Climate Dynamics

, Volume 50, Issue 9–10, pp 3251–3279 | Cite as

The atmospheric hydrologic cycle in the ACME v0.3 model

  • Christopher R. TeraiEmail author
  • Peter M. Caldwell
  • Stephen A. Klein
  • Qi Tang
  • Marcia L. Branstetter


We examine the global water cycle characteristics in the Accelerated Climate Modeling for Energy v0.3 model (a close relative to version 5.3 of the Community Atmosphere Model) in atmosphere-only simulations spanning the years 1980–2005. We evaluate the simulations using a broad range of observational and reanalysis datasets, examine how the simulations change when the horizontal resolution is increased from 1° to 0.25\(^{\circ }\), and compare the simulations against models participating in the the Atmosphere Model Intercomparison Project of the 5th Coupled Model Intercomparison Project (CMIP5). Particular effort has been made to evaluate the model using the best available observational estimates and verifying model biases with additional datasets when differences are known to exist among the observations. Regardless of resolution, the model exhibits several biases: global-mean precipitation, evaporation, and precipitable water are too high, light precipitation occurs too frequently, and the atmospheric residence time of water is too short. Many of these biases are shared by the multi-model mean climate of models participating in CMIP5. The reasons behind regional biases in precipitation are discussed by examining how different fields, such as local evaporation and transport of water vapor, contribute to the bias. Although increasing the horizontal resolution does not drastically change the water cycle, it does lead to a few differences: an increase in global mean precipitation rate, an increase in the fraction of total precipitation that falls over land, more frequent heavy precipitation (>30 mm day\(^{-1}\)), and a decrease in precipitable water. One of the most notable changes is the shift of precipitation produced by the convective parameterization to that produced by the large-scale microphysics parameterization. We analyze how changes in moisture and circulation with resolution contribute to this shift in the precipitation partitioning. Because changing horizontal resolution requires some re-tuning, the effect of that tuning was evaluated by performing an additional simulation at 1\(^{\circ }\) but using the tunings from the 0.25\(^{\circ }\) simulation. The evaluation shows that the more frequent heavy precipitation, the decrease in precipitable water, and the shift from convective to large-scale precipitation are predominantly due to resolution changes, while tuning changes have a major influence on the global mean precipitation and the land/ocean partitioning of precipitation.


Water cycle Precipitation Climate modeling Horizontal resolution Model evaluation 



The authors acknowledge and thank the scientists and staff of the ACME Atmosphere team whose efforts were instrumental in the development, coordination, and execution of the model simulations and whose input and discussions have improved the study. The authors also thank two anonymous reviewers for comments that have helped improved the manuscript. This research used computing resources of the Argonne Leadership Computing Facility (ALCF), the Oak Ridge Leadership Computing Facility (OLCF), and the National Energy Research Scientific Computing Center (NERSC), all of which are supported by the Office of Science of the Department of Energy (DOE). ALCF, OLCF, and NERSC are supported under Contract nos. DE-AC02-06CH11357, DE-AC05-00OR22725, and DE-AC02-05CH11231, respectively. The ACME v0.3 model output used in this study can be obtained by contacting the corresponding author ( A number of observational datasets are compared against the model output. The GPCP v2.2 precipitation dataset can be obtained from GPCP 1DD and TRMM 3B42 data are available from the NASA/GSFC Mesoscale Atmospheric Processes Laboratory ( and Mirador (, respectively. The CORE v2 ocean evaporation dataset was obtained and is available from the NCAR Research Data Archive at The LandFlux-EVAL merged benchmark synthesis products of ETH Zurich were produced under the aegis of the GEWEX and ILEAPS projects ( NVAP precipitable water data can be obtained from the NASA Langley ASDC User Services (, and the RSS precipitable data were obtained from Remote Sensing Systems ( The ECMWF Interim Reanalysis fields are publicly available from The US Department of Energy’s (DOE) Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure for CMIP5 in partnership with the Global Organization for Earth System Science Portals. The model output can be obtained from the Earth System Grid Federation at We thank all of the programs who made this data available. The efforts of C. R. Terai, P. M. Caldwell, Q. Tang, and M. L. Branstetter are supported as part of the Accelerated Climate Modeling for Energy (ACME) program, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. The efforts of S. A. Klein are supported by the Regional and Global Climate Modeling program of the United States Department of Energy’s Office of Science. This work was performed under the auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-706823.


