Development of an Indian Ocean Moored Buoy Array for Climate Paul Freitag NOAA/PMEL SEACORM Bali, Indonesia 7-9 June 2006

Scientific Background and Societal Context

Indian Ocean Science Drivers Improved description, understanding and prediction of:  Seasonal monsoon variability  Monsoon ENSO interactions  Indian Ocean Dipole (El Niño-like phenomenon in the Indian Ocean)  Intraseasonal oscillations and both near and far field impacts (Asian monsoon active/break periods; west coast US rainfall, Atlantic hurricane formation, ENSO)  SST warming trends since the 1970s  Indonesian Throughflow  General ocean circulation, ocean heat transport, and their variability Indian Ocean Dipole Webster et al, 1999, Nature

Efforts to develop an Indian Ocean component to the Global Ocean Observing system for climate studies are accelerating  Compelling unanswered scientific questions;  Potential societal benefits from development of skillful monsoon prediction models;  One of the most poorly sampled regions of the world ocean;  Growing ocean science investments from India, Japan, and U.S., and nations surrounding the Indian Ocean;  Inauguration of plans for the Global Earth Observing System of Systems (GEOSS) in 2003;  Inauguration of a CLIVAR/GOOS Indian Ocean Panel in 2004;

Network Design  All Components  Moored Array

CLIVAR/GOOS Indian Ocean Panel Progress on Sustained Observing System ● Basin scale mooring array ● Integrated approach to moorings, Argo floats, XBT lines, drifters, and satellites ● Alternative observational strategies assessed by observing system simulations ● Report: The role of the Indian Ocean in the climate system—implementation plan for sustained observations

Integrated, Multi-platform Ocean Observing System Carbon/hydro cruise High density XBT Frequently repeated XBT Enhanced XBT lines to monitor Indonesian Throughflow, inflow to western boundary, Java upwelling and 10°S thermocline ridge Emphasis on ocean, but will provide surface met data as well Argo floats 3°x 3° Drifters 5°x 5° ~20 real-time tide gauges for IOTWS Regional mooring arrays

Satellite and In Situ Obs  Satellite remote sensing of SST, wind, sea level, ocean color, precipitation, and salinity* is critical  Spatial and temporal coverage of surface properties  In situ measurements provide the 3-dimensional perspective  Combined observations used for model validation & development, initialization of climate & weather forecasts, and ocean state estimation. * First salinity remote sensing satellites will fly in 2007 (ESA/SMOS) and in 2009 NASA/Aquarius) Cloud penetrating capabilities of TMI yields an SST data suitable for intraseasonal analysis

Draft Strategy for Indian Ocean Moored Buoy Array *Actual sea days in 2006: involves more than just mooring work

Moored Measurement Suite Standard  Met: Wind, RH, AT, SWR, Rain  Ocean: SST, SSS, T(z:10 depths), S(z: 5 depths), P (z: 2 depths); v (10 m) Flux Sites: Standard plus--  Met: LWR, BP  Ocean: Additional T(z), S(z), v (z) in upper 100 m All data transmitted to shore in real-time via Service Argos

Design Considerations

Indian Ocean “Dipole” or “Zonal Mode” First reported in Nature by Saji et al and Webster et al in 1999

Ocean Circulation and its Relation to SST Cross-equatorial transport carried by -- Somali Current north -- Ekman transport south What is relative strength of the different upwelling centers? How do they vary from intraseasonal to decadal time scales? What is relation of ITF to general circulation in the Indian Ocean? Schott et al., 2002

Intraseasonal & MJO Variations “Building Block for the Monsoons”  Elongated zone between 0°-10°S exhibits elevated intraseasonal SST, wind, and OLR variance  Air-sea interaction & mixed layer dynamics are critical to understanding and predicting variations in this region. Band-passed (30-90 day periods) standard deviation for DJF TOP: SST (color), wind speed (m/s) BOT: SST (color), OLR (W/m 2 )

Rationale for Flux Sites Courtesy of Lisan Yu, WHOI

Implementation of Moored Array and Initial Data

 41 Day Cruise  4 ATLAS & 1 ADCP mooring Mooring Implementation ORV Sagar Kanya Cruise 9 October – 17 November 2004  PMEL in collaboration with the National Institute of Oceanography (NIO) and the National Center for Antarctic and Ocean Research (NCAOR), Goa, India.

Meteorological Data at 0°, 90°E

ATLAS at 0, 80.5E MLD based on  =0.15 kg m -3 from surface value Transition winds (Nov-Dec) Northeast Monsoon (Feb-Mar) Wyrtki Jet (Nov-Dec) Northeast Monsoon Current (Feb-Mar) O(1°C) week-to-week and seasonal SST changes 50 m intraseasonal MLD changes (Nov-Jan) Shallow and steady during NE monsoon (Feb-Mar)

Preliminary Results Indo/US TAO Mooring Mixed layer heat balance Surface heat fluxes

Indian Ocean Moored Buoy Data Assembly Center (DAC)  Modeled after TAO/ TRITON and PIRATA data processing and dissemination systems.  PMEL and JAMSTEC initial contributors.  Hosted at PMEL; potential for mirror sites outside the US (e.g. in Indonesia).

Challenges & Plans

FY06 Budget for NOAA Climate Observations and Services “…[Funds] to expand the Tropical Atmosphere Ocean array… into the Indian Ocean. This expansion will enhance NOAA's capability to accurately document the state of ocean climactic conditions and improve seasonal forecasting capability.” ( Other activities covered by this funding:  Add salinity sensors to the TAO array to improve seasonal-interannual forecasting.  Upgrades for 4 TAO and 3 PIRATA moorings to ocean reference station quality.  Provide 4 additional buoys for the PIRATA array in the hurricane-genesis region of the Atlantic Ocean for improved understanding of ocean-atmosphere interactions on hurricane development.  Support the technological development of the next generation of moored buoys

New 2006 JAMSTEC Budget Initiative JAMSTEC received a new 5-year budget starting in Japanese FY2005 for promoting the Indian Ocean mooring array as a part of "JEPP: Japan EOS (Earth Observation System) Promotion Program" which is a new program of Japanese government related GEOSS. JEPP will enable the development of new small size TRITON buoy and the continuation of the present TRITON sites in the Indian Ocean.

Challenges: Ship Time Requirements:  > 140 days per year to maintain full array  Must be available routinely and with regularity  Assumes 1-year mooring design lifetime and annual servicing cruises

Ship time needs based on these hypthetical tracks

Near-Term Mooring Array Plans RV Baruna Jaya 2006 (BRKT/NOAA) ORV Sagar Kanya August 2006 RV Suroit February 2007 (CIRENE) RV Mirai Nov 2006 (MISMO) SOA/NOAA Cooperation (?)

Near-Term Mooring Array Plans Deploy 2 new ATLAS moorings (4ºN, 8ºN) Repair 2 existing ATLAS moorings (0º, 1.5ºN if required) Jakarta to Padang 14 days for transit and mooring operations (assuming 10 kt)

Summary  The international community has developed plans for an integrated Indian Ocean observing system for climate research and forecasting.  The array design is based on observing, understanding, and predicting key ocean and climate phenomena that have significant socio-economics impacts on countries surrounding the basin and that affect global climate variability.  The plan has been endorsed by CLIVAR and GOOS; implementation is underway.  The newest component of the observing system is a basin scale moored buoy array, with initial contributions from the U.S., India, and Japan.  There are many challenges to full implementation of this array, but success promises significant scientific and societal benefits.

Issues  Developing partnerships that enable efficient and sustained implementation of the array  Identifying and overcoming resource limitations, especially ship time  Building capacity for transfer of scientific and technical expertise in mooring operations and data analysis to regional partners  Coordinating international contributions (underway through CLIVAR/GOOS IOP and through bilateral agreements)  Ensuring integration and dissemination of all observing system data for climate research, model development and forecasting  Leveraging available resources for development of multi-hazard warning systems, e.g. for climate, weather, tsunami*, etc. * see McKinnie talk for tsunami

Thank You