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The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.
Funded by the German Ministry for Education and Research (BMBF) a major research project called MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) was launched and global as well as regional predictive ensemble hindcasts have been generated. The aim of the project is to demonstrate for past climate change whether predictive models have the capability of predicting climate on time scales of decades. This includes the development of a decadal forecast system, on the one hand to support decision making for economy, politics and society for decadal time spans. On the other hand, the scientific aspect is to explore the feasibility and prospects of global and regional forecasts on decadal time scales. The focus of this paper lies on the description of the regional hindcast ensemble for Europe generated by COSMO-CLM and on the assessment of the decadal variability and predictability against observations. To measure decadal variability we remove the long term bias as well as the long term linear trend from the data. Further, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e. we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict regional temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill.
In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44◦(∼50 km), 0.22◦ (∼25 km) and 0.11◦ (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.
In the last decade, the Climate Limited-area Modeling Community (CLM-Community) has contributed to the Coordinated Regional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM-Community model, ERA-Interim reanalysis and eight global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44∘ (∼ 50 km), 0.22∘ (∼ 25 km), and 0.11∘ (∼ 12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia, and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Federation (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version, and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modeling communities is needed to increase the reliability of the GCM–RCM modeling chain.
The frequency of extreme events has changed, having a direct impact on human lives. Regional climate models help us to predict these regional climate changes. This work presents an atmosphere–ocean coupled regional climate system model (RCSM; with the atmospheric component COSMO-CLM and the ocean component NEMO) over the European domain, including three marginal seas: the Mediterranean, North, and Baltic Sea. To test the model, we evaluate a simulation of more than 100 years (1900–2009) with a spatial grid resolution of about 25 km. The simulation was nested into a coupled global simulation with the model MPI-ESM in a low-resolution configuration, whose ocean temperature and salinity were nudged to the ocean–ice component of the MPI-ESM forced with the NOAA 20th Century Reanalysis (20CR). The evaluation shows the robustness of the RCSM and discusses the added value by the coupled marginal seas over an atmosphere-only simulation. The coupled system is stable for the complete 20th century and provides a better representation of extreme temperatures compared to the atmosphere-only model. The produced long-term dataset will help us to better understand the processes leading to meteorological and climate extremes.
A twentieth century-long coupled atmosphere-ocean regional climate simulation with COSMO-CLM (Consortium for Small-Scale Modeling, Climate Limited-area Model) and NEMO (Nucleus for European Modelling of the Ocean) is studied here to evaluate the added value of coupled marginal seas over continental regions. The interactive coupling of the marginal seas, namely the Mediterranean, the North and the Baltic Seas, to the atmosphere in the European region gives a comprehensive modelling system. It is expected to be able to describe the climatological features of this geographically complex area even more precisely than an atmosphere-only climate model. The investigated variables are precipitation and 2 m temperature. Sensitivity studies are used to assess the impact of SST (sea surface temperature) changes over land areas. The different SST values affect the continental precipitation more than the 2 m temperature. The simulated variables are compared to the CRU (Climatic Research Unit) observational data, and also to the HOAPS/GPCC (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, Global Precipitation Climatology Centre) data. In the coupled simulation, added skill is found primarily during winter over the eastern part of Europe. Our analysis shows that, over this region, the coupled system is dryer than the uncoupled system, both in terms of precipitation and soil moisture, which means a decrease in the bias of the system. Thus, the coupling improves the simulation of precipitation over the eastern part of Europe, due to cooler SST values and in consequence, drier soil.
