NASA/TM_2002-104606, Vol. 23

Techmcal Report • • _J Series on Global Modehn_,

and Data Assimilation

Volume 23

Prospects for Improved Forecasts of Weather and

Short-Term Climate Variability on Subseasonal

(2-Week to 2-Month) Time Scales

S. Schubert, R. Dole, H. van den DooL MI Suarez, and D. Waliser

Ptvceedings flvm a _fbrkshop Sponsored hy the Earth

Sciences Directorate at NASA's Goddard Space Flight

Centez Co-sponsored by 2v_dSA Seasonal-to-bm_rannual

Prediction Project and NAS_d Data Assimilation OJfice

April 16-18, 2002

Nc_vember__ 2002
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NASA/TM_2002-104606, VoL 23

Technical Report Series on Global Modeling

and Data Assimilation

Volume 23

Prospects for Improved Forecasts of Weather and

Short-Term Climate Variability on Subseasonal

(2-Week to 2-Month) Time Scales

Siegfried Schubert, Goddard Space Flight Centez Greenbelt, AID

Randall Dole, NOAA- Coopelvtive hzstitute /br Research in Envh_mmenml Sciences

Climate Di_gnostics Centez Boulder, CO

Huug van den Dool, NOAA- Climate Prediction Center, Camp 5_rings, MD

Mcc¢ Suarez, Goddard Space Flight Centel; Greenbelt, MD

1-)uane t4_liser, lnstitute fi_r Terrestrial and Planetaty A tmo_pheres

State University of New York. Stony Brook, NY

National Aeronautics and

Space Administration

Goddard Space Hight Center

Greenbelt, Maryland 20771

November 2002
NASA Center for AeroSpace Inlk_rmation

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Available from:

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Price Code: A10
Table of Contents

Abstract .............................................................................................................................. 5

I. Introduction .................................................................................................................... 7

II. Summary of Sessions ...................................................................................................... 9

i. Current operational methods and their skill ............................................................ 9

ii. Predictability of extra-tropical "modes". .............................................................. 11

iii-iv) Predictability of the ISO/MJO and tropical/extra-tropical interactions .............. 14

v. Role of land surface processes ............................................................................ 17

vi. Link between low frequency and weather/regional phenomena ............................ 19

Ill. Discussion and Summary .............................................................................................. 22

i. Role of tropical heating and the MJO ................................................................... 23

ii. Extra- tropical modes of variability ....................................................................... 24

iii. Soil moistuxe and snow ...................................................................................... 25

iv. Links with weather and other regional phenomena .............................................. 26

v. Methodology and data ........................................................................................ 27

1V. Recommendations ........................................................................................................ 28

i. High priority research and development ................................................................ 28

ii. High priority action items ..................................................................................... 29

Acknowledgments .............................................................................................................. 31

References ......................................................................................................................... 31

Appendix - List of participants ........................................................................................... 33

Agenda .............................................................................................................................. 37

Extended Abstracts ............................................................................................................ 43

3
Abstract

A workshop was held in April of 2002 that brought together various experts in the Earth Sciences to

focus on the subseasonal prediction problem. While substantial advances have occurred over the last

few decades in both weather and seasonal prediction, progress in improving predictions on these

intermediate time scales (time scales ranging from about two weeks to two months) has been slow. The

goals of the workshop were to get an assessment of the "state of the art" in predictive skill on these time

scales, to determine the potential sources of "untapped" predictive skill, and to make recommendations

for a course of action that will accelerate progress in this area. A remarkable aspect of the workshop

was the multi-disciplinary natuxe of the attendees, consisting of about 100 scientists with specialties in

areas that included stratospheric dynamics, hydrology and land surface modeling, the monsoons, the

Madden-Julian Oscillation (MJO) and other tropical variability, extratropical variability including

extratropical-tropical interactions, coupled atmosphere- ocean-land modeling, weather prediction,

seasonal prediction, and various aspects of statistical modeling, analysis, and prediction. This broad

range of expertise reflected the wide array of physical processes that are deemed potentially important

sources of predictive skill on subseasonal time scales.

One of the key conclusions of the workshop was that there is compelling evidence for predictability at

forecast lead times substantially longer than two weeks. Tropical diabatic heating and soil wetness were

singled out as particularly important processes affecting predictability on these time scales.

Predictability was also linked to various low-frequency atmospheric phenomena such as the annular

modes in high latitudes (including their connections to the stratosphere), the Pacific/North American

pattern (PNA), and the MJO. The latter, in particular, was highlighted as a key source of untapped

predictability in the tropics and subtropics, including the Asian and Australian monsoon regions.

The key recommendations of the workshop are:

a) That a coordinated and systematic analysis of current subseasonal forecast sldll be conducted by

generating ensembles of 30-day hindcasts for the past 30-50 years with several "frozen"

AGCMs. Specific goals include, sampling all seasons, and generating sufficiently large

ensembles to estimate the evolution of the probability density function.

5
b) Thataseriesof workshopsbeconvenedfocusedonmodelingtheMJO,andthatacoordinated

multi-nation/multi-model experimental predictionprogrambedevelopedfocusedontheMJO.

e) That new satellite observations and new long-term consistent reanalysis data sets be developed

for initialization and verification, with high priority given to improvements in estimates of

tropical diabatic heating and cloud processes, soil moisture, and surface fluxes (including

evaporation over land).

d) That NASA and NOAA develop a collaborative program to coordinate, focus, and support

research on predicting subseasonal variability.

Specific steps to implement the above recommendations are: 1) to begin immediately to develop a

framework for an experimental MJO prediction program, 2) to convene a follow-up workshop in the

spring of 2003 to organize the AGCM hindcast project, and conduct initial meetings on modeling the

MJO, and 3) for NASA and NOAA to put out a joint announcement of opportunity within the next year

to focus research, modeling and data development efforts on the subseasonal prediction problem.
I. Introduction

"It seems quite plausible from general experience that in any mathematical problem it is easiest to

determine the solution for shorter periods, over which the extrapolation parameter is small. The next

most difficult problem to solve is that of determining the asymptotic conditions - that is, the conditions

that exist over periods for which the extrapolation parameter is very large, say near infinity. Finally, the

most difficult is the intermediate range problem, for which the extrapolation parameter is neither very

small nor very large. In this case the neglect of either extreme is forbidden. On the basis of these

considerations, it follows that there is a perfectly logical approach to any computational treatment of the

problem of weather prediction. The approach is to try first short-range forecasts, then long range

forecasts &those properties of the circulation that can perpetuate themselves over arbitrarily long

periods of time (other things being equal), and only finally to attempt forecast for medium-long time

periods which are too long to treat by simple hydrodynamic theory and too short to treat by the general

principles of equilibrium theory".

John von Neumann (1955)

Almost a half centuxy after the eminent mathematician, John von Neuxnann, spoke those words it

appears that we are finally ready to tackle the "medium-long" time scale prediction problem. In fact we

have, over the last few decades, made important strides in both weather (short time scale) and

seasonal/climate (very long time scale) prediction. The critical roles of initial atmospheric conditions in

the former, and boundary conditions in the latter, have helped to guide and prioritize research and

development, as well as to establish new observing systems targeting these prediction problems.

As surmised by von Neuman, progress in predicting time scales between those of weather and shortterm climate (time scales roughly between 15 and 60 days) has been at best modest. The variability on

these time scales is rich with well known phenomena such as blocking, the PNA, and the MJO, yet the

important mechanisms involved, their predictability, and the ability of cuxrent models to simulate them

are still in question. Improvements made in predicting these time scales are in many ways an important

step in making fuxther progress in weather and climate prediction. For weather, these time scales offer

the hope for extending (at least occasionally) the range of useful forecasts of weather and/or weather

statistics, while for the seasonal and longer term climate prediction problem they are a key component of

the atmospheric "noise" that is a limiting factor in the climate prediction problem.

How predictable is the PNA? How predictable is the MJO? What is the connection between the

variations in predictability of weather and the PNA, blocking, the MJO, or various other subseasonal

modes? What is the role of the stratosphere? What is the link between subseasonal variability and the
E1Nino/SouthemOscillation(ENSO)?Isintraseasonal variabilitythekeytounderstandingand

predictingtheinterannual variabilityoftheIndianmonsoon?Howimportantisoceancouplingto

subseasonal prediction?WhatistherelativecontributionofSSTandatmosphericinitialconditionsto

thepredictabilityof subseasonal variabilityonweeklytomonthlytimescales?Areourmodelsgood

enoughtocaptuxethedominantmodesofvariability?Aretheuncertaintiesinthepredictionsdominated

by errorsintheinitialconditions(e.g.thetropicsorsoilmoistuxe)ordeficienciesinthemodels?These

aresomeofthekeyissuesthatneedtobeaddressedfor achievingusefulongrangeweatherpredictions,

andforimprovinganddeterminingthelimitsofseasonalndlongerpredictions.

Thisdocumentsummarizestheproceedingsof aworkshopthatwasorganizedtobringtogethervarious

expertsin thefieldtofocusonthesubseasonal predictionproblem.Thebasicgoaloftheworkshopwas

togetanassessment of ourcurrentunderstandingof theaboveissues,andtodeterminewhatwecando

tohelpmakeprogressonthesubseasonal predictionproblem:thisincludesnecessaryadvancesin

models,analysis,theory,andobservations.

TheworkshopwasheldattheNewtonWhiteMansioninMitchellville,MarylandonApril 16-18,2002.

Therewereabout100attendees(eeAppendix),with43presentations(seeextendedabstracts),

includingsixinvitedtalks.TheworkshopwasjointlysponsoredbytheNASASeasonal toInterannual

PredictionProject(NSIPP)andtheDataAssimilationOffice(DAO),withsupportandfundingfromthe

GoddardEarthSciencesDirectorate.AdditionalfundingwasprovidedbytheEarthSciencesEnterprise

atNASAheadquarters. TheworkshoporganizingcommitteeconsistedofSiegfriedSchubert(chair,

NASA/DAOandNSIPP),MaxSuarez(NSIPP),RandallDole(NOAA-CIRESClimateDiagnostics

Center),HuugvandenDool(NOAA/ClimatePredictionCenter),andDuaneWaliser(SUNY/Institutefor

Terrestrial andPlanetaryAtmospheres).

Inthefollowingsectionwepresentsummariesofeachsession.Theoverallsummaryisgiveninsection

III, andtherecommendations arepresented in section 1V. The final sections of the document include

the list of participants, the agenda of the workshop, and the abstracts (some of them extended) of the

talks.
II. Summary of sessions

The workshop was opened with welcoming remarks from Franco Einaudi, the Chief of the Goddard

Earth Sciences Directorate. The keynote talk was given by Eugenia Kalnay, Chair of the Department of

Meteorology at the University of Maryland, and a leading expert on dynamical extended range

prediction. The rest of the workshop was organized into six sessions. Each session began with a 45

minute invited tall, followed by a number of 20 minute contributed presentations. In the following we

provide summaries of each session.

The keynote talk by Eugenia Kalnay (see extended abstract) reviewed some of the key areas that provide

opportunities for making progress on the subseasonal prediction problem. These opporm_nities include

the use of coupled atmosphere-ocean models to more realistically simulate extratropical atmosphereocean feedbacks, improved simulations of the MJO including "interim" empirical/statistical approaches

that nudge the GCMs to produce more realistic MJO characteristics, taldng better advantage of the

history of 15-day ensemble forecasts routinely made by the U.S. National Weather Service, and

improved initialization and ensemble methodologies with coupled models. These basic ideas were, in

fact, at the core of many of the presentations that followed.

i) Current operational methods and their skill (Chair: Randy Dole)

The session opened with an invited presentation by Huug van den Dool on "Climate Prediction Center

(CPC) Operational Methods and Skill in the Day 15 - Day 60 Forecast Range" (see extended abstract).

Van den Dool emphasized the great challenges posed by forecasts in the 15-60 day time range. This time

scale is intermediate between short-range and long-range forecasts, with the former predominantly

determined by initial conditions and the latter by boundary forcing. Forecasting in this time range

therefore constitutes a mixed problem in which both initial and boundary conditions are likely to play

important roles. Van den Dool described current CPC operational practice, which is to provide a 30-day

forecast for U.S. precipitation and temperatures for the U.S. at a lead-time of two weeks; i.e., effectively

a forecast for mean conditions over days 15-45. The forecasts are developed through subjective

procedures that effectively combine output from both statistical-empirical and numerical model
predictions.PredictorsincludeOptimalClimateNormals,Canonical CorrelationAnalysis,andNational

CentersforEnvironmental Prediction(NCEP)modelforecasts. Whenapplicable,E1Nino- Southern

Oscillationcomposites, soil-moisturestate,andpotentialimpactsfromtheMJOarealsoconsidered.

Comparisonsbetweenmonthlyandseasonal forecastsshowthatatpresentthespatialpatternof the

monthlyforecastisusuallysimilartothatoftheseasonal forecast,withmodestpositivesldllbuta

somewhat lowersignaltonoiseratio.

FollowingthepresentationbyvandenDool,FredericVitartprovidedanoverviewofmonthly

forecastingattheEuropeanCenterfor MediumRangeWeatherForecasts(ECMWF,seeextended

abstract).HedescribedanewmonthlyforecastingprojectatECMWFdesignedto fill thegapbetween

medium-rangeforecasting(outto 10days)andseasonal forecasts.TheECMWFsystemisbasedona

51-memberensembleof coupledocean-atmosphere modelintegrations, withtheatmosphericcomponent

beingrunatT159L40resolution,andtheoceanicomponent atazonalresolutionof 1.4degreesand29

verticallevels.Sofar,severalmonthlyforecastshavebeenperformed.Preliminaryresultssuggestthat

themonthlyforecastingsystemmayproduceusefulforecastsouttoweek4,althoughthemodelis

deficientinsimulatingarealisticMJOmorethan10daysinadvance. Plansaretoruntheexperimental

systemeverytwoweeksforthenextfewyearsinordertoassesstheskillofthiscoupledmodel.

ZoltanToththendescribedtheNCEPGlobalEnsembleForecastSystemanddiscussedapproachesto

extendingforecastsbeyond16days.Tothemphasizedfourissues:1)Thisisacombinedinitial

condition-boundary conditionproblem,asstatedpreviously;2)thatpotentialpredictabilityinthisrange

isrelativelylow,butvariableintimeandspace;3)thatakeyissueispredictingregimechanges; and4)

thatthemodelsbeingusedarefarfromperfect.Withregardtoeachof theseissues,hesuggested: 1)we

needtoemploycoupledmodelsaspartof thepredictionstrategy;2)thatensemblepredictionmethods

areessential forenhancingthepredictablesignal,detectingvariationsinpredictability, andproviding

probabilisticforecasts;3)thatmajorregimechangesaresometimeswellpredicted,butcase-to-case

variationsaregreat,andtheextenttowhichthesevariationsareintrinsicorduetomodelerrorsor

observational deficienciesiunknown;and4)thatnewapproachesareneededto correctfortheeffects

ofmodelerrors,includingbiasesinboththefirstandsecondmoments.Acriticalissueisthelackof

adequatevariabilityinthemodels,whichleadsto"overconfident"forecasts. It isvitaltoadjustforthis

biasinordertoimproveforecastestimatesofprobabilitydistributions.

10
SteveColucciprovidedanoverviewofrecentresultsonensemblepredictionsofbbcldng(seeextended

abstract).HediscussedresultsofresearchwiththeNCEPMRFmodel,andunpublishedworkby J.L.

PellyandB.J.HoskinswiththeECMWFmodel.Thesestudiesexaminedtheclimatologyofblocldng

over3-5yearperiods.Preliminaryresultssuggestthatblocldngfrequencyismnderpredicted inboththe

NCEPandECMWFmodel,butthatthisbiascanbeatleastpartiallycorrectedtoproducecalibrated

probabilisticforecastshatextendtherangeof sldllfulblocldngforecasts.

ArranKumardiscussedtheimpactof atmosphericinitialconditionsonmonthly-meanmodelhindcasts

withanatmosphericgeneral circulationmodel(AGCM).TheAGCMswerermnfromatmospheric

conditionsstartingatleadtimesfrom1-4monthsinadvance. TheresultssuggestthatJanuary

simulationswithashorter(1month)leadtimehaveahighersignal-to-noiseratio,especiallyathigher

latitudes,andthereforeconsiderationshouldbegiventousingobservedinitialconditionsinthistimeof

year.

Thefinalpresentationofthesession,givenbyThomasReichler,alsoexaminedtheroleofatmospheric

initialconditionsonlong-rangepredictability.ThestudydesignconsistedofmxmingensembleAGCM

predictabilityexperimentswiththeNCEPseasonal forecastingmodelatT42L28resolutionfroma

varietyof initialcondition(IC)andboundarycondition(BC)states,includingENSOandnon-ENSO

(neutral)years.Thebasicconclusionswerethatinitialconditionshaveanoticeableinfluenceonweekly

hindcastsldllinwinterouttoweeks2 to6,withtheeffectsmostpronouncedathighlatitudes,the

middleatmosphere, atlowerlevelsovertheIndianOcean,duringactivephasesoftheAntarctic

Oscillation(AAO),andwhenENSOisweak.

ii) Predictability of extra-tropical "modes" (Chair: Max Suarez)

This session dealt with various aspects of a number of coherent atmospheric teleconnection patterns that

have time scales sufficiently long to afford predictability on weekly and longer time scales.

The session began with an invited talk by Mike Wallace about a study on the impact of the Arctic

Oscillation (AO) and the PNA on subseasonal variability, carried out by Roberta Quadrelli and Mike

11
Wallace(seeextendedabstract).TheanalysiswasbasedonNCEP/NCARreanalysisdataforDJFMfor

theperiod1958-1999.Theyexaminedthevariabilityof 10-daymeansof sealevelpressure,500mb

heightand1000-500mbthicknessfieldsduringtheextremesinthetwopolaritiesofboththeAOand

PNA. TheyfoundthatboththeAOandthePNAhaveasubstantial impactonthefrequencyof

occurrenceof weatheroftenassociatedwithcoldairoutbreaksinmiddlelatitudes.FortheAO,thisis

characterizedby enhancedvariabilityduringthelowindexstate(weaksub-polarwesterlies).

MarkBaldwinreportedonworkwithTimDu_rlkertonthatexaminedtheabilitytopredictheAO

usingstatisticaltechniques(eeextendedabstract).TheynotethattheAOisthesurface

expressionoftheNorthernAnnularMode(NAM):thelatterismostpersistentatstratospheric

levels,peakinginthelowermoststratosphere, wheretheDJFe-foldingtimescaleexceeds30

days.TheyfurthershowedthatthepersistenceoftheAOhasastrongseasonalitywith

substantiallymorepersistence(-foldingtimeof 15-20days)du_rmgthewinterseasonwhenthe

planetarywavecouplingtothestratosphereisstrongest.Theyfoundalinearelationship

betweenthelowerstratosphericNAM andtheaverageAO 10-40dayslater,thatshouldmake

possiblepredictionsoftheAOatthe10-40dayrange.

StevenFeldsteinexaminedthedynamical mechanismsof thegrowthanddecayof theNorthAtlantic

Oscillation(NAO).ThestudyinvolvedadiagnosticanalysisusingNCEP/NCARreanalysisdataaswell

ascalculationswithaforced,barotropicmodetTheresultsshowedalifecycleofaboutwoweeks.

Bothhigh-frequency(period<10days)andlow-frequency(period>10days)transienteddyfluxeswere

foundtodrivetheNAOgrowth,whilethedecayoftheNAOoccursthroughboththedivergencetrm

andthelow-frequencytransienteddyfluxes.Theresultsfurthershowedanimportantdifference

betweentheNAOandPNApatterns,inthattheNAOlifecycleisdominatedbynonlinearprocesses,

whereasthePNAevolutionisprimarilylinear.

GrantBranstatorshowedexamplesfromtheobservationsandtwodifferentmodelsimulations(the

NSIPP-1andNCARmodels)ofwavetrainsthataremeridionallyconfinedandzonally-elongatedasa

resultofbeingtrappedwithinthewaveguideof themeanwinterNorthemHemispherejts(seeextended

abstract).Hediscussedhowthesewavetrainsareimportantforthesubseasonal predictionproblem

becausetheyactto connectwidelyspacedlocationsaroundtheglobewithinaboutaweek,theyimpact

12
thetimemeanandbandpasseddystatistics, andtheyeffectvariousotherteleconnectionpatternsuchas

theNAO andtheENSOresponse.

Suxnant Nigamexaminedthematuxe-phasedynamicsofPNAvariability.HenotedthatthePNA

representscirculationandprecipitationvariabilityonbothintraseasonal andseasonal time-scales,and

thatthePNAhassometimesbeenerroneouslyassociatedwithENSOvariabilityasit canbeexcited

duringENSOwintersaswell. Thepatternwassimulatedwithforcing(diagnosedfromreanalysisdata)

usingasteadylinearprimitiveequationmodel. Themodelresultsindicatedthatzonal/eddycoupling

andsub-monthlyvorticitytransientsareimportantinthepattern'sgeneration.

TimDelsoleintroducedtheconceptof anoptimalpersistencepattern(OPP)asacomponent of atimevaryingfieldthatremainsauto-correlatedforthelongesttimelags.HeshowedhowOPPscanbeused

forisolatingpersistentpatternsin stationarytimeseries,andfordetectingtrends,discontinuities, and

otherlow-frequencysignalsinnon-stationarytimeseries.Theresultsofhisanalysishowed,among

otherthings,thatthePNAisthemostpredictable(inalinearsense)atmosphericpattern.

WilburChenexaminedsubseasonal variabilityandteleconnectivityforvarioustimescalesassociated

withthePNA,NAOandAOusingNCEP/NCARreanalysesfortheperiod1971-2001(seeextended

abstract).Heshowedthatasthetimescaleincreases(7-day,31-dayand61-daymeans),the

teleconnections notonlybecomestrongerandbetterestablished, butalsomuchmoreorganizedand

locatedincertainpreferredregions.HefurtheredillustratedthesensitivityoftheJanuary-March

teleconnectionpatternstothebasedpointusedtodefinethecorrelationpatterns.

Hyuxl-KymngKimpresentedworkcarriedoutwithWayneHigginstomonitorthePNA,theArctic

Oscillation(AO),theNorthAtlanticOscillation(NAO),andtheAntarcticOscillation(AAO). Indices

of thesepatternsweredevelopedfortheperiodJanuary1950to thepresent,usingtheNCEP/NCAR

CDAS/Reanalysis. Forecastsof theindicesaremadebyprojectingtheloadingpatternofeachmode

ontotheMRFandensembleforecastdata.Theindicesandforecastsforthemostrecent120daysare

postedonthemonitoringweatherandclimatewebsiteof NCEP/CPCandupdateddaily.

(http://www.cpc.ncep.noaa. gov/products/precip/CWlink ).

13
iii-iv) Predictability of the ISO/MJO (Chair: Cecile Penland) and Tropical/extratropical interactions (Chair: Duane Waliser)

In view of the strong connections between the topics on the ISO/MJO and tropical-extratropical

interactions, this section is a synthesis of both sessions III and 1V. While the names intraseasonal

oscillation (ISO) and MJO are often used interchangeably, it is now becoming common practice to refer

to the boreal summer variability as the ISO, while the MJO is a boreal winter or cold season phenomena.

For convenience we have chosen not make that distinction here, so that in the following we refer to both

phenomena as simply the MJO. Note also that in this section, the references to the speakers appear in

parenthesis and usually at the end of the sentence. Invited presentations were made by Duane Waliser

(session iii, see extended abstract), and Prashant Sardeshmukh (session iv, see extended abstract).

Tropical intraseasonal variability was shown to interact with and/or influence a wide range of

phenomena including local weather in the tropical Indo-Pacific region, onsets and breaks of the AsianAustralian monsoons, persistence extra-tropical circulation anomalies in the Pacific-America sectors,

extreme precipitation events along the westem United States, the development of tropical

storms/hurricanes in the Pacific/Atlantic sectors, and even the initiation of E1 Nino / La Nina events

(Duane Waliser). This latter aspect was even extended to the point that the MJO may help characterize

the intrinsic time scale, at least its biennial character of ENSO (William Lau, see extended abstract).

Additional influences from the MJO include the modulation of N. Pacific cyclone activity (Mike Chen)

and the persistence of the S. America convergence zone (Leila Carvahlo). There is also the indication

that an independent intraseasonal oscillation may exist over S. America (Jiayu Zhou, see extended

abstract).

A number of presentations (e.g., Prashant Sardeshmukh, Duane Waliser, Ken Sperber) showed that

improvements in tropical diabatic heating variability (e.g., MJO, ENSO, other equatorial "waves")

will/should result in enhanced (long-lead and seasonal) forecasting sldll in the Tropics as well as midlatitudes via tropical/extra-tropical teleconnections. For example, one idealized dynamical predictability

study showed that with a reasonable GCM representation of the tropical MJO, the expected limit of

useful predictability for the MJO might be about 25 days (Duane Waliser). Unfortunately, GCMs in

both climate simulation and prediction or numerical weather forecasting settings still exhibit rather poor

14
simulationsofMJOvariabilityexceptforafewisolatedcases(DuaneWaliser,KenSperber,Suranjana

Saha).Infact,eventheinterannual variabilityof featuressuchastheAsianmonsoonappeartobepoorly

simulatedueto thepoorrepresentationfintraseasonal variabilityandits(albeit)onlyweak

dependenceonboundaryconditions(KenSperber).Apartfromtropicaldiabaticheating,one

presentation(KlausWeickmann)notedthatatmosphericintraseasonal variabilitycanariseduetothe

effectofmountainandfrictionaltorquesontheadjustment oftheatmosphereto stochastically-varying

flowovermountains.Thus,toproperlyaccountfortropical- extratropical interactionsonintraseasonal

timescales,it maybeimportanttounderstandtheseparaterolesandinfluencesfromtheabove

stochastically-forced processandfromthemoredeterministictropicaldiabaticheatingprocess(i.e.

MJO).

Onepresentation(PrashantSardeshmuXh) showedthatforecastsbasedonamultivariatelinearinverse

model(LIM) couldpredictseven-dayaveragesofnorthernhemispherestreamfunctionatleadtimesof

abouthreeweeksatskillssuperiortotheNCEPMedium-RangeForecastmodel(MRF).This

presentation, alongwithanother(Newman),showedhowtheabovepredictabilitycameinlargepart

fromanabilitytodescribetheevolutionoftropicalheating,aphenomenonwhichisnotdescribedwell

bytheMRF. BothpresentationsemphasizedhowLIM'spredictionsldllwastiedtotheidentificationof

threeoptimalstructuresforgrowth:onedominatedbytropicalheating,onewhichcombinedtropical

heatingandmidlatitudedynamics, andathirddominatedbymidlatitudedynamics.In addition,it was

notedthatwhiletheLIM canlikelyprovideusefulsubseasonal predictionsofmidlatitudevariability,it

maybenecessarytousenon-linearmodelstoprovidebetterestimatesof mncertainty- i.e.forecasting

forecastsldll. Of course,thesenon-linearmodelsneedtorepresent thestrengthandvariabilityof

tropicalheatingproperly.

Monitoringandforecastingof MJOwasdiscussedinanumberof contexts.Forexample,atleastwo

empiricalreal-timeforecastingschemesappeartobeforthcoming(MatthewWheeler- seeextended

abstract,CharlesJones,YanXue-seeextendedabstract),inadditiontotheonethatalreadyexistsvia

shallow-water modelwavefiltering(MatthewWheeler).Theseactivitiescanbeexpectedtoprovide

usefulreal-timeMJOforecastsoutto 10-20dayslead-time,particularlyinthecuxrentenvironment in

whichnooperational modelhasbeenshowntodowellatsimulating/forecasting theMJO. Oneresearch

grouphasbuiltanempirical mid-latitudeforecastingmodelbasedonthecanonical relationshipbetween

15
theMJOandmid-latitudeanomaliesoverthePacific- NorthAmericansector(YanXue).Suchamodel

(andassociatedwebsite)isdesignedtoprovideanaidforextendedrangeweatherand/orshort-term

climateforecasts.Additionalpresentationsexaminedthestructural variabilityaswellasfrequencyand

propagationcharacteristicsof MJOevents(Jones)tobetterunderstandthedifferentypesMJOevents

andtheirassociatedprobabilitiesfor occurrence, includingforexamplehowENSOinfluencesthese

probabilities(orviceversa).Alongthesesamelines,it waspointedout(DuaneWaliser,WilliamLau)

thatanumberofrecentanalyseshaveshownthatthereisnoobviousrelationbetweeninterannual SST

variabilitymthetropicsandtheoverallactivityof theMJO,exceptthatMJOeventstypicallypropagate

furthereastduringE1Nmoconditions.However,theanalysisfromonepresentation(WilliamLau)

suggestedthatthebiennialtendencyofENSOmaympartbederivedfromthecouplingbetweenthe

mterannualndmtraseasonal timescales.

Twopresentations(SuranjanaSaha,HilarySpencer- seeextendedabstract)pointedoutspecific

instancesof theextremesensitivitythattheMJOsimulationcharacterhasonevensubtlechangesm

modelparameterization. Oneofthesepresentationsstressedtheneedto simulateacorrectbasicstatem

anumericalweatherpredictionmodel,forboththesakeof obtaininganaccurateclimatologyandthus

lessdriftwithleadtimebutalsotoobtainabetterepresentationftransientshatdependonthemean

state(SuranjanaSaha).In fact,veryrecentstudieshaveshownthatthesimulationqualityoftheMJO

canbeparticularlysensitivetothebasic(e.g.,low-levelzonalwinddirectionmtheIndian/westem

PacificOceans).Theotherpresentation(HilarySpencer)highlightedtherathersignificantchangemthe

simulationqualityoftheMJOsimplyduetoanincreasemtheverticalresolution(namelymthemidtroposphere). Alongsomewhat similarlines,it wasshownthatassimilationof totalcolumnwatervapor

contentintotheGEOSACJCMcansignificantlyinfluencethemodel'srepresentationftheMJO(Man

LiWu- seeextendedabstract),furtheringthesuggestingthatcorrecttreatmentofmoistconvectionand

thehydrological cyclearevitaltothesimulationoftheMJO.Anotherstudyextendedthisnotiontoa

completelygeneralframework,pointingoutthatouranalysesandre-analysesdatasetsareseverely

hamperedbynotonlyalackofdatabutalsoduetoincorrectmoistphysicalparameterizations (Arthur

Hou- seeextendedabstract).Thisstudyhighlightedtheimprovementsthatcanbegamedmthesesorts

ofvalidatingdatasetsviatheassimilationof satellite-basedprecipitationestimates.

16
Anumberof talksstressedtheimportanceof whatmighthavepreviouslybeenthoughtof as

unimportant detailsasactuallybeingsignificanttothesubseasonal predictionproblem.Inonecase

study,it wasshownthattheevolutionoftheseasonal meancirculationoverthePacificNorthAmerican

regionwasdeterministicallyinfluencedbysubseasonal SSTvariability(BenKirtman).In anothercase,

it wasshownthatpropersimulationofthedetailedcharacteristicsof atropicalatmosphericheat

anomalyassociatedwithagivenE1Nino,includingthecorrectpartitionbetweenbasicstateand

anomaly,werecrucialto obtainingapropermid-latitudeteleconnectionpropertiesforthatgivenevent

(HilarySpencer).Alongsimilarlines,it wasshownthatdistinctsub-seasonal (i.e.monthly)extratropicalatmosphericsignalsdooccurinresponsetoENSOandthatseasonalveragesnotonlycan

smeartheseoutbutcanobscurethementirely(MarryHoerling).

v) Role of land surface processes (Chair: Huug van den Dool)

Randy Koster gave the invited presentation for this session (see extended abstract). There were four

additional papers, by Masao Kanamitsu et al. (see extended abstract), Adam Schlosser et al., Paul

Dirmeyer and Mike Bosilovich et al. (see extended abstract). To this I will add some comments made

by Huug van den Dool in his invited talk about operational methods that relate to the land surface. To

paraphrase (and extend slightly) on Koster's list of requirements:

1) Soil moisture needs to have an effect on the atmosphere. This effect has to be quantifiable.

2) Soil moisture needs to have a memory, either through a long autocorrelation (lifetime "on the spot")

or in a more Lagrangian sense as prediction skill where soil moisture anomalies are allowed to evolve

and move around.

3) Finally, does realistic soil moisture actually help in the prediction?

Koster et al made several shortcuts and studied "potential predictability" in a model to find "where" soil

moisture could make a difference. Different models give, mnformanately, very different estimates of

where soil moisture could make a difference. Kanamitsu et al have gone ahead and made a fairly

realistic soil moisture data set for 1979-present by manipulating the precipitation that enters the land

scheme of the so-called Reanalysis-2. A large focus here is on verification, which leads to a focus on

DATA. Model forecasts with and without realistic soil moisture show improved sldll in temperature.

17
Theimpactonprecipitationisunclear.Kanamitsudoubtsthatweneeddynamical modelsif allwecan

harvestis theimpactontemperature. VandenDoolshowedextensivelybyempiricalmeanshowdry

(wet)antecedent soilleadstowarmer(colder)conditionsforthenextseveralmonths.Suchtoolsare

alreadyinplaceforuseinCPC'smonthlyforecastsandarebeingrelieduponforthewarmseason.

Schlossershowedthattheautocorrelationof soilmoisturevariesfromweeksto seasons. Healsoargued

thatthebeneficial impactofrealisticinitialsoilmoistureinamodelbecomesimpossibletofindwhen

themodelbiasesoverwhelmanysmalleffectswearelooldngfor.Thefocushereisona-prioriremoval

of systematicerrors.Dirmeyerdelineatedthatthemodeldriftconsistsof threeparts,eachwiththeirown

timescale.Thefirstiscausedbyprecipitationbias,thesecondbyradiationbiases,andthethirdwould

becouplingeffects.Dirmeyeralsoraisedthequestionastowhetherweshouldbeusingmodel

consistent soilmoistureorrealisticsoilmoisture.Nearlyeverybodywantedtoknowwhymodelshave

precipitationbiases,andwhatit wouldtaketoreducethemThetracerstudyby Bosilovichadded

anotherperspectivetothequestionaboutheoriginofprecipitationinagivenarea.

Closingcomments:

A)Althoughtheeffectof soilmoistureisexpectedin"thewarmseason"it isimportantyearroundfora

numberofreasons:i) Thereisawarmseasonsomewhereontheplanetallthetime,andby

teleconnectiontheimpactscouldbefaraway,especiallywhentrendsinlanduseoverlargeareas(South

EastAsia)areconsidered, ii) Soilmoisturecalculationsneedtobedoneyearroundandiii) Theremay

becarryovereffectfromsnowcover/deptho soilmoistureanomalies.

B) GCMsappearto overdotheimpactof soilmoistureonforinstancethenearsurfacetemperature.

C)Weneeddataatthemostbasicleveltoverifymodelresults.Thereappearstobeanalmostcomplete

lackofevaporationdata.Needbrightmindstothinkofmeasuxing(surface)evaporationonascalelarger

thanatowerhereandthere.

D)Weneedtofindnon-localimpactsof soilmoistureforthisfieldofscientificendeavortobereally

important(overandabovesimpleempiricalmethods)forforecastsin dayl5-60range.

E)Whatarethecausesoflargeprecipitationbiasesin GCMs,andhowtoimprovethesituation?

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vi) Link between low frequency and weather/regional phenomena

(Chair: Siegfried Schubert)

This session consisted of a number of talks dealing with various aspects of weather and climate

variability and predictability, including diagnostic studies of the errors (both random and systermtic)

that affect AGCM simulations and forecasts.

The invited presentation was made by Jeff Whitaker on the subject of"Storm Track Prediction" by Jeff

Whitaker and Tom Hamill (see extended abstract). The transition between weather and climate

prediction was defined to occur at the forecast lead time at which all sldll is lost in predicting individual

storms. Prediction of weather beyond that transition is based on the assumption that the large scales

organize weather to allow predicting the statistics of weather, in particular the short waves that are the

major precipitation producers during the cold season.

A number of results were presented from 23 years of three week "reforecasts" for JFM with the MRF

model (T62L28) that was operation during the first half of 1998. They found that in the extratropics, the

short waves lose sldll after 5 days, while the long waves have sldll well into week two. The results of a

CCA analysis showed that the most predictable pattern for day 10 forecasts is the PNA. A regression

analysis further showed a time evolution in which an initial broad anomaly develops in the North

Pacific, followed by rapid intensification in the eastern Pacific of a baroclinic wave, followed by

downstream propagation. Results for week 2 were quite similar, while the three week averages showed

a link to the west Pacific pattern. The above evolution is associated with well-defined storm track shifts.

Furthermore, the storm track shifts were more coherent for longer averaging periods (more individual

storms). In the second part of the talk, Jeff examined week two quantitative precipitation forecasts

(QPFs) with the above forecast model, and based on his analysis of the storm track shifts. The results

were based on near real time 15 member ensemble runs at CDC since 1 Dec 2001. The results showed

that sldllful QPFs in week 2 are possible, but that ensemble bias correction is crucial.

The next talk was given by Yehui Chang on "Extreme weather events and their relationship to low

frequency teleconnections" by Y. Chang and S. Schubert (see extended abstract). The presentation

described a method for characterizing extremes in daily precipitation over the continental United States

19
usingacombinationofcompositingandlinearegression.Theresultshowedthattheextremeshave

bothregionalandseasonal dependencies. Forexample,duringthecoldseason,theextremesin

precipitationtendtobeassociatedwithwell-known"large-scale"weathersystems, whileduringthe

warmseasontheyareassociatedwithmorelocalizedconvectivesystems.Theresultswereappliedto

boththeobservationsandtheresultsfromsimulationswiththeNASA/NCARAGCMrunwithidealized

warm,neutralandcoldENSOSST.TheAGCMresultsandtoalesserextentheobservations(1963-

1999)showedevidencefor asubstantial impactofENSOonthestatisticsof theextremesevents.

