Historical and future learning about climate sensitivity

Geophysical Research Letters

Volumn 41, Issue 7, 2014, Pages 2543-2552

  Urban, Nathan M ^a,b   Holden, Philip B ^c   Edwards, Neil R ^c   Sriver, Ryan L ^d   Keller, Klaus ^e,f  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b PRINCETON UNIVERSITY   (United States)

^c OPEN UNIVERSITY   (United Kingdom)

^d UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^e PENNSYLVANIA STATE UNIVERSITY   (United States)

^f CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

climate sensitivity; feedback; learning; ocean heat; transient; uncertainty

Indexed keywords

ATMOSPHERIC TEMPERATURE; CARBON DIOXIDE; FEEDBACK; TRANSIENTS;

CLIMATE SENSITIVITY; ENERGY BALANCE MODELS; INTERGOVERNMENTAL PANEL ON CLIMATE CHANGES; LEARNING; MITIGATION POLICIES; OCEAN HEAT; SURFACE TEMPERATURES; UNCERTAINTY;

CLIMATE CHANGE;

ATMOSPHERIC FORCING; CARBON DIOXIDE; CLIMATE CHANGE; CLIMATE FEEDBACK; CLIMATE PREDICTION; ENERGY BALANCE; HEAT FLUX; HISTORICAL PERSPECTIVE; INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE; LEARNING; MITIGATION; PRECAUTIONARY PRINCIPLE; SENSITIVITY ANALYSIS; SURFACE TEMPERATURE; UNCERTAINTY ANALYSIS;

EID: 84899036813     PISSN: 00948276     EISSN: 19448007     Source Type: Journal    
DOI: 10.1002/2014GL059484     Document Type: Article

References (32)