  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4(6):1147–1167. doi: 10.1175/1525-7541(2003)004<1147:tvgpcp>;2 CrossRefGoogle Scholar
  2. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):224. doi: 10.1038/nature01092 CrossRefGoogle Scholar
  3. Bacmeister JT, Wehner MF, Neale RB, Gettelman A, Hannay C, Lauritzen PH, Caron JM, Truesdale JE (2014) Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J Clim 27(9):3073–3099. doi: 10.1175/jcli-d-13-00387.1 CrossRefGoogle Scholar
  4. Barsugli JJ, Battisti DS (1998) The basic effects of atmosphere-ocean thermal coupling on midlatitude variability. J Atmos Sci 55(4):477–493. doi: 10.1175/1520-0469(1998) 0550<477:tbeoao>;2 CrossRefGoogle Scholar
  5. Behrangi A, Lebsock M, Wong S, Lambrigtsen B (2012) On the quantification of oceanic rainfall using spaceborne sensors. J Geophys Res Atmos. doi: 10.1029/2012JD017979 Google Scholar
  6. Bentsen M, Bethke I, Debernard JB, Iversen T, Kirkevag A, Seland O, Drange H, Roelandt C, Seierstad IA, Hoose C, Kristjansson JE (2013) The Norwegian Earth System Model, NorESM1-M - Part 1: Description and basic evaluation of the physical climate. Geosci Model Dev 6(3):687–720. doi: 10.5194/gmd-6-687-2013 CrossRefGoogle Scholar
  7. Bi D, Dix M, Marsland SJ, O’Farrell S, Rashid HA, Uotila P, Hirst AC, Kowalczyk E, Golebiewski M, Sullivan A, Yan H, Hannah N, Franklin C, Sun Z, Vohralik P, Watterson I, Zhou X, Fiedler R, Collier M, Ma Y, Noonan J, Stevens L, Uhe P, Zhu H, Griffies SM, Hill R, Harris C, Puri K (2013) The ACCESS coupled model: description, control climate and evaluation. Austr Meteorol Oceanogr J 63(1):41–64CrossRefGoogle Scholar
  8. Boyle J, Klein SA (2010) Impact of horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP-ICE period. J Geophys Res Atmos. doi: 10.1029/2010JD014262 Google Scholar
  9. Bretherton CS, Battisti DS (2000) An interpretation of the results from atmospheric general circulation models forced by the time history of the observed sea surface temperature distribution. Geophys Res Lett 27(6):767–770. doi: 10.1029/1999gl010910 CrossRefGoogle Scholar
  10. Bretherton CS, Park S (2009) A new moist turbulence parameterization in the Community Atmosphere Model. J Clim 22(12):3422–3448. doi: 10.1175/2008jcli2556.1 CrossRefGoogle Scholar
  11. Chahine MT, Pagano TS, Aumann HH, Atlas R, Barnet C, Blaisdell J, Chen L, Divakarla M, Fetzer EJ, Goldberg M, Gautier C, Granger S, Hannon S, Irion FW, Kakar R, Kalnay E, Lambrigtsen BH, Lee SY, Le Marshall J, McMillan WW, McMillin L, Olsen ET, Revercomb H, Rosenkranz P, Smith WL, Staelin D, Strow LL, Susskind J, Tobin D, Wolf W, Zhou L (2006) Improving weather forecasting and providing new data on greenhouse gases. Bull Am Meteorol Soc 87(7):911. doi: 10.1175/bams-87-7-911 CrossRefGoogle Scholar
  12. Cox PM, Betts RA, Collins M, Harris PP, Huntingford C, Jones CD (2004) Amazonian forest dieback under climate-carbon cycle projections for the 21st century. Theoret Appl Climatol 78(1–3):137–156. doi: 10.1007/s00704-004-0049-4 Google Scholar
  13. Dai A (2006) Precipitation characteristics in eighteen coupled climate models. J Clim 19(18):4605–4630. doi: 10.1175/jcli3884.1 CrossRefGoogle Scholar
