Feedbacks: their inconstancy & dependence on SST pa6erns

Transcription

1 Feedbacks: their inconstancy & dependence on SST pa6erns Timothy Andrews, Mark Webb & Jonathan Gregory Ringberg 2015

2 Thinking fast & slow: SST pa3erns & feedbacks The surface warming pa6ern is not constant, e.g. delayed east tropical Pacific and Southern Ocean warming. Atmospheric feedbacks change with the pa6ern of warming, giving more posigve feedbacks (shallower slope) a[er ~20yrs involves local and nonlocal processes. Largely robust across CMIP5 AOGCMs. All feedbacks show some change, though the largest increase is in cloud feedback. Andrews, T., J.M. Gregory and M.J. Webb, 2015: The dependence of radiagve forcing and feedback on evolving pa6erns of surface temperature change in climate models. J. Climate, 28, , doi: /jcli- D

3 Science gaps Curvature (i.e. inconstant feedbacks) appears to be an issue in AOGCMs Process understanding: What is going on? What are the physical processes? Why do feedbacks become more posigve rather than more negagve? Why do some models show larger curvature than others? What is the role of atmospheric versus ocean physics? How does variability affect SST pa6erns and feedbacks? Implica=ons: How may it manifest itself in the 20 th century? What does this imply for observed esgmates of climate sensigvity? Are they biased low? Not going to answer all these today, but I will show some new experimental designs/ results that may help us move forward very much work in progress...

4 Process understanding of the dependence & spread of climate feedbacks on the pa3ern of SST change 4xCO 2 fast SST pa6ern 4xCO 2 slow SST pa6ern obs SST pa6ern CFMIP proposal: amip4kfast, amip4kslow, and now amip4k20c? Equivalent to the CFMIP amip4k uniformed & pa6erned experiments, but with pa6erns of SST change derived from the transient behaviour of AOGCMs and observagons Allows us to readily determine the atmospheric feedbacks to any given SST pa6ern and gives us a clear regional signal with process diagnosgcs to help understand the mechanisms involved across models Allows us to test whether observed 20 th century warming generates feedbacks of relevance to long term climate change and ECS

5 HadGEM2- A feedbacks from amip4k experiments with observed & simulated SST pa3erns 4xCO 2 fast SST pa6ern 4xCO 2 slow SST pa6ern obs SST pa6ern dn/dt = Wm - 2 K - 1 ECS ~ 3.3K dn/dt = Wm - 2 K - 1 ECS ~ 5K dn/dt = ECS ~ 2.3K Wm- 2 K- 1 Reproduces the curvature in the Gregory plot, i.e. amip4kslow has more posigve feedbacks (larger ECS) than amip4kfast 20 th century warming pa6ern gives very small ECS (~½ of known ECS for this model). Why is this? Does it implies observed esgmates of climate sensigvity will be biased low?

6 HadGEM2- A feedbacks from amip4k experiments with observed & simulated SST pa3erns 4xCO 2 fast SST pa6ern 4xCO 2 slow SST pa6ern obs SST pa6ern dn/dt = Wm - 2 K - 1 ECS ~ 3.3K dn/dt = Wm - 2 K - 1 ECS ~ 5K dn/dt = ECS ~ 2.3K Wm- 2 K- 1 Climate sensigvity halved just by changing warming pa6ern, note no change in model physics!

7 An example of process understanding from marine low clouds off the coast of California All experiments have similar dt in this region, but large spread in cloud feedback (0-3 Wm - 2 K - 1 ) Spread in warming profile & lower tropospheric stability (LTS) strongly correlates with spread in cloud feedback. 20 th century SSTs produce such a large LTS increase it thro6les the cloud feedback. Perhaps a thermodynamic posigve cloud feedback, which is then thro6led or enhanced by LTS changes that are sensigve to the warming pa6ern?

8 Exploring tendency terms will give more physical insight into the underlying processes/mechanisms Humidity and temperature tendency terms allow us to probe the processes further and test various hypothesis regarding mechanisms of cloud changes (e.g. Webb and Lock, 2013)

9 Science Gap: Are climate feedbacks during the 20 th century different to those ac=ng on long term climate change? CFMIP proposal: amip with pre- industrial forcings amippiforcing (Andrews, 2014) HadGEM2- A forced with observed sea- ice and SSTs over last 30yrs gives a feedback parameter of ~- 2.4 Wm - 2 K - 1. This implies a climate sensigvity of ~1.3K, way below known ECS of 4.5K. Andrews, T., 2014: Using an AGCM to diagnose historical effecgve radiagve forcing and mechanisms of recent decadal climate change. J. Climate, 27, , doi: /jcli- D

10 Science Gap: Are climate feedbacks during the 20 th century different to those ac=ng on long term climate change? Obs: HadCRUT4 v Allan etal. (2014) Apply a similar technique to all CMIP5 AMIP runs by excluding volc yrs & assuming a decadal trend in forcing (~0.4 Wm - 2 dec - 1, IPCC AR5). CMIP5 AMIP models give very low ECS values (~1-2K), no discrepancy with observagons when using models forced with observed SSTs Not a surprise, the feedback response to the last 30yrs of dt (which includes east Pacific cooling) is strongly negagve. Implies esgmates of feedbacks and climate sensigvity from the satellite era will be biased low compared to long term esgmates. But no 1:1 map between them yet...

11 Summary & discussion Targeted AGCM experiments with various SST pa6erns traceable to AOGCM transient behaviour and observagons are a valuable tool in understanding the mechanisms and processes related to Gme varying feedbacks Feedbacks are very sensigve to warming pa6erns: I can get ECS values from 1.3 to 5K just by changing the warming pa6ern in HadGEM2! HadGEM2- A results give some insight into why cloud feedback varies with SST pa6erns, relates to LTS change for low clouds. CMIP5 AMIP experiments forced with observed SST variagons give feedback and ECS esgmates (~1-2K) in agreement with observagons. Perhaps no discrepancy between models and observagonal esgmates of climate sensigvity a[er all, its just the historical record esgmates are biased low because of variability in SST pa6erns InteresGng that emergent constraints do not rely on simple linear feedback model, and give higher ECS esgmates (e.g. Sherwood et al., Fasullo & Trenberth), unifies models and various observagonal ECS esgmates? Much more to understand and do. Detailed results mostly from a single model. Some of these experiments have been proposed for CMIP6 and/or CFMIP3 please do them, they are cheap runs, early mulg- model pilot study welcome!