Water Vapor and Cloud Feedbacks Dennis L. Hartmann in collaboration with Mark Zelinka Department of Atmospheric Sciences University of Washington PCC Summer Institute 2010

Basic Greenhouse Effect The atmosphere is translucent to solar radiation. Because water vapor, other greenhouse gases and clouds are opaque to Earth’s thermal emission, And because the temperature decreases with altitude, The emission from Earth comes from the atmosphere about 5km up, where it is about 30˚C colder than the surface of Earth.

Greenhouse Effect BB Curve minus OLR 10  m 20  m 5  m 50  m Harries, QJ, 1996 Surface 30km Greenhouse Effect

Greenhouse Effect = Surface Emission - Outgoing Energy =390 Wm Wm Wm -2

W m -2

Water Vapor Feedback Saturation water vapor pressure increases about 7% for every 1˚K increase in temperature So if relative humidity is relatively constant The greenhouse effect of water vapor increases with temperature Giving strong water vapor FEEDBACK.

Emission Temperature Lapse Rate and water Vapor Fixed Absolute Humidity Fixed Relative Humidity Emission Temperature Runaway Greenhouse

Water Vapor Feedback Since Manabe and Wetherald (1967) it has been estimated that fixed relative humidity is a good approximation and vapor feedback roughly doubles the sensitivity of climate. Because water vapor is so strongly positive and interacts with other feedbacks, small deviations from the fixed relative humidity behavior would be significant and are worth studying.

Manabe & Wetherald ppm Fixed Clouds Clear Fixed Absolute Humidity  T = 1.33˚K  T = 1.36˚K Fixed Relative Humidity  T = 2.36˚K  T = 2.92˚K

Lapse Rate Feedback The Greenhouse effect depends on the lapse rate of temperature. If the lapse rate decreases with global warming, that is a negative feedback, since the difference in surface temperature and emission temperature will decrease, all else being equal. But all else is not equal, Water vapor feedback tends to lessen the importance of lapse rate feedback. See Cess, Tellus, 1975, page 193.

When relative humidity is fixed, so that absolute humidity is a function of temperature, lapse rate and relative humidity feedbacks on OLR tend to cancel. Since water vapor is the primary greenhouse gas, and depends only on temperature, emissivity is an increasing function of temperature. Emission Temperature

Water Vapor Feedback vs Lapse Rate Feedback in AR4 models Slope = -1

Validation for Fixed RH Assumption Seasonal Variation – Manabe and Wetherald 1967 Many observational studies. Volcanic Eruption – Soden et al 2002 Science All the models do it fairly closely – Sherwood et al 2010 JGR. El Niño – La Niña Difference – Zelinka 2011

ENSO Response – Mark Zelinka 30S-30N

ENSO Response 1˚K Tropical Warming - Zelinka

Temperature and Humidity Response to ENSO Models(top) vs AIRS(bottom) AIRS Data AR4 Model Control

Cloud Feedback Clouds have a strong impact on the radiation balance of Earth Reduce OLR by about 30 Wm -2 Reduce Absorbed Solar Radiation ~ 50Wm -2 Net effect about -20 Wm -2 If their radiative effects change with global warming, the effect could be a very significant cloud feedback.

Feedback Analysis using radiative Kernels Soden et al Where i represents a vertical level. And x represents water vapor w, Temperature T, and surface albedo a. Cloudiness C, is too nonlinear and is done as a residual.

Longwave Kernels: Temperature and Humidity Average Cloudiness

Longwave Radiative Kernels: Surface Vs Atmospheric Temperature Surface Contribution Atmosphere Contribution Total For Uniform 1˚K Temperature increase Zero

Longwave Radiative Kernels: Surface vs Atmospheric Temperature Surface Total Heavy Lines for modeled AR4 Temperature Change Atmosphere

Vertically Integrated Feedback Multiply the Kernels times the changes and integrate vertically to get the change in top-of- atmosphere energy flux required by feedback processes. Radiation Balance Change = Radiative Kernel x Change in state variable

Temperature and Humidity Changes SRES A2 Scenario AR4 Ensemble Normalized for 1˚K Global Mean Surface Temperature increase

Feedback = Kernel x Response Normalized to 1˚K global warming TotalClear

Vertically Integrated Feedbacks TOA Net Positive feedback near Equator Some Consistent Wiggles Implies increased poleward heat flux due to feedbacks

Integrated Feedbacks

LW and Shortwave Cloud feedbacks

Consistency of Longwave Cloud Feedback Zelinka from AR4 SRES A2

Longwave Feedbacks Only

Summary of Feedback Analysis Net positive feedback near equator comes from longwave water vapor and cloud feedbacks that seem robust. Consistent wiggle in Southern Ocean comes from shortwave cloud feedback and ocean upwelling, which provide heat sinks and cause atmosphere to increase its transport.

Feedbacks and Meridional Transport If you subtract the global mean and integrate the feedback over a polar cap, you get the change in meridional transport associated with feedback processes for each degree of global warming. If you combine this with the change in surface heat fluxes you can obtain the changes in atmospheric and oceanic heat flux. See also Dargan’s talk on Thursday.

Feedbacks including surface fluxes

Warming induced Surface Flux changes: More heat from atmosphere to surface in high latitudes, especially in SH: Heat Uptake by Ocean Warming induced Net flux into atmosphere from combined TOA and surface flux changes. Note net loss, and gradient in net loss are greater than for surface fluxes.

Transport Changes Annual Averages for AR4 Model Ensemble Feedbacks Flux Feedbacks Net O & A Flux Feedbacks In AR4 sRES A2 Model Ensemble: Oceanic Heat fluxes decrease, but atmospheric fluxes overcompensate to increase net flux ~ 0.1 PW K -1. Cause: Atmospheric Feedbacks.

Main Points Combined Temperature, Water Vapor and Cloud longwave feedbacks give a net positive feedback in the equatorial region. In AR4 models and we think also in nature. Cloud Shortwave feedback is still uncertain, but models seem to give a consistently negative feedback in high latitudes. Atmospheric feedbacks and ocean heat uptake combine to give interesting changes in meridional heat transport in the atmosphere and ocean.

The End

Radiative Kernels: Temperature Average Cloudiness Clear

Radiative Kernels: Water Vapor Average Cloudiness Clear

Relative humidity in Models Sherwood et al 2010

Models vs Observed ENSO Response of RH to Warming Models: Sherwood et al 2010 JGRAIRS Data Tropical SST Regression: Zelinka

Longwave Radiative Kernels: Surface Vs Atmospheric Temperature

Greenhouse Effect Sensitivity of OLR to Water Vapor Harries, QJ,  m20  m 5  m 50  m

Manabe & Wetherald 1967

Longwave Radiative Kernels: Temperature Clear vs Cloudy Average Cloudiness Clear

Water vapor Longwave Radiative Kernels: Clear vs Cloudy Average Cloudiness Clear

Cloud Feedback Cloud feedback has been identified as one of the primary uncertainties in global warming projections for at least 20 years. Longwave cloud feedback seems to be more consistently modeled Shortwave cloud feedback seems to be very poorly constrained in models and uncertain in nature.