The response of US summer rainfall to quadrupled CO2 climate change in conventional and superparameterized versions of the NCAR community atmosphere model

Abstract

Observations and regional climate modeling (RCM) studies demonstrate that global climate models (GCMs) are unreliable for predicting changes in extreme precipitation. Yet RCM climate change simulations are subject to boundary conditions provided by GCMs and do not interact with large-scale dynamical feedbacks that may be critical to the overall regional response. Limitations of both global and regional modeling approaches contribute significant uncertainty to future rainfall projections. Progress requires a modeling framework capable of capturing the observed regional-scale variability of rainfall intensity without sacrificing planetary scales. Here the United States summer rainfall response to quadrupled CO₂ climate change is investigated using conventional (CAM) and superparameterized (SPCAM) versions of the NCAR Community Atmosphere Model. The superparameterization approach, in which cloud-resolving model arrays are embedded in GCM grid columns, improves rainfall statistics and convective variability in global simulations. A set of 5 year time-slice simulations, with prescribed sea surface temperature and sea ice boundary conditions harvested from preindustrial and abrupt four times CO₂ coupled Community Earth System Model (CESM/CAM) simulations, are compared for CAM and SPCAM. The two models produce very different changes in mean precipitation patterns, which develop from differences in large-scale circulation anomalies associated with the planetary-scale response to warming. CAM shows a small decrease in overall rainfall intensity, with an increased contribution from the weaker parameterized convection and a decrease from large-scale precipitation. SPCAM has the opposite response, a significant shift in rainfall occurrence toward higher precipitation rates including more intense propagating Central United States mesoscale convective systems in a four times CO₂ climate.

References Allan, R. P., and B. J. Soden (2008), Atmospheric warming and the amplification of precipitation extremes, Science, 321, 1481–1484. Allen, M. R., and W. J. Ingram (2002), Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 224–232. Andrews, T., J. M. Gregory, M. J. Webb, and K. E. Taylor (2012), Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models, Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607. Benedict, J. J., and D. A. Randall (2009), Structure of the Madden-Julian oscillation in the superparameterized CAM, J. Atmos. Sci., 66(11), 3277–3296. Brooks, H. E. (2013), Severe thunderstorms and climate change, Atmos. Res., 123, 129–138. Bukovsky, M. S., and D. J. Karoly (2011), A regional modeling study of climate change impacts on warm-season precipitation in the Central United States, J. Clim., 24, 1985–2002. Carbone, R. E., and J. D. Tuttle (2008), Rainfall occurrence in the US warm season: The diurnal cycle, J. Clim., 21(16), 4132–4146. Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier (2002), Inferences of predictability associated with warm season precipitation episodes, J. Atmos. Sci., 59(13), 2033–2056. Dai, A. (2006), Precipitation characteristics in eighteen coupled climate models, J. Clim., 19, 4605–4630. DeMott, C. A., D. A. Randall, and M. Khairoutdinov (2007), Convective precipitation variability as a tool for general circulation model analysis, J. Clim., 20, 91–112. Deser, C., A. Phillips, M. Alexander, and B. Smoliak (2013), Projecting North American climate over the next 50 years: Uncertainty due to internal variability, J. Clim., 27, 2271–2296, doi:10.1175/JCLI-D-13-00451.1. Grabowski, W. W. (2001), Coupling cloud processes with the large-scale dynamics using the cloud-resolving convection parameterization (CRCP), J. Atmos. Sci., 58(9), 978–997. Gregory, J. M., W. J. Ingram, M. A. Palmer, G. S. Jones, P. A. Stott, R. B. Thorpe, J. A. Lowe, T. C. Johns, and K. D. Williams (2004), A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747. Held, I. M., and B. J. Soden (2006), Robust responses of the hydrological cycle to global warming, J. Clim., 19, 5686–5699. Khairoutdinov, M. F., and D. A. Randall (2001), A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results, Geophys. Res. Lett., 28(18), 3617–3620. Khairoutdinov, M. F., and D. A. Randall (2003), Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities, J. Atmos. Sci., 60, 607–625. Khairoutdinov, M., C. DeMott, and D. Randall (2008), Evaluation of the simulated interannual and subseasonal variability in an AMIP-Style simulation using the CSU multiscale modeling framework, J. Clim., 21(3), 413–431. Khairoutdinov, M., D. Randall, and C. DeMott (2005), Simulations of the atmospheric general circulation using a cloud-resolving model as a super-parameterization of physical processes, J. Atmos. Sci., 62(7), 2136–2154. Kooperman, G. J., M. S. Pritchard, and R. C. J. Somerville (2013), Robustness and sensitivities of central U.S. summer convection in the super-parameterized CAM: Multi-model intercomparison with a new regional EOF index, Geophys. Res. Lett., 40, 3287–3291, doi:10.1002/grl.50597. Kopparla, P., E. M. Fischer, C. Hannay, and R. Knutti (2013), Improved simulation of extreme precipitation in a high-resolution atmosphere model, Geophys. Res. Lett., 40, 5803–5808, doi:10.1002/2013GL057866. Lee, M.-I., S. D. Schubert, M. J. Suarez, I. M. Held, N.-C. Lau, J. J. Ploshay, A. Kumar, H.-K. Kim, and J.-K. E. Schemm (2007), An analysis of the warm-season diurnal cycle over the continental United States and northern Mexico in general circulation models, J. Hydrometeorol., 8(3), 344–366. Li, F., D. Rosa, W. D. Collins, and M. F. Wehner (2012), “Super-parameterization”: A better way to simulate regional extreme precipitation?, J. Adv. Model. Earth Syst., 4, M04002, doi:10.1029/2011MS000106. Li, G., and S. Xie (2013), Tropical biases in CMIP5 multi-model ensemble: The excessive equatorial Pacific cold tongue and double ITCZ problems, J. Clim., 27, 1765–1780, doi:10.1175/JCLI-D-13-00337.1. Lin, J.-L., et al. (2006), Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals, J. Clim., 19, 2665–2690. Liu, X., et al. (2012), Toward a minimal representation of aerosols in climate models: Description and evaluation in the community atmosphere model CAM5, Geosci. Model Dev., 5(3), 709–739. Maloney, E., et al. (2013), North American climate in CMIP5 experiments: Part III: Assessment of 21st century projections, J. Clim., doi:10.1175/JCLI-D-13-00273.1, in press. Mapes, B. E., and R. B. Neale (2011), Parameterizing convective organization to escape the entrainment dilemma, J. Adv. Model. Earth Syst., 3, M06004, doi:10.1029/2011MS000042. McCrary, R. R. (2012), Seasonal, synoptic and intraseasonal variability of the West African monsoon, PhD dissertation, 160 pp., Colo. State Univ., Fort Collins, Colo. Mearns, L. O., et al. (2013), Climate change projections of the North American regional climate change assessment program (NARCCAP), Clim. Change, 120, 965–975. Moncrieff, M. W. (1992), Organized convective systems: Archetypal dynamic-models, mass and momentum flux theory, and parametrization, Q. J. R. Meteorol. Soc., 118(507), 819–850. Moncrieff, M. W. (2010), The multiscale organization of moist convection at the intersection of weather and climate, in Why Does Climate Vary?, Editors D.-Z. Sun, and F. Bryan, Geophys. Monogr. Ser., vol. 189, pp. 3–26, AGU, Washington, D. C. Morrison, H., and A. Gettelman (2008), A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, version 3 (CAM3). Part I: Description and tests, J. Clim., 21, 3642–3659. Muller, C. J., P. A. O'Gorman, and L. E. Back (2011), Intensification of precipitation extremes with warming in a cloud-resolving model, J. Clim., 24, 2784–2800. Neale, R. B., et al. (2010), NCAR Technical Note: Description of the NCAR Community Atmosphere Model (CAM 5.0), Natl. Cent. for Atmos. Res., Boulder, Colo. O'Gorman, P. A. (2012), Sensitivity of tropical precipitation extremes to climate change, Nat. Geosci., 5, 697–700. O'Gorman, P. A., and T. Schneider (2009a), The physical basis for increases in precipitation extremes in simulations of 21st-century climate change, Proc. Natl. Acad. Sci. U. S. A., 106(35), 14,773–14,777. O'Gorman, P. A., and T. Schneider (2009b), Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM, J. Clim., 22, 5676–5685. Park, S., and C. S. Bretherton (2009), The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the community atmosphere model, J. Clim., 22(12), 3449–3469. Patricola, C. M., and K. H. Cook (2013a), Mid-twenty-first century warm season climate change in the Central United States. Part I: Regional and global model predictions, Clim. Dyn., 40, 551–568. Patricola, C. M., and K. H. Cook (2013b), Mid-twenty-first century climate