# WORKSHOP ON STATISTICAL DOWNSCALING OF CLIMATE SCENARIOS AND
ADAPTATION OF SCENARIOS FOR USE IN IMPACT STUDIES

## Background

Empirical methods have become crucial for producing climate
scenarios for e.g. impact assessment. Improved methods for statistical
downscaling of climate scenarios, as well as statistical refinement of
output from regional models, have been developed during the later
years. Various groups have developed different techniques and tested
out different strategies, predictors & predictands, optimal domain
sizes, etc. We now believe it could be fruitful for representatives
from different groups working with these problems to meet in order to
1) summarize what we have learned, 2) point out central problems which
still need to be solved and eventually suggest how to deal with them,
and 3) to discuss the possibility of future cooperation, e. g. in a
"Statistical Model Intercomparison Project" (SMIP) where we suggest
standards for comparisons of our methods.

## Workshop-sketch

We plan a 2-day workshop with 3 introductory presentations
addressing the workshop themes. Other participants are invited to give
short (10-15 min) prepared presentations, but most of all we want an
open discussion where everyone contribute with their experience and
expertise. To make this setting efficient, we believe that the number
of participants should be restricted to about 20. The results from the
workshop should be a report summarizing status, recommendations and
problems concerning statistical downscaling, and possibly also plans
for future cooperation.

Possible themes for discussion (in arbitrary order)

- Internal consistence for statistically downscaled scenarios with
respect to covariation between different variables, and/or to
autocorrelation in space and/or time. Advantages/ disadvantages for
different methods.
- Extremes: Are some indicators better/ more robust than others? Methods?
- Domain size in statistical downscaling: How sensitive are the
results to domain size? Is there an optimal choice? Is it possible
to give general recommendations?
- Predictands and predictors: Can we agree on recommendations
concerning predictors for local temperature and precipitation? What
about local wind, snow cover/ water equivalent and other variables?
- Methods: Can we give general recommendations here, or is this
totally dependent on the problem?
- Uncertainty associated with statistical downscaling.
- Aapplicability of statistical downscaling methods.
- "Statistical Model Intercomparison Project": Could we agree on
some basic principles/ standards for comparisons of our models,
e.g. in a common project (SMIP)?