Research Project:
Assessing the influence of background state and climate variability on tropical cyclones using initialized ensembles and mesh refinement in E3SM

dc.contributor.departmentAtmospheric Sciences
dc.contributor.memberTAMU
dc.contributor.pdachttps://hdl.handle.net/20.500.14641/227
dc.contributor.sponsorDOE-Office Of Science
dc.creator.copiChang, Ping
dc.creator.piSaravanan, Ramalingam
dc.date2023-08-31
dc.date.accessioned2025-03-11T17:18:23Z
dc.date.available2025-03-11T17:18:23Z
dc.descriptionGrant
dc.description.abstractThe scientific goal of this project is to assess the influence of the background state and horizontal resolution of climate models on the simulation of extreme events like intense precipitation, tropical cyclones, atmospheric rivers, and heatwaves. The main results are: - Initialized ensembles of 4-week forecasts using a climate model (E3SM) show that dynamic (flow) and thermodynamic (moisture) biases asymptote to their climatological values at different rates—dynamic bias grows rapidly and approaches its climatological value within about 2 weeks whereas the thermodynamic bias takes about 4 weeks. - Moisture bias has a bigger impact on extreme events like tropical cyclones and intense precipitation than flow bias, and this impact is non-monotonic over bias evolution time. - Climate models are capable of simulating the flow features associated with unprecedented extreme events like the Western North America heatwave of June 2021, but moisture biases can weaken the surface manifestation of extreme heatwaves. Reducing model bias may be more important than increasing model resolution in improving the fidelity of heatwave simulations in climate models. - Climate models are capable of simulating weather patterns associated with “power droughts”, i.e., periods with low solar and wind power generation, provided model biases in the simulation of the background state can be corrected. - Regional mesh refinement can improve the simulation of extreme weather events but model biases may still persist.
dc.description.chainOfCustody2025-03-11T17:20:47.795079571 David Hubbard (35aca544-f5e8-4e99-90c9-c0033655efed) added Chang, Ping (63ffbc7a-d369-4df7-9d17-610a9c2e316d) to null (031145d7-f1b6-4c1d-b92c-e0f57149483e) 2025-03-11T17:21:54.874329705 David Hubbard (35aca544-f5e8-4e99-90c9-c0033655efed) removed Chang, Ping (63ffbc7a-d369-4df7-9d17-610a9c2e316d) from null (031145d7-f1b6-4c1d-b92c-e0f57149483e) 2025-03-11T17:22:19.896024852 David Hubbard (35aca544-f5e8-4e99-90c9-c0033655efed) added Saravanan, Ramalingam (64b3388b-fa2a-4e64-95cd-5ea75880b56d) to null (031145d7-f1b6-4c1d-b92c-e0f57149483e)en
dc.identifier.otherM1903532
dc.identifier.urihttps://hdl.handle.net/20.500.14641/800
dc.relation.profileurlhttps://scholars.library.tamu.edu/vivo/display/n2bf78472
dc.titleAssessing the influence of background state and climate variability on tropical cyclones using initialized ensembles and mesh refinement in E3SM
dc.title.projectAssessing the influence of background state and climate variability on tropical cyclones using initialized ensembles and mesh refinement in E3SM
dspace.entity.typeResearchProject
local.awardNumberDE-SC0020072
local.pdac.nameSaravanan, Ramalingam
local.projectStatusTerminated

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