Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation?
Three different resolution (50, 12, and 1.5 km) regional climate model simulations are compared in terms of their ability to simulate moderate and high daily precipitation events over the southern United Kingdom. The convection-permitting 1.5-km simulation is carried out without convective parametrisation. As in previous studies, increasing resolution (especially from 50 to 12 km) is found to improve the representation of orographic precipitation. The 50-km simulation underestimates mean precipitation over the mountainous region of Wales, and event intensity tends to be too weak; this bias is reduced in both the 12- and 1.5-km simulations for both summer and winter. In south–east England lowlands where summer extremes are mostly convective, increasing resolution does not necessary lead to an improvement in the simulation. For the 12-km simulation, simulated daily extreme events are overly intense. Even though the average intensity of summer daily extremes is improved in the 1.5-km simulation, this simulation has a poorer mean bias with too many events exceeding high thresholds. Spatial density and clustering of summer extremes in south–east England are poorly simulated in both the 12- and 1.5-km simulations. In general, we have not found any clear evidence to show that the 1.5-km simulation is superior to the 12-km simulation, or vice versa at the daily level.
KeywordsHigh resolution models Dynamical downscaling Hydroclimate Precipitation
This research is part of the CONVEX project—a collaboration between Newcastle University, the Met Office, and the University of Exeter. CONVEX is supported by the United Kingdom NERC Changing Water Cycle programme (grant NE/I006680/1), and the presented model simulations are supported by the Met Office. The lead author is financially supported by Newcastle University, and is a visiting scientist at the Met Office Hadley Centre in Exeter, United Kingdom.
- Arakawa A, Lamb VR (1977) Computational design of the basic dynamical processes of the UCLA general circulation model. Methods Comput Phys 17:173–265Google Scholar
- Besag JE (1977) Comments on ripley’s paper. J R Stat Soc 39(2):193–195Google Scholar
- Charney JG, Phillips NA (1953) Numerical integration of the quasi-geostrophic equations for barotropic and simple baroclinic flows. J Meteorol 10:71–99. doi: 10.1175/1520-0469(1953)010<0071:NIOTQG>2.0.CO;2
- 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, Hölm EV, Isaksen L, Kallberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park PK, Peubey C, de Rosnay P, Tavolato C, Thêpaut 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. doi: 10.1002/qj.828 CrossRefGoogle Scholar
- Efron B, Tibshirani RJ (1993) An introduction to the bootstrap, monographs on statistics and applied probability, vol 57. Chapman and Hall, New YorkGoogle Scholar
- Essery R, Best M, Cox P (2001) MOSES 2.2 technical documentation. Hadley Centre Technical Note 30, Hadley Centre, Met Office, Fitzroy Road, Exeter, UKGoogle Scholar
- Faulkner D (1999) Flood estimation handbook, vol 2. Rainfall frequency estimation. NERC Centre for Ecology and HydrologyGoogle Scholar
- Fowler HJ, Ekstrom M, Blenkinsop S, Smith AP (2007) Estimating change in extreme European precipitation using a multi-model ensemble. J Geophys Res 112(D18):art No. D18104Google Scholar
- Giorgi F, Marinucci MR (1996) An investigation of the sensitivity of simulated precipitation to the model resolution and its implication for climate studies. Mon Weather Rev 124:148–166. doi: 10.1175/1520-0493(1996)124<0148:AIOTSO>2.0.CO;2
- Gregory D, Rowntree PR (1990) A mass-flux convection scheme with representation of cloud ensemble characteristics and stability dependent closure. Mon Weather Rev 118:1483–1506. doi: 10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2
- Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional–climate model. I: assessment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121:1413–1449Google Scholar
- Jones RG, Murphy JM, Noguer M, Keen AB (1997) Simulation of climate change over Europe using a nested regional–climate model. II: comparison of driving and regional model responses to a doubling of carbon dioxide concentration. Q J R Meteorol Soc 123:265–292Google Scholar
- Meehl GA, Karl T, Easterling DR, Changnon S, Changnon D, Pielke R Jr, Evans J, Groisman PY, Knutson TR, Kunkel KE, Mearns LO, Parmesan C, Pulwarty R, Root T, Sylves RT, Whetton P, Zwiers F (2000) An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull Am Meteorol Soc 81(3):413–416. doi: 10.1175/1520-0477(2000)081<0413:AITTIE>2.3.CO;2
- Perry M, Hollis D, Elms M (2009) The generation of daily gridded datasets of temperature and rainfall for the UK. Met Office National Climate Information Centre, FitzRoy Road, Exeter, Devon EX1 3PB, UKGoogle Scholar
- Ripley BD (1977) Modelling spatial patterns. J R Stat Soc 39(2):172–212Google Scholar
- Ripley BD (1979) Tests of ‘randomness’ for spatial point patterns. J R Stat Soc 41(3):368–374Google Scholar
- Seth A, Giorgi F (1998) The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model. J Climate 11:2698–2712. doi: 10.1175/1520-0442(1998)011<2698:TEODCO>2.0.CO;2
- Walters DN, Best MJ, Bushell AC, Copsey D, Edwards JM, Falloon PD, Harris CM, Lock AP, Manners JC, Morcrette CJ, Roberts MJ, Stratton RA, Webster S, Wilkinson JM, Willett MR, Boutle IA, Earnshaw PD, Hill PG, MacLachlan C, Martin GM, Moufouma-Okia W, Palmer MD, Petch JC, Rooney GG, Scaife AA, Williams KD (2011) The Met Office Unified Model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci Model Devel 4:919–941. doi: 10.5194/gmd-4-919-2011 CrossRefGoogle Scholar