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Impact of resolution and downscaling technique in simulating recent Atlantic tropical cylone activity

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Abstract

Using the global environmental multiscale (GEM) model, we investigate the impact of increasing model resolution from 2° to 0.3° on Atlantic tropical cyclone activity. There is a clear improvement in the realism of Atlantic storms with increased resolution, in part, linked to a better representation of African easterly waves. The geographical distribution of a Genesis Potential Index, composed of large-scales fields known to impact cyclone formation, coincides closely in the model with areas of high cyclogenesis. The geographical distribution of this index also improves with resolution. We then compare two techniques for achieving local high resolution over the tropical Atlantic: a limited-area model driven at the boundaries by the global 2° GEM simulation and a global variable resolution model (GVAR). The limited-area domain and high-resolution part of the GVAR model coincide geographically, allowing a direct comparison between these two downscaling options. These integrations are further compared with a set of limited-area simulations employing the same domain and resolution, but driven at the boundaries by reanalysis. The limited-area model driven by reanalysis produces the most realistic Atlantic tropical cyclone variability. The GVAR simulation is clearly more accurate than the limited-area version driven by GEM-Global. Degradation in the simulated interannual variability is partly linked to the models failure to accurately reproduce the impact of atmospheric teleconnections from the equatorial Pacific and Sahel on Atlantic cyclogenesis. Through the use of a smaller limited-area grid, driven by GEM-Global 2°, we show that an accurate representation of African Easterly Waves is crucial for simulating Atlantic tropical cyclone variability.

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Notes

  1. In the cases where two minima in pressure were recorded within a <2° radius of each other, the highest pressure coordinate was discarded.

  2. This value was chosen in order to allow direct comparison with the storms stored in the National Hurricane Center best track database (HURDAT; available online at http://www.nhc.noaa.gov/pastall.shtml).

  3. The jumpy looking tracks in GEM 2°, and to a lesser extent in GEM 1°, are caused by the coarse resolution of these simulations.

  4. Details on MPI are given in Appendix.

  5. \(\hbox{SOI} =\frac{\hbox{Standardized MSLP Tahiti}- \hbox{Standardized MSLP Darwin}}{\hbox{Monthly standard deviation}}.\)

References

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version 2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Aiyyer AR, Thorncroft C (2006) Climatology of vertical wind shear over the tropical Atlantic. J Clim 19:2969–2983

    Article  Google Scholar 

  • Ashok K, Behera SK, Rao SA, Weng H, Yamagata T (2007) El Niño Modoki and its possible teleconnection. J Geophys Res 112:C1100710. doi:10.1029/2006JC003798

    Article  Google Scholar 

  • Bélair S, Mailhot J, Girard C, Vaillancourt P (2005) Boundary layer and shallow cumulus clouds in a medium-range forecast of a large-scale weather system. Mon Wea Rev 133:1938–1959

    Article  Google Scholar 

  • Bélair S, Crevier L-P, Mailhot J, Bilodeau B, Delage Y (2003) Operational implementation of the ISBA land surface scheme in the Canadian regional weather forecast model. Part I: warm season results. J Hydrometeor. 4:352–370

    Article  Google Scholar 

  • Bélair S, Mailhot J, Strapp JW, MacPherson JI (1999) An examination of local versus nonlocal aspects of a TKE-based boundary layer scheme in clear convective conditions. J Appl Meteor 38:14991518

    Article  Google Scholar 

  • Bender MA, Ginis I, Tuleya R, Thomas B, Marchok T (2007) The operational GFDL coupled hurricane-ocean prediction system and a summary of its performance. Mon Wea Rev 135:3965–3989

    Article  Google Scholar 

  • Bengtsson L, Botzet M, Esch M (1996) Will greenhouse gas-induced warming over the next 50 years lead to a higher frequency and greater intensity of hurricanes? Tellus 48A:57–73

    Article  Google Scholar 

  • Bister M, Emanuel KA (1998) Dissipative heating and hurricane intensity. Meteorol Atmos Phys 52:233240

    Google Scholar 

  • Bougeault P, Lacarrère P (1989) Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon Wea Rev 117:18721890

    Article  Google Scholar 

  • Camargo S, Zebiak S (2002) Improving the detection and tracking of tropical cyclones in atmospheric general circulation models. Wea Forecast 17:1152–1162

    Article  Google Scholar 

  • Camargo SJ, Barnston AG, Zebiak SE (2005) A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus 57A:589–604

