Abstract
The multi-year simulation of tropical cyclones (TCs) over the Western North Pacific (WNP) in the variable resolution (VR) CAM-MPAS model is studied. Experiments with the global quasi-uniform low resolution of 120 km (MPAS-UR) and the variable resolution mesh of 30–120 km refined over East Asia (MPAS-VR) are integrated from 1980 to 2005 following the Atmospheric Model Intercomparison Project protocol. By utilizing an objective detection method, TCs in ERA5 reanalysis and model simulations are tracked and compared against observations. MPAS-VR shows significant advantages over MPAS-UR as indicated by more realistic TC counts, intensities, lifetime distribution, and seasonal variation. The large-scale circulation and precipitation patterns associated with TCs are also improved in MPAS-VR relative to MPAS-UR. Based on the theory of Dynamic Genesis Potential Index, the multi-year TC records are further used to quantify the dependence of TC genesis on various dynamical environmental factors from the perspective of seasonal variation. We find that in ERA5, the relative contribution of the 500 hPa vertical pressure velocity term to TC genesis exceeds that of the 200–850 hPa vertical wind shear term, which is responsible for the August peak and strong seasonal variation of TC genesis. MPAS-UR fails to capture such relationship while MPAS-VR performs much better in this regard, suggesting that the higher skills in simulating the relative contributions from different dynamical environmental factors to the simulated seasonal cycle of TC genesis may explain the improvements from MPAS-UR to MPAS-VR.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The IBTrACS data is available at https://www.ncei.noaa.gov/products/international-best-track-archive?name=ib-v4-access. The 3-hourly TRMM 3B42 version 7 data provided by NASA GSFC is available at https://doi.org/10.5067/TRMM/TMPA/3H/7. The 6-hourly ERA5 reanalysis data provided by the Copernicus Climate Change Service can be downloaded from https://doi.org/10.24381/cds.bd0915c6, and the monthly ERA5 data is available at https://doi.org/10.24381/cds.6860a573. The CAM-MPAS model outputs are available from the corresponding authors on reasonable request.
References
Balaguru K, Foltz GR, Leung LR, Hagos SM (2022) Impact of rainfall on tropical cyclone-induced sea surface cooling. Geophys Res Lett. https://doi.org/10.1029/2022gl098187
Balaguru K, Leung LR, Van Roekel LP, Golaz JC, Ullrich PA, Caldwell PM et al (2020) Characterizing tropical cyclones in the energy exascale earth system model version 1. J Adv Model Earth Syst. https://doi.org/10.1029/2019MS002024
Bretherton CS, Park S (2009) A new moist turbulence parameterization in the community atmosphere model. J Clim 22:3422–3448. https://doi.org/10.1175/2008jcli2556.1
Camargo SJ (2013) Global and regional aspects of tropical cyclone activity in the CMIP5 models. J Clim 26:9880–9902. https://doi.org/10.1175/Jcli-D-12-00549.1
Camargo SJ, Emanuel KA, Sobel AH (2007) Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Clim 20:4819–4834. https://doi.org/10.1175/Jcli4282.1
Camargo SJ, Wing AA (2016) Tropical cyclones in climate models. Wiley Interdiscipl Rev-Clim Change 7:211–237. https://doi.org/10.1002/wcc.373
Chan JCL (2008) Decadal variations of intense typhoon occurrence in the western North Pacific. Proc R Soc A-Math Phys Eng Sci 464:249–272. https://doi.org/10.1098/rspa.2007.0183
Daloz AS, Chauvin F, Roux F (2012) Impact of the configuration of stretching and ocean-atmosphere coupling on tropical cyclone activity in the variable-resolution GCM ARPEGE. Clim Dyn 39:2343–2359. https://doi.org/10.1007/s00382-012-1561-3
Done JM, Holland GJ, Bruyere CL, Leung LR, Suzuki-Parker A (2015) Modeling high-impact weather and climate: lessons from a tropical cyclone perspective. Clim Change 129:381–395. https://doi.org/10.1007/s10584-013-0954-6
Fu D, Chang P, Patricola CM, Saravanan R (2019) High-resolution tropical channel model simulations of tropical cyclone climatology and intraseasonal-to-interannual variability. J Clim 32:7871–7895. https://doi.org/10.1175/Jcli-D-19-0130.1
