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Earth Systems and Environment

, Volume 3, Issue 3, pp 367–379 | Cite as

Effect of 1-km Subgrid Land-Surface Heterogeneity on the Multi-year Simulation of RCM-Modelled Surface Climate Over the Region of Complex Topography

  • Imran NadeemEmail author
  • Herbert Formayer
  • Asma Yaqub
Original Article
  • 16 Downloads

Abstract

Effects of parameterization of subgrid-scale topography and land cover scheme (SubBATS) at 1-km resolution were investigated over the Alpine region using a regional climate model. Two multi-year simulations were carried out with the Regional Climate Model of International Centre for Theoretical Physics. The control simulation was carried out at 10-km horizontal resolution using standard land-surface model; while for the SubBATS simulation, the land-surface model was employed at much higher resolution (1 km) to investigate the effect of land-surface heterogeneity on the Alpine climate. In SubBATS, near-surface atmospheric state variables from coarse (10-km) atmospheric model were disaggregated to 1 km before passing to high-resolution land surface scheme. Comparison of these two multi-year simulation was done for the Great Alpine Region. The analysis shows the added value imparted by very high-resolution SubBATS in simulating hydrology processes in the complex terrain. The direct effects of the scheme are evident on height-dependent variables; temperature and snow pack. The better representation of topographic height in sub-scale scheme leads to more refined temperature field which subsequently results in more realistic representation of snow cover and snow melt. At 1-km resolution, the influence of resolved mountain peaks and valleys results in decrease of snow-covered area. The subgrid scheme not only improves the overall simulation by feedback process but also provides high-resolution meteorological fields that can be used for adaptation and impact studies. Therefore, more accurate representation of land-surface heterogeneity in sub-grid approach improves the temperature and snow fields over the complex terrain and can be useful for coupling with impact models, although further improvements are desirable.

Keywords

Regional climate models Subgrid heterogeneity SubBATS scheme Subgrid-scale topography and landuse 

Notes

Acknowledgements

This study presented in this paper was partially funded by EC project CECILIA. The scholarship provided by Higher Education Commission of Pakistan to first author also helped to complete this study. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the data providers in the ECA&D project (https://www.ecad.eu). Our special thanks to ECMWF for providing ERA-Interim reanalysis data. We are also very thankful to Swiss Federal Institute of Technology Zürich for providing observational precipitation dataset for this study.

