An Ensemble Covariance Framework for Quantifying Forced Climate Variability and Its Time of Emergence

Vineel Yettella Department of Atmospheric and Oceanic Sciences, and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by Vineel Yettella in
Current site
Google Scholar
PubMed
Close
,
Jeffrey B. Weiss Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by Jeffrey B. Weiss in
Current site
Google Scholar
PubMed
Close
,
Jennifer E. Kay Department of Atmospheric and Oceanic Sciences, and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by Jennifer E. Kay in
Current site
Google Scholar
PubMed
Close
, and
Angeline G. Pendergrass National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Angeline G. Pendergrass in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Climate variability and its response to increasing greenhouse gases are important considerations for impacts and adaptation. Modeling studies commonly assess projected changes in variability in terms of changes in the variance of climate variables. Despite the distant and impactful covariations that climate variables can exhibit, the covariance response has received much less attention. Here, a novel ensemble framework is developed that facilitates a unified assessment of the response of the regional variances and covariances of a climate variable to imposed external forcings and their time of emergence from an unforced climate state.

Illustrating the framework, the response of variability and covariability of land and ocean temperatures is assessed in the Community Earth System Model Large Ensemble under historical and RCP8.5 forcing. The results reveal that land temperature variance emerges from its preindustrial state in the 1950s and, by the end of the twenty-first century, grows to 1.5 times its preindustrial level. Demonstrating the importance of covariances for variability projections, the covariance between land and ocean temperature is considerably enhanced by 2100, reaching 1.4 times its preindustrial estimate. The framework is also applied to assess changes in monthly temperature variability associated with the Arctic region and the Northern Hemisphere midlatitudes. Consistent with previous studies and coinciding with sea ice loss, Arctic temperature variance decreases in most months, emerging from its preindustrial state in the late twentieth century. Overall, these results demonstrate the utility of the framework in enabling a comprehensive assessment of variability and its response to external climate forcings.

© 2018 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: Vineel Yettella, vineel.yettella@colorado.edu

Abstract

Climate variability and its response to increasing greenhouse gases are important considerations for impacts and adaptation. Modeling studies commonly assess projected changes in variability in terms of changes in the variance of climate variables. Despite the distant and impactful covariations that climate variables can exhibit, the covariance response has received much less attention. Here, a novel ensemble framework is developed that facilitates a unified assessment of the response of the regional variances and covariances of a climate variable to imposed external forcings and their time of emergence from an unforced climate state.

Illustrating the framework, the response of variability and covariability of land and ocean temperatures is assessed in the Community Earth System Model Large Ensemble under historical and RCP8.5 forcing. The results reveal that land temperature variance emerges from its preindustrial state in the 1950s and, by the end of the twenty-first century, grows to 1.5 times its preindustrial level. Demonstrating the importance of covariances for variability projections, the covariance between land and ocean temperature is considerably enhanced by 2100, reaching 1.4 times its preindustrial estimate. The framework is also applied to assess changes in monthly temperature variability associated with the Arctic region and the Northern Hemisphere midlatitudes. Consistent with previous studies and coinciding with sea ice loss, Arctic temperature variance decreases in most months, emerging from its preindustrial state in the late twentieth century. Overall, these results demonstrate the utility of the framework in enabling a comprehensive assessment of variability and its response to external climate forcings.

© 2018 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: Vineel Yettella, vineel.yettella@colorado.edu
Save
  • Addo-Bediako, A., S. L. Chown, and K. J. Gaston, 2000: Thermal tolerance, climatic variability and latitude. Proc. Roy. Soc. London, 267B, 739745, https://doi.org/10.1098/rspb.2000.1065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Årthun, M., T. Eldevik, E. Viste, H. Drange, T. Furevik, H. L. Johnson, and N. S. Keenlyside, 2017: Skillful prediction of northern climate provided by the ocean. Nat. Commun., 8, 16152, https://doi.org/10.1038/ncomms16152.

    • Search Google Scholar
    • Export Citation
  • Bindoff, N. L., and Coauthors, 2013: Detection and attribution of climate change: From global to regional. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 867–952, https://doi.org/10.1017/CBO9781107415324.022.

    • Crossref
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boer, G. J., 2009: Changes in interannual variability and decadal potential predictability under global warming. J. Climate, 22, 30983109, https://doi.org/10.1175/2008JCLI2835.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ciais, P., and Coauthors, 2005: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature, 437, 529533, https://doi.org/10.1038/nature03972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, M., and M. R. Allen, 2002: Assessing the relative roles of initial and boundary conditions in interannual to decadal climate predictability. J. Climate, 15, 31043109, https://doi.org/10.1175/1520-0442(2002)015<3104:ATRROI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391397, https://doi.org/10.1038/ngeo868.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., 1981: Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika, 68, 589599, https://doi.org/10.1093/biomet/68.3.589.