Preliminaryevidencewasalsopresentedforimpactsof subseasonal modesofvariability(e.g.thePNA

andtheNAO)onthestatisticsof theextremevents.

J.ShuMapresentedtheresultsofastudyonthe"RelationshipbetweenthePNAinternalpatternandthe

ENSO-forcedpattern:timescalesfromdailyto seasonal", byDavidStrausandJ.ShuMa.Thestudy

involvedtheseparationof seasonal meanvariabilityduringborealwinterintothatforcedbySSTand

thatgeneratedinternally.Thestudywasbasedon30wintersofensemblesofsimulationswiththe

COLAAGCMforcedwithobservedSSTaswellas,longsimulationswithclimatologicallyvarying

SST.TheydistinguishbetweentheENSO-forcedresponse(orexternalpatternofvariability)andthe

'PNA"-likepatternof internalvariability.Theyfoundthatwhenthesepatternsareprojectedonto

pentaddata,theprobabilitydensityfunction(pdf)obtainedfortheENSOpattemisdistinctfromthatof

thePNApattern.TheyfurtherfoundthatduringwarmeventsthePNApatternhasgreaterchanceof

havingbothpolarities,whileduxmgcoldeventstheENSOpdfiswiderthanthatofthePNA,andthere

isenhancedintraseasonal variability.

RobBlackpresentedatalkonan"Assessment ofmidlatitudesubseasonal variabilityinNASA/GSFC

generalcirculationmodels",byBlack,RobinsonandMcDaniel(seeextendedabstract).They

performedapreliminaryassessment ofhestormtracksandanomalousweatherregimesinAMIP-style

integrationsof twodifferentmodels.Theseconsistof theNASA/NCARAGCMusedbytheDAOfor

dataassimilation, andtheNASASeasonal-to-Interannual PredictionProject(NSIPP)-1AGCMusedby

NSIPPtoassesspredictabilityandtocarryoutpredictionsonseasonal-to-interannual timescales.The

resultsshowedthatthemodelsregionalpatternsof uppertroposphericeddykineticenergy(EKE)are

wellrepresentedforboththesynopticandlow frequencyeddies,andrepresentanimprovement over

earliermodels.Bothmodels,however,haveweakerthanobservedlowfrequencyandsynopticEKE(by

20
20-30%).TheNSIPPmodelinparticular,hassynopticEKEthatisonly64%oftheobservedvalues.

Theauthorspresentanumberof diagnosticsto attempttoexplainthemodeldiscrepancies. A diagnosis

of thebaroclinicandbarotropicaspectsofthemodeldynamicshowsthattheNSIPPmodelsynoptic

eddiesareexperiencingenhancedbarotropicenergylossestothemeanflowinthejet exitregion,while

thebackgroundbaroclinicforcingis closertotheobservedvalues.Theauthorspeculatethatthe

existingdeficienciesinthelow frequencyEKEofbothmodelsmayberelatedtothescaleinteraction

betweenthesynopticandlowfrequencyeddies.

MingCaipresentedatalkon"DiagnosticsofClimateVariabilityandTrendUsingPotential Vorticity

Maps"byMingCaiandEugeniaKalnay.Theyexaminedthestrongwarmingtrendinhighlatitudesof

theNH duringthelast2decades, andconjecturedthatchangesinextratropical frontalactivitymay

explainpartofthemuch-amplifiedwarmingtrendinhighlatitudes.Theirdiagnostictechniquecentered

onananalysisofpotentialvorticitysurfacesinwhichtheydefinePVfoldingzones.Theinterannual

variabilityof anextratropical PVFoldingIndex(PVFI)andextremesurfacecoldandwarmeventswere

bothshowntohaveastrongQBOsignal.Theyfurthershowedthattheinterannual variabilityofthe

PVFIiscorrelatedwithinterannual variability&warm and cold events. An advantage of using the

PVFI, over say the AO index is that it measures both mobile and standing parts of polar vortex

variability, and is not constrained by "inactive" periods or warmer seasons.

Glen White's talk focused on the systematic errors of the NCEP operational 0-15 day forecasts (see

extended abstract). He showed that the systematic errors are similar to the bias in long (multi-year)

model simulations, and suggested that improvements in the short-range systematic errors should help to

improve the 2 week to two month forecasts. An advantage of focusing on the short-range forecasts is

that they should be easier to diagnose, since they occur before nonlinearity dominates the forecast

evolution. The analysis addressed, in particular, the systematic errors in the surface fluxes. Glen

showed that considerable differences exist between different estimates of air-sea fluxes. Current global

forecast systems have problems with cloudiness that produce inaccurate short wave fluxes and problems

with moistuxe that affect long wave fluxes. A key deficiency of cuxrent models appears to be in the

representation of low-level oceanic stratus clouds.

21
MarkHelfandpresentedresultsfromastudyof theinterannual variabilityoftheUnitedStatesGreat

PlainsLow-LevelJet(GPLLJ)duringMaythroughAugust(seeextendedabstract).Theresults,baseon

17yearsof GEOS-1reanalysisdatashowedthattheGPLLJisoneof themostpersistentandstable

featuresofthelow-levelcontinental flow. Theinterannual variancewasfoundtohave3maximawith

oneovertheupperGreatPlains,anotheroverTexas,andathirdoverthewesternGulfof Mexico.Mark

showedevidenceforanintermittent biennialoscillationinthemaximaoverTexas.Ofparticular

relevancetothesubseasonal predictionproblemisthefindingthatthetypicaldurationof theinterannual

anomaliesisontheorderof severalweeks,withthemoresoutherlymaximahavingthelongesttime

scales.Furtheranalysisisrequiredtobetterunderstandthenatureof thebiennialoscillationand

robustnessoftheweek-to-weekcoherenceoftheseanomalies.

III. Discussion and Summary

The final discussion session, chaired by J. Shu_kla, summarized the main findings of the workshop and

outline steps that should be taken to make progress on the subseasonal prediction problem. J. Shukla

highlighted some of the lessons learned from the numerical weather prediction (NWP) problem. In

particular, it was pointed out that much of the improvement in weather forecast sldll over the last 30

years has come about because of improvements to the models. It remains to be seen whether this will

also be true for the subseasonal prediction problem, though it is already clear that models will have to do

many things right (e.g., land, weather, MJO, stratosphere, etc.) to make substantial progress towards the

prediction problem throughout the globe. He also outlined a set of baseline forecast experiments that

could serve to better assess the status of our current subseasonal prediction capabilities (see below).

The following is a synthesis of the summary session and includes further (post-workshop) summaries

and analysis provided by members of the organizing committee. Specific recommendations are given in

section IV.

Forecasting on time scales longer than weather but shorter than one season (about 2 weeks to 2 months)

is perhaps the most challenging weather/climate forecasting problem we will face in the coming years.

This workshop was held in order to take stock of current capabilities, and to examine recent progress in

22
anumberof differentareasofweatherandclimateresearchthatofferpotentiallysubstantial gainsin

forecastskillon2weekto2monthtimescales.Theworkshoparticipants, therefore,included

scientistsfromtraditionallydisparatecommuxtities, includingresearcherswithspecialtiesin

stratosphericdynamics,hydrologyandlandsurfacemodeling,themonsoons, theMJOandothertropical

variability,extratropical variabilityincludingextratropical-tropical interactions, coupledatmosphereocean-landmodeling,weatherprediction,seasonal prediction,andvariousaspectsof statistical

modeling,analysis,andprediction.

Thekeyfindingoftheworkshopisthatthereiscompellingevidenceforpredictiveskillatleadtimes

substantiallybeyondtwoweeks.Itisunderstoodthatatthesetimesscalesthepredictionsarelargely

probabilistic(e.g.,phaseinformationaboutindividualstormsystemsislost),andthattheultimategoal

shouldbethepredictionoftheevolutionoftheprobabilitydensityfunction(PDF),therebybridging

boththeweatherandseasonal forecastproblems.Thevariouspresentationssuggestedthatweshould

notexpectto findasingledominantsourceofpredictiveskillonthesetimescales.Infactpredictive

skillwill likelycomefromahostof differentphenomenadependingonregion,season,andtimescales

ofinterest.Forexample,duringtheborealwinterthereisevidenceforenhancedpredictabilityinthe

middleandhighlatitudesassociatedwiththeArcticOscillation,thestratosphere, thePNApattern,and

tropicalforcing.EvidenceforanalogoussourcesofpredictabilityexistintheSouthernHemisphere

(e.g.,theAntarcticoscillation).TheMJOoffersthepotentialforimprovedforecastskill,especiallyof

theAsian-Australianmonsoon.Memoryofthesoilmoisture(andsnowmelt)offershopeforskillful

warmseasonpredictionsatleadtimesbeyondtwoweeksinanumberofdifferentcontinental regions,

primarilyof surfacetemperatureandto alesserextentprecipitation.

In the following we summarize some of the key unresolved issues and outline specific steps that should

help translate the various potential sources of predictability into measurable improvements in forecast

skill.

i. Role of tropical heating and the MJO

This is one of the most promising sources of predictability and represents a major opportunity for

improvements. Current GCMs do poorly in simulating many aspects of tropical convection and the

23
linkstotheextratropics, especiallytheheatingassociatedwiththeMJO.Simpler(comparedwith

GCMs)linearinversemodels(LIMs)showskillat3weeksintheextratropicsassociatedwithtropical

heating,asldllnotrealizedincurrentGCMs.Statistical andothermodelingstudiesuggestthat

deficienciesintheabilityofGCMstosimulateMJOsareaseriousimpediment toimprovingforecastsof

subseasonal variabilityoftheAsian-Australianmonsoons, thepredilectionforhumcaneformation,and

othersubseasonal variabilityincludingthatoverthesouthwesternUnitedStates.It wasalsopointedout

thatimprovingtherepresentationftropicaltransientsinGCMswill likelyhaveamuchgreaterimpact

onforecastshanimprovementsintheextratropics, andthiswill havemajorimpactsonalargerportion

oftheworld'spopulation.

Whilemodelingdeficienciesareamajorprobleminthisarea,deficienciesintheestimatesoftropical

heating(bothforinitialconditionsandformodelverification)arealsoimportant.It isclearthatwewill

needbetterestimatesof tropicalheating.Littleworkhasbeendonetodatetoaddresspredictability

associatedwithtropicalheatingonsubseasonal timescalesusingGCMs.Howimportantarethedetails

of theheatingfields(howaccuratelymustthesebeobserved)andcanweexpecttopredicthesebeyond

twoweeks.LIMsof GCMsofferanimportantdiagnostictoolto addresstheseissues.

Considerableworkisneededtobetterunderstandthenatureandpredictabilityofthetropicallyforced

modes.Forexample,issuespecifictotheMJOinclude,thesensitiveof MJOpredictabilityestimates

totheGCM,season,ENSO,SSTcoupling,andmid-latitudevariability.HowdoesMJOpredictability

influencemid-latitudecirculationandextremeventpredictability?Canweuseempiricalforecastsof

MJOheatingandassimilatethatintoforecastmodelsasashort-termmeanstoimprovemediumto

extendedrangepredictions?Theroleof subseasonal SSTvariationsnotdirectlycoupledtotheMJO

alsorequiresfurtherstudy.

ii. Extratropical modes of variability

Annular "modes" provide one of the most promising prospects for sldH beyond two weeks in the middle

and high latitudes. These modes appear to have long enough time scales and strong enough influences

on the surface that memory &the initial conditions can produce useful forecasts at long lead times. For

example, the Arctic Oscillation has a strong influence on middle and high latitude surface temperatures.

24
Thereisintriguingevidenceof stratospheric influence:strengthof wintertimestratosphericvortex

influencesthesubsequent troposphericcirculation.Anotherlowfrequencypatternthathasasubstantial

impactontheNorthernHemisphereextratropical cimateisthePNApattem.In fact,thePNAwas

identifiedasthemostpredictableof alltheextratropical modesexamined.Blockingepisodesrepresent

anothersourceofpredictability,thoughthisisyet-to-be-realizedsincecuxrentmodelstendtounder

predictblocldngfrequency.Stillotherlowfrequencyteleconnectionpattemshavebeenrecently

discoveredinwhichthetroposphericjetsactaswaveguides,resultinginzonallyelongatedwave

patternsthatlinkdistantpartsoftheglobe.Thepredictabilityassociatedwiththosemodesisyettobe

determined.

Keyissuesthatneedtobefurtheraddressedincludethenatureofthelink oftheannularmodestothe

stratosphereandthesignaltonoiseratio(i.e.howlargeisthepredictablesignalfromthestratosphere

comparedwiththetotalvariability).Thereisaneedtobetterunderstandthesourcesofpredictability

associatedwithblockingandthePNA(e.g.tropicalheating,weather,ENSO).Howdothevariationsin

thejetsaffectpredictabilityassociatedwiththewaveguidingmechanism.Howwelldomodels

reproducetheseandotherlow frequencymodes,theinteractionswiththestratosphere, andinteractions

withweather/extremes? Doesthesignalgetlostbeyondtwoweeksduetomodeldrift'?Whatisthe

sensitivitytohorizontal andverticalresolution?

iii. Soil moisture and snow

Soil moisture may be very important on subseasonal time scales (perhaps more so even than for the

seasonal problem) - consistent with intrinsic time scales of soil moisture anomalies (weeks to one

month).

Among the outstanding issues is the fact that LSMs currently do not agree on the strength of interactions

between land and atmosphere. Little has been done to address the predictability of snow in cuxrent

GCMs. Results from cuxrent studies suggest strong regional and seasonal dependence (need to pick the

right season and the right region).

25
Theavailabilityof landsurfacedataispoor,yettheneedisgreat(especiallysoilmoisture,snowand

estimatesofevaporation).Canwegetevaporationfromsatellites?Weneedtoconsiderin

predictabilitystudiesthatsoilmoistureanomaliesmovearound-thisrelatesto thedifferencebetween

thepredictabilitytimescaleandtheautocorrelation. Modelbiasisanissue,especiallyforrainfall.

Whilesoilmoisturesensitivityisasummerphenomenon, weneedto gettherightseasonal cycle,sothat

snowisimportant(howthatimpactsoilmoistureinspring).Currentresultssuggestthat80%of the

soilmoistureffectisontemperature, whiletheimpactonprecipitationisstilluncertain.

iv. Links with weather and other regional phenomena

Numerous studies have demonstrated that weather or synoptic variability is influenced by (and

influences) low frequency variations such as the MJO, the PNA pattem, and blocldng. For the

subseasonal prediction problem we need to better understand how these interactions affect predictability,

and determine those properties (or statistics) of weather that are predictable at lead times beyond which

individual storm systems can be predicted. Examples of weather properties that are potentially

predictable include changes in storm tracks, changes in regions of preferred hurricane formation, and

changes in extreme events. A key issue in addressing these problems concerns the ability of climate

models to simulate weather systems. For example, are detailed high-resolution simulations of individual

hurricanes needed'? Or, is it sufficient to only predict large-scale changes in the factors influencing

hurricane formation?

A related issue concerns the resolution that is necessary in climate models to make them useful for

addressing predictability on regional scales? Various local climatological features, such as the United

States Great Plains Low Level Jet (LLJ), have a profound impact on regional climates, making it

uxllikdy that climate models that do not adequately resolve such phenomena can provide useful

predictions on regional scales.

In general, we need to begin to define more stringent quality measures to assess the veracity of model

simulations that directly link errors in the models to uncertainties and errors in predictions at longer time

scales. Clearly, climate drift plays an important role in that it often exceeds the signal that we are trying

to predict. The diurnal cycle is a key example of large systematic errors that are common to most

26
climatemodelsandthatlikelyimpactourabilitytomakeusefulregionalpredictions- especially over

continents during the warm season.

In addition to reducing systematic errors, useful and reliable regional predictions (and for that matter

predictions at all scales) require that models produce realistic variability and show sensitivity to initial

conditions. This is necessary so that the spread of ensemble members will provide realistic assessments

of forecast uncertainties (more on this below).

v. Methodology and data

A number of suggestions for improving forecast sldll at the subseasonal time scale dealt with the

application and development of new forecast methodologies. These included the use of atmosphereocean coupled models to better simulate the interactions between the atmosphere and ocean, especially

in the extratropics. The key argument here is that this should provide more realistic time scales of the

atmospheric anomalies. There is also evidence that ocean feedbacks can produce more realistic

simulations of the MJO. Another suggestion was to use ocean mixed-layer models: these might provide

most of the benefits of a full ocean model, but their use avoids the problems of spin-up and other shocks

to the system that often occur when starting up a fully-coupled atmosphere ocean model forecast.

There is a clear need to improve the initialization of coupled atmosphere/land/ocean models in a way

that minimizes model shocks and retains the information in the initial conditions. This includes

improvements in how one samples from the initial PDF, including uncertainties in the tropical heating

and other important forcing mechanisms. Simpler models should be used where possible to help

diagnose the full AGCMs. Other uses of simpler (e.g. linear and/or lower order) models include more

economical estimates of higher order statistics that could be used, for example, for forecasting forecast

sldll.

New data sets are needed for both initialization and verification of model forecasts, and for validating

models. These include long term comprehensive and consistent reanalysis data sets of the atmosphere,

land, surface fluxes, and sea surface temperatures for a host of studies, including hindcast experiments

that address issue of model forecast sldll and predictability. The reanalysis data sets also need to

27
provideimproveddiabatic(latentandradiative)heating,precipitation, andcloudsto allowbetter

tropicalanalysesforinitialization, aswellastohelpvalidateconvectionschemes.Longtermsoil

moisture,snowand,if possible,evaporationobservationsareneededforreanalysis, forinitializingthe

land,andimprovinglandsurfacemodels.Thereisalsoaneedforbetterobservationsof thediurnal

cycleoverwarmseasoncontinentstoimprovethediurnalcycleofprecipitation/convection inGCMs.

IV. Recommendations

The results presented at the workshop suggest that substantial progress on the subseasonal prediction

problem will likely be the result &progress in a number of disparate lines &research and development.

As such, we believe that it will be crucial that the major climate and weather centers (in particular

NASA and NOAA) work together to help coordinate and focus these various efforts. In the following

we outline areas of high priority research and make specific recommendations on near term "action

items" that we believe are most likely to lead to substantial improvements of predictions on subseasonal

time scales.

i. High priority research and development

Tropical heating

• Improvements in the ability of GCMs to simulate the MJO

• Improved satellite estimates of subseasonal variability in tropical heating, especially

vertical profiles, and related phenomena such as precipitation and clouds.

• Development of long-term consistent reanalysis data sets with improved representation

of the tropics

• Improved initialization and ensemble methodologies

• Determine the role of subseasonal SST variability, and assess the gains in forecast sldll

from employing coupled atmosphere-ocean models, including models with only

mixed layer components

• Develop and exploit simplified models to help diagnose and benchmark the sldll of full

GCMs

• Improve our understanding oftropical/extratropical interactions

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Soilmoistureandsnow

• Improvedmodelsimulationsofsoilmoistureandsnow- determinerealismof the

simulatedsoilmemory

• Improvedsatelliteobservationsof soilmoistureandsnow

• Development oflongtermconsistent datasetsofsoilmoisture,evaporation, andsnow,

includingthosefromglobalreanalysissystems

• Improveunderstandingof thecouplingbetweenthelandsurfaceandatmospheric

processes

Extratropicalmodesof variability

• Improved understanding and simulation of annular modes, and their impact on the

surface climate

• Improved understanding of the link to the stratosphere and associated potential

predictability in the troposphere

• Improved long-term reanalysis datasets with a well-resolved stratosphere

• Improved understanding of the nature and predictability of various other modes of

subseasonal variability such as the PNA, as well as an assessment of how well

models are able to capture these modes

• Improved understanding of how subseasonal variability impacts weather and how

predictable the impacts are

ii. High priority action items:

a) That a coordinated and systematic analysis of current subseasonal forecast skill be conducted by

generating ensembles of 30-day hindcasts for the past 30-50 years with several "frozen"

AGCMs. Specific goals include, sampling all seasons, and generating sufficiently large

ensembles to estimate the evolution of the probability density function.

b) That a series of workshops be convened focused on modeling the MJO, and that a coordinated

multi-nation/multi-model experimental prediction program be developed focused on the MJO.

29
c) Thatnewsatelliteobservationsandnewlong-termconsistentreanalysisdatasetsbedeveloped

forinitializationandverification, withhighprioritygiventoimprovementsinestimatesof

tropicaldiabaticheatingandcloudprocesses, soilmoisture,andsurfacefluxes(including

evaporationoverland).

d) ThatNASAandNOAAdevelopacollaborativeprogramtocoordinate, focus,andsupport

researchonpredictingsubseasonal variability.

Specificstepstoimplementtheaboverecommendations are:1)tobeginimmediatelytodevelopa

frameworkfor anexperimental MJOpredictionprogram,2)toconveneafollow-upworkshopin the

springof 2003toorganizetheAGCMhindcastproject,andconductinitialmeetingsonmodelingthe

MJO,and3)forNASAandNOAAtoputoutajoint announcement of opportuxfitywithinthenextyear

tofocusresearch, modelinganddatadevelopment effortsonthesubseasonal predictionproblem.

30
Acknowledgments

This workshop was supported by the Goddard Earth Sciences Directorate as part of a crosscutting program to coordinate activities on weather and climate prediction. Additional fmndmg

was provided by NASA headquarters through the Global Modeling and Analysis Program, and

by the NASA Seasonal-to-interannual Prediction Program (NSIPP).

We wish to thank Nefertari Johnson for her invaluable and tireless effort in putting the various

disparate pieces of this document together.

References

Von Neuxnan, John, 1955: Some remarks on the problem of forecasting climate fluctuations. In

"Dynamics of Climate": The Proceedings of a Conference on the Application of Nuxnerical

Integration Techniques to the Problem of the General Circulation held October 26-28, 1955.

Richard L. Pfeffer, Ed, published in 1960 by Pergamon Press, pp 137.

31
Appendix- List of Participants

Jordan

Phil

Bob

Julio

Mark

Anjuli

Anthony

Lennart

Robert

Michael

Grant

Antonio

Ming

Kenneth

Peter

Juan Carlos

Marco

Leila

Arun

Simon

Yehui

Winston

Muthnbel

Mike

Wilbur

Ngar

Stephen

Timothy

Paul

Randall

Timothy

Franco

Steven

Rosana

Mary

Christian

Ron

Lisa

Milt

Jongli

Mark

Wayne

Alpert

Arkin

Atlas

Bacmeistar

Baldwin

Bamzai

Barnston

Bengtsson

Black

Bosilovich

Branstator

Busalacchi

Cai

Campana

Caplan

Jusem

Carrera

Carva Iho

Chakraborty

Chang

Chang

Chao

Chelliah

Chen

Chen

Cheung Lau

Colucci

Delsole

Dirmeyer

Dole

Dunkerton

Einaudi

Feldstein

Ferreira

Floyd

Franzke

Gelaro

Goddard

Halem

Han

Helfand

Higgins

NWS/NCEP/EMC

Earth System Sciences Interdisciplinary Center

DAO/NASA/Goddard Space Flight Center

UMBC/GEST NSIPP

Northwest Research Associates

NOAA OGP

International Research Institute (IRI)

Max Planck Institute fur Meteorologie / COLA

Georgia Tech, School of EAS

NASA/Goddard Space Flight Center

NCAR

Earth System Sciences Interdisciplinary Center

The University of Maryland

NOAA/NCEP

NCEP

SAI C/NASA/G S FC

NOAA/Climate Prediction Center

University of California, Santa Barbara

Florida State University

Office of Naval Research

DAO/NASA/Goddard Space Flight Center

NASA/Goddard Space Flight Center

CPC/NCEP/NWS

Iowa State University

NOAA/NWS/NCEP/CPC

GFDL/NOAA

Cornell University

Center for Ocean-Land-Atmosphere Stud ies

Center for Ocean-Land-Atmosphere Stud ies

NOAA-ClRES Climate Diagnostics Center

Northwest Research Associates

NASA Goddard Space Flight Center

The Pennsylvania State University

GEST/NSIPP/NASA Goddard Space Flight Center

Westover Consultants, Inc.

EMS Environmental Institute, Penn State

DAO/NASA/Goddard Space Flight Center

International Research Institute (IRI)

NASA/Goddard Space Flight Center

NCEP/RSIS

DAO/NASA/Goddard Space Flight Center

Climate Prediction Center/NCEP/NWS/NOAA

33
Marty

Fiona

Arthur

Jin

Ming

Charles

Hann-Ming

Eugenia

Masao

Richard

Hyun-Kyung

Kyu-Myong

Jim

Ben

Michael

Randall

Vernon

V.

Arun

Bill

Jim

Tsengdar

Stefan

Larry

Michiko

David

Kingtse

Shrinivas

Steven

William

Matt

Sumant

Warwick

Ed

Hua-Lu

Michael

Philip

Malaquias

Peitao

Cecilie

Roberta

Michael

Oreste

Thomas

Michele

John

Hoerling

Horsfall

Hou

Huang

Ji

Jones

Juang

Kalnay

Kanamitsu

Kerr

Kim

Kim

Kinter

Kirtman

Kistler

Koster

Kousky

Krishnamurthy

Kumar

Lau

Laver

Lee

Liess

Marx

Masutani

Melendes

Mo

Moorthi

Mullen

Neff

Newman

Nigam

Norton

Olenic

Pan

Patterson

Pegion

Pena

Peng

Penland

Quadrelli

Rabinowitz

Reale

Reichler

Reinecker

Roads

NOAA-ClRESClimateDiagnosticsCenter

NOAA

NASA/GoddardSpace FlightCenter

NOAA/OGP

NOAA/OGP

ICESS

NCEP/NOAA

Universityof Maryland

ECPC/SIOI/UCSD

Science

NCEP/CPC

NASA/GoddardSpace FlightCenter

COLA

COLA

NASASeasonalto Inter.PredictionProject

NASA/GoddardSpace FlightCenter

NOAA

COLA, IGES

NCEP/NOAA

NASA/GoddardSpace FlightCenter

NOAA/NWS/CPC

NASAHeadquarters

MaxPlanckInstitutefur Meteorologie

COLA

NOAA/EMC

NOAA

NOAA/ClimatePredictionCenter

NOAA/NCEP

UniversityofArizona

NOAA

NOAA-ClRESClimateDiagnosticsCenter

Universityof Maryland

Universityof Oxford,UK

NOAA/CPC

NOAA/NCEP

NOAA/OGP

NASA- NSIPP/SAIC

Universityof Maryland

CPC/NCEP/NOAA

NOAA-ClRESClimateDiagnosticsCenter

Universityof Washington/JISAO

Universityof Marylandand NASA/GSFC

GEST/DAO

Scripps - UCSD

NASA/GoddardSpace FlightCenter

Universityof California,SanDiego

34
Andrew

Suranjana

Prashant

Jae-Kyung

Adam

Siegfried

J.

Hilary

Ken

David

Bill

David

Max

Liqiang

Naomi

Marina

Zoltan

Steve

Alberto

Huug

Frederic

Duane

Mike

Wanqiu

Klaus

Matthew

Jeff

Glenn

ManLi

Yan

Song

Soo-Hyun

Jiayu

Yaping

Yuejian

Robertson

Saha

Sardeshmukh

Schemm

Schlosser

Schubert

Shukla

Spencer

Sperber

Starr

Stern

Straus

Suarez

Sun

Surgi

Timofeyeva

Toth

Tracton

Troccoli

van den Dool

Vitart

Waliser

Wallace

Wang

Weickmann

Wheeler

Whitaker

White

Wu

Xue

Yang

Yoo

Zhou

Zhou

Zhu

InternationalResearchInstitute

EMC/NCEP/NWS/NOAA

UniversityColorado

CPC/NCEPINWS/ NOAA

COLA

NASA/Goddard Space Flight Center

GMU/COLA

University of Reading

PCMDI, Lawrence Livermore National Laboratory

NASA/Goddard Space Flight Center

GFDL / NOAA

COLA

NASA/Goddard Space Flight Center

International Research

Environmental Modeling Center, NCEP

NOAA

SAIC-NCEP/EMC

NOAA/NCEP

NASA/UMBC

NOAA/Climate Prediction Center

ECMWF

SUNY, Stony Brook

University of Washington/J ISAO

NOAA

NOAA-ClRES Climate Diagnostics Center

Bureau of Meteorology Research Centre

NOAA-ClRES Climate Diagnostics Center

NOAA/EMC

NASA/Goddard Space Flight Center

NCEP/Climate Prediction Center

Climate Prediction Center / NOAA / NCEP

CPC/NOAA

GEST/NASA Goddard Space Flight Center

RS IS/CP C/N CE P

EMC/NCEP/NOAA

.35
Agenda

Tuesday, April 16

Page # for extended abstracts

08:30 Introduction (Franco Einaudi)

08:45 INVITED KEYNOTE TALK: EUGENIA KALNAY

Some opportu_ties in Dynamic Extended Range Forecasting

i) Current operational methods and their skill (Chair: Randy Dole)

09:30 INVITED TALK: HUUG VAN DEN DOOL

CPC operational methods and skill in the day 15 - day 60 forecast range

10:15 30-day weather forecasting at ECMWF

Frederic Vitart

10:35 The NCEP global ensemble: How to go beyond 16 days?

Z. Toth, Y. Zhu, R. Wobus, and T. Marchok

10:55 BREAK

11:25 Ensemble Prediction of Blocking

Stephen J. Colucci

11:45 The impact of atmospheric initial conditions on monthly mean hindcasts

Aman Kumar and Michael Phelps

12:05 The Role of Atmospheric ICs for Long-Range Predictability

T. J. Reichler and J. O. Roads

12:25 LUNCH

ii) Predictability of extra-tropical "modes" (Chair: Max Suarez)

01:30 INVITED TALK: MIKE WALLACE

Impacts of the Arctic Oscillation and the PNA

pattern on Weather

02:15 Statistical Prediction of the Arctic Oscillation

Mark P. Baldwin and Timothy J. DuxLkerton

02:35 How can the stratosphere influence the troposphere?

Walter A. Robinson and Yucheng Song

43

45

47

51

54

55

59

60

61

63

67

37
2:55 Dynamicalmechanismsassociatedwiththegrowthanddecayofthe 68

NorthAtlanticOscillation(NAO)teleconnectionpattem

StevenFeldstein

03:15 BREAK

03:45 Distanteleconnectionsviathetroposphericjet streamwaveguide

GrantBranstator

04:05 Ontheoriginandmatuxe-phasedynamicsofPNAvariability

Suxnant Nigam

04:25 LimitsofPredictabilityDeterminedbyOptimalPersistencePattems

TimDelsole

04:45 Transientteleconnectionnatureof climatevariability

onsub-seasonal timescales

WilburY. Chen

05:05 MonitoringtheAO/PNAindicesusingNCEP/NCARCDAS/Reanalysis

HyuJa-kyuJag KimandWayneHiggins

05:30 DISCUSSION

Wednesday, April 17

iii) Predictability of the ISO/MJO (Chair: Cecile Penland)

08:30 INVITED TALK: DUANE WALISER

Predictability and Forecasting Issues Related to the MJO/ISO

09:15 MJO-ENSO relationship: A re-examination

William K. M. Lau and K. M. Kim

09:35 Recent failures of monsoon-ENSO relationships viewed from coupled

model results

Aruan Chahaborty and T. N. Krishnamuxti

09:55 Variability of Tropical Intraseasonal Convective Anomalies and their

Statistical Forecast Skill

Charles Jones and Leila M. V. Carvalho

10:15 BREAK

10:45 An All- Season Real- time Multivariate MJO Index: Development of the

index for monitoring and prediction in Australia

Matthew Wheeler and Harry Hendon

69

71

72

73

77

79

81

88

92

93

94

38
11:05 Evidenceof Independent 30-60-dayOscillationoverTropicalSouth 98

AmericaduringAustralSummer

JiayuZhouandWilliamK.-M. Lau

11:25 DynamicalSeasonal Predictabilityof theAsianSummerMonsoon

K. R.Sperber,C.Brankovic,M. Deque,C.S.Fredericksen,

R.Graham,A.Kitoh,C.Kobayashi,T.Palmer,K. Purl,

W.Tennant,andE.Volodin

11:45 ExperimentswithaDataAssimilationSystemtoDiagnoseAGCM

Hindcastsof theMJO.M.-L. C.Wu,S.Schubert,D.VanPelt,and

D.Waliser

iv) Tropical/extra-tropical interactions (Chair: Duane Waliser)

01:00 INVITED TALK: PRASHANT SARDESHMUKH

The importance of tropical influences in the predictability

of subseasonal extratropical variations

01:45 Mid-latitude mountains and the MJO: A prototype for tropicalextratropical interaction?

Klaus Weickmann

02:05 Influence of the Madden Julian Oscillations on U.S. Tempemtuxe and

Precipitation Patterns during ENSO-Neutral and Weak ENSO Winters.

Yan Xue, Wayne Higgins, and Vernon Kousky

02:25 The role of MJO activity modulating the SACZ persistence, intensity

and form, and regional impacts on extreme precipitation events

Leila M. V. Carvalho, Charles Jones and Brant Liebmann

02:45 Modulation of the North Pacific-Pacific Northwest Storm Activity

by MJO

T.- C.(Mike) Chen, David Li, Peter Hsieh, and Jordan Alpert

03:05 BREAK

03:30 Monthly Variability of ENSO Signals and the Relevance for Monthly

Predictions

Martin P. Hoerling

03:50 Modeling the Influence of Tropical Pacific SST anomalies on the

circulation over the North Pacific and North America

Hilary Spencer and Julia Slingo

04:10 The impact of sub-seasonal sea surface temperature variability on the

simulation of the seasonal mean extra-tropical circulation

Ben Kirtman

103

104

109

111

115

116

120

121

122

123

128

39
04:30 Simulationof timemeantropicalcirculationanditsvariationsinAMIP 129

runsusingdifferentversions of the operational NCEP global model

Suxanj ana Saha

04:50 An empirical estimate ofsubseasonal predictability

Matt Newman.

05:10 Role of satellite rainfall information in improving understanding of

the dynamical link between the tropics and extratropics,

Arthur Y. Hou

5:30 DISCUSSION

Thursday, April 18

v) Role of land surface processes (Chair: Huug van den Dool

08:30 INVITED TALK: RANDY KOSTER

Predictability associated with land surface moisture states: Studies

with the NSIPP system

09:15 The predictability of soil moisture and near surface temperatuxe

based on the hindcasts of NCEP Seasonal Forecast Model

M. Kanamitsu, C.- S. Lu and W. Ebisuzaki

09:35 The Impact of Land Initialization and Assimilation on Climate

Predictability and Prediction.

C. Adam Schlosser, P. C. D. Milly, Paul A. Dirmeyer and David Mocko.

09:55 Three modes of climate drift in coupled land-atmosphere systems

Paul A. Dirmeyer

10:15 BREAK

11:15 GCM Simulation of the Large-scale North American Monsoon

including Water Vapor Tracer Diagnostics

M. G. Bosilovich, S. D. Schubert and G. K. Walker

vi) Link between low frequency and weather/regional phenomena

(Chair: Siegfried Schubert)

11:35 INVITED TALK: JEFF WHITAKER

Storm Track Prediction

12:20 LUNCH

01:20 Extreme weather events and their relationship to low frequency

teleconnection patterns. Yehui Chang and Siegfried Schubert

130

131

133

135

139

142

143

144

149

151

153

40
01:40 TheRelationshipBetweentheClassicPNAPatternandthe 158

ENSO-ForcedPatternonTimeScalesfromDailytoSeasonal

DavidM. StrausandJ.Shukla

02:00 Assessment ofmidlatitudesubseasonal variabilityinNASA/GSFC

GeneralCirculationModels.RobertX. BlackandDennisP.Robinson

159

02:20 Diagnosticsof ClimateVariabilityandTrendUsingPotentialVorticity

Maps.MingCaiandEugeniaKalnay

163

02:40 SystematicErrorsandSurfaceFluxesIntheNCEPglobalmodel

GlennWhite

164

3:00 Configurationandintraseasonal durationofinterannual anomaliesof

theGreatPlainsLowLevelJet.MarkHelfand

170

3:20 BREAK

3:50 DISCUSSION (Chair: Shukla)

5:30 END OF WORKSHOP

41
Some Opportunities in Dynamic

Extended Range Forecasting

Eugenia Kalnay, Ming Cai, and Malaquias Pena

University of Maryland

College Park, MD 20742

Extracting useful predictability from the Dynamic Extended Range Forecasting (DERF)

time range, two weeks to two months, is not easy. D. Bumdge (pers. comm.) referred to

this forecast range as the "dry patch of predictability", meaning that it is too long for

weather forecasting from information in the initial conditions, and too short for

influences such as ENSO forcing to set in and provide predictability from boundary

conditions. Nevertheless, it should be both economically very important and

scientifically possible to advance significantly beyond what we are doing today.

In this talk we review some of the opportunities that we believe have not yet been

exploited within the DERF "dry patch".

Extratropical ocean-atmosphere coupling. It is common to see AMIP runs referred to as

"the upper limit on the skill that one can possibly obtain, since the SSTs are perfect".

This statement is not really true, since in AMIP (perfect SST) runs the interaction

between the ocean and atmosphere in the extratropics is seriously flawed. Away from the

deep tropics the atmosphere is mostly forcing the ocean, and AMIP runs are set up so that

instead, the ocean always forces the atmosphere, and the atmosphere provides no

feedback. Pena et al (2002) have shown that a) locally coupled anomalies last much

longer than locally uncoupled anomalies, b) in the extratropics, atmosphere-driving

anomalies last longer than ocean-driving anomalies, and the opposite is true in the

tropics. As could be expected from these results, they also found that c) the AMIP runs

have shorter persistent anomalies than the reanalysis. This indicates that "perfect"

coupled ocean-atmosphere models should be able to predict persistent anomalies much

better than AMIP models. Another result obtained by Pena et al (2002) is that in AMIP

runs, high skill in the extratropics is correlated with ocean-driving situations in the

reanalysis and vice versa, indicating that it may be possible to identify a priori expected

skill.

