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  • Abdul-Razzak, H., and S. J. Ghan, 2000: A parameterization of aerosol activation: 2. Multiple aerosol types. J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Aggarwal, P. K., U. Romatschke, L. Arguas-Arguas, D. Belachew, F. J. Longstaffe, P. Berg, C. Schumacher, and A. Funk, 2016: Proportions of convective and stratiform revealed in water isotope ratios. Nat. Geosci., 9, 624–629, https://doi.org/10.1038/ngeo2739.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Alapaty, K., D. Niyogi, F. Chen, P. Pyle, A. Chandrasekar, and N. Seaman, 2008: Development of the flux-adjusting surface data assimilation system for mesoscale models. J. Appl. Meteor. Climatol., 47, 2331–2350, https://doi.org/10.1175/2008JAMC1831.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Alapaty, K., J. A. Herwehe, T. L. Otte, C. G. Nolte, O. R. Bullock, M. S. Mallard, J. S. Kain, and J. Dudhia, 2012: Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling. Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL054031.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Alapaty, K., J. S. Kain, J. S. Herwehe, O. R. Bullock, M. S. Mallard, T. L. Spero, and C. G. Nolte, 2014: Multiscale Kain-Fritsch scheme: Formulations and tests. 13th Annual CMAS Conf., Chapel Hill, NC, University of North Carolina, https://www.cmascenter.org/conference/2014/agenda.cfm#inline1503.

  • Albrecht, B. A., 1989: Aerosols, cloud microphysics, and fractional cloudiness. Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bae, S. Y., S.-Y. Hong, and K.-S. S. Lim, 2016: Coupling WRF double-moment 6-class microphysics schemes to RRTMG radiation scheme in Weather Research Forecasting Model. Adv. Meteor., 2016, 5070154, https://doi.org/10.1155/2016/5070154.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bender, F. A.-M., 2012: Aerosol forcing: Rappoteur’s report and summary. Surv. Geophys., 33, 693–700, https://doi.org/10.1007/s10712-011-9160-0.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bergeron, T., 1935: On the physics of clouds and precipitation. Proces Verbaux de l’Association de Météorologie, International Union of Geodesy and Geophysics, 156–178.

  • Boucher, O., and Coauthors, 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 571–657.

  • Bullock, O. R., K. Alapaty, J. A. Herwehe, M. S. Mallard, T. L. Otte, R. C. Gilliam, and C. G. Nolte, 2014: An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing. J. Appl. Meteor. Climatol., 53, 20–33, https://doi.org/10.1175/JAMC-D-13-030.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Campbell, P., and Coauthors, 2015: A multi-model assessment for the 2006 and 2010 simulations under the Air Quality Model Evaluation International Initiative (AQMEII) phase 2 over North America: Part I. Indicators of the sensitivity of O3 and PM2.5 formation regimes. Atmos. Environ., 115, 569–586, https://doi.org/10.1016/j.atmosenv.2014.12.026.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/US7 MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Clough, S. A., M. W. Shephard, J. E. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: a summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Colarco, P., A. da Silva, M. Chin, and T. Diehl, 2010: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth. J. Geophys. Res., 115, D14207, https://doi.org/10.1029/2009JD012820.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Cooper, W. A., 1986: Ice initiation in natural clouds. Precipitation Enhancement—A Scientific Challenge, Meteor. Monogr., No. 21, Amer. Meteor. Soc., 29–32.

    • Crossref
    • Export Citation

  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • DeMott, P. J., and Coauthors, 2010: Predicting global atmospheric ice nuclei distributions and their impacts on climate. Proc. Natl. Acad. Sci. USA, 107, 11 217–11 222, https://doi.org/10.1073/pnas.0910818107.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Denman, K. L., and Coauthors, 2007: Couplings between changes in the climate system and biogeochemistry. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 499–587.

  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Findeisen, W., 1938: Kolloid-meteorologische Vorgänge bei Neiderschlags-bildung. Meteor. Z., 55, 121–133.

