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Kevin Debeire

Researcher at German Aerospace Center

Publications -  4
Citations -  271

Kevin Debeire is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Climate model & Coupled model intercomparison project. The author has an hindex of 3, co-authored 3 publications receiving 69 citations.

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Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Claudia Tebaldi, +61 more
TL;DR: In this paper, the authors present a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations, focusing mainly on the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation.
Journal ArticleDOI

Earth System Model Evaluation Tool (ESMValTool) v2.0 - An extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Veronika Eyring, +57 more
TL;DR: Large-scale diagnostics of the second major release of the ESMValTool tool, a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models participating in the Coupled Model Intercomparison Project (CMIP), are described.
Posted ContentDOI

ESMValTool v2.0 Extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Veronika Eyring, +57 more
TL;DR: Large-scale diagnostics of the second major release of the ESMValTool tool, a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth System Models participating in the Coupled Model Intercomparison Project (CMIP).

Bootstrap aggregation and confidence measures to improve time series causal discovery

TL;DR: In this paper , a novel bootstrap aggregation (bagging) and confidence measure method that is combined with time series causal discovery is introduced, which allows measuring confidence for the links of the time series graphs calculated by causal discovery methods.