    Article  Google Scholar 

  • Camargo SJ, Sobel AH, Barnston AG, Emanuel KA (2007a) Tropical cyclone genesis potential index in climate models. Tellus 59A:428–443. doi:10.1111/j.1600-0870.2007.00238.x

    Article  Google Scholar 

  • Camargo SJ, Emanuel KA, Sobel AH (2007b) Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Clim 20:4819–4834

    Article  Google Scholar 

  • Camargo SJ, Wheeler MC, Sobel AH (2009) Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J Atmos Sci 66:3061–3074. doi:10.1175/2009JAS3101.1

    Article  Google Scholar 

  • Caron L-P, Jones CG (2008) Analysing present, past and future tropical cyclone activity as inferred from an ensemble of Coupled Global Climate Models. Tellus 60A:80–96

    Google Scholar 

  • Chauvin F, Royer J-F, Déqué M (2006) Response of hurricane-type vortices to global warming as simulated by ARPEGE-climat at high resolution. Clim Dyn 27:377–399

    Article  Google Scholar 

  • Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon W-T, Laprise R, Magaa Rueda V, Mearns L, Menndez CG, Risnen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Côté J, Gravel S, Methot A, Patoine A, Roch M, Staniforth A (1998) The operational CMC/MRB global environmental multiscale (GEM) model: part I—design considerations and formulation. Mon Wea Rev 126:1373–1395

    Article  Google Scholar 

  • Courtier P, Geleyn J-F (1988) A global numerical weather prediction model with variable resolution: application to the shallow-water equations. Q J R Meteorol Soc 114:1321–1346

    Article  Google Scholar 

  • Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455:92–95

    Article  Google Scholar 

  • Emanuel KA (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 326:686–688

    Article  Google Scholar 

  • Emanuel KA, Nolan D (2004) Tropical cyclone activity and global climate. In: 26th conference on hurricanes and tropical meteorology. Amer. Meteor. Soc., Miami, pp 240–241

  • Emanuel KA (1995) Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics. J Atmos Sci 52:39693976

    Google Scholar 

  • Evan AT, Vimont DJ, Heiginger AK, Kossin JP, Bennartz R (2009) The role of aerosols in the evolution of tropical North Atlantic ocean temperature anomalies. Science doi:10.1126/science.1167404

  • French JR, Drennan WM, Zhang JA, Black PG (2007) Turbulent fluxes in the hurricane boundary layer. Part I: momentum flux. J Atmos Sci 64:1089–1102

    Article  Google Scholar 

  • Fox-Rabinovitz MS, Takacs LL, Govindaraju RC, Suarez MJ (2001) A variable-resolution stretched-grid general circulation model: regional climate simulation. MWR 129:453–469

    Article  Google Scholar 

  • Gleckler P (1996) Atmospheric model intercomparison project newsletter. No. 8, PCMDI/LLNL. http://www-pcmdi.llnl.gov/projects/amip/NEWS

  • Goldenberg SB, Landsea CW, Mestas-Nuez AM, Gray WM (2001) The recent increase in Atlantic hurricane activity: causes and implications. Science 293:474–479

    Article  Google Scholar 

  • Goldenberg SB, Shapiro LJ (1996) Physical mechanism for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J Clim 9:1169–1187

    Article  Google Scholar 

  • Gray WM, Landsea CW, Mielke PW Jr., Berry KJ (1993) Predicting Atlantic basin seasonal tropical cyclone activity by 1 August. Wea Forecast 8:73–86

    Article  Google Scholar 

  • Gray WM (1984) Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb quasi-biennal oscillation influences. Mon Wea Rev 112:1649–1668

    Article  Google Scholar 

  • Gray WM (1975) Tropical cyclone genesis. Dept. of Atmospheric Science Paper, No. 234, Colorado State University, Fort Collins, CO, p 121

  • Gualdi S, Scoccimarro E, Navarra A (2008) Changes in tropical cyclone activity due to global warming: results from a high-resolution coupled general circulation model. J Clim 21: 5204–5228

    Article  Google Scholar 

  • Haarsma RJ, Mitchell JFB, Senior CA (1993) Tropical disturbances in a GCM. Clim Dyn 8:247–257

    Article  Google Scholar 

  • Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and application in convective parameterization. J Atmos Sci 47:2784–2802

    Article  Google Scholar 

  • Kim H-M, Webster PJ, Curry JA (2009) Impact of shifting patterns of Pacific ocean warming on North Atlantic tropical cyclones. Science 325:77–80

