Abstract
Dynamical downscaling has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical downscaling for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in downscaling due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical downscaling were also described.
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Alexandru, A., R. de Elia, R. Laprise, L. Separovic, and S. Biner, 2009: Sensitivity study of regional climate model simulations to large-scale nudging parameters. Mon. Wea. Rev., 137, 1666–1686.
Annamalai, H., J. Sling, K. R. Sperber, and K. Hodges, 1999: The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP-NCAR reanalysis. Mon. Wea. Rev., 127, 1157–1186.
Anthes, R. A., 1983: Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev., 111, 1306–1335.
Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 2493–2525.
—, and C.-M. Wu, 2013: A unified representation of deep moist convection in numerical modeling of the atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992, doi: 10.1175/JAS-D-12-0330.1.
Bennett, A. F., 1976: Open boundary conditions for dispersive waves. J. Atmos. Sci., 33, 176–182.
Brunet, G., and Coauthors, 2010: Collaboration of the weather and climate communities to advance sub-seasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 91, 1397–1406, doi: 10.1175/2010BAMS3013.1.
Castro, C. L., R. A. Pielke Sr., and G. Leoncini, 2005: Dynamical downs-caling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res., 110, D05108, doi:10.1029/2004JD004721.
Davies, H. C., 1976: A lateral boundary formulation for multi-level pre-diction models. Quart. J. Roy. Meteor. Soc., 102, 405–418, doi:10.1002/ qj.49710243210.
Di Luca, A., R. Elía, and R. Laprise, 2013: Potential for small scale added value of RCM’s downscaled climate change signal. Clim. Dynam., 40, 601–618.
Dimitrijevic, M., and R. Laprise, 2005: Validation of the nesting technique in a RCM and sensitivity tests to the resolution of the lateral boundary conditions during summer. Clim. Dynam., 25, 555–580.
de Ela, R., R. Laprise, and B. Denis, 2002: Forecasting skill limits of nested limited-area models: a perfect-model approach. Mon. Wea. Rev., 130, 2006–2023.
Denis, B., R. Laprise, D. Caya, and J. Côté, 2002: Downscaling ability of one-way nested regional climate model: the Big-Brother Experiment. Clim. Dynam., 18, 627–646.
Decker, M., M. A. Brunke, Z. Wang, K. Sakaguchi, X. Zeng, and M. G. Bosilovich, 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944.
Dudhia, J., 1993: A nonhydrostatic version of the Penn State / NCAR mesoscale model: Validation tests and simulations of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513.
Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn, 2011: Regional climate models add value to global model data: A review and selected examples. Bull. Amer. Meteor. Soc., 92, 1181–1192.
Fox-Rabinovitz, M., J. Côté, B. Dugas, M. Déqué, and J. L. McGregor, 2006: Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP). J. Geophys. Res., 111, D16104, doi:10.1029/2005JD006520.
Fu, C., and Coauthors, 2005: Regional climate model intercomparison project for Asia. Bull. Amer. Meteor. Soc., 86, 257–266.
Giorgi, F., 1990: Simulation of regional climate using a limited area model nested in a general circulation model.J. Climate, 3, 941–963.
—, and G. T. Bates, 1989: The climatological skill of a regional model over complex terrain. Mon. Wea. Rev., 117, 2325–2347.
—, and X. Bi, 2000: A study of internal variability of a regional climate model. J. Geophys. Res., 105, 29503–29521.
—, M. R. Marinucci, G. Bates, and G. DeCanio, 1993: Development of a second generation regional climate model (RegCM2). II. Con-vective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 2814–2832.
—, and Coauthors, 2001: Regional climate information-evaluation and projections. Chapter 10 of: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (J. T. Houghton et al., eds), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 583–638.
Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, doi:10.1029/ 2007JD008972.
Grell, G. A., and S. R. Freitas, 2013: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys. Discuss., 13, 23845–23893, doi:10.5194/acpd-13-23845-2013.
Gutmann, E. D., R. M. Rasmussen, C. Liu, K. Ikeda, D. J. Gochis, M. P. Clark, J. Dudhia, and G. Thompson, 2012: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Climate, 25, 262–281.
Ham, S., and S.-Y. Hong, 2013: Sensitivity of simulated intraseasonal oscillation to four convective parameterization schemes in a coupled climate model. Asia-Pac. J. Atmos. Sci., 49, 483–496.