Intraseasonal variability. The Madden-Julian Oscillation (MJO) is a large amplitude,

strong organizing influence in the atmosphere, with time scales of several weeks. Waliser

and others have shown that few models are able to reproduce it realistically (one exception

is the GLA 4th order model developed at NASA/GSFC in the early 1980's!). Until the

models become good enough to predict the MJO with the right frequency and amplitude, it

should be possible to use the robust statistical characteristics of the MJO to nudge or

otherwise force forecast models to reproduce in a statistically optimal way the MJO during

a forecast. This would increase the skill of both the tropical and extratropical forecasts

during the first few weeks.

15-day ensemble forecasts. The 2-weeks daily ensembles of the NWS contain a treasure

of information on potential predictability that is not being exploited to its full potential.

For example, during the Oklahoma-Texas drought of the summer of 1998, it was

43
apparent that the ensemble captured well the persistent nature of the drought, maintained

by a large extent by the local soil moisture feedback (Hong and Kalnay, 2000, 2002).

However, this was not being conveyed to the public in real time.

Improved initial conditions and coupled ensembles. Improving the initial conditions of

coupled models could enhance predictability from initial conditions in the DERF time

scales. One approach for doing this is the use of breeding to determine the subspace of

dominant errors in the forecasts used as background or first guess for the analysis

(Kalnay and Toth, 1994, Patil et al, 2001, Corazza et al, 2002, Ott et al, 2002). Cai et al

(2002) have shown promising results for improved data assimilation and ensemble

forecasting using the Zebiak-Cane model. Cai and Kalnay (2002) have experimented

with breeding in the NSIPP coupled atmospheric-ocean system, and their preliminary

results indicate that by performing breeding based on ocean rescaling, it is possible to

capture the dominant instability of the coupled system even if the atmospheric noise

saturates. This should allow using the observations in a more effective way, by

minimizing the errors in the initial conditions that project on this and other coupled

growing modes. It should also allow the creation of more effective initial perturbations

for ensemble forecasting.

References:

Cai, Ming, E. Kalnay and Z. Toth, 2002: Bred Vectors of the Zebiak-Cane Model and

Their Application to ENSO Predictions. Accepted in J. of Climate.

Cai, Ming and Eugenia Kalnay, 2002: Coupled ENSO instabilities derived with breeding.

WMO, Research activities in atmospheric and oceanic modeling. Report #23, [Available

from WMO, C.P. No 2300, CH1211, Geneva, Switzerland].

Corazza, M., E. Kalnay, D. J. Patil, R. Morss, I. Szunyogh, B. R. Hunt, E. Ott, and M.

Cai, 2001: Use of the breeding technique to estimate the structure of the analysis "errors

of the day". Submitted to Nonlinear Processes in Geophysics, European Geophysical

Society.

Hong, Song-you and E. Kalnay, 2000: Origin and maintenance of the Oklahoma-Texas

drought of 1998. Natuxe, 842-845.

Hong, Song-you and Eugenia Kalnay, 2002: The 1998 Oklahoma-Texas Drought:

Mechanistic Experiments with NCEP Global and Regional Models. J. of Climate, in

press.

Kalnay, E., and Z. Toth, 1994: Removing growing errors in the analysis cycle. Preprints

of the Tenth Conference on Numerical Weather Prediction, Amer. Meteor. Soc., 1994,

212-215, Boston, MA.

Ott, Edward, B.R. Hunt, I. Szunyogh, M. Corazza, E. Kalnay, D.J. Patil and J. Yorke,

2002: Exploiting Local Low Dimensionality of the Atmospheric Dynamics for Efficient

Kalman Filtering. ArXiv:physics/0203058vl 19 March 2002.

Patil, D.J., B. Hunt, E. Kalnay, E. Ott, J. Yorke, 2001: Local Low Dimensionality of

Atmospheric Dynamics. Phys. Rev. Letters, 86, 5878.

Pena, M., E. Kalnay and M. Cai, 2002: The life span ofintraseasonal atmospheric

anomalies: dependence on the phase relationship with the ocean. WMO, Research

activities in atmospheric and oceanic modeling. Report #23, [Available from WMO, C.P.

No 2300, CH1211, Geneva, Switzerland].

Pena, M., E. Kalnay and M. Cai, 2002: Statistics of coupled ocean and atmosphere

intraseasonal anomalies in Reanalysis and AMIP data. Submitted to the Nonlinear

Processes in Geophysics, European Geophysical Society.

44
Session I

Current Operational Methods and Their Skill

45
CPC operational methods and skill in the

day 15 - day 60 forecast range

Huug van den Dool (CPC)

1. Introduction

The Climate Prediction Center has only one operational forecast in the day 15 - day 60 range, which

is, as predicted by Von Neumann(1955), the most difficult of all ranges. We do publish a forecast

for monthly mean conditions over the US at a lead of 2 weeks. This forecast has been released once

a month since Dec 1994. We will discuss briefly the tools underlying this forecast, in particular

OCN (optimal climate normals), CCA (canonical correlation analysis), ENSO composites (when

applicable) and, during summer, local and non-local soil moisture tools such as the Constructed

Analogue. The weight given to the NCEP two tiered 'coupled' model is rather low so far. Relying

on a-priori skill estimates over an historical evaluation period, the tools are combined each month

into an official forecast. We present here a discussion of the sldll of these official monthly forecasts

for 1995-present. A comparison to the skill of the seasonal forecast shows that CPC's monthly

forecast is basically a watered down version of the seasonal forecast, but with somewhat lower

signal to noise ratio, as expected. Much of the forecast map shows the dreaded CL, a forecast for

equal chances.

2. Time scales

The word monthly or seasonal (forecast) sounds like a monthly or seasonal time scale. However,

one needs to carefully distinguish three time scales:

-) The lead time, i.e. the time between the issuance of the forecast and the first moment &validity

-) The averaging time (if any), and

-) The time scale of the physical process contributing to the predictability or sldll of the forecast

While we can choose the first two time scales, (before 1995 we had a zero lead monthly forecast)

we have no control over the third. The great apparent contradiction, as we shall see, is that

interdecadal variability contributes to sldll of seasonal and, yes, even monthly forecasts.

3. Tools

The tools used at CPC for the 1Amonth lead monthly forecast include:

1) Optimal Climate Normals (OCN), Huang et a1(1996)

2) Canonical Correlation Analysis (CCA), Bamston(1994)

3) NCEP Dynamical Seasonal Forecast Model (CPM)

4) ENSO composites (when judged applicable)

5) Soil Moisture based tools (when judged applicable)

6) MJO extrapolation and more

47
Thegroupingreflectstheoverallimportanceof thesetools.Thefirstthreetoolsarealways

available.Tools4 and5areimportant,butareusedonlyoccasionally, i.e.ENSOcompositesare

invokedwhenanENSOisanticipated, andthesoilmoisturetoolsaregivensomeweightduringthe

warmhalfof theyear.Withtheexceptionof MJOextrapolation, noneof thesetoolsisan

atmosphericinitialvalueapproach.Rather,thelowerboundary,beit SSTorsoilmoisture,isrelied

uponwhenformulatingtheforecast.

In somedetailthesetoolsaredesignedasfollows:

1)OptimalClimateNormals(Huangetal1996a)isessentiallyapersistenceof theclimaticanomaly

averagedoverthelast10years.Thistoolspicksuponanyrecentclimatetrends,whetherit be

naturalorman-made. Thefactthata10yearaverageis 'optimal'forUStemperatureisaconcise

statement abouthepowerspectrumofclimatevariationinthispartof theworld.

2)Canonical CorrelationAnalysisisappliedfollowingBarnston(1994). CCAisamultivariate

linearegressiontechniquethata-prioricompressesthestateof theclimateisalimitednumberof

modes.PredictorsincludeglobalSSToverthelastyear,antecedent heightfieldsandlocal

predictors.Thepredictorcanbeanything,butforCPC'soperationsincludesall 50states.

BothOCNandCCAhavebeentrainedondatafor 1950'spresent,withmuchattentionforcross

validation,i.e.anattempttoestimatea-priorisldllhonestly.

3)The2-tieredcoupledmodel(CPM)followstheexampleofNWP,butnowwithanoceanmodel

(Pacificonly)included.Thesemodelcomponentsaresubjecttocontinualchange.Arecent

descriptionisgiveninKanamitsueta1(2002).

4)ENSOwarm(cold)compositesareessentiallyanensembleofrealizationsduringpreviouswarm

(cold)events.Thistoolisinvokedwhenawarm/coldeventisanticipated, adecisionthatincludes

somesubjectivity.Theideagoesbackatleasto RopelewsldandHalpert(1986).Theselectionof

years,andthewayonedealswithotherclimatevariationsissuesimultaneouslymakescomposites

asomewhatsubjectivetools.

5)Localeffectsof soilmoistureontemperaturehavebeendescribedinHuangetal(1996b),anda

localtoolthereofwasused.Gradually,atoolincludingnon-localeffects(evenonprecipitation)has

subsumedthelocaltool.Thelatertoolisbasedonananalogueconstructedto 'todays'nationwide

soilmoistureanomaly(seeVandenDool1994forthemethod).Dailyupdatescanbefoundat

_://www.c_ov/soilms,;/mdex _, seeCASforecastsbutton.

6)WhenastrongMJOisinprogress,someof theforecastersprojectitsevolutionintothefuture

month(day15-day60).Thiscomponent isverysubjectiveandcouldmostdefinitelybenefitfrom

anobjectiveunderpinningbydevelopingspecificMJOtools,astrongthemeofthismeeting.

4. Skill

Withreferenceto definitionsgiveninVandenDooleta1(1996, 1999),thethreeclassHeidkesldll

scoresfor 1995onwardaregivenintheTablebelow.TheSS1(0isuseless,100perfect)isthe

Heidkeskill scoreforthoseareasjudgeda-priori to be high sldll areas, where we make a 'non-CL'

(i.e. a non-climatological probabilities) forecast, which is about 44% of all places/times (averaged

over all leads). The lower SS2 score (=SSl*fractional coverage) is for the whole nation and should

be used to intercompare the skill of tools.

{{For those unfamiliar with Heidke: It might help to remember that SS1 is about half the expected

correlation, i.e. SS1=25 corresponds to an expected 0.5 correlation, and the permissible probability

48
anomalyis onaverage2/3rdsofSS1,i.e.forSS1=25weareentitledto throwa34/66type'coin',a

considerabledeparturefrom50/50(thenosldlllowerbound)}.

TEMPERATURE

SS1 SS2 Coverage SS1 SS2 Coverage

OFF 25 11 44% 10 2 20%

CCA 27 5 20 2 0 6

OCN 26 9 33 10 1 11

CMP 11 3 27 3 0 8

PRECIP'N

Note that we have little skill in precipitation. For Temperature the official forecast (OFF) appears to

combine the strengths &various tools, and has the highest coverage and highest SS2. Of the

individual tools OCN has scored the highest over 1995-2001. Each tool is expected to have SS1 in

excess of 15, if prepared properly - with the exception of CMP this is the case for Temp, but for

Precip we are generally unable to identify the high sldll areas a-priori. Appropriately, the coverage

is low.

The above Table applies strictly speaking to the seasonal forecast. However, the sldll of the monthly

forecast (far less studied) follows the above, except that SS2 and coverage are lower. As reported in

van den Dool (1998), repeated as a Table below, the comparison of seasonal and monthly forecasts

on temperature (2 week lead only) proves that, as expected, the monthly forecast as practiced at

CPC just acts as a watered down version of the seasonal forecast, with similar sources of skill, and

barely an atmospheric initial value approach. The best single tool is OCN, a crude attempt to

harvest 'climate change' as a source of skill for CPC operational forecasts. The coverage of the

monthly forecast is low: only 34%, i.e. most of the time we plead a lack of knowledge and or

predictability and resort to climatological probabilities.

OFF

OFF

TEMPERATURE

SS1 SS2 Coverage

25 13 53 (seasonal from vdDool et a1(1998)

19 6 34 (monthly "

49
References:

Bamston, Anthony G., 1994: Linear Statistical Short-Term Climate Predictive Sldll in the Northem

Hemisphere. Journal of Climate: Vol. 7, No. 10, pp. 1513-1564.

Huang, J., H. M. van den Dool and A. G. Barnston, 1996a: Long-Lead Seasonal Temperature

Prediction Using Optimal Climate Normals. J. Climate., 9, 809- 817.

Huang, J., H. M. van den Dool and K. G. Georgakakos, 1996b: Analysis of model-calculated soil

moisture over the US (1931-1993) and applications to long range temperature forecasts. [

Climate., 9, 1350-1362.

Kanamitsu, M. Arun Kumar, Hann-Ming Henry Juang, Jae-Kyung Schemm, Wanqui Wang,

Fanglin Yang, Song-You Hong, Peitao Peng, Wilber Chen, Shrinivas Moorthi, and Ming Ji,,

2002: NCEP Dynamical Seasonal Forecast System 2000. BAMS, 83, 1019-1037.

Ropelewsld, C.F., M.S. Halpert, 1986: North American Precipitation and Temperature Patterns

Associated with the E1 Ni? o/Southem Oscillation (ENSO). Monthly Weather Review: Vol.

114, No. 12, pp. 2352-2362.

Qin, J., and H. M. van den Dool, 1996: Simple extensions of an NWP model.Mon. Wea Rev., 124,

277-287.

Van den Dool, H.M.,1994: Searching for analogues, how long must one wait? Tellus, 46A, 314-

324.

Van den Dool, J. Hoopingamer, E.O'Lenic, A.J.Wagner, A.G.Barnston, R.E.Livezey, D.Unger,

A.Artusa, M. Masutani, J. Huang and R.Churchill, 1996 • 1st Annual review of sldll of CPC

real time long lead predictions. Proceedings of the 21 rd _1 Climate Diagnostics and

Prediction Workshop, Oct. 28-Nov 1, 1996, Huntsville, Alabama. p l 3-16.

Van den Dool, H. M., J. Hoopingamer, E.O'Lenic, A.J.Wagner, A.G.Bamston, R.E.Livezey,

D.Unger, A.Artusa and R.Churchill, 1999:3 rd Annual review of sldll of CPC real time long

lead predictions: How well did we do during the great ENSO event. Proceedings of the 23 rd

Annual Climate Diagnostics and Prediction Workshop, Oct. 26-30, 1998, Miami, Florida, pp

9-12.

Von Neumann, J., 1960: Some remarks on the problem of forecasting climatic fluctuations.

Proceedings of Dynamics of Climate Conference, 1955 Institute for Advanced Study

Princeton, Edited by R. Pfeffer. p9-11.

5O
Monthly Forecasting At ECMWF

Frederic Vitart

European Centre For Medium-Range Weather Forecasts

1. Introduction

Medium-range weather forecasting and seasonal forecasting are currently operational at ECMWF.

These two systems have different physical bases. Medium-range forecasting targets the period from

day 4 to day 10, and is usually considered as an atmospheric initial condition problem. It is

generally believed that the time range is too small for the oceanic variability to play a significant

role. Therefore, the medium-range forecasting systems at ECMWF are based on atmosphere-only

integrations forced by initially observed SST's. Deterministic medium-range weather forecasts are

issued using a single integration of T511 with 60 vertical levels. Probabilistic forecasts called EPS

are issued using a 51-member ensemble of T255L40 integrations twice a day.

Seasonal forecasting at ECMWF focuses on the period between 1 and 6 months. It is considered as

a boundary condition problem, since most of the predictability at the time scale is justified by the

long predictability of the oceanic circulation and its impact on the atmospheric circulation. Because

of the strong role of the oceanic variability at such long time-scales, seasonal forecasts at ECMWF

are based on coupled ocean-atmosphere integrations. They are probabilistic based on a 40-member

ensemble of coupled T95L40 integrations each month.

In order to fill the gap between medium-range weather forecasting and seasonal forecasting, a new

project is being developed at ECMWF called monthly forecasting. Its aim is to evaluate the

predictability between day 10 and day 30. It is likely to be both an initial condition problem like

seasonal forecasting. Therefore, the monthly forecasting system at ECMWF has been designed as a

combination of EPS and seasonal forecasting.

2. Description of the monthly forecasting system

The ECMWF monthly forecasting system is based on a 51-member ensemble of coupled oceanatmosphere integrations. The atmospheric component is IFS (from ECMWF) with a T159L40

resolution. The oceanic component is HOPE (from Max Plank Institute) with a zonal resolution of

1.4 degrees with 29 vertical levels. The oceanic and atmospheric components are coupled every

ocean time step (1 hour) using OASIS from CERFACS.

The atmospheric initial conditions are proved by ERA40 or the ECMWF operational analysis. The

ocean initial conditions are provided by the last ECMWF ocean analysis. Since the ocean analysis

lags 12 days behind real time, ocean-only integrations are performed for 12 days with this ocean

analysis as initial condition and forced by atmospheric fluxes from the ECMWF analysis in order to

estimate the ocean initial conditions for the coupled man.

51
TheatmosphericpermxbationsaresimilartothoseusedforEPS.Theyincludesingularvectorsto

perturbtheatmosphereinitialconditionsandtheuseof stochasticphy6sicstopermxbthe

atmosphericmodelateachtimestep.Theoceanperturbationsareidenticaltothoseappliedfor

seasonal forecasting.TheincludeSSTperturbationsintheoceanicinitialconditionsandwind

stresspermxbationsduringtheoceanicdataassimilation.

Thepresentsystemdisplaysasystematicerrorthatgrowsalmostlinearlywithtime.Thiserroris

quitesmallduringthefirst10daysofintegrations, butbecomesignificantafter10days,and

therefore,needstobecorrected.Abackgroundsetof statisticsiscreatedin orderto correcthe

modeldrift. A 5-memberensembleisintegratedatthesamecalendardateastherealtimeforecast

from1990to 2001.Thisrepresentsa60-memberensemble, whichisrunalternatelywiththereal

timeforecast.This60-meterensembleallowsustoestimatethedriftof themodel,andthereforeto

correcthereal-timeforecastafterwards.

Thebackgroundstatisticsarealsoveryusefultoestimatewhatistheprobabilitydistribution

function(pdf)of oursystem.After10days,thespreadofthereal-timeforecastensemblegetsvery

large,andforecastsareissuedbycomparingthepdfof therealtimeforecasttothepdfofthemodel

climatology.Statistical testsareusedtodetermineif the51-memberensembleof therealtime

forecastissignificantlydifferentfromthe60-memberensembleofthemodelclimatology.

3. CaseStudies

Severalmonthlyforecastshavebeenperformed.Forecastsstartingon1January2001and10April

2002havebeendisplayed.Interestingly, theseforecastssuggestthatthemodelstilldisplaysome

potentialpredictabilityby week4. Inthepresentpaper,wesaythereispotentialpredictability

whenthe51-memberensembledistributionof therealtimeforecastandthe60-memberensemble

oftheclimatological distributionaresignificantlydifferent,withasignificanceexceeding90%.

Thereisof couxselesspotentialpredictabilityin week4 thaninweekl,butthemodelisstill ableto

predictsomethingforweek4thatissignificantlydifferentfromclimatology.

Thebackgroundstatisticsarealsoneededto estimatetheskillofthecoupledsystemduringpast

years.A timeseriesof thetemperatureat850hPafrom1January1990to 1January2000,suggests

thatthemodelhassomesldllinpredictingitsinterannual variabilityuptoweek3overSouthern

Europe,althoughtheskillforweek3 doesnotexceedthesldllobtainedbysimplypersistingthe

forecastof week2. However,overotherregions,forinstancetheeasternU.S.,themodeldisplaysa

skillthatbeatspersistencetill week4. Theensemblesizeistoosmalltomakedefinitive

conclusions, butthissuggestshatthemonthlyforecastingsystemmayproduceusefulforecastsup

toweek4.

LastwinterwasaninterestingtestcaseoverEurope,withablocldngeventaldngplacein December

followedby azonalregimein earlyJanuary.Asaconsequence, theweatheroverwesternEurope

wasverycoldanddryinDecemberandunusuallywarmandwetin earlyJanuary.In ordertocheck

if themonthlyforecastingsystemwassuccessful inpredictingthisregimetransition,a51-member

ensemblehasbeencreatedstartingon15December2001.Theweatherpatternshavebeen

classifiedasblocked,zonalandintermediate.Mostmembersoftheensemblepredictablocked

regimeinthemediumrangeandazonalregimeduringthelastweekoftheforecast,butthereis

considerablevariabilityinthewaythetransitionoccurs.Asaconsequence, the2-meter

temperatureforecastsfor week1andweek4verifywellwithobservations, whereastheforecasts

52
forweek2andweek3werenotassuccessful.Anotherensembleof monthlyforecastswasrealized

startingon15Novembertotesttheabilityofthecoupledsysteminpredictingtheblockingevent.

Only10membersof theensemblepredictedablocldng.About3ofthesememberspredictedatime

evolutiontill day25veryconsistent withobservations.

4. SimulatingtheMJO

Theatmosphericcomponent ofthemonthlyforecastingsystemdisplaysldllinpredictingthe

evolutionof theMJOtill day10.Afterabout10daysofintegrations, theMJOtendsto disappearin

themodelintegrations.Thisis alsotruewithcoupledintegrations, wherethemodelsucceedsin

predictingtheevolutionoftheMJOonlywhenit ispresentintheinitialconditions.Changesinthe

cumulusparametefizationhavehelpedimprovethesimulationof theMJOinthemodel.However,

thesechangeshadundesirableeffectsinotherareas,leadingto adegradationof medium-range

scoresoverEurope.ImprovingtheMJOwithoutgeneratingnegativeimpactselsewhereisstillan

activeareaofresearchatECMWF.If successful thiswouldlikelyleadtoimprovedmonthly

forecasts.

5. Conclusion

A monthlyforecastingsystemhasbeensetupatECMWF.Thismodelisbasedon51-member

ensembleintegrations.It isanexperimental systemthatisrunevery2weeksandstartedon27

March2002.Thissystemwill beroutinelyintegratedforatleastacoupleof yearsinordertoget

enoughcasestoassessthesldllof thiscoupledmodel.Thissystemisdeficientinsimulatinga

realisticMJOmorethan10daysinadvance.

53
The NCEP Global Ensemble:

How to Go Beyond 16 Days?

Z. Toth, Y. Zhu, R. Wobus, and T. Marchok

Environmental Modeling Center

NCEP/NWS/NOAA

The NCEP global ensemble system cuxrently generates operational forecasts out to 16 days. In this

talk different verification statistics will be presented to explore the sldll of these forecasts in

predicting daily weather events out to 16 days. Through case studies the possible impact of regime

transitions on forecast sldll will also be addressed.

Additionally, experimental ensemble forecasts run out to 35 days lead time will be evaluated in

terms of their skill. Possible future ensemble configurations that could contribute to a seamless

suite of forecasts from day 1 to the climate time scales will also be discussed.

54
Ensemble Prediction of Blocking

Stephen J. Colucci

Department of Earth and Atmospheric Sciences

1116 Bradfield Hall

Cornell University

Ithaca, New York 14853

sjc25@cornell.edu

Recent unpublished work (Pelly and Hosldns 2002; Watson and Colucci 2002) reveals that

ensemble prediction systems (EPSs) from the European Center for Mediuxn-range Weather

Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) can skillfully

predict the occurrence of blocking over the Northern Hemisphere at extended ranges (ten days in

advance and beyond). In the Pelly and Hosldns study, blocldng is defined by a persistent,

longitudinally coherent reversal of the normal meridional potential temperature gradient on the 2-

PVU surface. With this definition, a blocldng climatology was constructed from five years of

ECMWF data and EPS forecasts were evaluated over one year. In the Watson and Colucci study,

blocldng is defined by a persistent, longitudinally coherent reversal of the normal meridional 500-

mb height gradient over middle latitudes, following Tibaldi et al. (1994). With this definition, a

blocking climatology was constructed from 40 years of NCEP data; EPS forecasts were evaluated

over three winters. In both climatologies, maximum blocldng frequency was found over the

Atlantic and Pacific Oceans, allowing a separation of the data into Atlantic and Pacific sectors.

Blocking frequency is underpredicted by both the ECMWF and NCEP EPSs. Results are shown for

the NCEP forecasts in Figures (1-3). The underprediction bias can be corrected to produce

calibrated probabilistic forecasts that extend the range of sldllful blocking forecasts in the NCEP

EPS, especially over the Atlantic (Figure 4). Block onset is found to be more difficult to forecast

than block decay in the ECMWF EPS (not shown).

These results are preliminary, since they are based upon a limited sample (three years of NCEP

forecasts, one year of ECMWF forecasts), and therefore need to be checked for authenticity in a

larger sample of forecasts.

References

Pelly, J. L., and B. J. Hoskins, 2002: How well does the ECMWF

Ensemble Prediction System predict blocldng? Submitted to the

Quarterly Joumal of the Royal Meteorological Society.

55
Tibaldi,S.,E.Tosi,A.NavarraandL.Pedulli,1994:Northernand

SouthemHemisphereseasonal variabilityofblocldngfrequency

andpredictability.MonthlyWeatherReview,122,1971-2003.

Watson,J.S.,andS.J.Colucci:Evaluationofensemblepredictionsof

blocldngintheNCEPGlobalSpectralModel.Conditionallyaccepted

forpublicationintheMonthlyWeatherReview.

7O

-- Pacific--0--Atlantic]

6O

50

40

30

20

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Days Prior to Block Onset

16

Figure 1. Percentage of available ensemble members (up to 17) predicting blocldng on the block

onset day, averaged over all three seasons studied (1995-96, 1996-97, 1997-98) as a function of

forecast range in the NCEP Global Spectral Model. Forecasts of the probability of block onset from

this information would become uaasldllful relative to climatology (13% probability over the Atlantic,

12% probability over the Pacific) by Day 12 over the Atlantic and Day 15 over the Pacific.

56
0I

P_

_

1.8-

1.6-

1.4-

1.2-

i0.8-

0.6-

0.4-

0.2-

0

1

0.8

0.6

0.4

0.2

0

0 1 2 3 4 5 6 7 8 9 10111213141516

Forecast Day

Figure 2. Average sldll scores (HSS), biases and False Alarm Ratios (FAR) for NCEP ensemble

forecasts of Pacific block onsets. The skill scores were averaged over individual forecast days (0 -

16) over all ensemble members for the three years of verification data.

P_

............................................ ............................ .............

1._T....................................................................................................................................................... 7-1

1.6 0.8

1.4

°°!

0.8 0.4 m

0.6

0.4 0.2 _

0.2n

V I I I I I I I I I I I I I I I I I I 0

0 1 2 3 4 5 6 7 8 9 10111213141516

Forecast Day

Figure 3. Same as in Fig. 2, but for Atlantic sector forecasts.

57
0.80

"_ _0.60

0.40

_> o2o

o o.oo

-0.20 .......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i.......... i

1 2 8 4 5 6 7 8 9 I0 ii 12 18 14 15 16 17

Equation

Figure 4. Calibrated NCEP forecast skill relative to climatology for Pacific and Atlantic block

onsets, as a function of "equation" ( = forecast day plus one). Climatological block relative

frequencies for the Pacific (0.115) and Atlantic (0.131) are used as the reference forecasts. Two

seasons of forecasts (1996-97, 1997-98) were used to develop the forecast scheme which was tested

on the third season (1995-96).

58
Arun Kumar

CPC/NCEP

5200 Auth Road, Rm 807

Camp Springs, MD 20746

Micheal W. Phelps

COAPS, FSU

Tallahassee, FL

In this analysis AGCM simulations for the monthly mean hindcasts with different lead times are

analyzed. For example, for the 1979-99 period, AGCM simulations starting from the atmospheric

initial conditions in September, October, November, and December are analyzed for the hindcasts

for the month of January. The purpose of this analysis is to evaluate the impact of atmospheric

initial conditions on the hindcasts, for it is expected that hindcasts with the shortest lead (for

example, the hindcasts from December for the target month of January compared to the hindcasts

from September for the month of January) will have a higher level of simulation sldll. Different

measures to assess the role of initial conditions on the monthly mean variability are used. These

include the analysis of intemal and extemal variances with different lead times, the analysis of

simulation sldll with different lead times, and the analysis of low- frequency modes of the

atmospheric variability with different lead times.

59
The Role of Atmospheric Initial Conditions

for Long-Range Predictability

T. J. Reichler and J. O. Roads

Experimental Climate Prediction Center

Scripps Institution of Oceanography,

University of California San Diego, 0224

9500 Gilman Dr.

La Jolla, CA 92093-0224

We examine the relative importance of the atmospheric initial state and boundary forcing for longrange atmospheric predictability using the seasonal forecasting model from the National Centers for

Environmental Prediction. A series of ensemble predictability experiments are conducted, using

different combinations of initial and boundary conditions. The experiments are verified globally

against a reference run with the same model as well as against reanalysis. We analyze the ranaveraged forecast skill from day one out to a season and compare it with seasonally averaged data.

We find that from initial conditions alone, there is significant instantaneous forecast skill out to 2

months. Different initial conditions show different predictability using the same kind of boundary

forcing. Boundary forcing leads to measurable instantaneous forecast skill at any lead time: It starts

to impact predictability after 10 days, it is equally important as initial conditions after 4 weeks, and

it completely determines the forecast after 6 weeks. During events with strong tropical forcing,

these time scales are somewhat shorter. For seasonally averaged skill, using observed atmospheric

initial conditions can lead to a significant increase in overall skill, especially during periods with

weak tropical forcing. We conclude that the long-term memory of the initial conditions is important

for seasonal forecasting, and that good atmospheric initial conditions should be included in current

seasonal forecasts.

60
Session II

Predictability of Extratropical "Modes"

61
Impacts of the Arctic Oscillation

and the PNA Pattern on Weather

Roberta Quadrelli and John M. Wallace

JISAO, Seattle

Over large areas of the Northern Hemisphere, the influence of the AO upon the frequency of

occurrence of extreme cold is stronger than one would expect, based on its effect on mean

temperature alone. Apparently, the temperature field in these regions exhibits stronger temporal

variability when the AO is in its low index polarity, with relatively weak subpolar westerlies, than

when it is in its high index polarity. In order to elucidate this behavior, we examine the circulation

regimes observed during contrasting polarities of the AO (and the PNA pattern as well). After

presenting the phase space defined by the two modes together and its impact on the large scale

temperature variability we show a series of "spaghetti diagrams"- selected contours extracted from

the 10-day mean maps of the 500-mb height, sea-level pressure, and 1000-500-mb thickness fields

for the 30 highest and lowest mean values of each of the indices. The plots reveal that the AO and

the PNA pattem do, indeed, exert a strong influence on the frequency of occurrence of the

distinctive flow regimes that are often associated with cold air outbreaks in midlatitudes.

RESULTS

In this study the AO and the PNA pattems are defined as the leading modes of wintertime

variability of the Northern Hemisphere SLP. Their time series are given by PC1 and PC2 of

monthly SLP anomalies, for the months DJFM of the period 1958-99. This follows the standard

way of defining the AO (Thompson and Wallace, 1998), while the PNA pattem thus defined differs

from the conventional PNA (Wallace and Gutzler, 1981) for the presence of an Atlantic center of

action (Wallace and Thompson, 2002). The latter is a real signal though, since it also shows up in

the one-point covariance map of the Pacific center of action for 500 hPa data.

Figure 1 presents a simple schematic of how some modes of low frequency variability project onto

the phase space defined by the PC1 and PC2 SLP patterns. We notice how the "regional'"

definitions of NAO (Hurrell, 1995) and PNA (Wallace and Gutzler, 1981) represent an orthogonal

basis as well as the "global" definitions offered by the EOF analysis. In figure 2 we show how AO

and PNA affect the monthly temperature field on a global scale. At each gridpoint the arrow

direction indicates which linear combination of the two modes is that most highly correlated with

the local temperature time series.

Warm anomalies over Europe and cold anomalies over North Africa are associated with the positive

polarity of AO; the PNA signature is quite strong over the Pacific sector and, somewhat

surprisingly, over Eurasia as well.

To investigate the variability of AO (and of PNA) in its contrasting polarities, we show some

"spaghetti" diagrams where selected contours of atmospheric variables are plotted for the 30

"highest'" and 30 "lowesf' index 10-day averages. The Z500 contour presented in figure 3 shows

63
thatthepolarvortexisclearlydefinedduringhighAOindex(H1),whileit isbrokenapartduring

lowindex(L1)whentheglobalvariabilityisalsomuchhigher.

TheSLPcontoursinfigure4revealmarkeddifferences:H1ischaracterizedbyahigherfrequency

of occurrenceof anticyclonesoverthesubtropical eastemoceansandanalmostcompleteabsence

ofthemoverhigherlatitudes;SLPoftendropsbelow992hPaintheDavisStraits,overthefar

NorthAtlanticandtheEuropeansectoroftheArcticandmuchlessofteninthevicinityofthe

AleutianLow;theL1regimeischaracterizedbyahighfrequencyofoccurrenceof anticyclonesthat

encompasspartsoftheArcticandarelativeabsenceof cyclonesatlowerlatitudes.

Thethicknesscontourshowninfiguxe5alsoexhibitsgreatervariabilityoverthehemispheric

domain(withtheexceptionofEuxasia)duringL1thanduringH1. Thefavoredlocationofthe

contourisstrikinglydifferentovertheEuropeanregion,hardlytouchedbyit duringH1andalways

experiencingit duringL1;agreaterlatitudinalspreadisobservedduringL1allacrossUS,witha

muchhigherpossibilityofcoldoutbreaksfarthersouth.Anexampleof dramaticchangein

variabilityduringthepolaritiesofPNA (H2andL2)isshowninfiguxe6(500hPa).Focusingon

thenorthwesternAmericanregionwenoticethatduringH2theshapeof thecontourshowstherea

sharplydefinedridgewithanaxisalongthewestcoast;duringL2thesameregionisinstead

characterized byextremelyhighvariability.

Consistently, figure7showsthetwotypesofblockinganomaliesthatarelikelytooccurduringH2

andL2;thePacificNortwestwill bewarmerandcolderthanaverageduringthetwoPNApolarities,

respectively.

Figure 1 : Projections of various indices on the phase space defined by PC 1 and PC2 of monthly DJFM SLP anomalies.

ALE and ICE denote time series of monthly SLP anomalies at the center of the Aleutian Low and the Icelandic Low,

with signs inverted; COWL denotes the COWL pattern, and NAO denotes seasonal-means of an index of the North

Atlantic Oscillation. Length of solid line proportional

to the explained correlation (from 0 to 1).

64
g0"N

00*N

a0"N

Figure 2: Vectorial map showing correlation coefficients between monthly mean 850-hPa temperature and PCs 1 (xcomponent) and 2 (y-component) of monthly DJFM SLP anomalies• Only correlation coefficients significant at 95 {\%}

level for both PC's 1 and 2 are shown• Upper right arrow corresponds to a correlation coefficient of 0.5.

HI_EST

m

--,. ,,.:,:_i_:_!!_i_ !_iii_i

Figure 3: Plots of the 5220 m contour of Z500 for the 30 10-day mean maps with the most positive and the 30 10-day

mean maps with the most negative projections upon EOF 1 ofmonthly-meaaa SLP.

HIGHE_r

I

m

Figure 4: Plots of specified SLP contours for the 30 10-day mean maps with the most positive and the 30 10-day mean

maps with the most negative projections upon EOF 1 ofmonthly-meaaa SLP.

65
HW

m

Figure 5: Plots of the 5220 m contour of the 500 hPa-1000 hPa thickness for the 30 10-day mean maps with the most

positive and the 30 10-day mean maps with the most negative projections upon EOF 1 of monthly-mean SLP.

Figure 6: Plots of the 5220 m contour of Z500 for the 30 10-day mean maps with the most positive and the

30 10-day mean maps with the most negative projections upon EOF 2 of monthly-mean SLP.

Figure 7: Composite mean of 500 hPa height for the 20 10-day mean maps with the highest anomalies at the grid point

(150 W, 65 N) based on the subset of the data consisting of the 80 10-day mean maps with the strongest positive (left)

and negative (right) projections upon EOF 1. Contour interval: 60 m; the 5100, 5400 and 5700 m contours are bold.

REFERENCES

Hurrell, J.W., 1995: Decadal trends in the North Atlantic Oscillation: Regional temperatuxes

and precipitation, Science, 269, 676-679.

Thompson, D.W.J., and J.M. Wallace, 1998: The Arctic Oscillation signature in the wintertime

geopotential height and temperature fields, Geophys. Res. Letters, 25, 1297-1300.

Wallace, J.M., and D.S. Gutzler, 1981: Teleconnections in the geopotential height field during

the northern hemisphere winter, Mon. Wea. Rev, 109, 784-812.

Wallace, J.M., and D.W.J. Thompson, 2002: The Pacific center of action of the Northem

hemisphere annular mode: real or artifact?, J. Climate, 2002, 15, 1987-1991.

66
Statistical Prediction of the Arctic Oscillation

Mark P. Baldwin and Timothy J. Dunkerton

Northwest Researd_ Associates, Bellevue, WA USA

mark@nwra.com, tim@myra.con3

The Arctic Oscillation (AO) is the sup]hoe expression of' the

N_rthern Annular Mode (NAM), which can be defined at aW

pressure level fi'om 1000 hPa to the mesosphere. Here we ex

plore the possibility of predicting the avarage wflue of the AO

10 40 clays in lke furore, based on today_ value of tl_e AO and

values of the N)kM at stratospheric lewqs_

We first note that there is a strong seasonality in the

autocorrelation of the AO; it is substantially more persislent

during the winter season (the season when planetary wave cou

pling to the stratosphere is strongest). On]side the wintar sea

son, the e folding time scale of the At) is _15 20 days during

DJF, and _-5 6 days fi'onlApriDOctober (Figure 1). The NAM

is more parsistent at stratospheric levels, peaking at 150 hPa in

the lowermost stratosphere, where the DJF e fokling time scale

exceeds 30 days.