    Article  Google Scholar 

  • Kossin JP, Knapp KR, Vimont DJ, Murnane RJ, Harper BA (2007) A globally consistent reanalysis of hurricane variability and trends. Geophys Res Lett 34:L04815. doi:10.1029/2006GL028836

    Article  Google Scholar 

  • Knutson TR, Sirutis JJ, Garner ST, Held IM, Tuleya RE (2007) Simulation of the recent multidecadal increase of Atlantic hurricane activity using an 18-km-grid regional model. Bull Am Meteorol Soc 88:1549–1565

    Article  Google Scholar 

  • Kuo HL (1965) On formation and intensification of tropical cyclones through latent heat release by cumulus convection. J Atmos Sci 22:4063

    Article  Google Scholar 

  • Landman WA, Seth A, Camargo SJ (2005) The effect of regional climate model domain choice on the simulation of tropical cyclone-like vortices in the Southwestern Indian ocean. J Clim 18:1263–1274

    Article  Google Scholar 

  • Landsea G, Bell D, Gray WM, Goldenberg SB (1998) The extremely active 1995 Atlantic hurricane season: environmental, conditions and verification of seasonal forecasts. Mon Wea Rev 126:1174–1193

    Article  Google Scholar 

  • Larow TE, Lim Y-K, Shin DW, Chassignet EP, Cocke S (2008) Atlantic basin seasonal hurricane simulations. J Clim 21:3191–3206

    Article  Google Scholar 

  • Li J, Barker HW (2005) A radiation algorithm with correlated-k distribution. Part I: local thermal equilibrium. J Atmos Sci 62:286–309

    Article  Google Scholar 

  • Maloney ED, Hartmann DL (2000) Modulation of hurricane activity in the Gulf of Mexico by the Madden–Julian oscillation. Science 287:2002–2004

    Article  Google Scholar 

  • Manabe S, Holloway JL Jr., Stone HM (1970) Tropical circulation in a time-integration of a global model of the atmosphere. J Atmos Sci 27:580–613

    Article  Google Scholar 

  • Mann M, Emanuel K (2006) Atlantic hurricane trends linked to climate change. Eos Trans Am Geophys Union 87:233241

    Google Scholar 

  • Marbaix P, Gallée H, Brasseur O, van Ypersele J (2003) Lateral boundary conditions in regional climate models: a detailed study of the relaxation procedure. MWR 131:461479

    Article  Google Scholar 

  • McFarlane NA (1987) The effect of orographically excited gravity-wave drag on the circulation of the lower stratosphere and troposphere. J Atmos Sci 44:1175–1800

    Article  Google Scholar 

  • Moon I-J, Ginis I, Hara T, Thomas B (2007) A physic-based parameterization of air-sea momentum flux at high wind speeds and its impact on hurricane intensity predictions. Mon Wea Rev 135:2869–2878

    Article  Google Scholar 

  • Oouchi K, Yoshimura J, Yoshimura H, Mizuta R, Kusunoki S, Noda A (2006) Tropical cyclone climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model: frequency and wind intensity analyses. J Meteorol Soc Jpn 84(2):259–276

    Article  Google Scholar 

  • Powell MD, Vickery PJ, Reinhold TA (2003) Reduced drag coefficient for high wind speeds in tropical cyclones. Nature 422:279–283

    Article  Google Scholar 

  • Reed RJ, Norquist DC, Recker EE (1977) The structure and properties of African easterly wave disturbances as observed during Phase III of GATE. Mon Wea Rev 105:317–333

    Article  Google Scholar 

  • Royer J-F, Chauvin F, Timbal B, Araspin P, Grimal D (1998) A GCM study of the impact of greenhouse gas increase on the frequency of occurrence of tropical cyclones. Clim Change 38:307–343

    Article  Google Scholar 

  • Ryan BF, Watterson IG, Evans JL (1992) Tropical cyclone frequencies inferred from Gray’s yearly genesis parameter: validation of GCM tropical climates. Geophys Res Lett 19:1831–1834

    Article  Google Scholar 

  • Simmons AJ, Burridge DM (1981) An energy and angular-momentum conserving finite-difference scheme and hybrid vertical coordinates. Mon Wea Rev 109:758–766

    Article  Google Scholar 

  • Sundqvist H, Berge E, Kristjansson JE (1989) Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon Wea Rev 117:1641–1657

    Article  Google Scholar 

  • Thorncroft C, Hodges K (2001) African easterly wave variability and its relationship to Atlantic tropical cyclone activity. J Clim 14:1166–1179