Hong, S.-Y., and E.-C. Chang, 2012: Spectral nudging sensitivity experi-ments in a regional climate model. Asia-Pac. J. Atmos. Sci., 48, 345–355.
—, and J. Dudhia, 2012: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies. Bull. Amer. Meteor. Soc., 93, ES6–ES9, http://dx.doi.org/10.1175/2011BAMS3224.1.
—, and E. Kalnay, 2000: Role of sea surface temperature and soil-moisture feedback in the 1998 Oklahoma-Texas drought. Nature, 408, 842–844.
—, and H.-L. Pan, 1998: Convective trigger function for a mass flux cumulus parameterization scheme. Mon. Wea. Rev., 126, 2599–2620.
—, and H.-L. Pan, 2000: Impact of soil moisture anomalies on seasonal, summertime circulation over North America in a regional climate model. J. Geophys. Res., 105, D24, 29625–29634.
—, H. M. Kim, J.-E. Kim, S.-O. Hwang, and H. Park, 2011: The impact of model uncertainties on the analyzed data in a global data assimilation system. Terr. Atmos. Oceanic. Sci., 22, 41–47.
—, and Coauthors, 2013a: The Global/Regional Integrated Model System (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219–243, doi:10.1007/s13143-013-0023-0.
—, M.-S. Koo, J. Jang, J.-E. E. Kim, H. Park, M.-S. Joh, J.-H. Kang, and T.-J. Oh, 2013b: An evaluation of the software system dependency of a global atmospheric model. Mon. Wea. Rev., 141, 4165–4172, doi: 10.1175/mwr-d-12-00352.1.
Hu, H., and W. T. Liu, 2002: QuikSCAT reveals the surface circulation of the Catalina Eddy. Geophys. Res. Lett., 29, 1821, doi:10.1029/2001GL-014203, 2002.
Huffman, G.J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydro-meteor., 2, 36–50.
Hwang, S.-O., and S.-Y. Hong, 2012: The impact of observation systems on medium-range weather forecasting in a global forecast system. Asia-Pac. J. Atmos. Sci., 48, 159–170.
—, and —, 2010: Investigation of moisture field assimilation in the NCEP/DOE reanalysis system. J. Atmos. Sol. -Terr. Phys., 72, 556–564.
—, —, and M. Kanamitsu, 2010: Impacts of assimilated data on reanalysis climatology. Asia-Pac. J. Atmos. Sci., 46, 185–197.
Im, E.-S., J.-B. Ahn, and D.-W. Kim, 2012a: An assessment of future dryness over Korea based on the ECHAM5-RegCM3 model chain underA1B emission scenario. Asia-Pac. J. Atmos. Sci., 48, 325–337, doi: 10.1007/s13143-012-0031-5.
—, B.-J. Lee, J.-H. Kwon, S.-R. In, and S.-O. Han, 2012b: Potential increase of flood hazards in Korea due to global warming from a high-resolution regional climate simulation. Asia-Pac. J. Atmos. Sci., 48, 107–113, doi:10.1007/s13143-012-0010-x. Intergovernmental Panel on Climate Change (IPCC), 1995: Climate Change 1995. The science of climate change. J. T. Houghton, L. G. Meira Filho, B. A. Callander, N. Harris, A. Kattenberg and K. Maskell, Eds, Cambridge University Press, 572 pp.
—, 2007: Climate Change 2007: Synthesis Report.IPCC, 104 pp.
Jo, S., Y. Lim, J. Lee, H.-S. Kang, and H.-S. Oh, 2012: Bayesian regression model for seasonal forecast of precipitation over Korea. Asia-Pac. J. Atmos. Sci., 48, 205–212, doi:10.1007/s13143-012-0021-7.
Jolliffe, I. T., and D B. Stephenson, 2011: Forecast Verification: A Practitioner’s Guide in Atmospheric Science. 2nd Edition, 292 pp.
Juang, H.-M. H., and M. Kanamitsu, 1994: The NMC nested regional spectral model. Mon. Wea. Rev., 122, 3–26.
—, and S.-Y. Hong, 2001: Sensitivity of the NCEP regional spectral model to domain size and nesting strategy. Mon. Wea. Rev., 129, 2904–2922.
—, —, and M. Kanamitsu, 1997: The NCEP regional spectral model: An update. Bull. Amer. Meteor. Soc., 78, 2125–2143.
