Consortium For Small-scale Modeling Research Articles

Reasons for the Extremely High-Ranging Planetary Boundary Layer over the Western Tibetan Plateau in Winter

Abstract The planetary boundary layer (PBL) over the Tibetan Plateau (with a mean elevation about 4 km above sea level) reaches an unmatched height of 9515 m above sea level. The proximity of this height to the tropopause facilitates an exchange between the stratosphere and the boundary layer. However, the underlying mechanisms responsible for this unique PBL have remained uncertain. Here, the authors explore these mechanisms and their relative importance using measurements of the PBL, the associated surface fluxes, and single-column and regional numerical simulations, as well as global reanalysis data. Results indicate that the dry conditions of both ground soil and atmosphere in late winter cannot explain the special PBL alone. Rather, the results from a single-column model demonstrate the key influence of the stability of the free atmosphere upon the growth of extremely deep PBLs over the Tibetan Plateau. Simulations with the numerical weather prediction model Consortium for Small-Scale Modelling (COSMO) exhibit good correspondence with the observed mean PBL structure and realistic turbulent kinetic energy distributions throughout the PBL. Using ERA-Interim, the authors furthermore find that weak atmospheric stability and the resultant deep PBLs are associated with higher upper-level potential vorticity (PV) values, which in turn correspond to a more southerly jet position and higher wind speeds. Upper-level PV structures and jet position thus influence the PBL development over the Tibetan Plateau.

Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA)

An ensemble Kalman filter for convective‐scale data assimilation (KENDA) has been developed for the COnsortium for Small‐scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi‐operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.

Assimilation of 3D radar reflectivities with an ensemble Kalman filter on the convective scale

An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection‐permitting numerical weather prediction model of the COnsortium for Small‐scale MOdelling (COSMO) based on the Kilometre‐scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state‐of‐the‐art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short‐term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case‐study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase.

Multi-year wind dynamics around Lake Tanganyika

Lake Tanganyika is the second largest freshwater lake in the world by volume and is of prime importance for the regional economy in East Africa. Although the lake is recognized as a key component of the regional climate system, little is known about atmospheric dynamics in its surroundings. To understand this role, we analyze winds around Lake Tanganyika as modeled by a high resolution (7 km) regional climate model (Consortium for Small-scale Modeling in Climate Mode) over the period 1999–2008. Modeled surface wind speed and direction are in very good agreement with high resolution (12.5 km) Quick Scatterometer (QuikSCAT) satellite wind observations during the dry season. Comparison of a control run with a model simulation where all lake pixels are replaced by representative land pixels indicates that mean surface wind speed over Lake Tanganyika almost doubles due to lake presence. Furthermore, a region of higher surface wind speed in the central part of the lake is identified and confirmed by QuikSCAT observations. A combination of wind channeling along valley mountains and wind confluence on the upwind side of the lake is responsible for this speed-up. The lower wind speeds in the rest of the lake result from blocked conditions due to more pronounced orography. Finally, the model captures a zone of higher wind speed at around 2 km height, associated with the low-level Somali jet. These results demonstrate that high resolution climate modeling allows a detailed understanding of wind dynamics in the vicinity of Lake Tanganyika.

Climatology of Wind, Kinetic Energy, and Temperature Spectra Using a High-Resolution Climate Model for Mid-Europe

Abstract Spectra of wind, kinetic energy, and temperature are investigated for a dataset of 10 years of regional climate simulations for mid-Europe. The nonhydrostatic Consortium for Small-Scale Modeling (COSMO) model in Climate Mode [COSMO-CLM (CCLM)] climate model is used in a hindcast mode for 1991–2000. The three-step nesting chain starts with a CCLM run with 18-km resolution covering all of Europe nested in ERA-40 reanalyses and then a run with a resolution of 4.4 km is performed for mid-Europe. Finally, the 1.3-km run focuses on the region of mid-Germany and Luxembourg. In the present study, only results for the 4.4- and 1.3-km runs are shown. Different methods based on the Fourier and cosine transformations for the computation of the spectra are evaluated. The kinetic energy spectra show the expected slope in the mesoscale (up to the effective resolution), while the spectrum of the vertical wind shows a zero-slope behavior. The spectra of the horizontal wind components and temperature compare well to the observations. The effective model resolution was found to be about 7–10 (5–7) times the horizontal grid spacing for the one-dimensional (two-dimensional) spectral methods. A comparison between the different model resolutions shows a benefit of the 1.3-km simulations for the boundary layer for horizontal scales up to 25 km. The multiyear time-scale simulations allow for a climatological study of the seasonal cycle. The kinetic energy spectrum is found to have the largest values in summer.

