Abstract
At the same temperature, below 0 °C, the saturation vapor pressure (SVP) over ice is slightly less than the SVP over liquid water. Numerical models use the Clausius–Clapeyron relation to calculate the SVP and relative humidity, but there is not a consistent method for the treatment of saturation above the freezing level where ice and mixed-phase clouds may be present. In the context of current challenges presented by cloud microphysics in climate models, we argue that a better understanding of the impact that this treatment has on saturation-related processes like cloud formation and precipitation, is needed. This study explores the importance of the SVP calculation through model simulations of the Indian summer monsoon (ISM) using the regional spectral model (RSM) at 15 km grid spacing. A combination of seasonal and multiyear simulations is conducted with two saturation parameterizations. In one, the SVP over liquid water is prescribed through the entire atmospheric column (woIce), and in another the SVP over ice is used above the freezing level (wIce). When SVP over ice is prescribed, a thermodynamic drying of the middle and upper troposphere above the freezing level occurs due to increased condensation. In the wIce runs, the model responds to the slight decrease in the saturation condition by increasing, relative to the SVP over liquid water only run, grid-scale condensation of water. Increased grid-scale mean seasonal precipitation is noted across the ISM region in the simulation with SVP over ice prescribed. Modification of the middle and upper troposphere moisture results in a decrease in mean seasonal mid-level cloud amount and an increase in high cloud amount when SVP over ice is prescribed. Multiyear simulations strongly corroborate the qualitative results found in the seasonal simulations regarding the impact of ice versus liquid water SVP on the ISM’s mean precipitation and moisture field. The mean seasonal rainfall difference over All India between wIce and woIce is around 10% of the observed interannual variability of seasonal All India rainfall.
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References
Abhik S, Halder M, Mukhopadhyay P, Jiang X, Goswami BN (2013) A possible new mechanism for northward propagation of boreal summer intraseasonal oscillations based on TRMM and MERRA reanalysis. Clim Dyn 40:1611–1624
Abhik S, Krishna RPM, Mahakur M, Ganai M, Mukhopadhyay P, Dudhia J (2017) Revised cloud processes to improve the mean and intraseasonal variability of the Indian summer monsoon in climate forecast system: part 1. J Adv Model Earth Syst 9:1002–1029
Ashok K, Guan Z, Yamagata T (2001) Impact of the Indian Ocean dipole on the relationship between Indian monsoon rainfall and ENSO. Geophys Res Lett 28:4499–4502. https://doi.org/10.1029/2001GLO13294
Bony S, Stevens B, Frierson DMW, Jakob C, Kageyama M, Pincus R, Shepherd TG, Sherwood SC, Siebesma AP, Sobel AH, Watanabe M, Webb MJ (2015) Clouds, circulation and climate sensitivity. Nat Geosci 8:261–268. https://doi.org/10.1038/ngeo2398
Chang FL, Li Z (2005) A near-global climatology of single-layer and overlapped clouds and their optical properties retrieved from Terra/MODIS data using a new algorithm. J Clim 18:4752–4771
Chou M-D (1992) A solar radiation model for use in climate studies. J Atmos Sci 49:762–772
Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation model. Technical report series on global modeling and data assimilation, NASA/TM-1994-104606, vol 3, p 85
Dessler AE (2010) A determination of the cloud feedback from climate variations over the past decade. Science 330(6020):1523–1527. https://doi.org/10.1126/science.1192546
Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz J, Ginoux P, Lin S, Schwarzkopf MD, Austin J, Alaka G, Cooke WF, Delworth TL, Freidenreich SM, Gordon CT, Griffies SM, Held IM, Hurlin WJ, Klein SA, Knutson TR, Langenhorst AR, Lee H, Lin Y, Magi BI, Malyshev SL, Milly PC, Naik V, Nath MJ, Pincus R, Ploshay JJ, Ramaswamy V, Seman CJ, Shevliakova E, Sirutis JJ, Stern WF, Stouffer RJ, Wilson RJ, Winton M, Wittenberg AT, Zeng F (2011) The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J Clim 24:3484–3519. https://doi.org/10.1175/2011JCLI3955.1
Fowler LD, Randall DA, Rutledge SA (1996) Liquid and ice cloud microphysics in the CSU general circulation model. Part I: model description and simulated microphysical processes. J Clim 9:489–529