change in the Central United States. Part II: Climate change processes, Clim. Dyn., 40, 569–583. Pendergrass, A., and D. Hartmann (2014a), The atmospheric energy constraint on global-mean precipitation change, J. Clim., 27, 757–768, doi:10.1175/JCLI-D-13-00163.1. Pendergrass, A., and D. Hartmann (2014b), Two modes of change of the distribution of rain. J. Clim., submitted. (http://www.atmos.washington.edu/~dennis/pendergrasshartmannsubmittedp1.pdf) Pritchard, M. S., and R. C. J. Somerville (2009), Assessing the diurnal cycle of precipitation in a multi-scale climate model, J. Adv. Model. Earth Syst., 1, 12, doi:10.3894/JAMES.2009.1.12. Pritchard, M. S., M. W. Moncrieff, and R. C. J. Somerville (2011), Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection, J. Atmos. Sci., 68(8), 1821–1840. Randall, D., M. Khairoutdinov, A. Arakawa, and W. Grabowski (2003), Breaking the cloud parameterization deadlock, Bull. Am. Meteorol. Soc., 84(11), 1547–1564. Romps, D. M. (2011), Response of tropical precipitation to global warming, J. Atmos. Sci., 68, 123–138. Rosa, D., and W. D. Collins (2013), A case study of sub-daily simulated and observed continental convective precipitation: CMIP5 and multiscale global climate models comparison, Geophys. Res. Lett., 40, 5999–6003, doi:10.1002/2013GL057987. Scheff, J., and D. Frierson (2012), Robust future precipitation declines in CMIP5 largely reflect the poleward expansion of model subtropical dry zones, Geophys. Res. Lett., 39, L18704, doi:10.1029/2012GL052910. Schumacher, R. S., and R. H. Johnson (2005), Organization and environmental properties of extreme-rain-producing mesoscale convective systems, Mon. Weather Rev., 133, 961–976. Smith, A., and R. Katz (2013), U.S. billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases, Nat. Hazards, 67, 387–410. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller (Eds.) (2007), 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 Univ. Press, Cambridge, U. K. Stackhouse, P. W., Jr., S. K. Gupta, S. J. Cox, T. Zhang, J. C. Mikovitz, and L. M. Hinkelman (2011), 24.5-year SRB data set released, GEWEX News, 21(1), 10–12. Stensrud, D. J. (1996), Importance of low-level jets to climate: A review, J. Clim., 9, 1698–1711. Stephens, G. L., and T. D. Ellis (2008), Controls of global-mean precipitation increases in global warming GCM experiments, J. Clim., 21, 6141–6155. Stephens, G. L., T. L'Ecuyer, R. Forbes, A. Gettlemen, J.-C. Golaz, A. Bodas-Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532. Stocker, T. F., D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley (Eds.) (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, U. K. Stouffer, R. J., et al. (2006), Investigating the causes of the response of the thermohaline circulation to past and future climate changes, J. Clim., 19, 1365–1387. Sun, Y., S. Solomon, A. Dai, and R. W. Portmann (2006), How often does it rain?, J. Clim., 19, 916–934. Thayer-Calder, K., and D. A. Randall (2009), The role of convective moistening in the formation and progression of the MJO, J. Clim., 66, 3297–3312. Trapp, R. J., N. S. Diffenbaugh, and A. Gluhovsky (2009), Transient response of severe thunderstorm forcing to elevated greenhouse gas concentrations, Geophys. Res. Lett., 36, L01703, doi:10.1029/2008GL036203. Trenberth, K. E. (2011), Changes in precipitation with climate change, Clim. Res., 47, 123–138. Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons (2003), The changing character of precipitation, Bull. Am. Meteorol. Soc., 84, 1205–1217. Wang, M., et al. (2011), The multi-scale aerosol-climate model PNNL-MMF: Model description and evaluation, Geosci. Model Dev., 4(1), 137–168. Wehner, M. F. (2013), Very extreme seasonal precipitation in the NARCCAP ensemble: Model performance and projections, Clim. Dyn., 40, 59–80. Wehner, M. F., R. L. Smith, G. Bala, and P. Duffy (2010), The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model, Clim. Dyn., 34, 241–247. Wheeler, M. C., and H. H. Hendon (2004), An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction, Mon. Weather Rev., 132, 1917–1932. Wilcox, E. M., and L. J. Donner (2007), The frequency of extreme rain events in satellite rain-rate estimates and an atmospheric general circulation model, J. Clim., 20(1), 53–69. Zhang, G. J., and N. A. McFarlane (1995), Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate center general-circulation model, Atmos. Ocean, 33(3), 407–446.