The tropopause inversion layer in baroclinic life-cycle experiments: the role of diabatic processes

Abstract. Recent studies on the formation of a quasi-permanent layer of enhanced static stability above the thermal tropopause revealed the contributions of dynamical and radiative processes. Dry dynamics leads to the evolution of a tropopause inversion layer (TIL), which is, however, too weak compared to observations and thus diabatic contributions are required. In this study we aim to assess the importance of diabatic processes in the understanding of TIL formation at midlatitudes. The non-hydrostatic model COSMO (COnsortium for Small-scale MOdelling) is applied in an idealized midlatitude channel configuration to simulate baroclinic life cycles. The effect of individual diabatic processes related to humidity, radiation, and turbulence is studied first to estimate the contribution of each of these processes to the TIL formation in addition to dry dynamics. In a second step these processes are stepwise included in the model to increase the complexity and finally estimate the relative importance of each process. The results suggest that including turbulence leads to a weaker TIL than in a dry reference simulation. In contrast, the TIL evolves stronger when radiation is included but the temporal evolution is still comparable to the reference. Using various cloud schemes in the model shows that latent heat release and consecutive increased vertical motions foster an earlier and stronger appearance of the TIL than in all other life cycles. Furthermore, updrafts moisten the upper troposphere and as such increase the radiative effect from water vapor. Particularly, this process becomes more relevant for maintaining the TIL during later stages of the life cycles. Increased convergence of the vertical wind induced by updrafts and by propagating inertia-gravity waves, which potentially dissipate, further contributes to the enhanced stability of the lower stratosphere. Finally, radiative feedback of ice clouds reaching up to the tropopause is identified to potentially further affect the strength of the TIL in the region of the clouds.

Projected Changes in Semi Permanent Systems of Indian Summer Monsoon in CORDEX-SA Framework

The semi-permanent systems such as Seasonal Heat Low (HL), Monsoon Trough (MT), Tibetan Anticyclone (TA), Tropical Easterly Jet (TEJ) and Low Level Jet (LLJ) or Somali jet are observed over Indian region during Indian summer monsoon season (June through September). These systems play a vital role in defining the strength of the Indian summer monsoon rainfall as a whole. Here we evaluate the ability of Consortium for Small-Scale Modeling (COSMO) regional Climate Model (COSMO-CLM), a high resolution regional climate model within the Coordinated Regional Climate Downscaling Experiment for South Asia (CORDEX-SA) framework, to simulate these systems of Indian summer monsoon. The historical runs of the COSMO-CLM for the period 1951-2000 are analysed. Overall the COSMO-CLM is able to simulate these components reasonably well. Possible changes in the position and the strength of these systems and their role in changing rainfall pattern over India are examined to assess the impact of global warming, under the RCP 4.5 simulations towards the end of the century (2051-2100). The analysis shows that the semi permanent systems may not strengthen in the future as compared to the present climate. The summer monsoon rainfall does not show uniform changes over the region. It is likely to enhance over the southern parts of the country, south of 20?S while it is projected to decrease in the northern parts under the global warming scenario.

Studies regarding the quality of numerical weather forecasts of the WRF model integrated at high-resolutions for the Romanian territory

The aim of this paper is to evaluate the quality of high-resolution weather forecasts from the Weather Research and Forecasting (WRF) numerical weather prediction model. The lateral and boundary conditions were obtained from the numerical output of the Consortium for Small-scale Modeling (COSMO) model at 7km horizontal resolution. The WRF model was run for January and July 2013 at two horizontal resolutions (3 and 1km). The numerical forecasts of the WRF model were evaluated using different statistical scores for 2 m temperature and 10 m wind speed. Results showed a tendency of the WRF model to overestimate the values of the analyzed parameters in comparison to observations.

Evaluation of ABL parametrization schemes in the COSMO, a regional non-hydrostatic atmospheric model over an inhomogeneous environment

The boundary-layer processes characterized through turbulent motion are generally very small scale and sub grid and are represented through different parametrization schemes in the atmospheric models. Here, we evaluate the performance of the atmospheric boundary layer (ABL) parametrization schemes in the COnsortium for Small-scale MOdelling (COSMO), a non-hydrostatic atmospheric model by comparing the model simulations with the concurrent in situ observations over Thiruvananthapuram (8.5 $$^{\circ }$$ N, 76.9 $$^{\circ }$$ E, India). With a view to investigating the role of master length scale (l) in simulation of the ABL features, the default parametrization scheme based on the 1-D diagnostic closure, is modified by adopting a new mathematical formulation for l, and the new approach is implemented in the COSMO model. A total of three parametrization approaches, including the two in-built schemes, are designed and the model simulations with these three distinct schemes are carried out for a total of 9 days. Results obtained from the present study reveal the role of l in the estimation of eddy diffusivity coefficients ( $$K_m$$ and $$K_h$$ ) and the associated vertical turbulent mixing. The study also attempts to highlight the significance of ABL parametrization schemes over non-homogeneous environment, hitherto least explored.

