A globally consistent local-scale assessment of future tropical cyclone risk.

There is considerable uncertainty surrounding future changes in tropical cyclone (TC) frequency and intensity, particularly at local scales. This uncertainty complicates risk assessments and implementation of risk mitigation strategies. We present a novel approach to overcome this problem, using the statistical model STORM to generate 10,000 years of synthetic TCs under past (1980-2017) and future climate (SSP585; 2015-2050) conditions from an ensemble of four high-resolution climate models. We then derive high-resolution (10-km) wind speed return period maps up to 1000 years to assess local-scale changes in wind speed probabilities. Our results indicate that the probability of intense TCs, on average, more than doubles in all regions except for the Bay of Bengal and the Gulf of Mexico. Our unique and innovative methodology enables globally consistent comparison of TC risk in both time and space and can be easily adapted to accommodate alternative climate scenarios and time periods.

Generation of a global synthetic tropical cyclone hazard dataset using STORM.

Over the past few decades, the world has seen substantial tropical cyclone (TC) damages, with the 2017 Hurricanes Harvey, Irma and Maria entering the top-5 costliest Atlantic hurricanes ever. Calculating TC risk at a global scale, however, has proven difficult given the limited temporal and spatial information on TCs across much of the global coastline. Here, we present a novel database on TC characteristics on a global scale using a newly developed synthetic resampling algorithm we call STORM (Synthetic Tropical cyclOne geneRation Model). STORM can be applied to any meteorological dataset to statistically resample and model TC tracks and intensities. We apply STORM to extracted TCs from 38 years of historical data from IBTrACS to statistically extend this dataset to 10,000 years of TC activity. We show that STORM preserves the TC statistics as found in the original dataset. The STORM dataset can be used for TC hazard assessments and risk modeling in TC-prone regions.

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.