Reenvisioning the Ocean: The View from Space A RESPONSE

Slides:

Advertisements

Similar presentations

Detecting Change in the Global Ocean Biosphere using SeaWiFS Satellite Ocean Color Observations David Siegel Earth Research Institute, UC Santa Barbara.

Advertisements

Global trends in air-sea CO 2 fluxes based on in situ and satellite products Rik Wanninkhof, NOAA/AOML ACE Ocean Productivity and Carbon Cycle (OPCC) Workshop.

REMOTE SENSING OF SOUTHERN OCEAN AIR-SEA CO 2 FLUXES A.J. Vander Woude Pete Strutton and Burke Hales.

Zuchuan Li, Nicolas Cassar Division of Earth and Ocean Sciences Nicholas School of the Environment Duke University Estimation of Net Community Production.

Satellite Ocean Color Overview Dave Siegel – UC Santa Barbara With help from Chuck McClain, Mike Behrenfeld, Bryan Franz, Jim Yoder, David Antoine, Gene.

What Can We Learn About Ocean Biogeochemistry from Satellite Data? Dave Siegel UC Santa Barbara With help from: Stéphane Maritorena, Norm Nelson, Mike.

Beyond Chlorophyll: Ocean color ESDRs and new products S. Maritorena, D. A. Siegel and T. Kostadinov Institute for Computational Earth System Science University.

Experiments with Monthly Satellite Ocean Color Fields in a NCEP Operational Ocean Forecast System PI: Eric Bayler, NESDIS/STAR Co-I: David Behringer, NWS/NCEP/EMC/GCWMB.

U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis U.S. ECoS Science Team* ABSTRACT. The U.S. Eastern Continental.

Oceanography Team A Multiple Linear Regression of pCO 2 Against Sea Surface Temperature, Salinity, and Chlorophyll a at Station ALOHA and its potential.

Influence of phytoplankton size structure on ocean carbon cycling and on ocean colour Bob Brewin 1,2,3, Shubha Sathyendranath 1,3, with contributions from.

VARIABILITY OF OCEAN CO 2 PARTIAL PRESSURE AND AIR-SEA CO 2 FLUXES IN THE SUBANTARCTIC ZONE OF THE SOUTHERN OCEAN J. Boutin (1), L. Merlivat (1) and K.

PROJECTING THE ENVIRONMENTAL NICHE FOR SUMMERTIME COCCOLITHOPHORE BLOOMS IN THE NORTH ATLANTIC ABSTRACT Coccolithophore blooms are one of the few phytoplankton.

2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.

Modelling the export of biogenic particulates from upper ocean Philip Boyd.

GOES-R 3 : Coastal CO 2 fluxes Pete Strutton, Burke Hales & Ricardo Letelier College of Oceanic and Atmospheric Sciences Oregon State University 1. The.

Lecture 10: Ocean Carbonate Chemistry: Ocean Distributions Controls on Distributions What is the distribution of CO 2 added to the ocean? See Section 4.4.

Temporal scales of coastal variability and land-ocean processes J. Salisbury, J. Campbell, D. Vandemark, A. Mahadevan, B. Jonsson, H. Xue, C. Hunt.

Remote Assessment of Phytoplankton Functional Types Using Retrievals of the Particle Size Distribution from Ocean Color Data Tihomir Kostadinov, David.

A Multiple Linear Regression of pCO2 against Sea Surface Temperature, Salinity, and Chlorophyll a at Station BATS and its Potential for Estimate pCO2 from.

Janelle Fleming Interdisciplinary Seminar September 16, 1998 The North Pacific Ocean event: A unique climate shift, natural decadal variability,

Light Absorption in the Sea: Remote Sensing Retrievals Needed for Light Distribution with Depth, Affecting Heat, Water, and Carbon Budgets By Kendall L.

Bio-optical observations of the North Atlantic Spring Bloom Toby K. Westberry 1, Giorgio Dall’Olmo 1, Mike Behrenfeld 1, Emmanuel Boss 2 1 Department of.

Hydrography of Chromophoric Dissolved Organic Matter (CDOM) in the Pacific Ocean Chantal Swan, David Siegel, Norm Nelson, Craig Carlson Institute for Computational.

Objectives of the Workshop, Results of SMOS+ Surface Ocean Salinity (SOS) Ellis Ash (SatOC) Christine Gommenginger, Chris Banks, Eleni Tzortzi (NOC) Jacqueline.

Equatorial Pacific primary productivity: Spatial and temporal variability and links to carbon cycling Pete Strutton College of Oceanic and Atmospheric.

Potential benefits from data assimilation of carbon observations for modellers and observers - prerequisites and current state J. Segschneider, Max-Planck-Institute.

Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.

Norm Nelson, Dave Siegel Institute for Computational Earth System Science, UCSB Bermuda Bio-Optics Project Decade-Plus Perspective on Ocean Color.