Kanamitsu, M., and H. Kanamaru, 2007: Fifty-Seven-year California Reanalysis Downscaling at 10 km (CaRD10). Part I: System detail and validation with observations. J. Climate, 20, 5553–5571.
—, and L. DeHaan, 2011: The Added Value Index: A new metric to quantify the added value of regional models. J. Geophys. Res., 116, D11106, doi:10.1029/2011JD015597.
—, E. Yulaeva, H. Li, and S.-Y. Hong, 2013: Catalina Eddy as revealed by the historical downscaling of reanalysis. Asia-Pac. J. Atmos. Sci., 49, 467–481, doi:10.1007/s13143-013-0041-y.
—, K. Yoshimura, Y.-B. Yhang, and S.-Y. Hong, 2010: Errors of interannual variability and trend in dynamical downscaling of reanalysis. J. Geophys. Res., 115, D17115, doi:10.1029/2009JD013511.
Kanamaru, H., and M. Kanamitsu, 2006: Scale selective bias correction in a downscaling of global analysis using a regional model. Mon. Wea. Rev., 135, 334–350, doi:10.1175/MWR3294.1.
Kida, H., T. Koide, H. Sasaki, and M. Chiba, 1991: A new approach for coupling a limited area model to a GCM for regional climate simulations. J. Meteor. Soc. Japan, 69, 723–728.
Kim, J.-W., J.-T. Chang, N. L. Baker, D. S. Wilks, and W. L. Gates, 1984: The statistical problem of climate inversion: Determination of the relation- ship between local and large-scale climate. Mon. Wea. Rev., 112, 2069–2077.
Kim, J., and Coauthors, 2013: Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim. Dynam., doi:10.1007/s00382-013-1751-7.
Koo, M.-S., and S.-Y. Hong, 2010: Diurnal Variations of Simulated Precipitation over East Asia in Two Regional Climate Models. J. Geophys. Res., 115, D05105, doi:10.1029/2009JD012574.
—, —, and J. Kim, 2009: An evaluation of the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) data over South Korea.Asia-Pac. J. Atmos. Sci., 45, 265–282.
Laprise, R., 2008: Regional climate modelling. J. Comput. Phys., 227, 3641–3666.
—, and Coauthors, 2012: Considerations of domain size and large-scale driving for nested regional climate models: Impact on internal variability and ability at developing small-scale details. Climate Change, 181–199.
Lee, D.-K., D.-H. Cha, C.-S. Jin, and S.-J. Choi, 2013: A regional climate change simulation over East Asia. Asia-Pac. J. Atmos. Sci., 49, 655–664, doi:10.1007/s13143-013-0058-2.
Lee, J.-W., and S.-Y. Hong, 2013: Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model. Theor. Appl. Climatol., doi: 10.1007/s00704-013-1034-6.
—, —, E.-C. Chang, M.-S. Suh, and H.-S. Kang, 2013: Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP. Clim. Dynam., doi:10.1007/s00382-013-1841-6
Leung, R., L. O. Mearns, F. Giorgi, and R. L. Wilby, 2003: Regional climate research: Needs and opportunities. Bull. Amer. Meteor. Soc., 84, 89–95.
Li, H., M. Kanamitsu, and S.-Y. Hong, 2012: California reanalysis downscaling at 10 km using an ocean-atmosphere coupled regional model system. J. Geophys. Res., 117, D12118, doi:10.1029/2011-JD017372.
—, —, —, K. Yoshimura, D. R. Cayan, and V. Misra, 2013: A high-resolution ocean-atmosphere coupled downscaling of the present climate over California. Clim. Dynam., doi:10.1007/s00382-013-1670-7.
Lord, S. J., 1982: Interaction of a cumulus cloud ensemble with the large-scale environment. Part III: Semi-prognostic test of the Arakawa-Schubert cumulus parameterization. J. Atmos. Sci., 39, 88–103.
Lorenz, E. N., 1993: The essence of chaos. Seattle, University of Washington Press, 240 pp.
Lucas-Picher, P., D. Caya, S. Biner, and R. Laprise, 2008: Quantification of the lateral boundary forcing of a regional climate model using an aging tracer. Mon. Wea. Rev., 136, 4980–4996.
—, F. Boberg, J. H. Christensen, P. B., and P. Berg, 2013: Dynamical downscaling with reinitializations: A method to generate finescale climate datasets suitable for impact studies. J. Hydrometeor., 14, 1159–1174.