We find that 150 hPa is the optimum single NAM level for

forecasting futme values of the AO, far better than using the

AO itself to predict the AO. The 150 I-tPA NAM provides not

only greater predictability during DJF, but extends the season

of predictability t_ include Novembar and March (black curve

it] Figure 2). ]t is also possible to combine levels it] a multiple

regression (dotted curve, which uses 1000,150, and 10 hPa) to

account for somewhat more w]riance, especially in early win

ter.

o3o Varial_e of 10MOKlayA,O , , ,

...".

025 NAM at 1000+150+10 ,; ",\

-- NAM at 150 hPa orlly ,," ", ..

>_ 015

010

005

000

J A S O N D J F M A M J

Mohth

Figure 2

NAM 150 mid M_I AO 10 40 days h_l

....... I ;_: i ='

o

J A s o N DMollthJ F M A M

Figure 1

As predictors of" the average value of" the AO 10 40 days in

the fhture, we use either todw's AO value or the NAM at 150

hPa. Outside of the winter season, persistence of the AO pro

vides almost rio skill; during DJF the AO valne a_x:ounLs lbr

._13% oftheAO 10 40 days in the future (gray curve in Figure

2).

Ore" previous work suggested that the effect of stratospheric

NAM anomalies on the AO is confined t_ extreme events (of'

either sign) which appear to propagate from the upper stratn

sphere down to Earth's surface. We find here that the elt_!ct is

simplar, with a linear relationship between the lower strat_

spheric NAM (150 hPa) and the avarage AO 10 ,t0 da}_ later

(Figure 3 ). The ckmd of points shows all of the DJF (laity NAM

150valuesplotledagainsttl?eAO 10 40 days later.Thestraight

line is a linear fit to the data, and the solid line shows the aw_r

age AO values for the range of NAM 150 values. Simihr linear

resuffs are obtained for olher NAM levels from 1000 lo 10 hPa.

, r , i , i , , i , i , i ,

4 3 2 1 0 1 2 3 4

NAMlS0ina_xa_msDJF

Fignre 3

Our results are not sensitive to the time intarva[ predicted or

the exact level of the NAM used. The results are nearty identi

cal if we predict the NAO instead of the AO (the daily AO and

NAO have a year round correlation of 0.93). We have assuiTted

that only tnciay's NAM values are awfilab]e from ciata analysis.

If' the NAM can be predicted numerically (especially at 150

hPa) several days it] the future, then the 10 ,t0 day range would

effectivdy be e×tended.

We have performed a similar study in the Son]hem Hemi

sphere. We find that the eb'_O shows enhanced persistence not

dining Austral winter, b u/rather dining March MW at]<] No

vembar December the seasons when planetary wave cou

piing to the stratosphare is strongest in the Southarn Hemi

sphere.

67
Dynamical Mechanisms Associated With the Growth and Decay of the

North Atlantic Oscillation (NAO)

Teleconnection Pattern

Steven Feldstein

EMS Environment Institute

The Pennsylvania State University

This investigation performs both diagnostic analyses with NCEP/NCAR Reanalysis data and

forced, barotropic model calculations to examine the dynamical mechanisms associated with the

growth and decay of the North Atlantic Oscillation (NAO) teleconnection pattern. The diagnostic

calculations include projection and composite analyses of each term in the streamfmnction tendency

equation.

The results of the analyses reveal a complete lifecycle of growth and decay within approximately

two weeks. The positive NAO phase is found to develop after anomalous wavetrain propagation

across the North Pacific to the east coast of North America. This contrasts with the negative NAO

phase which appears to develop in-situ.

Both high-frequency (period 10 days) and low-frequency (period 10 days)transient eddy fluxes

drive the NAO growth. After the NAO anomaly attains its maximum amplitude, the high-frequency

transient eddy fluxes continue to drive the NAO anomaly in a manner that is consistent with a

positive feedback process. The decay of the NAO occurs through both the divergence term and the

low-frequency transient eddy fluxes. The temporal and spatial properties of the divergence term are

found to be consistent with Ekman pumping. These results illustrate many important differences

between the NAO and Pacific/North American (PNA) teleconnection pattems, perhaps most

striking being that the NAO lifecycle is dominated by nonlinear processes, whereas the PNA

evolution is primarily linear. In addition, the relation between the NAO and the zonal index is

discussed.

68
To appear in proceedings of workshop on Prospects for Improved Forecasts of Weather and Shortterm Climate Variability on Subseasonal (2 week to 2 month) Time Scales, NASA/GSFC, April 16-18,

2002

Distant Teleconnections via the Tropospheric Jetstream Waveguide

Grant Branstator

National Center for Atmospheric Research,

Bo_11der_CO 80307

Planetary wave theory suggests that low-frequency

covariability between widely spaced points on the globe

can be achieved without resorting to annular disturbances. This is able to happen because the time averaged tropospheric jets can act as waveguides which

meridionally trap and zonally elongate wavetralns in

their vicinity, as seen in solutions of the barotropic

vorticity equation linearized about boreal winter upper tropospheric conditions. We examine the behavior

of seasonal and subseasonal mean circulation anomalies

in nature and in NSIPP and NCAR general circulation

models to determine whether this behavior is observed.

We find that indeed there are locations on opposite

sides of the North Hemisphere that covary during winter. An example of such behavior is shown in Fig. la,

which portrays a one point correlation map for a base

point in the waveguide when seasonal mean internal

DJF 300rob streamfunction variability from an ensemble of AMIP-like integrations is analyzed. The meridional confinement and zonal elongation of the pattern

in this example is quite distinct and contrasts with the

more familiar arching structure of the pattern in Fig.

lb, which represents the situation for a base point not

in the waveguide. This comparison indicates how the

waveguide can lead to covariability between much more

widely separated points than usually considered. A systematic investigation of one-point correlation plots indicares that this property is generic; pairs of midlatitude

points that are both widely separated and strongly ternporally correlation are invariably near the core of the

time-averaged jet.

The example in Fig. 1 has a prominent zonal mean

component, but further calculations show that this is

not essential for distant teleconnections to exist. If sireilar calculations are done with the v-component of the

nondivergent wind (which has no zonal mean), one still

finds that locations on the opposite side of the globe

can be highly correlated. Rather than possession of a

zonal mean component, what is important for a field to

capture the effect of the waveguide is that it have significant amplitude in the subtropics, where the mean jets

tend to be located in boreal winter. This means that

streamfunction and the v-component of the wind are

90°N

60°N

30°N

0 o

30°S

90°N

60°N

_ I I I I I I I I I I

b ___._'_l_.JJ[ : ::: ::::_ _ _ "///r_._-_. _ _._'._ I

i , i , i , i , i , i , i

0 ° 60°E 120°E 180 ° 120°W 60°W 0 °

30 ° N

0 o

30°S

90°N

60 ° N

0 o 60°E 120°E 180 ° 120°W 60°W 0 o

30 ° N

0 o

30°S

0 o 60OE 120°E 180 ° 120°W 60°W 0 o

Figure 1. One point correlation plots of DJF 300rob

streamfunction internal variability from a CCM3 22

member ensemble of 45 year integrations with observed

SSTs. The base point is at a) (60E, 24N), b) (172W,

24N), c) (90E, 29N). Contour interval is 0.1.

effective choices for investigating the waveguide while

the more commonly used geopotential height is not.

A distinguishing characteristic of waveguide patterns

of variability is that within the jet they do not appear

to have a preferred longitudinal phase. If, as in Fig. lc,

one chooses a basepoint to the east of that used in Fig.

la, the portion of the pattern in the Asian jet retains

its zonal wave five structure and shifts eastward. On

the other hand, from a global perspective the waveguide pattern is sensitive to phase; tbr the phase in Fig.

lc, the pattern of variability is not as global in extent

with a gap appearing over the eastern North Pacific and

North America. So the pattern of Fig. la is special and

69
mightbethoughtofasbeingthe circumglobal waveguide teleconnection pattern.

Additional analysis indicates that for a number of

reasons waveguide disturbances should be important fbr

the subseasonal fbrecasting problem. First, they can

act to connect widely separated locations in surprisingly

short times. Calculations using a linear inverse model as

a diagnostic tool indicate that waveguide-trapped disturbances in one part of the globe can affect locations on

the opposite side of the hemisphere within about a week.

Second, these disturbances influence not only the time

mean circulation but also weather parameters including bandpass eddy statistics and rainfall rates. Third,

calculations show that waveguide influenced variability

is embedded in and thus affects the structure of some

of the best known atmospheric teleconnection patterns,

including the NAO and the response of the extratropics

to El Nino. That the pattern of variability associated

with the NAO appears to be affected by the waveguide can be seen in Fig. 2a, which shows contours of

the correlation between upper tropospheric streamfunction, again for internal variability in an AMIP-like ensemble, and a near surface North Atlantic sector index

of the NAO. In addition to the familiar North Atlantic

and zonally symmetric features of variability associated

with the NAO, the five centers of variability seen in Fig.

la are seen in this diagram. Whether this indicates a

physical link between these two phenomena or simply

reflects the similarity in their structure over the North

Atlantic is not clear.

As explained in Branstator (2002), most of our analysis is carried out with GCM data because of the high

statistical significance of results produced when GCM

data are used. A similar analysis with data from hature shows similar characteristics, to the extent that

the short record allows conclusions to be drawn. As an

example of the situation in nature, Fig. 2b shows correlations between intraseasonal 300mb streamfunction

and a near surface North Atlantic index of the NAO,

calculated in an analogous way to that ibr Fig. 2a.

Again, the influence of the waveguide pattern on the

centers of covariability that are remote from the North

Atlantic are apparent, an indication that the effects of

the waveguide need to be taken into account when considering subseasonal and longer variability in nature as

well as in GCMs.

Reference

Branstator_ G._ 2002: Circumglobal teleconnections, the

jet stream waveguide_ and the North Atlantic oscillation.

J. Climate_ in press.

b

Figure 2. a) Correlation between DJF 300mb streamfunction CCM3 internal variability and projections

onto the leading EOF of North Atlantic sector 850rob

streamfunction, b) Same as a) ibr intraseasonal reanalysis perturbations. Contour interval is 0.1.

70
On the Origin and Mature-Phase

Dynamics of PNA Variability

Sumant Nigam

University of Maryland

A prominent mode of winter climate variability is the geographically well-defined

Pacific North American (PNA) pattem, which represents circulation and precipitation variability on

both intraseasonal and seasonal time-scales. The mode is linked with midlatitude Pacific SST

variability, but has sometimes been erroneously associated with ENSO variability as it can be

excited during ENSO winters as well. The talk will focus on the extraction of PNA structure

(rotational and divergent circulations) and forcing (diabatic heating, vorticity and thermal

transients) from reanalysis data sets. The pattem is then simulated from the diagnosed forcing using

a steady linear primitive equation model. A good simulation allowed pursuit of the dynamical

diagnosis strategy, the objective of which is to reveal the mature-phase maintenance mechanisms.

Modeling analysis indicates the considerable role of zonal/eddy coupling and sub-monthly vorticity

transients in the pattem's generation.

Ongoing analysis of PNA evolution at weekly resolution indicates that a typical 2-week long

episode begins with an eastward extension of the east Asian jet, with zonal wind anomalies at

200mb extending 20-30 ° beyond the dateline. From this point onwards, a sequence of events,

including displacement of Pacific storm tracks, occur, spurred, in part, by the balance-restoring

secondary circulations. The origin of the precursor phase jet extensions is unclear at present,

although linkage with Kuxoshio variability is being investigated. A better understanding of PNA

excitation and dynamics can clearly influence predictions of short-term climate variability.

71
Limits of Predictability Determined

by Optimal Persistence Patterns

Tim Delsole

Center for Ocean-Land-Atmosphere Studies

In this talk I introduce the concept of an optimal persistence pattem and discuss how it

can be used to define limits of predictability strictly from observations. An optimal persistence

pattern (OPP) is a component of a time-varying field that remains auto-correlated for the longest

time-lags. Techniques for extracting OPPs provide an efficient method not only for isolating

persistent patterns in stationary time series, but also for detecting trends, discontinuities, and other

low-frequency signals in nonstationary time series. In this talk, we discuss how OPPs can be used

to filter out low-frequency signals to render a time series stationary, then used to determine the

patterns with the maximum decorrelation time. This approach clarifies the fundamental time scales

of phenomena such as the PNA, NAO, etc. The resulting patterns can be shown to provide a

rigorous upper limit of predictability for all linear prediction models. The sldll of statistical

predictions of the leading OPPs will be discussed.

72
Variability Within the PNA, NAO, and AO Regimes

On Sub-Seasonal Time Scales

Wilbur Y. Chen

Climate Prediction Center

NCEP/NWS/NOAA

Variability over the oceans is much more prominent than over the lands, as shown in Fig. 1. Its

impacts on remote regions through teleconnections are well recognized. For instance, PNA and

TNH patterns show impact of the Pacific sector on North America sector. Both PNA and TNH

teleconnections can develop due to tropical forcing and/or intrinsic internal dynamics. This study

focuses on the latter. That is, through the intrinsic internal dynamical processes, how does a large

oceanic variability impact North America? Particular attention is focused on the variability of a

telconnection pattern within the PNA, NAO, and AO regimes.

Variability and teleconnectivity are examined for various sub-seasonal time-scales (figs. 1 and 2).

Variability decreases rapidly as time-scale increases, as shown in Fig. 1. Also, as timescale

increases, the teleconnection not only become stronger and better established but also much more

organized and located in certain restricted regions, as illustrated in Fig. 2. The main points relevant

to medium-range predictions over the North America are:

1. Significant variability within the PNA, NAO, and AO regimes, as shown in Figures 3 to 5.

2. A gradual transformation from PNA to TNH pattern (top panels of Fig. 3). Large anomalies over

most of the North Pacific basin induce a development of a same-signed anomaly over the Southeast

of the U.S. However, a reverse-signed anomaly will develop ranging from Alaska to eastern Canada

depending on the location of the Pacific anomaly, as shown in Fig. 3. Also shown is the gradual

transformation between PNA and TNH patterns (top 3 panels).

3. In Fig. 4: The large-scale anomalies over the North Atlantic along 40 N (which may be

considered to be the southern components of the NAO) make same-signed impacts on various

interior parts of the U.S. If the Atlantic anomaly is over the Europe, the impact is then over the

eastern U.S. If the Atlantic anomaly is located over the mid-western ocean, the impacted area then

extends westward across the U.S. interior, reaching as far as southern New Mexico. It is interesting

to note that the impacts take on an arch shape, from Florida coastal areas up to the Great Lakes and

northern Plains and then down to New Mexico.

4. In Fig. 5: Here we focus on large anomaly located over north of 65N (the green circle). Ira large

anomaly is located at 11 O'clock position, the impact is over the northern China. When the Arctic

anomaly shifts eastward, another impacted region start to develop over the Europe, as shown in the

top-right panel. Further movement of the Arctic anomaly to the 5 O'clock position, a third impacted

region develops over the mid-Atlantic region of the U.S.

5. An Arctic anomaly that is heavily weighted in the Pacific side (say, at 7-9 O'clock position) is

found not to affect the North Pacific and the U.S. mainland. This result is somewhat surprising,

when comparing to the conventional AO loading pattern. Could it be an artifact of an EOF analysis?

73
6.Withinaconventional AOindex,therecanbelargevariationsofanomalydistribution.The

variationsof actioncenterwithinArcticregioncanmaketheimpactedremoteregiontobehalfthe

worldapart.Thelongitudinal positionofalargeanomalyappearstobeanimportantconsideration

insub-seasonal predictionapplications.

Wintertime Z500 Variability on various time-scales

evaluated for Dec-Mar during the pealed ef 1971-2001

Standard Devi'aUortof 31-day meone Standard (_laUon el' 61-d0y meane

la la 110 110 1/11' 7lI m ao lit

Fig. 2 Correlation Coef with N PAC klGx[mum V<_riability Center

(160W & 45N) an various t[mescoles evaluated far Dec-Mar 1971-2001

far varfabl[ty af 7-do.y means far var'mbii_ aF 31-day means far variabirrbyot"51-day means

I ......

74
.3 Teleconnecton PaLtern with N Pec Variability Center

at variou9 locations, evaluated for Jan-March (1971-2001)

NP center Ioeo_d ot 1BOW ond 37.5N

NP center Ioco'[lld {It 1._OW 4TXl 4_N

NP c_nt_r Ioeot_l oL 160W and 35.0N

f" .......... ',

\" !:2!;.......

NP cerr_r Io_lbld 4t 140W ohd 4INN

....

I_P _n(er Io_nb_:l o1_ 1lOW ond 32-gN

• ...,.-., -./.. ] i%

) / <',"<_<:>

"<... -.> f ./... /

Fig. 4- Teleconnec_ion PoStern wiEh N AEI Var[abilff.y Cen_r

at various locations, evaluated for Jan-March (1971-2001)

HA _nter Iocot4d 41 _OE ond 4.IN

HA eerier I_t_d (it 2SW nnd dON

HA _enter Ioc0t4d 01. DE grid 4_I_I

N_k _entes- Io_b_l ot Z_OW nnd 37.51

'_, "a

75

I_ center 1oceted ot 2Dr/ and

i i ] " i i

-. % ;s:,"_,

NA e_t_r I_t_d at 3SW nnd 37.5N

_'"f"'"__ " • %.....
Fig, 5 Teleconnection Pattern with Arctic Center at

various longitudes, evaluated for wintertime Jan-March (1971-2001)

Arctic center IocaEod aL got and B2.SN

//N

-I10 -70 -M _ -4a Ilo

Ar'cb'c ccn_r located ct 12DW end 52,5N

.........:,..,A......;: " , ..

__iiiiiiiiiiiiiiiiiiiiiiii_m -_ -u -_ -4o n_

Arctic center Iocatod at ]DE and 82.5N

i..... 7]_. -, , i......]."],, "";_=,'" '?

-*_.__iiiiiiiiiiiiiiiiiiiiiii

-W -70 -N -4)9 .-4D BO

Arcb'c cen_er located ot 60W end B2,SN

===================================================-m -_ -m _ _

76
Monitoring the AO/PNA Indices Using NCEP/NCAR

CDAS/Reanalysis

Hyun-kyung Kim and Wayne Higgins

CPC/NCEP

The Arctic Oscillation (AO) and the Pacific / North American pattern (PNA) are two of the

dominant modes of atmospheric variability in the Northern Hemisphere (NH), in particular during

cold season. These indices are strongly associated with weather and climate variability over a large

portion of the NH.

For the purpose of improving weather and climate prediction at NCEP, these indices were

developed for the period from January 1950 to the present using the NCEP/NCAR

CDAS/Reanalysis. Since there is no explicit way to predict these indices yet, the "predicted"

indices are generated by projecting the loading pattern of each mode onto the MRF and Ensemble

forecast data in order to help the weather and climate prediction.

The indices and forecasts for the most recent 120 days are posted on the monitoring weather and

climate web site of NCEP/CPC and updated daily. (http://www.cpc.ncep.noaa.gov/products/

precip/CWlink )

For the comparison with AO, the North Atlantic Oscillation (NAO) has also been monitored. The

Antarctic Oscillation (AAO) has also been monitored as the counterpart of the AO in the Southern

Hemisphere. All monitoring products are updated on a daily basis. Several of these will be

demonstrated in our talk.

77
Session III

Predictability of the ISO/MJO

79
Predictability and Forecast Issues

Associated with the MJO/ISO

Duane E. Waliser

Institute for Terrestrial and Planetary Atmospheres

State University of New York, Stony Brook, NY 11794-5000

__oedu _ (631) 632-8647 (voice) -6252(fax)

Since its discovery by Madden and Julian (1971) over two decades ago, the Madden and Julian

Oscillation [MJO/ISO; a.k.a. Intraseasonal Oscillation (ISO)] has continued to be a topic of

significant interest due to its complex nature (Madden and Julian, 1994) and the wide range of

phenomena it interacts with. The onset and break activity of the Asian-Australian monsoon system

are strongly influenced by the propagation and evolution of MJO/ISO events (e.g., Yasunari, 1980;

Hendon and Liebmann, 1990). Apart from this significant local influence, there are also important

downstream influences that arise from the MJO/ISO. For example, the development of persistent

North Pacific (PNP) circulation anomalies du_rmg Northem Hemisphere winter has been linked to

the evolution and eastward progression of convective anomalies associated with MJO/ISO events

(e.g., Liebmann and Hartman 1984; Weickmann et al. 1985; Higgins and Mo 1997). In fact, a

strong link has been shown to exist between rainfall variability along the westem United States,

including extreme events, and the longitudinal position of MJO/ISO convective anomalies (Mo and

Higgins 1998a; Jones 2000; Higgins et al. 2000). In addition, MJO/ISO convective activity has

been linked to Northem Hemisphere summer time precipitation variability over Mexico and South

America as well as to wintertime circulation anomalies over the Pacific - South American Sector

(Mo and Higgins 1998b; Jones and Schemm 2000; Paegle et al. 2000; Mo 2000). Recently, studies

have also shown that particular phases of the MJO/ISO are more favorable than others in regards to

the development of tropical storms/hurricanes in both the Atlantic and Pacific sectors (Maloney and

Hartmann, 2000; Mo, 2000; Higgins and Shi, 2001). Finally, the passage of MJO/ISO events over

the westem Pacific Ocean has been found to significantly modify the thermocline structure in the

equatorial eastern Pacific Ocean via their connection to westerly wind bursts (e.g., McPhaden and

Taft, 1988). This latter interaction has even been suggested to play an important role in triggering

variations in E1 Nino - Southern Oscillation (ENSO) (e.g., Lau and Chan 1988; Weickmann 1991;

Kessler and Kleeman 2000).

As influential as the MJO/ISO is on our weather and climate, a fundamental question yet to be

adequately addressed concerns its theoretical limit of predictability. For example, it is well known

that useful sldll associated with deterministic prediction of most "weather" phenomena is limited to

about 6-10 days (e.g., Lorenz 1965; Palmer 1993; van den Dool 1994). Similarly, it has been found

that the likely limit of predictability for ENSO is on the order of 12-18 months (e.g., Cane et al.

1986). However, it is still yet to be determined what the corresponding metric is for the MJO/ISO

phenomenon. The somewhat well behaved nature of the MJO/ISO (e.g., equatorially-trapped;

preference for warm SSTs, seasonality) along with its intraseasonal time scale suggests that useful

predictive skill might exist out to at least 15-25 days and maybe longer. Support for this suggestion

comes from statistical predictive models of the MJO/ISO, which indicate useful sldll out to at least

15-20 days lead time (e.g, Waliser et al. 199%; Lo and Hendon 2000; Wheeler and Weickmann

2001; Mo 2001). However, as with any statistical model, these models are sorely limited in the

81
totalityof theweather/climatesystemtheycanpredict,theirabilityto adaptoarbitraryconditions,

andtheirabilitytotakeadvantageofknownphysicalconstraints.

Whiletherehavebeenanumberofdynamical predictiveskillstudiesoftheMJO/ISO(i.e.,

comparingforecaststoobservations), thesestudiestypicallywereperformedwithforecastmodels

thatexhibitedratherpoorsimulationsof theMJO/ISO.Forexample,thestudiesby Chenand

Alpert(1990),LauandChang(1992),Jonesetal.(2000),Hendonetal.(2000)wereallperformed

onthemostrecentorpreviousversionsoftheNCEP(orNMC)mediumrangeforecast(MRF)

model'sDynamicExtendedRangeForecasts(DERFs).In general thesestudiesshowedusefulsldll

outtoabout7-10days.However,theseskilllimitsarelikelytobesignificantunderestimates ofthe

potentialpredictabilityduetotheveryweakMJO/ISOsignatureinthemodel.Moreover,since

thesestudieswerereallymeasuringforecastskillof themodel,theirsldllversuslead-timestimates

areadditionallyhamperedbythedifferenceinphasespeedsbetweenthemodelandobservations

andtheinfluencefrompoorlyknown/specifiedinitialconditions.Thus,ascertainingevenagross

estimateof thelimit ofpredictabilityfortheMJO/ISOfromthesestudiesisneitherappropriatenor

feasible.

In additiontotheabovedynamical studies,therehavealsobeenanumberofpredictivesldllstudies

of theMJO/ISO(i.e.,comparingforecaststoobservations) usingstatisticalforecastmodels(e.g,

Waliseretal. 1999a;Lo andHendon2000;Mo2001;WheelerandWeickman,2001).Whilethese

sortsofmodelsaretypicallymoreadeptatforecastingMJO/ISOvariabilitythanpresent-day

dynamicalforecastmodels,theystillmaynotprovideausefuloradequatemeasureof MJO/ISO

predictabilityduetotheirrelativelysimplistic,andthusfarlinear,natuxe.

Theremainingportionof thisabstractwill describetheresultsoftworecentstudies(Waliseretal.,

2001,2002)designedtomoredirectlyestimatethetheoretical limit of dynamicpredictabilityof the

MJO/ISOusingsetsoftwinnumericalpredictabilityexperiments(e.g.,Lorenz1965;Shukla1985).

ThesestudiesutilizedtheNASAGLAgeneralcirculationmodelduetoitsrelativelyrealistic

MJO/ISOrepresentation(e.g.,Slingoetal.1996;Sperbereta1.1996).A 10-yearcontrolsimulation

usingspecifiedannualcycleSSTswasperformedin ordertoprovideinitialconditionsfromwhich

toperformanensembleof twinpredictabilityexperiments. Notethatthepredictabilityanalysis

describedbelowwasperformedseparatelyonN.H.winterMJO/ISOactivity(i.e.thatwhich

typicallytravelseastwardalongtheequatorandSPCZ)andN.H.summerMJO/ISOactivity(i.e.

thatwhichtypicallytravelsnortheastwardintoIndian/S.E. Asia).Theanalysisframeworkbelow

describestheN.H.winterstudyandanyitemin [parenthesis] describesthedifferencesassociated

with the N.H. summer analog.

Initial conditions were taken from periods of strong MJO/ISO activity identified via extended

empirical orthogonal function (EOF) analysis of 30-90 day bandpassed tropical rainfall during the

Oct-Apr [May-Sep] season. From the above analysis, 15 cases were chosen when the MJO/ISO

convection was located over the Indian Ocean, Maritime continent, western Pacific Ocean, and

central Pacific Ocean [central Indian Ocean, N.E. Indian Ocean, S.E. Asia, and northwestern

Tropical Pacific Ocean], respectively, malting 60 cases in total. In addition, 15 cases were selected

which exhibited very little to no MJO/ISO activity. Two different sets &small random

pertuxbations were added to these 75 initial states. Simulations were then performed for 90 days

from each of these 150 pertuxbed initial conditions. A measure of potential predictability was

constructed based on a ratio of the signal associated with the MJO/ISO, in terms ofbandpassed

rainfall or 200 hPa velocity potential (VP200), and the mean square error between sets of twin

82
(bandpassed) forecasts.Predictabilitywasconsideredusefulif thisratiowasgreaterthanone,and

thusif themeansquareerrorwaslessthanthesignalassociatedwiththeMJO/ISO.Theresults(see

Figure1)indicatethatusefulpredictabilityforthismodel'sMJO/ISOextendsouttoabout20to30

daysforVP200andto about10to 15daysforrainfall.Thisisincontrasttothetimescalesof

usefulpredictabilityforpersistence, whichforthismodelisabout12daysforVP200and7 daysfor

rainfall.Notethatthepersistenceforecastsgainadditionalsldllfromthebandpassingoperation

performedontheforecasts.

Thepredictabilitymeasurexhibited(notshown)modestdependenceonthephaseof theMJO/ISO,

withgreaterpredictabilityfortheconvectivephaseatshort(<~5days)leadtimesandforthe

suppressedphaseatlonger(> ~15days)leadtimes.Inadditionto thedependenceonthe

convectivephaseoftheMJO/ISO,themodel'spredictabilityalsoexhibitsdependenceonthe

strengthof theMJO,particularlyfortheN. H.wintermode.DuringperiodsofweakMJOactivity,

thepredictabilityassociatedwiththeregionof strongMJOvariability(i.e.EasternHemisphere) is

diminishedcomparedtoperiodsof strongMJOactivity.Effectively,thesamepredictabilityratios

arefoundatsignificantlyshorterleadtimes(~5-10daydifference)forthenullcasesversusthe

activeMJOevents.Thisdiminishedpredictabilityisaresultof anerrorgrowthratecomparableto

thestrongMJOactivitycasesin combinationwithweakerintraseasonal signals.Meansquare

forecasterrorswerealsocomputedforEOFamplitudetimeseriesofthebandpassedmodeloutput

tohighlighthefactthattheenhancedpredictabilityatextendedrangeisderivedmostlyfromthe

first2modes,i.e.,thosethatcapturethemodel'srepresentationftheMJO/ISO.

TheaboveresultshaveimportantimplicationsforboththelocalregionsthattheMJO/[SOrainfall

variationsimpactdirectlyaswellasregionsthatareinfluencedbytheMJO/ISOvia

teleconnections. Presentdayatmosphericforecastsarelargelydirectedtowardpredictingshorttermweathervariationsfromanalyzedinitialconditionsaswellasseasonal climatevariations

associatedwithseasonal/interannual chngesin surfaceboundaryconditions, namelyfromtropical

SSTs.Asyet,operational weatherforecastshavelargelybeenunabletoexploitherelativelystrong

signalandslowevolutionassociatedwiththeMJO/ISO(e.g.,Waliseretal.1999a;Jonesetal.

2000;Hendonetal.2000).ThisisduetothegenerallypoorrepresentationftheMJO/ISOinmost

AGCMs,exceptfor afewresearch-oriented models(e.g.,Slingoetal.1996).However,if the

MJO/ISOcouldbebetterrepresentedin operational weatherforecastmodels,theaboveresults

implythatextended-range tropicalforecastsintheregionsdirectlyimpactedby theMJO/ISOcould

begreatlyenhancedand/orextended.Thisincludesameanstobetterpredictheonsetandbreak

periodsoftheAsian-Australiansummermonsoonsthataresostronglydeterminedbyintraseasonal

variationssuchastheMJO/ISO.In thisregard,theimprovement inforecastskillthatmightbe

possiblewithamodelcapableof simulatingtheMJO/ISOoveronethatpoorlyrepresentsthe

MJO/ISOcanbeinferredfromtheenhancedpredictabilityassociatedwiththeactiveversusnull

MJOcasesdiscussedabove(i.e.about10dayimprovement inleadtime).

In additiontothelocalimpactsthatimprovedMJO/ISOpredictionmightoffer,thereareanumber

ofremoteprocesseswhosepredictionmayimproveaswell. Thesewerediscussedinthe

introductionandincludewintertimemid-latitudecirculationanomalies(e.g.,Ferrantietal.,1992),

summertimeprecipitationvariabilityoverMexicoandSouthAmerica,extremeventsinrainfall

variabilityalongthewesternUnitedStates,andthedevelopment of tropicalstorms/hurricanes in

boththeAtlanticandPacificsectors.

Thereareanumberofcaveatsthatshouldbenotedregardingtheaboveresults.Forexample,there

aremodelshortcomingsthatsuggesttheaboveresultsmightbeanunderestimateofpredictabilityof

83
theMJO/ISO.First,themodeltendstohavetoomuchhighfrequency,lowwave-number activity

(Slingoetal.1996).RelativetotheMJO/ISO,thisvariabilitywouldbeconsideredtobeunorganized,errantconvectiveactivitythatmayerodetherelativelysmoothevolutionofthe

MJO/ISOandthusdiminishitspredictability.Second,thesesimulationswerecarriedoutwith

fixedclimatological SSTvalues.AprevioustudywiththismodelshowedthatcoupledSSTstend

tohaveanenhancingandorganizinginfluenceontheMJO/ISO,maltingit strongerandmore

coherent(Waliseretal.1999b).ThustheexclusionofSSTcouplingmayleadtoanunderestimate

ofthepredictabilityaswell. Thethirdaspectthatmayleadtoanunderestimatethepredictabilityis

thefactthatthemodelcontainstoolittlevariabilityoverthewesternIndianOceanandsouthem

Maritimecontinentregion.TheweakenedMJO/ISOrainfallvariationsoverthisregionmayleadto

areducedpredictabilityduetothemodel'srelativelyweakconvectionpassingthroughthisregion,a

regionthatexhibitsarelativelyrobustconvectivesignalintheobservations.

Anumberof aspectsassociatedwiththemodeland/oranalysisuggestthattheaboveresultsmight

overestimatethepredictabilityoftheMJO/ISO.Thefirstisthatthemodel'scoarseresolutionand

inherentreducedegreesof freedomrelativetothetrueatmospheremaylimittheamountof smallscalevariabilitythatwouldtypicallyerodelargetimeandspacescalevariability.However,it is

importanttonoteinthisregardthattheloworderEOFsofintraseasonallyfilteredmodeloutput

typicallydonotcaptureasmuchvariabilityasanalogousEOFsofobservedquantities.Thusthe

model'sMJO/ISOitselfstillhasroomtobemorerobustandcoherentwhichwouldtendtoenhance

predictability.In additiontomodelshortcomings, thesimplemannerthatpermxbationswereadded

totheinitialconditionsmayalsoleadtoanoverestimateof thepredictability.Theperturbation

structureandthesizeof theperturbationsmaybetooconservativeandnotadequatelyrepresentthe

typeof initialconditionerrorthatwouldbefoundinanoperational context.However,evenif that

isthecase,it wouldseemthatadequatesize"initial"errorswouldoccurintheforecastin amatter

of adayortwoandthusonewouldexpectthisaspectto overestimatethepredictabilitybyonlya

coupledays,if atall.Futurestudieswill examinethesensitivityoftheseresultstotheAGCMused,

toSSTcoupling,mid-latitudevariability,andE1Ninostate,aswellasexaminehowsensitivethese

resultsaretotheinitialconditionpermxbationsanddefinitionofpredictability.

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Higgins, R. W., and K. C. Mo, 1997: Persistent North Pacific circulation anomalies and the tropical

intraseasonal oscillation. J. Clim., 10, 224-244.

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western United States related to tropical forcing. J. Climate, 13. 793-820.

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interannual variabilityoftheN.Americanmonsoonsystem.J.Climate.InPress.

Jones,C.,2000:Occurrenceof extremeprecipitationeventsin Californiaandrelationshipswiththe

Madden-JulianOscillation.J.Climate,13,3576-3587.

Jones,C.,D.E.Waliser,J.K.Schemm,andW.K.Lau,2000:Predictionskillof theMadden-Julian

OscillationinDynamicalExtendedRangeForecasts. Clim.Dyn.,16,273-289.

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weatherforecastsoverSouthAmerica.Mon.Wea.Rev.128,486-494.

Kessler,W.S.andR.Kleeman,2000:RectificationoftheMadden-JulianOscillationintothe

ENSOcycle.J.Climate,13,3560-3575.

Lau,K.M. andP.H. Chan,1988:Interannual andintraseasonal variationsoftropicalconvection:

A possiblelinkbetweenthe40-50dayoscillationandENSO?J.Atmos.Sci.,44,506-521.

Lau,K. M.andF.C.Chang,1992:Tropicalintraseasonal oscillationanditspredictionby theNMC

operational model.J.Climate,5, 1365-1378.

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McPhaden, M. J., and B. A. Taft, 1988: Dynamics of seasonal and intraseasonal variability in the

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325-357.

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Meteor. Soc., Submitted.

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Weickmann, K., M., 1991:E1 Nino/Southem oscillation and Maddem-julian (30-60 day)

oscillations during 1981-1982. J. Geophys. Res., 96, 3187-3195.

Wheeler, M. and K. Weickmann, 2001: Real-time monitoring and prediction of modes of coherent

synoptic to intraseasonal tropical variability. Mon. Wea. Rev., 129, 2677-2694.

Yasunari, T., 1980: A quasi-stationary appearance of the 30-40 day period in the cloudiness

fluctuations during the summer monsoon over India. J. Met. Soc. Japan., 59, 336-354.

86
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Figure 1. (top) Predictability ratio for N.H. winter mode VP200 (left) and rainfall (right) from the dynamical (solid) and

persistence+ (dotted; see text for details) forecasts for the region 8N-16S and 120-165E. (bottom) The same but for the N.H.

summer mode and for the region 4-24N and 75-130E.

87
The MJO-ENSO Relationship: A re-assessment

William K. M. Lau and K. M. Kim

Climate and Radiation Branch, Laboratory for Atmospheres

NASA/Goddard Space Flight Center

Greenbelt, MD 20771

1. Introduction

Noting the similarities among the spatial patterns of outgoing longwave radiation among

MJO and ENSO, Lau and Chan (1987, 1988) speculated a possible relationship between the two

phenomena. This speculation received a substantial boost in credibility after the 1997-98 E1

Nino, when MJO activities were found to be substantially enhanced prior to the onset of the

warm phase, and clear signals of oceanic Kelvin waves forced by MJO induced anomalous

surface wind were detected as possible triggers of ENSO (McPhaden 1999, McPhaden and Yu

1999). Yet statistical and modeling studies have so far yielded either nil or at best, very weak

relationship between MJO activities and SST (Hendon et al. 1999, Fink and Speth 1997, and

Slingo et al. 1999). Recently Kessler (2001) suggested using an MJO index which includes

convective variability in the equatorial central Pacific lead to a more robust MJO-ENSO

relationship. Clearly, while MJO might have been instmxnental in triggering some E1Nino,

there are other events that can occur without any MJO trigger.

A possible reason for the above conundrum is that MJO-ENSO relationship, if exists, is likely

to be strongly nonlinear, and therefore will not have a one-to-one correspondence in individual

cases. Even within the 20-70 or 20-90 day windows commonly used by most investigators to

capture the MJO, there are possibly different modes &variability that may act as trigger, or

merely response to, or simply independent of ENSO SST variability. Many previous studies of

MJO-ENSO relationship focused on MJO in one particular season, particular boreal winter or

summer. Yet, MJO wind forcings on the ocean are likely to be continuously modulated

throughout the year. Indeed, oceanic Kelvin waves forced by MJO are likely to be found along

the equatorial waveguides, when the forcing is symmetric with respect to the equator, i.e. during

the spring and fall, and not during boreal winter or summer when the center of MJO activities are

away from the equator. Furthermore, although MJO has dominant signals in the tropics, it also

has strong extratropical expressions, suggesting interaction between tropics and the extratropics.