  • Gantt, B., J. He, X. Zhang, Y. Zhang, and A. Nenes, 2014: Incorporation of advanced aerosol activation treatments into CESM/CAM5: Model evaluation and impact on aerosol indirect effects. Atmos. Chem. Phys., 14, 7485–7497, https://doi.org/10.5194/acp-14-7485-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Glotfelty, T., J. He, and Y. Zhang, 2017a: Improving organic aerosol treatments in CESM/CAM5: Development, application, and evaluation. J. Adv. Model. Earth Syst., 9, 1506–1539, https://doi.org/10.1002/2016MS000874.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Glotfelty, T., J. He, and Y. Zhang, 2017b: The impact of future climate policy scenarios on air quality and aerosol/cloud interactions using an advanced version of CESM/CAM5: Part I. Model evaluation for the current decadal simulations. Atmos. Environ., 152, 222–239, https://doi.org/10.1016/j.atmosenv.2016.12.035.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Grell, G. A., S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, and B. Eder, 2005: Fully coupled online chemistry within the WRF model. Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • He, J., and Y. Zhang, 2014: Improvement and further development in CESM/CAM5: Gas-phase chemistry and inorganic aerosol treatments. Atmos. Chem. Phys., 14, 9171–9200, https://doi.org/10.5194/acp-14-9171-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • He, J., T. Glotfelty, K. Yahya, K. Alapaty, and S. Yu, 2017: Does temperature nudging overwhelm aerosol radiative effects in regional integrated climate models? Atmos. Environ., 154, 42–52, https://doi.org/10.1016/j.atmosenv.2017.01.040.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., and J. Dudhia, 2012: Next-generation numerical weather prediction: Bridging parameterization, explicit clouds, and large eddies. Bull. Amer. Meteor. Soc., 93, ES6–ES9, https://doi.org/10.1175/2011BAMS3224.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Houze, R. A., Jr., 1997: Stratiform precipitation in regions of convection: a meteorological paradox? Bull. Amer. Meteor. Soc., 78, 2179–2196, https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hu, Y., S. Rodier, K. Xu, W. Sun, J. Huang, B. Lin, P. Zhai, and D. Josset, 2010: Occurrence, liquid water content, and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurements. J. Geophys. Res., 115, D00H34, https://doi.org/10.1029/2009JD012384.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hurrell, J. W., and Coauthors, 2013: The community earth system model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long–lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Igel, A. L., S. C. van den Heever, C. M. Naud, S. M. Saleeby, and D. J. Posselt, 2013: Sensitivity of warm-frontal processes to cloud-nucleating aerosol concentrations. J. Atmos. Sci., 70, 1768–1783, https://doi.org/10.1175/JAS-D-12-0170.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Janjić, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp., http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 24, Amer. Meteor. Soc., 165–170.

