WCRP Grand Challenge Workshop: Clouds, Circulation and Climate Sensitivity

Transcription

1 WCRP Grand Challenge Workshop: Clouds, Circulation and Climate Sensitivity Schloss Ringberg, 3700 Rottach-Egern, Germany March 24-28, 2014 This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA Lawrence Livermore National Security, LLC

2 Main Questions Raised How many believable emergent constraints exist for clouds? Should we organize as a community to make judgments about the reliability of emergent constraints? Will we do (or is it even possible to do) the hard work to determine the robustness of the physics underlying emergent constraints? Lawrence Livermore National Laboratory 2

3 1. Snow-Albedo Feedback over Northern Hemisphere continents Inter-model spread in the temperature sensitivity of surface albedo on shorttime-scales is correlated with that over climate change Hall and Qu (2006) based upon CMIP3 and verified for CMIP5 by Qu and Hall (2013) Lawrence Livermore National Laboratory 3

4 2. Subtropical marine lowcloud amount 3. Optical depth (~cloud albedo) and in-cloud liquid water content Estimate from satellites Climate Change of Low- Cloud Amount A per unit SST rise (% K -1 ) δa/δsst from Inter-annual Variability Qu et al. (2013) Gordon and Klein (2014, under review) based upon Tselioudis et al. (1992, 1998) For both, observations suggest the true cloud feedback is more positive than present in the average climate model Lawrence Livermore National Laboratory 4

5 Long term cloud feedback (W/m 2 /K) 4. Global mean cloud feedback Aggregation over too many processes with different sensitivities obscures relationships only somewhat 1,40 1,20 1,00 0,80 0,60 0,40 0,20 0,00-0,20 y = x R² = ,00 0,50 1,00 1,50 2,00 5. Climate sensitivity Comes with physical hypothesis that is potentially testable Current climate variable is a state property (degree of lowertropospheric mixing) and not a sensitivity coefficient Short term cloud feedback (W/m 2 /K) Dessler (2013) from CMIP3 Models with even better correlation for CMIP5 models (Zhou and Dessler, in preparation) Sherwood et al. (2014) Lawrence Livermore National Laboratory 5

6 Reliability Criteria for Emergent Constraints Do we let studies proliferate without trying to resolve them? Would a community effort to assess reliability be worthwhile? Or leave that to IPCC? Should we develop a best practices document? Would a community statement motivate climate model developers to pay attention to emergent constraints? Cautionary tale! Inter-model spread in surface albedo feedback is just as large in CMIP5 as it was in CMIP3 Plausible physical mechanism (but this is really hard to prove) Reliability across modeling ensemble (CMIP3 vs. CMIP5) Focus on individual processes, not aggregations of processes Strength of relationship (but statistical significance is hard to prove) Sample widest possible structural uncertainties (single model ensembles are insufficient) Examines sensitivity of the system to the environment (and not state vectors) Consistent behavior across all time-scales or an understanding as to why a time-scale does not exhibit the behavior Lawrence Livermore National Laboratory 6

7 Main Questions Raised Is it time and is it necessary for the credibility of the science to address the grand challenge to observe and explain long-term (> 20 years) cloud trends? Should we examine and look for the cloud trends that models agree upon as well as those that they disagree upon? Different signals will emerge from natural variability on different time-scales. Are climate models up to the challenge of attributing cloud trends to the combination of greenhouse gases, aerosols, ozone, and natural variability? Lawrence Livermore National Laboratory 7

8 Three common signals Poleward expansion of cloud features (i.e. circulation expansion) High-clouds increase their altitude (as the extra-tropical tropopause and level of tropical radiative cooling rises) cloudy get cloudier and clear get clearer pattern According to the models, when do signals emerge from natural variability? What are the satellite observed trends for these signals? Detection and attribution study spliced historical RCP 8.5 simulations from CMIP5 models (Kate Marvel et al., in preparation) Lawrence Livermore National Laboratory 8

9 DJF Lawrence Livermore National Laboratory 9

10 % Latitude Common trend for in CMIP5 simulations Lawrence Livermore National Laboratory 10

11 % Latitude Common trend for in CMIP5 simulations Lawrence Livermore National Laboratory 11

12 % Lawrence Livermore National Laboratory 12

13 Height Latitude Combined Amount Lawrence Livermore National Laboratory 13

14 Poleward Expansion Cloudy-Get- Cloudier & Clear-Get- Clearer High Clouds Rising (Tropics/Subtropi cs Only) ISCCP PATMOS Should artifact-corrected cloud datasets be developed for key indicators of climate change? Explained by other forcing (ozone and aerosols) that is not correctly represented in models? Satellites disagree on cloud amount trends Satellites see the predicted rise in highcloud altitude, a signal that should be beginning to emerge from the noise Lawrence Livermore National Laboratory 14

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17 Most studies find that models which better simulate the current climate have a larger climate sensitivity Are these chance associations? Justifying any of these relationships on pure statistical grounds is highly unlikely (Masson and Knutti 2013, Caldwell et al. 2014) Current Climate Quantity Net Radiation of the Southern Hemisphere Upper-tropospheric relative humidity in subtropical subsidence regions Skill score for cloud properties Shortwave cloud radiative forcing in subtropical oceanic subsidence regions Lateral entrainment rate, Ice fall speed, etc. Future Climate Quantity Equilibrium Climate Sensitivity Equilibrium Climate Sensitivity Global mean net and shortwave cloud feedback Equilibrium climate sensitivity Climate sensitivity Reference Trenberth and Fasullo (2010) Fasullo and Trenberth (2012) Klein et al. (2013) Klocke et al. (2011) Sanderson et al. (2008, 2011) Lawrence Livermore National Laboratory 17

18 Main Questions Raised Which cloud observations needs systematization and attention for long record? Can we prioritize observations needed to constrain models (both trends and sensitivities)? Potential observational constraints coming soon. Are we prepared to constrain models? Lawrence Livermore National Laboratory 18

19 Poleward Expansion Index Cloudy-Get- Cloudier Index High Clouds Rising Index Lawrence Livermore National Laboratory 19

20 Models generally predict that the Peruvian and Namibian stratocumulus should increase relative to Californian Stratocumulus Why? Sea surface temperatures do not warm as much in the southern hemisphere stratocumulus cloud decks Qu et al. (2013) Lawrence Livermore National Laboratory 20

21 ( ) Trenberth and Fasullo (2013) It is likely PDO phase shift dominates current trends through lower SST in the Californian and Peruvian stratocumulus cloud decks, higher stability, and more Scu in both hemisphere (Clement et al. 2009) Norris and Evan (2014, submitted) Lawrence Livermore National Laboratory 21

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26 Models, obs agree on 2/3 points ISCCP: possible spurious decreasing trend Both datasets show height rise (PATMOSx shows more) Lawrence Livermore National Laboratory 26

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