Projections of future tropical cyclone damage with a high-resolution global climate model
High-resolution climate model simulations and a tropical cyclone damage model are used to simulate the economic damage due to tropical cyclones. The damage model produces reasonable damage estimates compared to observations. The climate model produces realistically intense tropical cyclones over a historical simulation, with significant basin scale correlation of the inter-annual variability of cyclone numbers to observed storm numbers. However, the climate model produces too many moderate tropical cyclones, particularly in the N. Pacific. Annual mean cyclone damage with simulated storms is similar to estimates with the damage model and observed storms, and with actual economic losses. Ensembles of future simulations with different mitigation scenarios and different sea surface temperatures (SSTs), as well as societal changes, are used to assess future projections of cyclone damage. Damage estimates are highly dependent on the internal variability of the coupled system. Using different ensemble members or different SSTs affects damage results by ±40 %. Experiments indicate that despite decreases in storm numbers in the future, strong landfalling storms increase in E. Asia, increasing global storm damage by ∼50 % in 2070 over 2015. Little significant benefit is seen from mitigation, but only one ensemble is available. Projected increases in vulnerable assets increase damage from simulated storms by more than threefold (∼300 %, assuming no adaptation) indicating future growth will swamp potential changes in tropical cyclones.
The National Center for Atmospheric Research is funded by the U.S. National Science Foundation. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. Nighttime lights of the world DMSP Image and Data processing by NOAA’s National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency.
- Bacmeister JT, Reed KA, Hannay C, Lawrence P, Bates S, Truesdale JE, Rosenbloom N, Levy M (2016) Projected changes in tropical cyclone activity under future warming scenarios using a high-resolution climate model. Clim Chang:1–14. doi: 10.1007/s10584-016-1750-x
- Bresch DN (2014) CLIMADA Model Code and DescriptionGoogle Scholar
- Done JM, PaiMazumder D, Towler E, Kishtawal CM (2015) Estimating impacts of North Atlantic tropical cyclones using an index of damage potential. Clim Chang:1–13. doi: 10.1007/s10584-015-1513-0
- Kay JE, Deser C, Phillips A, Mai A, Hannay C, Strand G, Arblaster JM, Bates SC, Danabasoglu G, Edwards J, Holland M, Kushner P, Lamarque JF, Lawrence D, Lindsay K, Middleton A, Munoz E, Neale R, Oleson K, Polvani L, Vertenstein M (2014) The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull Amer Meteor Soc. doi: 10.1175/BAMS-D-13-00255.1
- Raible CC, Kleppek S, Wüest M, Bresch DN, Kitoh A, Murakami H, Stocker TF (2012) Atlantic hurricanes and associate insurance loss potentials in future climate scenarios: Limitations of high-resolution AGCM simulations. Tellus A 64 (15672). doi: 10.3402/tellusa.64i0.15672
- Sanderson BM, Oleson KW, Strand WG, Lehner F, O’Neill BC (2015) A new ensemble of GCM simulations to assess avoided impacts in a climate mitigation scenario. Clim Chang:1–16. doi: 10.1007/s10584-015-1567-z
- van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109(1-2):5–31. doi:10.1007/s10584-011-0148-z CrossRefGoogle Scholar
- Wehner MF, Reed KA, Li F, Prabhat, Bacmeister J, Chen CT, Paciorek C, Gleckler PJ, Sperber KR, Collins WD, Gettelman A, Jablonowski C (2014) The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. J Adv Model Earth Syst 6(4):980–997. doi: 10.1002/2013MS000276 CrossRefGoogle Scholar
- World Bank (2015) World Development Indicators 2015. World Bank PublicationsGoogle Scholar