Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature.     

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.     

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.     

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...     

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.     

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.     

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.     

Evaluation of regional climate simulations over the CORDEX-EA-II domain using the COSMO-CLM model

The COSMO-CLM (CCLM) model is applied to perform regional climate simulation over the second phase of CORDEX-East Asia (CORDEX-EA-II) domain in this study. Driven by the ERAInterim reanalysis data, the model was integrated from 1988 to 2010 with a high resolution of 0.22°. The model’s ability to reproduce mean climatology and climatic extremes is evaluated based on various aspects. The CCLM model is capable of capturing the basic features of the East Asia climate, including the seasonal mean patterns, interannual variations, annual cycles and climate extreme indices for both surface air temperature and precipitation. Some biases are evident in certain areas and seasons. Warm and wet biases appear in the arid and semi-arid areas over the northwestern and northern parts of the domain. The simulated climate over the Tibetan Plateau is colder and wetter than the observations, while South China, East China, and India are drier. The model biases may be caused by the simulated anticyclonic and cyclonic biases in low-level circulations, the simulated water vapor content biases, and the inadequate physical parameterizations in the CCLM model. A parallel 0.44° simulation is conducted and the comparison results show some added value introduced by the higher resolution 0.22° simulation. As a result, the CCLM model could be an adequate member for the next stage of the CORDEX-EA project, while further studies should be encouraged.     

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

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

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Evaluation of the regional climate model ALADIN to simulate the climate over North America in the CORDEX framework

In this study, an ensemble of four multi-year climate simulations is performed with the regional climate model ALADIN to evaluate its ability to simulate the climate over North America in the CORDEX framework. The simulations differ in their driving fields (ERA-40 or ERA-Interim) and the nudging technique (with or without large-scale nudging). The validation of the simulated 2-m temperature and precipitation with observationally-based gridded data sets shows that ALADIN performs similarly to other regional climate models that are commonly used over North America. Large-scale nudging improves the temporal correlation of the atmospheric circulation between ALADIN and its driving field, and also reduces the warm and dry summer biases in central North America. The differences between the simulations driven with different reanalyses are small and are likely related to the regional climate model’s induced internal variability. In general, the impact of different driving fields on ALADIN is smaller than that of large-scale nudging. The analysis of the multi-year simulations over the prairie and the east taiga indicates that the ALADIN 2-m temperature and precipitation interannual variability is similar or larger than that observed. Finally, a comparison of the simulations with observations for the summer 1993 shows that ALADIN underestimates the flood in central North America mainly due to its systematic dry bias in this region. Overall, the results indicate that ALADIN can produce a valuable contribution to CORDEX over North America.     

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.     

Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over South America

The purpose of this study was to evaluate the accuracy and skill of the UK Met Office Hadley Center Regional Climate Model (HadRM3P) in describing the seasonal variability of the main climatological features over South America and adjacent oceans, in long-term simulations (30 years, 1961–1990). The analysis was performed using seasonal averages from observed and simulated precipitation, temperature, and lower- and upper-level circulation. Precipitation and temperature patterns as well as the main general circulation features, including details captured by the model at finer scales than those resolved by the global model, were simulated by the model. However, in the regional model, there are still systematic errors which might be related to the physics of the model (convective schemes, topography, and land-surface processes) and the lateral boundary conditions and possible biases inherited from the global model.     

Regional climate simulations over South America: sensitivity to model physics and to the treatment of lateral boundary conditions using the MM5 model

In this study the capability of the MM5 model in simulating the main mode of intraseasonal variability during the warm season over South America is evaluated through a series of sensitivity experiments. Several 3-month simulations nested into ERA40 reanalysis were carried out using different cumulus schemes and planetary boundary layer schemes in an attempt to define the optimal combination of physical parameterizations for simulating alternating wet and dry conditions over La Plata Basin (LPB) and the South Atlantic Convergence Zone regions, respectively. The results were compared with different observational datasets and model evaluation was performed taking into account the spatial distribution of monthly precipitation and daily statistics of precipitation over the target regions. Though every experiment was able to capture the contrasting behavior of the precipitation during the simulated period, precipitation was largely underestimated particularly over the LPB region, mainly due to a misrepresentation in the moisture flux convergence. Experiments using grid nudging of the winds above the planetary boundary layer showed a better performance compared with those in which no constrains were imposed to the regional circulation within the model domain. Overall, no single experiment was found to perform the best over the entire domain and during the two contrasting months. The experiment that outperforms depends on the area of interest, being the simulation using the Grell (Kain–Fritsch) cumulus scheme in combination with the MRF planetary boundary layer scheme more adequate for subtropical (tropical) latitudes. The ensemble of the sensitivity experiments showed a better performance compared with any individual experiment.     

RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields

To enable downscaling of seasonal prediction and climate change scenarios, long-term baseline regional climatologies which employ global model forcing are needed for South America. As a first step in this process, this work examines climatological integrations with a regional climate model using a continental scale domain nested in both reanalysis data and multiple realizations of an atmospheric general circulation model (GCM). The analysis presents an evaluation of the nested model simulated large scale circulation, mean annual cycle and interannual variability which is compared against observational estimates and also with the driving GCM for the Northeast, Amazon, Monsoon and Southeast regions of South America. Results indicate that the regional climate model simulates the annual cycle of precipitation well in the Northeast region and Monsoon regions; it exhibits a dry bias during winter (July–September) in the Southeast, and simulates a semi-annual cycle with a dry bias in summer (December–February) in the Amazon region. There is little difference in the annual cycle between the GCM and renalyses driven simulations, however, substantial differences are seen in the interannual variability. Despite the biases in the annual cycle, the regional model captures much of the interannual variability observed in the Northeast, Southeast and Amazon regions. In the Monsoon region, where remote influences are weak, the regional model improves upon the GCM, though neither show substantial predictability. We conclude that in regions where remote influences are strong and the global model performs well it is difficult for the regional model to improve the large scale climatological features, indeed the regional model may degrade the simulation. Where remote forcing is weak and local processes dominate, there is some potential for the regional model to add value. This, however, will require improvments in physical parameterizations for high resolution tropical simulations.     

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.     

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.     

Simulation of the summer circulation over South America by two regional climate models. Part I: Mean climatology

Summary This study investigates the capabilities of two regional models (the ICTP RegCM3 and the climate version of the CPTEC Eta model – EtaClim) in simulating the mean climatological features of the summer quasi-stationary circulations over South America. Comparing the results with the NCEP/DOE reanalysis II data it is seen that the RegCM3 simulates a weaker and southward shifted Bolivian high (BH). But, the Nordeste low (NL) is located close to its climatological position. In the EtaClim the position of the BH is reproduced well, but the NL is shifted towards the interior of the continent. To the east of Andes, the RegCM3 simulates a weaker low level jet and a weaker basic flow from the tropical Atlantic to Amazonia while they are stronger in the EtaClim. In general, the RegCM3 and EtaClim show, respectively a negative and positive bias in the surface temperature in almost all regions of South America. For both models, the correlation coefficients between the simulated precipitation and the GPCP data are high over most of South America. Although the RegCM3 and EtaClim overestimate the precipitation in the Andes region they show a negative bias in general over the entire South America. The simulations of upper and lower level circulations and precipitation fields in EtaClim were better than that of the RegCM3. In central Amazonia both models were unable to simulate the precipitation correctly. The results showed that although the RegCM3 and EtaClim are capable of simulating the main climatological features of the summer climate over South America, there are areas which need improvement. This indicates that the models must be more adequately tuned in order to give reliable predictions in the different regions of South America.