Deep Convection: Onset, Diurnal Cycle, and Role in Climate Variability and Sensitivity (Final Report)
The ASR SGP testbed for continuous modeling offers new opportunities to improve GCM parameterizations. We conducted an ASR investigation of convective and cloud microphysical processes that used existing ARM data as a testing ground for meaningful approaches to model evaluation and set the stage for later strategic use of the testbed to improve cumulus parameterizations and better understand deep convective cloud feedback. Our research consisted of the following tasks: Diurnal cycle of continental convection Deep convection at the SGP occurs in two forms: Organized mesoscale convection that originates upstream and produces peak precipitation at night; and local shallow convection that develops into deep convection that peaks in late afternoon. Local transitions are a logical next step from the shallow convection that will be the early focus of the testbed. This transition constrains entrainment and cold pools, since GCMs produce a continental rain peak near noon. We studied ARSCL-observed transition cases using the WRF, determined the sources of SCM diurnal cycle errors and made parameterization changes as appropriate. Factors controlling convective detrainment and anvil evolution Convective detrainment is a major source of high cloud ice water content and cloud forcing. The GISS GCM diagnoses a convective updraft speed profile, assumes a particle size distribution (PSD), and uses size-fallspeed relations to determine the fraction of condensate carried upward vs. precipitating. We used SCM case studies for TWP-ICE and MC3E for which vertical velocity has been retrieved to test whether entrainment improvements produce realistic updraft speeds and then improved the updraft microphysics parameterization. We compared detrained ice water contents and fall speeds to ARM and other field experiment data to refine the GCM’s fall speed and PSD assumptions. Role of convective cloud radiative processes in the Madden-Julian Oscillation (MJO) Changes in entrainment and rain evaporation and a cold pool parameterization have produced an MJO in the GISS GCM for the first time and allowed us to reproduce the KAZR dependence of convection depth on humidity during AMIE-Gan. We determined whether recent model changes including cold pools improve or degrade the MJO and made adjustments accordingly.
- Research Organization:
- NASA Goddard Institute for Space Studies, New York, NY (United States)
- Sponsoring Organization:
- Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
- DOE Contract Number:
- SC0014382
- OSTI ID:
- 1479864
- Report Number(s):
- DOE-NASAGISS-14382
- Resource Relation:
- Related Information: Del Genio, A.D., J. Wu, A.B. Wolf, Y.H. Chen, M.-S. Yao, and D. Kim, 2015: Constraints on cumulus parameterization from simulations of observed MJO events. J. Climate, 28, no. 16, 6419-6442.Jensen, M.P., W.A. Petersen, A. Bansemer, N. Bharadwaj, L.D. Carey, D.J. Cecil, S.M. Collis, A.D. Del Genio, B. Dolan, J. Gerlach, S.E. Giangrande, A. Heymsfield, G. Heymsfield, P. Kollias, T.J. Lang, S.W. Nesbitt, A. Neumann, M. Poellot, S.A. Rutledge, M. Schwaller, A. Tokay, C.R. Williams, D.B. Wolff, S. Xie, and E.J. Zipser, 2016: The Midlatitude Continental Convective Clouds Experiment (MC3E). Bull. Amer. Meteorol. Soc., 97, no. 9, 1667-1686.Randall, D.A., A.D. Del Genio, L.J. Donner, W.D. Collins, and S.A. Klein, 2016: The impact of ARM on climate modeling. In The Atmospheric Radiation Measurement Program: The First 20 Years. D.D. Turner and R.G. Ellingson, Eds., AMS Meteorological Monograph 57. American Meteorological Society, pp. 26.1-26.16.Wood, R., M.P. Jensen, J. Wang, C.S. Bretherton, S.M. Burrows, A.D. Del Genio, A.M. Fridlind, S.J. Ghan, V.P. Ghate, P. Kollias, S.K. Kruger, R.L. McGraw, M.A. Miller, D. Painemal, L.M. Russell, S.E. Yuter, and P. Zuidema, 2016: Planning the next decade of coordinated research to better understand and simulate marine low clouds. Bull. Amer. Meteorol. Soc., 97, no. 9, 1699-1702.Zheng, Y., K. Alpaty, J.A. Herwehe, A.D. 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. Weather Rev., 144, no. 3, 833-860.Elsaesser, G.S., A.D. Del Genio, J. Jiang, and M. van Lier-Walqui, 2017: An improved convective ice parameterization for the NASA GISS Global Climate Model and impacts on cloud ice simulation. J. Climate, 30, no. 1, 317-336.Nazarenko, L., D. Rind, K. Tsigaridis, A.D. Del Genio, M. Kelley, and N. Tausnev, 2017: Interactive nature of climate change and aerosol forcing. J. Geophys. Res. Atmos., 122, no. 6, 3457-3480.
- Country of Publication:
- United States
- Language:
- English
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