• Andrews, T., J. M. Gregory, and M. J. Webb, 2015: The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J. Climate, 28, 16301648, doi:10.1175/JCLI-D-14-00545.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andry, O., R. Bintanja, and W. Hazeleger, 2017: Time-dependent variations in the Arctic’s surface albedo feedback and the link to seasonality in sea ice. J. Climate, 30, 393410, doi:10.1175/JCLI-D-15-0849.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Armour, K. C., C. M. Bitz, and G. H. Roe, 2013: Time-varying climate sensitivity from regional feedbacks. J. Climate, 26, 45184534, doi:10.1175/JCLI-D-12-00544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellucci, A., and Coauthors, 2013: Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses. Climate Dyn., 40, 14831497, doi:10.1007/s00382-012-1468-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellucci, A., and Coauthors, 2015a: Advancements in decadal climate predictability: The role of nonoceanic drivers. Rev. Geophys., 53, 165202, doi:10.1002/2014RG000473.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellucci, A., and Coauthors, 2015b: An assessment of a multi-model ensemble of decadal climate predictions. Climate Dyn., 44, 27872806, doi:10.1007/s00382-014-2164-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bombardi, R. J., and Coauthors, 2015: Evaluation of the CFSv2 CMIP5 decadal predictions. Climate Dyn., 44, 543557, doi:10.1007/s00382-014-2360-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., F. Robertson, L. Takacs, A. Molod, and D. Mocko, 2017: Atmospheric water balance and variability in the MERRA-2 reanalysis. J. Climate, 30, 11771196, doi:10.1175/JCLI-D-16-0338.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Branstator, G., and H. Teng, 2012: Potential impact of initialization on decadal predictions as assessed for CMIP5 models. Geophys. Res. Lett., 39, L12703, doi:10.1029/2012GL051974.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, M., and J. Lu, 2009: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part II: Method demonstrations and comparisons. Climate Dyn., 32, 887900, doi:10.1007/s00382-008-0424-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chikamoto, Y., M. Kimoto, M. Watanabe, M. Ishii, and T. Mochizuki, 2012: Relationship between the Pacific and Atlantic stepwise climate change during the 1990s. Geophys. Res. Lett., 39, L21710, doi:10.1029/2012GL053901.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, J., S.-W. Son, Y.-G. Ham, J.-Y. Lee, and H.-M. Kim, 2016: Seasonal-to-interannual prediction skills of near-surface air temperature in the CMIP5 decadal hindcast experiments. J. Climate, 29, 15111527, doi:10.1175/JCLI-D-15-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colman, R. A., and S. B. Power, 2010: Atmospheric radiative feedbacks associated with transient climate change and climate variability. Climate Dyn., 34, 919933, doi:10.1007/s00382-009-0541-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corti, S., and Coauthors, 2015: Impact of initial conditions versus external forcing in decadal climate predictions: A sensitivity experiment. J. Climate, 28, 44544470, doi:10.1175/JCLI-D-14-00671.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, Y., T.-W. Park, and M. Cai, 2012: Process-based decomposition of the global surface temperature response to El Niño in boreal winter. J. Atmos. Sci., 69, 17061712, doi:10.1175/JAS-D-12-023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, Y., T.-W. Park, and M. Cai, 2013: Radiative and dynamical forcing of the surface and atmospheric temperature anomalies associated with the Northern Annular Mode. J. Climate, 26, 51245138, doi:10.1175/JCLI-D-12-00431.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., and Coauthors, 2013: Initialized near-term regional climate change prediction. Nat. Commun., 4, 1715, doi:10.1038/ncomms2704.

  • ECMWF, 2009: ERA-Interim project. National Center for Atmospheric Research Computational and Information Systems Laboratory Research Data Archive, accessed 30 March 2014, doi:10.5065/D6CR5RD9.

