Sensitivity of winter North Atlantic-European climate to resolved atmosphere and ocean dynamics.

Northern Hemisphere western boundary currents, like the Gulf Stream, are key regions for cyclogenesis affecting large-scale atmospheric circulation. Recent observations and model simulations with high-temporal and -spatial resolution have provided evidence that the associated ocean fronts locally affect troposphere dynamics. A coherent view of how this affects the mean climate and its variability is, however, lacking. In particular the separate role of resolved ocean and atmosphere dynamics in shaping the atmospheric circulation is still largely unknown. Here we demonstrate for the first time, by using coupled seasonal forecast experiments at different resolutions, that resolving meso-scale oceanic variability in the Gulf Stream region strongly affects mid-latitude interannual atmospheric variability, including the North Atlantic Oscillation. Its impact on climatology, however, is minor. Increasing atmosphere resolution to meso-scale, on the other hand, strongly affects mean climate but moderately its variability. We also find that regional predictability relies on adequately resolving small-scale atmospheric processes, while resolving small-scale oceanic processes acts as an unpredictable source of noise, except for the North Atlantic storm-track where the forcing of the atmosphere translates into skillful predictions.

Towards reliable extreme weather and climate event attribution.

Climate change is shaping extreme heat and rain. To what degree human activity has increased the risk of high impact events is of high public concern and still heavily debated. Recent studies attributed single extreme events to climate change by comparing climate model experiments where the influence of an external driver can be included or artificially suppressed. Many of these results however did not properly account for model errors in simulating the probabilities of extreme event occurrences. Here we show, exploiting advanced correction techniques from the weather forecasting field, that correcting properly for model probabilities alters the attributable risk of extreme events to climate change. This study illustrates the need to correct for this type of model error in order to provide trustworthy assessments of climate change impacts.

Using climate models to estimate the quality of global observational data sets.

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection.