1. Subseasonal Variability CLIVAR SUMMIT Keystone, CO August 2005 Duane Waliser Duane Waliser Water & Carbon Cycle Sciences Division JPL

2. Principal 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 Relevance

3. MJO 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 ~8 Empirical Models ~4 Dynamical Studies Fu et al. 2005

4. CLIVAR/Monsoon gcm intercomparison project N.H. Summer Subseasonal Rainfall Variability 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

5. Equatorial Waves & MJO MODELING in IPCC Models Lin et al., 2005 Difficult to get all Parts of the Variability Right

6. Wang, 2005 Theory & Physical Processes Fundamental Components Important Feedbacks Annual/Seasonal Modulation Basic State: Summer: Easterly vertical Shear Winter: low-level westerlies Vertical Resolution Cloud Radiative Feedbacks

7. Coupled SST Feedback Fu and Wang, 2004 Zheng, Waliser, Stern, Jones, 2004

8. Coupled SST Feedback Zheng, Waliser, Stern, Jones, 2004 Fu 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

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

10. Recent “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/ 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.

11. Other 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 Issues

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