Hence identifying the full spectmxn &variability associated with MJO, including seasonality,

tropical and extratropical variabilities, and separating modes of intraseasonal variability with

respect to SST forcings are essential in understanding MJO-ENSO relationship.

In this paper, we re-assess the MJO-ENSO relationship, by identifying dominant space-time

modes of intreaseasonal variability and their possible separate roles in triggering or respond to

ENSO. Using 50 years of NCEP wind reanalysis, we have computed the space-time extended

empirical orthogonal functions (EEOF) of 20-70 filtered 850 mb streamfuanctions to identify

dominant modes of intraseasonal variability all year round. The first EEOF mode describes an

eastward, circum-propagating planetary pattem identified as the "classical" MJO. The second

88
modedepictsthequasi-stationary component of theMJO,associatedwithwavesignals

emanatingfromtheIndo-Pacificregionalongthesubtropical jetstreams.TwoindicesofMJO

activitieshavebeenconstructedbasedonthe90-daywindowedvariancesof theprincipal

componentsof thesetwomodes.TheMJOactivitiesshowstrongseasonality, withtheeastward

propagatingmode(EPM)andthestandingmode(SM)havingitspeakactivityintheboreal

spring,andwinterrespectively(notshown).

2. Results

Figure 1 shows a composite of the EPM with respect to Nino -3 SST variation. Enhanced

EPM (PC 1+2) activity is found during the cold-to-warm transition of E1 Nino. Immediately

after entering into the warm phase of E1 Nino, EPM activity is drastically reduced.,and remains

suppressed throughout the entire warm phase. This suggests that the EPM may be instrumental

in triggering or promoting the onset of E1 Nino. Yet, as a response to the spreading out &the

warm water, the global eastward propagation of the MJO is stalled in favor of the development

of the stationary component. As seen in the lower panel, SM activity appears to evolve

coherently with the Nino-3, with a delayed response of about 3 to 6 months.

Figure 1 Composite as a function of calendar months for a) Nino-3 SST (°C), b) PC1 +2

and c) PC3+4. The PCs are in relative units

The possible triggering of EL Nino by the EPM can be seen in the lagged time-longitude

cross sections of EPM index with respect to monthly 850mb zonal wind and SST along the

equator, shown in Figs. 2 a and b. Before enhanced EPM activity, the evolution pattems are

quite disorganized. However, simultaneous with and immediately following EPM activity, there

are clearly eastward propagating wind signals from the Indian Ocean to the eastern Pacific

(Fig.2a), accompanied by coherent SST signals over the eastern Pacific (Fig.2b). Note that the

89
dominanttimescaleisquasi-biennial andnotENSO. Incontrast,similartimelaggedcrosssectionsofSM (Figs.3aandb) depictaspace-timepatterncharacteristics ofE1Ninoandwitha

dominanttimescaleof 3.5to4years.AlsoclearisthattheenhancedSMactivitylaggedthe

warmSSTbyabout3months-furtherevidencethatit maybearesponsetoE1Nino.

Figure 2 Lagged-time-longitude corrvariance of EPM with respect to a) 850 mb zonal wind, and b) SST, along the equator.

Units are in rrEs and °C per 1-? changes in the PC.

3. Conclusion

Our results show that the EPM component of the MJO, by virtue of its enhanced activity

during boreal spring may be instrumental in exciting a warm event. When the naturally

occurring E1 Nino is properly phased with enhanced EPM activity in boreal and summer, a

rapidly warming phase, following by a rapidly cooling phase may occur. Recently Kim and Lau

(2001) has suggested a combination of winter and summer monsoon anomalous surface wind

forcings in the equatorial western Pacific may induce a biennial tendency in the ENSO cycle.

The present results suggest such biennial tendency in ENSO may be effected through the

seasonal modulation of the MJO.

90
iiiiiii;

_ _i_ _i_Z: _i_ i_i_#__ ̧ ̧ ̧_W _! _ i_i i_i i_i_N} '_!2_W ii_Wi i_3

Fig. 3 Same as Fig.2, except for SM.

Reference

Kim, K. M., and K. M. Lau, 2000: Dynamics of monsoon-induced biennial variability in ENSO.

Geophys. Res. Letters, 28,315-318.

Lau, K. M. and P. H. Chan, 1986: The 40-50 day oscillation and the E1 Nino/Southem Oscillation: a new

perspective. Bull. Amer. Meteor. Soc., 67, 533-534.

Lau, K. M. and P. H. Chan, 1988: Intraseasonal and interannual variability of tropical

convection: a possible link between the 40-50 day oscillation and ENSO. J. Atmos. Sci., 45,

506-521

Weickmann, K. M., 1991:E1 Nino/Southem Oscillation and the Madden and Julian (30-60 day)

oscillations during 1981-82. J. Geophys. Res., 96, 3187-3195.

McPhaden, M. J., 1999: Climate oscillations - Genesis and evolution of the 1997-98 E1Nino.

Science 283,950-940.

McPhaden, M. J., and X. Yu, 1999: Equatorial waves and the 1997-98 E1Nino. Geophys. Res.

Lett., 26, 2961-2964.

Kessler, W. S., 2001: EOF representations of the Madden-Julian Oscillation and its connection

with ENSO. J. Climate, 14, 3055-3061

Slingo J.M., Rowell D. P., Sperber K. R., 1999: On the predictability of the interannual

behaviour of the Madden-Julian Oscillation and its relationship with E1Nino, Q J Roy

Meteor Soc 125: (554) 583-609.

Hendon, H. H., C. Zhang, J. D. Glick, 1999: Interannual Variation of the Madden-Julian

Oscillation during Austral Summer. Jouxnal of Climate: Vol. 12, No. 8, pp. 2538-2550.

91
Recent Failures of Monsoon-ENSO Relationships

Viewed From Coupled Model Results

Arun Chakraborty and T. N. Krishnamurti

Department of Meteorology

Florida State University

Tallahassee, Florida 32306-4520

We are in the course of preparing data sets for a multimodel ensemble of seasonal forecasts.

These are based on eight versions of the FSU global coupled model following LaRow and

Krishnamurti (1998, Tellus). In addition to those we have prepared parallel data sets for as many

as four external non-FSU global coupled model runs. Although the entire array covering several

decades of seasonal forecasts experiments are not yet completed, some preliminary results from

several single model runs were possible at this stage. One of the questions we address here is on

the recently reported failures of the ENSO - Monsoon relationship. Prior to 1995 there was the

general feeling that E1-Nifio years were to be treated as deficient in Indian monsoon rainfall with

the converse being the case for the La-Nifia years. Recent papers by several authors observed the

failure of this relationship since roughly 1995. Our study examines the coupled model results on

the relationship of ENSO index and the all India summer monsoon rainfall (AISMR). We find

that the model output does confirm a breakdown of this relationship. In the course of further

diagnosis of this relationship, we noted a prominent featuxe that prevailed prior to 1995 that was

absent in the subsequent years. The model output showed that the amplification of the MJO

signals in the zonal winds at 850hPa levels and of the ENSO signals were both large during years

of above normal AISMR. During years of deficient AISMR the MJO signal amplitudes were

small. The differences in the amplitudes of ENSO and MJO signals were inversely related to the

AISMR. Even that relationship broke down after 1995. Since the complete global coupled model

output is available at intervals of every week we feel that this data sets provides an opportunity

for the examination of other non-conventional parameters that might provide a clue to the

behavior of the breakdown of these relationships and to search for other possible candidates.

These results will be presented at the workshop on subseasonal climate prediction.

92
Variability of Tropical Intraseasonal

Convective Anomalies and Their

Statistical Forecast Skill

Charles Jones, Leila M. V. Carvalho, Wayne Higgins,

Duane Waliser and Jae-K. E. Schemm

Institute for Computational Earth System Science (ICESS)

University of California

Santa Barbara, CA 93106-3060

The tropical atmosphere exhibits significant amounts of variance on time scales of 20-90 days.

In particular, the Madden-Julian Oscillation (MJO) is the main mode of tropical intraseasonal

variation with significant influences on the Asian-Australian Monsoons, Indo-Pacific

thermocline variability and mid-latitude teleconnections. Previous works have shown that

tropical intraseasonal convective anomalies (TICA) display different modes of eastward

propagation, seasonal and interannual occurrences.

This study examines the characteristics of eastward propagating TICA events. An objectivetracldng algorithm is applied to 22 years of Outgoing Longwave Radiation (OLR) data to

characterize their propagation, life cycle, zonal displacement and structural properties (minimum

OLR anomaly, variance of OLR anomaly, area, number of cold clusters, rate of growth and

decay). The zonal wind (U) components at 850 hPa and 200 hPa from 22 years of NCEP/NCAR

reanalysis are used to describe the large-scale circulation associated with TICA events. Four

main TICA types are recognized depending on their eastward propagation and region of most

significant influence (Indian Ocean, India/China Sea, SPCZ and Pacific Ocean). A statistical

analysis is carried out to determine if differences in structural properties of convective anomalies

are also associated with different TICA types. Probability curves are then constructed to

determine the likelihood of TICA occurrences according to their zonal displacement and

frequency per season. In the last part of this work, a combined (CEOF) analysis of OLR, U850,

and U200 is performed to build a statistical forecast model based on multiple linear regressions

using the first two leading EOF modes. The statistical forecast skills of different TICA types are

analyzed and their implications for midlatitude teleconnections are discussed.

93
An All-Season Real-time Multivariate MJO Index: Development of the index

for monitoring and prediction in Australia

Matthew C. Wheeler and Harry H. Hendon

Climate Forecasting Group

Bureau of Meteorology Research Centre

P.O. Box 1289K

Melbourne, Victoria 3001

m.wheeler@bom.gov.au & h.hendon@bom.gov.au

1. Introduction

As part of an ongoing research project at the Australian Bureau of Meteorology on the use of the MaddenJulian Oscillation (MJO) for forecasting on the intraseasonal time scale, we have developed an index for

the MJO based on the first two Empirical Orthogonal Functions (EOFs) of the combined fields of nearequatorial 850 hPa zonal wind, 200 hPa zonal wind, and satellite-observed outgoing longwave radiation

(OLR). Projection of the daily observed data onto such multivariate EOFs, with the annual cycle and E1

Nino variability removed, yields principal component (PC) time series that vary mostly on the

intraseasonal time scale of the MJO only. That is, the projection of daily data onto the two EOFs is a very

effective filter for the MJO, without the need for time filtering, and thus an effective index for real-time

use. In this way, it is thought to be an improvement over the method that is currently performed daily at

the Bureau, of Wheeler and Weickmann (2001). This paper explores the properties of the index along

with its relationship to other important weather and climate parameters. Prediction of the index will

follow in later work and publications.

2. Data and Analysis

Perform EOF analysis on the daily fields of equatorially-averaged (10°S to 10°N) OLR, 850 hPa zonal

wind, and 200 hPa zonal wind for the period of 1979 to 2001 (23 years). EOF analysis is of the

covariance matrix. For the combined (multivariate, i.e. OLR and winds) analysis, each field is

normalized by its global variance first. Before the EOF analysis, however, we do the following: Remove

the mean and annual cycle (3 harmonics) from each field at each grid-point. Remove the variability

associated with E1Nino (that which is linearly related an ENSO SST index). Remove a 120-day mean of

the most recent 120 days at each point. All of these steps can be performed in real-time. Figure 1 presents

the first two EOFs, which we feel capture the variability of the MJO well.

3. Initial Results

We call the two PC time series that form the index the Real-time Multivariate MJO series 1 (RMM1), and

RMM2. For the 1979 to 2001 period, for all seasons, RMM1 and RMM2 respectively have 58 and 61% of

their total variance occurring for the 30- to 80-day time scale (Fig. 1), and their cross-spectrum reveals a

coherence-squared value of-0.8 on this time scale with RMM1 leading RMM2 by a quarter cycle. In the

two-dimensional phase-space defined by the vector M = (RMM1,RMM2) (Fig. 2), the MJO thus

manifests itself as an anti-clockwise rotation with time, while times of little or no MJO activity are

represented by seemingly random movement of the vector M near the origin.

94
Figure1:Spatialstructure,exampletimeseries,andspectraof thosetimeseries,forthefirsttwo

empiricalorthogonal functionsof thecombinedfieldsof OLR,850hPazonalwind,and200hPa

zonalwind.Wecalltheprincipalcomponent timeseriesresultingfromtheprojectionof thedailydata

ontotheseEOFsRMM1andRMM2.

95
iiiiiiiiiiiiiiiiiiii_i_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_i_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_i2iiiiiiiiiiiiiiiiiiiiiiiiiiiiii_ii_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii2iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii8iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_i

Figure 2: The phase space representation of the RMM1 and RMM2 indices for the southern

summer months of December, January, February, March, and April for the full 23 years of

data. When the MJO is active the (RMM1,RMM2) vector describes large anti-clockwise

circles.

This phase-space representation of the two RMM series is one of a number of ways to use the index for

the purpose of producing composites and forming relationships with other climate or weather indicators.

a. Composites

Given that the RMM series are computed using the near-equatorial (10°S to 10°N average) OLR and

winds for any season, one aspect that is important to demonstrate is whether they can capture the known

varied structure and influence of the MJO from season to season. Composites of total OLR and wind

fields for eight defined phases of the MJO for the different seasons show that indeed they do capture such

seasonal changes. The strongest direct influence of the MJO on the OLR field (and presumably rainfall) is

strongest over Australia in southern summer, but it is mostly confined north of about 20°S. There is no

discernible influence over the Australian continent in northern summer, but in autumn, there is some

indication of an influence through northwest cloudbands stretching to western Australia from the Indian

Ocean.

b. Relationship with the onset of the Australian monsoon at Darwin

Relationships with a completely independent indicator of the onset and retreat of the Australian monsoon

can also be demonstrated. Figure 3 shows the location of the (RMM1,RMM2) vector in its phase space

when the monsoon was defined to onset at Darwin by a measure defined by Drosdowsky (1996). This

measure is determined by the radio-sonde westerly winds at Darwin only, and is thus a very strict point

measurement of the monsoon, that is, not a large-scale indicator like the MJO. Yet we see in Fig. 3 that

the monsoon for the last 23 years has only onset at times when the MJO is in only about half of its 8

defined phases. The monsoon at Darwin rarely "onsets" when the convection of the MJO is over the

96
Indian Ocean or the Western hemisphere. Such a relationship is instructive for the potential use of the

MJO and the RMM series for prediction.

iiiiiiiiiiiiiiiiiiiiiii iiiiiiiiiiiiiiiiiiiii_ii_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_ __iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii_iiiiiiiii_ i!iii_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_ ___i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_i_ _i_i_

iiiiiiiiiiiiiiiiiiiiiii_iiiiiiii ii_iiiiiiiii ii ii ii iii ii ii iii ii i_i_ i_ i i_i i iii ii ii ii iii ii ii i_ _3.i_i_iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii i3i i_ i_ i_ ii iii ii ii ii iii ii ii iii ii i_ i iii i_i i i

Figure 3" The location of the (RMM1,RMM2) vector each year when the monsoon is

determined to onset at Darwin. Each point is labeled according to the year of the December of

the monsoon season. For example, the point indicating the position for the 1978/1979 monsoon

onset is labeled as "78".

c. Prediction of the RMM1 and RMM2 series

Prediction of the RMM series, and thus prediction of the MJO, is work to be described in future

publications. One method of prediction is along the lines of the work of Lo and Hendon (2000), an

empirical technique utilizing multiple step-wise lag regression. Predictions from numerical models could

also be projected onto the same EOFs to obtain the RMM series.

References

Drosdowsky, W., 1996: Variability of the Australian Summer Monsoon at Darwin: 1957-1992. J.

Climate, 9, 85-96.

Lo, F. and H. H. Hendon, 2000: Empirical extended-range prediction of the Madden-Julian Oscillation.

Mon. Wea. Rev., 128, 2528-2543.

Wheeler, M. and K. M. Weickmann, 2001: Real-time monitoring and prediction of modes of coherent

synoptic to intraseasonal tropical variability. Mon. Wea. Rev., 129, 2677-2694.

97
Evidence of an Intrinsic Intraseasonal Oscillation

over Tropical South America during Austral Summer

Jiayu Zhou 17 and William K_-M. Lau 2

1Goddard Earth Sciences and Technology Center, UMBC, Baltimore, MD 21250

2Climate and Radiation Branch, Laboratory for Atmospheres,NASA/GSFC, Greenbelt, MD 20771

1. Introduction

The intraseasonal variation (ISV) in the 30-60 day band, also known as Madden-Julian oscillation (MJO),

has been studied for decades. Madden and Julian (1971, 1972, 1994) showed that the oscillation

originated from the western Indian Ocean, propagated eastward, got enhanced over the maritime

continent and weakened after passing over the dateline. Composite studies showed evidences of a signal

in upper and lower level zonal wind propagating around the globe during an oscillation (Knutson and

Weickmann 1987). Theoretical studies pointed out that the interaction with the warm ocean surface and

the coupling with the convective and radiative processes in the atmosphere could manifest the oscillation,

which propagates eastward via mutual feedbacks between the wave motions and the cumulus heating

(Chang 1977; Yamagata and Hayashi 1984; Lau and Peng 1987; Salby and Garcia 1987; Wang 1988; Hu

and Randall 1994).

Over tropical South America, no independent 30-60 day oscillation has been reported so far, despite that

Amazon is the most distinct tropical convection center over the western hemisphere and the fluxes from

its surface of tropical rainforests are close to that from the warm tropical ocean. Liebmann et al. (1999)

showed a distinct spectral peak of 40-50 day oscillation in outgoing longwave radiation (OLR) over

tropical South America and considered that was manifested by the MJO propagation. Nogues-Paegle et

al. (2000) focused on a dipole pattern of the OLR anomaly with centers of action over the South Atlantic

Convergence Zone (SACZ) and the subtropical plain. They used the regional 10-90 day filtered data and

demonstrated this pattern could be represented by the fifth mode of the rotated empirical orthogonal

function. Its principal component was further analyzed using the singular specmun analysis. Their result

showed two oscillatory modes with periods of 36-40 days and 22-28 days, of which the former was

related to the MJO influence and the latter linked to the remote forcing over southwest of Australia, which

produced a wave train propagating southeastward, rounding the southern tip of South America and

returning back toward the northeast. The 22-28 day mode has distinct impact on SACZ, responsible for

the regional seesaw pattern of alternating dry and wet conditions.

In this study we will focus on the 30-60-day spectral band and investigate whether the independent

oscillation source over tropical South America is existed. First, we will show the seasonal dependence of

the tropical South American ISV in Section 3. Then, the leading principal modes of 30-60 day bandpass

filtered 850-hPa velocity potential (VP850) will be computed to distinguish the stationary ISV over

tropical South America (SISA) from the propagating MJO in the austral summertime in Section 4. The

importance of SISA in representing the regional ISV over South America will be discussed. In Section 5,

we will demonstrate the mass oscillation regime of SISA, which is well separated from that of MJO by

the Andes, and the convective coupling with rainfall. The dynamical response of SISA and the impact on

_ Corresponding author address: Dr. Jiayu Zhou, GEST, Code 913, NASA/GSFC, Greenbelt, MD20771.

E-maih zhou@climate.gsfc.nasa.gov

98
theSouthAmericansummermonsoon(SASM)willbepresentedinSection6. Finally,wewill givethe

concludingremarks.

2. Data

ThedatausedinthisstudyaretheGoddardEarthObservingSystemofversionone(GEOS-1)reanalysis

(Schubert et al. 1993), the European Center for Medium Range Weather Forecast (ECMWF) reanalysis

(Gibson et al. 1997), the National Oceanic and Atmospheric Administration / National Centers for

Environmental Prediction (NOAA/NCEP) OLR (Janowiak et al. 1985), and CPC (Climate Prediction

Center) Merged Analysis of Precipitation (CMAP) (Xie and Arkin 1997). To focus on the 30-60 day

band, the Lanczos filter (Duchon 1979) was used, prior to which the pentad mean was constructed. A 9-

grid spatial smoother was also applied to the CMAP rainfall data to remove small-scale features. The

austral summer half-year is defined as from mid-October to the following mid-April.

3. Seasonal dependence of ISV over South America

The seasonal variations of precipitation, wind and temperature over South America have been addressed

by many previous studies (Aceimno 1988, Horel et al. 1989, Kousky and Ropelewski 1997, among

others). It is now recognized that these variations are organized by the South American monsoon system

(Nogues-Paegles et al. 2002), which is forced by the summertime regional thermal infrastructure,

responding to the annual cycle of solar heating in an interactive ocean-atmosphere-land entity and being

characterized by the systematic rainfall migration and the reversal of seasonal perturbation wind in the

tropical and subtropical regions. Zhou and Lau (1998) showed coherent convective 40-45 day

oscillations over tropical-subtropical South America during 1989/90 summer monsoon season. These

oscillations were related to pre-monsoon, monsoon onset and development, monsoon mature and

withdrawal and post-monsoon phases.

From the wavelet analysis of OLR averaged over eastern tropical South America from November 1982 to

November 1995, we can clearly see the annual cycle was enhanced during E1 Nifio years and the

submonthly and the 1-3 month time-scale ISV activated only in summer half years. Being dynamically

consistent, the similar features can also be found in VP850 of both GEOS and ECMWF reanalyses. Over

eastern tropical South America, the temporal correlation between the 30-60 day band-pass filtered

ECMWF 850-hPa velocity potential and OLR in the summer half-year is about 0.67. The summer halfyear composite of VP850 spectrum, averaged over eastern tropical South America, shows more

significant spectral peak at 45-day period in GEOS reanalysis.

The geographic distributions of the 30-60 day band averaged VP850 spectral density, are composited for

the summer and the winter half-year, respectively. Due to short record for each half-year span, the

spectral analysis of maximum entropy method (MEM) is used to sharpen the spectral features. Overall,

the composites show the oscillation in ECMWF reanalysis is weaker than that in GEOS reanalysis. The

intensity of the former is about 80% of the latter. Both reanalyses show two centers, one zonally

elongated over the maritime continent of the western Pacific, which is consistent with the illustration of

MJO by Madden and Julian (1972), and the other extending northwest-southeastward over tropical South

America in alignment with the monsoon migration. The intensity of the later is about 60% of the former.

The semiannual composites show that the oscillation is much stronger in the summer half-year than in the

winter half-year (almost a factor of 3/2 seen over tropical South America in both reanalyses). More

realistically, the ECMWF reanalysis demonstrates a seasonal preferred location of the oscillation center,

which follows the seasonal migration of the convective activities associated with American monsoons

over Central-South America.

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4. IdentificationofSISA

Lookingfortheintrinsic30-60dayoscillationovertropicalSouthAmerica,weconductedarotated

empiricalorthogonal function(REOF)analysisforthe30-60daybandpassfilteredVP850in streamer

half-years.TheVarimaxrotation,whichremovesthespatialbutkeepsthetemporalorthogonality, is

appliedtoidentifyregionallyconfinedpatterns(NigamandShen1993).Theresultshowsthefirstand

thesecondmodes,whichdisplaya90-degreephaseshiftinbothspaceandtime,represent the

propagatingMJO.Thethirdmode,thoughexplaininglessvariancethantheleadingtwomodes(7%in

GEOSand14%inECMWF),concentrates itsmostvarianceovereasterntropicalSouthAmerica.This

featureisevenmoreprominent intheECMWFreanalysis.

Todemonstratewhethertheoscillationrepresentedbythethirdmodeisindependent ofMJO,lagcorrelationsbetweenthethirdrotatedprincipalcomponent (RPC)andthe30-60daybandpassfiltered

globalVP850arecomputedforacompleteoscillationcycle.Theresultevidentlyshowsthestanding

characteristics ofSISA,whichformslocallyandnotrelatedtotheeastwardpropagatingMJO.

TheimportanceofthistropicalSouthAmericanmodeisfurtherdemonstrated bythereconstructionof

VP850usingtheleadingthreeREOFmodesforsummerhalf-years.WecanseethatovertropicalSouth

AmericathethirdmodetogetherwithMJOcanalmostmimicthetotalvariabilitybybothreanalyses. The

roleplayedbyindividualoscillatingcomponent isalsochecked.Forexample, during1991/92summer

half-year,thereconstructionshowsweakMJOinfluenceandstronglocaloscillationovertropicalSouth

Americafromaustralspringto earlysummerof 1991.Afterwards, theeastwardpropagatingMJO

becomesdominantbutdistinctdisturbancesofSISAcanstillbeseenwhenwavesof MJOpassingover

tropicalSouthAmerica.

5. Oscillationregimeandconvectivecoupling

InassociationwiththeoscillationsofOLRandVP850,distinctmassfluctuationsarealsofound.The

summerhalf-yearcompositeofMEMspectral densityofsealevelpressure(SLP)averagedovereastern

tropicalSouthAmericashowsconsistent threespectral peaksattheperiodsof36-60day,20dayand12

day.ThecorrelationsofSLPwiththethreeleadingRPCsofVP850showsthattheareashighly

correlatedwithMJO(greaterthan0.4)areconfinedtothePacificandtheIndianOcean.Thestationary

ISVoverSouthAmericahashighcorrelationwithSLPvariabilityovertheareafromtropicalSouth

AmericatothewestcoastoftropicalAfrica. ThetwostrongimpactregimesofMJOandSISAarewell

separatedbytheAndes,whichblockedthelow-levelinfluencesfromthePacificside.EastoftheAndes,

the30-60daymassvariabilityismorecloselyrelatedtoSISA.Thiscanbefurtherdemonstrated by

computingone-gridcorrelationsof30-60daybandpassfilteredSLPwiththebasegridateasternBrazil

andwesterncentralAfrica,respectively.Fromtheresultwecanseeawell-definedspatialregime

extendingfromtropicalSouthAmericaeastoftheAndestocentralAfrica,whichisconsistent withthe

areahighlycorrelatedwithSISA.It showsevidentlythatthe30-60dayoscillationoverSouthAmerica

canbelocallymanifestedanddoesnotnecessarilydependonMJO.

Beingconvectivelycoupledwiththeoscillation, therainfallvariabilityovereasterntropicalSouth

Americashowsthesamespectralcharacteristics asthatofSLPinthesummerhalf-year.Todistinguish

thegeographicmpactof each30-60dayISVmode,wecomputetheregressionof eachRPCofVP850

withrainfallandcomparetheresultwiththecorrespondingaturalmodeofrainfallvariationsrepresented

bytheindependent EOF.Forthefirstandthesecondmodes,largeamplitudesarefoundovertheIndian

OceanandthewesternPacific,whereMJOprevails,andthepatternsbetweentheregressionandEOFs

arehighlycorrelated.OvertropicalSouthAmericaamoderaterainfallsignalassociatedwithMJOmoves

fromwesternto easterntropicalSouthAmerica.Forthethirdmode,theregressionshowsasimilar

patternwiththerainfallEOF3overtropicalSouthAmericabutlesscoherentoverthewesternPacific.

100
TheimportanceofthethirdmodetotheSouthAmericanregionalrainfallvariabilityisfurther

investigatedbycomputingthepercentageofthelocalvariancexplainedbytheregressionwitheach

mode.Wefindtheimpactofeachmodehasdistinctgeographicpreference.It showsthattheMJOhas

largeimpactovernortheasternBrazilandthenearbyAtlanticIntertropical ConvergenceZone(ITCZ).

WhiletheimpactofSISAisconcentratedoncentralBrazil,whichextendssoutheastward andinfluences

theSouthAtlanticConvergenceZone(SACZ),andtheareaover10°NofthewesternAtlantic.Aswe

will seeinthenextsection,thesetwopreferredSISAimpactareasarelocatedalongthepathofthe

monsoonflow,indicatingcloserelationshipbetweenSISAandSASMvariations.

6. Dynamical responseandimpactonSASM

Thedynamical responsesoftheleadingthreemodesof 30-60dayISVareillustratedbyregressingeach

RPCofVP850withstreamfunctionat200and850hPa,respectively.Intheuppertroposphere, the

responsesofthefirstandthesecondmodesshowapatternsimilartotheillustrationof Matthews(1993)

andSperberet al. (1997), who depicted twin anticyclones (cyclones) over west (east) of the convection

accompany MJO propagating eastward. This pattern is vertically out of phase in the tropics and in phase

poleward, and can be explained by the counteraction between the streamfunction tendencies induced by

the horizontal advection of planetary vorticity and the divergence of planetary vorticity flux (Chen and

Chen 1997). The impact of the propagation of this pattern on the tropical easterlies can be clearly seen

over equatorial South America.

Consistent with the summertime heating over tropical South American continent, the response of the third

RPC shows two anomalous cyclonic (anticyclonic) circulations, which are symmetric about the equator,

at east (west) of tropical South America in the upper troposphere. A small anticyclonic cell can be seen

over southern Peruvian highland, where intense heating is found during the summertime. In the lower

level, a large anticyclonic disturbance is over northwestern Africa and a cyclonic per_bation along the

South Atlantic convergence zone (SACZ). As a result, anomalous flows, denoted by streamlines, follow

the SASM flow pattern, indicating a close relationship between SISA and the intraseasonal variation of

SASM. Recently, Jones and Carvalho (2002) identified two flow regimes in 10-70 day time-scale in

association with SASM active and break phases, according to the phase of the low-level zonal flow over

Rondonia State of Brazil. They showed that during the monsoon active phase, the anomalous flow

traveled along the tropical north Atlantic, crossed the equator over the east of the Andes, ran toward

Southeast Brazil and closed in a cyclonic anomalous circulation off the coast of Argentina and Uruguay.

During the break phase, the flow reversed completely. By making composites of 850-hPa wind and OLR

for two SISA extreme phases, of which the VP850 RPC3 amplitudes exceed 1.2 standard deviation, we

find the anomalous low-level flow of SISA swings between the active and break monsoon flow regimes.

The OLR anomaly, extending northwest-southeastward over tropical South America, flips the phase in

association with the anomalous monsoon flow convergence. Evidently, the variation of SISA is related to

the break and revival of SASM.

7. Concluding remarks

Using GEOS-1 and ECMWF reanalyses, NCEP OLR and CMAP rainfall data we have investigated the

ISV source over tropical South America during austral summer half-year. Rotated EOF analysis of 30-60

day bandpass filtered VP850 reveals that the first two leading modes are closely related to MJO. The third

mode, SISA, which shows standing oscillation characteristics and accounts for a large portion of ISV over

tropical South America, suggests a local forcing mechanism. The total 30-60 day variability of VP850

over tropical South America can be reconstructed by the three leading REOF modes. Further investigation

reveals a well-defined oscillation regime of SISA, which is separated from that of MJO by the Andes,

extending from tropical-subtropical South America east of the Andes to central Africa. The dynamical

impact of SISA on the upper tropospheric rotational flow shows a typical wave pattern of symmetric

101
variationsinstreamfunction, i.e., a leading pair of cyclones and a trailing pair of anticyclones in

association with localized heat sources and sinks. The impact of SISA on the regional climate shows a

close relationship with SASM break and revival. Our results suggest that in the austral summer half-year

the tropical South American region possesses a source of ISV that is distinct from that due to the MJO.

Acknowledgments. This research is supported by the Earth Observing System / Interdisciplinary Science

investigation on hydrological processes and climate, and the Global Modeling and Analysis Program of

NASA, Mission to Planet Earth Office.

102
Dynamical Seasonal Predictability

of the Asian Summer Monsoon

K. R. Sperber, C. Brankovic, M. Deque, C. S. Frederiksen,

R. Graham, A. Kitoh, C. Kobayashi, T. Palmer,

K. Puri, W. Tennant, and E. Volodin

Ensemblesoflaindcastsfromsevenmodelsareanalyzedtoevaluatedynamical seasonal predictability

of 850hPa wind and rainfall for the Asian summer monsoon (ASM) during 1987, 1988, and

1993. These integrations were performed using ob-served sea surface temperatures and from

observed initial conditions. The experiments were designed by the CLIVAR Worldng Group on

Seasonal to Interannual Prediction as part of the Seasonal prediction Model Intercomparison

Project (SMIP). Integrations from the European Union PROVOST (Prediction of climate

Variations on Seasonal to interannual Timescales) experiment are also evaluated.

The National Centers for Environmental Prediction/National Center for Atmospheric Research

and European Centre for Medium-Range Weather Forecasts reanalyses and observed pentad

rainfall form the baseline against which the hindcasts are judged. Pattem correlations and rootmean-square differences indicate errors in the simulation &the time-mean low-level flow and

the rainfall exceed observational uncertainty. Most models simulate the subseasonal EOF's that

are associated with the dominant variations of the 850hPa flow during the ASM, but not with the

fidelity exhibited by the reanalyses as determined using pattem correlations. Pattem correlations

indicate that the first EOF, associated with the tropical convergence zone being located over the

continental landmass, is best simulated. The higher order EOF's are less well simulated, and

errors in the magnitude and location of their associated precipitation anomalies compromise

dynamical seasonal predictability, and are related to errors of the mean state. In most instances

the models fail to properly project the subseasonal EOF's/PC's onto the interannual variability

with result that hindcasts of the 850hPa flow and rainfall are poor. In cases where the observed

EOF's are known to be related to the boundary forcing, the failure of the models to properly

project the EOF's onto the interannual variability indicates that the models are not setting up

observed teleconnection patterns.

Sperber, K. R., C. Brankovic, M. Deque, C. S. Frederiksen, R. Graham, A. Kitoh, C. Kobayashi,

T. N. Palmer, K. Purl, W. Tennent, and E. Volodin, 2001: Dynamical Seasonal Predictability

of the Asian Summer Monsoon. Mon. Wea. Rev., 129, 2226-2248.

103
Experiments with a Data Assimilation System to Diagnose

AGCM Hindcasts of the MJO

Man Li C. Wu, Siegfried D. Schubert, Derek Van Pelt

Data Assimilation Office

Goddard Laboratory for Atmospheres

and

Duane Waliser

Institute for Terrestrial and Planetary Atmospheres

State University of New York

I. Introduction

The objectives of this study are to (1) develop a better understanding of how observations

constrain/impact the MJO in a data assimilation system with the aim of improving the

representation of the MJO, and (2) to carry out AGCM predictability/forecast experiments under

various observational constraints to assess model errors and sensitivity to initial conditions. Our

current focus is on the second objective.

We use the NASA-NCAR AGCM to carry out this study. The model is based on the finitevolume dynamical core developed at the DAO (Lin and Rood 1998), with physical

parameterizations from the NCAR CCM-3 (Kiehl et al. 1996). The data assimilation system is

the finite volume Data Assimilation System (fvDAS) of the DAO (DAO Office Note, 1999).

The ability of the cuxrent version of the AGCM to simulate the MJO is highlighted in Figs. 90

through 94 in Chang et al, 2001. These figures show that the model generally reproduces the

large-scale structure of the MJO variability. However, the model MJO is too weak and the period

is too short (around 20 to 30 days) compared to that found in the NCEP/NCAR reanalysis (40 to

60 days). A major problem with the simulated mean climate is the lack of precipitation over

much of the westem Pacific warm pool and Indonesia region, and too much rainfall over the

western Indian Ocean and East China (Figs. 26 and 27 of Chang et al, 2001).

In order to address the AGCM deficiencies above, we made the following experiments coveting

the period August and September of 2000: (a) a full assimilation run with the fvDAS, (b) an

ensemble of 9 AGCM hindcasts with different atmospheric initial conditions, and (c) an

assimilation run in which only atmospheric moisture observations where ingested

(fvDAS only q) - the aim here was to examine the impact of correcting the moisture bias in the

AGCM on both the climate and on the representation of the MJO. In the following discussion,

the focus is on the impact of the moisture correction to the representation of the MJO.

104
II. Results

Figuxe 1 is a longitude-time diagram of velocity potential anomalies averaged over between 10S

and 10N. Both the fvDAS and NCEP/NCAR reanalysis show a well defined eastward

propagation associated with the MJO. The AGCM ensemble mean initially follows the

observations, but loses skill in a matter of a few days. The simulated anomalies travel to the east

too fast. Afterwards, the central Pacific is dominated by a sinldng branch of the MJO for more

than a month. The ensemble variance shows that the limit of predictability in the AGCM for this

case is about 10 days to 2 weeks.

A diagnosis of the mean moisture field indicates a substantial bias. Specifically, in the tropics

between 10S to 10N, the AGCM is too wet over the Indian Ocean and too dry over most of the

western Pacific Ocean. Around the ITCZ (latitudes 12N to 16N), the dry area extends into the

eastern Pacific and the dry situation is most severe.

In the assimilation with the moistuxe-only correction (fvDAS_only q rmn), the moisture bias are

largely corrected. In addition, the results show a better precipitation field. In particular, the

insufficient precipitation over Indonesia and western Pacific warm pool region is corrected and

the excessive rainfall over the western Indian Ocean is being reduced. Bias in the velocity

potential over the corresponding regions is also improved.

The impact of the moisture correction on the representation of the MJO as it cycles around the

globe is also positive. With the assimilation &moisture, the rising branch of the MJO in the

Central Pacific is enhanced so as the eastward propagation. The results also improve the sldll of

MJO hindcasts (especially EOF2). EOF 2 has a dipole centered over the central Pacific and

western Indian Ocean.

In the diagnoses of the moist heating of the fvDAS data, we found that the moist heating leads

the rising branch of the MJO, and the rising branch of the MJO also enhances the moist heating.

One &the apparent reasons for the failure of the AGCM to produce a reasonable MJO is that the

AGCM is too dry over the westem Pacific region and it cannot generate enough moist heating

ahead of the rising branch of the MJO.

When we compare the fvDAS with the AGCM and the fvDAS only q results, we find that the

AGCM heating over the central Pacific is too weak. When the moisture bias is corrected, the

moist heating in the central Pacific is improved, which in tuxn enhances the rising branch of the

MJO in the central Pacific and improves the eastward propagation of the MJO.

III. Conclusions

The MJO in the current version of the NASA/NCAR model is too weak and has dominant

periods that are too short: these are problems that are common to many other AGCMs. On the

other hand, the MJO is quit reasonable within an assimilation system that employs that AGCM.

