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U.S. CLIVAR Report Highlights from the Panels

U.S. CLIVAR Report Highlights from the Panels. U.S. CLIVAR, thru its Committee and Panels, serves to guide and implement the CLIVAR research program (aimed toward developing a predictive understanding of climate) in the broad functional goals of predictability/prediction;

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U.S. CLIVAR Report Highlights from the Panels

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  1. U.S. CLIVAR Report Highlights from the Panels U.S. CLIVAR, thru its Committee and Panels, serves to guide and implement the CLIVAR research program (aimed toward developing a predictive understanding of climate) in the broad functional goals of predictability/prediction; process and model improvement, and phenomena/observations/synthesis. The Panels develop and coordinate research plans and activities, provide input to agency programs, and assess achievement using measurable performance metrics US CLIVAR Summit 15-17July 2009

  2. U.S. CLIVAR Themes (formerly Foci) I Drought II Decadal Variability/Predictions Science Themes Are Increasingly Being Motivated by Interactions with Service and Decision Making Communities

  3. U.S. CLIVAR Organization US CLIVAR Scientific Steering Committee Ex-Com (3 persons) Inter-Agency Group (IAG) Federal Program Managers Committee U.S. CLIVAR Office “Best Practices”, Recommendations Predictability, Predictions & Applications Interface (PPAI) Panels Phenomenology, Observations, & Synthesis (POS) Process Studies & Model improvement (PSMI) • As of June 2009: • Western Boundary Current • High Latitude Air-Sea Fluxes • Decadal Predictability • AMOC (Incubated)http://www.atlanticmoc.org/ • MJO (finished) • Drought (finished) International CLIVAR Working Groups (short-term) Working Groups

  4. Process Study & Model Improvement Panel (PSMI) Co-Chairs: Sonya Legg, Paquita Zuidema Mission: to reduce uncertainties in climate models through an improved understanding and representation of the physical processes governing climate and its variations.

  5. PSMI Guidance for Climate Process and Modeling Teams • PSMIP performed review of pilot CPTs:best practices for future CPTs • CPT (joint NSF + NOAA) AO: $3M+/yr funding; proposals on ocean, atmos, polar/ice sheet, coupled processes • Modeling centers from NSF (NCAR), NOAA (GFDL/NCEP), NASA (GMAO), DOE (LLNL) encouraged to participate • PSMIP review is a critical component of the AO • ° CPTs now accepted as effective framework in USGCRP and WCRP

  6. BAMS, July 2009 Nowcast Best Practices for Process Studies

  7. PSMIP Goal: Use field studies to quantify climatically important processes (provide observing system guidance) VOCALS Regional Experiment Oct-Nov 2008 VOCALS analysis/modeling assessment well underway, 2nd working meeting earlier this week……

  8. DIMES DYNAMO Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean(US/UK) DYNAmics of the MjO -US cruise early 2009 Proposed US component to international field exp. in tropical Indian Ocean Further discussion led by Chidong Zhang

  9. Phenomenology, Observations and Synthesis Panel (POS) Co-Chair: Mike Alexander, Sarah Gille Mission: To improve the understanding of climate variations in the past, present, and future; develop syntheses of critical climate parameters; and sustain/improve the global climate observational system

  10. Activities of the POS Panel • Data Stewardship • Worked with GRACE satellite team to support better archiving of NOAA DART pressure gauge data collected as part of tsunami warning system. • Ongoing discussions/draft text on “best practices for ocean data stewardship” and strategies for “orphan” data with no natural archival home. • Ecosystems & Climate • NCAR Advanced Study Program Colloquium, August 2-14 2009 • Integrated Earth System Analysis - Identified as an important area of new scientific activity. • Extreme Events/ Tipping Possible involvement in UT Austin workshop • OceanObs’09 POS members have contributed to white papers for the meeting

  11. Argo shows the pattern of multi-decadal ocean warming. From Roemmich and Gilson (2009) ‏[5Yr ARGO Mean - Climatological data (World Ocean Atlas 2001)] Zonal average ΔT. 0-100 m ΔT Courtesy of Dean Roemmich, SIO)‏

  12. Predictions, Predictability, and Applications Interface Panel (PPAI) Co-Chairs:Arun Kumar, Ben Kirtman Mission: To foster improved practices in the provision, validation and uses of climate information and forecasts through coordinated participation within the U.S. and international climate science and applications communities.