    • Crossref
    • Export Citation

  • Khairoutdinov, M., and Y. Kogan, 2000: A new cloud physics parameterization in a large-eddy simulation model of marine stratocumulus. Mon. Wea. Rev., 128, 229–243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kogan, Y., 2013: A cumulus cloud microphysics parametrization for cloud-resolving models. J. Atmos. Sci., 70, 1423–1436, https://doi.org/10.1175/JAS-D-12-0183.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kooperman, G. J., M. S. Pritchard, S. J. Ghan, M. Wang, R. C. J. Somerville, and L. M. Russell, 2012: Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5. J. Geophys. Res., 117, D23204, https://doi.org/10.1029/2012JD018588.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kudzotsa, I., V. T. J. Phillips, S. Dobbie, M. Formenton, J. Sun, G. Allen, A. Bansemer, D. Spracklen, and K. Pringle, 2016a: Aerosol indirect effects on glaciated cloud. Part 1: Model description. Quart. J. Roy. Meteor. Soc., 142, 1958–1969, https://doi.org/10.1002/qj.2791.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kudzotsa, I., V. T. J. Phillips, and S. Dobbie, 2016b: Aerosol indirect effects on glaciated clouds. Part 2: Sensitivity tests using solute aerosols. Quart. J. Roy. Meteor. Soc., 142, 1970–1981, https://doi.org/10.1002/qj.2790.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lauer, A., and K. Hamilton, 2013: Simulating clouds with global climate models: A comparison of CMIP5 results with CMIP3 and satellite data. J. Climate, 26, 3823–3845, https://doi.org/10.1175/JCLI-D-12-00451.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lim, K.-S. S., J. Fan, L. R. Leung, P.-L. Ma, B. Singh, C. Zhao, Y. Zhang, G. Zhang, and X. Song, 2014: Investigation of aerosol indirect effects using a cumulus microphysics parameterization in a regional climate model. J. Geophys. Res. Atmos., 119, 906–926, https://doi.org/10.1002/2013JD020958.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, X. H., and J. E. Penner, 2005: Ice nucleation parameterization for global models. Meteor. Z., 14, 499–514, https://doi.org/10.1127/0941-2948/2005/0059.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, X. H., J. E. Penner, S. J. Ghan, and M. Wang, 2007: Inclusion of ice microphysics in the NCAR Community Atmospheric Model version 3 (CAM3). J. Climate, 20, 4526–4547, https://doi.org/10.1175/JCLI4264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Y., and P. H. Daum, 2004: Parameterization of the autoconversion process. Part I: Analytical formulation of the Kessler-type parameterizations. J. Atmos. Sci., 61, 1539–1548, https://doi.org/10.1175/1520-0469(2004)061<1539:POTAPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lohmann, U., 2002: A glaciation indirect aerosol effect caused by soot aerosols, Geophys. Res. Lett., 29, 1052, https://doi.org/10.1029/2001GL014357.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Lohmann, U., and J. Feichter, 2005: Global indirect aerosol effects: A review. Atmos. Chem. Phys., 5, 715–735, https://doi.org/10.5194/acp-5-715-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ma, P.-L., P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson, X. Liu, S. J. Ghan, and B. Singh, 2014: Assessing the CAM5 physics suite in the WRF-Chem model: Implementation, resolution sensitivity, and a first evaluation for a regional case study. Geosci. Model Dev., 7, 755–778, https://doi.org/10.5194/gmd-7-755-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ma, P.-L., and Coauthors, 2015: How does increasing horizontal resolution in a global climate model improve the simulation of aerosol-cloud interactions? Geophys. Res. Lett., 42, 5058–5065, https://doi.org/10.1002/2015GL064183.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mahoney, K. M., 2016: The representation of cumulus convection in high-resolution simulations of the 2013 Colorado Front Range Flood. Mon. Wea. Rev., 144, 4265–4278, https://doi.org/10.1175/MWR-D-16-0211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Monin, A. S., and A. M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere (in Russian). Tr. Geofiz. Inst., Akad. Nauk SSSR, 24, 163–187.

    • Search Google Scholar
    • Export Citation

  • Morrison, H., and A. Gettelman, 2008: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmospheric Model (CAM3). Part I: Description and numerical tests. J. Climate, 21, 3642–3659, https://doi.org/10.1175/2008JCLI2105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrison, H., J. A. Curry, and V. I. Khvorostyanov, 2005: A new double-moment microphysics parameterization for application in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677, https://doi.org/10.1175/JAS3446.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Morrison, H., G. Thompson, and V. Tatarskii, 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. Wea. Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden, 2012: Does nudging squelch the extremes in regional climate modeling? J. Climate, 25, 7046–7066, https://doi.org/10.1175/JCLI-D-12-00048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Partanen, A.-I., and Coauthors, 2014: Global modelling of direct and indirect effects of sea spray aerosol using a source function encapsulating wave state. Atmos. Chem. Phys., 14, 11 731–11 752, https://doi.org/10.5194/acp-14-11731-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Penner, J. E., and Coauthors, 2006: Model intercomparison of indirect aerosol indirect effects. Atmos. Chem. Phys., 6, 3391–3405, https://doi.org/10.5194/acp-6-3391-2006.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Phillips, V. T. J., P. J. DeMott, and C. Andronache, 2008: An empirical parameterization of heterogeneous ice nucleation for multiple chemical species of aerosol. J. Atmos. Sci., 65, 2757–2783, https://doi.org/10.1175/2007JAS2546.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Posselt, R., and U. Lohmann, 2009: Sensitivity of the total anthropogenic aerosol effect to the treatment of rain in a global climate model. Geophys. Res. Lett., 36, L02805, https://doi.org/10.1029/2008GL035796.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., U. Lohmann, G. B. Raga, C. D. O’Dowd, M. Kulmala, S. Fuzzi, A. Reissell, and M. O. Andreae, 2008: Flood or drought: How do aerosols affect precipitation? Science, 321, 1309–1313, https://doi.org/10.1126/science.1160606.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Song, X., and G. J. Zhang, 2011: Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests. J. Geophys. Res., 116, D02201, https://doi.org/10.1029/2010JD014833

