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In order to downscale the outcomes of the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) to the specific requests for climate services at the Belgian scale, the following four targets are set:


Target 1: Contribution to the CORDEX project

In order to study the climate at the regional scale, the “COordinated Regional Climate Downscaling EXperiment” (CORDEX) was created as a counterpart of the Coupled Model Intercomparison Project (CMIP). CORDEX aims to coordinate regional climate runs worldwide, by building a data archive of the model runs and to make it available for the climate community to perform climate impact studies. Akin to CMIP5, it provides ensembles of regional climate simulations with varying Global Climate Model (GCM) simulations, varying greenhouse gas (GHG) concentration scenarios, natural climate variability and different downscaling methods to sample the uncertainties in regional climate change (Giorgi et al., 2009; Jacob et al., 2014). The runs are done on commonly defined regional domains (e.g. Europe) with predefined resolutions, and aims to produce a predefined set of meteorological variables as specified in Christensen et al. (2012). Moreover the CORDEX downscaling activities are based on the latest set of GCM climate runs from the CMIP Phase 5 (CMIP5).

The consortium plans to contribute to this CORDEX project with four RCMs (ALARO-0, 2xCOSMO-CLM and MAR, see here for model description) by providing data to the EURO-CORDEX archive.


Target 2: Beyond CORDEX: High-Resolution Limited Area Model runs

Whereas the highest prescribed resolution of CORDEX is 12.5 km (Target 1), aims to go beyond the spatial resolution of CORDEX. The four RCM groups will produce Limited Area Model (LAM) runs at convection-permitting resolutions of ±4 km on a domain centred over Belgium. Compared to the EURO-CORDEX runs, the runs thus provide more detailed as well as more realistic descriptions of the model physics. The Belgian micro-ensemble therefore uses the model configurations adopted by the different Belgian climate groups, but at a higher resolution than the CORDEX resolution of 12.5 km. The same runs as planned for the EURO-CORDEX domain will be performed on a domain over Belgium, i.e. an evaluation run, historical and RCP runs. These simulations will be validated with respect to observations and more sophisticated and tailored data such as Global Navigation Satellite System (GNSS)-derived products (Ning et al., 2013).


Target 3: Beyond CORDEX: Local-Impact Models

The model outputs of the micro-ensemble will be used to drive the Local-Impact Models (LIMs). A detailed description of the LIMs and their main technical features can be found here. The LIMs used in are: the land-surface models with urban modules (UrbClim and SURFEX), a crop model (REGCROP), a model for tides and storms (COHERENS), a wave-height model (WAM) and a model for biogenic emissions (MEGAN-MOHYCAN). The outcomes of these runs will be high-resolution past and future time series including GNSS-derived products (e.g. hourly Zenith tropospheric Total Delay, ZTD), Urban Heat-Island indices for Brussels, yields for the dominant arable crops across the different Belgian agro-ecological zones, storm surges and wave heights and biogenic emmisions. The results will be past and future time series of severity indices, directly usable for impact studies.


Target 4: Inferring the climate uncertainties to the Belgian level

Target 4 aims to infer the uncertainties from the CORDEX project to the Belgian level. More specifically, the runs of the micro-ensemble will be situated with respect to the spread in model results from the ensemble of the EURO-CORDEX archive. To this end, some existing techniques originating in the discipline of statistical downscaling and model output statistics will be used. The data produced for Targets 1, 2 and 3 must be processed into coherent climate information with a best estimate of their uncertainties using the ensemble. A scientific innovation of this initiative can be found in the methodology for the data processing, both by applying innovative techniques of statistical downscaling as well as impact-centric climate scenario development for new specific applications and by validating the methods.