Hadley Circulation Cloud Feedback and Climate Sensitivity Yuk

Hadley Circulation, Cloud Feedback and Climate Sensitivity Yuk L. Yung Caltech Academia Sinica Seminar, 20 March, 2015 Collaborators: Hui Su, Jonathan H. Jiang, Chengxing Zhai, Janice T. Shen David J. Neelin, Graeme L. Stephens

Introduction § “The equilibrium climate sensitivity is defined as the change in global mean surface temperature at equilibrium that is caused by a doubling of the atmospheric CO 2 concentration. Equilibrium climate sensitivity is likely in the range 1. 5°C to 4. 5°C (high confidence), extremely unlikely less than 1°C (high confidence), and very unlikely greater than 6°C (medium confidence). ” – IPCC AR 5, 2013 § The spread of ECS has resisted reduction for decades. “ We estimate the most probable global warming for a doubling of CO 2 to be near 3°C with a probable error of ± 1. 5°C” – the Charney Report, 1979

IPCC First Assessment Report (FAR, 1990) “The key areas of scientific uncertainty are Clouds ……” u

Cess et al. (1989)

Cloud Feedback § Cloud feedback is one of the leading contributors to the intermodel spread in climate sensitivity (Stephens, J. Climate, 2005)

Motivation § Model differences in cloud feedback is a leading contributor to the uncertainty of climate sensitivity. § What process drives the inter-model spread in cloud feedbacks to increasing CO 2? ? Cloud Feedback Climate Sensitivity § Satellite observations have been used extensively to evaluate model performance of present-day climate. § Can satellite observations provide indications of future climate change?

Motivation Ø What drives the inter-model spread in cloud feedback? ? Cloud Feedback Climate Sensitivity Ø How can we constrain model estimates of ECS using observations? § Design model performance metrics directly relevant to climate sensitivity

Relative Humidity and Cloud Feedback § Subtropical free-tropospheric relative § Models that are close to the humidity (RH) bears a strong negative observed RH have relatively high correlation with ECS. (Fasullo and Trenberth, Science, 2012)

Convective Mixing Process and Low Cloud Feedback Lower Tropospheric Mixing Index (LTMI) = Small-scale mixing (S) + Large-scale mixing (D) S = (∆R 700 -850/100% − ∆T 700 -850/9 K) / 2 D = <∆H(∆)H(− ω1)>/< − ω2 H(− ω2)> where ∆ = ω2− ω1 ω2 the average of at 600 h. Pa, 500 h. Pa and 400 h. Pa ω1 the average of at 850 h. Pa and 700 h. Pa (Sherwood et al. , Nature, 2014)

Large-scale Circulation and Clouds (Emanuel, 1994)

Clouds Sorted by ω500 (Su et al. , JGR, 2013)

Changes of the Hadley Circulation, Clouds and Cloud Radiative Effects in the RCP 4. 5 Multi-model-mean from 15 CMIP 5 coupled models ∆ = 2074 -2098 in “RCP 4. 5” – 1980 -2004 in “historical run”

The Equatorial Tropics (around 5°S to 5°N) S W W S WW S ∆ = 2074 -2098 in “RCP 4. 5” – 1980 -2004 in “historical run”

The Flanks of Deep Tropics (around 5° to 15°N/S) S W W S WW S ∆ = 2074 -2098 in “RCP 4. 5” – 1980 -2004 in “historical run”

Equator-ward side of Descent Zone (about 15°-30°N/S) S W W S WW S ∆ = 2074 -2098 in “RCP 4. 5” – 1980 -2004 in “historical run”

Poleward-side of Descent Zone (about 30°-45°N/S) S W W S WW S ∆ = 2074 -2098 in “RCP 4. 5” – 1980 -2004 in “historical run”

High and Low Sensitivity Model Composites Similar in pattern, but different in magnitude.

Quantifying the Model Differences in Circulation and Relation with Cloud Radiative Effect Changes The explained variance by the 1 st EOF is 57% • Area-weighted CRE changes for the weakening and strengthening segments account for 54% and 46% of the total CRE change within the HC. • The amplitudes of the 1 st EOF mode differ by two orders of magnitude in models. • Differences in the Hadley Circulation are highly correlated with the intermodel spread in net CRE.

Normalized Response Largest Circulation Change Smallest Circulation Change

Moisture Deficit Smallest Circulation Change Largest Circulation Change

Weakened Subsidence and Boundary Layer Drying

Normalized CRE Changes Net CRE Largest Circu. Change Smallest Circu. Change SW CRE LW CRE

Circulation, Cloud Feedback and Climate Sensitivity Inter-model Spread Circulation Response Cloud Feedback Climate Sensitivity “ Three Cs”

Comparing to Satellite Observations The Hadley Circulation Cloud. Sat/CALIPSO Cloud Fraction and AIRS/MLS Relative Humidity

Quantitative Model Performance Metrics to Represent the Hadley Circulation Structure OBS

Quantitative Model Performance Metrics to Represent the Hadley Circulation Structure OBS

ECS (°C) Satellite-based “Best Estimates” of ECS The best estimates of ECS range from 3. 6 to 4. 7°C, with a mean of 4. 1°C and a standard deviation of 0. 4°C, compared to the multimodel-mean of 3. 4°C and a standard deviation of 0. 9°C.

Precipitation and Water Vapor ΔP (mm/mon) ΔW (mm/mon) (A) Deseasonalized time series of oceanic precipitation from GPCP V 2. 1 and SSM/I. (B) Deseasonalized time series of oceanic water vapor from SSM/I, AIRS, AMSR-E, and NVAP. 28

Spatial Pattern of the Mean Precipitation for 1988 -2008 29

Temporal Variations of Precipitation over High & Low Precipitation Areas 30

Conclusions (1) • Changes of the Hadley Circulation exhibit latitudinally alternating weakening and strengthening structures, with nearly equal contributions by the weakening or strengthening segments to the integrated cloud radiative effect changes within the Hadley Cell. • Model differences in circulation change is correlated with cloud feedback strength and explains about 1520% of the inter-model spread in cloud radiative effect changes.

Conclusions (2) • High sensitivity models simulate better the spatial variations of clouds and relative humidity in association with the Hadley Circulation than the low sensitivity models, consistent with previous studies (Fasullo and Trenberth, 2012; Klein et al. , 2013; Sherwood et al. , 2014). • Based on the model performance metrics that emphasize the cloud fraction and relative humidity distributions within the entire Hadley Cell, the best estimates of equilibrium climate sensitivity are higher than the multi-model-mean.

Li, L. M. , X, Jiang, M. T. Chahine, E. T. Olsen, E. J. Fetzer, L. K. Chen, Y. L. Yung. (2011). “The recycling rate of atmospheric moisture over the past two decades (1988 -2009). ” Environmental Research Letters 6 (3) Article Number: 034018. DOI: 10. 1088/1748 -9326/6/3/034018 Su, H. , J. H. Jiang, C. Zhai, T. J. Shen, J. D. Neelin, G. L. Stephens, and Y. L. Yung. (2014). “Weakening and Strengthening in the Hadley Circulation Change under Global Warming and Implications for Cloud Response and Climate Sensitivity”, J. Geophys. Res. , 119, 5787 -5805. DOI: 10. 1002/2014 JD 021642