  14. DeAngelis AM, Qu X, Zelinka MD, Hall A (2015) An observational radiative constraint on hydrologic cycle intensification. Nature 528(7581):249. doi: 10.1038/nature15770 CrossRefGoogle Scholar
  15. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thepaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  16. Demory ME, Vidale PL, Roberts MJ, Berrisford P, Strachan J, Schiemann R, Mizielinski MS (2014) The role of horizontal resolution in simulating drivers of the global hydrological cycle. Clim Dyn 42(7–8):2201–2225. doi: 10.1007/s00382-013-1924-4 CrossRefGoogle Scholar
  17. Dennis JM, Edwards J, Evans KJ, Guba O, Lauritzen PH, Mirin AA, St-Cyr A, Taylor MA, Worley PH (2012) CAM-SE: A scalable spectral element dynamical core for the Community Atmosphere Model. Int J High Perform Comput Appl 26(1):74–89. doi: 10.1177/1094342011428142 CrossRefGoogle Scholar
  18. Donner LJ, Wyman BL, Hemler RS, Horowitz LW, Ming Y, Zhao M, Golaz JC, Ginoux P, Lin SJ, 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 HC, Lin YL, Magi BI, Malyshev SL, Milly PCD, 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 FR (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(13):3484–3519. doi: 10.1175/2011jcli3955.1 CrossRefGoogle Scholar
  19. Duffy PB, Govindasamy B, Iorio JP, Milovich J, Sperber KR, Taylor KE, Wehner MF, Thompson SL (2003) High-resolution simulations of global climate, Part 1: Present climate. Clim Dyn 21(5–6):371–390. doi: 10.1007/s00382-003-0339-z CrossRefGoogle Scholar
  20. Dufresne JL, Foujols MA, Denvil S, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, de Noblet N, Duvel JP, Ethe C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix JY, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre MP, Lefevre F, Levy C, Li ZX, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40(9–10):2123–2165. doi: 10.1007/s00382-012-1636-1 CrossRefGoogle Scholar
  21. Gates WL (1992) AMIP—the atmospheric model intercomparison project. Bull Am Meteorol Soc 73(12):1962–1970. doi: 10.1175/1520-0477(1992)<073>1962:atamip;2 CrossRefGoogle Scholar
  22. Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang MH (2011) The Community Climate System Model version 4. J Clim 24(19):4973–4991. doi: 10.1175/2011jcli4083.1 CrossRefGoogle Scholar
  23. Gettelman A, Liu X, Ghan SJ, Morrison H, Park S, Conley AJ, Klein SA, Boyle J, Mitchell DL, Li JLF (2010) Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. J Geophys Res Atmos. doi: 10.1029/2009jd013797 Google Scholar
  24. Hagemann S, Arpe K, Roeckner E (2006) Evaluation of the hydrological cycle in the ECHAM5 model. J Clim 19(16):3810–3827. doi: 10.1175/jcli3831.1 CrossRefGoogle Scholar
  25. Hertwig E, von Storch JS, Handorf D, Dethloff K, Fast I, Krismer T (2015) Effect of horizontal resolution on ECHAM6-AMIP performance. Clim Dyn 45(1–2):185–211. doi: 10.1007/s00382-014-2396-x CrossRefGoogle Scholar
  26. Hourdin F, Foujols MA, Codron F, Guemas V, Dufresne JL, Bony S, Denvil S, Guez L, Lott F, Ghattas J, Braconnot P, Marti O, Meurdesoif Y, Bopp L (2013) Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model. Clim Dyn 40(9–10):2167–2192. doi: 10.1007/s00382-012-1411-3 CrossRefGoogle Scholar
  27. Huffman GJ, Adler RF, Morrissey MM, Bolvin DT, Curtis S, Joyce R, McGavock B, Susskind J (2001) Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeorol 2(1):36–50. doi: 10.1175/1525-7541(2001)002<0036:gpaodd>;2 CrossRefGoogle Scholar
