Subseasonal VariabilitySubseasonal VariabilityCLIVAR SUMMITCLIVAR SUMMITKeystone, CO August 2005Keystone, CO August 2005

Duane WaliserDuane WaliserWater & Carbon Cycle

Sciences DivisionJPL

Principal Mechanisms of SS VariabilityPrincipal Mechanisms of SS Variability• Madden-Julian Oscillation (MJO) - emphasized here.• Pacific/North American pattern (PNA)• Arctic / North Atlantic Oscillation (AO / NAO)• Mid-latitude blocking• Equatorial wave activity (e.g., TIW, Kelvin)• Soil Moisture

• These phenomena have influence on basin/global scales and interact with phenomena at both shorter time scales (e.g., mid-latitude weather, tropical cyclones) as well as longer time scales time scales (e.g., ENSO, monsoons).

• However in most cases, the important mechanisms involved, their mutual interactions, their predictability, and the ability of current models to simulate them are still in question.

• Improvements in predicting these time scales are an important step in making progress in weather (e.g., modulation of background flows, statistics) and climate simulation/prediction (e.g., important component of “noise”).

Overarching RelevanceOverarching Relevance

MJO Impacts/InteractionsMJO Impacts/Interactions• Monsoon active and break periods• Clustering of Monsoon Tropical Depressions• Tropical Storm/Hurricane Modulation - including W. Hemisphere• Mid-Latitude Circulation Anomalies US West Coast Extreme Precipitation• ENSO state modulation• Weather In High Latitudes e.g. Alaska• Tropical Ocean Chl• Tropical Ocean Diurnal Cycle

Predictability Predictability

~8 Empirical Models~4 Dynamical Studies

Fu et al. 2005

CLIVAR/MonsoonCLIVAR/Monsoongcm intercomparison gcm intercomparison

projectproject

N.H. Summer N.H. Summer SubseasonalSubseasonal

RainfallRainfallVariabilityVariability

•Variable Strength•Too little C. IO variability •N.H. peaks often okay•Often split about equator•Spurious S. IO peak

Waliser et al. 2003

Modeling Modeling

Equatorial Equatorial Waves & MJOWaves & MJOMODELINGMODELING

ininIPCCIPCC Models Models

Lin et al., 2005

Difficult to get allParts of the

Variability Right

Wang, 2005

Theory & Physical ProcessesTheory & Physical Processes

Fundamental Components

ImportantFeedbacksAnnual/Seasonal

Modulation

•Basic State: Summer: Easterly vertical Shear Winter: low-level

westerlies•Vertical Resolution•Cloud Radiative

Feedbacks

Coupled SST FeedbackCoupled SST Feedback

Fu and Wang, 2004Zheng, Waliser, Stern, Jones, 2004

Coupled SST FeedbackCoupled SST Feedback

Zheng, Waliser, Stern, Jones, 2004Fu and Wang, 2004

•Phase Errors in Tropical Heating ~ 7 days or ~2000km

•Subseasonal Predictions MUST Include SST Coupling

•Two-Tier Approach Inadequate For Subseasonal Problem

New Horizons - ModelingNew Horizons - Modeling • Super-parameterization• Global Cloud Resolving: Earth Simulator• Not enough/practical…..

New Horizons - DataNew Horizons - Data

• BOBMEX, JASMINE , GAME-GEWEX, SCSMEX , CEOP• CLIVAR/AGCM Intercomparison Project, AMIP, CMIP• Indian Ocean moored array and drifter program • TRMM, NASA A-Train (e.g., AIRS, MODIS, CloudSat)

Recent “Programmatic” HistoryRecent “Programmatic” History

Apr 2002 - 1st Subseasonal Meeting (NASA) Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales • Compelling evidence for predictability at leads substantially longer than 2 weeks.• Predictability linked to low-frequency high latitude annular modes, PNA, MJO.• Tropical diabatic heating and soil wetness particularly important at these time scales.

http://www.cdc.noaa.gov/MJO/http://www.cdc.noaa.gov/MJO/

June 2003 - 2st Subseasonal Meeting (USCLIVAR IAG/NOAA,NASA,NSF)

Modeling, Simulation and Forecasting of Subseasonal Variability •Framework for conducting a systematic evaluation of current subseasonal forecast skill.•Assess current state of MJO modeling capabilities. (Poor->Marginal; w/ Optimism)•Implementation plan for an experimental MJO prediction program.

Other Relevant ActivitiesOther Relevant Activities• Subseasonal Hindcasts: NCEP/CFS, NASA/GOES5• NOAA, NASA, NSF - Modest size portfolios of subseasonal research• NOAA-CPC/EMC - “Seamless Suite”, “weather-climate” links.• NOAA-CDC - LIM, Experimental MJO Prediction Project Host• International CLIVAR AAMP• Asian Pacific Climate Center (APCC): Proposed Case Studies (IS-SI)

• Multi-scale interactions (cumulus<->planetary, weather <-> SI)

• Mean state Simulation (IO, double ITCZ, eq. westerlies, v. shear).

• Data - mainly lack data on microphysics, latent heating profiles, boundary layer processes/structure & cloud-radiative interactions.

• Subseasonal Forecasting Methodology???: coupling, ICs vs BCs, super-ensemble, data assimilation issues.• Coordinating Mechanism(s).

Challenging IssuesChallenging Issues

Based on the discussion above, as well as:• the cross-cutting nature of the MJO as well as other subseasonal variability (e.g., annular modes, PNA, soil moisture) in terms of

– time scale (weather-climate link; modulates low-frequency variability)– global reach (Indian Ocean to Americas, Tropics to high latitudes)– phenomenological interaction (monsoons, ENSO, trop cyclones, extra-trop weather)

• the importance of having this component of variability represented in our weather and S-I models• the breadth of activity occurring in this area• the overall enthusiasm for this area of research and development (e.g., over 100 participants at 1st subseasonal meeting and over 80 at 2nd subseasonal

meeting - mostly US in each case)• the need for coordinated subseasonal follow-on activities•SS variability is the means fill the present-day weather-SI prediction gap

that a that a Working Group on Subseasonal VariabilityWorking Group on Subseasonal Variability be established in order to coordinate and best leverage ongoing activities in this area and be established in order to coordinate and best leverage ongoing activities in this area and plan future directions. plan future directions.

RecommendationRecommendation