Reliability of regional climate simulations
Abstract
Quantification of uncertainty becomes more and more a key issue for assessing the trustability of future climate scenarios. In addition to the mean conditions, climate impact modelers focus in particular on extremes. Before generating such scenarios using e.g. dynamic regional climate models, a careful validation of present-day simulations should be performed to determine the range of errors for the quantities of interest under recent conditions as a raw estimate of their uncertainty in the future. Often, multiple aspects shall be covered together, and the required simulation accuracy depends on the user's demand. In our approach, a massive parallel regional climate model shall be used on the one hand to generate "long-term" high-resolution climate scenarios for several decades, and on the other hand to provide very high-resolution ensemble simulations of future dry spells or heavy rainfall events. To diagnosis the model's performance for present-day simulations, we have recently developed and tested a first version of a validation and visualization chain for this model. It is, however, applicable in a much more general sense and could be used as a common test bed for any regional climate model aiming at this type of simulations. Depending on the user's interest, integrated quality measures can be derived for near-surface parameters using multivariate techniques and multidimensional distance measures in a first step. At this point, advanced visualization techniques have been developed and included to allow for visual data mining and to qualitatively identify dominating aspects and regularities. Univariate techniques that are especially designed to assess climatic aspects in terms of statistical properties can then be used to quantitatively diagnose the error contributions of the individual used parameters. Finally, a comprehensive in-depth diagnosis tool allows to investigate, why the model produces the obtained near-surface results to answer the question if the model performs well from the modeler's point of view. Examples will be presented for results obtained using this approach for assessing the risk of potential total agricultural yield loss under drought conditions in Northeast Brazil and for evaluating simulation results for a 10-year period for Europe. To support multi-run simulations and result evaluation, the model will be embedded into an already existing simulation environment that provides further postprocessing tools for sensitivity studies, behavioral analysis and Monte-Carlo simulations, but also for ensemble scenario analysis in one of the next steps.
- Publication:
-
EGS - AGU - EUG Joint Assembly
- Pub Date:
- April 2003
- Bibcode:
- 2003EAEJA....13178A