Gates WL (1992) AMIP - the atmospheric model intercomparison project. Bull Am Meteor Soc 73:1962–1970. https://doi.org/10.1175/1520-0477(1992)073%3c1962:Atamip%3e2.0.Co;2
Gettelman A, Morrison H (2015) Advanced two-moment bulk microphysics for global models. part I: off-line tests and comparison with other schemes. J Clim 28:1268–1287. https://doi.org/10.1175/Jcli-D-14-00102.1
Guo YP, Tan ZM (2018) Westward migration of tropical cyclone rapid-intensification over the Northwestern Pacific during short duration El Nino. Nat Commun 9:1507. https://doi.org/10.1038/s41467-018-03945-y
Harris LM, Lin SJ (2013) A two-way nested global-regional dynamical core on the cubed-sphere grid. Mon Weather Rev 141:283–306. https://doi.org/10.1175/Mwr-D-11-00201.1
Harris LM, Lin S-J, Tu CY (2016) High-resolution climate simulations using GFDL HiRAM with a stretched global grid. J Clim 29:4293–4314. https://doi.org/10.1175/JCLI-D-15-0389.1
Hashimoto A, Done JM, Fowler LD, Bruyere CL (2016) Tropical cyclone activity in nested regional and global grid-refined simulations. Clim Dyn 47:497–508. https://doi.org/10.1007/s00382-015-2852-2
Hersbach H, Bell B, Berrisford P, Hirahara S, Horanyi A, Munoz-Sabater J et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/10.1002/qj.3803
Horn M, Walsh K, Zhao M, Camargo SJ, Scoccimarro E, Murakami H, Wang H, Ballinger A, Kumar A, Shaevitz DA, Jonas JA, Oouchi K (2014) Tracking scheme dependence of simulated tropical cyclone response to idealized climate simulations. J Clim 27:9197–9213. https://doi.org/10.1175/JCLI-D-14-00200.1
Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM Multi-Satellite Precipitation Analysis (TMPA). In: Gebremichael M, Hossain F (eds) Satellite Rainfall Applications for Surface Hydrology. Springer, Dordrecht, pp 3–22
Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res Atmos. https://doi.org/10.1029/2008jd009944
Ju L, Ringler T, Gunzburger M (2011) Voronoi tessellations and their application to climate and global modeling. In: Lauritzen P, Jablonowski C, Taylor M, Nair R (eds) Numerical techniques for global atmospheric models. Springer, Berlin, pp 313–342
Kim HM, Webster PJ, Curry JA (2011) Modulation of North Pacific tropical cyclone activity by three phases of ENSO. J Clim 24:1839–1849. https://doi.org/10.1175/2010jcli3939.1
Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) THE INTERNATIONAL BEST TRACK ARCHIVE FOR CLIMATE STEWARDSHIP (IBTrACS) Unifying Tropical Cyclone Data. Bull Am Meteor Soc 91:363–376. https://doi.org/10.1175/2009bams2755.1
Li JX, Bao Q, Liu YM, Wang L, Yang J, Wu GX et al (2021) Effect of horizontal resolution on the simulation of tropical cyclones in the Chinese Academy of Sciences FGOALS-f3 climate system model. Geosci Model Dev 14:6113–6133. https://doi.org/10.5194/gmd-14-6113-2021
Li RCY, Zhou W (2012) Changes in western pacific tropical cyclones associated with the El Nino-Southern Oscillation Cycle. J Clim 25:5864–5878. https://doi.org/10.1175/Jcli-D-11-00430.1
Li Z, Yu WD, Li T, Murty VSN, Tangang F (2013) Bimodal character of cyclone climatology in the Bay of Bengal modulated by monsoon seasonal cycle. J Clim 26:1033–1046. https://doi.org/10.1175/Jcli-D-11-00627.1
Liang Y, Yang B, Wang MH, Tang JP, Sakaguchi K, Leung LR et al (2021) Multiscale simulation of precipitation Over East Asia by variable resolution CAM-MPAS. J Adv Model Earth Syst. https://doi.org/10.1029/2021MS002656
Liu X, Ma PL, Wang H, Tilmes S, Singh B, Easter RC et al (2016) Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model. Geosci Model Dev 9:505–522. https://doi.org/10.5194/gmd-9-505-2016
Lu MQ, Xiong R (2019) Spatiotemporal profiling of tropical cyclones genesis and favorable environmental conditions in the Western Pacific Basin. Geophys Res Lett 46:11548–11558. https://doi.org/10.1029/2019gl084995
Lui YS, Tse LKS, Tam CY, Lau KH, Chen JL (2021) Performance of MPAS-A and WRF in predicting and simulating western North Pacific tropical cyclone tracks and intensities. Theoret Appl Climatol 143:505–520. https://doi.org/10.1007/s00704-020-03444-5
