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Technical Notes Santander Meteorology Group (CSIC-UC) SMG:2.2011 User Guide of

Technical Notes Santander Meteorology Group (CSIC-UC) SMG:2.2011 User Guide of the ENSEMBLES Downscaling Portal (version 2) J.M. Guti´ errez1, D. San Mart´ ın1,2, A.S. Coﬁ˜ no3, S. Herrera1,2, R. Manzanas1, M.D. Fr´ ıas3 1Instituto de F´ ısica de Cantabria, CSIC-Universidad de Cantabria, Santander, Spain. 2 Predictia Intelligent Data Solutions, Santander, Spain. 3 Dpto. Matem´ atica Aplicada y C.C. Universidad de Cantabria. Santander, Spain correspondence: gutierjm@ifca.unican.es, daniel@predictia.es, coﬁnoa@unican.es version: v3–November 2012 Abstract http://www.meteo.unican.es/ensembles. This report describes the structure and usage of the statistical downscaling portal developed by the Santander Meteorology Group (http://www.meteo.unican.es) with the technical assistance of Predictia (http://www.predicita.es) as part of the activities of the EU-funded ENSEMBLES project (2004-2009, see http://ensembles-eu.metofﬁce.com). The current operational version (version 2) is a complete reimplementation of the portal, allowing particular adaptations and views for supporting projects and institutions (see the acknowledgements at the end). The three main actions necessary to create a downscaling method (deﬁning the predictors, choosing the local/regional target variable to be downscaled and creating the downscaling method) are described step by step, illustrating the different options available from the portal. Afterwards, the application of the method to downscale GCM climate scenarios is described and some information about validating and interpreting the results is provided. Therefore, this document is intended to be a brief user guide for the downscaling portal and requires additional “good practice documents” to learn about the optimum regions and predictors for the statistical downscaling process. 1 Introduction Statistical downscaling is a sound and mature ﬁeld which allows adapting the coarse-resolution (typically 250 km) global climate change scenarios provided by the Global Cli- mate Models (GCMs) to regional or local scale. These meth- ods link the large scale outputs of GCMs (typically large- scale ﬁelds such as 500 mb geopotential height) with simul- taneous local historical observations (typically surface vari- ables such as precipitation or temperature) in the region of interest. Therefore, these techniques allow ﬁlling the gap be- tween the low-resolution GCM outputs and the models used in different impact sectors —such as agriculture, energy or health— which require daily meteorological inputs in spe- cial high-resolution grids, or gauge networks. Statistical downscaling is nowadays a complex multi- disciplinary discipline involving a cascade of different scien- tiﬁc applications to access and process large amounts of het- erogeneous data. Therefore, interactive user-friendly tools are necessary in order to ease the downscaling process for end users, thus maximizing the exploitation of the available climate projections. The ENSEMBLES Downscaling Portal described in this document was initially developed within the EU-funded ENSEMBLES project (2004-2009) following an end-to-end approach. Afterwards, a complete reimplemen- tation (version 2) was performed to ensure the appropriate adaptation of the portal (different views for different users) to the needs of future supporting projects and institutions (see the acknowledgements at the end for the current list of sup- porting projects and institutions). This user guide is intended for end-users with some ba- sic knowledge on statistical downscaling and focus on the steps to be followed to undertake a particular downscal- ing experiment using the downscaling portal. As an illus- trative example, the portal includes a “demo” experiment Iberia demo, which focuses on maximum temperature in ﬁve locations/cities for the 2091-2100 decade. This experiment is available for all users (in write-protect mode) and can be fol- lowed step to step through the different panels of the portal in order to see a typical application. The know-how information about selecting appropriate predictors, calibrating/validating the downscaling method, selecting the appropriate GCMs and scenarios, assumptions of the statistical downscaling methodology, etc., is not dealt with in this document. Thus, before using the portal, we strongly recommend the user to read the Guidelines for Use of Climate Scenarios Developed from Statistical Downscal- ing Methods1 (which constitutes “supporting material” of the Intergovernmental Panel on Climate Change, IPCC). Finally, we want to remark that this portal should not be used as a black-box input-output tool since, other- wise, the obtained regional projections could be mislead- ing or even wrong. Therefore, some background knowledge about the meteorological conditions in the area of interest and the main large scale drivers inﬂuencing the climate is needed to appropriately use the downscaling tool and to ob- tain meaningful results. Moreover, the results obtained from the ENSEMBLES Downscaling Portal should not be directly used in impact applications without the necessary knowledge about the assumptions and limitations of this methodology. Thus, we strongly advise end-users to work in collaboration with downscaling groups, or at least have some support from them, in order to deﬁne the experiments and to appropriately analyze and use the results. 