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 October 2008

The US CLIVAR Drought Working Group U.S. Membership Tom Delworth NOAA GFDL Rong Fu Georgia Institute of Technology Dave Gutzler (co-chair) University of New Mexico Wayne HigginsNOAA/CPC Marty HoerlingNOAA/CDC Randy KosterNASA/GSFC Arun KumarNOAA/CPC Dennis LettenmaierUniversity of Washington Kingtse MoNOAA CPC Sumant NigamUniversity 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 Alex Hall Jerry Meehl Jin Huang John Marshall Adam Sobel Max Suarez Phil Pegion Tim Palmer Entin, Jared K. Donald Anderson Rong Fu Doug Lecomte Hailan Wang Junye Chen Eric Wood Aiguo Dai Alfredo Ruiz-Barradas Jae Kyung E Schemm Clara Deser Kirsten Findell Mark Helfand Scott J. Weaver Kit K. Szeto Chunzai Wang Adam Phillips Matias Mendez Hugo Berbery

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 ftp://gmaoftp.gsfc.nasa.gov/pub/data/clivar_drought_wg/README/www/index.html (contact: Hailan Wang)

Leading Rotated EOFs of annual mean SST ( ) Linear Trend Pattern Pacific Pattern Atlantic Pattern

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 WarmPacific Cold

Annual Mean Tsfc Response (°C) Pacific WarmPacific Cold

Annual Mean Tsfc Response (°C) Pacific WarmPacific Cold

Annual Mean Tsfc Response (°C) Atlantic WarmAtlantic Cold

Annual Mean Tsfc Response (°C) Atlantic WarmAtlantic Cold

Annual Mean Tsfc Response (°C) Warm Trend+ (Warm Pacific +Cold Atlantic) Warm Trend+ (Cold Pacific +Warm Atlantic)

Annual Mean Tsfc Response (°C) Warm TrendSpatially Uniform Warm Trend

Annual Precipitation (mm/day) Pacific ColdAtlantic Warm Tendency for US Drought!

Annual Precipitation (mm/day) Pacific Cold+Atlantic WarmPacific Warm+Atlantic Cold US Drought! US Pluvials!

Annual Precipitation (mm/day) Pacific Cold+Atlantic WarmPacific Warm+Atlantic Cold US Drought! US Pluvials!

Seasonal Evolution of Response

DJF - Cold Weak and shifted anti-cyclonic anomalies Contours: 200mb height anomalies Vectors: 850mb wind anomalies Colors: precipitation anomalies

MAM - Cold General consistency in height anomalies but CFS again shifted south

JJA - Cold Cyclonic anomalies in IAS

SON - Cold Cyclonic anomalies in IAS

R= (x-y)/s xy ( ¯ ): 50 yr mean X: seasonal mean from experiment Y: seasonal mean from control (forced with climatological SST) s 2 xy = (S 2 X +S 2 Y )/ 2 S 2 X variance of seasonal mean from experiment S 2 Y : variance of seasonal mean from control Signal to Noise Ratio ( R)

GP SE SW NW 

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

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

Uncertainties in Noise

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

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

Tsfc and Precip Noise Look at Pacific warm and cold SST cases

MAM Pacific: Great Plains Warm Precip mm/d Tsfc °C Cold Precip mm/d Tsfc °C

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!)

WW WW EE EE W (soil moisture) Pacific Warm Pacific Cold “Noise” in Great Plains in Spring/Summer driven by land/atmosphere feedbacks but also depends on SST Forcing! Schubert et al JCLIM Greater ∆E for given change ∆W

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 JCLIM

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