Manganello JV, Hodges KI, Kinter JL, Cash BA, Marx L, Jung T et al (2012) Tropical cyclone climatology in a 10-km global atmospheric GCM: Toward weather-resolving climate modeling. J Clim 25:3867–3893. https://doi.org/10.1175/Jcli-D-11-00346.1
Mendelsohn R, Emanuel K, Chonabayashi S, Bakkensen L (2012) The impact of climate change on global tropical cyclone damage. Nat Clim Chang 2:205–209. https://doi.org/10.1038/Nclimate1357
Murakami H (2022) Substantial global influence of anthropogenic aerosols on tropical cyclones over the past 40 years. Sci Adv 8:eabn9493. https://doi.org/10.1126/sciadv.abn9493
Murakami H, Hsu PC, Arakawa O, Li T (2014) Influence of model biases on projected future changes in tropical cyclone frequency of occurrence. J Clim 27:2159–2181. https://doi.org/10.1175/Jcli-D-13-00436.1
Murakami H, Wang B, Kitoh A (2011) Future change of Western North Pacific typhoons: projections by a 20-km-Mesh global atmospheric model. J Clim 24:1154–1169. https://doi.org/10.1175/2010jcli3723.1
Park S, Bretherton CS (2009) The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the community atmosphere model. J Clim 22:3449–3469. https://doi.org/10.1175/2008jcli2557.1
Park S, Bretherton CS, Rasch PJ (2014) Integrating cloud processes in the community atmosphere model, Version 5. J Clim 27:6821–6856. https://doi.org/10.1175/Jcli-D-14-00087.1
Ringler TD, Jacobsen D, Gunzburger M, Ju LL, Duda M, Skamarock W (2011) Exploring a multiresolution modeling approach within the Shallow-Water equations. Mon Weather Rev 139:3348–3368. https://doi.org/10.1175/Mwr-D-10-05049.1
Roberts MJ, Camp J, Seddon J, Vidale PL, Hodges K, Vanniere B et al (2020) Impact of model resolution on tropical cyclone simulation using the HighResMIP-PRIMAVERA multimodel ensemble. J Clim 33:2557–2583. https://doi.org/10.1175/Jcli-D-19-0639.1
Sakaguchi K, Leung LR, Zhao C, Yang Q, Lu J, Hagos S et al (2015) Exploring a multiresolution approach using AMIP simulations. J Clim 28:5549–5574. https://doi.org/10.1175/Jcli-D-14-00729.1
Sakaguchi K, Lu J, Leung LR, Zhao C, Li YJ, Hagos S (2016) Sources and pathways of the upscale effects on the Southern Hemisphere jet in MPAS-CAM4 variable-resolution simulations. J Adv Model Earth Syst 8:1786–1805. https://doi.org/10.1002/2016ms000743
Seo E, Lee MI, Kim D, Lim YK, Schubert SD, Kim KM (2019) Inter-annual variation of tropical cyclones simulated by GEOS-5 AGCM with modified convection scheme. Int J Climatol 39:4041–4057. https://doi.org/10.1002/joc.6058
Shan KY, Yu XP (2020) Interdecadal variability of tropical cyclone genesis frequency in western North Pacific and South Pacific ocean basins. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab8093
Shan KY, Yu XP (2021) Variability of tropical cyclone landfalls in China. J Clim 34:9235–9247. https://doi.org/10.1175/Jcli-D-21-0031.1
Skamarock WC, Klemp JB, Duda MG, Fowler LD, Park SH, Ringler TD (2012) A multiscale nonhydrostatic atmospheric model using centroidal voronoi tesselations and C-grid staggering. Mon Weather Rev 140:3090–3105. https://doi.org/10.1175/Mwr-D-11-00215.1
Stansfield AM, Reed KA, Zarzycki CM (2020a) Changes in precipitation from North Atlantic tropical cyclones under RCP scenarios in the variable-resolution community atmosphere model. Geophys Res Lett. https://doi.org/10.1029/2019GL086930
Stansfield AM, Reed KA, Zarzycki CM, Ullrich PA, Chavas DR (2020b) Assessing tropical cyclones’ contribution to precipitation over the eastern united states and sensitivity to the variable-resolution domain extent. J Hydrometeorol 21:1425–1445. https://doi.org/10.1175/Jhm-D-19-0240.1
Strachan J, Vidale PL, Hodges K, Roberts M, Demory ME (2013) Investigating global tropical cyclone activity with a hierarchy of AGCMS: the role of model resolution. J Clim 26:133–152. https://doi.org/10.1175/Jcli-D-12-00012.1
Ullrich PA, Zarzycki CM, McClenny EE, Pinheiro MC, Stansfield AM, Reed KA (2021) TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets. Geosci Model Dev 14:5023–5048. https://doi.org/10.5194/gmd-14-5023-2021
Walsh KJE, Fiorino M, Landsea CW, McInnes KL (2007) Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses. J Clim 20:2307–2314. https://doi.org/10.1175/JCLI4074.1