Spatial and temporal variability of clouds and precipitation over Germany: multiscale simulations across the "gray zone"

Abstract. This paper assesses the resolution dependance of clouds and precipitation over Germany by numerical simulations with the COnsortium for Small-scale MOdeling (COSMO) model. Six intensive observation periods of the HOPE (HD(CP)2 Observational Prototype Experiment) measurement campaign conducted in spring 2013 and 1 summer day of the same year are simulated. By means of a series of grid-refinement resolution tests (horizontal grid spacing 2.8, 1 km, 500, and 250 m), the applicability of the COSMO model to represent real weather events in the gray zone, i.e., the scale ranging between the mesoscale limit (no turbulence resolved) and the large-eddy simulation limit (energy-containing turbulence resolved), is tested. To the authors' knowledge, this paper presents the first non-idealized COSMO simulations in the peer-reviewed literature at the 250–500 m scale. It is found that the kinetic energy spectra derived from model output show the expected −5/3 slope, as well as a dependency on model resolution, and that the effective resolution lies between 6 and 7 times the nominal resolution. Although the representation of a number of processes is enhanced with resolution (e.g., boundary-layer thermals, low-level convergence zones, gravity waves), their influence on the temporal evolution of precipitation is rather weak. However, rain intensities vary with resolution, leading to differences in the total rain amount of up to +48 %. Furthermore, the location of rain is similar for the springtime cases with moderate and strong synoptic forcing, whereas significant differences are obtained for the summertime case with air mass convection. Domain-averaged liquid water paths and cloud condensate profiles are used to analyze the temporal and spatial variability of the simulated clouds. Finally, probability density functions of convection-related parameters are analyzed to investigate their dependance on model resolution and their impact on cloud formation and subsequent precipitation.

Evaluation of ensemble NWP models for dynamical downscaling of air temperature over complex topography in a hot climate: A case study from the Sultanate of Oman

This paper evaluates the use of ensemble numerical weather prediction (NWP) models for dynamical downscaling of temperature over a complex, hot region. This approach delivers information about the uncertainty of the NWP models and provides probabilistic information for comparison with the currently used single NWP model. An ensemble system was constructed using four members with a 7km resolution over Oman. Two limited-area models (LAMs), the high-resolution model (HRM) and the model from the Consortium for Small-Scale Modeling (COSMO) formed the ensemble members. The two LAMs were derived and initialized using the general circulation model (GCM) data from the German Global Model (GME), which runs at 40km resolution, using two different initial atmospheric states. The first initial state was provided by the 3Dvar data assimilation system at the German Weather Service (Deutscher Wetterdienst, DWD), and the second initial state was provided from the reanalysis data (ERA-Interim) from the European Centre for Medium-Range Weather Forecasts (ECMWF). The results reveal the uncertainty in temperature prediction related to the uncertainty of the NWP models that were used and indicate that there is no best model for the entire domain. On average, the ensemble mean performed better than individual members.

Comparison and Combination of Regional and Global Ensemble Prediction Systems for Probabilistic Predictions of Hub-Height Wind Speed

Abstract The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.

Spatial and temporal correlation length as a measure for the stationarity of atmospheric dust aerosol distribution

Abstract Fields of dust aerosol optical depth (AOD) from numerical models and satellite observations are widely used data sets for evaluating the actual distribution of atmospheric dust aerosol. In this study we investigate the use of estimates of spatial and temporal correlation lengths (CLs) calculated from simulations using the regional model system COSMO-MUSCAT (COSMO: Consortium for Small-scale Modelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) to characterize the spatial and temporal variability of atmospheric aerosol distribution, here mineral dust, and to provide an estimate on the temporal model output interval required in order to represent the local evolution of atmospheric dustiness. The CLs indicate the scales of variability for dust and thus provide an estimate for the stationarity of dust conditions in space and time. Additionally, CLs can be an estimate for the required resolution in time and space of observational systems to observe changes in atmospheric dust conditions that would be relevant for dust forecasts. Here, two years of dust simulations using COSMO-MUSCAT are analyzed. CLs for the individual years 2007 and 2008 are compared to the entire two-year period illustrating the impact of the length of time series on statistical analysis. The two years are chosen as they are contrasting with regard to mineral dust loads and thus provide additional information on the representativeness of the statistical analysis. Results from the COSMO-MUSCAT CL analysis are compared against CL estimates from satellite observations, here dust AOD inferred from IASI (Infrared Atmospheric Sounding Interferometer), which provides bi-daily information of atmospheric dust loading over desert land and ocean. Although CLs estimated from the satellite observations are at a generally lower level of values, the results demonstrate the applicability of daily observations for assessing the atmospheric dust distribution. Main outcomes of this study illustrate the applicability of CL for characterizing the spatio-temporal variability in atmospheric dustiness. This is in particular of interest for determining time intervals at which for example dust forecasts need to be provided. Results from this study further demonstrate that bi-daily satellite dust observations are sufficient for assessing the dust distribution over regions such as the Mediterranean region that are far from the dust sources.

