Tropical Cyclone Impact Sensitivity to Different Aerosol Conditions with CLIMADA Risk Platform

Tropical Cyclone Impact Sensitivity to Different Aerosol Conditions with CLIMADA Risk PlatformTropical cyclones are one of the most severe natural hazards and pose major risks to coastal regions through extreme winds, heavy precipitation, flooding, and storm surges. During the period 1980 - 2011 these hazards contributed to 47% of all U.S. billion-dollar natural hazard losses (Smith and Katz, 2013), which are expected to increase with climate change and increasing coastal exposure (IPCC, 2021). To mitigate tropical cyclone risk, it is crucial to understand their evolution under future climate conditions. It is expected that reductions in anthropogenic emissions will lead to a decline in aerosol concentrations (Riahi et al., 2017). We already know that aerosols influence the formation and evolution of tropical cyclones by affecting cloud microphysics and precipitation patterns (Khain et al., 2010). Depending on the location of aerosol intrusion the convective invigoration can either weaken or strengthen the storm winds and further modulate the precipitation intensities (Hoarau et al., 2018; Lin et al., 2023). However, these effects remain largely unaccounted for in many operational forecasting and risk assessment models.This thesis aims to understand how aerosol concentrations may alter tropical cyclone wind and precipitation patterns with a focus on the 2020 hurricane season in North America. Looking at different aerosol concentrations, we combine numerically simulated tropical cyclone tracks from ICON with the open-source probabilistic risk assessment platform CLIMADA (Aznar-Siguan and Bresch, 2019). This platform integrates hazard, exposure and vulnerability so we can link the impacts of different aerosol concentrations on the seasonal tropical cyclone activity to resulting economic damages and population exposure. With this, we evaluate how the inclusion of aerosol effects in tropical cyclone risk models could improve the risk assessment. This can support decision-makers in implementing more effective adaptation strategies in affected coastal regions. REFERENCESAznar-Siguan, G., Bresch, D.N., 2019.  https://doi.org/10.5194/gmd-12-3085-2019Hoarau, T., Barthe, C., Tulet, P., Claeys, M., Pinty, J.-P., Bousquet, O., Delanoë, J., Vié, B., 2018. https://doi.org/10.1029/2017JD028125IPCC, 2021. https://doi.org/10.1017/9781009157896Khain, A., Lynn, B., Dudhia, J., 2010. https://doi.org/10.1175/2009JAS3210.1Lin, Y., Wang, Y., Hsieh, J.-S., Jiang, J.H., Su, Q., Zhao, L., Lavallee, M., Zhang, R., 2023. https://doi.org/10.5194/acp-23-13835-2023Riahi, K., van Vuuren, D.P., Kriegler, E., Edmonds, J., O’Neill, B.C., Fujimori, S., Bauer, N., Calvin, K., Dellnik, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J.C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Tavoni, M., 2017. https://doi.org/10.1016/j.gloenvcha.2016.05.009Smith, A.B., Katz, R.W., 2013. https://doi.org/10.1007/s11069-013-0566-5