Uncovering the Forced Climate Response from a Single Ensemble Member Using Statistical Learning

被引:53
作者
Sippel, Sebastian [1 ,2 ]
Meinshausen, Nicolai [2 ]
Merrifield, Anna [1 ]
Lehner, Flavio [3 ]
Pendergrass, Angeline G. [3 ]
Fischer, Erich [1 ]
Knutti, Reto [1 ]
机构
[1] Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Zurich, Switzerland
[2] Swiss Fed Inst Technol, Seminar Stat, Zurich, Switzerland
[3] Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA
基金
美国国家科学基金会;
关键词
Atmospheric circulation; Climate change; Climate prediction; Regression analysis; Statistical techniques; Climate variability; ATMOSPHERIC CIRCULATION; NATURAL VARIABILITY; PRECIPITATION TRENDS; DYNAMICAL ADJUSTMENT; NORTH-ATLANTIC; TEMPERATURE; UNCERTAINTY; MODEL; REGULARIZATION; PROJECTIONS;
D O I
10.1175/JCLI-D-18-0882.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Internal atmospheric variability fundamentally limits predictability of climate and obscures evidence of anthropogenic climate change regionally and on time scales of up to a few decades. Dynamical adjustment techniques estimate and subsequently remove the influence of atmospheric circulation variability on temperature or precipitation. The residual component is expected to contain the thermodynamical signal of the externally forced response but with less circulation-induced noise. Existing techniques have led to important insights into recent trends in regional (hydro-) climate and their drivers, but the variance explained by circulation is often low. Here, we develop a novel dynamical adjustment technique by implementing principles from statistical learning. We demonstrate in an ensemble of Community Earth System Model (CESM) simulations that statistical learning methods, such as regularized linear models, establish a clearer relationship between circulation variability and atmospheric target variables, and need relatively short periods of record for training (around 30 years). The method accounts for, on average, 83% and 78% of European monthly winter temperature and precipitation variability at gridcell level, and around 80% of global mean temperature and hemispheric precipitation variability. We show that the residuals retain forced thermodynamical contributions to temperature and precipitation variability. Accurate estimates of the total forced response can thus be recovered assuming that forced circulation changes are gradual over time. Overall, forced climate response estimates can be extracted at regional or global scales from approximately 3-5 times fewer ensemble members, or even a single realization, using statistical learning techniques. We anticipate the technique will contribute to reducing uncertainties around internal variability and facilitating climate change detection and attribution.
引用
收藏
页码:5677 / 5699
页数:23
相关论文
共 73 条
  • [1] Constraints on future changes in climate and the hydrologic cycle
    Allen, MR
    Ingram, WJ
    [J]. NATURE, 2002, 419 (6903) : 224 - +
  • [2] Alquier P, 2013, J MACH LEARN RES, V14, P243
  • [3] Boé J, 2006, J GEOPHYS RES-ATMOS, V111, DOI [10.1029/2005JD006889, 10.1029/JD006889]
  • [4] Cleveland W., 1991, LOCAL REGRESSION MOD, P309
  • [5] Challenges and opportunities for improved understanding of regional climate dynamics
    Collins, Matthew
    Minobe, Shoshiro
    Barreiro, Marcelo
    Bordoni, Simona
    Kaspi, Yohai
    Kuwano-Yoshida, Akira
    Keenlyside, Noel
    Manzini, Elisa
    O'Reilly, Christopher H.
    Sutton, Rowan
    Xie, Shang-Ping
    Zolina, Olga
    [J]. NATURE CLIMATE CHANGE, 2018, 8 (02) : 101 - 108
  • [6] Colucci S, 2015, ENCY ATMOSPHERIC SCI, P273
  • [7] Removing ENSO-Related Variations from the Climate Record
    Compo, Gilbert P.
    Sardeshmukh, Prashant D.
    [J]. JOURNAL OF CLIMATE, 2010, 23 (08) : 1957 - 1978
  • [8] A Significant Component of Unforced Multidecadal Variability in the Recent Acceleration of Global Warming
    DelSole, Timothy
    Tippett, Michael K.
    Shukla, Jagadish
    [J]. JOURNAL OF CLIMATE, 2011, 24 (03) : 909 - 926
  • [9] Deser C, 1997, J CLIMATE, V10, P393, DOI 10.1175/1520-0442(1997)010<0393:AOIOWT>2.0.CO
  • [10] 2