Gettelman A, Collins WD, Fetzer EJ, Eldering A, Irion FW, Duffy PB, Bala G (2006) Climatology of upper-tropospheric relative humidity from the atmospheric infrared sounder and implication for climate. J Clim 19:6104–6120
Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg H-D, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners K-H, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the coupled model intercomparison project phase 5. J Adv Model Earth Syst 5:572–597. https://doi.org/10.1002/jame.20038
Goswami BN (1998) Interannual variation of Indian summer monsoon in a GCM: external condition versus internal feedbacks. J Clim 11:501–522
Goswami BB, Krishna RPM, Mukhopadhyay P, Khairoutdinov M, Goswarmi BN (2015) Simulation of the Indian summer monsoon in the superparameterized climate forecast system version 2: preliminary results. J Clim 28:8988–9012
Hahn DG, J Shukla (1976) An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J Atmos Sci 33:2461–2462
Huffman GJ, Bolvin DT, Adler RF (2012) GPCP version 1.2 1-degree daily (1DD) precipitation data set. World Data Center A, National climatic Data Center, Asheville
Jiang X, Waliser DE, Li JL, Woods C (2011) Vertical cloud structures of the boreal summer intraseasonal variability based on CloudSat observations and ERA-interim reanalysis. Clim Dyn 36:2219–2232. https://doi.org/10.1007/s00382-010-0853-8
Juang H-MH, Kanamitsu M (1994) The NMC nested regional spectral model. Mon Weather Rev 122.1:3–26
Kanamaru H, Kanamitsu M (2007) Scale-selective bias correction in a downscaling of global reanalysis using a regional model. Mon Weather Rev 135:334–350
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis. Bull Am Meteorol Soc 83:1631–1643
Kanamitsu M, Yoshimura K, Yhang Y, Hong S (2010) Errors of interannual variability and multi-decadal trend in dynamical regional climate downscaling and its corrections. J Geophys Res 115:D17115
Korolev A, Isaac GA (2006) Relative humidity in liquid, mixed-phase, and ice clouds. J Atmos Sci 63:2865–2880. https://doi.org/10.1175/JAS3784.1
Korolev A, Mazin IP (2003) Supersaturation of water vapor in clouds. J Atmos Sci 60:2957–2974
Krishnamurthy V, Shukla J (2000) Intraseaonal and interannual variability of rainfall over India. J Clim 13:4366–4377
Krishnamurthy V, Shukla J (2007) Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall. J Clim 20:3–20. https://doi.org/10.1175/JCLI3981.1
Kumar S, Hazra A, Goswami BN (2014) Role of interaction between dynamics, thermodynamics and cloud microphysics on the summer monsoon precipitating cloud over the Myanmar Coast and the Western Ghats. Clim Dyn 43:911–924. https://doi.org/10.1007/s00382-013-1909-3
Li H, Misra V (2014) Thirty-two-year ocean-atmosphere coupled downscaling of global reanalysis over the Intra-American Seas. Clim Dyn 43:2471–2489. https://doi.org/10.1007/s00382-014-2069-9
Li H, Kanamitsu M, Hong S-Y, Yoshimura K, Cayan DR, Misra V (2013a) A high-resolution ocean-atmosphere coupled downscaling of a present climate over California. Clim Dyn. https://doi.org/10.1007/s00382-013-1670-7
Li H, Kanamitsu M, Hong S-Y, Yoshimura K, Cayan DR, Misra V, Sun L (2013b) Projected climate change scenario over California by a regional ocean-atmosphere coupled model system. Clim Change. https://doi.org/10.1007/s10584-013-1025-8
Liebmann B, Smith CA (1996) Description of a complete (interpolated) outgoing longwave radiation dataset. Bull Am Metorol Soc 77:1275–1277
Lohmann U, Roeckner E (1996) Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model. Clim Dyn 12:557–572
Marx L (2002) New calculation of saturation specific humidity and saturation vapor pressure in the COLA atmospheric general circulation model. COLA Tech Rep 130:1–23 (Available from the Center for Ocean–Land–Atmosphere Studies, Calverton)
Matus AV, L’Ecuyer TS (2017) The role of cloud phase in Earth’s radiation budget. J Geophys Res Atmos 122:2559–2578
Meehl GA, Washington WM, Arblaster JM, Hu A, Teng H, Tebaldi C, Sanderson BN, Lamarque J, Conley A, Strand WG, White JB (2012) Climate system response to external forcings and climate change projections in CCSM4. J Clim 25:3661–3683. https://doi.org/10.1175/JCLI-D-11-00240.1
Moorthi S, Suarez MJ (1992) Relaxed Arakawa–Schubert. A parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002
Moorthi S, Pan HL, Caplan P (2001) Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech Proced Bull 484:14
Murphy DM, Koop T (2005) Review of the vapor pressures of ice and supercooled water for atmospheric applications. Q J R Meteorol Soc 131:1539–1565. https://doi.org/10.1256/qj.04.94
