Information about possible future climate change under global warming is becoming an integral part of developing suitable adaptation and mitigation strategies at global, national, regional and local levels. The use of downscaling techniques (regional climate models and statistical methods) as a source of high-resolution regional climate information has been well established and recognised with a remarkable progress within the Coordinated Regional climate Downscaling Experiment (). During the recent years a large number of downscaled climate change scenarios for different regions worldwide were generated and made openly available.
Even though a large volume of regional climate data is available, still there are many gaps in our understanding of regional climate; additionally data does not necessary mean information. Now the main focus is on how to translate this regional climate data to regional climate information relevant and usable for decision and policy making. To advance such translation it is necessary to fill the gaps in our understanding of regional climate and address scientific questions not fully explored at moment.
- What is the contribution to regional climate from local (e.g., snow, soil moisture, soil moisture-precipitation feedback) and global (e.g. teleconnections) drivers in different regions?
- What is the source of contradicting messages across different climate data? What kind of local processes are involved in these contradicting messages?
- What level of uncertainties does bias adjustment (correction) introduce to regional climate information?
- How to provide an integral measure of uncertainty for the entire downscaling chain (global climate models / downscaling / bias adjustment / regional impact studies)?
- What is the spatial and temporal scale dependency of uncertainties in regional climate information?
- Can smaller subsets of climate simulations from a grand ensemble be representative and how to select such subsets?
To address these scientific challenges a number of new and novel approaches are being applied, namely:
- advanced statistical analysis in order to clarify contribution of large-scale and local processes to regional climate and the added value of downscaling
- new targeted downscaling experiments
- different bias adjustment methods
- different methodologies for selection of reduced ensembles of climate simulations.
All the above steps aim to provide ‘transformed’ scale-relevant climate information necessary for impact studies and are ongoing in close cooperation with international partners and with other groups at SMHI. Such activities are inline with the Global Framework for Climate Services (GFCS) coordinated by WMO and will help in establishing climate services infrastructure.
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