This page provides
information about statistical downscaling tools including a link to the
ENSEMBLES downscaling portal.
scenarios constructed to assess climate change impacts require finer
scales than those provided by global climate models. This transformation
is done by dynamical or statistical downscaling. Statistical downscaling
involves the application of relationships identified in the observed
climate, between the large and smaller-scale, to climate model output.
It assumes that the relationships between predictors (large-scale
variables) and predictands (small-scale surface variables) do not vary under
climate change conditions. The process required to adapt model outputs
to end-user demands is complex.
Thus ENSEMBLES has developed a
statistical downscaling portal that allows end-users to produce
downscaled data without worrying about technical details. However, the
portal should not be used as a black-box since this could lead to
unreliable outputs or inappropriate use of downscaled data. Hence, the
portal offers a support system and user guidelines.
on the identification of robust statistical downscaling methods
developed by the STARDEX project
may also be of interest to users of statistical downscaling tools and
ENSEMBLES downscaling portal
The ENSEMBLES downscaling
portal for statistical downscaling provides user-friendly web access
to statistical downscaling techniques and simulations produced in
ENSEMBLES. It allows users to choose a method of statistical downscaling
and produce high-resolution predictions either using as predictands
(target data), observational datasets already mounted on the server or
data uploaded by the user. In this portal, GCM forecasts
(seasonal-to-decadal and climate change) are downscaled to local
stations or uniform observation grids using any of the available
downscaling algorithms. This process is performed from a web browser
following three steps: predictor selection, predictand selection and
downscaling method. The portal also includes a data access tool for reanalysis, GCM and observed data sets.
The portal allows downscaling over Europe of seasonal-to-decadal hindcasts (from DEMETER and ENSEMBLES) and anthropogenic climate change simulations (from ENSEMBLES). It will be extended to West Africa in collaboration with the AMMA project.
||A first prototype of web service for downscaling at seasonal-to-decadal timescales
|| ERA-40 based predictor data set for statistical downscaling
||GCM-based predictor data set for statistical downscaling
||Extension of the ENSEMBLES web-based service for downscaling
||Journal paper on a test case application of the downscaling portal for seasonal forecasts in agriculture or energy sectors
Statistical Downscaling activities in ENSEMBLES
As well as developing a downscaling tool, ENSEMBLES partners have addressed the challenges of applying statistical downcaling in a probabilistic framework
||Methodology for Markov chain modelling of sequences of atmospheric circulation patterns for implementation with a conditional model of extreme hydrometeorological events
||Recommendations for the application of statistical downscaling methods to seasonal-to-decadal hindcasts in ENSEMBLES
||Recommendations for the modification of statistical downscaling methods for the construction of probabilistic projections
||Improved conditional weather generator for extreme precipitation events
||Technical protocol for the construction of ENSEMBLES statistical downscaling and scenario generator tools
Two non-ENSEMBLES statistical downscaling tools are the statistical downscaling model (SDSM) developed in the UK (Wilby, R.L., Dawson, C.W. and Barrow, E.M., 2002: 'SDSM - a decision support tool for the assessment of regional climate change impacts'. Environmental Modelling Software, 17, 145-157) and the automated statistical downscaling (ASD) tool developed in Canada. A package of R functions for statistical downscaling - clim.pact - has also been developed.
SDSM calculates statistical relationships,
based on multiple linear regression techniques, between
large-scale (the predictors) and local (the predictand) climate.
The downscaling models are calibrated using NCEP Reanalysis as large-scale predictors, and predictors are also provided for a number of GCM climate change simulations, including HadCM3. Version 4.1 was released in April 2007.|
||SDSM provided the starting point for development of the ASD tool by CCSN in Canada.
|| A software package for retrieving data, making climate analysis and statistical downscaling of monthly mean and daily mean global climate scenarios written by Rasmus Benestad, Deliang Chen and Inger Hanssen-Bauer. This package uses the
free and open source data analysis environment R and is supported by a detailed compendium.