Murray BJ, O’Sullivan D, Atkinson JD, Webb ME (2012) Ice nucleation by particles immersed in supercooled cloud droplets. Chem Soc Rev 41:6519–6554
Parthasarathy B, Munot AA, Kothawale DR (1994) All India monthly and seasonal rainfall series: 1871–1993. Theor Appl Climatol 49:217–224
Platnick S, King MD, Ackerman SA, Menzel WP, Baum BA, Riedi JC, Frey RA (2003) The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans Geosci Remote Sens 41:459–473
Platnick S et al. (2015) MODIS atmosphere L3 daily product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA. https://doi.org/10.5067/MODIS/MOD08_D3.006
Pruppacher HR, Klett JD (1978) Microphysics of clouds and precipitation. Reidel, Dordrecht
Rahman SH, Sengupta D, Ravichandran M (2009) Variability of Indian summer monsoon rainfall in daily data from gauge and satellite. J Geophys Res 114:D17113
Rajeevan M, Rohini P, Kumar KN, Srinivasan J, Unnikrishnan CK (2013) A study of vertical cloud structure of the Indian summer monsoon using CloudSat data. Clim Dyn 40:637–650. https://doi.org/10.1007/s00382-012-1374-4
Ramanathan V (1987) The role of earth radiation budget studies in climate and general circulation research. J Geophys Res 92:4075–4095. https://doi.org/10.1029/JD092iD04p04075
Ramanathan V, Cess RD, Harrison EF, Minnis P, Barkstrom BR, Ahmad E, Hartmann D (1989) Cloud-radiative forcing and climate: results from the earth radiation budget experiment. Science 243(4887): 57–63. https://doi.org/10.1126/science.243.4887.57
Ramu DA, Sabeerali CT, Chattopadhyay R, Rao DN, George G, Dhakate AR, Salunke K, Srivastava A, Rao SA (2016) Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: impact of atmospheric horizontal resolution. J Geophys Res 121:2205–2221
Randall DA et al (2007) Climate change 2007: the physical science basis. In: Solomon S et al (ed) Contributions of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge Univ. Press, Cambridge
Rasmusson EM, Carpenter TH (1983) The relationship between the eastern Pacific seas surface temperature and rainfall over India and Sri Lanka. Mon Weather Rev 111:354–384
Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Clim 20:5473–5496
Rossow WB, Schiffer RA (1991) ISCCP cloud data products. Bull Am Meteor Soc 72:2–20
Rossow WB, Schiffer RA (1999) Advances in understanding clouds from ISCCP. Bull Am Meteor Soc 80:2261–2287
Rotstayn LD, Ryan BF, Katzfey JJ (2000) A scheme for the calculation of the liquid fraction of mixed-phase stratiform clouds in large scale models. Mon Weather Rev 128:1070–1088. 10.1175/1520-0493(2000)128<1070:ASFCOT>2.0.CO;2
Sabeerali CT, Rao SA, Dhakate AR, Salunke K, Goswami BN (2014) Why ensemble mean projection of south Asian monsoon rainfall by CMIP5 models is not reliable? Clim Dyn 45:161–174. https://doi.org/10.1007/s00382-014-2269-3
Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y, Chuang H, Iredell M, Ek M, Meng J, Yang R, Mendez MP, van den Dool H, Zhang Q, Wang W, Chen M, Becker E (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1
Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363
Schmidt GA, Kelley M, Nazarenko L, Ruedy R, Russell GL, Aleinov I, Bauer M, Bauer SE, Bhat MK, Bleck R, Canuto V, Chen Y-H, Cheng Y, Clune TL, Del Genio A, de Fainchtein R, Faluvegi G, Hansen JE, Healy RJ, Kiang NY, Koch D, Lacis AA, LeGrande AN, Lerner J, Lo KK, Matthews EE, Menon S, Miller RL, Oinas V, Oloso AO, Perlwitz JP, Puma MJ, Putman WM, Rind D, Romanou A, Sato M, Shindell DT, Sun S, Syed RA, Tausnev N, Tsigaridis K, Unger N, Voulgarakis A, Yao M-S, Zhang J (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6(1):141–184. https://doi.org/10.1002/2013MS000265
Shimpo A, Kanamitsu M, Iacobellis S, Hong S-Y (2008) Comparison of four cloud schemes in simulating the seasonal mean field forced by the observed sea surface temperature. Mon Weather Rev 136:2557–2575
Slingo JM (1980) A cloud parameterization scheme derived from GATE data for use with a numerical model. Q J R Meteorol Soc 106:747–770
Slingo JM (1987) The development and verification of a cloud predicition scheme for the ECMWF model. Q J R Meteorol Soc 113:899–927
Slingo A, Slingo JM (1991) Response of the National Center for Atmospheric Research Community Climate Model to improvements in the representation of clouds. J Geophys Res 96:341–357