Probabilistic wind speed forecasting on a grid based on ensemble model output statistics

Probabilistic forecasts of wind speed are important for a wide range of applications, ranging from operational decision making in connection with wind power generation to storm warnings, ship routing and aviation. We present a statistical method that provides locally calibrated, probabilistic wind speed forecasts at any desired place within the forecast domain based on the output of a numerical weather prediction (NWP) model. Three approaches for wind speed post-processing are proposed, which use either truncated normal, gamma or truncated logistic distributions to make probabilistic predictions about future observations conditional on the forecasts of an ensemble prediction system (EPS). In order to provide probabilistic forecasts on a grid, predictive distributions that were calibrated with local wind speed observations need to be interpolated. We study several interpolation schemes that combine geostatistical methods with local information on annual mean wind speeds, and evaluate the proposed methodology with surface wind speed forecasts over Germany from the COSMO-DE (Consortium for Small-scale Modelling) ensemble prediction system.

Microphysical processing of aerosol particles in orographic clouds

Abstract. An explicit and detailed treatment of cloud-borne particles allowing for the consideration of aerosol cycling in clouds has been implemented into COSMO-Model, the regional weather forecast and climate model of the Consortium for Small-scale Modeling (COSMO). The effects of aerosol scavenging, cloud microphysical processing and regeneration upon cloud evaporation on the aerosol population and on subsequent cloud formation are investigated. For this, two-dimensional idealized simulations of moist flow over two bell-shaped mountains were carried out varying the treatment of aerosol scavenging and regeneration processes for a warm-phase and a mixed-phase orographic cloud. The results allowed us to identify different aerosol cycling mechanisms. In the simulated non-precipitating warm-phase cloud, aerosol mass is incorporated into cloud droplets by activation scavenging and released back to the atmosphere upon cloud droplet evaporation. In the mixed-phase cloud, a first cycle comprises cloud droplet activation and evaporation via the Wegener–Bergeron–Findeisen (WBF) process. A second cycle includes below-cloud scavenging by precipitating snow particles and snow sublimation and is connected to the first cycle via the riming process which transfers aerosol mass from cloud droplets to snowflakes. In the simulated mixed-phase cloud, only a negligible part of the total aerosol mass is incorporated into ice crystals. Sedimenting snowflakes reaching the surface remove aerosol mass from the atmosphere. The results show that aerosol processing and regeneration lead to a vertical redistribution of aerosol mass and number. Thereby, the processes impact the total aerosol number and mass and additionally alter the shape of the aerosol size distributions by enhancing the internally mixed/soluble Aitken and accumulation mode and generating coarse-mode particles. Concerning subsequent cloud formation at the second mountain, accounting for aerosol processing and regeneration increases the cloud droplet number concentration with possible implications for the ice crystal number concentration.

