Datensatz

Zusammenfassung

The Earth’s land surface and the atmosphere are strongly interlinked through the exchange of energy and matter
(e.g. water and carbon). This coupled behaviour causes various land-atmosphere feedbacks and an insufficient understanding
of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface
in exacerbating summer heat waves in mid-latitude regions has been identified empirically for high-impact heatwaves, but
individual 5 climate models differ widely in their respective representation of land-atmosphere coupling. Here, we combine an
ensemble of observations-based and simulated temperature (T) and evapotranspiration (ET) datasets and investigate coincidences
of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm
temperatures. We demonstrate that a relatively large fraction of state-of-the-art climate models from the Coupled Model Intercomparison
Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative
10 ET anomalies in mid-latitude regions during the warm season and in several tropical regions year-round. Further, we show
that these coincidences (high T, low ET), as diagnosed by the land-coupling coincidence metrics, are closely related to the
variability and extremes of simulated temperatures across a multi-model ensemble. Thus, our approach offers a physically consistent,
diagnostic-based avenue to evaluate these ensembles, and subsequently reduce model biases in simulated and predicted
extreme temperatures. Following this idea, we derive a land-coupling constraint based on the spread of 54 combinations of
15 T-ET benchmarking datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour
that is compatible with these observations-based benchmark estimates. The constrained multi-model projections exhibit lower
temperature extremes in regions where models show substantial spread in T-ET coupling, and in addition, biases in the climate model ensemble are consistently reduced.