Oumarou Nikiema

Considerations of Domain Size and Large-Scale Driving for Nested Regional Climate Models: Impact on Internal Variability and Ability at Developing Small-Scale Details

The premise of dynamical downscaling is that a high-resolution, nested Regional Climate Model (RCM), driven by large-scale atmospheric fields at its lateral boundary, generates fine scales that are dynamically consistent with the large scales. An RCM is hence expected to act as a kind of magnifying glass that will reveal details that could not be resolved on a coarse mesh. The small scales represent the main potential added value of a high-resolution RCM.

Budget study of the internal variability in ensemble simulations of the Canadian Regional Climate Model at the seasonal scale

Previous investigations with nested regional climate models have revealed that simulations are sensitive to the initial conditions (IC). This results in internal variability (IV) in ensembles of simulations initialized with small differences in IC. In a previous study, a quantitative budget calculation has documented the physical processes responsible for the rapid growth of IV in simulations with the Canadian Regional Climate Model (CRCM). By using an ensemble of 20 simulations performed for the 1993 summer season, we extend the previous study to further our understanding about the physical processes responsible for the maintenance and fluctuations of IV in a seasonal simulation with CRCM. We have identified and quantified various terms in the prognostic budget equations of IV for the potential temperature and the absolute vorticity. For these studied variables, the covariance of fluctuations acting on the gradient of the ensemble mean of variables generally contributes to increasing the IV, indicating that the transport of heat and vorticity is down the gradient of ensemble mean potential temperature and absolute vorticity. The horizontal transport of IV by ensemble mean flow acts as a sink term, the IV transport out of the study domain contributing to reduce the IV. On average in the troposphere and at the seasonal scale, results confirm that there is no trend in IV although it greatly fluctuates in time. Our results also show that IV is a natural phenomenon arising from the chaotic nature of the atmosphere. In a time-averaged sense, the IV budget reduces to a balance between generation and destruction terms.

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.

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

Dynamical downscaling of climate projections over a limited-area domain using a Regional Climate Model (RCM) requires boundary conditions (BC) from a Coupled Global Climate Model (CGCM) simulation. Biases in CGCM-generated BC can have detrimental effects in RCM simulations, so attempts to improve the BC used to drive the RCM simulations are worth exploring. It is in this context that an empirical method involving the bias correction of the sea-surface conditions (SSCs; sea-surface temperature and sea-ice concentration) simulated by a CGCM has been developed: The 3-step dynamical downscaling approach. The SSCs from a CGCM simulation are empirically corrected and used as lower BC over the ocean for an atmosphere-only global climate model (AGCM) simulation, which in turn provides the atmospheric lateral BC to drive the RCM simulation. We analyse the impact of this strategy on the simulation of the African climate, with a special attention to the West African Monsoon (WAM) precipitation, using the fifth-generation Canadian Regional Climate Model (CRCM5) over the CORDEX-Africa domain. The Earth System Model of the Max-Planck-Institut für Meteorologie (MPI-ESM-LR) is used as CGCM and a global version of CRCM5 is used as AGCM. The results indicate that the historical climate is much improved, approaching the skill of reanalysis-driven hindcast simulations. The most remarkable effect of this approach is the positive impact on the simulation of all aspects of the WAM precipitation, mainly due to the correction of SSCs. In fact, our results show that proper sea surface temperature (SST) in the Gulf of Guinea is a necessary condition for an adequate simulation of WAM precipitation, especially over the equatorial region of West Africa. It was found that the climate-change projections under RCP4.5 scenario obtained with the 3-step approach are substantially different from those obtained with usual downscaling approach in which the RCM is directly driven by the CGCM output; in the WAM region most of the differences in the projected climate changes came mainly from the empirical correction of SST.

Arctic budget study of intermember variability using HIRHAM5 ensemble simulations

One of the challenges in evaluating and applying regional climate models (RCMs) is the nonlinear behavior of atmospheric processes, which is still poorly understood. The nonlinearities induce chaos which leads to an internal variability in the model. Therefore, an ensemble of RCM simulations has been run and a budget study for potential temperature has been applied to investigate the internally generated variability. Hence, the physical processes associated with diabatic and dynamical terms inducing the intermember variability have been analyzed. The study is applied over the Arctic on an ensemble of 20 members, differing in their initial conditions, simulated with the RCM HIRHAM5 during summer 2012. This time period is of particular importance because of the melting sea ice and its influence on atmospheric circulation and the resulting effect on the intermember variability. The amplitude of the intermember variability of the simulations fluctuates strongly both temporally and spatially. During the beginning of August 2012 the intermember variability is strongest and coincides with the great Arctic cyclone event. The most important contributions for the intermember variability tendency are the horizontal and vertical “baroclinic” terms. Both terms have largest absolute values along the coastlines of the Arctic Ocean which are associated with the Arctic frontal zone leading to the cyclone maximum over the Arctic Ocean during summer.