Teleconnection responses in multi-GCM driven CORDEX RCMs over Eastern Africa

The ability of climate models to simulate atmospheric teleconnections provides an important basis for the use and analysis of climate change projections. This study examines the ability of COordinated Regional climate Downscaling EXperiment models, with lateral and surface boundary conditions derived from Coupled Global Climate Models (CGCMs), to simulate the teleconnections between tropical sea surface temperatures and rainfall over Eastern Africa. The ability of the models to simulate the associated changes in atmospheric circulation patterns over the region is also assessed. The models used in the study are Rossby Centre regional atmospheric model (RCA) driven by eight CGCMs and COnsortium for Small scale MOdeling (COSMO) Climate Limited-area Modelling (COSMO-CLM or CCLM) driven by four of the same CGCMs. Teleconnection patterns are examined using correlation, regression and composite analysis. In order to identify the source of the errors, CGCM-driven regional climate model (RCM) results are compared with ERA-Interim driven RCM results. Results from the driving CGCMs are also analyzed. The RCMs driven by reanalysis (quasi-perfect boundary conditions) successfully capture rainfall teleconnections in most examined regions and seasons. Our analysis indicates that most of the errors in simulating the teleconnection patterns come from the driving CGCMs. RCMs driven by MPI-ESM-LR, HadGEM2-ES and GFDL-ESM2M tend to perform relatively better than RCMs driven by other CGCMs. CanESM2 and MIROC5, and their corresponding downscaled results capture the teleconnections in most of the sub-regions and seasons poorly. This highlights the relative importance of CGCM-derived boundary conditions in the downscaled product and the need to improve these as well as the RCMs themselves. Overall, the results produced here will be very useful in identifying and selecting CGCMs and RCMs for the use of climate change projecting over the Eastern Africa.

Indian Summer Monsoon Rainfall Processes in Climate Change Scenarios

Abstract Indian summer monsoon rainfall was examined in two different greenhouse gas emission scenarios: the Special Report on Emissions Scenarios (SRES; B1) and a similar greenhouse gas scenario, the new representative concentration pathways (RCPs; RCP4.5). The rainfall change in the climate model projections through remotely induced changes in precipitation processes and through changes in precipitation efficiency processes was discussed. To that end, two model setups were applied: 1) the regional climate model (RCM) Consortium for Small-Scale Modelling in Climate Mode (COSMO-CLM), nested in the global climate model (GCM) ECHAM5/Max Planck Institute ocean model (ECHAM5/MPIOM), applying the greenhouse gas scenario B1; and 2) the RCM nested in a newer version of the GCM, ECHAM6/MPIOM, incorporating the RCP4.5 scenario. Both GCM simulations showed a slight increase in precipitation over central India toward the end of the twenty-first century. This slight increase was the result of two largely compensating changes: increase of remotely induced precipitation and decrease of precipitation efficiency. The RCM with the scenario RCP4.5 followed this trend, but with smaller changes. However, the RCM with B1 showed a decreasing trend in precipitation because of a slightly larger absolute change of the reduced precipitation efficiency compared to the change caused by the remote processes. Changes of these processes in the scenario simulations were larger than the natural variability, as simulated in an unperturbed preindustrial greenhouse gas control (CTL) climate simulation. Results indicated that the projection of the Indian summer monsoon rainfall is still a key challenge for both the GCM and the RCM.

Reaching the lower stratosphere: validating an extended vertical grid for COSMO

Abstract. This study presents an extended vertical grid for the regional atmospheric model COSMO (COnsortium for Small-scale MOdeling) reaching up to 33 km. The extended setup has been used to stably simulate 11 months in a domain covering central and northern Europe. Temperature and relative humidity have been validated using radiosonde data in polar and temperate latitudes, focussing on the polar and mid-latitude stratosphere over Europe. Temperature values are reproduced very well by the model. Relative humidity could only be met in the mean over the whole time period after excluding data from Russian stations, which showed significantly higher values. A sensitivity study shows the stability of the model against different forcing intervals and damping layer heights.

Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models

In the framework of the coordinated regional climate downscaling experiment (CORDEX), an ensemble of climate change projections for Africa has been created by downscaling the simulations of four global climate models (GCMs) by means of the consortium for small-scale modeling (COSMO) regional climate model (RCM) (COSMO-CLM, hereafter, CCLM). Differences between the projected temperature and precipitation simulated by CCLM and the driving GCMs are analyzed and discussed. The projected increase of seasonal temperature is found to be relatively similar between GCMs and RCM, although large differences (more than 1 °C) exist locally. Differences are also found for extreme-event related quantities, such as the spread of the upper end of the maximum temperature probability distribution function and, in turn, the duration of heat waves. Larger uncertainties are found in the future precipitation changes; this is partly a consequence of the inter-model (GCMs) variability over some areas (e.g. Sahel). However, over other regions (e.g. Central Africa) the rainfall trends simulated by CCLM and the GCMs show opposite signs, with CCLM showing a significant reduction in precipitation at the end of the century. This uncertain and sometimes contrasting behaviour is further investigated by analyzing the different models’ response to the land–atmosphere interaction and feedback. Given the large uncertainty associated with inter-model variability across GCMs and the reduced spread in the results when a single RCM is used for downscaling, we strongly emphasize the importance of exploiting fully the CORDEX-Africa multi-GCM/multi-RCM ensemble in order to assess the robustness of the climate change signal and, possibly, to identify and quantify the many sources of uncertainty that still remain.

Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

Abstract A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012. The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread. Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.