Book contents
- Frontmatter
- Contents
- Acknowledgement
- OPENING ADDRESS OF THE FIRST GEORGE KOVACS COLLOQUIUM
- HETEROGENEITY AND SCALING LAND-ATMOSPHERIC WATER AND ENERGY FLUXES IN CLIMATE SYSTEMS
- SCALE PROBLEMS IN SURFACE FLUXES
- REMOTE SENSING – INVERSE MODELLING APPROACH TO DETERMINE LARGE SCALE EFFECTIVE SOIL HYDRAULIC PROPERTIES IN SOIL–VEGETATION–ATMOSPHERE SYSTEMS
- THE IMPORTANCE OF LANDSCAPE POSITION IN SCALING SVAT MODELS TO CATCHMENT SCALE HYDROECOLOGICAL PREDICTION
- THE INFLUENCE OF SUBGRID-SCALE SPATIAL VARIABILITY ON PRECIPITATION AND SOIL MOISTURE IN AN ATMOSPHERIC GCM
- MODELLING THE HYDROLOGICAL RESPONSE TO LARGE SCALE LAND USE CHANGE
- AN APPROACH TO REPRESENT MESOSCALE (SUBGRID-SCALE) FLUXES IN GCMs DEMONSTRATED WITH SIMULATIONS OF LOCAL DEFORESTATION IN AMAZONIA
- A HIERARCHICAL APPROACH TO THE CONNECTION OF GLOBAL HYDROLOGICAL AND ATMOSPHERIC MODELS
- STOCHASTIC DOWNSCALING OF GCM-OUTPUT RESULTS USING ATMOSPHERIC CIRCULATION PATTERNS
- DEPENDENCIES OF SPATIAL VARIABILITY IN FLUVIAL ECOSYSTEMS ON THE TEMPORAL HYDROLOGICAL VARIABILITY
- PROBLEMS AND PROGRESS IN MACROSCALE HYDROLOGICAL MODELLING
- PREDICTABILITY OF THE ATMOSPHERE AND CLIMATE: TOWARDS A DYNAMICAL VIEW
- FROM SCALAR CASCADES TO LIE CASCADES: JOINT MULTIFRACTAL ANALYSIS OF RAIN AND CLOUD PROCESSES
- FRACTALS ET MULTIFRACTALS APPLIQUÉS À L'ÉTUDE DE LA VARIABILITÉ TEMPORELLE DES PRÉCIPITATIONS
STOCHASTIC DOWNSCALING OF GCM-OUTPUT RESULTS USING ATMOSPHERIC CIRCULATION PATTERNS
Published online by Cambridge University Press: 05 November 2011
- Frontmatter
- Contents
- Acknowledgement
- OPENING ADDRESS OF THE FIRST GEORGE KOVACS COLLOQUIUM
- HETEROGENEITY AND SCALING LAND-ATMOSPHERIC WATER AND ENERGY FLUXES IN CLIMATE SYSTEMS
- SCALE PROBLEMS IN SURFACE FLUXES
- REMOTE SENSING – INVERSE MODELLING APPROACH TO DETERMINE LARGE SCALE EFFECTIVE SOIL HYDRAULIC PROPERTIES IN SOIL–VEGETATION–ATMOSPHERE SYSTEMS
- THE IMPORTANCE OF LANDSCAPE POSITION IN SCALING SVAT MODELS TO CATCHMENT SCALE HYDROECOLOGICAL PREDICTION
- THE INFLUENCE OF SUBGRID-SCALE SPATIAL VARIABILITY ON PRECIPITATION AND SOIL MOISTURE IN AN ATMOSPHERIC GCM
- MODELLING THE HYDROLOGICAL RESPONSE TO LARGE SCALE LAND USE CHANGE
- AN APPROACH TO REPRESENT MESOSCALE (SUBGRID-SCALE) FLUXES IN GCMs DEMONSTRATED WITH SIMULATIONS OF LOCAL DEFORESTATION IN AMAZONIA
- A HIERARCHICAL APPROACH TO THE CONNECTION OF GLOBAL HYDROLOGICAL AND ATMOSPHERIC MODELS
- STOCHASTIC DOWNSCALING OF GCM-OUTPUT RESULTS USING ATMOSPHERIC CIRCULATION PATTERNS
- DEPENDENCIES OF SPATIAL VARIABILITY IN FLUVIAL ECOSYSTEMS ON THE TEMPORAL HYDROLOGICAL VARIABILITY
- PROBLEMS AND PROGRESS IN MACROSCALE HYDROLOGICAL MODELLING
- PREDICTABILITY OF THE ATMOSPHERE AND CLIMATE: TOWARDS A DYNAMICAL VIEW
- FROM SCALAR CASCADES TO LIE CASCADES: JOINT MULTIFRACTAL ANALYSIS OF RAIN AND CLOUD PROCESSES
- FRACTALS ET MULTIFRACTALS APPLIQUÉS À L'ÉTUDE DE LA VARIABILITÉ TEMPORELLE DES PRÉCIPITATIONS
Summary
ABSTRACT A methodology is presented for downscaling GCM-output results to regional scale precipitation using atmospheric circulation patterns. A sequence of observed daily air pressure distributions is used to define circulation patterns. The classification of the circulation patterns is done with the help of a neural network. A multivariate stochastic model describes their link to observed daily precipitation amounts at a number of selected locations. To assess precipitation under changed climate circulation patterns derived from GCM output pressure values are used to condition the stochastic precipitation model. The methodology is demonstrated by results obtained for a selected location (Essen, Germany).
INTRODUCTION
Climate change will have a major influence on the hydrological cycle. It is of vital importance to assess the possible impacts as soon as possible, in order to find strategies to adapt to these changes.
The only physically based tools in predicting climate change effects are General Circulation Models (GCM). GCMs deliver meteorological variables in a fine time resolution (30 minutes to a few hours) but in a very coarse spatial grid (200–500 km × 200–500 km). Many climatic parameters like temperature, precipitation, wind, clouds, radiation, snow cover and soil moisture can be simulated by these models.
Unfortunately precipitation, which is the main input in hydrological models, cannot be well modelled by the GCMs. GCM control runs for the present climate indicate that single grid values cannot be taken as representative rainfall amounts of the corresponding area.
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 1995
- 2
- Cited by