Energy cycle associated with inter-member variability in a large ensemble of simulations with the Canadian RCM (CRCM5)

In an ensemble of Regional Climate Model (RCM) simulations where different members are initialised at different times but driven by identical lateral boundary conditions, the individual members provide different, but equally acceptable, weather sequences. In others words, RCM simulations exhibit the phenomenon of Internal Variability (or inter-member variability—IV), defined as the spread between members in an ensemble of simulations. Our recent studies reveal that RCM’s IV is associated with energy conversions similar to those taking place in weather systems. By analogy with the classical work on global energetics of weather systems, a formulation of an energy cycle for IV has been developed that is applicable over limited-area domains. Prognostic equations for ensemble-mean kinetic energy and available enthalpy are decomposed into contributions due to ensemble-mean variables and those due to deviations from the ensemble mean (IV). Together these equations constitute an energy cycle for IV in ensemble simulations of an RCM. A 50-member ensemble of 1-year simulations that differ only in their initial conditions was performed with the fifth-generation Canadian RCM (CRCM5) over an eastern North America domain. The various energy reservoirs of IV and exchange terms between reservoirs were evaluated; the results show a remarkably close parallel between the energy conversions associated with IV in ensemble simulations of RCM and the energy conversions taking place in weather systems in the real atmosphere.

Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

As part of the CORDEX project, the fifth-generation Canadian Regional Climate Model (CRCM5) is used over the Arctic for climate simulations driven by reanalyses and by the MPI-ESM-MR coupled global climate model (CGCM) under the RCP8.5 scenario. The CRCM5 shows adequate skills capturing general features of mean sea level pressure (MSLP) for all seasons. Evaluating 2-m temperature (T2m) and precipitation is more problematic, because of inconsistencies between observational reference datasets over the Arctic that suffer of a sparse distribution of weather stations. In our study, we additionally investigated the effect of large-scale spectral nudging (SN) on the hindcast simulation driven by reanalyses. The analysis shows that SN is effective in reducing the spring MSLP bias, but otherwise it has little impact. We have also conducted another experiment in which the CGCM-simulated sea-surface temperature (SST) is empirically corrected and used as lower boundary conditions over the ocean for an atmosphere-only global simulation (AGCM), which in turn provides the atmospheric lateral boundary conditions to drive the CRCM5 simulation. This approach, so-called 3-step approach of dynamical downscaling (CGCM-AGCM-RCM), which had considerably improved the CRCM5 historical simulations over Africa, exhibits reduced impact over the Arctic domain. The most notable positive effect over the Arctic is a reduction of the T2m bias over the North Pacific Ocean and the North Atlantic Ocean in all seasons. Future projections using this method are compared with the results obtained with the traditional 2-step dynamical downscaling (CGCM-RCM) to assess the impact of correcting systematic biases of SST upon future-climate projections. The future projections are mostly similar for the two methods, except for precipitation.

Limited-area atmospheric energetics: illustration on a simulation of the CRCM5 over eastern North America for December 2004

The seminal work of Lorenz on global atmospheric energetics has allowed understanding much on the physical processes responsible for the general circulation, its maintenance and the development of synoptic-scale weather systems. In mid-latitudes, potential energy generated by the differential heating of the planet by the Sun is converted into kinetic energy by the weather systems and eventually dissipated by friction. While a corresponding study of atmospheric energetics over a limited region would have the advantage of focusing on the details of individual storms and the processes taking place over a domain of interest, such study has encountered several pitfalls and has been challenging. Here we build upon our earlier work on the energy cycle of inter-member variability in ensembles of limited-area model simulations to develop a regional-scale atmospheric energy cycle formulated in terms of available enthalpy and kinetic energy. The approach is then applied to study the energetics of a short simulation made with the fifth-generation of the Canadian Regional Climate Model (CRCM5) over an eastern North American domain for December 2004. The results obtained for a specific storm and monthly mean climatology confirm the current understanding that available enthalpy of the atmospheric time-mean state is mainly generated by the covariance of diabatic heating and temperature, and that the most important conversions of energy are found to correspond to baroclinic processes that take place along the storm track, where perturbations of temperature and wind are important. Finally, transient-eddy kinetic energy is mainly dissipated by friction in the boundary layer.

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

The ability of state-of-the-art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA-Interim, National Centers for Environmental Prediction-Climate Forecast System Reanalysis, National Aeronautics and Space Administration-Modern-Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency-Japanese 55-year reanalysis) in winter and summer for 1981-2010 period. In addition, we compare cyclone statistics between ERA-Interim and the Arctic System Reanalysis reanalyses for 2000-2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large-scale spectral nudging show a better agreement with ERA-Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables. (Less)

Energetics of transient-eddy and inter-member variabilities in global and regional climate model simulations

Available Enthalpy (AE) and Kinetic Energy (KE) associated with the natural Transient-Eddy (or Time-Variability, TV) and models’ Internal Variability (or Inter-member Variability, IV) are studied using two ensembles of simulations, one from a nested Regional Climate Model (RCM) driven by reanalyses over a regional domain covering eastern North America, and one from a Global Climate Model (GCM), and the Era-interim reanalyses as reference. The fields of TV and IV energies are first examined, both globally and over the regional domain. Results from GCM simulations reveal that GCM TV is similar to that of reanalyses, confirming the realism of the GCM simulations, and TV and IV are approximately equal, in agreement with the ergodicity property. For RCM simulations, TV energies are similar to those of reanalyses driving them. On the other hand, the IV energies of reanalyses-driven RCM simulations are much smaller than those of the GCM, because of the control exerted by the lateral boundary conditions imposed in nested models. While GCM IV energies present similar seasonal variations as the TV energies, the RCM IV greatly fluctuates in time, with short episodes of large variations. The second part of this study is devoted to the analysis of TV and IV energetic budgets. Results indicate similar physical interpretations of conversions, generations and destructions for both TV and IV energetics, although TV is associated with natural phenomena of weather disturbances and IV is a model feature contributing to the uncertainties of simulations.