    • Crossref
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, doi:10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Q., and K. N. Liou, 1992: On the correlated k-distribution method for radiative transfer in nonhomogeneous atmospheres. J. Atmos. Sci., 49, 21392156, doi:10.1175/1520-0469(1992)049<2139:OTCDMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fu, Q., and K. N. Liou, 1993: Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci., 50, 20082025, doi:10.1175/1520-0469(1993)050<2008:POTRPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W. S. Lee, and K. von Salzen, 2011: Skillful predictions of decadal trends in global mean surface temperature. Geophys. Res. Lett., 38, L22801, doi:10.1029/2011GL049508.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • García-Serrano, J., and F. J. Doblas-Reyes, 2012: On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ENSEMBLES decadal hindcast. Climate Dyn., 39, 20252040, doi:10.1007/s00382-012-1413-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • García-Serrano, J., V. Guemas, and F. J. Doblas-Reyes, 2015: Added-value from initialization in predictions of Atlantic multi-decadal variability. Climate Dyn., 44, 25392555, doi:10.1007/s00382-014-2370-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

  • Guemas, V., F. J. Doblas-Reyes, F. Lienert, Y. Soufflet, and H. Du, 2012: Identifying the causes of the poor decadal climate prediction skill over the North Pacific. J. Geophys. Res., 117, D20111, doi:10.1029/2012JD018004.

    • Search Google Scholar
    • Export Citation
  • Guemas, V., S. Corti, J. García-Serrano, F. J. Doblas-Reyes, M. Balmaseda, and L. Magnusson, 2013: The Indian Ocean: The region of highest skill worldwide in decadal climate prediction. J. Climate, 26, 726739, doi:10.1175/JCLI-D-12-00049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazeleger, W., V. Guemas, B. Wouters, S. Corti, I. Andreu-Burillo, F. J. Doblas-Reyes, K. Wyser, and M. Caian, 2013a: Multiyear climate predictions using two initialization strategies. Geophys. Res. Lett., 40, 17941798, doi:10.1002/grl.50355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hazeleger, W., and Coauthors, 2013b: Predicting multiyear North Atlantic Ocean variability. J. Geophys. Res. Oceans, 118, 10871098, doi:10.1002/jgrc.20117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hermanson, L., R. Eade, N. H. Robinson, N. J. Dunstone, M. B. Andrews, J. R. Knight, A. A. Scaife, and D. M. Smith, 2014: Forecast cooling of the Atlantic subpolar gyre and associated impacts. Geophys. Res. Lett., 41, 51675174, doi:10.1002/2014GL060420.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, X., S. Yang, and M. Cai, 2016: Contrasting the eastern Pacific El Niño and the central Pacific El Niño: Process-based feedback attribution. Climate Dyn., 47, 24132424, doi:10.1007/s00382-015-2971-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, X., Y. Li, S. Yang, Y. Deng, and M. Cai, 2017: Process-based decomposition of the decadal climate difference between 2002–13 and 1984–95. J. Climate, 30, 43734393, doi:10.1175/JCLI-D-15-0742.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2015: Climate drift of AMOC, North Atlantic salinity and Arctic sea ice in CFSv2 decadal predictions. Climate Dyn., 44, 559583, doi:10.1007/s00382-014-2395-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karspeck, A., S. Yeager, G. Danabasoglu, and H. Teng, 2015: An evaluation of experimental decadal predictions using CCSM4. Climate Dyn., 44, 907923, doi:10.1007/s00382-014-2212-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-M., P. J. Webster, and J. A. Curry, 2012: Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts. Geophys. Res. Lett., 39, L10701, doi:10.1029/2012GL051644.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klocke, D., J. Quaas, and B. Stevens, 2013: Assessment of different metrics for physical climate feedbacks. Climate Dyn., 41, 11731185, doi:10.1007/s00382-013-1757-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, J., and M. Cai, 2009: A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: Formulation. Climate Dyn., 32, 873885, doi:10.1007/s00382-008-0425-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mears, C. A., and F. J. Wentz, 2009: Construction of the RSS V3.2 lower tropospheric dataset from the MSU and AMSU microwave sounders. J. Atmos. Oceanic Technol., 26, 14931509, doi:10.1175/2009JTECHA1237.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and H. Teng, 2012: Case studies for initialized decadal hindcasts and predictions for the Pacific region. Geophys. Res. Lett., 39, L22705, doi:10.1029/2012GL053423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and H. Teng, 2014a: CMIP5 multi-model hindcasts for the mid-1970s shift and early 2000s hiatus and predictions for 2016–2035. Geophys. Res. Lett., 41, 17111716, doi:10.1002/2014GL059256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and H. Teng, 2014b: Regional precipitation simulations for the mid-1970s shift and early-2000s hiatus. Geophys. Res. Lett., 41, 76587665, doi:10.1002/2014GL061778.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2009: Decadal prediction: Can it be skillful? Bull. Amer. Meteor. Soc., 90, 14671485, doi:10.1175/2009BAMS2778.1.

