The US CLIVAR Drought Working Group (formed in December 2006)

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

• Jose Marengo CPTEC/INPE

Terms of Reference

1) Propose a working definition of drought and related model predictands of drought (developed model-based indices);

2) Coordinate evaluations of existing relevant model simulations (developed list of relevant simulations);

3) Suggest new experiments (coupled and uncoupled) designed to address some of the outstanding uncertainties mentioned above (carried out a coordinated set of experiments);

4) Coordinate and encourage the analysis of observational data sets to reveal antecedent linkages of multi-year drought (developed list of datasets); and

5) Organize a community workshop to present and discuss the results (joint with CDPW in October 2008)

http://www.usclivar.org/Organization/drought-wg.html

Coordinated Global Model Experiments Addressing the Role of SSTs and Soil Moisture in

Regional Drought

• The idea is that several modelling groups would do identical idealized experiments to address issues of model dependence on the response to SSTs (and the role of soil moisture), and to look in more detail at the physical mechanisms linking the SST changes to drought

• All runs 50+ years, fixed SST patterns added to seasonally varying SST climatology

• Participating groups/models: NASA (NSIPP1), Lamont(CCM3), NCEP(GFS), GFDL (AM2.1), NCAR (CAM3.5), and COLA/Univ. of Miami/ (CCM3.0)

SST Forcing

• Leading patterns of annual mean SST variability– (base on on 1901-2004 HADISST)

• Climatological SST (control run) • Separate patterns of low (decadal) and high

(ENSO) SST variability• Tropics-only component of above SST forcing• Uniform SST warming• Fixed soil moisture • AMIP runs (provide link to observed variations)

Leading EOFs and Time series (annual mean SST - 1901-2004)

Linear Trend Pattern (LT)

Pacific Pattern (Pac)

Atlantic Pattern (Atl)

Annual 200mb Height Anomalies (m) QuickTime™ and a

TIFF (Uncompressed) decompressorare needed to see this picture.

Pacific Warm Pacific Cold

Annual Tskin Anomalies (°C) QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Pacific Warm Pacific Cold

Annual 200mb Height Anomalies (m)

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

Atlantic warm Atlantic cold

QuickTime™ and aTIFF (Uncompressed) decompressor

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Annual Tskin Anomalies (°C)

Atlantic warm Atlantic cold

Composite of the Atlantic Warm Pool (AWP)

• ERSST from 1854-2006.

• AWP variability is large.

• Large AWPs are almost three times larger than the small ones.

Courtesy Chunzai Wang

NOAA AOML

The Caribbean Low-Level Jet (CLLJ) and Great Plains Low-Level Jet (GPLLJ) transport moisture from the AWP to the eastern North Pacific and the central United States, respectively.

Vertically integrated moisture flux in summer (JJA):

CLLJ

GPLLJ

Version 3.1; CAM3.1

Annual Precipitation (mm/day)

Pacific Cold and Atlantic Warm Pacific Warm and Atlantic Cold

Multi Model Annual Mean response to idealized SST forcing

Surface Temperature (K)

Contours are the multi model mean and shading indicates where all model have the same sign anomaly.Multi model mean for Pacific and Atlantic responses include NSIPP, GFS, GFDL, CCM3, and CAM3.5 models.The linear trend response does not include CAM3.5

200 mb Height (m)

Courtesy Phil Pegion NOAA/CPC

Precipitation (mm/day)

Courtesy Phil Pegion NOAA/CPC

Annual Mean GP Precipitation (Y-axis) and Temperature (X-axis)

Pacific Warm Pacific Cold

Annual Mean GP Precipitation (Y-axis) and Temperature (X-axis)

Atlantic Warm Atlantic Cold

T anomaly (JJA) during driest years minus mean T for control (PnAn)

Mean T for experiment minus mean T for control (PnAn)

T anomaly (JJA) during driest years minus mean T for experiment

= +( )

Land Impacts on Temperature During Drought

Total T anomaly during drought

T change from climate shift

impact of land-atmosphere feedback (strengthened or weakened)

Courtesy Randy Koster NASA/GMAO

T anomaly (JJA) during driest years minus mean T for control (PnAn)

Mean T for experiment minus mean T for control (PnAn)

T anomaly (JJA) during driest years minus mean T for experiment

For PwAc, drastically reduced drought warming relative to control…

…is due in part to a climate shift…

…and in part to changes in the feedback character of the land surface.

