An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models

Journal of Advances in Modeling Earth Systems

Volumn 7, Issue 4, 2015, Pages 1810-1827

  Ma, H Y ^a   Chuang, C C ^a   Klein, S A ^a   Lo, M H ^b   Zhang, Y ^a,a   Xie, S ^a   Zheng, X ^a   Ma, P L ^c   Phillips, T J ^a  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

Author keywords

GCM; hindcast; initial conditions; model evaluation; transpose AMIP

Indexed keywords

AEROSOLS; ATMOSPHERIC AEROSOLS; ATMOSPHERIC HUMIDITY; ATMOSPHERIC TEMPERATURE; MOISTURE; OCEANOGRAPHY; PRECIPITATION (METEOROLOGY); SEA ICE; SOIL MOISTURE; SURFACE WATERS;

GCM; HINDCASTS; INITIAL CONDITIONS; MODEL EVALUATION; TRANSPOSE-AMIP;

CLIMATE MODELS;

AIR TEMPERATURE; CLIMATE MODELING; DATA ASSIMILATION; GENERAL CIRCULATION MODEL; GLOBAL CLIMATE; HINDCASTING; PARAMETERIZATION; SEA ICE; SEA SURFACE TEMPERATURE; SOIL MOISTURE;

EID: 84959496097     PISSN: None     EISSN: 19422466     Source Type: Journal    
DOI: 10.1002/2015MS000490     Document Type: Article

References (71)

