Climate data – basis for understanding climate change
- What is the effect of GCM-based ensemble (model uncertainty) on future projections of hydrological model results for Yakutia? (Using the C3S data).
- What is the consensus and discrepancy of different meteorological forcing data on the hydrological forecasts for Yakutia (different reanalysis, ECVs)?
The understanding of large-scale climate drivers, their changes and their influence on the local hydrological regime is one of the key elements of the project. However, the analysis of long-term changes of climate parameters is hampered by reliable in-situ observations (Bulygina et al., 2011; Ge and Gong, 2008). Data from meteorological stations provide the highest quality with regard to local conditions, but station network have become sparser over time, especially in the Arctic. Several on-going and recent projects (e.g. Copernicus C3S, ESA Climate Change Initiative (CCI)) have increased the availability of data for Essential Climate variables (ECVs) such as air temperature, precipitation, snow cover, snow water equivalent with global or Arctic coverage. The data quality varies in terms of e.g. temporal and spatial coverage, consistency, principle of generation (observations, models, re-analyses), findability, accessibility, interoperability, and re-usability (the FAIR principle), update frequency, documentation, and metadata standards. ECV data for the climate change analyses in this project will be gathered from different data providers, and carefully assessed for temporal and spatial consistency and biases. Impacts of different re-analysis data on hydrological model results and consensus and discrepancies between global climate models and forecast models of the study area (Yakutia) will be analysed.