1http://www.ipcc-data.org/guidelines/dgm no2 v1 09 2004.pdf User Guide of the ENSEMBLES Downscaling Portal (version 2) 2 Downscaling Method Predictor Predictor dataset Predictand dataset Definition Validation (perfect prognosis) Downscaled dataset Reanalysis datasets Obseved datasets GCMs datasets Predictand Downscaling Definition/Calibration of the SDM RCM SDM Figure 1: Scheme of the downscaling process using either Statistical Downscaling Methods (SDM) or Regional Climate Models (RCM); in the former case, besides the Global Circulation Model (GCM) scenarios, reanalysis and observed local data are necessary to perform the downscaling. Details of the deﬁnition/calibration of the statistical downscaling approach are shown. 2 Downscaling Elements To ﬁll the gap between the coarse-scale GCM outputs and the local/regional needs of end-users, a number of dynami- cal models (Regional Climate Models, RCMs) and statisti- cal methods (Statistical Downscaling Methods, SDMs) have been developed. On the one hand, RCMs are directly cou- pled to the outputs of the GCMs (GCMs datasets) and pro- vide high-resolution (typically 25 km) gridded downscaled datasets for the variables of interest, as simulated from the physical equations and parameterizations included in the RCM (see the scheme of this downscaling process in Fig. 1, left panel). On the other hand, SDMs combine the in- formation of retrospective GCM analysis/forecasts databases (Reanalysis datasets) with simultaneous historical observa- tions of the variables of interest (Observed datasets, either station networks or grids of interpolated observations) to in- fer appropriate statistical transfer models. Therefore, besides the GCM datasets, two basic ingredients of the statistical downscaling methodology are the Reanalysis and Observa- tions datasets, which are required to deﬁne and calibrate the statistical downscaling methods. The diagram in Fig. 1 (right panel) shows how the dif- ferent ingredients of the statistical downscaling process are used to deﬁne a SDM for a particular application. A par- ticular subset (geographical region, variables and historical temporal window) of the reanalysis constitutes the predictor dataset, whereas the historical records (for the same temporal window) from a goal variable on a number of stations over the region of interest forms the predictand dataset. These data are used to calibrate and validate a particular downscal- ing method before using it for downscaling purposes (i.e. for projecting GCM datasets). These three basic ingredients are the basis of the portal workﬂow, as described in the following sections. The skill of the downscaling methods depends on the variable, season and region of interest, with the latter vari- ation dominating. Thus, for each particular application and case study, an ensemble of statistical downscaling methods needs to be tested and validated to achieve the maximum skill and a proper representation of uncertainties. Thus, validation is a key issue in the ENSEMBLES downscaling portal and, as we will show later, it is automatically performed when a downscaling method is deﬁned. 3 Structure of the Portal The portal has been organized in different windows (tabs) to gradually access the information necessary to deﬁne a down- scaling task: (1) Predictor, (2) Predictand, (3) Downscaling Method and (4) Downscale. (1-3) correspond to the calibra- tion/validation of a particular downscaling method, whereas (4) corresponds to the actual downscaling process, apply- ing the calibrated method to different GCMs and scenarios. These windows can be accessed from the corresponding up- per tabs of the portal, as shown in Fig. 2 (1). A ﬁrst window (My home) is shown after login to the por- tal (see Fig. 2) and provides information about the existing downscaling experiments (2) and the status of the submit- ted jobs (3), as well as the user’s account proﬁle (4). The Experiment manager panel shows the details of the exper- iments already created by the user —a unique experiment, “Iberia demo”, in this case; see Fig. 2 (5)—, each includ- ing a set of predictors (6) deﬁned in a particular region from a reanalysis project —MSLP, T850,Q850 and Z500 from ERA40, in this case— and one, or several, predictands — maximum temperature in ﬁve stations in the Iberian penin- sula from GSOD Europe database, labeled as “Tmax cities” as shown in Fig. 2 (7)—. Each of the predictands may have one or several associated downscaling methods —in this case, only the default analog method (8)—. The user can browse the information and navigate through the panel by clicking in the different components. Tech. Notes Santander Meteorology Group (CSIC-UC): GMS:2.2011;1–16 User Guide of the ENSEMBLES Downscaling Portal (version 2) 3 Figure 2: Main window of the downscaling portal. Management of the experiments (left) and the jobs/tasks (right). The uploads/Geographie/ user-guide 63 .pdf