Data Policy and SeaBASS Evolution Giulietta S. Fargion CHORS April 11, 2006.

Using in-situ measurements of inherent optical properties to study biogeochemical processes in aquatic systems. Emmanuel Boss Funded by:

Ocean Color Remote Sensing Pete Strutton, COAS/OSU.

Prospects for Using Historical Transmissometer Data in Large-Scale Assessment of Particular Organic Carbon A.V. Mishonov, W.D. Gardner, & M.J. Richardson.

Interannual Time Scales: ENSO Decadal Time Scales: Basin Wide Variability (e.g. Pacific Decadal Oscillation, North Atlantic Oscillation) Longer Time Scales:

Ocean Color Products: The challenge of going from stocks to rates

Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes R.W. Gould, Jr. & R.A. Arnone Naval Research Laboratory, Code 7333,

Definition and assessment of a regional Mediterranean Sea ocean colour algorithm for surface chlorophyll Gianluca Volpe National Oceanography Centre, Southampton.

Impact of Watershed Characteristics on Surface Water Transport of Terrestrial Matter into Coastal Waters and the Resulting Optical Variability:An example.

120 o E 5o5o 15 o 25 o 35 o 5o5o 15 o 25 o 160 o W160 o E120 o W 5o5o 15 o 25 o 35 o 5o5o 15 o 25 o 140 o E80 o W Nitrate Concentrations (  M)

1 Using Satellite Data for Climate Modeling Studies: Representing Ocean Biology-induced Feedback Effect in the Tropical Pacific Rong-Hua Zhang CICS-ESSIC,

Science Questions Societal Relevance Observational Requirements Observational Strategies Satellite Missions Scientific Basis for NASA OBB Mission Planning.

Radiative Coupling in the Oceans using MODIS-Aqua Ocean Radiance Data Watson Gregg, Lars Nerger Cecile Rousseaux NASA/GMAO Assimilate MODIS-Aqua Water-Leaving.

Approach: Assimilation Efficiencies The Carbon based model calculates mixed layer NPP (mg m -3 ) as a function of carbon and phytoplankton growth rate:

1 Autonomous measurements of the subpolar North Atlantic spring bloom: early results from the NAB08 experiment Core PIs, students and responsibilities.

The effect of wind on the estimated plume extension of the La Plata River Erica Darken Summer 2004.

V This project is supported by the NASA Interdisciplinary Science Program Map of the U.S. East Coast showing the Mid-Atlantic and South Atlantic Bights.

Interannual Time Scales: ENSO Decadal Time Scales: Basin Wide Variability (e.g. Pacific Decadal Oscillation, North Atlantic Oscillation) Longer Time Scales:

Reducing uncertainty in satellite ocean color products with measurements made from gliders and floats M.J. Perry, UMaine Herve Claustre, LOV Ken Johnson,

Ocean Biological Modeling and Assimilation of Ocean Color Data Watson Gregg NASA/GSFC/Global Modeling and Assimilation Office Assimilation Objectives:

ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING Julia Uitz, Dariusz Stramski, and Rick A. Reynolds Scripps Institution.

Lecture 10: Ocean Carbonate Chemistry: Ocean Distributions

Open Ocean CDOM Production and Flux

U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis U.S. ECoS Science Team* ABSTRACT. We present results from the U.S.

Metrics and MODIS Diane Wickland December, Biology/Biogeochemistry/Ecosystems/Carbon Science Questions: How are global ecosystems changing? (Question.

Filling the Gap in the Ocean Color Record Watson Gregg and Nancy Casey NASA/Global Modeling and Assimilation Office ABSTRACT A critical.

Assimilation of Aqua Ocean Chlorophyll Data in a Global Three-Dimensional Model Watson Gregg NASA/Global Modeling and Assimilation Office.

Carbon cycling and optics in the Gulf of Maine: Observations and Modeling Joe Salisbury Doug Vandemark Janet Campbell Fei Chai Huijie Xue Amala Mahatavan.

From satellite-based primary production to export production Toby K. Westberry 1 Mike J. Behrenfeld 1 David A. Siegel 2 1 Department of Botany & Plant.

Incorporating Satellite Time-Series data into Modeling Watson Gregg NASA/GSFC/Global Modeling and Assimilation Office Topics: Models, Satellite, and In.

The Dirty Truth of Coastal Ocean Color Remote Sensing Dave Siegel & St é phane Maritorena Institute for Computational Earth System Science University of.

Teaching Oceanography at a Liberal Arts College

Carbon-based primary production & phytoplankton physiology from ocean color data Toby Westberry1, Mike Behrenfeld1, Emmanuel Boss2, Dave Siegel3, Allen.

Status and Outlook Evaluating CFSR Air-Sea Heat, Freshwater, and Momentum Fluxes in the context of the Global Energy and Freshwater Budgets PI: Lisan.

