of 2.8°. The mesoscale precipitation signal in the RCM simulations is more dominant during the rather dry December 1982 than during December 1988. The improved SMA scheme contributed to a realistic partition between latent and sensible heat fluxes at the ground-atmosphere boundary and consequently a realistic diurnal cycle of ground temperature. Simulated differences in the spatial distribution of rainfall between December 1982 and December 1988 are more realistic with the improved scheme. Received June 28, 2001 Revised August 27, 2001     

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23?years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.     

Interannual variability and regional climate simulations

Summary An assessment is made of a regional climate model's skill in simulating the mean climatology and the interannual variability experienced in a specific region. To this end two ensembles comprising three realizations of month-long January and July simulations are undertaken with a limited are a operational NWP model. The modelling suite is driven at its lateral boundaries by analysed meteorological fields and the computational domain covers Europe and the North-western Atlantic with a horizontal resolution of 56 km.Validation is performed against both operational ECMWF analyses and objectively analysed precipitation fields from a network of ~ 1400 SYNOP rain gauge stations. Analysis of the simulated ensemble-mean climatology indicates that the model successfully reproduces both the winter and summer distributions of the primary dynamical and thermodynamical field, and also provides a reasonable representation of the measured precipitation over most of Europe. Typically the domain averaged model-biases are below 0.5 K for temperature and 0.1 g/kg for specific humidity. Analysis of the interannual variability reveals that the model captures the wintertime changes including that of the precipitation distribution, but in contrast the summertime precipitation totals for the individual years is not simulated satisfactorily and only partially reproduces the observed regional interannual variability.The latter shortcomings are related to the following factors. Firstly the model bias in the dynamical fields is somewhat larger for summer than winter, while at the same time summertime interannual variability is associated with weaker effects in the dynamical fields. Secondly the summertime precipitation distribution is more substantially affected by small-scale moist convection and surface hydrological processes. Together these two factors suggest that summertime precipitation over continental extratropical land masses might be intrinsically less predictable than wintertime synoptic scale precipitation.With 17 Figures     

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

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

A review on vegetation models and applicability to climate simulations at regional scale

The lack of accurate representations of biospheric components and their biophysical and biogeochemical processes is a great source of uncertainty in current climate models. The interactions between terrestrial ecosystems and the climate include exchanges not only of energy, water and momentum, but also of carbon and nitrogen. Reliable simulations of these interactions are crucial for predicting the potential impacts of future climate change and anthropogenic intervention on terrestrial ecosystems. In this paper, two biogeographical (Neilson’s rule-based model and BIOME), two biogeochemical (BIOME-BGC and PnET-BGC), and three dynamic global vegetation models (Hybrid, LPJ, and MC1) were reviewed and compared in terms of their biophysical and physiological processes. The advantages and limitations of the models were also addressed. Lastly, the applications of the dynamic global vegetation models to regional climate simulations have been discussed.     

Sensitivity to domain size of mid-latitude summer simulations with a regional climate model

The issue of Regional Climate Model (RCM) domain size is studied here by using a perfect-model approach, also known as the Big-Brother experiment. It is known that the control exerted by the lateral boundary conditions (LBC) on nested simulations increases when reducing the domain size. The large-scale component of the simulation that is forced by the LBC influences the small-scale features that develop along the large-scale flow. Small-scale transient eddies need space and time to develop sufficiently however, and small domains can impede their development. Our tests performed over eastern North America in summer reveal that the small-scale features are systematically underestimated over the entire domain, even for domain as large as 140 by 140 grid points. This result differs from that obtained in winter where the small scales were mainly underestimated on the west (inflow) side of the domain. This difference is due to the circulation regime over Eastern Canada, which is characterized by weak and variable flow in summer, but strong and westerly flow in winter. For both seasons, the small-scale transient-eddy amplitudes are systematically underestimated at higher levels, but this problem is less severe in summer. Overall the model is more skilful in regenerating the small scales in summer than in winter for comparable domain sizes, which can be related to the weaker summer flow and stronger physical processes occurring in this season.     