Wang B, Murakami H (2020) Dynamic genesis potential index for diagnosing present-day and future global tropical cyclone genesis. Environ Res Lett. https://doi.org/10.1088/1748-9326/abbb01
Wu L, Chou C, Chen CT, Huang RH, Knutson TR, Sirutis JJ et al (2014) Simulations of the present and late-twenty-first-century Western North Pacific tropical cyclone activity using a regional model. J Clim 27:3405–3424. https://doi.org/10.1175/Jcli-D-12-00830.1
Wu XN, Reed KA, Callaghan P, Bacmeister JT (2022) Exploring Western North Pacific tropical cyclone activity in the high-resolution community atmosphere model. Earth and Space Science. https://doi.org/10.1029/2021EA001862
Zarzycki CM (2016) Tropical cyclone intensity errors associated with lack of two-way ocean coupling in high-resolution global simulations. J Clim 29:8589–8610. https://doi.org/10.1175/Jcli-D-16-0273.1
Zarzycki CM, Jablonowski C (2014) A multidecadal simulation of Atlantic tropical cyclones using a variable-resolution global atmospheric general circulation model. J Adv Model Earth Syst 6:805–828. https://doi.org/10.1002/2014ms000352
Zhang GJ, Mcfarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the canadian climate center general-circulation model. Atmos Ocean 33:407–446. https://doi.org/10.1080/07055900.1995.9649539
Zhao C, Leung LR, Park SH, Hagos S, Lu J, Sakaguchi K et al (2016) Exploring the impacts of physics and resolution on aqua-planet simulations from a nonhydrostatic global variable-resolution modeling framework. J Adv Model Earth Syst 8:1751–1768. https://doi.org/10.1002/2016ms000727
Zhao M, Held IM, Lin SJ, Vecchi GA (2009) Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J Clim 22:6653–6678. https://doi.org/10.1175/2009jcli3049.1
Zhong Z, Chen X, Yang XQ, Ha Y, Sun Y (2019) The relationship of frequent tropical cyclone activities over the western North Pacific and hot summer days in central-eastern China. Theoret Appl Climatol 138:1395–1404. https://doi.org/10.1007/s00704-019-02908-7
Zhou XY, Lu RY (2019) Interannual variability of the tropical cyclone landfall frequency over the Southern and Northern Regions of East Asia in Autumn. J Clim 32:8677–8686. https://doi.org/10.1175/Jcli-D-19-0057.1
Zhou YH, Zhang Y, Li J, Yu RC, Liu Z (2020) Configuration and evaluation of a global unstructured mesh atmospheric model (GRIST-A20.9) based on the variable-resolution approach. Geosci Model Dev 13:6325–6348. https://doi.org/10.5194/gmd-13-6325-2020
Zscheischler J, Westra S, van den Hurk BJJM, Seneviratne SI, Ward PJ, Pitman A et al (2018) Future climate risk from compound events. Nat Clim Chang 8:469–477. https://doi.org/10.1038/s41558-018-0156-3
Acknowledgements
We greatly thank the High Performance Computing Center (HPCC) of Nanjing University and the Kunshan Supercomputing Center for providing the computational resources used in this work. We are grateful for the comments from two anonymous reviewers and the editor, which helped improve the original manuscript.
Funding
This work is supported by the National Natural Science Foundation of China (NSFC; 41925023) and the Fundamental Research Funds for the Central Universities (020714380170). This research is also supported by NSFC (91744208 and 41621005), the Ministry of Science and Technology of the People’s Republic of China (2017YFA0604002), and the Collaborative Innovation Center of Climate Change, Jiangsu Province.
Author information
Authors and Affiliations
Contributions
BY and MW contributed to the study conception and design. Material preparation, data collection and analysis were performed by YL. The first draft of the manuscript was written by YL and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no competing interests.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liang, Y., Yang, B., Wang, M. et al. Tropical cyclone strength, precipitation, and environment in variable resolution CAM-MPAS simulations over Western North Pacific. Clim Dyn 61, 2253–2267 (2023). https://doi.org/10.1007/s00382-023-06677-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-023-06677-y