Understanding Climate Feedbacks Using Radiative Kernels

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1 Understanding Climate Feedbacks Using Radiative Kernels Brian Soden Rosenstiel School for Marine and Atmospheric Science University of Miami Overview of radiative kernels Recent advances in understanding cloud feedback: Cloud feedback is not negative High cloud feedback is positive and robust Low cloud feedback is neutral to positive and highly variable

2 Global Mean Surface Temperature Radiative Forcing Radiative Feedbacks Change in Global Surface Temperature IPCC AR4 GCMs IPCC The likely range of climate sensitivity is: to 4.5 C to 4.5 C to 4.5 C to 4.5 C

3 IPCC Assessments Water Vapor Feedback Cloud Feedback 1990: 1992: 1995: 2001: 2007: The best understood feedback mechanism is water vapor feedback, and this is intuitively easy to understand There is no compelling evidence that water vapor feedback is anything other than positive although there may be difficulties with upper trop. water vapor Feedback from the redistribution of water vapor remains a substantial source of uncertainty in climate models The balance of evidence favours a positive clear-sky water vapour feedback of magnitude comparable to that found in (model) simulations Observational and modelling evidence provide strong support for a combined water vapour/lapse rate feedback of around the strength found in GCMs Feedback mechanisms related to clouds are extremely complex The effects of clouds remain a major area of uncertainty in the modeling of climate change In previous IPCC reports cloud feedback was identified as a major source of uncertainty. Considerable research efforts have further reinforced this conclusion. there has been no apparent narrowing of the uncertainty range associated with cloud feedbacks Cloud feedback has been confirmed as a primary source of uncertainty.

4 Climate Feedbacks: Kernel Method T s G G = radiative forcing R = net radiation at TOA = climate sensitivity parameter (rate of radiative damping) dr dt dt dt s dr dw dw dt s dr dc dc dt s dr d d dt s Temperature Water Vapor Cloud Sfc Albedo Feedback Feedback Feedback FeedbacK Climate = Feedback dr/dx Radiative Transfer X dx/dt s Climate Response

5 Water Vapor Feedback using Kernels Water Vapor Kernel (from RT code) Water Vapor Response to 2xCO2 (from GCM) x dr dw dw dt s Radiation is most sensitive to upper Fractional changes in water troposphere because clouds mask vapor are also largest in upper contributions from lower Water levels Vapor Feedback = Kernel x troposphere Response due to C-C. =

6 Ensemble Mean Feedbacks: IPCC AR4 GCMs

7 Climate Feedbacks in IPCC Models Bony et al Water vapor provides the strongest positive feedback in GCMs. Water vapor and lapse-rate are strongly correlated. There is no model with a negative cloud feedback.

8 Lapse Rate and Water Vapor Feedbacks: IPCC AR4 GCMs Water vapor feedback is larger in models with greater warming of upper troposphere Models with greater lowlatitude warming have larger lapse-rate feedback.

9 Climate Feedbacks in IPCC Models Bony et al Water vapor provides the strongest positive feedback in GCMs. Water vapor and lapse-rate are strongly correlated. There is no model with a negative cloud feedback.

10 Intercomparison of Climate Feedbacks Cess et al. (1989) Cloud feedback is primary cause of uncertainty

11 Causes of Intermodel Spread in Cloud Feedback High (0.07) Mixed (0.18) Low (0.75) High cloud feedback is positive and robust Consistent with FAT hypothesis of Zelinka and Hartmann (2010) Low cloud feedback is highly variable Soden and Vecchi (2011)

12 Local contribution to intermodel spread in cloud feedback Most of intermodel spread arises from low stratocumulus/cumululs regions Possible links to weakening of atmospheric circulation Soden and Vecchi (2011)

13 Remaining Challenges Why is low cloud feedback positive in models? What role do changes in the large-scale circulation (subsidence) regulate low cloud changes in the tropics? What other factors besides subsidence are important? Is there observational evidence to support any of this?

14 Extra Slides

15 Why is High Cloud Feedback Positive? High cloud changes in GCMs follow a nearly constant temperature (rather than constant altitude). This behavior is supported by observations (Z&H 20011) Zelinka and Hartmann (2010)

16 Change in OLR due to constant RH increase in WV Water Vapor Kernel (zonal, annual mean) Total Sky Upper Tropospheric amplification due to C-C: de s /e s ~dt/t 2 T=300 T=200 Clear Sky Largest feedback comes from upper troposphere because that is where the fractional change in water vapor is greatest. W/m 2 /K/100 mb

17 Ensemble Mean Cloud Feedback W/m 2 /K W/m 2 /K

18 Why is High Cloud Feedback Positive? Zelinka and Hartmann (2010) As climate warms, there is an upward shift in the level of divergence (and Q R ) due to increased water vapor

19 Observational Evidence for PHAT Observed interannual changes in tropical high clouds follow FAT/PHAT. Zelinka and Hartmann (2011)

20 How Good Are Model Simulations of Water Vapor? Multi Model Ensemble Mean Specific Humidity IPCC AR4 GCMs - AIRS Pierce et al John et al The biases in current GCM simulations are large: Moist biases of up 100% in upper troposphere. Dry biases of ~25% in lower troposphere.

21 Model Simulations of Water Vapor The biases in water vapor vary substantially from model to model yet all have very similar wv feedbacks.

22 Change in Total Column Water Vapor (%) The Consistency of Water Vapor Feedback Change in Temperature (C) The absorption by water vapor increases in proportion to the logarithm of its concentration. Consistent fractional changes = consistent feedback from water vapor.

23 Importance of Water Vapor Feedback W/m 2 /K Positive feedbacks mutually amplify their impact on climate sensitivity.

24 El Nino (warm) La Nina (cold) El Nino La Nina Satellite-Observed and Model-Simulated Changes in Atmospheric Water Vapor? Pinatubo

25 Uncertainty in Aerosol Radiative Forcing: 2xCO2 vs. A1b Uncertainty in CO2: ~0.5 W/m 2 ~3 W/m 2

26 Climate Sensitivity and Climate Feedbacks T s G G = radiative forcing R = net radiation at TOA = climate sensitivity parameter (rate of radiative damping) dr dt dt dt s dr dw dw dt s dr dc dc dt s dr d... d dt s Temperature Water Vapor Cloud Sfc Albedo Feedback Feedback Feedback FeedbacK W/m2/K