    • Crossref
    • Search Google Scholar
    • 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, https://doi.org/10.5194/gmd-9-1937-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, 2007: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 50815099, https://doi.org/10.1175/JCLI4288.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fischer, E. M., J. Rajczak, and C. Schär, 2012: Changes in European summer temperature variability revisited. Geophys. Res. Lett., 39, L19702, https://doi.org/10.1029/2012GL052730.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friendly, M., G. Monette, and J. Fox, 2013: Elliptical insights: Understanding statistical methods through elliptical geometry. Stat. Sci., 28, 139, https://doi.org/10.1214/12-STS402.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and X. Bi, 2009: Time of emergence (TOE) of GHG-forced precipitation change hot-spots. Geophys. Res. Lett., 36, L06709, https://doi.org/10.1029/2009GL037593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hannachi, A., I. T. Jolliffe, and D. B. Stephenson, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol., 27, 11191152, https://doi.org/10.1002/joc.1499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawkins, E., and R. Sutton, 2012: Time of emergence of climate signals. Geophys. Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087.

  • Hawkins, E., and Coauthors, 2014: Uncertainties in the timing of unprecedented climates. Nature, 511, E3E5, https://doi.org/10.1038/nature13523.

  • Hegerl, G., and F. Zwiers, 2011: Use of models in detection and attribution of climate change. Wiley Interdiscip. Rev.: Climate Change, 2, 570591, https://doi.org/10.1002/wcc.121.

    • Search Google Scholar
    • Export Citation
  • Henze, N., and B. Zirkler, 1990: A class of invariant consistent tests for multivariate normality. Commun. Stat. Theory Methods, 19, 35953617, https://doi.org/10.1080/03610929008830400.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holmes, C. R., T. Woollings, E. Hawkins, and H. de Vries, 2016: Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection. J. Climate, 29, 22212236, https://doi.org/10.1175/JCLI-D-14-00735.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huntingford, C., P. D. Jones, V. N. Livina, T. M. Lenton, and P. M. Cox, 2013: No increase in global temperature variability despite changing regional patterns. Nature, 500, 327330, https://doi.org/10.1038/nature12310.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., and Coauthors, 2013: The Community Earth System Model: A framework for collaborative research. Bull. Amer. Meteor. Soc., 94, 13391360, https://doi.org/10.1175/BAMS-D-12-00121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 1990: Climate Change: The IPCC Scientific Assessment. Cambridge University Press, 414 pp., https://www.ipcc.ch/ipccreports/far/wg_I/ipcc_far_wg_I_full_report.pdf.

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., https://doi.org/10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jahn, A., J. E. Kay, M. M. Holland, and D. M. Hall, 2016: How predictable is the timing of a summer ice-free Arctic? Geophys. Res. Lett., 43, 91139120, https://doi.org/10.1002/2016GL070067.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I. T., 2002: Principal Component Analysis. 2nd ed. Springer, 487 pp.

  • Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climatic Change, 21, 289302, https://doi.org/10.1007/BF00139728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 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. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K. E., R. A. Pielke Jr., and S. A. Changnon, 1999: Temporal fluctuations in weather and climate extremes that cause economic and human health impacts: A review. Bull. Amer. Meteor. Soc., 80, 10771098, https://doi.org/10.1175/1520-0477(1999)080<1077:TFIWAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LaJoie, E., and T. DelSole, 2016: Changes in internal variability due to anthropogenic forcing: A new field significance test. J. Climate, 29, 55475560, https://doi.org/10.1175/JCLI-D-15-0718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lamarque, J.-F., and Coauthors, 2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys., 10, 70177039, https://doi.org/10.5194/acp-10-7017-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larsen, J., 2006: Setting the record straight: More than 52,000 Europeans died from heat in summer 2003. Earth Policy Institute, accessed 20 August 2017, http://www.earth-policy.org/plan_b_updates/2006/update56.

  • Leeds, W. B., E. J. Moyer, and M. L. Stein, 2015: Simulation of future climate under changing temporal covariance structures. Adv. Stat. Climatol. Meteor. Oceanogr., 1, 114, https://doi.org/10.5194/ascmo-1-1-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leith, C. E., 1978: Predictability of climate. Nature, 276, 352355, https://doi.org/10.1038/276352a0.

  • Liu, Y., J. Stanturf, and S. Goodrick, 2010: Trends in global wildfire potential in a changing climate. For. Ecol. Manage., 259, 685697, https://doi.org/10.1016/j.foreco.2009.09.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and C. Tebaldi, 2004: More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305, 994997, https://doi.org/10.1126/science.1098704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213241, https://doi.org/10.1007/s10584-011-0156-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mora, C., and Coauthors, 2013: The projected timing of climate departure from recent variability. Nature, 502, 183187, https://doi.org/10.1038/nature12540.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perry, S. J., S. McGregor, A. Sen Gupta, and M. H. England, 2017: Future changes to El Niño–Southern Oscillation temperature and precipitation teleconnections. Geophys. Res. Lett., 44, 10 60810 616, https://doi.org/10.1002/2017GL074509.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Politis, D. N., J. P. Romano, and M. Wolf, 1999: Subsampling. Springer, 347 pp.