Ma, W., Y. Ma, M. Li, Z. Hu, L. Zhong, Z. Su, H. Ishikawa, and J. Wang, 2009: Estimating surface fluxes over the north Tibetan Plateau area with ASTER imagery. Hydrol. Earth Syst. Sci., 13, 57–67, doi:10.5194/hess-13-57-2009.
Marbaix, P., H. Gallée, O. Brasseur, and J.-P. van Ypersele, 2003: Lateral boundary conditions in regional climate models: A detailed study of the relaxation procedure. Mon. Wea. Rev., 131, 461–479.
McGregor, J. L., 1997: Regional climate modeling. Meteor. Atmos. Phys., 63, 105–117.
—, 2013: Recent developments in variable-resolution global climate modelling. Climatic Change, 119, doi:10.1007/s10584-013-0866-5.
—, and Dix, M. R., 2008. An updated description of the conformal-cubic atmospheric model. High Resolution Simulation of the Atmosphere and Ocean, Hamilton, K. and Ohfuchi, W., Eds., Springer, 51–76.
Mesinger, F., and K. Veljovic, 2013: Limited area NWP and regional climate modeling: A test of the relaxation vs Eta lateral boundary conditions. Meteor. Atmos. Phys., 119, 1–16, doi:10.1007/s00703-012-0217-5.
Mearns, L. O., and Coauthors, 2012: The north American regional climate change assessment program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 1337–1362.
Misra, V., 2007: Addressing the issue of systematic errors in a regional climate model. J. Climate, 20, 801–818.
Murphy, J., 1999: An evaluation of statistical and dynamical techniques for downscaling local climate. J. Climate, 12, 2256–2284.
Oh, S.-G., M.-S. Suh, and D.-H. Cha, 2013: Impact of lateral boundary conditions on precipitation and temperature extremes over South Korea in the CORDEX regional climate simulation using RegCM4. Asia-Pac. J. Atmos. Sci., 49, 497–509, doi:10.1007/s13143-013-0044-8.
Omrani, H., P. Drobinski, and T. Dubos, 2012: Spectral nudging in regional climate modelling: how strongly should we nudge? Quart. J. Roy. Meteor. Soc., 138, 1808–1813, doi: 10.1002/qj.1894.
Pan, Z., E. Takle, W. Gutowski, and R. Turner, 1999: Long simulation of regional climate as a sequence of short segments. Mon. Wea. Rev., 127, 308–321.
Park, B.-K., and S.-Y. Hong, 2013: Effects of physics packages on medium-range forecasts in a global forecasting system. J. Atmos. Sol. -Terr. Phys., 100-101, 50–58.
Peña-Arancibia, J. L., A. I. J. M. van Dijk, L. J. Renzullo, and M. Mulligan, 2013: Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an snsemble method for regions in Australia and South and East Asia. J. Hydrometeor., 14, 1323–1333.
Pfeifroth, U., R. Mueller, and B. Ahrens, 2013: Evaluation of satellite-based and reanalysis precipitation data in the tropical Pacific. J. Appl. Meteor. Climatol, 52, 634–644.
Phelps, M. W., A. Kumar, and J. J. O’Brien, 2004: Potential predictability in the NCEP CPC dynamical seasonal forecast system. J. Climate, 17, 3775–3785.
Pielke, R. A., Sr, 2013: Comments on “The North American Regional Climate Change Assessment Program: Overview of Phase I Results”. Bull. Amer. Meteor. Soc., 1075–1077.
—, and R. L. Wilby, 2012: Regional climate downscaling: What’s the point? Eos. Amer. Geophys. Union, 93, 52–53, doi:10.1029/2012-EO050008.
Randall, D., M. Khairoutdinov, A. Arakawa, W. Grabowski, 2003: Breaking the cloud parameterization deadlock. Bull. Amer. Meteor. Soc., 84, 1547–1564.
Skamarock, W. C., J. B. Klemp, M. G. Duda, L. D. Fowler, and S.-H. Park, 2012: A multiscale nonhydrostatic atmospheric model using centroidal voronoi tesselations and C-grid staggering. Mon. Wea. Rev., 140, 3090–3105.
—, and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-4751STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf]..
Schmidli, J., C. M. Goodess, C. Frei, M. R. Haylock, Y. Hundecha, J. Ribalaygua, and T. Schmith, 2007: Statistical and dynamical down-scaling of precipitation: An evaluation and comparison of scenarios for the European Alps.J. Geophys. Res., 112, D04105, doi:10.1029/2005 JD007026.