  28. Huffman GJ, Adler RF, Bolvin DT, Gu GJ, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55. doi: 10.1175/jhm560.1 CrossRefGoogle Scholar
  29. Hunke EC, Lipscomb WH (2010) TCICE: the Los Alamos Sea Ice Model documentation and software users manual version 4.1. Tech. Rep. LA-CC-06-012, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, Los Alamos, New MexicoGoogle Scholar
  30. Jeffrey S, Rotstayn L, Collier M, Dravitzki S, Hamalainen C, Moeseneder C, Wong K, Syktus J (2013) Australia’s CMIP5 submission using the CSIRO-Mk3.6 model. Austr Meteorol Oceanogr J 63(1):1–13CrossRefGoogle Scholar
  31. Jung T, Miller MJ, Palmer TN, Towers P, Wedi N, Achuthavarier D, Adams JM, Altshuler EL, Cash BA, Kinter JL, Marx L, Stan C, Hodges KI (2012) High-resolution global climate simulations with the ECMWF model in Project Athena: experimental design, model climate, and seasonal forecast skill. J Clim 25(9):3155–3172. doi: 10.1175/jcli-d-11-00265.1 CrossRefGoogle Scholar
  32. Kay JE, Deser AC, Phillips Mai A, Hannay C, Strand G, Arblaster SCJM, Bates Danabasoglu G, Edwards J, Holland M, Kushner P, Lamarque JF, Lawrence D, Lindsay K, Middleton A, Munoz E, Neale R, Oleson K, Polvani L, Vertenstein M (2015) The Community Earth System Model (CESM) Large Ensemble Project: a community resource for studying climate change in the presence of internal climate variability. Bull Am Meteorol Soc 96(8):1333–1349. doi: 10.1175/BAMS-D-13-00255.1 CrossRefGoogle Scholar
  33. Kitoh A, Arakawa O (1999) On overestimation of tropical precipitation by an atmospheric GCM with prescribed SST. Geophys Res Lett 26(19):2965–2968. doi: 10.1029/1999GL900616 CrossRefGoogle Scholar
  34. Klein SA, Jiang XN, Boyle J, Malyshev S, Xie SC (2006) Diagnosis of the summertime warm and dry bias over the US Southern Great Plains in the GFDL climate model using a weather forecasting approach. Geophys Res Lett. doi: 10.1029/2006gl027567 Google Scholar
  35. Kobayashi S, Ota Y, Harada Y, Ebita A, Moriya M, Onoda H, Onogi K, Kamahori H, Kobayashi C, Endo H, Miyaoka K, Takahashi K (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn 93(1):5–48. doi: 10.2151/jmsj.2015-001 CrossRefGoogle Scholar
  36. Kooperman GJ, Pritchard MS, Burt MA, Branson MD, Randall DA (2016) Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model. J Adv Model Earth Syst 8(1):140–165. doi: 10.1002/2015MS000574 CrossRefGoogle Scholar
  37. Large WG, Yeager SG (2009) The global climatology of an interannually varying air-sea flux data set. Clim Dyn 33(2–3):341–364. doi: 10.1007/s00382-008-0441-3 CrossRefGoogle Scholar
  38. L’Ecuyer TS, Beaudoing HK, Rodell M, Olson W, Lin B, Kato S, Clayson CA, Wood E, Sheffield J, Adler R, Huffman G, Bosilovich M, Gu G, Robertson F, Houser PR, Chambers D, Famiglietti JS, Fetzer E, Liu WT, Gao X, Schlosser CA, Clark E, Lettenmaier DP, Hilburn K (2015) The observed state of the energy budget in the early twenty-first century. J Clim 28(21):8319–8346. doi: 10.1175/jcli-d-14-00556.1 CrossRefGoogle Scholar
  39. Liu WT, Katsaros KB, Businger JA (1979) Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface. J Atmos Sci 36(9):1722–1735. doi: 10.1175/1520-0469(1979) 036<1722:bpoase>;2 CrossRefGoogle Scholar
