This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.
Internal variability Regional climate model Spectral nudging High spatial resolution Climate change
This is a preview of subscription content, log in to check access.
This work is supported under a military interdepartmental purchase request from the Strategic Environmental Research and Development Program, RC-2242, through US Department of Energy (DOE) contract DE-AC02-06CH11357. The CCSM4 data are downloaded from https://www.earthsystemgrid.org/home.htm. Computational resources are provided by the DOE-supported National Energy Research Scientific Computing Center.
Alexandru A, de Elía R, Laprise R (2007) Internal variability in regional climate downscaling at the seasonal time scale. Mon Weather Rev 135:3221–3238CrossRefGoogle Scholar
Braun M, Caya D, Frigon A, Slivitzky M (2012) Internal variability of the Canadian RCM’s hydrological variables at the basin scale in Quebec and Labrador. J Hydrometeor 13:443–462. doi:10.1175/JHM-D-11-051CrossRefGoogle Scholar
Caya D, Biner S (2004) Internal variability of RCM simulations over an annual cycle. Clim Dyn 22:33–46CrossRefGoogle Scholar
Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
Gao Y, Leung LR, Lu J, Liu Y, Huang M, Qian Y (2014) Robust spring drying in the southwestern US and seasonal migration of wet/dry patterns in a warmer climate. Geophys Res Lett. doi:10.1002/2014GL059562Google Scholar
Giorgi F, Bi X (2000) A study of internal variability of regional climate model. J Geophys Res 105:29503–29521CrossRefGoogle Scholar
Girard E, Bekcic B (2005) Sensitivity of an arctic regional climate model to the horizontal resolution during winter: implications for aerosol simulation. Int J Climatol 25:1455–1471CrossRefGoogle Scholar
Grell GA, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29:1693–1697CrossRefGoogle Scholar
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 113:D13103. doi:10.1029/2008JD009944CrossRefGoogle Scholar
Lucas-Picher P, Caya D, de Elía R, Laprise R (2008) Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain. Clim Dyn 31:927–940CrossRefGoogle Scholar
Morrison H, Thompson G, Tatarskii V (2009) Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon Weather Rev 137:991–1007. doi:10.1175/2008MWR2556.1CrossRefGoogle Scholar
Nikiéma O, Laprise R (2011) Budget study of the internal variability in ensemble simulations of the Canadian RCM at the seasonal scale. J Geophys Res Atmos 116:D16112. doi:10.1029/2011JD015841CrossRefGoogle Scholar
Noh Y, Cheon WG, Hong SY, Raasch S (2003) Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound Layer Meteorol 107:401–427CrossRefGoogle Scholar
Vanitsem S, Chomé F (2005) One-way nested regional climate simulations and domain size. J Clim 18:229–233CrossRefGoogle Scholar
von Storch H, Langenberg H, Feser F (2000) A spectral nudging technique for dynamical downscaling purposes. Mon Weather Rev 128:3664–3673CrossRefGoogle Scholar
Wang J, Kotamarthi VR (2013) Assessment of dynamical downscaling in near-surface fields with different spectral nudging approaches using the nested regional climate model (NRCM). J Appl Meteorol Climatol 52:1576–1591CrossRefGoogle Scholar
Wang J, Kotamarthi VR (2014) Downscaling with a nested regional climate model in near-surface fields over the contiguous United States. J Geophys Res Atmos 119:8778–8797. doi:10.1002/2014JD021696CrossRefGoogle Scholar
Wang J, Kotamarthi VR (2015) High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth’s Future. doi:10.1002/2015EF000304Google Scholar