    • Crossref
    • Export Citation
  • Poppick, A., D. J. McInerney, E. J. Moyer, and M. L. Stein, 2016: Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances. Ann. Appl. Stat., 10, 477505, https://doi.org/10.1214/16-AOAS903.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porter, J. R., and M. A. Semenov, 2005: Crop responses to climatic variation. Philos. Trans. Roy. Soc. London, 360B, 20212035, https://doi.org/10.1098/rstb.2005.1752.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Räisänen, J., 2001: CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability. J. Climate, 14, 20882104, https://doi.org/10.1175/1520-0442(2001)014<2088:CICCIC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rajagopalan, B., and U. Lall, 1998: Interannual variability in western US precipitation. J. Hydrol., 210, 5167, https://doi.org/10.1016/S0022-1694(98)00184-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robine, J.-M., S. L. K. Cheung, S. Le Roy, H. Van Oyen, C. Griffiths, J.-P. Michel, and F. R. Herrmann, 2008: Death toll exceeded 70,000 in Europe during the summer of 2003. C. R. Biol., 331, 171178, https://doi.org/10.1016/j.crvi.2007.12.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2004: The role of increasing temperature variability in European summer heatwaves. Nature, 427, 332336, https://doi.org/10.1038/nature02300.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scherrer, S. C., M. A. Liniger, and C. Appenzeller, 2008: Distribution changes of seasonal mean temperature in observations and climate change scenarios. Climate Variability and Extremes during the Past 100 Years, S. Brönnimann et al., Eds., Advances in Global Change Research, Vol. 33, Springer, 251–267, http://doi.org/10.1007/978-1-4020-6766-2_17.

    • Crossref
    • Export Citation
  • Schneider, T., and S. M. Griffies, 1999: A conceptual framework for predictability studies. J. Climate, 12, 31333155, https://doi.org/10.1175/1520-0442(1999)012<3133:ACFFPS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010: Increasing fall-winter energy loss from the Arctic Ocean and its role in Arctic temperature amplification. Geophys. Res. Lett., 37, L16707, https://doi.org/10.1029/2010GL044136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and R. G. Barry, 2011: Processes and impacts of Arctic amplification: A research synthesis. Global Planet. Change, 77, 8596, https://doi.org/10.1016/j.gloplacha.2011.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., J. M. Wallace, and G. W. Branstator, 1983: Barotropic wave propagation and instability, and atmospheric teleconnection patterns. J. Atmos. Sci., 40, 13631392, https://doi.org/10.1175/1520-0469(1983)040<1363:BWPAIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stouffer, R. J., and R. T. Wetherald, 2007: Changes of variability in response to increasing greenhouse gases. Part I: Temperature. J. Climate, 20, 54555467, https://doi.org/10.1175/2007JCLI1384.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutton, R., E. Suckling, and E. Hawkins, 2015: What does global mean temperature tell us about local climate? Philos. Trans. Roy. Soc. London, 373A, 20140426, https://doi.org/10.1098/rsta.2014.0426.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300, https://doi.org/10.1029/98GL00950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., E. A. Barnes, C. Deser, W. E. Foust, and A. S. Phillips, 2015: Quantifying the role of internal climate variability in future climate trends. J. Climate, 28, 64436456, https://doi.org/10.1175/JCLI-D-14-00830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Toth, Z., 1991: Circulation patterns in phase space: A multinormal distribution? Mon. Wea. Rev., 119, 15011511, https://doi.org/10.1175/1520-0493(1991)119<1501:CPIPSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., and D. J. Shea, 1987: On the evolution of the Southern Oscillation. Mon. Wea. Rev., 115, 30783096, https://doi.org/10.1175/1520-0493(1987)115<3078:OTEOTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetherald, R. T., 2009: Changes of variability in response to increasing greenhouse gases. Part II: Hydrology. J. Climate, 22, 60896103, https://doi.org/10.1175/2009JCLI2834.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, T. R., P. Q. Craufurd, R. H. Ellis, J. R. Porter, and P. V. Vara Prasad, 2000: Temperature variability and the yield of annual crops. Agric. Ecosyst. Environ., 82, 159167, https://doi.org/10.1016/S0167-8809(00)00224-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. International Geophysics Series, Vol. 100, Academic Press, 676 pp.

  • Yettella, V., and J. E. Kay, 2017: How will precipitation change in extratropical cyclones as the planet warms? Insights from a large initial condition climate model ensemble. Climate Dyn., 49, 17651781, https://doi.org/10.1007/s00382-016-3410-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 434 138 3
PDF Downloads 335 66 3