  40. Lucarini V, Ragone F (2011) Energetics of climate models: net energy balance and meridional enthalpy transport. Rev Geophys. doi: 10.1029/2009RG000323 Google Scholar
  41. Ma HY, Xie X, Boyle JS, Klein SA, Zhang Y (2013) Metrics and diagnostics for precipitation-related processes in climate model short-range hindcasts. J Clim 26(5):1516–1534. doi: 10.1175/JCLI-D-12-00235.1 CrossRefGoogle Scholar
  42. Martin GM, Bellouin N, Collins WJ, Culverwell ID, Halloran PR, Hardiman SC, Hinton TJ, Jones CD, McDonald RE, McLaren AJ, O’Connor FM, Roberts MJ, Rodriguez JM, Woodward S, Best MJ, Brooks ME, Brown AR, Butchart N, Dearden C, Derbyshire SH, Dharssi I, Doutriaux-Boucher M, Edwards JM, Falloon PD, Gedney N, Gray LJ, Hewitt HT, Hobson M, Huddleston MR, Hughes J, Ineson S, Ingram WJ, James PM, Johns TC, Johnson CE, Jones A, Jones CP, Joshi MM, Keen AB, Liddicoat S, Lock AP, Maidens AV, Manners JC, Milton SF, Rae JGL, Ridley JK, Sellar A, Senior CA, Totterdell IJ, Verhoef A, Vidale PL, Wiltshire A, Had GEMDT (2011) The HadGEM2 family of Met Office Unified Model climate configurations. Geosci Model Dev 4(3):723–757. doi: 10.5194/gmd-4-723-2011 CrossRefGoogle Scholar
  43. Mueller B, Hirschi M, Jimenez C, Ciais P, Dirmeyer PA, Dolman AJ, Fisher JB, Jung M, Ludwig F, Maignan F, Miralles DG, McCabe MF, Reichstein M, Sheffield J, Wang K, Wood EF, Zhang Y, Seneviratne SI (2013) Benchmark products for land evapotranspiration: landFlux-EVAL multi-data set synthesis. Hydrol Earth Syst Sci 17(10):3707–3720. doi: 10.5194/hess-17-3707-2013 Google Scholar
  44. Neale RB, Chen CC, Gettelman A, Lauritzen PH, Park S, Williamson DL (2012) Description of the NCAR Community Atmosphere Model (CAM 5.0). Tech. Rep. NCAR/TN-486 + STR, National Center For Atmospheric Research, Boulder, ColoradoGoogle Scholar
  45. Neggers RAJ, Neelin JD, Stevens B (2007) Impact mechanisms of shallow cumulus convection on tropical climate dynamics. J Clim 20(11):2623–2642. doi: 10.1175/jcli4079.1 CrossRefGoogle Scholar
  46. Oleson KW, Lawrence DM, Bonan GB, Flanner MG, Kluzek E, Lawrence PJ, Levis S, Swenson SC, Thornton PE, Dai A, Decker M, Dickinson R, Feddema J, Heald CL, Colette L Lamarque CL, Mahowald N, Niu Gy, Qian T, Randerson J, Running S, Sakaguchi K, Yang L, Zeng X, Zeng X (2010) Technical description of version 4.0 of the Community Land Model (CLM). Tech Report NCAR/TN- 478 + STR, National Center For Atmospheric Research, Boulder, ColoradoGoogle Scholar
  47. Onogi K, Tsutsui J, Koide H, Sakamoto M, Kobayashi S, Hatsushika S, Matsumoto T, Yamazaki N, Kamahori H, Takahashi K, Kadokura S, Wada K, Kato K, Oyama R, Ose T, Mannoji N, Taira R (2007) The JRA-25 reanalysis. J Meteorol Soc Jpn Ser II 85(3):369–432. doi: 10.2151/jmsj.85.369 CrossRefGoogle Scholar
  48. Park S, Bretherton CS (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. doi: 10.1175/2008jcli2557.1 CrossRefGoogle Scholar
  49. Pendergrass AG, Hartmann DL (2014a) The atmospheric energy constraint on global-mean precipitation change. J Clim 27(2):757–768. doi: 10.1175/jcli-d-13-00163.1 CrossRefGoogle Scholar