Sperber KR, Annamalai H, Kang IS, Kitoh A, Moise A, Turner A, Wang B, Zhou T (2012) The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Clim Dyn 41:2711–2744. https://doi.org/10.1007/s00382-012-1607-6
Stefanova L, Misra V, Chan S, Griffin M, O’Brien JJ, Smith III TJ (2012) A proxy for high-resolution regional analysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses. Clim Dyn 38:2449–2466. https://doi.org/10.1007/s00382-011-1230-y
Stephens GL (2005) Cloud feebacks in the climate system: a critical review. J Clim 18:237–273. https://doi.org/10.1175/JCLI-3243.1
Su H, Jiang JH, Teixeira J, Gettelman A, Huang X, Stephens G, Vane D, Perun VS (2011) Comparison of regime-sorted tropical cloud profiles observed by CloudSat with GEOS5 analyses and two general circulation model simulations. J Geophys Res 116:D09104. https://doi.org/10.1029/2010JD014971
Tan I, Storelvmo T, Zelinka MD (2016) Observational constraints on mixed-phase clouds imply higher climate sensivity. Science 353:224–227
Tremblay A, Glazer A (2000) An improved modeling scheme for freezing precipitation forecasts. Mon Weather Rev 128:1289–1308. 10.1175/1520-0493(2000)128<1289:AIMSFF>2.0.CO;2
Waliser DE, Jin K, Kang IS, Stern WF, Schubert SD, Wu MLC, Lau KM, Lee MI, Krishnamurthy V, Kitoh A, Meehl GA, Galin VY, Satyan V, Mandke SK, Wu G, Liu Y, Park CK (2003) AGCM simulation of intraseasonal variability associated with the Asian summer monsoon. Clim Dyn. https://doi.org/10.1007/s00382-003-0337-1
Waliser DE, Li JLF, Woods CP, Austin RT, Bacmeister J, Chern J, Del Genio A, Jiang JH, Kuang Z, Meng H, Minnis P, Platnick S, Rossow WB, Stephens GL, Sun-Mack S, Tao WK, Tompkins AM, Vane DG, Walker C, Wu D (2009) Cloud ice: a climate model challenge with signs and expectations of progress., J Geophys Res 114:D00A21. https://doi.org/10.1029/2008JD010015
Waliser DE, Li J-LF, L’Ecuyer TS, Chen WT (2011) The impact of precipitating ice and snow on the ration balance of global climate models. Geopys Res Lett 38:L06802. https://doi.org/10.1029/2010GL046478
Wang T, Wong S, Fetzer EJ (2015) Cloud regime evolution in the Indian monsoon intraseasonal oscillation: connection to large-scale dynamical conditions and the atmospheric water budget. Geophys Res Lett 42:9465–9472. https://doi.org/10.1002/2015GL066353
Weare BC (2004) A comparison of AMIP II model cloud layer properties with ISCCP D2 estimates. Clim Dyn 22:281–292
Webster PJ, Magaña VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res 103:14451–14510
WMO (1988) General meteorological standards and recommended practices, appendix A. Technical Regulations, WMO-No. 49. World Meteorological Organization, Geneva
WMO (2015a) Measurement of upper-air pressure, temperature and humidity (J. Nash). Instruments and Observing Methods Report No. 121. Geneva
WMO (2015b) Recommended algorithms for the computation of marine metrological variables, I.6. JCOMM Technical Report No. 63. World Meteorological Organization, Geneva
Wood R, Field PR (2000) Relationships between total water, condensed water, and cloud fraction in stratiform clouds examined using aircraft data. J Atmos Sci 57:1888–1905
Xu K-M, Randall DA (1996) A semi-empirical cloudiness parameterization for use in climate models. J Atmos Sci 53:3084–3102
Yau MK, Rogers RR (1989) A short course in cloud physics. In: Yau MK, Rogers RR (eds) Formation of cloud droplets, Ch. 6. Elsevier, Amsterdam
Acknowledgements
The authors would like to thank the contributions of two anonymous reviewers to previous versions of the manuscript which greatly improved this study. The authors gratefully acknowledge the financial support given by the Earth System Science Organization, Ministry of Earth Sciences, Government of India (Grant number MM/SERP/FSU/2014/SSC-02/002) to conduct this research under Monsoon Mission. We thank the Indian Meteorological Department for the availability of the daily rain analysis over India. Computing resources were provided by the Texas Advanced Computing Center at the University of Texas and XSEDE under Grant number ATM10010 and Florida State University’s High Performance Computer. The authors would also like to acknowledge Dr. Akhilesh Mishra at FSU COAPS for his advice and assistance in this work.
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Glazer, R.H., Misra, V. Ice versus liquid water saturation in simulations of the Indian summer monsoon. Clim Dyn 51, 3847–3863 (2018). https://doi.org/10.1007/s00382-018-4116-4
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DOI: https://doi.org/10.1007/s00382-018-4116-4