References

  1. Ban N, Schmidli J, Schaer C (2014) Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J Geophys Res Atmos.  https://doi.org/10.1002/2014JD021478 CrossRefGoogle Scholar
  2. Chan SC, Kendon EJ, Fowler HJ, Blenkinsop S, Ferro CAT, Stephenson DB (2013) Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation? Climate Dyn 41(5):1475–1495.  https://doi.org/10.1007/s00382-012-1568-9 CrossRefGoogle Scholar
  3. Chatré B, Lanzinger G, Macaluso M, Mayrhofer W, Morandini M, Onida M, Polajnar B (2010) The Alps: people and pressures in the mountains, the facts at a glance. Permanent secretariat of the alpine convention, Insbruck, Austria. https://www.alpconv.org/fileadmin/user_upload/publikationen/vademecum/Vademecum_web.pdf
  4. Cholette M, Laprise R, Thériault JM (2015) Perspectives for very high-resolution climate simulations with nested models: illustration of potential in simulating St. Lawrence River Valley channelling winds with the fifth-generation canadian regional climate model. Climate 3(2):283.  https://doi.org/10.3390/cli3020283. http://www.mdpi.com/2225-1154/3/2/283 CrossRefGoogle Scholar
  5. de Vries H, Lenderink G, van Meijgaard E (2014) Future snowfall in western and central Europe projected with a high-resolution regional climate model ensemble. Geophys Res Lett 41(12):4294–4299.  https://doi.org/10.1002/2014GL059724 CrossRefGoogle Scholar
  6. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597.  https://doi.org/10.1002/qj.828 CrossRefGoogle Scholar
  7. Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphere-atmosphere transfer scheme (BATS) Version 1e as coupled to the NCAR community climate model. NCAR Technical Note NCAR/TN-387+STR.  https://doi.org/10.5065/D67W6959
  8. Dimri A (2009) Impact of subgrid scale scheme on topography and landuse for better regional scale simulation of meteorological variables over the western Himalayas. Climate Dyn 32:565–574.  https://doi.org/10.1007/s00382-008-0453-z CrossRefGoogle Scholar
  9. Fritsch J, Chappel C (1980) Numerical prediction of convectively driven mesoscale pressure systems. Part I: convective parameterization. J Atmos Sci 37(8):1722–1733.  https://doi.org/10.1175/1520-0469(1980)037<1722:NPOCDM>2.0.CO;2 CrossRefGoogle Scholar
  10. Garvert MF, Smull B, Mass C (2007) Multiscale mountain waves influencing a major orographic precipitation event. J Atmos Sci 64(3):711–737.  https://doi.org/10.1175/JAS3876.1 CrossRefGoogle Scholar
  11. Giorgi F, Marinucci M, Bates G, Decanio G (1993) Development of a second-generation regional climate model (RegCM2). Part II: convective processes and assimilation of lateral boundary conditions. Mon Weather Rev 121(10):2814–2832.  https://doi.org/10.1175/1520-0493(1993)121<2814:DOASGR>2.0.CO;2 CrossRefGoogle Scholar
  12. Giorgi F, Francisco R, Pal J (2003) Effects of a subgrid-scale topography and land use scheme on the simulation of surface climate and hydrology. Part I: effects of temperature and water vapor disaggregation. J Hydrometeorol 4(2):317–333.  https://doi.org/10.1175/1525-7541(2003)4<317:EOASTA>2.0.CO;2 CrossRefGoogle Scholar
  13. Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla MB, Bi X, Elguindi N, Diro GT, Nair V, Giuliani G, Turuncoglu UU, Cozzini S, Guettler I, O’Brien TA, Tawfik AB, Shalaby A, Zakey AS, Steiner AL, Stordal F, Sloan LC, Brankovic C (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Climate Res 52(1,29):7–29.  https://doi.org/10.3354/cr01018 CrossRefGoogle Scholar
  14. Grell G (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 121(3):764–787.  https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2 CrossRefGoogle Scholar
  15. Halenka T (2010) Cecilia-EC FP6 project on the assessment of climate change impacts in central and eastern Europe. In: Alexandrov V, Gajdusek MF, Knight CGF, Yotova A (eds) Global environmental change: challenges to science and society in Southeastern Europe. Springer, Amsterdam, pp 125–137.  https://doi.org/10.1007/978-90-481-8695-2_11 CrossRefGoogle Scholar
  16. Hantel M, Ehrendorfer M, Haslinger A (2000) Climate sensitivity of snow cover duration in Austria. Int J Climatol 20(6):615–640.  https://doi.org/10.1002/(SICI)1097-0088(200005)20:6<615::AID-JOC489>3.0.CO;2-0 CrossRefGoogle Scholar
  17. Haylock MR, Hofstra N, Tank AMGK, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res Atmos.  https://doi.org/10.1029/2008JD010201 CrossRefGoogle Scholar
  18. Hohenegger C, Brockhaus P, Schaer C (2008) Towards climate simulations at cloud-resolving scales. Meteorol Z 17(4, SI):383–394.  https://doi.org/10.1127/0941-2948/2008/0303 CrossRefGoogle Scholar
  19. Holtslag A, Debruijn E, Pan H (1990) A high resolution air mass transformation model for short-range weather forecasting. Mon Weather Rev 118(8):1561–1575.  https://doi.org/10.1175/1520-0493(1990)118<1561:AHRAMT>2.0.CO;2 CrossRefGoogle Scholar
  20. Im ES, Coppola E, Giorgi F, Bi X (2010) Validation of a high-resolution regional climate model for the alpine region and effects of a subgrid-scale topography and land use representation. J Climate 23(7):1854–1873.  https://doi.org/10.1175/2009JCLI3262.1 CrossRefGoogle Scholar