= +( )

Land Impacts on Temperature During Drought

Total T anomaly during drought

T change from climate shift

impact of land-atmosphere feedback (strengthened or weakened)

Perpetual ENSO and Drought over the United States

Kingtse Mo and Jae Schemm

Climate Prediction Center

NCEP/NWS/NOAA

ENSO composites

• Project SSTA from 1915-2007 onto the first REOF for annual SSTA to obtain RPC

• For each season, composites of SPI6, Soil moisture anomalies and SRI6 (for runoff) for warm and cold ENSO were formed when the RPC was greater (less) than one (negative) standard deviation.

• Plot composite difference between cold and warm ENSO and average over 4 seasons together (Fig.1)

• Drought: SPI6 < -0.8 and SRI6 < -0.8

Multi model ensemble ENSO Composites Cold-warm (Obs)

Multi model ensemble captures the relationships between ENSO & drought over the United States

Conclusions

• Drought is measured by persistent positive precipitation, soil moisture and runoff anomalies using indices SPI6, SRI6 and SM anomalies.

• All indices indicate that drought is more likely to occur over the Southwest, and the Great Plains during cold ENSO.

• The individual model experiment differs , but the multi model ensemble captures the relationships between ENSO and drought well

Evaluation of the links between SE US summer rainfall variability and SSTA

simulated by NSIPP, GFDL AM2.0 and CCSM3

(we do not have AMIP run for CCSM3.5)

Courtesy Rong Fu Georgia Tech

How well can models reproduce the relationship between SE US summer rainfall anomalies (P) and N. tropical Atlantic SSTA?

GFDL AM2.1

Observed

0o: in phase

90o: NAtlLeads PI

CCM3

NSIPP capture the observed correlation between Natl SSTA and P at 1- year period during early 80s and early 90s, but phase relation is not realistic.

The correlation at 4-year scale is too strong in all three models compared to observation.

Per

iod

(Y

ear)

1980 1990 2000

NSIPP

Great Plains Precipitation Anomaly

Seasonal precipitation anomalies smoothed by 12

applications of 1-2-1 averagingThe smoothed PRECIP index mimics PDSI (correlation of

detrended series ~0.85)

Full Century (1901-2002) Correlations [CRU_P, CCM3_P] =0.36 (0.37 detrend) [CRU_P, CAM3.5_P]=0.32 (0.33 detrend) [CRU_P, GFDLAM2.1_P]=0.26 (0.37 detrend) [CRU_P, PDSI] =0.82 (0.84 detrend)

Half Century (1950-2000) Correlations[CRU_P, CCM3_P] =0.58 (0.50 detrend)[CRU_P, CAM3.5_P]=0.38 (0.19 detrend)[CRU_P, GFS_P] =0.28 (0.19 detrend)[CRU_P, GFDLAM2.1_P] =0.49 (0.46 detrend)[CRU_P, NSIPP_P] =0.27 (0.04 detrend)

Full Century (1901-2002)

Half Century (1950-2000)

Simulation of 20th Century North American Hydroclimate Variability by the Drought Working Group Models

Alfredo Ruiz-Barradas, Sumant Nigam, U of Maryland

Dust Bowl 1950s Drought

SST Correlations of the Great Plains Summer PRECIP Indices

(1950-2000)

All-season precipitation indices are first detrended and then smoothed(as before) to generate PDSI proxies.

The summertime proxy indices are then correlated

with detrended SSTs. •GFS has fairly realistic correlations over the Pacific but not the Atlantic.

•CCM3’s Pacific correlations are too strong, NSIPP’s too weak, and CAM3.5’s and GFDLAM2.1’s somewhere in between.

•Atlantic links are comparable to the Pacific ones in observations but weaker in model simulations (with less accord among them as well).

•CCM3 and CAM3.5 exhibit Indian Ocean connectivity, with little support from observations

CR

UT

S2.

1C

AM

3.5

GF

SC

CM

3N

SIP

PG

FD

LAM

2.1

The spread in models’ performance makes the CLIVAR Drought

Modeling exercise worthwhile

CMIP - Runs Courtesy Marty Hoerling

(Link to Ben Kirtman’s Results)

Other Applications of the Runs

• E.g., hurricanes

Shear

Shear

Shear

Shear

Shear

Shear

Summary

• The USCLIVAR WG on drought is making progress on achieving its research goals to improve our understanding of long term regional drought

• A series of coordinated global model experiments is providing important new information on the mechanisms (SST and soil moisture) that lead to long term regional drought including an assessment of model dependence

– The idealized runs represent a substantial community investment in computing (thousands of years of simulation involving most of the major US modeling centers)

– Initial results will be presented and discussed in a joint CDP and USCLIVAR workshop in October

– The results of all the experiments will be made available in October of this year to the general community for analysis (some groups have already made their runs available)

Relevant PublicationsSchubert, S., R. Koster, M. Hoerling, R. Seager, D. Lettenmaier, A. Kumar, and D. Gutzler, 2007: Predicting Drought on Seasonal-to-Decadal Time Scales. Bull. Amer. Meteor. Soc., 88, 1625–1630

Gutzler, D. and S. Schubert, 2007: The U.S. CLIVAR Working Group on Long-Term Drought,”, U.S. CLIVAR Variations (Spring 2007, volume 5, No. 1).