1. Adler, R. F., et al., (2003), The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present), J. Hydrometeorol., 4 (6), 1147-1167.
2. Barton, N. P., S. A. Klein, J. S. Boyle, and, Y. Zhang, (2012), Arctic synoptic regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5 during similar dynamics, J. Geophys. Res., 117, D15205, doi: 10.1029/2012JD017589.
3. Barton, N. P., S. A. Klein, and, J. S. Boyle, (2014), On the contribution of longwave radiation to global climate model biases in Arctic lower tropospheric stability, J. Clim., 27, 7250-7269.
4. Bodas-Salcedo, A., et al., (2008), Evaluating cloud systems in the met office global hindcast model using simulated Cloudsat radar reflectivities, J. Geophys. Res., 113, D00A13, doi: 10.1029/2007JD009620.
5. Bond, D., (2005), Soil water and temperature system (SWATS) handbook, ARM Tech. Rep. TR-063, U.S. Dep. Of Energy, Washington, D. C.
6. Boyle, J., and, S. A. Klein, (2010), Impact of model horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP-ICE period, J. Geophys. Res., 115, D23113, doi: 10.1029/2010JD014262.
7. Boyle, J., D. Williamson, R. Cederwall, M. Fiorino, J. Hnilo, J. Olson, T. Phillips, G. Potter, and, S. Xie, (2005), Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at atmospheric radiation measurement sites, J. Geophys. Res., 110, D15S15, doi: 10.1029/2004JD005042.
8. Boyle, J., S. A. Klein, G. Zhang, S. Xie, and, X. Wei, (2008), Climate model forecast experiment for TOGA COARE, Mon. Weather Rev., 136, 808-832.
9. Bretherton, C. S., and, S. Park, (2009), A new moist turbulence parameterization in the Community Atmosphere Model, J. Clim., 22 (12), 3422-3448.
10. Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell, and, A. Shelly, (2012), Unified modeling and prediction of weather and climate, Bull. Am. Meteorol. Soc., 93, 1865-1877.
11. Cai, X., Z.-L. Yang, Y. Xia, M. Huang, H. Wei, L. R. Leung, and, M. B. Ek, (2014), Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS testbed, J. Geophys. Res. Atmos., 119, 13,751-13,770, doi: 10.1002/2014JD022113.
12. Chandra, A. S., C. Zhang, S. A. Klein, and, H.-Y. Ma, (2015), Low-cloud characteristics over the tropical western Pacific from ARM observations and CAM5 simulations, J. Geophys. Res. Atmos., 120, 8953-8970, doi: 10.1002/2015JD023369.
13. Dee, D. P., et al., (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553-828.
14. Dirmeyer, P. A., (2011), A history and review of the Global Soil Wetness Project (GSWP), J. Hydrometeorol., 12, 729-749.
15. Doelling, D. R., N. G. Loeb, D. F. Keyes, M. L. Nordeen, D. Morstad, C. Nguyen, B. A. Wielicki, D. F. Young, and, M. Sun, (2013), Geostationary enhanced temporal interpolation for CERES flux products, J. Atmos. Oceanic Technol., 30, 1072-1090.
16. English, J., J. Kay, A. Gettelman, X. Liu, Y. Wang, Y. Zhang, and, H. Chepfer, (2014), Contributions of clouds, surface albedo, and mixed-phase ice nucleation schemes to Arctic radiation biases in CAM5, J. Clim., 27, 5174-5197.
17. Gettelman, A., et al., (2010), Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the community atmosphere model, J. Geophys. Res., 115 (D18), doi: 10.1029/2009JD013797.
18. Hannay, C., D. L. Williamson, J. J. Hack, J. T. Kiehl, S. A. Klein, C. S. Bretherton, and, M. Kohler, (2009), Evaluation of forecasted southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF models, J. Clim., 22, 2871-2889.
19. Hoke, J. E., and, R. A. Anthes, (1976), The initialization of numerical models by a dynamic-initialization technique, Mon. Weather Rev., 104, 1551-1556.
20. 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. Atmos. Res., 113 (D13), 103, doi: 10.1029/2008JD009944.
21. Jeuken, A. B. M., P. C. Siegmund, L. C. Heijboer, J. Feichter, and, L. Bengtsson, (1996), On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation, J. Geophys. Res., 101, 16,939-16,950, doi: 10.1029/96JD01218.
22. Jiang, J. H., et al., (2012), Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA A-Train satellite observations, J. Geophys. Res., 117, D14105, doi: 10.1029/2011JD017237.
23. 2: Its timescale and the role of land-sea contrast, Clim. Dyn., 41, 3007-3024.
24. Klein, S. A., X. Jiang, J. Boyle, S. Malyshev, and, S. Xie, (2006), Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach, Geophys. Res. Lett., 33, L18805, doi: 10.1029/2006GL027567.
25. Klein, S. A., Y. Zhang, M. D. Zelinka, R. Pincus, J. Boyle, and, P. J. Gleckler, (2013), Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator, J. Geophys. Res. Atmos., 118, 1329-1342, doi: 10.1002/jgrd.50141.