PHYTOPLANKTON GROWTH RATES AND CARBON BIOMASS FROM SPACE

Developing NPP algorithms for the Arctic

Jian Wang, Ph.D IMCS Rutgers University

Multi-disciplinary real-time moorings

Coastal CO2 fluxes from satellite ocean color, SST and winds

Fig. 1. Location of the study site (23-28 S, W)

Influence of land - ocean exchange on carbonate mineral saturation state Joe Salisbury, Mark Green, Doug Vandemark, Chris Hunt, Dwight Gledhill, Wade McGillis,

Presentation transcript:

Reenvisioning the Ocean: The View from Space A RESPONSE Dave Siegel University of California, Santa Barbara

6 12.011 C Carbon

Remote Sensing Carbon Stocks Components Fluxes pCO2, DIC, DOC, POC & PIC Components Community structure, calcifiers, N2 fixers, etc. Fluxes Air-sea, export, net community production, etc.

Remote Sensing Carbon Ocean color is not perfect for this task Optical properties are not carbon species CDOM is not DOC, optical backscattering is not POC Need to think like paleo-oceanographers… Develop “quantitative proxies” But … test them using real observations

Some Examples… Stocks Components Fluxes POC pCO2 CDOM (This is a shameless plug for our CDOM poster!!) Components Calcifiers - coccolithophorid bloom occurrence Fluxes Air-sea CO2 fluxes

Remote Sensing of POC April 1998 Relate POC to estimates of optical backscattering by particle Loisel et al. [2002] GRL

Remote Sensing of POC SeaWiFS chlorophyll concentration SeaWiFS POC April 1998 SeaWiFS chlorophyll concentration SeaWiFS POC

Remote Sensing of POC Average for the North Atlantic (0 to 60oN) “POC” Chlorophyll Average for the North Atlantic (0 to 60oN)

Remote Sensing of POC POC patterns are very different from chlorophyll Validation at BATS & HOT is OK, but not great Ratios of satellite to field POC are 1.22 (± 0.37) @ BATS & 0.94 (±0.27) @ HOT Enables POC budgets to be assessed Mean POC = 54 mg C m-3 Large uncertainty in conversion from optics to POC Estimated to be ~40% - Improvements are underway

Remote Sensing of Calcifiers Coccolithophorid bloom classification Iglesias-Rodríguez et al. [2002] GBC

Remotely Sensing of Calcifiers Classification analysis based on observations of coccolithophorid blooms Enables space/time characteristics of coccolithophorid blooms to be assessed First (& probably easiest) step towards determining phytoplankton community structure remotely

Air-Sea CO2 Fluxes Relate observations of pCO2 to SST Satellite SST to map pCO2 Highly variable relationship Lee et al. [1998] Nature

Air-Sea CO2 Fluxes Incorporating winds enables air-sea CO2 fluxes to be estimated

Air-Sea CO2 Fluxes Used regional relationships for pCO2 as f(SST) Drive with remote estimates of wind & SST Approach has promise, but we need a better way to predict pCO2 What if climate change, alters pCO2 =f(SST)??

6 12.011 C Carbon

Remote Sensing Carbon Ocean color is not easily related to carbon Chlorophyll is not carbon Key is using “real” observations to build simple models Global data are finally available – more in future The JGOFS legacy is its open data access We are really just at the beginning of this work

The Future is Remotely Sensible There are many applications under consideration Primary production & export fluxes Photochemical rxn rates (CO, CO2, COS, etc.) DMSP/DMS cycling & air-sea DMS fluxes Trichodesmium distributions Physiological status from fluorescence and many more …

Thank you!!

Remote Sensing of DOC Colored Detrital & Dissolved Organic Material Absorption Siegel et al. [2002] JGR

Remote Sensing of DOC NH Winter DOC Distribution Siegel et al. [2002] JGR

Remote Sensing of Trichodesmium Index for Trichodesmium occurrence Toby Westberry [work in progress]

Remote Sensing of DIC & pCO2 Empirical approach for the Tropical Pacific Model … DIC = f(SST,SSS) TA = f(SST,SSS) Loukos et al [2000] GRL Fall ‘92 Spring ’92 DIC pCO2

Remote Sensing of DIC & pCO2 DpCO2 GasEx Flux Remote Sensing of DIC & pCO2 Average from 5oS to 10oN 1982 to 1994 SST, SSS & wind products used

Remote Sensing of DIC & pCO2 SOI Anomalous evasion is well related to SOI

Remote Sensing of DIC & pCO2 Empirically model DIC & TA for Tropical Pacific Drive this with remote sensing (& other) data Find relationship between CO2 evasion & SOI BUT, evasion flux uncertainties are ~50% More field observations should help So would the remote sensing of sea surface salinity