The AGCM problems are reflected in hindcasts of a strong MJO event where the model loses

sldll within a matter of days and loses predictability in about one month. Initial experiments

105
correctingonlythemoisturefieldsinthemodelappeartohavepositiveeffectsonthe

precipitationclimatologyandimprovetherepresentationf theMJO.Follow-onexperimentsare

neededtodeterminewhethersimilarbenefitsof theMJOcanbeachievedwithasimplemoisture

biascorrection.

References

Chang, Y.-H., S. D. Schubert, S-.J Lin, S. Nebuda, and B. W. Shen, 2001:The Climate of the

FCCM-3 Model - The Technical report series on global modeling and data assimilation,

NASA/TM-2001-104606, Vol. 20, pp127.

da Silvar, A. and S-.J Lin, 1999, The DAO physical-space/fmite-voluxne data assimilation

system. Part I: Algorithm theoretical basis for the "violet' core system - Office Note series on

global modeling and data assimilation, DAO Office Note 2000-nn, pp 27.

Lin, S.-J. and R. B. Rood, 1998: A flu_x-form semi-Lagrangian general circulation model with a

Lagrangian control volume vertical coordinate. The Rossby- 1000 symposium, Stockholm,

Sweden.

Kiehl, J. T., J. J. Hack, and B. P. Briegleb, 1994: The simulated earth radiation budget of the

NCAR CCM2 and comparisons with the Earth Radiation Budget Experiment (ERBE). J.

Geophys. Res., 99, 20815-20827

106
Sep19

5ep 9

AUg31

:U_21

Au_11

Aug 1

Velocity Pol_ntial AnorT,_lies aL 2O0 hPo: Au_ju_t through 5epternber 2000 (los to 1oN)

NCEP/NCAR _vDAS AGCM AGCM

Re_nalysis Ensembl_ rrlean Enserrlble worlonce

10 m (m=_-') _ • _ _ 1 1 _ _ • g

o 5 ..... : ........ :........... 7_::-::' . _/-_,.:z_-,:_;../::... . :_-_-":-::._.,,...." f '.. ......................... :._

I--OF 2 (24.7%)

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NC'EP ,/N CAR {_DAS PcDASen I_q

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_iqnr_cant _t 1_ I_1

107
Session IV

Tropical/Extratropical Interactions

109
Tropical Influences on Subseasonal Extratropical

Variability and Predictability

Prashant D. Sardeshmukh

NOAA-CIRES Climate Diagnostics Center

Boulder, Colorado, USA

This talk emphasized three points: 1) A large fraction of the total power of extratropical tropospheric

height variability resides in the intraseasonal band; 2) Much of the predictability on this time scale

can be captured by simple multilinear Markov models; and 3) Tropical forcing is a major (though

not the only) source of this predictability.

In support of point 1, it was shown that to a first approximation the average wintertime power

spectrum of 500 mb heights for northern hemispheric gridpoints poleward of 20 N is well

approximated by that of red noise with a 3-day correlation scale. The hemispherically averaged

variances of daily, weekly, monthly and seasonal-average anomalies are also consistent with that of 3-

day red noise. It was further emphasized that about 88% of the total power of a red noise time series

lies between periods of 0.1 and 10 times the characteritic period, which for 3-day red noise is 2_ x 3

18 days. Thus a large part of the total power lies in the intraseasonal band. Shorter-scale synoptic

variability modifies this picture somewhat in the western ocean basins, but longer term interannual

and interdecadal variability remains modest in comparison, even in the eastern ocean basins. Also

consistent with this picture, one-point teleconnection maps of daily, weekly, monthly, and seasonalaverage anomalies for base points in the north Pacific and Atlantic have a remarkably similar spatial

structure across these time scales.

If simple univariate 3-day red noise captures so much of the structure of tropospheric variability, then

multivariate red noise (with multiple time scales and patterns) should do even better. Such a model

may be constructed directly from observed statistics through the process of Linear Inverse Modeling

(Penland 1989; Penland and Sardeshmukh 1995) as follows. In any multidimensional statistically

stationary system with components x_, one may define a time-lag covariance matrix C(_) with

elements C_ = < x/t+r) x;(t) >, where angle brackets denote a long-term average. In linear inverse

modeling, one assumes that the system satisfies C(r) = G(r)C(O), where G(r) = exp(Lr) and L is a

constant matrix, and uses this relationship to estimate L given observational estimates of C(O) and

C(ro) at some lag 30. In such a system any two states separated by a time interval r are related as x(t+r)

G(r) x(t) + e, where e is a random error vector with covariance E(r) = C(O) G(r)C(O)Gr(r). Such

a model may then be used to make forecasts. The vector G(r)x(t) represents an "infinite-member

ensemble-mean" forecast for x(t+r) given x(t), i.e the forecast signal, and E(r) represents the

expected covariance of its error, i.e the noise. As pointed out for example by by Sardeshmukh et al.

(2000), the expected correlation p_ of such forecasts with observations is given by p2 = 3_/[1+ 3_],

where S is the forecast signal to noise ratio. More generally, the predictability of any system

component may be estimated by defining the average 32 for that component as the appropriate

diagonal element of the signal covariance matrix G(r)C(O)Gr(r) divided by the corresponding

diagonal element of the noise covariance matrix E(r), and for the system as a whole as the ratio of

either the traces or the determinants of these matrices.

We have constructed such "linear inverse models" (LIMs) of 7-day running mean circulation

anomalies in the northern hemisphere and diabatic heating anomalies in the tropics from their

observed simultaneous and time-lag correlation statistics (see Winkler et al. 2001; Newman et al.

2002). The state-vector x has 37 components representing the first 30 combined EOFs of 250 and

750 mb streamfunction and the first 7 EOFs of colunm-averaged tropical diabatic heating. In both

winter and summer, the models' forecast skill at Week 2 (Days 8 to 14) is comparable to that of

NCEP's medium range forecast (MRF) model. Their skill at Week 3 (Days 15 to 21) is actually

higher, partly due to their better ability to forecast tropical heating variations and their influence on

the extratropical circulation. Furthermore, the geographical and temporal variations of forecast skill

are similar in the LIMs and the MRF model.

The simplicity of the LIM makes it an attractive tool for assessing and diagnosing atmospheric

predictability on subseasonal time scales. The model assumes that the dynamics of weekly averages

111
arelinear,asymptoticallystable,andstochasticallyforced.Thevalidityof theseassumptionshave

importantimplicationsfor understandingsubseasonal variabilityandpredictability.In aforecasting

context,thepredictablesignalisassociatedwiththedeterministiclineardynamicsandtheforecast

errorwiththeunpredictablestochasticnoise.In ourlow-orderlinearmodelof thehigh-orderchaotic

realsystem,thisstochasticnoiserepresentsheeffectsof bothchaoticnonlinearinteractionsand

unresolvedinitialcomponentsontheevolutionof theresolvedcomponents. Weassumeitsstatisticsto

bestate-independent.

An averagesignaltonoiseratioS is estimated at each grid point on the hemisphere in the manner

outlined above, and is then used to estimate the potential predictability poo of 7-day running mean

anomalies as a function of forecast lead time. The day on which poo drops below 0.5 in winter is

plotted in the left panel of Figure 1. The right panel shows the contribution to this from tropical

heating, obtained by performing similar calculations in which the effects of the heating are turned

off. It is clear that predictable tropical heating variations play an important role in enhancing the

potential predictability of weekly averages in the extratropics, especially over the Pacific ocean and

north America. The actual LIM forecast skill p (not shown) has a similar geographical structure but

weaker magnitude than the potential skill.

The LIM formalism is also useful for diagnosing variations of forecast skill from case to case. In this

framework, the predictable variations of forecast skill from case to case are associated with the

predictable variations of signal, rather than of noise. This contrasts with the traditional emphasis in

studies of shorter-term predictability on flow-dependent instabilities, i.e. on the predictable variations

of the noise statistics. In the LIM, the predictable variations of signal are associated with variations of

the initial state projection on the growing singular vectors of the LIM's propagator G, which have

relatively large amplitude in the tropics. As shown in Figure 2 for Week 3 forecasts in winter, at times

of strong projection on the first 3 singular vectors, the signal to noise ratio S is high, and the northern

hemispheric circulation is not only potentially (poo) but also actually (p) more predictable than at

other times. The version of the NCEP MRF model examined here is much less able to discriminate

between relatively high and low forecast skill cases in this way. This is partly due to its lesser ability to

forecast tropical heating variations in regions where the initial heating component of the growing

singular vectors is important. Our results thus suggest both that there is considerable predictability of

Week 2 and Week 3 averages in some regions of the northern hemisphere, and that it may currently

not be fully realized in some forecasting systems because of their error in predicting tropical heating

variations in sensitive areas.

References

Winkler, C.R., M. Newman and P.D. Sardeshmukh, 2001: A linear model of wintertime low-f

requency variability. Part I: Formulation and forecast skill. J. Climate, 14, 4474-4494.

Newman, M., P.D. Sardeshmukh, C.R. Winkler, and J.S. Whitaker, 2002: A study of subseasonal

predictability. Mort. Wea. Rev., submitted.

Penland, C., 1989: Random forcing and principal oscillation pattern analysis. Mort. Wea. Rev., 117,

2165-2185.

Penland, C., and P.D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature

anomalies. J. Climate, 8, 1999--2024.

Sardeshmukh, P.D., G.P. Compo and C. Penland, 2000: Changes of probability associated with E1

Nino. J. Climate, 13, 4268-4296.

112
113
_++++_++_d b__=++_++fl:__+_+_#_+_+ __+_+m+_ '

114
Mid-latitude Mountains and the MJO:

A Prototype for

Tropical-Extratropical Interaction?

Klaus M. Weickmann

NOAA-CIRES Climate Diagnostics Center

Boulder, Colorado 80303

The Madden-Julian oscillation produces a robust, repeatable signal in global and zonal

atmospheric angular momentum (AAM). Duxing northern winter, the MJO zonal wind

anomalies appear first on the equator in the westem hemisphere and then shift to the eastem

hemisphere, stretching from the north Indian Ocean to the central Pacific Ocean. Global AAM

anomalies increase and reach a maximum during this evolution. Studies of the torques that

produce the AAM variations show Asian and North American mountains contribute to the

mountain torque anomalies when analyzing 30-70 day filtered data but not when analyzing data

based on a MJO index. The Andes and African Highlands make a larger contribution during an

MJO. Subsequent observational studies of the mountain and frictional torque reveal an

independent coherent evolution of the mid-latitude circulation that involves the adjustment by

the atmosphere to stochastically-varying flow over the mid-latitude mountains. In this case, the

frictional torque responds to strong or persistent mountain torque events in a matter of days and

the response includes the PNA teleconnection pattern. The intraseasonal AAM variations thus

involve the interaction of mostly independent processes in the tropics and mid-latitudes. Some

recent cases are presented.

115
A Workshop on ProspectsJbr Improved Forecasts' of Weather and Short-term Climate Variability on Subseasonal (2

week to 2 month) Time Scales, NASA/Goddard Space Flight Center, April 16-18, 2002

Influences of the Madden Julian Oscillations on Temperature and Precipitation

in North America during ENSO-Neutral and Weak ENSO Winters

Yan Xue*, Climate Prediction Center, Yar,.Xue_gi!noaa.,zov

Wayne Higgins, Climate Prediction Center, Wu,_'ne. Higgins({_ nouu._ov

Vernon Kousky, Climate Prediction Center, Vernon.Kouskv(a;}noaa.eov

1. Introduction

A number of studies have shown that the MaddenJulian Oscillation (MJO) have a strong influence on the

atmospheric circulation and precipitation patterns in the

tropical and extratropics (Madden and Julian 1971; Lau

and Chan 1985; Knutson and Weickmann 1987; Kayano

and Kousky 1999). Recent studies also suggest that the

MJO-related tropical forcing is linked to precipitation

events along the west coast of the United State during the

winter season (Mo and Higgins 1998; Higgins et al.

2000).

Dynamical models generally simulate the MJO quite

poorly, partly because of inherent difficulties in

parameterizing tropical convection. Statistical prediction

models have shown a modest skill relating subseasonal

variations in tropical convection to week-2 prediction of

wintertime rainfall in western North America (Whitaker

and Weickmann 2001). Operational week two ensemble

predictions show levels of skill that are comparable to the

statistical forecasts. However, neither statistical or

dynamical prediction models have demonstrated useful

levels of skill in forecasting MJO-related impacts beyond

week 2.

We believe that improved monitoring and

assessment of the MJO and its impacts on the atmo spheric

circulation and precipitation patterns will lead to better

prediction. In this work we focus on ENSO-neutral and

weak ENSO winters since the MJO is known to be quite

active during those times. Velocity potential at 200-hPa

has been used to construct a composite index for life

cycles of the MJO (Knutson and Weickmann 1987). A

similar approach is taken here. An extended Empirical

Orthogonal Function (EEOF) analysis is applied to the

bandpass filtered (25-87 days) pentad velocity potential

at 200-hPa for ENSO-neutral and weak ENSO winters

(November-April) during 1979-2000. The first EEOF is

composed often time-lagged patterns. We construct ten

*Corresponding author address: Dr. Yan Xue,

NOAA/Climate Prediction Center, 5200 Auth Road #605,

Camp Springs, MD 20746.

MJO indices by regressing the filtered pentad data onto

the ten patterns of the first EEOF. Keyed on the ten MJO

indices, ten composites for the major MJO events are

derived for various fields, including surface air

temperature and precipitation. Our composites are

constructed with unfiltered pentad data in which the

winter season mean is removed.

Significant MJO-related influences on surface air

temperature and precipitation in North America are

found. The potential predictability for those fields derived

from the tropical MJO-related forcing is discussed.

2. Data

The data set used in this study consists of

nonoverlapping five-day (pentad) means for 200-hPa

velocity potential, 200-hPa zonal wind and 500-hPa

geopotential height derived from the NCEP/NCAR

CDAS/Reanalysis data (Kalnay et al. 1996). As a proxy

for deep tropical convection we use pentad OLR data

derived from measurements made by the NOAA

operational polar-orbiting satellites for the 1979-2000

period. We also use the GPCP pentad precipitation

analysis for its global coverage (Xie et al. 2002). The

pentad global surface air temperature is based on GTS

data ( Ping-Ping Xie, personal communication).

3. MJO indices

The ENSO-neutral and weak ENSO winters during

1979-2000 are selected according to the CPC ENSO

classification (http://www.cpc.ncep.noaa.gov/

research_papers/ncep_cpc_atlas/8/ensoyrs.txt). Based on

this, 15 winters are selected and used to calculate the

EEOF of velocity potential at 200-hPa (referred to as

CHI200 hereafter). The first EEOF, composed of ten

time-lagged patterns, describes an eastward propagation

of the MJO with a timescale of about 45 days (Fig. 1). We

construct ten MJO indices by regressing the filtered

pentad CHI200 onto each of the ten patterns. So positive

indices correspond to the convectively active phase of the

MJO at different longitudes. Those MJO indices have

been implemented for the real time monitoring of the

MJO at CPC: h I.tp://w w w .c_c.n ce_ moaa.b, ov/

116
pro duc ts/prec ip/Cwlink/daily_mj o_index/mj o_index.html.

EEOF1 Nov-Apt, CIEI200 (xE.E-7)

t t+zs_

20B_0. • :.. :r,, _ tJ _0g :-_' .. _: r:_::::_.. ":'.w$?: ": ._,_i :::::::::::::::::::::::::: _aN:,,' ":: :_i_:_*"":_i: :_

::::::::::::::::::::: ,,__:::::_: &_::::: :_f::,_

t+Sd t+5Od

_o_b:::::::: _ " :ii_!i!_il _::i# _ :i. : _i_i! '-f:}ii_ _0_ :::: iii_i:h,,' _iiiii: _s : iiii::j i:.

t+15d _+40d

t+;_Od L+45d

_0_ : _ ,_:_i::T'::TiTi'i'L '_::.. :_ " ' _0_ _."? '_Y: lI:::::::::: :::::::::::

"" "_HHHHHH{_ :III1HHh

l#S ".......... _:- lOS _ , ................

-:o,s o,7 o.e o,5 &4-o,a-_,g .o 02 o._ 0,4 o_5 o.4 o,7 oa

Fig. 1. The first EEOF of the pentad velocity potential at

200-hPa for the ENSO-neutral and weak ENSO winters

(November-April) during 1979-2000.

4. MJO composites

We identify MJO events when the maxima of the

standardized MJO indices exceed 0.8 standard

deviations. To increase the sample size, one to four

pentads centered on the maxima are used in composites,

depending on the amplitudes of the indices. So the

stronger events are weighted more in composites than the

weaker ones are. We restrict our cases to the extended

cold season (December to April). Based on this, about

25 to 29 MJO events are selected for each of the ten MJO

indices. The number of pentads included in each

composite varies from 67 to 83 since more than one

pentads are used for each MJO event. The statistical

significance of the composites at each grid point is

evaluated by comparing selected MJO pentads to the

remaining non-MJO pentads in the ENSO-neutral and

weak ENSO winters via a two-tailed student's t test.

The life cycle of the MJO and its influences on the

upper-tropospheric circulation are described by the

COMPOSITE PENTAD OLR; cini=4 _/rn_

INDEX 1 INDEX 6

ii?'i:::

INDEX 2 INDEX 7

_.- :Jff2,:" :)_ _) ::; :

_,Se i2_c l_O iz'_._ e6_ a 5 e,hE hiDE _bwe_ 5

INDEX 3 INDEX 8

r _: ::: q "3 :

INDEX 4 INDEX 9

_J _)'v__,.-_:,,:,,:, t',h' ,oJU°''_ ,N_:'_:_:,q/]

,3 6OE 120E i_o 12C_ BOW o 9 6BE 120E IBO 120_ I_0_' G

INDEX 5 INDEZ 10

Fig. 2. OLR composites keyed on the ten MJO indices.

The contour interval is 4 w/m2, and the zero contour is

omitted. The blue (yellow) shading denotes negative

(positive) anomalies significant at the 95% level.

composites of OLR and 200-hPa zonal wind (Fig. 2 and

3). The initial phase of the cycle is characterized by

enhanced convection in far eastern Africa and the Indian

Ocean, and suppressed convection in the western Pacific

and the South Atlantic Convergence Zone (SACZ) (index

1 and 2). There are twin anticyclones located to the west

and twin cyclones to the east of the tropical forcing,

which in turn retracts the East Asian Jet (Fig. 3). When

the enhanced convection shifts eastward towards

Indonesia, westerly wind anomalies prevail in the upper

troposphere over the gulf of Alaska and the Pacific North

West (PNW), which brings wet condition there (Fig. 4).

As the enhanced convection moves out of Indonesia and

suppressed convection sets up over the Indian Ocean, the

East Asian Jet extends eastward (index 5). This

circulation pattern remains quisi-stationary for about three

pentads (index 6-8 in Fig. 3). When the enhanced

convection moves to northern South America and Africa,

a new cycle begins (index 9-10 in Fig. 2).

117
COMPQSTE PENTAD 20D mb ZONAL WIND, c"n_=2 m/s

NDEX 1 INDEX 6

_e_ % v-- 8_N " % v-..

_, '" _. 4°. £" .:_ _.

Eo iil _o

6 NE IfOE I_0 12_ _ o _ 6_ 120E I_o 12_ _6_

NDEX 2 INDEX 7

_o, _ @_ .% v -. _oN :_" j:i;:: j_. "_ ._" -

ZeN , 20N [" _

58E 120E 180 12#'_ _Ta g 0 6_E 128E laO 120_ eOw

NDEX 3 NDE× 8

NDE_ 4 NDEX 9

oti

o 6BE 20E lab 12_# _0_ 0 O 6BE 13BE _0 120W 60_

NDEX b NDEX IC}

ZO_EO _ _" """--="'_ _-" 20NEo - "

o 6mE 20E 180 12_'# _'_ 13 0 6BE 12BE 180 120W SOw C"

INDEX 1 INDEX (_

....__._... . "_ . _ ._

INDEX 2 .... ]NDt-X 7

INDEX 3 INDEX 8

180 150 120 gO'_ _o_ 180 i_g lzQ_ _1 60_

INDEX 4 INDEX 9

INDEX 5 INDEX _0

/5D 40 _50 ±2{} 20 30 g{_ 50

Fig. 3. Same as Fig. 2 except for the 200-hPa zonal wind.

The contour interval is 2 m/s.

Concurrent with the tropical MJO-related forcing,

significant precipitation signals are found in the U.S. (Fig.

4). The MJO-related precipitation signal is most

pronounced along the west coast, which is consistent with

previous studies (Mo and Higgins 1998; Higgins et al.

2000). During index 3 and 4, the PNW is wet and

California is dry. However, the wet signal in the PNW is

negligible compared to the local climatology because

most MJO events happen during later winter and early

spring, while the PNW receives its peak precipitation in

Dec-Jan. The California precipitation begins as the

enhanced convection moves to the date line (index 7) and

persists for two to three pentads. This is probably

associated with the upper-tropospheric westerly wind

anomalies that act to steer weather systems into this

region (Whitaker and Weickmann 2001, hereafter WW).

We noticed that the OLR pattern of index 8 agrees very

well with the canonical predictor OLR pattern (at a 2-

week lag) for the week-2 prediction of the western north

American rainfall of WW. It appears that the precursor

OLR signal for the week-2 prediction of California

precipitation in WW is that around index 5.

Fig. 4. Differences of composites of MJO pentads and

non-MJO pentads. The contour interval is 0.2 mm/day,

and the zero contour is omitted. The shading denotes the

percentage departure relative to the climatological pentad

precipitation based on te Dec-Apr period.

Significant MJO-related influences on surface air

temperature in North America are found (Fig. 6), that are

consistent with the MJO-related influences on 500-hPa

geopotential height (Fig. 5). When the enhanced

convection is in the Indian Ocean (index 1 and 2), strong

blocking activity occurs in the northwestern Pacific and a

deep trough is located over the gulf of Alaska. As the

enhanced convection moves eastward toward Indonesia

(index 3 and 4), the composite 500-hPa geopotential

height resembles a negative PNA pattern, that is

characterized by a ridge in the northeastern Pacific, a

trough in the northwestern U.S. and a ridge in the

southeastern U.S.. This quisi-negative PNA pattern lasts

2-3 pentads, producing warm anomalies in the eastern

U.S. and cold anomalies in the western Canada (Fig. 6).

As the enhanced convection moves toward dateline, a

weak positive PNA pattern forms, causing warm

anomalies in the western Canada (index 8 in Fig. 5 and 6).

As the enhanced convection returns to Africa and the

118
Indian Ocean, anomalous trough covers the western U.S.,

producing cold anomalies inthe west (index 9). Averaged

over the cycle, the U.S. tends to be warm-than-normal in

the east and cold-than-normal in the west. These long

lasting warm anomalies (2-3 pentads) in the east will

undo ubtab ly contribute significantly to monthly fore casts

issued regularly at the CPC when the MJO is active

("forecasts of opportunity").

COMPOSITE PENTAD 500 mb HEIGHT, cint=lO m

INDEX 1 INDEX 8

B81 -,%: _(}N

_eH 4_Ni

Ze" 2_

13 61)E 12_{_E 1,_0 i2C'_ 8(/n 0 13 _2_OE 1013 12b_l 66e (k

INDEX 2 INDEx 7

,[18'1 _-_ r" aoN i_ :

_I)E iaO 12_'_ _0_ 0 _ _Og 120E leo 1Nw I_Ol_

INDEX 3 INDt_N 8

,_eN ::; ::. ., - a0N , , , .

58N : ' _P 80N ":_ ill

4e_l 4_N %:.

INDEX 4 IND=X 9

a0N aoN ,-i -- " . ,

# r_'4

INDEX 5 INDEX 10

- ! . #:

40"1 40N

o ,S_IE I_E 1a5 12_ _oo _ _ ,_ClE l_OE _eo 120,_ 60'_

Fig. 5. Same as Fig. 2 except for the 500-hPa geopotential

height. The contour interval is 10m.

REFERENCES

Higgins, R. W., J.-K. E. Schemm, W. Shi, and A.

Leetmaa, 2000: Extreme precipitation events in the

western united states related to tropical forcing. J.

Climate, 13,793-820.

Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-

year reanalysis project. Bull. Amer. Meteor. Soc., 77,

437-471.

Kayano, M. T., and V. E. Kousky, 1999: Intraseasonal

(30-60 day) variability in the global tropics: principal

modes and their evolution. Tellus, 51A, 373-386.

Knutson, T. R., and K. M. Weickmann, 1987:30-60 day

atmopsheric osciallations: composite life cycles of

convection and circulation anomalies. Mon. Wea.

Rev., 115, 1407-1436.

Lau, K.-M., and P. H. Chan, 1985: Aspects of the 40-50

day oscillation during the northern winter as inferred

from outgoing longwave radiation. Mort. Wea. Rev.,

113, 1889-1909.

Madden, R. A., and P. R. Julian, 1972: Description of

global scale circulation cells in the tropics with 40-50

day period. J. Atmos. Sci., 29, 1109-1123.

Mo, K. C. and R. W. Higgins, 1998: Tropical influences

on California precipitation.. J. Clim ate, 11,412-430.

Whitaker, J. S., and K. M. Weickmann, 2001:

Subseasonal variations of tropical convection and

week-2 prediction of wintertime western north

american rainfall. J. Climate, 14, 3279-3288.

Xie, P., J.E. Janowiak, P.A. Arkin, R. Adler, A. Gruber,

R. Ferraro, G. J. Huffman, and S. Curtis, 2002:

GPCP pentad precipitation analysis:An experimental

data set based on gauge observations and satellite

estimates. (Summitted to J. Climate).

COMPOSITE F'ENTAD TEMPERATURE, cint=O.5 ;C

INDEX 1 INDEX 6

ae_ aot4 ___

INDEX 2 INDI-X7

ao_ _ aotl .:

46_ _6N

150w 120_' g._ 1fog 120W 90_

INDEX 3 INDEX 8

ac.h......... : aott

_O. _Sn

INDEX ¢ INDEX 9

._ON aott

_e_ !on

4e,,, aou

2e_ ell

_6_ aOtl

Fig. 6. Same as Fig. 2 except for surface air temperature.

The contour interval is 0.5 degree.

119
The Role of MJO Activity Modulating

the SACZ Persistence, Intensity and

Form, and Regional Impacts on

Extreme Precipitation Events

Leila M. V. Carvalho, Charles Jones and Brant Liebmann

Institute for Computational Earth System Science (ICESS)

University of California

Santa Barbara, CA 93106-3060

The tropical atmosphere exhibits significant amounts of variance on time scales of 20-90 days.

In particular, the Madden-Julian Oscillation (MJO) is the main mode of tropical intraseasonal

variation with large influences on the Asian-Australian Monsoons and mid-latitude

teleconnections. Previous works have shown that intraseasonal variations play an important role

on the variability of the South American Monsoon System (SAMS). The South Atlantic

Convergence Zone is part of the SAMS and its variability affects large areas of tropical and

subtropical South America.

This study examines the influence of the MJO on the intensity, persistence, and form of the

SACZ and regional implications for the occurrence of extreme precipitation over Brazil. Daily

Outgoing Longwave Radiation (OLR) is examined during 22 years to characterize convective

activity in the SACZ. An objective algorithm is applied to identify OLR properties related to

intensity and form of the SACZ such as area, minimum OLR, fraction with minimum OLR,

eccentricity, and extent to the Atlantic Ocean and the Amazon. Factor analysis is then applied to

time series of OLR properties to characterize the SACZ features. Two factors explain ~ 65% of

the total variance of the convective activity patterns in the SACZ and characterize events

according to the intensity (Factor-1) and extent of the OLR features to the Atlantic Ocean

(Factor-2). Factor- 1 is then used as an index to infer persistence of intense SACZ episodes.

Factor-2 characterizes distinct situations when convection is displaced to the Atlantic Ocean or

more continental.

The MJO episodes are characterized independently using filtered OLR (10-90 days) data and

principal components analysis. The propagation ofmidlatitudes wave train is also investigated

using filtered OLR (10-90 days) data but with a distinct procedure. Precipitation over Brazil is

examined with data from stations interpolated in a grid of2x2 ° of latitude and longitude. It is

shown that MJO events modulate approximately 30% of the total SACZ episodes with

persistence greater than 3 days. The midlatitude wave trains modulate the SACZ extent to the

Atlantic Ocean. It is shown that there are significant regional impacts in the occurrence of

extreme precipitation over Brazil related to MJO occurrences and to the midlatitude wave train.

The relationships between the occurrence of MJO and the wave train are also investigated.

120
Modulation of the North Pacific-Pacific

Northwest Storm Activity by MJO

T. C. (Mike) Chen, David Li,

Peter Hsieh, and Jordan Alpert

Iowa State University

The x-t diagram of precipitation along 45°N were constructed over the North Pacific-Pacific

Northwest region for the last twenty years, with different data sources: precipitation generated by

the GEOS- 1 and NCEP/NCAR reanalysis, Goddard precipitation estimates, and OLR. A clear

30-60 day modulation of the eastward migration of storm tracks emerges from these x-t

diagrams. Storm tracks migrates eastward when the MJO deepens the Aleutian low. It was

shown by our previous study (Chen and Alpert 1990 MWR) that the descent forecast skill of the

planetary-scale divergent circulation can reach five days by the NMC mediuxn-range forecasts

(MRF). Thus, an effort is undertaldng to explore how many days the modulation of the North

Pacific-Pacific Northwest storm track activity by the MJO can properly predicted by the NCEP

MRF. Preliminary results show that the five-day forecast sldll generally can be obtained.

However, the forecast sldll may exceed this limit when the MJO is strong and well-organized.

121
Monthly Variability of ENSO Signals

and the Relevance for Monthly Predictions

Martin P. Hoerling

NOAA-CIRES Climate Diagnostics Center

Boulder, CO

The role of ENSO in the predictability of monthly averages (monthly prediction) is explored.

The simplest approach, and one commonly used, is to specify the seasonal mean atmospheric

signal related to ENSO. An effective "monthly resolution" to such signals is rendered by

calculating overlapping, seasonal averages. Yet, these could underestimate true monthly ENSOrelated predictability to the extent that distinct monthly signals exist, and vary within a season. It

is well established that the potentially predictable ENSO signal varies from one season to

another, and it is thus reasonable to expect that some variation occurs even within a season. This

could occur due to a strong sensitivity oftropical-extratropical interactions to modest changes in

the atmospheric base state. It could also be due to sub-seasonal changes in the tropical forcing

accompanying the ENSO cycle itself.

The focus of this study is to provide evidence for the existence of sub-seasonal variations in the

ENSO signal, and the analysis uses NSIPP climate simulations and observed data for 1950-2000.

Results are presented for the late boreal Fall season (October-November-December, OND),

during which the seasonal mean 500-mb ENSO signal is compared to the individual monthly

signals. Distinct atmospheric signals occur in each month. A strildng feature of the sub-seasonal

variation in the ENSO signal is that the October and December responses over the Pacific-North

American region are anti-correlated, and as such the associated seasonal mean signal is a

smeared residual of sharper patterns. The practical importance for monthly predictions is

discussed within a "signal-to-noise ratio" framework for predictability assessment.

122
The Influence of E1 Nifio on the circulation over the

North Pacific and North America

Hilary Spencer Julia M. Slingo

April 2002

1 Motivation

Simulating ENSO teleconnections with an atmospheric GCM (AGCM) is normally considered

to be of interest to seasonal forecasting. A long AGCM ensemble with observed SSTs is studied

and shows that there are significant systematic errors of seasonal means over the North Pacific

during the peak of E1 Nifio in DJF and after the peak in MAM. It has been shown by other

contributors that improved subseasonal forecasting (2 weeks to 2 months) requires reduced

systematic model errors. Therefore, reducing the sources of the seasonal mean systematic

error should be beneficial to shorter timescales. It is shown that these systematic errors can be

dramatically reduced with a small increase in the vertical model resolution.

2 Method

An ensemble of six integrations of the UK Met. Office model, HadAM3 (Rodwell and Folland,

2001), with observed SSTs (GISST, version 2.3b, Parker et al., 1995) was run at the Hadley

centre and is studied here. The model resolution is 2.5 ° latitude by 3.75 ° longitude and 19

vertical levels. The model is described further by Pope et al. (2000). HadAM3 has also been

integrated with observed SSTs from 1979-1996 at the Hadley centre with 30 vertical levels and

various horizontal resolutions (Pope and Stratton, 2001). This is an AMIPII integration and

so has slightly different SSTs from the 19 level integration. The E1 Nifio event of 1982/83 is

compared with NCEP reanalysis data (Kalnay et al., 1996) and with CMAP precipitation (Xie

and Arkin, 1996). The 1982/83 event is common to all four datasets. Other events have also

been studied with the same conclusions but not included here. A fuller discussion of the results

can be found in Spencer and Slingo (2002).

3 Results

The climatological, DJF SSTs from GISST and the DJF SSTs from 1982/83 from GISST and

the AMIPII SSTs are shown in figure 1. The two datasets are slightly different in the tropical

Pacific but the main differences between the HadAM3 integration with 19 levels (GISST SSTs

imposed) and that with 30 levels (AMIPII SSTs imposed) are thought to be due to the different

vertical resolutions.

The mean sea level pressure anomalies during DJF of the E1 Nifio of 1982/83 for the

HadAM3 integrations with both 19 and 30 levels and for the NCEP reanalysis are shown in

123
GISST clim.

..... 0o

180°E

GISST 1982/83

J -. i.... ..............

0o

180°E

AMIPII 1982/83

180°E

°C

6 10 14 18 22 26 27 28 29 30

Figure 1: Sea surface temperature during DJF, climatology and E1 Nifio

NCEP HadAM3, 19 levels HadAM3, 30 levels

180°E 180°E 180°E

mbar

-12.5 -10.5 -8.5 -6.5 -4.5 -2.5 -0.5 0.5 2.5 4.5 6.5 8.5 10.5 12.5

Figure 2: Mean sea level pressure anomalies during DJF of the E1 Nifio of 1982/83 for NCEP

reanalysis data and one member seasonal means of HadAM3 with observed SSTs

CMAP HadAM3, 19 levels HadAM3, 30 levels

180 E 180 E 180 E

mm/day

0 2 4 6 8 10 12 14 16 18 20

Figure 3: Total precipitation during DJF of the E1Niflo of 1982/83 for CMAP and one member

seasonal means of HadAM3 with observed SSTs

124
figure 2. This shows the tropical Pacific to be simulated accurately by both models but there

are large and highly significant errors in the 19 level model in the North Pacific region. Both

the reanalysis and HadAM3 with 30 vertical levels show low pressure in the central and east

of the North Pacific with high pressure to the west, whereas HadAM3 with 19 levels simulates

a very deep Aleutian low across the whole of the North Pacific. Although only one realisation

for each dataset is plotted, every member of the six with 19 vertical levels show very similar

behaviour and none have high pressure in the west of the North Pacific. However, whenever a

deepening of the Aluetian low is observed during a strong E1Niflo in DJF, it is accompanied by

a high pressure anomaly in the west of the North Pacific. Therefore, HadAM3 with 19 levels

never captures the observed response but one integration of one event with 30 vertical levels

captures this observed response well, with low pressure across the centre and east of the North

Pacific and high pressure in the west.

The reason for this improved simulation with 30 vertical levels can be traced to the tropical

Pacific precipitation. The 19 level model simulates the precipitation climatology very well (not

shown) and also simulates the approximate locations of reduced and enhanced precipitation

during E1 Nifio (also not shown). However, if the total precipitation during E1 Nifio is plotted

(figure 3), it can be seen that the enhanced precipitation simulated with 19 levels in the central

tropical Pacific is not sufficient to shift the peak of the total precipitation into the central tropical

Pacific as is observed. The CMAP observations show a broad peak in total precipitation across

the west and centre of the tropical Pacific. This is not identical to that simulated with 30 vertical

levels but the broad features south of the equator are similar. However, when simulated with 19

vertical levels, the peak precipitation remains in the west of the tropical Pacific and a separate,

smaller peak is simulated towards the east. This implies that the divergent circulation at upper

levels will be in a different longitudinal position in the simulation with 19 levels. It is this

divergent circulation which forces the extratropical response.

This is confirmed by plotting the total velocity potential at 200mb for DJF of 1982/83 for

the three datasets in figure 4. For each dataset, the velocity potential minimum, indicating the

centre of the divergence, is situated over the maximum precipitation in the tropical Pacific.

Hence the NCEP reanalysis and 30 level HadAM3 North Pacific are forced from the central

tropical Pacific whereas the 19 level HadAM3 North Pacific is forced from the western tropical

Pacific, in a similar manner to non-E1Nifio conditions. This explains the different longitudinal

positions of the extratropical responses of the three datasets.

In order to trace the source of the precipitation errors in the tropical Pacific, probability

density functions (p.d.f.s) of precipitation as a function of SST are calculated for each January

between 1979 and 1998 and for each grid box in the western and central tropical Pacific (160°E

to 120°W and 10°S to 5°N) for CMAP and the HadAM3 integrations. The p.d.f.s are calculated

using a two dimensional Gaussian kernel estimator (Marshall and Molteni, 1993). All three

datasets show suppressed precipitation over the cooler SSTs and more high precipitation as

SST increases. However, HadAM3 with 19 levels simulates the highest precipitation over the

warmest SSTs and few instances with low precipitation over the warmest SSTs, unlike CMAR

HadAM3 with 19 levels shows least variability of precipitation for any SST value which leads

to the precipitation distribution matching closely the observed SST distribution (figure 1). This

leads to the localised spikes of precipitation seen in figure 3 rather than the more smeared out

distribution of the other datasets.

125
NCEP HadAM3,19levels HadAM3,30levels

90oE 180o E 90°W 0°

Figure 4: Total 200mb velocity potential during DJF of the E1 Nifio of 1982/83 for NCEP

reanalysis data and one member seasonal means of HadAM3 with observed SSTs The contour

interval is 2 x 10%r_28 1 and negative contours are dashed and start at -1 x 106m2s 1

CMAP

3O

_25 ........... i ............ .................................................. i........... i...........

d is

._10-

ks

0 .......