Drought WG +, 2008: A USCLIVAR Project to Assess and Compare the Responses of Global Climate Models to Drought-Related SST Forcing Patterns. Manuscript in preparation

Schubert, S.D., M. J. Suarez, P. J. Pegion, R. D. Koster, H. Wang and J. T. Bacmeister, 2008: A mechanistic study of the impact of SSTs on drought and pluvial conditions over the United States. Manuscript in preparation.

Wang, A., T. J. Bohn, D. P. Lettenmaier, S. Mahanama, and R. D. Koster, 2008: Multimodel ensemble reconstruction of drought over the continental United States. Manuscript in preparation.

Koster, R. D., Z. Guo, P. A. Dirmeyer, R. Yang, and K. Mitchell, 2008: On the nature of soil moisture in land surface models. Manuscript in preparation

Expect many more in the coming months on model results …

For cold ENSO, all indices show:

Dryness over the Southwest, areas along the Gulf of Mexico and Great Plains

For warm ENSO, the situation reverses

Fig .1: composites based on the ENSO Pacific SST pattern

Procedures to analyze model runs• Pool all monthly mean P together from all 9

experiments for a given model.• Calculate 6 month standardized precipitation index

(SPI6). For drought, the 6-month SPI needed to be less than -0.8.

• For each experiment, we count the number of months (num) that SPI6 indicates drought. Obtain percentile by dividing num by the total months of the experiment.

• Multi model ensemble : Average of four models (GFS,NSIPP,CCM3 and GFDL) for each experiment.

• The statistical significant test was done using the Monte Carlo method.

For wPna: The ensemble shows that there are less drought events over the Southwest, Great Plains

For cPna: There are more drought events over the above areas

Fig.2 Ensemble: percentile of the number of months under drought averaged over GFS, NSIPP, CCM3 and GFDL.

How well can models AMIP runs reproduce the relationship between SE US summer rainfall anomalies (P) and Nino?

GFDL AM2.1

Observed

0o: in phase

90o: NinoLeads PI

CCM3 NSIPP

Joint wavelet coherence:

NSIPP and GFDLAM2.1 capture the observed correlation between Nino34 (lead) and SE US summer rainfall variability of 0.5-1 year period during mid 80s and early 90s.

ENSO forcing on P seems to be too strong and too persistent in all models. Only NSIPP shows decadal variability of P-NINO34 correlation.

Per

iod

(Y

ear)

1980 1990 2000

Correlationcoefficient

NCAR Community Atmospheric Model (Version 3.1;

CAM3.1) • A global spectral model (T42 with 26 vertical layers; equivalent to a

2.8°2.8° horizontal resolution).

• SST from the Hadley Centre (UK) as the model-forcing.

• The control (CTRL) ensemble (with 18 members) run: Climatological SST is prescribed globally.

• The large AWP (LAWP) ensemble run: SST composite for large AWP is used in the AWP region.

• The small AWP (SAWP) ensemble run: SST composite for small AWP is used in the AWP region.

• The difference is taken between the LAWP and SAWP runs.

Impact of the AWP on North Atlantic Subtropical High (NASH)

The AWP weakens the NASH (especially at its southwestern edge) and strengthens summer continental low over the North American monsoon region.

SLP’s response to AWP variability in JJA

Impact of the AWP on Rainfall during Summer (JJA)

Large (small) AWP decreases (increases) rainfall in the United States east of the Rocky Mountains, in agreement with observations.

Precipitation response to AWP variability

Simulation of 20th Century North American Hydroclimate Variability

by the Drought Working Group Models

Objective:

Assess capabilities of the DWG atmospheric models in simulating seasonal and low-frequency summer hydroclimate variability over North America

Models examined:– NSIPP (NASA/GSFC; 2.5lon x 2.0lat, 5th AMIP ens mem, 1930-2004)– CCM3 (LDEO; T-42 goga_new runs atm, 1st ens mem, 1856-2007)– CAM3.5 (NCAR; T-85, one run completed recently, 1871-2006)– GFS (NOAA/NCEP; T62L64 version of current CFS, 1950-2001)– AM2.1 (NOAA/GFDL; 144x90, 7th AMIP ens mem, 1870-1999)

Alfredo Ruiz-Barradas and Sumant Nigam

University of Maryland