  • Meehl, G. A., H. Teng, and J. M. Arblaster, 2014a: Climate model simulations of the observed early-2000s hiatus of global warming. Nat. Climate Change, 4, 898902, doi:10.1038/nclimate2357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2014b: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243267, doi:10.1175/BAMS-D-12-00241.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., A. Hu, and H. Teng, 2016: Initialized decadal prediction for transition to positive phase of the Interdecadal Pacific Oscillation. Nat. Commun., 7, 11718, doi:10.1038/ncomms11718.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Msadek, R., and Coauthors, 2014: Predicting a decadal shift in North Atlantic climate variability using the GFDL forecast system. J. Climate, 27, 64726496, doi:10.1175/JCLI-D-13-00476.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, T.-W., Y. Deng, and M. Cai, 2012: Feedback attribution of the El Niño–Southern Oscillation–related atmospheric and surface temperature anomalies. J. Geophys. Res., 117, D23101, doi:10.1029/2012JD018468.

    • Search Google Scholar
    • Export Citation
  • Pohlmann, H., D. M. Smith, M. A. Balmaseda, N. S. Keenlyside, S. Masina, D. Matei, W. A. Müller, and P. Rogel, 2013: Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system. Climate Dyn., 41, 775785, doi:10.1007/s00382-013-1663-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rugenstein, M. A. A., K. Caldeira, and R. Knutti, 2016: Dependence of global radiative feedbacks on evolving patterns of surface heat fluxes. Geophys. Res. Lett., 43, 98779885, doi:10.1002/2016GL070907.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sejas, S. A., M. Cai, A.-X. Hu, G. A. Meehl, W. Washington, and P. C. Taylor, 2014: Individual feedback contributions to the seasonality of surface warming. J. Climate, 27, 56535669, doi:10.1175/JCLI-D-13-00658.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Senior, C. A., and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. Geophys. Res. Lett., 27, 26852688, doi:10.1029/2000GL011373.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, D. M., R. Eade, and H. Pohlmann, 2013: A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction. Climate Dyn., 41, 33253338, doi:10.1007/s00382-013-1683-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, X., G. J. Zhang, and M. Cai, 2014a: Quantifying contributions of climate feedbacks to tropospheric warming in the NCAR CCSM3.0. Climate Dyn., 42, 901917, doi:10.1007/s00382-013-1805-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Song, X., G. J. Zhang, and M. Cai, 2014b: Characterizing the climate feedback pattern in the NCAR CCSM3-SOM using hourly data. J. Climate, 27, 29122930, doi:10.1175/JCLI-D-13-00567.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, D.-Z., and I. M. Held, 1996: A comparison of modeled and observed relationships between interannual variations of water vapor and temperature. J. Climate, 9, 665675, doi:10.1175/1520-0442(1996)009<0665:ACOMAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swingedouw, D., J. Mignot, S. Labetoulle, E. Guilyardi, and G. Madec, 2013: Initialisation and predictability of the AMOC over the last 50 years in a climate model. Climate Dyn., 40, 23812399, doi:10.1007/s00382-012-1516-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thoma, M., R. J. Greatbatch, C. Kadow, and R. Gerdes, 2015: Decadal hindcasts initialized using observed surface wind stress: Evaluation and prediction out to 2024. Geophys. Res. Lett., 42, 64546461, doi:10.1002/2015GL064833.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeager, S., A. Karspeck, G. Danabasoglu, J. Tribbia, and H. Teng, 2012: A decadal prediction case study: Late twentieth-century North Atlantic ocean heat content. J. Climate, 25, 51735189, doi:10.1175/JCLI-D-11-00595.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yeager, S., A. Karspeck, and G. Danabasoglu, 2015: Predicted slowdown in the rate of Atlantic sea ice loss. Geophys. Res. Lett., 42, 10 70410 713, doi:10.1002/2015GL065364.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 19 19 10
PDF Downloads 6 6 1