23 24 25 26 27 28 29 30 31

SST (°C)

30.

"_25

20.

._ 15.

N

}10

_5.

23

HadAM3, 19 levels

24 25 26 27 28 29 30 31

SST (°C)

HadAM3, 30 levels

30

"_25 - :- :

._ 15

N

}10

_5

23 24 25 26 27 28 29 30 31

SST (°C)

Figure 5: The probability density function of precipitation as a function of SST for each January between 1979 and 1998 and for each grid box in the western and central tropical Pacific

(160°E to 120°W and 10°S to 5°N) for CMAP and HadAM3.

4 Discussion

The close relationship between precipitation and SST for the simulation with 19 levels, is probably due to a lack of convective inhibition. Inness et al. (2001) found that using 30 model layers

with HadAM3, with enhanced vertical resolution around the freezing layer of convection, improved the simulation of the MJO. Deep convection is inhibited due to the convection being

capped by the inversion created by the freezing layer. Without this inversion being adequately

resolved, as is the case with 19 levels, deep convection can form too readily over a warm SST

anomaly. This leads to the close relationship between SST and precipitation modelled with 19

levels.

Many authors have shown that the position of the North Pacific response to E1 Nifio is

relatively insensitive to the longitudinal location of the tropical heating anomaly (eg. Geisler

et al., 1985; Sardeshmuckh and Hoskins, 1988). The latter argue that the longitudinal location

of the forcing is dependent on the location of strong meridional gradients ofvorticity. However,

for the case of a very strong E1 Nifio event such as 1982/83, the tropical heating anomaly is

sufficiently large to alter the basic state, shifting the location of maximum meridional gradients.

This emphasises the importance of studying absolute fields as well as anomalies.

126
References

Geisler, J., Brown, M., Bates, G., and Mufioz, S. (1985). Sensitivity of January climate response to the magnitude and position of equatorial pacific sea surface temperature amomalies. Journal of Atmospheric Science, 42(10), 1037-1049.

Inness, P., Slingo, J., Woolnough, S., Neale, R., and Pope, V. (2001). Organization of tropical

convection in a GCM with varying vertical resolution; implications of the Madden-Julian

Oscillation. Climate Dynamics, 17(10), 777-793.

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha,

S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, B., Chelliah, M., Ebisuzaki,

W., Higgins, W., Janowiak, J., Mo, K., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.

(1996). The NCEP/NCAR Reanalysis 40-year Project. Bulletine of the American Meteorological Society, 77, 437-471. Web site http://www.cdc.noaa.gov.

Marshall, J. and Molteni, F. (1993). Toward a dynamical understanding of planetary-scale flow

regimes. Journal of Atmospheric Science, 50(12), 1792 - 1818.

Parker, D. E., Folland, C. K., and Jackson, M. (1995). Marine surface temperature: observed

variations and data requirements. Climatic Change, 31,559-600.

Pope, V. and Stratton, R. (2001). The processes governing horizontal resolution sensitivity in

a climate model. Submitted to Climate Dynamics.

Pope, V., Gallani, M., Rowntree, P., and Stratton, R. (2000). The impact of new physical

parameterisations in the Hadley Centre climate model: HadAM3. Climate Dynamics, 16,

123-146.

Rodwell, M. and Folland, C. (2001). Atlantic air-sea interaction and seasonal predictability.

Quarterly Journal of the Royal Meteorological Society, 127, 1-33.

Sardeshmuckh, P. and Hoskins, B. (1988). The generation of global rotational flow by steady

idealized tropical divergence. Journal of Atmospheric Science, 45(7), 1228-1251.

Spencer, H. and Slingo (2002). The simulation of the seasonal evolution of ENSO by an

atmospheric general circulation model, submitted to J. Clim.

Xie and Arkin (1996). Analyses of global monthly precipitation using gauge observations,

satellite estimates, and numerical model predictions. Journal of Climate, 9, 840 -858.

127
The Impact of Sub-seasonal Sea Surface

Temperature Variability on the

Simulation of the Seasonal Mean

Extra-tropical Circulation

Ben Kirtman

Center for Ocean-Land-Atmosphere Studies

The purpose of this study is to examine the impact of sub-seasonal sea surface temperature (SST)

variability on the simulation of the seasonal mean extra-tropical circulation using a state-of-theart high resolution atmospheric general circulation model (AGCM). The format is a case study

for January through March 1989 (JFM89) and the primary emphasis is on regional scales over

North America. The SST boundary conditions in the AGCM simulations were prescribed using

observed weekly data. Experiments were made in which the week-to-week (sub-seasonal) SST

variability was suppressed. In terms of the largest spatial scales, the sub-seasonal SST variability

has only a modest impact; however, statistically significant modifications to the 500 mb height

anomalies over North America were detected. Consistent with these changes in the height field,

the seasonal mean North American rainfall anomalies were particularly sensitive to the subseasonal SST variations, especially over the Pacific Northwest.

Two possible mechanisms for this sensitivity were investigated with additional AGCM

experiments and model diagnostics. The first mechanism, referred to as a "stochastic" effect, is

defined by the hypothesis that the week-to-week SST variability only serves to enhance the

amplitude of tropical precipitation variability, which, in tum, modifies the mid-latitude response.

With this "stochastic" effect, the details of the sub-seasonal SST evolution do not matter. In

contrast, the second mechanism is a "deterministic" effect in that the details of the evolution of

the sub-seasonal SST matter. The experiments presented here indicate that the "stochastic"

effect is small and that the details of the sub-seasonal SST produce significant differences. This

conclusion is supported by experiments with very large ensembles using a somewhat lower

resolution AGCM and a non-linear barotropic model. Finally, some implications of these results

for real time forecasting are discussed.

128
Simulation of Time Mean Tropical

Circulation and Its Variations in AMIP

Runs Using Different Versions of the

Operational NCEP Global Model

Suranjana Saha

Environmental Modeling Center (EMC)

National Centers for Environmental Prediction (NCEP)

Washington, D.C.

Suranj ana.Saha@noaa.gov

A primary reason to believe there could be predictability beyond week-2 would be sldll in

forecasting organized transient tropical convection, such as the MJO which has time scales of

20-70 days. Assuming that the forecast should be made by a model (GCM), it has been a

perennial frustration to modelers that GCM's do not simulate, let alone predict, phenomena like

the MJO very well at long leads. At NCEP we are revisiting this topic anew, and have made

many 1 year (2001) AMIP style model integrations. Three different horizontal resolutions (T62,

T126 and T170), as well as three different vertical resolutions (L28, L42 and L64) have been

combined with two different convective parameterization schemes (SAS and RAS). In all 9 such

runs have been made. All experiments start on 15 Dec 2000 and end on 31 Dec 2001 ... 382 days.

The timeseries for most fields have been saved for 0Z, 6Z, 12Z and 18Z....total of 382 x 4 =1528

time levels. Monthly means for all diabatic heating components as well as other important 3_D

quantities have been saved and is available to the community. For a reality check we used three

operational NCEP analyses for the same 1 year period, specifically the high resolution T170L42

GDAS, the low resolution T62L28 GDAS, and the T62L28 CDAS.

The question is how well do we simulate upper level divergence or tropical velocity potential

anomalies and how do they depend on the convection scheme, horizontal or vertical resolution.

Fortunately, the year 2001 had strong MJO activity. Leaving aside questions of short range

forecast skill (first 2 weeks), we studied the amplitude of the anomalies, and the phase speed or

period in the extended integration. We found that most model versions had an acceptable

strength of velocity potential anomalies, but the westerly phase speed of the MJO appear to be

too low in nearly all model versions, except in a few. Study of the diabatic heating fields has yet

to establish whether the results are merely fortuitous, or correct for the right reason.

129
An Empirical Estimate of

Subseasonal Predictability

Matt Newman

NOAA-CIRES Climate Diagnostics Center

Boulder, Colorado

Linear inverse models (LIMs) suitable for studies of atmospheric extratropical variability and

predictability on longer than weekly time scales have been constructed for each season of the

year, using atmospheric observations of the past 30 years. Notably, these empirical-dynamical

models include tropical diabatic heating as a predicted model variable rather than as a forcing,

and also include, in effect, the feedback of the extratropical weather systems on the more slowly

varying circulation. The models are capable of reproducing both lagged covariance statistics

from independent data and the development of individual streamfunction and tropical heating

anomalies. In fact, week 2 predictions by the models have sldll comparable to that of the NCEP

MRF ensemble mean forecasts (forecasts are available at http://www.cdc.noaa.gov/lim).

Comparison to a 20-year dataset of"reforecasts" using the early 1998 operational version of the

MRF further shows that the LIM has notably higher week 3 sldll year-round, and may even have

higher week 2 sldll during spring. The LIM also has much greater sldll than the MRF for

forecasts of tropical diabatic heating.

Theoretical predictability limits derived for the LIM suggest that the model has useful mean

forecast skill at forecast lead times of between three and five weeks, depending upon both season

and geographical region. Some initial atmospheric states which result in strong deterministic

growth are associated with greater predictability because of a relatively high signal to noise ratio,

although these states too have strong seasonal dependence. Our analysis further suggests that

without inclusion of tropical heating, weekly averages may be predictable between about 1-2

weeks in the extratropics, but with tropical heating included, they may be predictable as far as 5-

7 weeks ahead.

Sensitivity of streamfunction anomaly growth to both the strength and location of tropical

diabatic heating anomalies is shown to shift from the central Pacific in winter to the West Pacific

and Indian oceans in summer. Such optimal anomaly growth is related not only to ENSO but

also to tropical intraseasonal variability. These results also have important implications for the

development of persistent anomalies, such as North American heat waves and droughts.

130
Role of Satellite Rainfall Information in Improving

Understanding of the Dynamical Link Between the Tropics and Extratropoics

Prospects of Improved Forecasts of Weather and Short-Term Climate Variability

on Sub-Seasonal Time Scales

Arthux Y. Hou

Data Assimilation Office

NASA Goddard Space Flight Center, Greenbelt, MD USA 20771

E-mail: arthur.hou@gsfc.nasa.gov

Abstract

The tropics and extratropics are two dynamically distinct regimes. The coupling between

these two regimes often defies simple analytical treatment. Progress in understanding of the

dynamical interaction between the tropics and extratropics relies on better observational

descriptions to guide theoretical development. However, global analyses currently contain

significant errors in primary hydrological variables such as precipitation, evaporation, moistuxe,

and clouds, especially in the tropics. Tropical analyses have been shown to be sensitive to

parameterized precipitation processes, which are less than perfect, leading to order-one

discrepancies between estimates produced by different data assimilation systems.

One strategy for improvement is to assimilate rainfall observations to constrain the

analysis and reduce uncertainties in variables physically linked to precipitation. At the Data

Assimilation Office at the NASA Goddard Space Flight Center, we have been exploring the use

of tropical rain rates derived from the TRMM Microwave Imager (TMI) and the Special Sensor

Microwave/Imager (SSM/I) instruments in global data assimilation. Results show that

assimilating these data improves not only rainfall and moisture fields but also related climate

parameters such as clouds and radiation (Fig. 1), as well as the large-scale circulation and shortrange forecasts (Fig. 2). These studies suggest that assimilation of microwave rainfall

observations from space has the potential to significantly improve the quality of 4-D assimilated

datasets for climate investigations (Hou et al. 2001).

In the next few years, there will be a gradual increase in microwave rain products

available from operational and research satellites, culminating to a target constellation of 9

satellites to provide global rain measurements every 3 hours with the proposed Global

Precipitation Measurement (GPM) mission in 2007. Continued improvements in assimilation

methodology, rainfall error estimates, and model parameterizations are needed to ensure that we

derive maximum benefits from these observations.

References

Hou, A. Y., S. Zhang, A. da Silva, W. Olson, C. Kuxnmerow, J. Simpson, 2001: Improving global

analysis and short-range forecast using rainfall and moisture observations derived from TRMM

and SSM/I passive microwave instruments. Bulletin of Amer. Meteor. Soc., 82,659-679.

131
Figure1.GEOSassimilationresultswithandwithoutTMIandSSM/IobservationsforJanuary1998.Left

panelshowerrorsinthemonthly-meantropicalprecipitationfieldsverifiedagainstGPCPcombined

Satellite-gauge estimate:TopisthedifferencebetweentheGEOScontrol(withoutrainfallandTPWdata)

andGPCP.BottomisthecorrespondingerrorinGEOSassimilationwithrainfallandTPWdata.Right

panelshowtheimpactontheoutgoinglongwaveradiation(OLR)verifiedagainstCIRES/TRMM

measurements. Percentagechangesinthetropical-meanerrorstandarddeviationrelativetotheGEOS

controlaregiveninparentheses.

Figure2. ImprovedstormtrackforecastsandQPFEquitableThreatScoresforHurricaneBonnie.Theleft

panelshowsthatthe5-daystormtrackforecastinitializedwith1°x 1° GEOSanalysiscontainingTMIand

SSM/Irainfalldata(blue)is in closeagreement withthebestrackanalysisfromNOAA.Thetrackfrom

thecontrolexperiment is shownin green.Theforecastsareinitializedat 12:00on20August1998.The

rightpanelshowstheconsistentlyhigherEquitableThreatScoresfor Day3 precipitationforecast(red)

initializedbytheanalysiswithrainfalldata.Resultsforthecontrolexperiment areshowninblue.A higher

ThreatScorecorrespondstogreaterforecastskills.

132
Session V

Role of Land Surface Processes

133
Predictability Associated with Land Surface Moisture States -

Studies with the NSIPP System

R. D. Koster and M. J. Suarez

1. Introduction

Hydrologists have long speculated that soil moisture information can be used to increase sldll in

monthly to seasonal forecast systems. For this to be true, though, three conditions must be

satisfied: (1) an imposed initial soil moisture anomaly in the forecast system must have some

memory, so that it persists into the forecast period; (2) the modeled atmosphere must respond in

a predictable way to the persisted anomaly; and (3) the forecast model must correctly represent

both the soil moistuxe memory and the atmospheric response as they occur in natuxe. In this

short paper, we review some recent work at NSIPP (NASA Seasonal-to-Interannual Prediction

Project) that addresses all three conditions.

2. Analysis of Requirement 1: Soil Moistuxe Memory

Koster and Suarez (2001) manipulated the water balance equation into an equation that relates

the autocorrelation of soil moisture - soil moistuxe memory - to four separate controls: (1) a

restoring force due to evaporation's sensitivity to soil moisture, (2) a restoring force due to

runoffs sensitivity to soil moistuxe, (3) seasonal variability in climatic forcing, and (4) any

correlation that may exist (through feedback or otherwise) between soil moisture at the

beginning of the time period and subsequent forcing. The equation was found to reproduce well

the autocorrelations that were actually simulated by the GCM. Thus, it can be used to analyze

the controls over soil moisture memory at any given grid cell. Koster and Suarez (2001) found,

for example, that the mJaoff sensitivity provides the main control over soil moistuxe memory in

the eastem U.S., whereas the evaporation sensitivity provides the main control in the west. In

the central U.S., the evaporation effect is counteracted somewhat by a nonzero soilmoistuxe/forcing correlation.

3. Analysis of Requirement 2: Atmospheric Response to Land Moisture Anomalies

The response of the atmosphere to soil moistuxe anomalies was investigated by Koster et al.

(2000) with two parallel 16-member ensembles of 45-year simulations. In the first ensemble, the

land surface was allowed to evolve freely with the atmosphere. In the second, it was not -

instead, an interannually-varymg time series of evaporation efficiency (as derived from one of

the simulations in the first ensemble) was prescribed at each grid cell. Thus, every member of

the second ensemble was effectively given the same time series of land surface moistuxe

boundary conditions. Also, interannually-varying SSTs were prescribed in both ensembles.

Thus, the first ensemble addresses the question, "What is the impact of a perfect knowledge

(now and into the future) of SSTs on precipitation", whereas the second addresses the question,

"What is the impact of a perfect knowledge (now and into the futuxe) of both SSTs and soil

moisture on precipitation?"

By determining the degree to which the precipitation amounts within each ensemble agree with

each other, we obtain a quantitative estimate of the impact of SSTs and soil moistuxe on

precipitation. This is illustrated in Figuxe 1, which shows, for North America, an index that

135
effectivelydescribesthefractionofregion'sprecipitationvariancethatisexplainedbythenoted

boundarycondition.

GivenPredictability ofSSTsandLandMoisture

_::::::::::::::::::: o_

Figure 1. (left) Index of precipitation predictability given predictability of SSTs. (right) Index of

precipitation predictability given predictability of SSTs and land moisture.

The figure shows, in essence, that foreknowledge of soil moisture could have a tremendous

impact on our ability to predict precipitation in a swath that extends from the Gulf of Mexico to

the northeast United States.

4. Combined Study: Impact of Land Moisture Initialization in the NSIPP system

In a recently submitted paper, Koster and Suarez (2002) describe a series of numerical

experiments that effectively combine the two requirements examined above. The experiments

consist of ensembles of 3-month precipitation forecasts that utilize "realistic" initial soil moisture

conditions, as obtained through the previous application of observed daily precipitation to the

GCM's land surface. The GCM's land and atmosphere are allowed to evolve together during

each forecast period. Thus, for the forecasted precipitation to be influenced by the initialization,

soil moisture memory must be significant and the atmosphere must be responsive to soil

moisture anomalies.

The index plotted in Figure 2 (top) shows where soil moisture initialization had a significant

impact on forecasted JJA precipitation at least once during the 5 boreal summers of study. The

salient feature of the plot is the relatively small influence the initialization had over much of the

globe. The influence is essentially limited to swaths in central North America, below the

Amazon, in central Africa, and in western Asia. The bottom plot of Figure 2 shows where we

might predict a significant impact based on: (1) a significant degree ofinterannual soil moisture

variability on day 1, as measured by the standard deviation of initial soil moisture (in essence, it

turns out, a surrogate for soil moisture memory); (2) an adequate sensitivity of evaporation to

soil moisture, as measured by the product of the mean net radiation and the fitted slope of the

evaporation ratio - soil moisture relationship; and (3) an adequate sensitivity of precipitation to

evaporation, as measured by the convective fraction. The predicted locations agree well with the

actual locations of impact, suggesting that we have correctly identified the controls of relevance.

136
PRODUCT OF THREE FACTORS

,.o,, 2. i

90S

'f 80 120W _OW 0 60E _20E ! 80

Figure 2. (top) Indication of where soil moisture initialization had some impact on forecasted precipitation

during 5 years of experiment. (bottom) Indication of where the three factors mentioned in the text are all

simultaneously large.

5. Analysis of Requirement 3: Realism of Model Results

Unformanately, global observations of soil moisture and/or land-atmosphere feedback are nonexistent, so we cannot verify many of our model results. The best we can do is demonstrate

indirectly that they are consistent with available observations.

A signature of land-atmosphere feedback over the United States was sought in the 50-year

"Unified Precipitation Database" generated by NCEP (Wayne Higgins, pers. comm.).

Specifically, the data were analyzed to determine if a band of high precipitation autocorrelation

(between pentads at lag 2) appears down the center of the U.S., as it does in the GCM due to

feedback. The observations do indeed show this band in July, when it is also strongest in the

GCM. This result is not conclusive, however, since the band of nonzero autocorrelations may

also reflect monsoon dynamics, long-term precipitation trends, or other external influences. In

any case, the magnitudes of the autocorrelations are much reduced in the observational data,

suggesting that the GCM overestimates the feedback.

In one recent study of model dependence (Koster et al., 2002), four different GCMs performed

the same highly controlled numerical experiment. The experiment was specifically designed to

isolate the strength of land-atmosphere coupling in each host model. The variations between the

models were surprisingly large, with the NSIPP model showing the largest coupling strength.

Due to the non-existence of relevant observations, the models could not be ranked in terms of

realism. Rather, the experiment served to showcase the uncertainty with which modelers

understand the coupling.

137
6. Summary

TheNSIPPcoupledland-atmosphere modelingsystemhasbeenusedtoinvestigatemanyaspects

ofland-atmosphere interaction.Theoverarchinggoalistodeterminethevalueofsoilmoisture

initializationforlong-termforecasts.

7. References

Koster,R.D.,M. J.Suarez,andM. Heiser,2000:Varianceandpredictabilityof precipitationat

seasonal-to-interannual timescales. J. Hydrometeorology, 1, 26-46.

Koster, R. D. and M. J. Suarez, 2001: Soil moisture memory in climate models. J.

Hydrometeorology, 2,558-570.

Koster, R. D., P. A. Dirmeyer, A. N. Hahmann, R. Ijpelaar, L. Tyahla, P. Cox, and M. J. Suarez,

2002: Comparing the degree of land-atmosphere interaction in four atmospheric general

circulation models. J. Hydrometeorology, 3,363-375.

Koster, R. D. and M. J. Suarez, 2001: Impact of soil moisture initialization on seasonal weather

prediction, submitted to J. Hydrometeorology.

138
The Predictability of Soil Moisture and Near Surface Temperature

Based on the Hindcasts of

NCEP Seasonal Forecast Model

By

Masao Kanamitsu (CRD/SIO, University of California, San Diego)

Cheng-Hsuan Lu (EMC/NCEP/NOAA)

Wesley Ebisuzaki (CPC/NCEP/NOAA) and

Jae Schemm (CPC/NCVEP/NOAA)

(Accepted in Journal of Climate after minor revision, Aug. 2002)

Using the NCEP/DOE Reanalysis soil moisture analysis and the NCEP Seasonal Forecast

System, the seasonal predictability of the soil moisture, and near surface temperature over the

globe were examined. It is shown that the NCEP/DOE Reanalysis soil moisture is reasonably

good over the United States compared to an independent soil moisture observation. This means

that the cuxrent experiments are conducted under more realistic conditions in contrast to modelbased studies performed in the past.

Two sets of forecasts were made, one starting from climatological soil moisture as initial

condition and the other starting from NCEP/DOE Reanalysis soil moisture. Each set is made of

10-member ensemble, 7-month integrations, 16 early Aprils as initial conditions for the 1979-

1996 period.

The predictability of the soil moisture was examined over several regions over the globe with

different climatological characteristics (Fig. 1), and the results are presented in Fig.2. It is found

that the prediction sldll of soil moisture is very high over regions where there is little or less

precipitation. Over these areas, the soil moisture evolution is largely determined by the

evaporation process, and the model does an excellent job in predicting this process. In many of

these relatively dry or low precipitation areas, model prediction surpasses the anomaly

persistency forecast. Over temperate zones with more precipitation, and over tropical monsoon

regions, the prediction sldll of the soil moisture drops quickly in the first 3-4 months. Over these

regions, the soil moisture evolves following the Markov process in which precipitation acts as a

white noise forcing and the soil moisture decays exponentially. The time scale of this Markov

process computed as a ratio between precipitation and potential evaporation showed that the time

scale is particularly short over the monsoon regions and some temperate zones. Over all the

regions noted above, the forecast starting from climatological soil moisture is very poor,

indicating that the soil moisture anomalies cannot be generated by model precipitation or by the

model evaporation. From a practical point of view, the soil moisture initial condition is essential

in malting seasonal prediction over these areas. In contrast, the characteristics are very different

over tropical South America. The forecast starting from climatological soil moisture quickly

catches up to the forecast sldll of initial soil moisture as well as the anomaly persistency.

Particularly in the Northeast region of South America, the anomaly persistency becomes the

worst forecast after 4 months. Over these areas, SST forcing determines the precipitation

anomaly over land, and subsequently forces the soil moisture evolution. The effect of initial soil

139
moistureinformationtendsto disappearafter3-4monthsovertheseareas.

Thenearsurfacetemperatureanomalyforecastiscloselyrelatedtothesoilmoistureanomaly

forecast.Thecorrelation,however,isnotperfect.Thusthenearsurfacetemperatureforecast

sldllislowerthanthesoilmoistureforecastskill. Ouranalysishowsthattheskillof

predictinglatentheatfluxislowerthanthatofthesoilmoisture,andthatof thesurface

temperatureisevenlower.Thisisbecauseadditionalphysicalprocesses,uchascloudinessand

nearsurfaceatmosphericconditionsarealsoinvolvedindeterminingthenearsurfaceair

temperature.

Theabundanceof nearsurfacetemperatureobservationallowsustoverifyourhindeastswith

independent datathatarenotusedintheNCEP/DOEReanalysis.Theverificationof

temperaturemadeagainstCD433dataclearlyindicatedthattheuseoftheNCEP/DOEreanalysis

soilmoistureinitialconditiontrulyimprovedtheforecasts.

Forbetterpredictionof soilmoistureandnearsurfacetemperatureduringsummer,it is

importantthatthesoilmoistureinitialconditionisaccuratelyspecifiedandthemodelproduces

betterprecipitationforecast.Unformanately, thesetworequirementsareverydifficulttosatisfy,

andfurtheradvancesin observational andmodelingstudiesareneeded.Independent measureof

thesoilmoistureisalsoextremelyimportanttoeliminateanymodeldependent result. Inthis

regard,satelliteobservationthatprovidesoilmoisturemeasurement isbecomingavailable,and

theuseof suchdataisstronglyencouraged.

BON15 Study Region

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140
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Anomaly pattern correlation in abscissa and lead-time (month) in ordinate. The first month score

is not plotted. Forecast from climatological soil moisture (dark solid), forecast from analysis soil

moisture (light solid) and anomaly persistency (dashed) are plotted.

141
The Impact of Land Initialization

and Assimilation on

Climate Predictability and Prediction

C. Adam Schlosser (GEST/UBMC/NASA),

P. C. D. Milly (USGS/GFDL),

Paul A. Dirmeyer (COLA),

and David Mocko (SAIC/GSC)

Analysis will be presented which explores the impact of land conditions on monthly to seasonal

climate simulations in a variety of atmospheric general circulation models (AGCMs). In one set

of experiments, the Geophysical Fluid Dynamics Laboratory (GDFL) AGCM is used to explore

the nature of soil-moisture predictability and associated climate predictability as an initial value

problem. For another set of experiments, the Center for Ocean Land Atmosphere (COLA) and

the Goddard Earth Observing System 2 (GEOS-2) AGCMs are used to investigate the impact of

realistic snow initialization and assimilation in retrospective climate forecasts for the northem

hemisphere spring (March-June).

In the GFDL AGCM experiments, soil-moisture predictability can be characterized by a firstorder Markov process, and thus directly linked to soil-moisture persistence. As such

predictability timescales of soil moisture are found to range from as little as a week (mid-latitude

summer, tropics, and subtropics) to 2-6 months (middle to high latitude fall and winter). The

degree of translation of soil-moisture predictability to atmospheric predictability is characterized

by the ratio of the fraction of explained variance of an atmospheric variable to that of soil

moisture. By this measure, high associated predictability (ratio greater than 0.5) of near- suxface

air temperature is found in the tropics, subtropics, and mid-latitude summers. In contrast, no

widespread, appreciable associated precipitation predictability is found. Other recent climatemodel experiments (e.g. Koster et al. 2001) that quantify the land's impact on precipitation

suggest that a significant range in the magnitude of the simulated precipitation response exists.

This poses a compelling task to quantify what the true magnitude of atmospheric response to

land conditions is in nature, and how this will guide future activities in land-data assimilation and

climate studies/prediction.

The diagnosis of the COLA AGCM snow initialization/assimilation experiments suggest that the

most notable and widespread impact (focusing on near-surface air temperature sldll) occurs

during the month of April, and a weak impact is seen in the remaining months of the simulations

(i.e. March, May, and June). The result is consistent with the intuitive expectation that (realistic)

snow-cover anomalies will support strong (and sldllful) temperature responses when coincident

with relatively high incident solar radiation (i.e., when the impact of albedo modulation from the

snow anomalies would be maximal). Results from the GEOS-2 AGCM, using a similar land

surface model as in the COLA AGCM but with an improved snow-physics scheme, will also be

analyzed and compared to the COLA AGCM results. The strength of the two AGCMs'

responses to the snow assimilation are assessed accordingly against the errors of simulated snow

in the control runs, as well as the representation of snow processes (and their functional link to

the surface energy budget) between the AGCMs.

142
Three Modes of Climate Drift in Coupled

Land-Atmosphere Systems

Paul A. Dirmeyer

Center for Ocean-Land-Atmosphere Studies

Three different modes of climate drift have been identified in a coupled land-atmosphere climate

model. The fastest growing error modes are those associated with errors in the atmospheric

initial conditions. The signatuxe of these modes are evident as drift in the time series of land

surface state variables. Second is the climate drift associated with systematic errors on the scale

from weeks to seasons. This drift is dominated by the error modes with the largest satuxation

amplitude. This drift tends to erase the "information" in the anomalies in the land surface initial

conditions, that would be expected to provide added predictability to medium-range to seasonal

forecasts. Third, there is drift due to the coupled error mode, where systematic errors in the

atmospheric physics can push the land surface state beyond its "observed" range or variability

over seasons-years. This mode is evident in the clear stratification of sldll (both in simulation of

mean states, and prediction of anomalies) between DSP-type experiments (1-4 month

simulations), and corresponding AMIP or C20C experiments (multi-decadal simulations). The

coupled drift mode is relatively weak during winter, but evident during all other seasons.

Examples of the three ldnds of drift will be shown in global climate simulations, and

methods for ameliorating these types of drift will be discussed.

143
GCM Simulation of the Large-scale North American Monsoon Including

Water Vapor Tracer Diagnostics

Michael G. Bosilovich, Siegfried D. Schubert,

Yogesh Sud and Gregory K. Walker

NASA/Data Assimilation Office and Climate

and Radiation Branch

Goddard Space Flight Center

Greenbelt MD

Corresponding Email: mikeb@dao.gsfc.nasa.gov

Introduction

In this study, we have applied GCM water vapor tracers (WVT) to simulate the North American

water cycle. WVTs allow quantitative computation of the geographical source of water for

precipitation that occurs anywhere in the model simulation. This can be used to isolate the

impact that local surface evaporation has on precipitation, compared to advection and

convection.

A 15 year 1°? 1.25 ° simulation has been performed with 11 global and 11 North American

regional WVTs. Figure 1 shows the source regions of the North American WVTs. When water

evaporates from one of these predefined regions, its mass is used as the source for a distinct

prognostic variable in the model. This prognostic variable allows the water to be transported and

removed (precipitated) from the system in an identical way that occurs to the prognostic specific

humidity. Details of the model are outlined by Bosilovich and Schubert (2002) and Bosilovich

(2002). Here, we present results pertaining to the onset of the simulated North American

monsoon.

Model and Methodology

The model used is the Finite Volume General Circulation Model (FVGCM) developed at

NASA/DAO. The dynamical core is the Lin-Rood semi-Lagrangian dynamics (Lin and Rood,

1996). This dynamical core conserves mass and has been shown to be very good at handling

atmospheric tracers. The physical parameterizations are taken from the NCAR Community

Climate Model version 3.

Reynolds SST observation data set provides the surface boundary conditions over the oceans.

Land data are derived from NCAR data sets. The land model includes prognostic soil

temperature and moisture, as well as vegetation temperatuxe, which determines land evaporation.

The model was initialized in January 1982, and allowed to spin up for 4 years. WVTs were

initialized at zero. Here we analyze the last 15 years (1986 - 2000).

Generally, the model simulates the dynamical circulation satisfactorily (S.-J. Lin, Personal

Communication, figuxes not shown for brevity). The atmospheric moistuxe over North America

is generally well represented, though the model may be drier than NVAP over the western third

of the United States and also Mexico (Figure 2). The model does seem to produce too much

mean summertime precipitation over land, including the maximum that extends too far to the

western states (Figure 2).

144
NA Monsoon Onset

The onset &monsoon precipitation is generally characterized by a decrease in precipitation in

Texas and increase in precipitation in Mexico (Barlow et al. 1998). Figure 3 shows the CPC

precipitation difference of July minus June averaged from 1986-1998. The model simulated

precipitation shows a signature comparable to the observations. This gives us confidence that the

model is able to simulate the large-scale dynamics associated with the monsoon. Note that the

model does not resolve the Gulf of California very well, which likely affects the models ability

to initiate the monsoon in Arizona. Further, the model evaporation differences seems to show

some correlation to the precipitation differences over land, but there seems to be little correlation

over the Gulf of Mexico and tropical Atlantic Ocean. It appears that the increase of surface

evaporation over the oceanic regions is being transported to the continent.

We next evaluate the geographical sources (Figure 1) of water contributing to the precipitation.

Figure 4 shows the percent contribution from each North American source region to precipitation

in Texas and western Mexico. June to July shows a jump in the fraction of water supplied from

the Gulf of Mexico to Texas, while the total precipitation is decreasing. Corresponding to the

decrease in total Texas precipitation is the decrease in local continental sources of water (MX

and SP). This is related to the decrease in land evaporation. The increase of the fraction of

precipitation from tropical Atlantic Ocean is likely related to the monsoon, and is certainly

important, but it seems to be part of the large-scale evolution of the annual cycle. In western

Mexico, the increase in July precipitation is related to a shift in the circulation, as Easterlies from

the tropical Atlantic replace the contribution from the Pacific, especially the region named Baja

Oceanic. One cannot overlook the contribution of water from continental Mexico. The fraction

of precipitation from MX (figure 1) is the dominant source in May-July in western Mexico.

Summary and conclusions

There certainly appears to be a connection between the surface evaporation and precipitation

over land. WVTs have helped to quantify this connection. Since the ratios of continental

precipitation drop over land along with the precipitation, we expect that the decrease of surface

evaporation is driving the decrease in precipitation in Texas. The surface evaporation drops,

which reduces the water available for convection and decreases the convective instability of the

region. Pentad and daily time series are being analyzed to verify this statement.

References

Barlow, M., S. Nigam and E. H. Berbery, 1998: Evolution of the North American monsoon

system. J. Climate, 11, 2238 - 2257.

Bosilovich M. G. and S. D. Schubert, 2002: Water vapor tracers as diagnostics of the regional

hydrologic cycle, J. Hydromet., 3, 149-165.

Bosilovich, M. G., 2002: On the Vertical Distribution of Local and Remote Sources of Water for

Precipitation. Accepted to Meteorology and Atmospheric Physics.

Lin, S.-J., and R. B. Rood, 1996: Multidimensional flux form semi-lagrangian transport schemes.

Mon. Wea. Rev., 124, 2046 - 2070.

145
Figures

North American Source Regions

Figure 1 Geographical source regions of water vapor tracers in the model simulation. SE - Southeast; SP

- Southern Plains; SW - Southwest; NW - Northwest; NP - Northern Plains; NE - Northeast; CA -

Canada; MX - Mexico; Nat - North Atlantic; Tat - Tropical Atlantic; CB - Caribbean; GM - Gulf of

Mexico; BO - Baja Oceanic and NP - North Pacific. (Global tracer sources not shown).

146
FVGCM TPW

NVAPTPW

Figure 2 Model validations of precipitation and TPW. Gauge precipitation is averaged over JJA 1986 -

1998 (Wayne Higgins data set, there is no data in Canada or over the ocean). TPW is averaged over the

NVAP period (1988-1994). Contours of respective fields are the same, and precipitation greater than 3

mm day -1 are shaded.

147
_g E Ju_y _iaus Juae {11_I,--I_9_}

i ................. ":. .,_OiI::.,',-C_,,:._' _;_;".,_-----_ ........ ,,

Figure 3 Average difference between June and July

model precipitation, gauge precipitation and model

evaporation (units mm day-1). Positive differences

are solid contours negative are dashed. Note the

increase of modeled evaporation over the Gulf of

Mexico, while the precipitation there decreases.

TX Percent sources

35 8

_20 N 2

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3O

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Figure 4 The mean annual cycle of the percent of precipitation that originated as evaporation from key

source regions (Figure 1) for Texas (TX) and western Mexico (MX) precipitation. The bar charts show

the July minus June difference of the percentages.

148
Session VI

Link Between Low Frequency

and Weather/Regional Phenomena

149
Storm Track Prediction

Jeffrey S. Whitaker and Thomas M. Hamill

NOAA Climate Diagnostics Center

The transition between a 'climate' and a 'weather' forecast can be defined to occur at the

point in the forecast when individual synoptic-scale, 'weather' producing systems can no longer

be sldllfully predicted. At this point, one must begin issuing forecasts of the statistics of

synoptic-scale storms, i.e. the storm tracks, instead of forecasts of the synoptic-scale storms

themselves. Storm tracks are predictable as long as the large-scale, low-frequency flow

modulates the storm tracks and remains predictable.

In this study we have utilized an "MRF reforecast datasef'

(h_:/,_y,,'w.cdconoaao_ov/-isw/re_s0, consisting of 23 years of forecasts with a recent version

of NCEP's medium range forecast model, to assess the predictability of storm tracks out to week

three. Coherent, predictiable modes of storm track variability are identified, and used as a basis

for for producing calibrated, quantitative precipitation probability forecasts (QPPF) in week two.

Storm tracks in the MRF reforecast dataset are defined in terms of accumulated

precipitation, which in wintertime is primarily produced by synoptic-scale weather systems.

Predictable storm track patterns are identified via

a canonical correlation (CCA) analysis that uses

model forecast precipitation as a predictor, and

analyzed precipitation from the NCEP/NCAR

reanalysis as a predictand. Figure 1 shows the

most predictable DJF storm track pattern for (A)

a day 10 forecast, (B) a week two forecast, and

(C) an average over the first three weeks of the

forecast.

The most predictable storm track pattern

is an eastward shift of the climatological Pacific

storm track for all three forecast ranges. This

shift is associated with a "PNA-like" mode

of variability of the tropospheric circulation.

However, a similar analysis (not shown) that keys

on the most predictable 500 mb height pattern

identifies the classic PNA pattern as most predictable

That pattern is slightly shifted to the north and east

of the pattern shown in Fig. 1, and hence does not as

effectively modulate the Pacific storm track.