26. Klingaman, N. P., et al., (2015), Vertical structure and physical processes of the Madden-Julian oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening, J. Geophys. Res. Atmos., 120, 4690-4717, doi: 10.1002/2014JD022374.
27. Koster, R. D., M. J. Suarez, P. Liu, U. Jambor, A. Berg, M. Kistler, R. Reichle, M. Rodell, and, J. Famiglietti, (2004), Realistic initialization of land surface states: Impacts on subseasonal forecast skill, J. Hydrometeorol., 5, 1049-1063.
28. Koster, R. D., et al., (2010), Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment, Geophys. Res. Lett., 37, L02402, doi: 10.1029/2009GL041677.
29. Lagman, K. B., K. Fennel, K. R. Thompson, and, L. Bianucci, (2014), Assessing the utility of frequency dependent nudging for reducing biases in biogeochemical models, Ocean Modell., 81, 25-35.
30. Li, J.-L. F., et al., (2012), An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data, J. Geophys. Res., 117, D16105, doi: 10.1029/2012JD017640.
31. Li, J.-L. F., D. E. Waliser, G. Stephens, S. Lee, T. L'Ecuyer, S. Kato, N. Loeb, and, H.-Y. Ma, (2013), Characterizing and understanding radiation budget biases in CMIP3/CMIP5 GCMs, contemporary GCM, and reanalysis, J. Geophys. Res. Atmos., 118, 8166-8184, doi: 10.1002/jgrd.50378.
32. Lin, Y., et al., (2012), TWP-ICE global atmospheric model intercomparison: Convection responsiveness and resolution impact, J. Geophys. Res., 117, D09111, doi: 10.1029/2011JD017018.
33. 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, 709-739, doi: 10.5194/gmd-5-709-2012.
34. Lynch, P., and, X. Y. Huang, (1992), Initialization of the HIRLAM model using a digital filter, Mon. Weather Rev., 120, 1019-1034.
35. Ma, H.-Y., S. Xie, J. S. Boyle, S. A. Klein, and, Y. Zhang, (2013), Metrics and diagnostics for precipitation-related processes in climate model short-range hindcasts, J. Clim., 26, 1516-1534.
36. Ma, H.-Y., et al., (2014), On the correspondence between mean forecast errors and climate errors in CMIP5 models, J. Clim., 27, 1781-1798.
37. Ma, P.-L., P. J. Rasch, H. Wang, K. Zhang, R. C. Easter, S. Tilmes, J. D. Fast, X. Liu, J.-H. Yoon, and, J.-F. Lamarque, (2013), The role of circulation features on black carbon transport into the Arctic in the Community Atmosphere Model version 5 (CAM5), J. Geophys. Res. Atmos., 118, 4657-4669, doi: 10.1002/jgrd.50411.
38. Ma, P.-L., P. J. Rasch, J. D. Fast, R. C. Easter, W. I. Gustafson Jr., 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, doi: 10.5194/gmd-7-755-2014.
39. Ma, P.-L., P. J. Rasch, M. Wang, H. Wang, S. J. Ghan, R. C. Easter, W. I. Gustafson Jr., X. Liu, Y. Zhang, and, H.-Y. Ma, (2015), How does increasing horizontal resolution in a global climate model improve the simulation of aerosol-cloud interactions?, Geophys. Res. Lett., 42, 5058-5065, doi: 10.1002/2015GL064183.
40. Martin, G. M., S. F. Milton, C. A. Senior, M. E. Brooks, S. Ineson, T. Reichler, and, J. Kim, (2010), Analysis and reduction of systematic errors through a seamless approach to modeling weather and climate, J. Clim., 15, 5933-5957.
41. Medeiros, B., D. L. Williamson, C. Hannay, and, J. G. Olson, (2012), Southeast Pacific stratocumulus in the Community Atmosphere Model, J. Clim., 25, 6175-6192.
42. 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 numerical tests, J. Clim., 21 (15), 3642-3659.
43. Neale, R., et al., (2012), Description of the NCAR Community Atmosphere Model (CAM5.0), NCAR Tech. Note NCAR-TN-486+STR, 274 p. [Available at http://www.cesm.ucar.edu/models/cesm1.2/cam/docs/description/cam5-desc.pdf.]
44. Neale, R. B., J. H. Richter, and, M. Jochum, (2008), The impact of convection on ENSO: From a delayed oscillator to a series of events, J. Clim., 21 (22), 5904-5924.
45. 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, 3449-3469.
46. Phillips, T. J., G. L. Potter, D. L. Williamson, R. T. Cederwall, J. S. Boyle, M. Fiorino, J. J. Hnilo, J. G. Olson, S. Xie, and, J. J. Yio, (2004), Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction, Bull. Am. Meteorol. Soc., 85, 1903-1915.
47. Polavarapu, S., M. Tanguay, and, L. Fillion, (2000), Four-dimensional variational data assimilation with digital filter initialization, Mon. Weather Rev., 128, 2491-2510.
48. Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and, W. Wang, (2002), An improved in situ and satellite SST analysis for climate, J. Clim., 15, 1609-1625.
