# Presentation: 200390

## The Iris Hypothesis Revisited:

**Analysis of Upper Tropospheric Cloud Variations with SST **
**using Aura MLS and Aqua AIRS Observations**
**Hui Su**
**Jonathan H. Jiang, Daniel Feldman (Caltech), Yu Gu (UCLA)**
**Acknowledgements:**
- William G. Read, Dong L. Wu, Michael J. Schwartz
- Joe W. Waters, Nathaniel J. Livesey, Michelle L. Santee
- Microwave Atmospheric Science Team
- Jet Propulsion Laboratory
- California Institute of Technology

## Introduction

**Cloud feedback is one of greatest uncertainties in climate modeling and climate prediction**
**Upper tropospheric (UT) clouds reduce outgoing longwave radiation (OLR) to space, causing a warming effect; they also increase planetary albedo and reduce incoming solar radiation, producing a  cooling effect **
**Upper tropospheric clouds are closely related to  UT humidity and its greenhouse effect (e.g. *****Su et al*****. *****2006*****). **
**It is important to quantify the net radiative effects of UT clouds and their changes with surface temperature, and the associated feedbacks**

## Existing Studies on Clouds and SST Relation

**Tropical deep convection increases with SST**** ***(Ramanathan and Collins 1991; Waliser et al. 1993; Collins et al. 1996; Lin et al. 1995; Lau et al. 1997; Bony et al. 1997; Tompkins and Craig 1999)*
**Do UT clouds increase or decrease with SST?** *(Lindzen et al. 2001; Hartmann and Michelsen 2002; Lin et al. 2001, 2004; Del Genio et al. 2002)*

**Do Clouds have positive or negative climate feedback?**

## Spatial Variation of UT Clouds with SST

***Su et al. (2006)***** showed that UT cloud ice increases with SST when SST is greater than ~300 K, leading to a moistened UT and enhanced water vapor greenhouse effect.**

## The Iris Hypothesis (Lindzen et al. 2001, BAMS)

**Scatterplots showing how cirrus coverage varies with cloud-weighted SST (From Fig. 5 in *****Lindzen et al., BAMS, 2001).***** They argued that cirrus cloud coverage normalized by a measure of cumulus coverage decreases about 22% per degree increase of SST, implying a negative climate feedback that would more than cancel all the positive feedbacks in current climate models.**

**Cloud     Fraction**

**Cloud     Fraction**

## Highlights of Lindzen et al. (2001) Analysis

** ****Simplify global circulation using a 3.5-box model**

**Extra-tropics**

- Tropics

**Clear/Dry**

**Clear/**
**moist**

**Cloudy**
**/moist**

** ****Used daily mean cloud fraction and cloud-weighted SST relation over W. Pacific to infer clouds-SST relation for climate change**
** ****Radiative transfer calculations were based on assumed optical properties of clouds to match radiation budget from ERBE**

## “Iris” Seen in AIRS Cloud Fraction-SST Relation

**Scatter plots of area-averaged daily Aqua-AIRS cirrus cloud fraction (with cloud top pressure less than 400 hPa) over the region 130********E-170********W, 30********S-30********N for the period of August 04 to November 05 versus Aqua-MODIS simultaneous SST.**

**Over the western Pacific convective region (130********E-170********W, 30********S-30********N), area-averaged AIRS cirrus cloud fraction decreases ~6% (20% relative change) with 1 K increase of SST.**** **

**However, cloud fraction represents only one aspect of cloud properties. Without cloud optical thickness information, it is not possible to compute the radiative effect of clouds. The MLS IWC measurement provides the 3-D information of cloud mass. Thus it is a better measure to account for cloud radiative effects. **

## Revisit the Iris Hypothesis 

**      ****Using Aura MLS cloud ice water content (IWC) measurements, and Aqua MODIS SST and CERES radiative fluxes data to tackle the clouds-SST relationship**
**Advantages of MLS cloud IWC data **
**Cloud mass measurement in the UT**
**Cloud vertical profiles**
**Simultaneous measurements of UT H****2****O and Temperature**
**Approach **
**Simplify tropical circulation using a two-box model: cloudy and clear boxes              **
**	****	****Note: the box boundaries are time-dependent, based on IWC > 0 mg/m****3**
**Examine how the mean IWC over the cloudy area changes with SST**
**Examine how UT H****2****O and T changes with SST**
**Compute the changes of top-of-atmosphere (TOA) and surface radiative fluxes associated with the changes of clouds, UT H****2****O and T using the Fu-Liou radiation model**
**Quantify the climate feedback parameters for each variable **