Correlations between predicted and

observed precipitation increase with increasing

averaging time (Fig. 1), although the predictability

of the associated 500 mb heights does not (not

shown). This indicates that the predictability of

storm- tracks inherently depends upon the

averaging interval. The larger the separation

between the averaging interval and the time-scale

Figure 1: Climatological storm track (thick

solid), 500 mb height regression (thin solid)

and correlation between the CCA predictor

index and analyzed precipitation for the most

predictable storm track pattern in (A) 10 day

forecasts, (B) week two forecasts and (C) an

average over the first three weeks of the

forecast.

151
of the synoptic-scalestormsthatmakeup the stormtrack,the higherthepredictability.In the

limit thattheaveragingintervalgoesto zero,(Fig.la) the"stormtrack"consistsof a snapshotof

aninvidualstorm,andpredictabilityis expectedonly outto theboundarybetweena weatherand

a climateforecast(by our definition). Conversely,as the averagingintervalbecomesmuch

greaterthanthelifetimeof an individualstorm,thepredictabilityis determinedby theunderlying

statistical/dynamical relationshipsbetweenthelarge-scaleflowandthestormtracks.

An experimental real-time15memberensemblehasbeenrunatCDCsinceDecember

2001usingthesamemodelusedtogeneratethe"reforecast"dataset.Thestatisticsof forecast

C.

D

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: i

o!

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.k  ̧ .. kk% ?b. S ; ..kC/i; ;

Figure 2: Reliability diagrams for experimental week two tercile probability forecasts for

precipitation, derived from a 15 member ensemble for the 90 days starting on Dec 1, 2001.

(A) is for probabilities derived from the raw ensemble (by counting the number of members

that fall in each tercile, and (B) for probabilities derived using a statistical correction to the

first and second moments of the forecast ensemble.

errors derived from this dataset allow for corrections to the first and second moments of the realtime ensemble. These corrections are essential for creating useful QPPF forecasts in week two

that take advantage of the predictable storm track modes we have identified. Figure 2 shows

reliability diagrams for tercile probability forecasts for week two precipitation over the PNA

region computed with and without our statistical correction technique. Without corrections to

the first and second moments the forecasts have virtually no reliability, and the rankedprobability skill score (RPSS) is actually worse than that of a climatological forecast. With the

corrections derived from the 23-year history of forecast errors, the forecasts are very reliable, and

the RPSS indicates that the errors are about 16% better than those of a climatological forecast. A

more detailed description of the algorithm for generating the calibrated probabilities is available

on our website (http://www.cdc.noaa.gov/--jsw/refcst). We expect that more sophisticated

procedures can be used when our ultimate goal of an full 15-member 23 year ensemble

reforecast dataset is achieved.

152
Extreme Weather Events and Their Relationship to Low Frequency

Teleconnection Patterns

Yehui Chang: GEST, NASA/GSFC, _;dao.,_sfcor_aSao_

Siegfried Schubert: NASA/GSFC, schubert_sfc.nasa.g#.2'

Introduction

A new method for identifying the structure and other characteristics of extreme weather events is

introduced and applied to both model simulations and observations. The approach is based on a

linear regression model that links daily extreme precipitation amounts for a particular point on

the globe to precipitation and related quantities at all other points. We present here some initial

results of our analysis of extreme precipitation events over the United States, including how they

are influenced by ENSO and various large-scale teleconnection patterns such as the PNA.

The results are based on simulations made with the NASA/NCAR AGCM (Lin and Rood 1996).

The quality of the simulated climate for the NASA/NCAR AGCM forced with observed SSTs is

described in Chang et al. (2001). The runs analyzed here consist of three 20-year runs forced

with idealized cold, neutral and warm ENSO SST anomalies (superimposed on the mean

seasonal cycle of SST). The idealized warm or cold SST anomalies are fixed throughout each 20-

year simulation and consist of the first EOF (+/- 3 standard deviations) of monthly SST data.

Comparisons are made with the results obtained from a similar analysis that uses daily NOAA

precipitation observations (Higgins et al. 1996) over the United States and NCEP/NCAR

reanalysis data (Kalnay et al. 1996) for the period 1949-1998.

Method

We consider the simple linear regression model in which a variable Yis regressed against the

daily precipitation extremes, X, at a base point o. The regression model has the form

Y(j,k) = a X(o) + e,

wherej indicates the jth grid point, k is the time lag in days with respect to the time of the

maydmuxn in X(o), and e is the error in the regression model. The regression links the daily

extreme (maximum) precipitation for a particular point X(o) with precipitation or other related

quantities Yat all other points j, and time lags k. We carry out the analysis for each month of the

year separately (e.g. all Januaries, all Februaries, etc.), allowing us to examine the seasonal

dependence of the extremes. In the following, we show the composite average of Yover all

times whenX(o) is an extreme event (the average conditions that occur when X(o) is a

maximum). We also show the regression coefficient a. These show the co-variability, or

structure and time evolution of the extreme events.

Results

In the following we present some initial results of our analysis. Figure 1 shows, for example, a

composite of the precipitation (shading), 300mb heights (contours) and 850mb wind (vectors)

during extreme precipitation events for each month at the base grid point (77.5W, 40N). The

upper figure shows the results based on 36 years (1949-1998) of daily NOAA precipitation

observations and NCEP/NCAR reanalysis. The lower figure shows the results based on 60 years

153
(20cold,20warmand20neutralyears)ofNASA/NCARmodelsimulations.Theresultsshow

theexpectedlargeregionalandseasonal changesinthestructureandscalesof theextreme

precipitationevents,withcontinental-scales andstrongdynamicalcontrolsduringthecoldseason,

andhighlylocalizedeventsduringthewarmseason.TheNASA/NCARAGCMdoesremarkably

wellinreproducingthebasicstructuresof theextremevents.Figure2 showstheregression

coefficienta relating the precipitation extremes at the base point (77.5W,40N) to precipitation at

other grid points and at different time lags relative to the maximum. The results show coherence

in time with a sequence ofpattems that is consistent (during the cold season) with the

northeastward motion of cyclones along the East Coast.

In order to study the link between the extreme events and various low frequency modes of

atmospheric variability we first identify the leading pattems of monthly variability using a

rotated EOF analysis of the 300mb height. The top panel &Figure 3 shows, for example, the

nine leading monthly EOFs for DJF for the GCM simulations. The first EOF is the ENSO

response, the second resembles the Arctic Oscillation, while the third resembles the

Pacific/North American (PNA) pattern. The bottom panel of Figure 3 shows scatter plots of the

principal components of the monthly EOFs (here the daily height data have been projected onto

the monthly EOFs) versus the amplitude of the linear regression pattem associated with the

extreme precipitation events at base point (82W,27N). For the latter, all daily values of

precipitation (not just the times with extreme values) are projected onto the regression patterns.

The height pattem associated with the precipitation extremes at this point over Florida (not

shown) consists of a storm system that apparently develops in the Gulf and moves up the east

coast. The point was chosen because it illustrates a case where the extremes are strongly

impacted by ENSO. This is evident from the top left panel of the bottom panel of Figure 3

where we find that the warm ENSO events are associated with more large daily precipitation

amounts (positive values of PC 1 or red dots) compared with cold events (blue dots). We also

see some impact from the PNA mode (top right panel of the lower panel of Figuxe 3), in that

there is a tendency for the positive values of the PNA pattern to be associated with the largest

daily precipitation amounts.

References

Chang, Y., and coauthors, 2001: The climate of the FVCCM-3 model. Technical Report Series on

Global Modeling and Data Assimilation. NASA/TM-2001-104606, Vol. 20.

Higgins, R. W., J. E. Janowiak, and Y. Yao, 1996: A gridded hourly precipitation database for

the United States (1963-1993). NCEP/Climate Prediction Center Atlas 1, 47pp.

Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer.

Meteor. Soc., 77,437-471.

Lin, S.-J., and R_B. Rood, 1996: Multidimensional flux form semi-lagrangian transport schemes.

Mort. Wea. Rev., 124, 2046 - 2070.

154
Figure 1. A composite of the precipitation (shading), 300mb heights (contours) and 850mb wind (vectors) during

extreme precipitation events for each month at base grid point (77.5W, 40N). Upper figure: The results based on 36

years (1963-1998) of daily NOAA precipitation observations and NCEP/NCAR reanalysis. Lower figure: The

results based on 60 years of NASA/NCAR model simulations. Precipitation has units of mm/day. Height contours

are 20m. Colored contours are significant at the 5% level.

155
_oN " ]

...... __-._,,_

<. /77_ ;_i i,._ i:;:,i.:_7 ..

20_ ) " °

120_ I0_ 60_V 120W 90_ 60_ 12DW 90W 60W 120%V _OW 6DW

...::--:--=:::.::..: 17_._(_1) Z_y( O} 7:7:.W _ +_) D_,7(÷_)

Figure 2. The regression coefficient a relating the precipitation extremes at the base point (77.5W,40N) to

precipitation at other grid points and at different time lags relative to the maximum. Top panel is for the

observations and bottom panel is for the model. The colors indicate the lag in days.

156
5O

_o

..1.-

fvccrn: 300rnb Height w Precipitution (DJF) et (82W,27N)

• 2x:'3.... , .. , .

1 _0

50

_,o

32

1 [}0

I O0

, _&._;!.:. "-: .

::,:_#. :._ :_!_S!'t:_':._... ";

t,_ __'__ . "?: @40_.z.......

-40 -2° 0 20 40 60 BO 100 -20 0 20 40 60 60 100

Precipitetion Precipitation

,.i:i__ii_.:: _:/

[ ?2-....

•,,.O_,_ _i_:;.,, : ,;

-2° 0 20 40 60 50 1 O0

Precipitation

Figure 3. Upper panel: The nine leading EOFs of the monthly mean 300mb height during DJF from the 60 years of

GCM simulations with idealized (20 warm, 20 neutral and 20 cold years) ENSO SST forcing. Lower panel: Scatter

plots of the daily amplitude of the linear regression model based on extreme precipitation at grid point (82W,27N)

versus the daily values of the principal component of the nine leading monthly EOFs of 300mb height shown above.

The red, green and blue colors show the warm, neutral and cold ENSO SST cases respectively.

157
"The Relationship Between the Classic

PNA Pattern and the ENSO-Forced Pattern

on Time Scales From Daily to Seasonal"

David M. Straus and J. Shukla

From the perspective of seasonal mean predictability, one can distinguish between the SSTforced extemal response and chaotic intemal variability. The former (latter) leads to predictable

(unpredictable) components of seasonal mean fields. A large number of ensemble seasonal

historical forecasts made with the COLA AGCM (forced by observed SSTs) for many winters

are used to distinguish patterns ofintemal variability (including the PNA pattem of Wallace and

Gutzler) from true forced response patterns, such as the dominant ENSO-forced pattem. The

former can be identified from EOF analysis of the deviations of seasonal means about the

ensemble means, and from long integrations using climatologically varying SSTs. The latter can

be identified from EOF analysis of the ensemble means or from the pattem which optimizes the

"signal to noise" ratio. A number of consistency checks are used to confirm the validity of these

identifications.

The presence ofintemal variability patterns in seasonal means is thought to be due in large part

to statistical residuals of shorter time episodes. Information on the strength and duration of these

episodes can be gleaned from projections of daily and 5-day mean fields from individual

integrations onto intemal variability patterns. Comparison to similar projections onto the

extemal pattems yields information on the shorter time scale manifestations of the forced

response.

Intemal variability can depend on the state of SST forcing, yielding probability shifts in for

example the PNA pattern between warm and cold ENSO winters. In addition, changes in the

nature of ENSO itself during the past 50 years have led to subtle changes in the forced patterns,

and the degree to which they are distinguishable from internal variability. These issues will also

be addressed using large numbers of COLA AGCM ensemble integrations.

158
ASSESSMENT OF MIDLATITUDE SUBSEASONAL VARIABILITY

IN NASA/GSFC GENERAL CIRCULATION MODELS

Robert X. Black*

Dennis P. Robinson

Brent A. McDaniel

Georgia Institute of Technology

Atlanta, Georgia

1. Introduction

The success of short-term climate prediction efforts

is critically dependent upon the veracity with which

atmospheric general circulation models (GCMs) are able

to simulate the atmospheric circulation. A minimum

expectation is that GCMs employed are capable of

realisticallyrepresenting important aspects of the current

climate. Regional climate is strongly influenced by

second order atmospheric circulation features such as

storm track variability and anomalous weather regimes,

both of which are dynamically linked to the midlatitude

jet stream and are associated with alterations in surface

weather. A proper representation of the behavior of

anomalous weather regimes and storm tracks is essential

for reliable climate simulations. A detailed assessment

of short-term climate variability thus provides an

important benchmark for a climate model.

We perform an exploratory study of storm tracks

and anomalous weather regimes in extended integrations

ofNASA/GSFC GCMs. This includes intercomparisons

of the representation of these natural phenomena in

AMIP-type simulations of NASA/NCAR (NASCAR)

and Aries (NSIPP) models. Specifically, we employthe

AMIP fvccm3-1.2.0 NASCAR run and the ens05 AMIP

run of NSIPP 1. We diagnose their statistics, structure,

and dynamical characteristics and contrast these results

to parallel observational reanalyses (NCEP/NCAR) to

isolate systematic errors. Long-term goals include (a)

determining the extent to which the models are able to

replicate observed characteristics of the phenomena and

(b), in cases where a specific shortcoming is identified,

performing targeted dynamical diagnoses aimed at

deducing the underlying physical reasons for the

systematic errors.

2. Intraseasonal eddy statistics

After removing long-term seasonal trend values, the

intraseasonal eddy activity is then bandpass filtered into

2-10 day (synoptic) and 10-90 day (low frequency)

period bands, respectively. The collective effect of the

synoptic eddies is manifested as "storm tracks" while

low frequency eddies are linked to large-scale anomalous

weather regimes. These filters were applied to the daily

NCEP/NCAR reanalyses and model simulation output

for the winter months during the AMIP-2 time period.

A first order characterization of the intraseasonal

eddy activity was obtained by calculating the upper

tropospheric eddy kinetic energy (EKE) distribution for

the entire intraseasonal eddy spectrum (synoptic plus low

frequency) which is displayed in Figure 1. This analysis

shows that, though energy magnitudes are comparatively

weak in both NSIPP and NASCAR models, the regional

EKE patterns are well represented. This result carries

over to separate considerations of the synoptic and low

frequency eddies (not shown) suggesting improvements

over earlier models (e.g., Black and Dole 2000). The

300 hPa EKE magnitudes averaged over the Northern

Hemisphere extratropics are summarized in Table 1. The

total intraseasonal EKE is 20-30% weaker in the models

than in observations. Considering the individual eddy

components, the most striking difference is the 36%

shortfall in synoptic EKE for NSIPP. Potential reasons

for this characteristic are discussed later.

(20 90°N) Synoptic Low Freq Total Eddies Eddies Intraseasonal

NCEP/NCAR

75m2s -2 86m2s -2 161m2s -2

Reanalyses

NSIPP 48 (64%) 65 (76%) 113 (70%)

NASCAR 60 (80%) 70 (81%) 130 (81%)

Table 1. The 300 hPa intraseasonal eddy kinetic energy

averaged over 20-90°N for observations (NCEP/NCAR)

and the two model simulations. Model values expressed

as absolute values and as percentage of observations.

159
Thehorizontal structureoftransient eddiescanbe

diagnosed byplottingeddymajoraxeswhichdepictthe

orientationandamountofeddyanisotropy. Eddiesare

elongatedalongtheeddymajoraxiswiththeaxislength

proportional tothedegreeofeddyanistotropy. Theeddy

majoraxisvanishesforcircular(isotropic) eddies.We

haveappliedthisanalysistothemodelsimulations (not

shown). BoththeNSIPPandNASCARmodelfaithfully

represents themeridional elongationofsynopticeddies

observedinthecoreoftheprimaryjetstreamsaswellas

theeast-west "bowing"thatoccursnorthandsouthof

thejetstreams. Bothmodelsarealsoabletorepresent

thegeneraleast-west orientationof low frequency

eddies.Thedegreeofanisotropyforthelowfrequency

eddiesismisrepresented inthemodels, however.This

likelyimpactslocalbarotropicinteractions betweenlow

frequencyeddies andtheclimatological-mean flow.The

structural characteristics areconfirmedbyensemble

analysesofanomalous weatherregimes(notshown).

3. Dynamical diagnoses of eddy behavior

It is of scientific and practical interest to delineate

dynamical reasons for existing model discrepancies in

representing short-term climate behavior. We approach

this by considering the main eddy forcing mechanisms.

Synoptic eddies are strongly forced by baroclinic growth

while barotropic deformation in the upper troposphere

impacts both synoptic and low frequency eddies. To this

end, we have performed preliminary diagnoses of the

baroclinic and barotropic aspects of the model dynamics.

Regional values of the baroclinic growth parameter

(not shown) are very well represented by both models. In

fact, one of the few differences from observations is that

in both model simulations the parameter is too strong

over the Atlantic storm track region. This result is

perhaps surprising given the aforementioned relative

weakness in simulated synoptic EKE, particularly for

NSIPP. This suggests that other dynamic processes are

likely responsible for this model feature. For synoptic

eddies, upper tropospheric barotropic deformation acts

as an EKE sink in the jet exit regions where large-scale

shearing and stretching processes tend to elongate the

eddies along their major horizontal axis. Our analyses

show that regional deformation magnitudes are generally

overestimated by the NSIPP model. This suggests that

the synoptic eddies are likely experiencing enhanced

barotropic energy losses to the mean flow in the jet exit

regions. This behavior is confirmed by analyzing local

barotropic energy conversions (not shown) which show

local synoptic EKE losses in the NSIPP model that are

similar to, and in some cases exceed, observed values

(noting that one would expect conversion values to be

reduced by 30% or more as barotropic EKE conversions

are proportional to the EKE, itself). Consistent with this

argument, for the NASCAR simulation we find synoptic

EKE levels and barotropic deformation magnitudes that

are both closer to observed values.

To summarize: synoptic eddies in the NSIPP model

encounter anomalously strong barotropic damping at

upper levels while the background baroclinic forcing is

closer to observations. The relative importance of

barotropic and baroclinic forcing can be quantified by

dividing the barotropic deformation magnitude by the

baroclinic growth parameter. Regions where this ratio is

low are more strongly influenced by baroclinic processes

than barotropic processes and vice-versa. Fig. 2 displays

this ratio for the models and observations and concisely

shows that barotropic deformation likely plays a stronger

role in the NSIPP simulation than in observations or the

NASCAR simulation (i.e., smaller areas of dark shading

in the storm track regions). This may help to explain the

relative weakness of synoptic EKE in the NSIPP model

compared to observations and NASCAR.

We note that the low frequency EKE is also underrepresented by both models (though proportionallybetter

than synoptic EKE). This is a puzzling result for NSIPP

since it is reasonably well accepted in the literature that

barotropic deformation acts as a source of EKE for low

frequency eddies in the midlatitude atmosphere. Indeed,

the NSIPP model actually provides a quantitatively good

representation of the local barotropic energy conversions

between the mean flow and low frequency eddies (not

shown) which are positive in the jet exit regions. Here

we speculate that existing deficiencies in low frequency

EKE may be related to the scale interaction between the

synoptic and low frequency eddies. First, individual

synoptic eddies often act as the "seeds" for the onset of

large-scale weather regimes. Secondly, synoptic eddies

collectively provide positive dynamical feedbacks that

help maintain anomalous weather regimes. In both cases,

relatively weak synoptic eddies will likely result in

anomalously weak low frequency eddies. This is clearly

an avenue for further investigation.

R@rence:

Black, R.X., and R.M. Dole, 2000: Storm tracks and

barotropic deformation in climate models.

J. Climate, 13, 2712-2728.

Acknowledgments,. This research is supported by the

Global Modeling Program of NASA's Office of Earth

Science under Grant NAG 5-10374.

160
Fig.1. The300hPaeddykineticenergyassociated

withintraseasonal transient eddies(units:m2/s2)

Top: NSIPPsimulation

Middle:NCEP/NCARreanalyses

Bottom:NASCARsimulation

161
Fig.2. Theratioofthemagnitudeofthe300hPa

barotropicdeformation tothetropospheric baroclinic

growthparameter (nondimensional). Valuesgreater

(lessthan)0.75areshadedlightly(darkly).

Top: NSIPPsimulation

Middle:NCEP/NCARreanalyses

Bottom:NASCARsimulation

162
Diagnostics of Climate Variability

and Trend Using Potential Vorticity Maps

Ming Cai and Eugenia Kalnay

Department of Meteorology

University of Maryland, College Park, MD 20742

ca_tmos_d.ed u

Daily pressure fields on a constant potential vorticity (PV) surface (PV = 2.5 unit) are analyzed

using NCEP/NCAR reanalysis II (1979-2000) dataset. Potential vorticity folding indexes (PVFI)

are developed to measure the mean latitudinal position, area, and intensity of the polar front zone

over the Northem Hemisphere. It is found that these indexes closely related to the interannual

and decadal variability of the cold air temperature anomalies over the high latitudes (north of 40

degrees). In general, these indexes are negatively correlated with the cold air temperature

anomalies near the surface. The interannual variability of these indexes has a strong QBO

signal. These indexes also exhibit strong interdecadal variability. Between early 80s and early

90s, these indexes all exhibit a negative trend, accompanied with which is a warming trend in

both cold/warm surface air temperature anomalies. During last 5 years of 90s, these indexes

have a positive trend superimposed on a QBO-like interannual variability. Interestingly, the

surface air temperature anomalies continuously exhibit a warm trend while their interannual

variability follows the interannual variability of PVFI. This seems to suggest that we are

beginning to observe a separation between the climate trend induced by change in circulation and

that induced by other factors, such as anthropogenic factors.

163
Systematic Errors and Surface Fluxes

in the NCEP Global Model

Glenn White

Environmental Modeling Center

National Centers for Environmental Prediction

NWS/NOAA/DOC

Camp Springs, MD

This paper reflects the following assumptions:

a) Improving the atmospheric analysis/forecast systems currently used for 0-15 day

numerical weather prediction and coupling them to complex land and ocean

analysis/forecast systems can significantly improve 15-60 day forecasts.

b) Improving either the 0-15 or 15-60 day forecasts will usually improve the other.

c) Investigating the shortcomings of the current forecast system is a key ingredient in

improving forecasts.

d) More realistic physics and tropics are required for successful 15-60 day forecasts than for

0-7 day mid-latitude forecasts. We may well not know the physics in the actual

atmosphere as well as we need to produce successful 15-60 day forecasts.

e) 15-60 day and seasonal forecasts need for model verification and diagnosis a reanalysis

consistent with the forecast model.

The Global Modeling branch plans to implement a global ocean data assimilation system and

coupled model for 15-60 day and seasonal forecasts within the next year, replacing the current

operational system that is coupled to a tropical Pacific Ocean analysis/forecast system. The

atmospheric portion of the system will be upgraded every 1 to 2 years based on changes to the

medium-range model. Significant changes to the MRF will be tested for their effects on seasonal

forecasts. If improvement is found, the new version will be frozen and tested extensively for

seasonal forecast model upgrade. This will mean that the seasonal forecast model will lag the

operational medium-range model by 1-2 years. A similar schedule will be applied to

improvements to the ocean model.

During the late winter of 2001-2002, operational forecasts of 500 hPa height by the MRF

reached 60% anomaly correlation (widely accepted as a measure of the limit of useful sldll) at

day 8 in the extra-tropical Northern Hemisphere, an improvement of 1 day from 10 years ago.

Anomaly correlations for operational week-2 (day 8-14 mean) forecasts for the same period were

50% for the MRF and 55% for the MRF ensemble; for extended periods in the late winter the

forecast sldll for week-2 exceeded 60%. Such levels of sldll reflect the efforts of many sldlled

scientists; however, the results also suggest that new approaches may be needed to extend

forecast sldll to 15-60 days.

Fig. 1 compares time-mean errors in zonal mean temperatuxe in one and fifteen-day operational

MRF forecasts for Dec. 2001-Feb. 2002 with the error in zonal mean temperatuxe from an

integration with observed SST of the operational model at T170 resolution from mid-December

2000. Both are verified against operational analyses. The pattems are quite similar, although

differences can be seen near the poles in the troposphere. The magnitudes of errors are

comparable in the troposphere, but the longer run shows considerable larger errors in the

164
stratosphere. Thetroposphericcoldbiasissaturatedby day15,butthestratosphericbias

continuesto growbeyondday15.

znl mean T l dGy er DJF0102 zonal mean take T17042prx-gdas 2001

Fig. 1 Zonal mean error in temperature

for (top) 1 day forecasts (middle) 15 day

forecasts and (bottom) integration from midDec. 2000 verified against analyses for Dec.

2001-Feb.2002.

165
Fig.2 comparesthezonalmeanerrorintransienteddykineticenergyintheT170m

integration,averagedover2001,andthesamefieldin 6-houxforecastsfor December

2001.Bothareverifiedagainstzonalmeantransienteddykineticenergyin operational

analyses. Thepatternsarequitesimilarandindicatethatthemodel'sunder-forecast of

eddyactivityreflectsproblemsthatbegininthefirst6hoursofintegration.Figures1

and2showthatproblemsthatdevelopinlongintegrationsoftheNCEPglobalmodel

clearlycanbeseenin short-termoperational medium-rangeforecastsandsuggestthat

reducingsucherrorsinshort-termforecastswill improve15-60dayandseasonal

forecasts.

Realisticair-seafluxesareessential forusefulcoupledmodelforecasts.Severalgroups

arecuxrentlystudyingair-seafluxes.TheWGNE/SCORWorkingGrouponAir-Fluxes

(http://www.soc.soton.ac.uX/JRD/MET/WGASF) hasrecentlypublishedareporton

validationof air-seafluxes(Taylor,ed.,2000)andheldaworkshoponair-seafluxes

(White,ed.,2001).TheGEWEXSEAFLUX(_aos.colorado.edu/-cun_)

workinggroupis comparingestimatesof air-seafluxesandemphasizestheuseof

satelliteobservationsindeterminingair-seafluxes.PeterGlecklerandJanPolcher

(GlecklerandPolcher,2002)aredevelopingSURFA,anongoingprojecto collect

surfacefluxesfromoperationNWPanalysis/forecast systemsandverifythemagainst

high-qualityin-situobservationsoverbothlandandocean.

GlobalMeanBalances

2001

CDAS GDAS Kiehland Range

Trenberth

P(mm/d) 2.84 2.97 2.69 2.69-3.1

E 2.86 3.1 2.69

P-E -.02 -.13

Sens.heat

(W/m2) 15.59 9.31 24 16-27

Latentheat 83.06 89.97 78 78-90

Sfcdsw 205.91 201.13 198

Usw 45.11 29.01 30

NSW 160.8 172.12 168 142-174

Dlw 336.17 333.6 324

Ulw 396.81 397.69 390

NLW 60.64 64.09 66 40-72

netrad 100.16 108.03 102 99-119

NHF +1.5 8.76 0

166
Table1GlobalmeansurfacewaterandenergybalanceintheCDASandGDASfor2001

andfromaclimatologybyKiehlandTrenberth(1997).Rangedisplaystherangeof

independent estimatesconsideredbyKiehlandTrenberth(1997).

Table1comparesglobalmeansurfacefluxesaveragedover2001fromCDAS(donewith

the1995NCEPoperational globaldataassimilationsystem)andthecurrentlyoperational

GDAStoclimatological estimatesbyKiehlandTrenberth(1997)andtherangeof

estimatestheyconsidered.Thelargerangeof estimatesionemeasureoftheuncertainty

in ourknowledgeof air-seafluxes. Thehydrological cycleismoreintenseintheNCEP

analysesthanintheclimatological estimate, butitsmagnitudelieswithin the range of

independent estimates. Sensible heat flux in the current system is lower than other

estimates and may reflect the introduction of a new boundary layer parameterization in

the late 1990s. CDAS had too high an oceanic surface albedo; short wave radiation in the

current GDAS is closer to the estimates of Kiehl and Trenberth. The current GDAS

however has a larger imbalance at the surface than CDAS.

Table 2 compares the global mean water and surface energy balance over the oceans from

CDAS and GDAS for 2001 to climatological estimates from da Silva et al. (1994), based

on COADS observations for 1981-1992, and from the Surface Radiation Budget (Damell

et al., 1992; Gupta et al., 1992), based on retrievals of surface radiation fields from top of

the atmosphere satellite observations and ISCCP clouds. The COADS-based estimate

produces a large imbalance, indicating a large uncertainty in our knowledge of air-sea

fluxes. The SRB net long wave is lower than other estimates (Kiehl and Trenberth, 1997).

Global Mean Balances

2001 Ocean

CDAS GDAS COADS SRB

P mm/day 3.08 3.24

E 3.35 3.64

P-E -.27 -.40

Sensible heat W/m 2 11.41 5.65 10.1

Latent heat 97.01 105.47 88

Sfc dsw 198.66 201.56

Usw 34.85 18.47

Nsw 163.81 183.09 170.4 173.4

Dlw 353.21 347.35

UIw 408.4 409.05

Nlw 55.19 61.7 49.2 41.9

net rad 108.62 121.39 121.1 131.5

NHF 0.2 10.27 23.3

167
Table 2 Global mean surface water and energy balance over the ocean in the CDAS and

GDAS for 2001 and from climatologies by da Silva et al. (1994) and from the Surface

Radiation Budget.

Fig. 3(upper panel) compares surface stress along the equator from 12-year climatologies

from the NCEP/NCAR reanalysis and from COADS. The reanalysis had too weak zonal

surface stress. Fig. 3 (lower panel) shows zonal surface stress along the equator for 2001

from CDAS (the NCEP/NCAR reanalysis), GDAS (the operational analysis), and 3

integrations from mid-Dec. 2000 with observed SSTs. GDAS and the parallel X (PRX)

give reasonable zonal surface stress in the east Pacific; two other versions of the model,

including the operational seasonal forecast model (SFM) and a different convection

(RAS) have too strong stress in the East Pacific.

5S-5N zonal sfa stress annual mean 81-92 ¢OADSoeear

ioiiiiii iii....... iiiiiiiiiii;iiiiiiiiiiiiiiii,iii iiii

't

.......... .//-

.............

-&l

us T62 amip 5S-5N ocean

.... l I

: : i i

m

-- prx

-- I'O_

......=lOS

L E_E 12_JE 1_'0 1_2y _ 0

Fig. 3 Annual mean zonal surface stress over the ocean averaged from 5S-5N for (upper)

the NCEP/NCAR reanalysis and COADS (da Silva et al., 1994) for 1981-92 and (lower)

GDAS, CDAS and 3 integrations from mid-Dec. 2000 for 2001.

Considerable work is needed to improve both the forecast system's air-sea fluxes and our

knowledge of air-sea fluxes. Considerable differences exist between different estimates

of air-sea fluxes; our current knowledge of air-sea fluxes is insufficient to close the

surface energy budget. Current global forecast systems have problems with cloudiness

168
that produce inaccurate short wave fluxes and problems with moisture that affect long

wave fluxes. Low-level oceanic stratus clouds are difficult for current systems to model

correctly.

References

da Silva, A., C.C. Young, and S. Levitus, 1994: Atlas of Surface Marine Data 1994.

Vol. 1: Algorithms and Procedures. NOAA Atlas NESDIS 6, U.S. Dept. of Commerce,

Washington, D.C., 83 pp.

Damell, W.L., W.F. Staylor, S.K. Gupta, N.A. Richey, and A.C. Wilber, 1992: Seasonal

variation of surface radiation budget derived from International Satellite Cloud

Climatology Project C1 Data. J. Geophys. Res., 97, 15741-15760.

Gleckler, P., and J. Polcher, 2002: Project SURFA: A WGNE Pilot Study. WCRP/SCOR

Workshop on Inter-comparison and validation of ocean-atmosphere flux fieM.. Bolger

Center, Potomac, MD, USA, 21-24 May 2001, WCRP-115, WMO/TD-No. 1083, 6-7.

Gupta, S., W. Damell, and A. Wilber, 1992: A parameterization of long wave surface

radiation from satellite data: Recent improvements. J. Appl. Meteor., 31, 1361-1367.

Kiehl, J. T., and K.E. Trenberth, 1997: Earth's annual global mean energy budget. Bull.

Amer. Meteor. Soc., 78, 197-208.

Taylor, P.K., Ed., 2001: Inter-comparison and validation of ocean-atmosphere energy

flux fields. Joint WCRP/SCOR Working Group on Air-Sea Fluxes Final Rep., WCRP112, WMO/TD-No. 1036, 306 pp.

White, G., Ed., 2002: WCRP/SCOR Workshop on Inter-comparison and validation of

ocean-atmosphere flux field.. Bolger Center, Potomac, MD, USA, 21-24 May 2001,

WCRP- 115, WMO/TD-No. 1083, 362 pp.

169
Configuration and Intraseasonal Duration of Interannual

Anomalies of the Great Plains

Low-Level Jet

H. M. Helfand

Data Assimilation Office, Laboratory for Atmospheres

NASA/Goddard Space Flight Center, Greenbelt, Maryland

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.

S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours,

it is one of the most persistent and stable climatological features of the low-level

continental flow during the warm-season months, May through August. We have used

significant-level data to validate the skill of the GEOS-1 Data Assimilation System

(DAS) in realistically detecting this jet and inferring its structure and evolution. We have

then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology

and mean diurnal cycle and to study its interannual variability.

Interannual anomalies of the meridional flow associated with the GPLLJ are much

smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and

than the mean wind itself. There are three maxima of low-level meridional flow variance

over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast

Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the

westem Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near

the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a

spatially coherent, jet-like pattern of low-level flow interannual variability. There are

also three dominant modes of interannual variability corresponding to the three variance

maxima, but not in a simple one-to-one relationship.

Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and

recognizable from year to year with only its eastward flank showing significant

variability. This variability, however, exhibits a distinct, biennial oscillation during the

first six to seven years of the reanalysis period and only then. This intermittent biennial

oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern Texas and is also evident in the

NCEP/NCAR reanalysis data set from about 1978 to 1985 or 1986 and again from 1995

to 2000. It is evident as well in surface pressure in both the GEOS- 1 and NCEP/NCAR

sets.

The interannual anomalies do not necessarily persist uniformly throughout an entire

season, but can fluctuate from one part of the season to the next. To estimate the

characteristic subseasonal time scales for coherence of these fluctuations, we have taken

the weekly anomaly of low-level wind at each point of the domain from the

climatological average for that given point and that given week of the season and

170
computed the covariance of its fluctuations over all weeks and over all years with the

weekly climatological anomaly of the meridional wind at each of the three reference

points discussed above. The typical duration of a coherent interannual anomaly within a

given warm season increases with decreasing latitude from 2 to 3 weeks over the UGP, to

6 to 7 weeks over eastern Texas. Coherence over the western Gulf of Mexico is

intermediate between the two with a typical duration of 4 to 5 weeks. There appears to

be evidence that the interannual anomalies over Texas the Gulf propagate to the UGP

after a week and those over the Gulf propagate there after 2 to 3 weeks. There also

appears to be some reverse propagation of interannual anomalies over the UGP to Texas

and to the Gulf after a period of about one week.

The interannual anomalies in southerly flow over eastem Texas seem to correlate well

with interannual anomalies of surface temperature and (negative) ground wetness and

over western Texas.

171
REPORT DOCUMENTATION PAGE 1 _M;_V°_'°°7°e4d-°'88

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Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Pape[work Reduction Project (0704-0188), Washington, DO 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

November 2002 Technical Memorandum

4, TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on Global Modeling and Data AssimilationVol. 23-

Prospects :[or hnproved Forecasts of weather and Short-Te_wn Climate

_vlmability on Subseasonal (2 week to 2 month) Time Scales Code 971

6. AUTHOR(S)

Sieglded Schubert, Randall Dole, Max Suarez,Huug van den Dool,

and Duane Waliser

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES) 8. PEFORMING ORGANIZATION

REPORT NUMBER

Goddard Space Flight (:enter 2002-00538-0

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES) 10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

National Aeronautics and Space Administration

Washington, DC 20546-0001 TM-2002-104606, Vol. 23

11. SUPPLEMENTARY NOTES

S. Schubert, M. Suarez, GSFC, Greenbelt, MD

R. Dole, NOA A, Boulder, CO, H. van den Dool, NOAA, Institute of Terrestrial and Planetary Atmospheres

Stony Brook, NY

12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

Unclassified-Unlimited

Subject Category: 46

Report available from the NASA Center lot AeroSpace Information,

7121 Standmd Drive, Hanover, MD 21076-1320. (301) 621-0390.

13. ABSTRACT(Maximum200 wo_ds)

This workshop, held in April 2002, brought together various Earth Sciences experts to focus on the

subseasonal prediction problem. While substantial advances have occurred over the last few decades in

both weather and seasonal prediction, progress in improving predictions on these intermediate time scales

(time scales ranging from about 2 weeks to 2 months) has been slow. The goals of the workshop were to

get an assessment of the "state of the art" in predictive skill on these time scales, to deterlrJne the potential

sources of "untapped" predictive skill, and to make recommendations for a course of action that will

accelerate progress in this area.

One of the key conclusions of the workshop was that there is compelling evidence for predictability at

forecast lead times substantially longer than 2 weeks. Tropical diabatic heating and soil wetness were

singled out as particularly important processes affecting predictability on these time scales. Predictability

was also linked to various low-frequency atmospheric "phenomena" such as the annular modes in high

latitudes (including their connections to the stratosphere), the Pacific/North American (PNA) pattern, and

the Madden Julian Oscillation (MJO). The latter, in particular, was highlighted as a key source of untapped

predictability in the tropics and subtropics, including the Asian and Australian monsoon regions.

14. SUBJECT TERMS 15, NUMBER OF PAGES

]7]

weather, forecasts, climate variability

16, PRICE CODE

17. SECURITY CLASSIFiCATiON 18. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. MM[TAT[ON OF ABSTRAC'T

OF REPORT OF THiS PAGE OF ABSTRACT

Unclassified Unclassified Unclassified UL

NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. Z39.18

298-102