  50. Pendergrass AG, Hartmann DL (2014b) Changes in the distribution of rain frequency and intensity in response to global warming. J Clim 27(22):8372–8383. doi: 10.1175/jcli-d-14-00183.1 CrossRefGoogle Scholar
  51. Pope VD, Stratton RA (2002) The processes governing horizontal resolution sensitivity in a climate model. Clim Dyn 19(3–4):211–236. doi: 10.1007/s00382-001-0222-8 Google Scholar
  52. Randel DL, VonderHaar TH, Ringerud MA, Stephens GL, Greenwald TJ, Combs CL (1996) A new global water vapor dataset. Bull Am Meteorol Soc 77(6):1233–1246. doi: 10.1175/1520-0477(1996)<077 1233:angwvd>;2 CrossRefGoogle Scholar
  53. Rauscher SA, O’Brien TA, Piani C, Coppola E, Giorgi F, Collins WD, Lawston PM (2016) A multimodel intercomparison of resolution effects on precipitation: simulations and theory. Clim Dyn 47(7):2205–2218. doi: 10.1007/s00382-015-2959-5 CrossRefGoogle Scholar
  54. Rodell M, Beaudoing HK, L’Ecuyer TS, Olson WS, Famiglietti JS, Houser PR, Adler R, Bosilovich MG, Clayson CA, Chambers D, Clark E, Fetzer EJ, Gao X, Gu G, Hilburn K, Huffman GJ, Lettenmaier DP, Liu WT, Robertson FR, Schlosser CA, Sheffield J, Wood EF (2015) The observed state of the water cycle in the early twenty-first century. J Clim 28(21):8289–8318. doi: 10.1175/jcli-d-14-00555.1 CrossRefGoogle Scholar
  55. Rogers RR, Yau MK (1989) A short course in cloud physics. Pergamon Press, New YorkGoogle Scholar
  56. RSS (2016) Monthly mean total precipitable water data set on a 1 degree grid made from Remote Sensing Systems version-7 microwave radiometer data, V07r01.
  57. von Salzen K, Scinocca JF, McFarlane NA, Li J, Cole JNS, Plummer D, Verseghy D, Reader MC, Ma X, Lazare M, Solheim L (2013) The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4). Part I: Representation of physical processes. Atmos Ocean 51(1):104–125. doi: 10.1080/07055900.2012.755610 CrossRefGoogle Scholar
  58. Schmidt GA, Kelley M, Nazarenko L, Ruedy R, Russell GL, Aleinov I, Bauer M, Bauer SE, Bhat MK, Bleck R, Canuto V, Chen YH, 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 MS, Zhang J (2014) Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J Adv Model Earth Syst 6(1):141–184. doi: 10.1002/2013ms000265 CrossRefGoogle Scholar
  59. Stephens GL, L’Ecuyer T (2015) The Earth’s energy balance. Atmos Res 166:195–203. doi: 10.1016/j.atmosres.2015.06.024 CrossRefGoogle Scholar
  60. Stephens GL, L’Ecuyer T, Forbes R, Gettlemen A, Golaz JC, Bodas-Salcedo A, Suzuki K, Gabriel P, Haynes J (2010) Dreary state of precipitation in global models. J Geophys Res Atmos. doi: 10.1029/2010jd014532 Google Scholar
  61. Stephens GL, Li J, Wild M, Clayson CA, Loeb N, Kato S, L’Ecuyer T, Stackhouse PW Jr, Lebsock M, Andrews T (2012) An update on Earth’s energy balance in light of the latest global observations. Nat Geosci 5(10):691–696. doi: 10.1038/ngeo1580 CrossRefGoogle Scholar
  62. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. doi: 10.1175/bams-d-11-00094.1 CrossRefGoogle Scholar
  63. Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47(1–2):123–138. doi: 10.3354/cr00953 CrossRefGoogle Scholar
  64. Trenberth KE, Asrar GR (2014) Challenges and opportunities in water cycle research: WCRP contributions. Surv Geophys 35(3):515–532. doi: 10.1007/s10712-012-9214-y CrossRefGoogle Scholar
