摘要：Assessing the adverse impacts caused by tropical cyclones has becomeincreasingly important as both climate change and human coastal developmentincrease 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 impactfunctions. In this study, we show that assessing tropical cyclone risk on aglobal level with one single impact function calibrated for the USA – whichis a typical approach in many recent studies – is problematic, biasing thesimulated damage by as much as a factor of 36 in the north West Pacific.Thus, tropical cyclone risk assessments should always consider regionaldifferences in vulnerability, too. This study proposes a calibrated model toadequately assess tropical cyclone risk in different regions by fittingregional impact functions based on reported damage data. Applying regionalcalibrated impact functions within the risk modeling framework CLIMADA (CLIMate ADAptation) at aresolution of 10 km worldwide, we find global annual average direct damagecaused by tropical cyclones to range from USD 51 up to USD 121 billion (value in 2014, 1980–2017) with the largest uncertainties in the WestPacific basin where the calibration results are the least robust. To betterunderstand the challenges in the West Pacific and to complement the globalperspective of this study, we explore uncertainties and limitations entailedin the modeling setup for the case of the Philippines. While using wind asa proxy for tropical cyclone hazard proves to be a valid approach ingeneral, the case of the Philippines reveals limitations of the model andcalibration due to the lack of an explicit representation of sub-perils suchas storm surges, torrential rainfall, and landslides. The globally consistentmethodology and calibrated regional impact functions are available online asa Python package ready for application in practical contexts like physicalrisk disclosure and providing more credible information for climateadaptation studies.