Seth, A., and F. Giorgi, 1998: The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model. J. Climate, 11, 2698–2712.
Shige, S., Y. N. Takayabu, W.-K. Tao, W., and C.-L. Shie, 2007: Spectral retrieval of latent heating profiles from TRMM PR data. Part II: Algorithm improvement and heating estimates over tropical ocean regions. J. Appl. Meteor. Climatol., 46, 7, 1098–1124.
Shin, H. H., S.-Y. Hong, 2013: Analysis of resolved and parameterized vertical transports in convective boundary layers at gray-zone resolutions. J. Atmos. Sci., 70, 3248–3261, doi: 10.1175/JAS-D-12-0290.1.
Shrestha, D. L., D. E. Robertson, Q. J. Wang, T. C. Pagano, and H. A. P. Hapuarachchi, 2013: Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrol. Earth Syst. Sci., 17, 1913–1931, doi:10.5194/hess-17-1913-2013.
Stern, W., and K. Miyakoda, 1995: Feasibility of seasonal forecasts inferred from multiple GCM simulations. J. Climate, 8, 1071–1085.
Stern, W. F., and J. J. Ploshay, 1992: A scheme for continuous data assimilation. Mon. Wea. Rev., 120, 1417–1432.
Sugi, M., R. Kawamura, and N. Sato, 1997: A study of SST-forced variability and potential predictability of seasonal mean fields using the JMA global model. J. Meteor. Soc. Japan, 75, 717–736.
Sun, L., H. Li, S. E. Zebiak, D. F. Moncunill, F. D. A. D. S. Filho, and A. D. Moura, 2006: An operational dynamical downscaling prediction system for Nordeste Brazil and the 2002-04 real-time forecast evaluation. J. Climate, 19, 1990–2007.
Takle, E. S., and Coauthors, 1999: Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results. J. Geo-phys. Res., 104(D16), 19443–19461, doi:10.1029/1999JD900352.
Thatcher M., and J. L. McGregor, 2009: Using a scale-selective filter for dynamical downscaling with the conformal cubic atmospheric model. Mon. Wea. Rev., 137, 1742–1752.
Tian, Y., and C. D. Peters-Lidard, 2010: A global map of uncertainties in satellite-based precipitation measurements. Geophys. Res. Lett., 37, L24407, doi:10.1029/2010GL046008.
Trenberth, K. E., and L. Smith, 2008: Atmospheric energy budgets in the Japanese Reanalysis: Evaluation and variability. J. Meteor. Soc. Japan, 86, 579–592.
von Storch, H., H. Langenberg, and F. Feser, 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 3664–3673.
Wang, X., Z. Zhong, Y. Hu, and H. Yuan, 2010: Effect of lateral boundary scheme on the simulation of tropical cyclone track in regional climate model RegCM3. Asia-Pac. J. Atmos. Sci., 46, 221–230, doi:10.1007/s13143-010-0019-y.
Wang Y., L. R. Leung, J. L. McGregor, D.-K. Lee, W.-C. Wang, Y.-H. Ding, and F. Kimura, 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteor. Soc. Japan, 82, 1599–1628.
Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611–627.
Yang, Z., and R.W. Arritt, 2002: Tests of a perturbed physics ensemble approach for regional climate modeling. J. Climate, 15, 2881–2896.
Yhang, Y.-B., and S.-Y. Hong, 2011: A study on large-scale nudging effects in regional climate model simulation. Asia-Pac. J. Atmos. Sci., 47, 235–243.
Yoon, J.-H., L. R. Leung, and J. Correia Jr., 2012: Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J. Geophys. Res., 117, D21109, doi:10.1029/2012JD017650.
Yoshimura, K., and M. Kanamitsu, 2008: Dynamical global downscaling of global reanalysis.Mon. Wea. Rev., 136, 2983–2998.
—, and —, 2013: Incremental correction for the dynamical downscaling of ensemble mean atmospheric fields. Mon. Wea. Rev., 141, 3087–3101, doi:10.1175/MWR-D-12-00271.1.
Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simu-lations to the parameterization of cumulus convection in the Canadian climate centre general circulation model. Atmos.-Ocean, 33, 407–446.
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Hong, SY., Kanamitsu, M. Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations. Asia-Pacific J Atmos Sci 50, 83–104 (2014). https://doi.org/10.1007/s13143-014-0029-2
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DOI: https://doi.org/10.1007/s13143-014-0029-2