A Process-Based Assessment of Decadal-Scale Surface Temperature Evolutions in the NCAR CCSM4’s 25-Year Hindcast Experiments

View More View Less
  • 1 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China, and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia
  • 2 Georgia Institute of Technology, Atlanta, Georgia
  • 3 School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China
© Get Permissions
Restricted access

Abstract

This study represents an initial effort in the context of the coupled atmosphere–surface climate feedback-response analysis method (CFRAM) to partition the temporal evolution of the global surface temperature from 1981 to 2005 into components associated with individual radiative and nonradiative (dynamical) processes in the NCAR CCSM4’s decadal hindcasts. When compared with the observation (ERA-Interim), CCSM4 is able to predict an overall warming trend as well as the transient cooling occurring during the period 1989–94. However, while the model captures fairly well the positive contributions of the CO2 and surface albedo change to the temperature evolution, it has an overly strong water vapor effect that dictates the temperature evolution in the hindcast. This is in contrast with ERA-Interim, where changes in surface dynamics (mainly ocean circulation and heat content change) dominate the actual temperature evolution. Atmospheric dynamics in both ERA-Interim and the model work against the surface temperature tendency through turbulent and convective heat transport, leading to an overall negative contribution to the evolution of the surface temperature. Impacts of solar forcing and ozone change on the surface temperature change are relatively weak during this period. The magnitude of cloud effect is considerably smaller compared to that in ERA-Interim and the spatial distribution of the cloud effect is also significantly different between the two, especially over the equatorial Pacific. The value and limitations of this process-based temperature decomposition are discussed.

© 2017 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: Dr. Yi Deng, yi.deng@eas.gatech.edu; Prof. Wenshi Lin, linwenshi@mail.sysu.edu.cn

This article is included in the CCSM4 Special Collection.

Abstract

This study represents an initial effort in the context of the coupled atmosphere–surface climate feedback-response analysis method (CFRAM) to partition the temporal evolution of the global surface temperature from 1981 to 2005 into components associated with individual radiative and nonradiative (dynamical) processes in the NCAR CCSM4’s decadal hindcasts. When compared with the observation (ERA-Interim), CCSM4 is able to predict an overall warming trend as well as the transient cooling occurring during the period 1989–94. However, while the model captures fairly well the positive contributions of the CO2 and surface albedo change to the temperature evolution, it has an overly strong water vapor effect that dictates the temperature evolution in the hindcast. This is in contrast with ERA-Interim, where changes in surface dynamics (mainly ocean circulation and heat content change) dominate the actual temperature evolution. Atmospheric dynamics in both ERA-Interim and the model work against the surface temperature tendency through turbulent and convective heat transport, leading to an overall negative contribution to the evolution of the surface temperature. Impacts of solar forcing and ozone change on the surface temperature change are relatively weak during this period. The magnitude of cloud effect is considerably smaller compared to that in ERA-Interim and the spatial distribution of the cloud effect is also significantly different between the two, especially over the equatorial Pacific. The value and limitations of this process-based temperature decomposition are discussed.

© 2017 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: Dr. Yi Deng, yi.deng@eas.gatech.edu; Prof. Wenshi Lin, linwenshi@mail.sysu.edu.cn

This article is included in the CCSM4 Special Collection.

Save