**where G is net TOA fluxes, and A can be IWC, T and H****2****O**

## Using Daily MODIS SST Data

**Shown on the left is the annual-mean IWC vs. NCEP and MODIS SSTs for all tropical oceanic boxes within 30S-30N (spatial variability). **
**In following discussions, we will examine the mean IWC changes averaged over the cloudy areas in relation to daily MODIS SST changes (temporal variability). **

**Compared to NCEP SST, the MODIS SST has approximately 2 K cold bias across the entire tropics. Other SST data (e.g. NCEP, TMI) will be used later.**

**NCEP SST: weekly or monthly**
**Aqua MODIS SST: daily, nearly simultaneous with Aura MLS IWC measurements**

## Use a time-dependent cloudy area

**The mean IWC over the tropical cloudy area (30S-30N) increases about ~10% per degree K increase of SST, while the fraction of cloudy-area is insensitive to SST changes.**

**215 hPa**

**147 hPa**

**100 hPa**

**y = 0.54 x – 152.63**
**R = 0.3**

**y = 0.23 x – 66.71**
**R = 0.3**

**y = 0.023 x – 6.69**
**R = 0.2**

**Cloudy area fraction**

**Cloudy area fraction**

**Cloudy area fraction**

**IWC**

**IWC**

**IWC**

## Simultaneous UT H2O and T changes

**The 316-147 hPa water vapor increases by ****~6%**** per degree increase of SST. **

**The 316-147 hPa temperature increases by ****~1 K ****per degree increase of SST. **

**y = 7.7  x – 1836.2**
**R = 0.08**

**y = 5.9  x – 1650.5**
**R = 0.3**

**y = 0.88  x – 247.8**
**R = 0.2**

**y = 1.2 x – 120.5**
**R = 0.4**

**y = 1.0  x – 85.3**
**R = 0.5**

**y = 0.62  x + 10.4**
**R = 0.4**

**H****2****O**

**H****2****O**

**H****2****O**

**Temperature**

**Temperature**

**Temperature**

## MLS-observed UT Cloud Forcing

|  | IRTOA | SWTOA | IRSFC | SWSFC |
| --- | --- | --- | --- | --- |
| 30S-30N
(W/m2) | 25.6
(Warming) | 6.8
(Cooling) | 1.3
(Warming) | 8.5
(Cooling)  |

**Define cloud forcing as**

## Radiative Impact of UT Cloud Change

|  | IRTOA | SWTOA | IRSFC | SWSFC |
| --- | --- | --- | --- | --- |
| 30S-30N
(W m2 K1) | 1.3
(Warming) | 0.4
(Cooling)  | 0.1
(Warming) | 0.5 
(Cooling) |

**When UT **(215 hPa and up)** IWC increases 10% for 1 K increase of SST, the changes of TOA fluxes are about  2 to 4 W m********2****, dominated by warming effect due to reduced OLR. **
**UT clouds have a positive feedback to global warming**** **

## Coupled UT H2O, Clouds and T Changes

|  | IRTOA | SWTOA | IRSFC | SWSFC |
| --- | --- | --- | --- | --- |
| 30S-30N
(W m2 K1) | 0.9
(Warming) | 0.4 
(Cooling) | 0.1
(Warming) | 0.5
(Cooling) |

**The increase of UT H****2****O at 6% per degree increase of SST would produce a **_**positive **_**feedback of ~ 0.1 W m********2 ****K********1****. The increase of UT temperature at 1 K per degree increase of SST  would provide a **_**negative**_** feedback, about 0.5 W m********2 ****K********1****.**** **

**Verification of these results using other datasets and model simulations  are needed!**

## Summary

**The UT clouds-SST relationship depends on spatial and temporal scales in question.  **
**Tropical daily cloudy-area averaged mean IWC increases approximately ~10% per degree of SST increase, accompanied by ~6% increase of UT water vapor  and about 1 K increase of UT temperature.**
** ****The MLS-observed UT clouds have a net warming effect to the climate system. Thus the increase of UT clouds with SST would produce a positive feedback to global warming. **
**The increase of UT water vapor enhances positive feedback by clouds, while the increase of UT temperature tends to mitigate the positive cloud feedback.**
**Our preliminary analysis using MLS IWC measurements shows disagreement with *****Lindzen et al.***** (2001) ‘Iris’ hypothesis in terms of cloud-climate feedback. **