# Presentation: 910942

## The USCLVAR Drought Working Group: A Multi-Model Assessment of the Impact of SST Anomalies on Drought

- By
- The  USCLIVAR Drought Working Group
*Presented by S. Schubert*
*NASA/GSFC*
*Global Modeling and Assimilation Office*

- NOAA's 33rd Climate Diagnostics and Prediction Workshop/
- CLIVAR Drought Workshop
- Lincoln, NE   20-24 October 2008

## The US CLIVAR Drought Working Group http://www.usclivar.org/Organization/drought-wg.html

**U.S.  Membership**
- Tom Delworth  	NOAA GFDL
- Rong Fu 	Georgia Institute of Technology
**Dave Gutzler (co-chair)** 	University of New Mexico
- Wayne Higgins	NOAA/CPC
- Marty Hoerling	NOAA/CDC
- Randy Koster	NASA/GSFC
- Arun Kumar	NOAA/CPC
- Dennis Lettenmaier	University of Washington
- Kingtse Mo	NOAA CPC
- Sumant Nigam	University of Maryland
- Roger Pulwarty 	NOAA- NIDIS Director
- David Rind 	NASA - GISS
**Siegfried Schubert (co-chair)** 	NASA GSFC
- Richard Seager 	Columbia University/LDEO
- Mingfang Ting 	Columbia University/LDEO
- Ning Zeng 	University of Maryland
**International Membership: Ex Officio**
- Bradfield Lyon 	International Research Institute for Climate
- Victor O. Magana 	Mexico
- Tim Palmer 	ECMWF
- Ronald Stewart 	Canada
- Jozef Syktus 	Australia

## Other interested participants
Lisa Goddard <goddard@iri.columbia.edu>	 
Alex Hall <alexhall@atmos.ucla.edu>	 
Jerry Meehl <meehl@ucar.edu>
Jin Huang <Jin.Huang@noaa.gov>
John Marshall <jmarsh@MIT.EDU>	 
Adam Sobel <ahs129@columbia.edu>
Max Suarez <Max.J.Suarez@nasa.gov>
Phil Pegion <pegion@gmao.gsfc.nasa.gov>
Tim Palmer <Tim.Palmer@ecmwf.int>
Entin, Jared K. <jared.k.entin@nasa.gov>
Donald Anderson <donald.anderson-1@nasa.gov>	 
Rong Fu <rf66@mail.gatech.edu>
Doug Lecomte <Douglas.Lecomte@noaa.gov>
Hailan Wang <hwang@climate.gsfc.nasa.gov>
Junye Chen <jchen@gmao.gsfc.nasa.gov>
Eric Wood <efwood@princeton.edu>
Aiguo Dai <adai@ucar.edu>
Alfredo Ruiz-Barradas <alfredo@atmos.umd.edu>
Jae Kyung E Schemm <Jae.Schemm@noaa.gov>
Clara Deser cdeser@cgd.ucar.edu
Kirsten Findell <Kirsten.Findell@noaa.gov>
Mark Helfand Mark.Helfand@nasa.gov
Scott J. Weaver <sweaver@gmao.gsfc.nasa.gov>
Kit K. Szeto <Kit.Szeto@ec.gc.ca>
Chunzai Wang <Chunzai.Wang@noaa.gov>
Adam Phillips <asphilli@cgd.ucar.edu>
Matias Mendez <matias@atmosfera.unam.mx>
Hugo Berbery <berbery@atmos.umd.edu>

- Lisa Goddard <goddard@iri.columbia.edu>
- Alex Hall <alexhall@atmos.ucla.edu>
- Jerry Meehl <_[meehl@ucar.edu>](mailto:meehl@ucar.edu)_
- Jin Huang <_[Jin.Huang@noaa.gov>](mailto:Jin.Huang@noaa.gov)_
- John Marshall <jmarsh@MIT.EDU>
- Adam Sobel <ahs129@columbia.edu>
- Max Suarez <Max.J.Suarez@nasa.gov>
- Phil Pegion <pegion@gmao.gsfc.nasa.gov>
- Tim Palmer <Tim.Palmer@ecmwf.int>
- Entin, Jared K. <jared.k.entin@nasa.gov>
- Donald Anderson <donald.anderson-1@nasa.gov>
- Rong Fu <rf66@mail.gatech.edu>
- Doug Lecomte <Douglas.Lecomte@noaa.gov>
- Hailan Wang <hwang@climate.gsfc.nasa.gov>
- Junye Chen <jchen@gmao.gsfc.nasa.gov>
- Eric Wood <efwood@princeton.edu>
- Aiguo Dai <adai@ucar.edu>
- Alfredo Ruiz-Barradas <alfredo@atmos.umd.edu>
- Jae Kyung E Schemm <Jae.Schemm@noaa.gov>
- Clara Deser cdeser@cgd.ucar.edu
- Kirsten Findell <Kirsten.Findell@noaa.gov>
- Mark Helfand Mark.Helfand@nasa.gov
- Scott J. Weaver <sweaver@gmao.gsfc.nasa.gov>
- Kit K. Szeto <Kit.Szeto@ec.gc.ca>
- Chunzai Wang <Chunzai.Wang@noaa.gov>
- Adam Phillips <asphilli@cgd.ucar.edu>
- Matias Mendez <matias@atmosfera.unam.mx>
- Hugo Berbery <berbery@atmos.umd.edu>