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.     

Contrasting regional and global climate simulations over South Asia

Climate Dynamics - Two ensembles of climate simulations, one global and one regional, are used to investigate model errors and projected climate change in seasonal mean temperature and...     

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.     

一种基于全球动力模式和SMART原理结合的统计降尺度区域季节气候预测方法

针对江苏夏季旱涝和高温热浪等异常气候的预测难题,以江苏夏季站点降水和气温为预测目标,建立了一种基于全球动力模式BCC_CSM1.1（m）和最优可预测气候模态和异常相对倾向（SMART）原理结合的统计降尺度季节气候预测方法。利用历史观测资料和SVD方法提取决定中国夏季降水异常相对倾向的同期热带地区向外长波辐射（Outgoing Longwave Radiation,OLR）和北半球中高纬500 hPa位势高度场异常相对倾向的最优可预测气候模态,并利用逐步回归法构建其与同期江苏站点降水和气温异常相对倾向同期关系的统计降尺度模型;将动力模式对最优可预测气候模态的预测带入统计降尺度模型,实现对区域降水和气温异常相对倾向的预测;最后通过引入观测的近期背景异常实现对降尺度的降水和气温总距平的预测。通过对1991—2019年江苏夏季降水和气温的回报检验表明,本文建立的统计降尺度模型效果较BCC_CSM1.1（m）动力模式的直接预测效果有显著提高,为区域精细化季节气候预测提供了一种有效的手段。     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Developing a likely climate scenario from multiple regional climate model simulations with an optimal weighting factor

This study presents a performance-based comprehensive weighting factor that accounts for the skill of different regional climate models (RCMs), including the effect of the driving lateral boundary condition coming from either atmosphere–ocean global climate models (AOGCMs) or reanalyses. A differential evolution algorithm is employed to identify the optimal relative importance of five performance metrics, and corresponding weighting factors, that include the relative absolute mean error (RAME), annual cycle, spatial pattern, extremes and multi-decadal trend. Based on cumulative density functions built by weighting factors of various RCMs/AOGCMs ensemble simulations, current and future climate projections were then generated to identify the level of uncertainty in the climate scenarios. This study selected the areas of southern Ontario and Québec in Canada as a case study. The main conclusions are as follows: (1) Three performance metrics were found essential, having the greater relative importance: the RAME, annual variability and multi-decadal trend. (2) The choice of driving conditions from the AOGCM had impacts on the comprehensive weighting factor, particularly for the winter season. (3) Combining climate projections based on the weighting factors significantly increased the consistency and reduced the spread among models in the future climate changes. These results imply that the weighting factors play a more important role in reducing the effects of outliers on plausible future climate conditions in regions where there is a higher level of variability in RCM/AOGCM simulations. As a result of weighting, substantial increases in the projected warming were found in the southern part of the study area during summer, and the whole region during winter, compared to the simple equal weighting scheme from RCM runs. This study is an initial step toward developing a likelihood procedure for climate scenarios on a regional scale using equal or different probabilities for all models.     

Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM

A new parametric bias correction method for precipitation with an extension for extreme values is compared to an empirical and an existing parametric method. The bias corrections are applied to the regional climate model COSMO-CLM (consortium for small-scale modelling – climate limited area modelling) with a resolution of 4.5 km for the time periods 1991–2000 and 2091–2100. In addition to a comparison in a cross-validation framework, a focus is laid on the investigation of extreme value correction and the effect of the bias correction on the climate change signal. According to the statistical methods used in this study, it was found that the empirical method outperforms both parametric alternatives. However, due to the limited length of the available time series, some outliers occurred, and all methods had problems correcting extreme values. The climate change signal is moderately influenced by all three methods, and the power of climate change detection is reduced. The largest effect was found for the number of dry days and the mean daily intensity, which are considerably altered after correction.     

Bias corrections of global models for regional climate simulations of high-impact weather

All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the six-hourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.     