49. Rodell, M., et al., (2004), The global land data assimilation system, Bull. Am. Meteorol. Soc., 85, 381-394.
50. Rodwell, M. J., and, T. N. Palmer, (2007), Using numerical weather prediction to assess climate models, Q. J. R. Meteorol. Soc., 133, 129-146, doi: 10.1002/qj.23.
51. Schneider, J. M., D. K. Fisher, R. L. Elliott, G. O. Brown, and, C. P. Bahrmann, (2003), Spatiotemporal variation in soil water: First results from the ARM SGP CART network, J. Hydrometeorol., 4, 106-120.
52. Sherwood, S. C., S. Bony, and, J.-L. Dufresne, (2014), Spread in model climate sensitivity traced to atmospheric convective mixing, Nature, 505, 37-42.
53. Sungsu, P., C. S. Bretherton, and, P. J. Rasch, (2014), Integrating Cloud Processes in the Community Atmosphere Model, Version 5, J. Clim., 27, 6821-6856.
54. Taylor, K. E., (2001), Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183-7192.
55. Taylor, K. E., R. J. Stouffer, and, G. A. Meehl, (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485-498.
56. Van Weverberg, K., C. J. Morcrette, H.-Y. Ma, S. A. Klein, and, J. C. Petch, (2015), Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models, Q. J. R. Meteorol. Soc., doi: 10.1002/qj.2603.
57. Waliser, D. E., et al., (2012), The "year" of tropical convection (May 2008-April 2010): Climate variability and weather highlights, Bull. Am. Meteorol. Soc., 93, 1189-1218.
58. Wang, H., R. C. Easter, P. J. Rasch, M. Wang, X. Liu, S. J. Ghan, Y. Qian, J.-H. Yoon, P.-L. Ma, and, V. Vinoj, (2013), Sensitivity of remote aerosol distributions to representation of cloud-aerosol interactions in a global climate model, Geosci. Model Dev., 6 (3), 765-782, doi: 10.5194/gmd-6-765-2013.
59. Williams, K. D., and, M. E. Brooks, (2008), Initial tendencies of cloud regimes in the met office unified model, J. Clim., 21 (4), 833-840, doi: 10.1175/2007JCLI1900.1.
60. Williams, K. D., A. Bodas-Salcedo, M. Déqué, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and, D. L. Williamson, (2013), The transpose-AMIP II experiment and its application to the understanding of southern ocean cloud biases in climate models, J. Clim., 26, 3258-3274.
61. Williamson, D. L., (2013), The effect of time steps and time-scales on parameterization suites, Q. J. R. Meteorol. Soc., 139, 548-560, doi: 10.1002/qj.1992.
62. Williamson, D. L., and, J. G. Olson, (2007), A comparison of forecast errors in CAM2 and CAM3 at the ARM Southern Great Plains site, J. Clim., 20, 4572-4585, doi: 10.1175/JCLI4267.1.
63. Williamson, D. L., et al., (2005), Moisture and temperature balances at the atmospheric radiation measurement Southern Great Plains site in forecasts with the Community Atmosphere Model (CAM2), J. Geophys. Res., 110, D15S16, doi: 10.1029/2004JD005109.
64. Winker, D. M., J. R. Pelon, and, M. P. McCormick, (2003), The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds, Proc. SPIE Int. Soc. Opt. Eng., 4893, 1-11, doi: 10.1117/12.466539.
65. Xavier, P. K., et al., (2015), Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range, J. Geophys. Res. Atmos., 120, 4749-4763, doi: 10.1002/2014JD022718.
66. Xie, S., R. T. Cederwall, and, M. H. Zhang, (2004a), Developing long-term single-column model/cloud system-resolving model forcing using numerical weather prediction products constrained by surface and top of the atmosphere observations, J. Geophys. Res., 109, D01104, doi: 10.1029/2003JD004045.
67. Xie, S., M. Zhang, J. S. Boyle, R. T. Cederwall, G. L. Potter, and, W. Lin, (2004b), Impact of a revised convective triggering mechanism on Community Atmosphere Model, Version 2, simulations: Results from short-range weather forecasts, J. Geophys. Res., 109, D14102, doi: 10.1029/2004JD004692.
68. Xie, S., J. Boyle, S. Klein, X. Liu, and, S. Ghan, (2008), Simulations of Arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE, J. Geophys. Res., 113, D04211, doi: 10.1029/2007JD009225.
69. Xie, S., H.-Y. Ma, J. S. Boyle, S. A. Klein, and, Y. Zhang, (2012), On the correspondence between short-And long-timescale systematic errors in CAM4/CAM5 for the years of tropical convection, J. Clim., 25, 7937-7955.
70. Zhang, K., H. Wan, X. Liu, S. J. Ghan, G. J. Kooperman, P.-L. Ma, P. J. Rasch, D. Neubauer, and, U. Lohmann, (2014), Technical note: On the use of nudging for aerosol-climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631-8645.
71. 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.