## Terms of Reference

- propose a working definition of drought and related model predictands of drought
- coordinate evaluations of existing relevant model simulations
**suggest new model experiments designed to address some of the outstanding uncertainties concerning the roles of the ocean and land in long term drought**
- coordinate and encourage the analysis of observational data sets to reveal antecedent linkages of multi-year drought
- organize a community workshop in 2008 to present and discuss results

## Model Experiments (SST and Soil Moisture Impacts)

- Force with the 3 leading REOFs of annual mean SST (+/- 2 std)
- Also fixed soil moisture experiments
- Also “tropics only” versions of some patterns
- Also high and low frequency Pacific SST patterns (separating ENSO, PDO)
- Also AMIP simulations
- Participating groups/models: NASA (NSIPP1), Lamont(CCM3), NCEP(GFS), GFDL (AM2.1), NCAR (CAM3.5), and COLA/Univ. of Miami/ (CCSM3.0)
- Web site with subset of monthly data ftp://gmaoftp.gsfc.nasa.gov/pub/data/clivar_drought_wg/README/www/index.html
- 	(contact: Hailan Wang)

## Leading Rotated EOFs of annual mean SST  (1901-2004)

- Linear Trend Pattern

- Pacific Pattern

- Atlantic Pattern

**Notes:**
Siegfried,
  Trend positive  (1965-2004)
  Trend negative (1901-1943)
Phil

Fields are the actual amplitudes of the forcing fields (°C) accpet for trend which should be divided by 2 (trend has global mean of 1.6°C).

## 		Annual Mean Response-all runs 50 years (35 for GFS)-force with each sign of the leading patterns and combinations of the patterns (e.g., cold Pacific, warm Pacific,                    warm Pacific + cold Atlantic, etc.)

## Annual Mean 200mb Height Response (m) 	

**Pacific Warm**

**Pacific  Cold**

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**Pacific Warm**

**Pacific  Cold**

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**Pacific Warm**

**Pacific  Cold**

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**Atlantic Warm**

**Atlantic  Cold**

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**Atlantic Warm**

**Atlantic  Cold**

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**	****Warm Trend+                           (Warm Pacific +****Cold Atlantic)**** **

**	****Warm Trend+                       (****Cold Pacific**** +Warm Atlantic) **

## Annual Mean Tsfc Response (°C) 	

- Annual Mean Tsfc Response (°C)

**Warm Trend**

**Spatially Uniform Warm Trend**

## Annual Precipitation (mm/day)

**Pacific Cold**

**Atlantic Warm**

**_x0015__x0013_ ****_x0004_****Tendency for US Drought!**

## Annual Precipitation (mm/day)

**Pacific Cold+****Atlantic Warm**

**Pacific Warm+****Atlantic Cold**

**_x0015__x0013_ _x0004_****US Drought!**

**_x0015__x0013_ _x0004_****US Pluvials!**

## Annual Precipitation (mm/day)

**Pacific Cold+****Atlantic Warm**

**Pacific Warm+****Atlantic Cold**

**_x0015__x0013_ _x0004_****US Drought!**

**_x0015__x0013_ _x0004_****US Pluvials!**

## Seasonal Evolution of Response

## DJF - Cold

**DJF - Cold**

**Weak and shifted anti-cyclonic anomalies**

- Contours: 200mb height anomalies

- Vectors: 850mb wind anomalies

- Colors: precipitation anomalies

## MAM - Cold

**MAM - Cold**

**General consistency in height anomalies but CFS again shifted south**

## JJA - Cold

**JJA - Cold**

**Cyclonic anomalies in IAS**

## SON - Cold

**SON - Cold**

**Cyclonic anomalies in IAS**

## 	R	= (x-y)/sxy	 
		(  ̄ ): 50 yr mean
X: seasonal mean from experiment
Y: seasonal mean from control (forced with climatological SST)
s2xy = (S2X+S2Y)/2
S2X variance of seasonal mean from experiment
S2Y : variance of seasonal mean from control

- 		( ** ̄ **): 50 yr mean
- X: seasonal mean from experiment
- Y: seasonal mean from control (forced with climatological SST)
- s2xy = (S2X+S2Y)/2
- S2X variance of seasonal mean from experiment
- S2Y : variance of seasonal mean from control

- Signal to Noise Ratio ( R)