1. Bhat, K.-S., M. Haran, R. Olson, and, K. Keller, (2012), Inferring likelihoods and climate system characteristics from climate models and multiple tracers, Environmetrics, 23, 345-362, doi: 10.1002/env.2149.
2. Charney, J. G. E. A., (1979), Carbon dioxide and climate: A scientific assessment, Tech. Rep., National Research Council, Woods Hole, Mass.
3. Fasullo, J.-T., and, K.-E. Trenberth, (2012), A less cloudy future: The role of subtropical subsidence in climate sensitivity, Science, 338, 792-794, doi: 10.1126/science.1227465.
4. Feldman, D.-R., C.-A. Algieri, W.-D. Collins, Y.-L. Roberts, and, P.-A. Pilewskie, (2011), Simulation studies for the detection of changes in broadband albedo and shortwave nadir reflectance spectra under a climate change scenario, J. Geophys. Res., 116, D24103, doi: 10.1029/2011JD016407.
5. Forest, C.-E., P.-H. Stone, A.-P. Sokolov, M.-R. Allen, and, M.-D. Webster, (2002), Quantifying uncertainties in climate system properties with the use of recent climate observations, Science, 295, 113-117, doi: 10.1126/science.1064419.
6. Goes, M., N.-M. Urban, R. Tonkonojenkov, M. Haran, and, K. Keller, (2010), What is the skill of ocean tracers in reducing uncertainties about ocean diapycnal mixing and projections of the Atlantic Meridional Overturning Circulation?, J. Geophys. Res., 115, C12006, doi: 10.1029/2010JC006407.
7. Gouretski, V., and, K.-P. Koltermann, (2007), How much is the ocean really warming?, Geophys. Res. Lett., 34, L01610, doi: 10.1029/2006GL027834.
8. Hannart, A., M. Ghil, J.-L. Dufresne, and, P. Naveau, (2013), Disconcerting learning on climate sensitivity and the uncertain future of uncertainty, Clim. Change, 119, 585-601, doi: 10.1007/s10584-013-0770-z.
9. Hansen, J., G. Russell, A. Lacis, I. Fung, D. Rind, and, P. Stone, (1985), Climate response times: Dependence on climate sensitivity and ocean mixing, Science, 229, 857-859, doi: 10.1126/science.229.4716.857.
10. Holden, P.-B., N.-R. Edwards, K.-I.-C. Oliver, T.-M. Lenton, and, R.-D. Wilkinson, (2010), A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1, Clim. Dyn., 35, 785-806, doi: 10.1007/s00382-009-0630-8.
11. Huang, Y., S. Leroy, P.-J. Gero, J. Dykema, and, J. Anderson, (2010), Determining longwave forcing and feedback using infrared spectra and GNSS radio occultation, J. Clim., 23, 6027-6035, doi: 10.1175/2010JCLI3588.1.
12. Huber, M., I. Mahlstein, and, M. Wild, (2011), Constraints on climate sensitivity from radiation patterns in climate models, J. Clim., 24, 1034-1052, doi: 10.1175/2010JCLI3403.1.
13. Intergovernmental Panel on Climate Change (IPCC) (2007), Summary for policymakers, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by, S. Solomon, et al., Cambridge Univ. Press, Cambridge, U. K., and New York.
14. Intergovernmental Panel on Climate Change (2013), Summary for policymakers, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by, T.-F. Stocker, et al., Cambridge Univ. Press, Cambridge, U. K., and New York.
15. Kelly, D.-L., and, C.-D. Kolstad, (1999), Bayesian learning, pollution, and growth, J. Econ. Dyn. Control, 23, 491-518, doi: 10.1016/S0165-1889(98) 00034-7.
16. Khesghi, H.-S., and, B.-S. White, (1993), Does recent global warming suggest an enhanced greenhouse effect?, Clim. Change, 23, 121-139, doi: 10.1007/BF01097333.
17. Knutti, R., and, G.-C. Hegerl, (2008), The equilibrium sensitivity of the Earth's temperature to radiation changes, Nat. Geosci., 1, 735-743, doi: 10.1038/ngeo337.
18. Knutti, R., T.-F. Stocker, F. Joos, and, G.-K. Plattner, (2002), Constraints on radiative forcing and future climate change from observations and climate model ensembles, Nature, 416, 719-723, doi: 10.1038/416719a.
19. Knutti, R., G.-A. Meehl, M.-R. Allen, and, D.-A. Stainforth, (2006), Constraining climate sensitivity from the seasonal cycle in surface temperature, J. Clim., 19, 4224-4233, doi: 10.1175/JCLI3865.1.
20. Kriegler, E., (2005), Imprecise probability analysis for integrated assessment of climate change, PhD thesis, Univ. of Potsdam, Germany.
21. Leach, A.-J., (2007), The climate change learning curve, J. Econ. Dyn. Control, 31, 1728-1752, doi: 10.1016/j.jedc.2006.06.001.
22. Levitus, S.-E.-A., (2012), World ocean heat content and thermosteric sea level change (0-2000 m), 1955-2010, Geophys. Res. Lett., 39, L10603, doi: 10.1029/2012GL051106.
23. Morice, C.-P., J.-J. Kennedy, N.-A. Rayner, and, P.-D. Jones, (2012), Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset, J. Geophys. Res., 117, D08101, doi: 10.1029/2011JD017187.
24. Nordhaus, W.-D., (2007), A Question of Balance: Weighing the Options on Global Warming Policies, Yale Univ. Press, New Haven, Conn.
25. Olson, R., R. Sriver, M. Goes, N.-M. Urban, H.-D. Matthews, M. Haran, and, K. Keller, (2012), A climate sensitivity estimate using Bayesian fusion of instrumental observations and Earth System model, J. Geophys. Res., 117, D04103, doi: 10.1029/2011JD016620.
26. Padilla, L.-E., G.-K. Vallis, and, C.-W. Rowley, (2011), Probabilistic estimates of the transient climate sensitivity subject to uncertainty in forcing and natural variability, J. Clim., 24, 5521-5537, doi: 10.1175/2011JCLI3989.1.
27. Ring, M.-J., and, M.-E. Schlesinger, (2007), Bayesian learning of climate sensitivity I: Synthetic observations, Atmos. Clim. Sci., 2, 464-473, doi: 10.4236/acs.2012.24040.
28. Sansõ, B., and, C. Forest, (2009), Statistical calibration of climate system properties, J. R. Stat. Soc. C, 58, 485-503, doi: 10.1111/j.1467-9876.2009.00669.x.
29. 2 and climate: Importance of the transient responses, J. Geophys. Res., 86, 3135-3147, doi: 10.1029/JC086iC04p03135.
30. Tanaka, K., E. Kriegler, T. Bruckner, G. Hooss, W. Knorr, and, T. Raddatz, (2007), Aggregated carbon cycle, atmospheric chemistry, and climate model (ACC2), Tech. Rep. Reports on Earth System Science No. 40, Max Planck Institute for Meteorology, Hamburg, Germany.
31. Urban, N., and, K. Keller, (2009), Complementary observational constraints on climate sensitivity, Geophys. Res. Lett., 36, L04708, doi: 10.1029/2008GL036457.
32. Urban, N.-M., and, K. Keller, (2010), Probabilistic hindcasts and projections of the coupled climate, carbon cycle and Atlantic meridional overturning circulation system: A Bayesian fusion of century-scale observations with a simple model, Tellus A, 62, 737-750, doi: 10.1111/j.1600-0870.2010.00471. x.