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited-area model. Part I: experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250–1277, https://doi.org/10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stauffer, D. R., and N. L. Seaman, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor., 33, 416–434, https://doi.org/10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tao, W.-K., J.-P. Chen, Z. Li, C. Wang, and C. Zhang, 2012: Impact of aerosols on convective clouds and precipitation. Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thompson, G., M. Tewari, K. Ikeda, S. Tessendorf, C. Weeks, J. Otkin, and F. Kong, 2016: Explicitly-coupled cloud physics and radiation parameterizations and subsequent evaluation in WRF high-resolution convective forecasts. Atmos. Res., 168, 92–104, https://doi.org/10.1016/j.atmosres.2015.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tonttila, J., H. Jarvinen, and P. Raisanen, 2015: Explicit representation of subgrid cariability in cloud microphysics yields weaker aerosol indirect effect in the ECHAM5-HAM2 climate model. Atmos. Chem. Phys., 15, 703–714, https://doi.org/10.5194/acp-15-703-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Twomey, S., 1977: The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, S.-Y., T.-C. Chen, and S. E. Taylor, 2009: Evaluations of NAM forecasts on midtropospheric perturbation-induced convective storms over the U.S. northern plains. Wea. Forecasting, 24, 1309–1333, https://doi.org/10.1175/2009WAF2222185.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wegener, A., 1911: Thermodynamik der Atmosphäre. Leipzig, 331.

  • White, B., E. Gryspeerdt, P. Stier, H. Morrison, G. Thompson, and Z. Kipling, 2017: Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects. Atmos. Chem. Phys., 17, 12 145–12 175, https://doi.org/10.5194/acp-17-12145-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Xie, X., and X. Liu, 2015: Aerosol-cloud-precipitation interactions in WRF model: Sensitivity to autoconversion parameterization. J. Meteor. Res., 29, 72–81, https://doi.org/10.1007/s13351-014-4065-8.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zelinka, M. D., T. Andrews, P. M. Forster, and K. E. Taylor, 2014: Quantifying components of aerosol-cloud-radiation interactions in climate models. J. Geophys. Res. Atmos., 119, 7599–7615, https://doi.org/10.1002/2014JD021710.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, G. J., and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre General Circulation Model. Atmos.–Ocean, 33, 407–446, https://doi.org/10.1080/07055900.1995.9649539.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhang, Y., P. Karamchandani, T. Glotfelty, D. G. Streets, G. Grell, A. Nenes, F. Yu, and R. Bennartz, 2012: Development and initial application of the global-through-urban weather research and forecasting model with chemistry (GU-WRF/Chem). J. Geophys. Res., 117, D20206, https://doi.org/10.1029/2012JD017966.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zheng, Y., K. Alapaty, J. S. Herwehe, A. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev., 144, 833–860, https://doi.org/10.1175/MWR-D-15-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI): Development, Evaluation, and Initial Application

Timothy GlotfeltyNational Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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Kiran AlapatyNational Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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Jian HeNational Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina

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Patrick HawbeckerDepartment of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina

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Xiaoliang SongScripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Guang ZhangScripps Institution of Oceanography, University of California, San Diego, La Jolla, California

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Print Publication:
01 May 2019
DOI:
https://doi.org/10.1175/MWR-D-18-0267.1
Page(s):
1491–1511
Restricted access

Abstract

The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-18-0267.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: alapaty.kiran@epa.gov

Abstract

The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/MWR-D-18-0267.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: alapaty.kiran@epa.gov

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