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.     

On the linear response of tropical African climate to SST changes deduced from regional climate model simulations

Summary Previous studies have highlighted the crucial role of sea surface temperature (SST) anomalies in the tropical Atlantic region in forcing the summer monsoon rainfall over subsaharan West Africa. Understanding the physical processes, relating SST variations to changes in the amount and distribution of African rainfall, is a key factor in improving weather and climate forecasts in this highly vulnerable region. Here, we present sensitivity experiments from a regional climate model with prescribed warmer tropical SSTs, according to enhanced greenhouse conditions at the end of the 21st century. This dynamical downscaling approach provides information about the nonlinear response of the atmosphere to oceanic heating. It has been suggested that the response is at least partly accounted for by the linear theory of tropical dynamics, involving a Kelvin and Rossby wave response to a tropical heat source. We compute the major modes of the linear Matsuno-Gill model for geopotential height and horizontal wind components and project the simulated response patterns onto these linear modes, in order to evaluate to which extent the simple linear theory may explain the SST-induced climate anomalies over Africa. A multivariate Hotelling T² test is used to evaluate whether these anomalies are statistically significant. Forcing the regional climate model by warmer SSTs leads to substantial climate anomalies over tropical Africa: Rainfall is increases over the Guinea Coast region (GCR) and tropical East Africa, but decreases over the Congo Basin and the Sahel Zone (SHZ). At the 850 hPa level, a trough develops over southern West Africa and the Gulf of Guinea, and is associated with stronger surface wind convergence over the GCR. These changes in the atmospheric dynamics strongly project onto the leading modes of the linear Matsuno-Gill model at various zonal wave numbers. The corresponding atmospheric heating pattern is highly reminiscent of the simulated nonlinear model reponse. The T² test statistics reveal that the SST forcing induces a statistically significant climate anomaly over tropical Africa if the climate state vector is reduced by projecting the simulated data onto the leading 10 linear modes. It is also shown that the linear response prevails in a long-term simulation with more realistic lower and lateral boundary conditions. Thus, linear tropical dynamics are assumed to be a major physical process on the ground of the prominent SST-African rainfall relationship.     

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.     

Interannual variability of Mediterranean evaporation and its relation to regional climate

Gridded monthly evaporation data for 1958–2006 from the Woods Hole Oceanographic Institution data set are used to investigate interannual variability of Mediterranean evaporation during cold and hot seasons and its relation to regional atmospheric dynamics, sea surface temperature and atmospheric elements of the hydrological cycle. The first EOF mode of Mediterranean evaporation, explaining more than 50% of its total variance, is characterized by the monopole pattern both in winter and summer. However, despite structural similarity, the EOF-1 of Mediterranean evaporation is affected by different climate signals in cold and hot seasons. During winter the EOF-1 is associated with the East Atlantic teleconnection pattern. In summer, there is indication of tropical influence on the EOF-1 of Mediterranean evaporation (presumably from Asian monsoon). Both in winter and summer, principal components of EOF-1 demonstrate clear interdecadal signals (with a stronger signature in summer) associated with large sea surface temperature anomalies. The results of a sensitivity analysis suggest that in winter both the meridional wind and the vertical gradient of saturation specific humidity (GSSH) near the sea surface contribute to the interdecadal evaporation signal. In summer, however, it is likely that the signal is more related to GSSH. Our analysis did not reveal significant links between the Mediterranean evaporation and the North Atlantic Oscillation in any season. The EOF-2 of evaporation accounts for 20% (11%) of its total variance in winter (in summer). Both in winter and summer the EOF-2 is characterized by a zonal dipole with opposite variations of evaporation in western and eastern parts of the Mediterranean Sea. This mode is associated presumably with smaller scale (i.e., local) effects of atmospheric dynamics. Seasonality of the leading modes of the Mediterranean evaporation is also clearly seen in the character of their links to atmospheric elements of the regional hydrological cycle. In particular, significant links to precipitation in some regions have been found in winter, but not in summer.