**Notes:**
t= sqrt(n/2)XPP

- GP

- SE

- SW

- NW

- 

## Precipitation Response to Warm and Cold Pacific (signal/noise)

- Precipitation Response to Warm and Cold Pacific (signal/noise)

- R

- R

**Notes:**
Each curve is a different model (red is for warm Pacific and blue is for cold Pacific).  The dashed lines are the results for the GFS model (that model was only run for 35 year mean versus 50 year mean for the other models).  The horizontal lines denote the 5% significant levels for the 50-year results (slight larger for the GFS results).
The noise is defined as "sqrt(sum of variances/2)". FYI:

signal/noise = (mean2-mean1)/sqrt((var2+var1/)/2) = tvalue * sqrt(2/n), where tvalue = (mean2-mean1)/sqrt((var2+var1)/n)

The 5% significance value for GFS signal/noise is about 0.49. In further detail, the 5% significance tvalue for ~34 degree of freedom is 2.03. Since 35 years of GFS data were used to compute the signal/noise for monthly mean (34 years for 3-month running mean), the signal/noise ~= 2.03*sqrt(2.0/34) = 0.49

## Tsfc Response to Warm/Cold Pacific (signal/noise)

- Tsfc Response to Warm/Cold Pacific (signal/noise)

- R

- R

## Uncertainties in Noise

## Noise (Z200mb):  Unforced Interannual Variance in Control Runs 

- Noise (Z200mb):  Unforced Interannual Variance in Control Runs

- DJF

- MAM

**Notes:**
plots of variance of Z_200mb for the control run (climatological SSTs). For clarification, the variance of Z_200mb here is based on seasonal means. 49 years data are used for all models except NCEP GFS (GFS uses 35 yrs data). The first year data of these model outputs have been discarded as spin-up. 

## JJA 

- JJA

- SON

- Noise (Z200mb):  Unforced Interannual Variance in Control Runs

**Notes:**
plots of variance of Z_200mb for the control run (climatological SSTs). For clarification, the variance of Z_200mb here is based on seasonal means. 49 years data are used for all models except NCEP GFS (GFS uses 35 yrs data). The first year data of these model outputs have been discarded as spin-up. 

## Tsfc and Precip Noise

- Look at Pacific warm and cold SST cases

## MAM Pacific: Great Plains

**MAM Pacific:**** Great Plains**

**Warm**

**Precip mm/d**

**Tsfc °C**

**Cold**

**Precip mm/d**

**Tsfc °C**

## JJA Pacific: Great Plains

**JJA Pacific:**** Great Plains**

**Warm**

**Precip mm/d**

**Tsfc °C**

**Cold**

**Precip mm/d**

**Tsfc °C**

## What Are the Determining Factors for Noise (unforced variability)?

**Seasonal Dependence?**
**Impact of SST (a signal in the noise!)**

## “Noise” in Great Plains in Spring/Summer driven by land/atmosphere feedbacks but also depends on SST Forcing!

- “Noise” in Great Plains in Spring/Summer driven by land/atmosphere feedbacks but also depends on SST Forcing!

- Schubert et al. 2008  JCLIM

- Greater ∆E for given change ∆W

**Notes:**
Focus on AMJ
Force model with 2 composite SST patterns
Positive: GP precip    >    +1 STD
Negative: GP precip  <    +1 STD
100 ensemble members (March 1 - June30) for each composite
Initial soil moisture conditions are from AMIP runs
Repeat both sets of runs with fixed soil moisture (fixed beta)

## 200mb  Variability (10-30 days)

*  **JFM 98 (El Nino)                      JFM 99 (La Nina)*

**Model**
- 120 ensemble members

**Obs**

- Subseasonal Noise is the result of barotropic instability of the jet - depends on SST!

- Schubert et al. 2001  JCLIM

**Notes:**
1998 versus 1999 Boreal Winter 
-impact of ENSO, on subseasonal and weather variability 
-large ensembles (~100)

## Some Basic Results: Over US

- Mean Responses
- Models tend to agree that
- Cold Pacific+Warm Atlantic => drought/warm
- Warm Pacific+Cold Atlantic => pluvial conditions/cold
- There are substantial differences in details of anomaly patterns
- There is a large seasonality in responses

- Potential Predictability (Pacific signal to noise)
- Largest in spring
- Models appear to agree more on precipitation than surface temperature responses!

## Some Basic Results-2

- Models show substantial differences in seasonally dependent controls
- Cold season (planetary waves/storm tracks)
- Warm season (land surface memory and feedbacks)
- Summer/fall (Low level response: LLJ/IAS)

- Models show considerable differences in basic noise levels
- For the upper level circulation this is likely tied to differences in climatological jet structures and related instabilities (weather, PNA, etc)
- For Precip and Tsfc over the Great Plains, land surface interactions may play a role during warm seasons