  13. Activities of the PPAI Panel • Decadal Predictability Working Group • - WG and its terms of references formed, WG meeting in June ‘09 • Drought Working Group • – J. Climate Special Issue • World Climate Conference • – 3 White Papers on Seasonal Forecast Producers and Users • International Group on Attribution of Climate Related Events • - first meeting of group in January 2009, Boulder CO. • OceanObs’09 • - organizing committee, contribution to community white paper • NRC Predictability Study on ISI time-scale • WGSIP - Climate-system Historical Forecast Project (CHFP)

  14. Toward Developing a Predictive Understanding of Climate: Advances in Understanding • Natural Variability and Warming Trends –Understanding Recent cooling(Easterling & Wehner, GRL 2008) • Oceans role in terrestrial temperature trends(Compo & Sardeshmukh, Clim. Dyn., 2008; Hoerling et al., GRL 2008)

  15. Toward Developing a Predictive Understanding of Climate: Emerging Issues • Explaining Causes for Eastern Pacific SST Cooling • Extent of Natural Low-Freq Variability • Warming hole in the Eastern US • LF ENSO variability and variation in skill • 2008 NA cooling • Initialized Decadal Predictions • Understanding the 70’s climate shift: Forced vs. Internal (Meehl et al., J. Climate 2008)

  16. Integrated US CLIVAR Approach to Drought Physics of drought-relevant processes: ° land surface interaction/cloud feedbacks/SSTs Relevant field campaigns for focused process study ---NAME, DYNAMO, IASCLIP & CPTs ---DRICOMP Attributing the causes for drought variability: ° analysis of the earth system (conditions & forcings) Relevant data/analysis strategies --- land and ocean data assimilation (surface/subsurface/deep ocean) ° diagnosis & simulation of historical period Case studies, intercomparison, assessing factors driving variability --- role of SSTs/land surface/aerosols/GHG forcing Predictability of drought: ° determining simulation skill and hindcast skill Relevant multi-model intercomparisons --- AMIP (uncoupled, unintialized), Drought WG --- DEMETER (coupled, initialized) --- CMIP4 (coupled uninitialized/initialized with GHG frcg).

  17. Integrated US CLIVAR Approach to Decadal Variability Processes Contributing to Decadal Variability : ° Observed vs modeled key physical oceanic processes Relevant field campaigns for focused process study ---KESS, CLIMODE, DIMES Attributing the causes for decadal variability: ° Distinguishing natural from anthropogenic factors --- Indian Ocean warming trend --- AMOC/AMO and the recent warmth Decadal predictability: ° Determining best prediction strategies (Decadal WG metrics) --- predictability due to GHG forcing alone --- predictability from initialization..which earth system elements? --- numerical vs empirical, coupled vs uncoupled approaches ° Perfect prog skill vs Hindcast skill --- what are the current limits to decadal predictability? --- what are data and model requirements to harvest skill?

  18. Extra Slides

  19. Historical ENSO Nino 3.4 SST anomaly from the most recent SODA ocean reanalysis that goes from 1890-2005 showing strong El Nino events at the beginning of the 20th Century. Ben Giese, TAMU

  20. Ocean Observations Assimilation Depicts large array of elements going into the 1/6 degree resolution regional "eddying" Southern Ocean State Estimate (SOSE). Assimilated observations include Argo, CLIVAR repeat hydrography, altimetry, data from CTDs attached to elephant seals, and now data from IPY. In contrast with SODA, which covers a century-long time period, SOSE has focused on just a few years (initially 2005-06, and now extending onward), with a goal of incorporating all available quality-controlled data. Matt Mazloff, SIO

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