Abstract. Assessing the adverse impacts caused by tropical cyclones has become increasingly important as both climate change and human coastal development increase the damage potential. In order to assess tropical cyclone risk, direct economic damage is frequently modeled based on hazard intensity, asset exposure, and vulnerability, the latter represented by impact functions. In this study, we show that assessing tropical cyclone risk on a global level with one single impact function calibrated for the USA – which is a typical approach in many recent studies – is problematic, biasing the simulated damage by as much as a factor of 36 in the north West Pacific. Thus, tropical cyclone risk assessments should always consider regional differences in vulnerability, too. This study proposes a calibrated model to adequately assess tropical cyclone risk in different regions by fitting regional impact functions based on reported damage data. Applying regional calibrated impact functions within the risk modeling framework CLIMADA (CLIMate ADAptation) at a resolution of 10 km worldwide, we find global annual average direct damage caused by tropical cyclones to range from USD 51 up to USD 121 billion (value in 2014, 1980–2017) with the largest uncertainties in the West Pacific basin where the calibration results are the least robust. To better understand the challenges in the West Pacific and to complement the global perspective of this study, we explore uncertainties and limitations entailed in the modeling setup for the case of the Philippines. While using wind as a proxy for tropical cyclone hazard proves to be a valid approach in general, the case of the Philippines reveals limitations of the model and calibration due to the lack of an explicit representation of sub-perils such as storm surges, torrential rainfall, and landslides. The globally consistent methodology and calibrated regional impact functions are available online as a Python package ready for application in practical contexts like physical risk disclosure and providing more credible information for climate adaptation studies.

The recent development of high-resolution climate models offers a promising approach in improving the simulation of precipitation, clouds and temperature. However, higher grid spacing is also a promising feature to improve the simulation of snow cover. In particular, it provides a refined representation of topography and allows for an explicit simulation of convective precipitation processes. In this study we analyze the snow cover in a set of decade-long high-resolution climate simulation with horizontal grid spacing of 2.2 km over the greater Alpine region. Results are compared against observations and lower resolution models (12 and 50 km), which use parameterized convection. The simulations are integrated using the COSMO (Consortium for Small-Scale Modeling) model. The evaluation of snow water equivalent (SWE) in the simulation of present-day climate, driven by the ERA-Interim reanalysis, against an observational dataset, reveals that the high-resolution simulation clearly outperforms simulations with grid spacing of 12 and 50 km. The latter simulations underestimate the cumulative amount of SWE over Switzerland over the whole annual cycle by 33% (12 km simulation) and 56% (50 km simulation) while the high-resolution simulation shows a spatially and temporally averaged difference of less than 1%. Scenario simulations driven by GCM MPI-ESM-LR (2081–2090 RCP8.5 vs. 1991–2000) reveal a strong decrease of SWE over the Alps, consistent with previous studies. Previous studies had found that the relative decrease becomes gradually smaller with elevation, but this finding was limited to low and intermediate altitudes (as a 12 km simulation resolves the topography up to 2500 m). In the current study we find that the height gradient reverses sign, and relative reductions in snow cover increases above 3000 m asl, where important parts of the cryosphere are present. In addition, the simulations project a transition from permanent to seasonal snow cover at high altitudes, with potentially important impacts to Alpine permafrost. This transition and the more pronounced decline of SWE emphasize the value of the higher grid spacing. Overall, we show that high-resolution climate models offer a promising approach in improving the simulation of snow cover in Alpine terrain.

Abstract. Assessing the adverse impacts caused by tropical cyclones has become increasingly important, as both climate change and human coastal development increase the damage potential. In order to assess tropical cyclone risk, direct economic damage is frequently modelled based on hazard intensity, asset exposure and vulnerability, the latter represented by impact functions. In this study, we show that assessing tropical cyclone risk on a global level with one single impact function calibrated for the USA – which is a typical approach in many recent studies – is problematic, biasing the simulated damages by as much as a factor of 36 in the North West Pacific. Thus, tropical cyclone risk assessments should always consider regional differences in vulnerability, too. This study proposes a calibrated model to adequately assess tropical cyclone risk in different regions by fitting regional impact functions based on reported damage data. Applying regional calibrated impact functions within the risk modelling framework CLIMADA at a resolution of 10 km worldwide, we find global annual average direct damage caused by tropical cyclones to range from 51 up to 121 billion USD (current value of 2014, 1980–2017), with the largest uncertainties in the West Pacific basin, where the calibration results are the least robust. To better understand the challenges in the West Pacific and to complement the global perspective of this study, we explore uncertainties and limitations entailed in the modelling setup for the case of the Philippines. While using wind as a proxy for tropical cyclone hazard proves to be a valid approach in general, the case of the Philippines reveals limitations of the model and calibration due to the lack of an explicit representation of sub-perils such as storm surge, torrential rainfall, and landslides. The globally consistent methodology and calibrated regional impact functions are available online as a Python package, ready for application in practical contexts like physical risk disclosure and providing more credible information for climate adaptation studies.