  21. Isotta FA, Frei C, Weilguni V, Tadic MP, Lassegues P, Rudolf B, Pavan V, Cacciamani C, Antolini G, Ratto SM, Munari M, Micheletti S, Bonati V, Lussana C, Ronchi C, Panettieri E, Marigo G, Vertacnik G (2014) The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int J Climatol 34(5):1657–1675.  https://doi.org/10.1002/joc.3794 CrossRefGoogle Scholar
  22. Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578.  https://doi.org/10.1007/s10113-013-0499-2 CrossRefGoogle Scholar
  23. Jonas T, Marty C, Magnusson J (2009) Estimating the snow water equivalent from snow depth measurements in the Swiss Alps. J Hydrol 378(1–2):161–167.  https://doi.org/10.1016/j.jhydrol.2009.09.021 CrossRefGoogle Scholar
  24. Jones P (1999) First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon Weather Rev 127(9):2204–2210.  https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2 CrossRefGoogle Scholar
  25. Kendon EJ, Roberts NM, Senior CA, Roberts MJ (2012) Realism of rainfall in a very high-resolution regional climate model. J Climate 25(17):5791–5806.  https://doi.org/10.1175/JCLI-D-11-00562.1 CrossRefGoogle Scholar
  26. Kendon EJ, Roberts NM, Fowler HJ, Roberts MJ, Chan SC, Senior CA (2014) Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat Climate Change 4(7):570–576.  https://doi.org/10.1038/NCLIMATE2258 CrossRefGoogle Scholar
  27. Kiehl J, Hack J, Bonan G, Boville B, Briegleb B, Williamson D, Rasch P (1996) Description of the NCAR community climate model (CCM3). NCAR Technical Note NCAR/TN–420+STR, Natl. Cent. for Atmos. Res., Boulder, CO 80307 USAGoogle Scholar
  28. Leung LR, Ghan SJ (1995) A subgrid parameterization of orographic precipitation. Theor Appl Climatol 52(1):95–118.  https://doi.org/10.1007/BF00865510 CrossRefGoogle Scholar
  29. Mölg T, Kaser G (2011) A new approach to resolving climate-cryosphere relations: Downscaling climate dynamics to glacier-scale mass and energy balance without statistical scale linking. J Geophys Res Atmos.  https://doi.org/10.1029/2011JD015669. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2011JD015669
  30. Nadeem I, Formayer H (2015) Sensitivity studies of high-resolution RegCM3 simulations of precipitation over the European Alps: the effect of lateral boundary conditions and domain size. Theor Appl Climatol.  https://doi.org/10.1007/s00704-015-1586-8 CrossRefGoogle Scholar
  31. Pal J, Small E, Eltahir E (2000) Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res Atmos 105(D24):29579–29594.  https://doi.org/10.1029/2000JD900415 CrossRefGoogle Scholar
  32. Pal JS, Giorgi F, Bi X, Elguindi N, Solmon F, Gao X, Rauscher SA, Francisco R, Zakey A, Winter J, Ashfaq M, Syed FS, Bell JL, Diffenbaugh NS, Karmacharya J, Konare A, Martinez D, da Rocha RP, Sloan LC, Steiner AL (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Am Meteorol Soc 88(9):1395.  https://doi.org/10.1175/BAMS-88-9-1395 CrossRefGoogle Scholar
  33. Pavlik D, Söhl D, Pluntke T, Mykhnovych A, Bernhofer C (2012) Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km. Environ Earth Sci 65(5):1475–1482.  https://doi.org/10.1007/s12665-011-1081-1 CrossRefGoogle Scholar
  34. Prein AF, Langhans W, Fosser G, Ferrone A, Ban N, Goergen K, Keller M, Tölle M, Gutjahr O, Feser F, Brisson E, Kollet S, Schmidli J, van Lipzig NPM, Leung R (2015) A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev Geophys 53(2):323–361.  https://doi.org/10.1002/2014RG000475 CrossRefGoogle Scholar
  35. Rasmussen R, Liu C, Ikeda K, Gochis D, Yates D, Chen F, Tewari M, Barlage M, Dudhia J, Yu W, Miller K, Arsenault K, Grubisic V, Thompson G, Gutmann E (2011) High-resolution coupled climate runoff simulations of seasonal snowfall over colorado: a process study of current and warmer climate. J Climate 24(12):3015–3048.  https://doi.org/10.1175/2010JCLI3985.1 CrossRefGoogle Scholar
  36. Reynolds R, Rayner N, Smith T, Stokes D, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Climate 15(13):1609–1625.  https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2 CrossRefGoogle Scholar
  37. Schicker I, Seibert P (2009) Simulation of the meteorological conditions during a winter smog episode in the Inn Valley. Meteorol Atmos Phys 103(1):211–222.  https://doi.org/10.1007/s00703-008-0346-z CrossRefGoogle Scholar
  38. Termonia P, Schaeybroeck BV, Cruz LD, Troch RD, Caluwaerts S, Giot O, Hamdi R, Vannitsem S, Duchêne F, Willems P, Tabari H, Uytven EV, Hosseinzadehtalaei P, Lipzig NV, Wouters H, Broucke SV, van Ypersele JP, Marbaix P, Villanueva-Birriel C, Fettweis X, Wyard C, Scholzen C, Doutreloup S, Ridder KD, Gobin A, Lauwaet D, Stavrakou T, Bauwens M, Müller JF, Luyten P, Ponsar S, den Eynde DV, Pottiaux E (2018) The CORDEX.be initiative as a foundation for climate services in Belgium. Climate Serv 11:49–61.  https://doi.org/10.1016/j.cliser.2018.05.001. http://www.sciencedirect.com/science/article/pii/S2405880717300195 CrossRefGoogle Scholar
  39. Zeng X, Zhao M, Dickinson R (1998) Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J Climate 11(10):2628–2644.  https://doi.org/10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2 CrossRefGoogle Scholar

Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Meteorology and ClimatologyUniversity of Natural Resources and Life Sciences Vienna (BOKU)ViennaAustria

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