  65. Trenberth KE, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of the global water budget and its annual cycle using observational and model data. J Hydrometeorol 8(4):758–769. doi: 10.1175/jhm600.1 CrossRefGoogle Scholar
  66. Trenberth KE, Fasullo JT, Kiehl J (2009) Earth’s global energy budget. Bull Am Meteorol Soc 90(3):311. doi: 10.1175/2008bams2634.1 CrossRefGoogle Scholar
  67. Trenberth KE, Fasullo JT, Mackaro J (2011) Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J Clim 24(18):4907–4924. doi: 10.1175/2011jcli4171.1 CrossRefGoogle Scholar
  68. Voldoire A, Sanchez-Gomez E, Melia DSY, Decharme B, Cassou C, Senesi S, Valcke S, Beau I, Alias A, Chevallier M, Deque M, Deshayes J, Douville H, Fernandez E, Madec G, Maisonnave E, Moine MP, Planton S, Saint-Martin D, Szopa S, Tyteca S, Alkama R, Belamari S, Braun A, Coquart L, Chauvin F (2013) The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn 40(9–10):2091–2121. doi: 10.1007/s00382-011-1259-y CrossRefGoogle Scholar
  69. Wang-Erlandsson L, van der Ent RJ, Gordon LJ, Savenjie HHG (2014) Contrasting roles of interception and transpiration in the hydrological cycle Part 1: Temporal characteristics over land. Earth Syst Dyn 5:441–469. doi: 10.5194/esd-5-441-2014 CrossRefGoogle Scholar
  70. Watanabe M, Suzuki T, O’Ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC5. Mean states, variability, and climate sensitivity. J Clim 23(23):6312–6335. doi: 10.1175/2010jcli3679.1 CrossRefGoogle Scholar
  71. Wild M, Folini D, Hakuba MZ, Schaer C, Seneviratne SI (2015) The energy balance over land and oceans: an assessment based on direct observations and CMIP5 climate models. Clim Dyn 44(11):33933429. doi: 10.1007/s00382-014-2430-z Google Scholar
  72. Williamson DL (2008) Convergence of aqua-planet simulations with increasing resolution in the Community Atmospheric Model, version 3. Tellus A 60(5):848–862. doi: 10.1111/j.1600-0870.2008.00339.x CrossRefGoogle Scholar
  73. Williamson DL (2013) The effect of time steps and time-scales on parametrization suites. Q J R Meteorol Soc 139:548–560. doi: 10.1002/qj.1992 CrossRefGoogle Scholar
  74. Yeager SG, Large WG (2008) CORE.2 global air-sea flux dataset. doi: 10.5065/D6WH2N0S
  75. Yin L, Fu R, Shevliakova E, Dickinson RE (2013) How well can CMIP5 simulate precipitation and its controlling processes over tropical South America? Clim Dyn 41(11–12):3127–3143. doi: 10.1007/s00382-012-1582-y CrossRefGoogle Scholar
  76. Yu LS, Weller RA (2007) Objectively analyzed air-sea heat fluxes for the global ice-free oceans (1981–2005). Bull Am Meteorol Soc 88(4):527. doi: 10.1175/bams-88-4-527 CrossRefGoogle Scholar
  77. Yu LS, Weller RA, Sun BM (2004) Improving latent and sensible heat flux estimates for the Atlantic Ocean (1988–99) by a synthesis approach. J Clim 17(2):373–393. doi: 10.1175/1520-0442(2004) 017<0373:ilashf>;2 CrossRefGoogle Scholar
  78. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Center General-Circulation Model. Atmos Ocean 33(3):407–446CrossRefGoogle Scholar

Copyright information

© GovernmentEmployee : [Lawrence Livermore National Laboratory] 2017

Authors and Affiliations

  1. 1.Cloud Processes Research GroupLawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Oak Ridge National LaboratoryOak RidgeUSA

Personalised recommendations