    Article  Google Scholar 

  • Trenberth KE, Shea DJ (2006) Atlantic hurricanes and natural variability in 2005. Geophys Res Lett 33:L12704. doi:10.1029/2006GL026894

    Article  Google Scholar 

  • Trenberth KE (1984) Signal versus noise in the Southern Oscillation. Mon Wea Rev 112:326–332

    Article  Google Scholar 

  • Uppala SM, Ilberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, et al. (2005) The ERA-40 reanalysis. Q J R Meteorol Soc 131:2961–3012

    Article  Google Scholar 

  • Vecchi GA, Soden BJ (2007) Increased tropical Atlantic wind shear in model projections of global warming. Geophys Res Lett 34:L08702. doi:10.1029/2006GL028905

    Article  Google Scholar 

  • Walsh KJE, Sandery P, Brassington GB, Entel M, Siegenthaler-LeDrian C, Kepert J, Darbyshire R (2009) Constraints on exchange coefficients in tropical cyclones at extreme wind speeds. J Geophys Res (Oceans) (submitted)

  • 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 reanalysis. J Clim 20:2307–2314

    Article  Google Scholar 

  • Walsh KJE, Nguyen K-C, McGregor JL (2004) Fine-resolution regional climate model simulations of the impact of climate change on tropical cyclones near Australia. Clim Dyn 22:47–56

    Article  Google Scholar 

  • Walsh K, Watterson IG (1997) Tropical cyclone-like vortices in a limited area model: comparison with observed climatology. J Clim 10:2240–2259

    Article  Google Scholar 

  • Zadra A, Caya D, Côté J, Dugas B, Jones C, Laprise R, Winger K, Caron L-P (2008) The next Canadian regional climate model. Phys Can 64:75–83

    Google Scholar 

  • Zadra A, Roch M, Laroche S, Charron M (2003) The subgrid scale orographic blocking parameterization of the GEM model. Atmos Ocean 41:155–170

    Article  Google Scholar 

  • Zhao M, Held IM, Lin S-J, Vecchi GA (2009) Simulations of global hurricane climatology, interannual variability and response to global warming using a 50 km resolution GCM. J Clim 33:6653–6678

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank ECMWF for making the ERA-40 reanalyses available and the National Hurricane Center for the use of their tropical cyclone best track data (HURDAT). The authors are also grateful to Kerry Emanuel for making the MPI FORTRAN routine available, to Bernard Dugas for many helpful discussions and to Fabrice Chauvin and an anonymous reviewer for their valuable recommendations in improving this paper. This research was supported by the Natural Sciences and Engineering Research Council of Canada and the Mathematics of Information Technology and Complex Systems (MITACS, grant number 61851).

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  1. CRCMD Network, UQAM, Case postale 8888, Succursale Centre-ville, Montreal, QC, H3C 3P8, Canada

    Louis-Philippe Caron & Katja Winger

  2. Rossby Centre, Swedish Meteorological and Hydrological Institute, SE-601 76 , Norrkoping, Sweden

    Colin G. Jones

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  1. Louis-Philippe Caron
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Correspondence to Louis-Philippe Caron.

A maximum potential intensity

A maximum potential intensity

The definition of maximum potential intensity is described in detail in Emanuel (1995) and Bister and Emanuel (1998). A Fortran subroutine to evaluate MPI provided by Kerry Emanuel is available at ftp://texmex.mit.edu/pub/emanuel/TCMAX. The formula used is

$$ V_{\rm m}^2= \frac{T_{\rm s}}{T_0}\frac{C_{\rm k}}{C_{\rm d}}\left( \hbox{CAPE}^*-\hbox{CAPE}^{\rm b} \right) $$

where V m is the maximum wind speed a storm can reach in current atmospheric condition (MPI), T s is the ocean temperature, T 0 them mean outflow temperature, C k the exchange coefficient for enthalpy and C d the drag coefficient. CAPE is the vertical integral of parcel buoyancy as a function of surface temperature, pressure, and specific humidity, as well as the vertical profile of virtual temperature. CAPE* is the value of CAPE for an air parcel lifted from saturation at sea level at the radius of maximum winds and CAPEb is the CAPE of the boundary layer air.

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Caron, LP., Jones, C.G. & Winger, K. Impact of resolution and downscaling technique in simulating recent Atlantic tropical cylone activity. Clim Dyn 37, 869–892 (2011). https://doi.org/10.1007/s00382-010-0846-7

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