# Presentation: R4SU5F6GGL4LNJTGPPGGWRS546QPSI5H

##  HOW WELL CAN WE SIMULATECLIMATE CHANGESIN MIDLATITUDE STORMS?

***Tony Del Genio***
***NASA/Goddard Institute for Space Studies***
***(with help from***
***Audrey Wolf, Mao-Sung Yao, Mike Bauer)****** ***

## Why should we pay attention to midlatitude storms?

**Regional cloud feedback important to regional temperature trends**
**Changes in storm frequency/strength affect drought/flood occurrence, water availability, forest fires**
**Community has mostly shifted attention to the tropics, forgotten that we never actually tested our ability to simulate these “well-understood” phenomena **
**Why do we need a good global precipitation/microphysics dataset at high temporal resolution?**

## Topics to be discussed:

**GCM predictions of climate changes in storm occurrence and strength**
**Simulated vs. analyzed vs. observed composite storm precipitation patterns**
**Use of precipitation data to improve analysis advective forcing**
**Simulated vs. observed storm cloud anomalies in response to 2xCO****2****-like advective forcing**
**Simulated vs. observed hydrometeor structure in midlatitude cold front passage case study**

## GCMs predict that midlatitude storms will be less frequent overall (decreased |T/y|) but strong storms will be more frequent (increased latent heating?) in the future

**Carnell and**
**Senior (1998)**

**Early 21****st**** C.**
**Late 21****st**** C.**

## GCM composite storm precipitation pattern centered more on surface low than frontal locations; reanalysis precipitation magnitudes differ from other and TRMM

**GCM                ERA-40                NCEP**

**TRMM merged 3-hr**

**Weak**
**Moderate**
**Strong**

## Constrained variational analysis (Zhang and Lin):  uses TOA and surface observational constraints to adjust analysis atmospheric state variables (s, q, u, v, ω) to conserve column mass, moisture, static energy, momentum 

## Constrained variational analysis (Zhang and Lin):  uses TOA and surface observational constraints to adjust analysis atmospheric state variables (s, q, u, v, ω) to conserve column mass, moisture, static energy, momentum 

**⃗**

**X**

**√**

**√**

**√**

**Apply to**
**analysis**
**products to**
**get long-term**
**forcing for**
**CRMs, SCMs;**
**precipitation**
**data are the**
**major impact**

## Improved vertical velocities during precipitating periods allow creation of long-term advective forcing data sets for SCMs, CRMs; with accurate global P could be applied to global reanalysis products to directly test parameterizations 

**sondes**
**    ****+**
**profilers**

**corrected**
**analysis**

**original**
**analysis**

**↑**

**rain rate**

**Xie et al. (2004)**

## 2xCO2 advective forcing anomalies correspond to upward shift in adiabatic cooling/moistening and warming/drying; current climate analogs for ω < 0 ~ strong synoptic storms

**Red = visible**
**Green = water vapor**
**Blue = window IR**

**Note:  Only 23 ISCCP**
**(3-hr) examples over 10 cold months**

## SCM overpredicts response of high, optically thick (= precipitating) clouds to climate forcing

**Del Genio et al. (2004)**

## GCM vs. observed temperature dependence of liquid-ice transition- dependence on dynamical regime (e.g., cyclogenesis vs. mature storms)?  Effect on precipitation? 

**Doutriaux-Boucher and Quaas (2004)**

## Liquid water content during cold front passage over Oklahoma – CRMs no better than SCMs, do not develop mesoscale dynamical structure

**cloud radar**

**CRMs**

**SCMs**

## Ice water content during cold front passage over Oklahoma – ditto

**cloud radar**

**CRMs**

**SCMs**

## Potential uses of GPM data for climate model studies of midlatitude storms:

**Statistical studies of large populations of “objects” (e.g., composite precipitation patterns around lows ****→ development of ageostrophic frontal circulation, liquid-ice transition, rain-snow formation parameterizations**
**Devlopment of precipitation intensity pdfs for input to land surface/ground hydrology parameterizations**
**Creation of accurate global adjusted reanalysis products for forcing SCMs, CRMs**
**Studies of response of model storms to climate-change-like forcing anomalies **