Impacts of Climate on EcoSystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) Kevin R. Arrigo Stanford University.

Slides:



Advertisements
Similar presentations
Lesson 12: Technology I Technology matters Most of the topics we’ve learned so far rely on measurement and observation: – Ocean acidification – Salinity.
Advertisements

Beyond Chlorophyll: Ocean color ESDRs and new products S. Maritorena, D. A. Siegel and T. Kostadinov Institute for Computational Earth System Science University.
U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis U.S. ECoS Science Team* ABSTRACT. The U.S. Eastern Continental.
Evaluation of Trends in Chlorophyll-a Concentration in Response to Climatic Variability in the Eastern Bering Sea from MODIS Puneeta Naik a,b and Menghua.
Environmental Variability, Bowhead Whale Distributions and Iñupiat Subsistence Whaling Carin Ashjian (Woods Hole Oceanographic Institution)
Bio-optical Gliders and Profiling floats in the Mediterranean ARGO SCIENCE WORKSHOP – MARCH 13 – 18, 2006 Fabrizio D’Ortenzio 1, Katarzyna Niewiadomska.
Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.
2 Remote sensing applications in Oceanography: How much we can see using ocean color? Adapted from lectures by: Martin A Montes Rutgers University Institute.
Water Level Sensor Physical processes related to bio-optical properties on the New York Bight inner continental shelf Grace C. Chang 1, Tommy D. Dickey.
1 Remote sensing applications in Oceanography: How much we can see using ocean color? Martin A Montes Ph.D Rutgers University Institute of Marine and Coastal.
Temporal and Spatial Variability of Physical and Bio-optical Properties on the New York Bight Inner Continental Shelf G. C. Chang, T. D. Dickey Ocean Physics.
The Ocean as a Microbial Habitat Matthew Church Marine Microplankton Ecology OCN 626/Fall 2008.
Hawaii Ocean Time-series (HOT) program Marine Microplankton Ecology
OSMOSIS Primary Production from Seagliders April-September 2013 Victoria Hemsley, Stuart Painter, Adrian Martin, Tim Smyth, Eleanor Frajka-Williams.
Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.
Abbie Harris - NOAA Ocean Acidification Think Tank #5 Current and Future Research at the Institute for Marine Remote Sensing Abbie Rae Harris Institute.
UNH Coastal Observing Center NASA GEO-CAPE workshop August 19, 2008 Ocean Biological Properties Ru Morrison.
Interannual and Regional Variability of Southern Ocean Snow on Sea Ice Thorsten Markus and Donald J. Cavalieri Goal: To investigate the regional and interannual.
In situ science in support of satellite ocean color objectives Jeremy Werdell NASA Goddard Space Flight Center Science Systems & Applications, Inc. 6 Jun.
Marine inherent optical properties (IOPs) from MODIS Aqua & Terra Marine inherent optical properties (IOPs) from MODIS Aqua & Terra Jeremy Werdell NASA.
1 Evaluating & generalizing ocean color inversion models that retrieve marine IOPs Ocean Optics Summer Course University of Maine July 2011.
Assessing Biodiversity of Phytoplankton Communities from Optical Remote Sensing Rick A. Reynolds, Dariusz Stramski, and Julia Uitz Scripps Institution.
Collaborative Research: Toward reanalysis of the Arctic Climate System—sea ice and ocean reconstruction with data assimilation Synthesis of Arctic System.
Some Thoughts on Ecology
Towards community-based approaches to estimating NPP & NCP from remotely-sensed optical properties Rick A. Reynolds Scripps Institution of Oceanography.
1 Applications of Remote Sensing: SeaWiFS and MODIS Ocean Color Outline  Physical principles behind the remote sensing of ocean color parameters  Satellite.
Data Policy and SeaBASS Evolution Giulietta S. Fargion CHORS April 11, 2006.
Assessing the Ecological Impact of the Antarctic Ozone Hole Using Multi-sensor Satellite Data Dan Lubin, Scripps Institution of Oceanography Kevin Arrigo,
The MEaSUREs PAR Project Robert Frouin Scripps Institution of Oceanography La Jolla, CA _______________________________________ OCRT Meeting, 4-6 may 2009,
1 Foundations VI: Discovery, Access and Semantic Integration Data Mining and Knowledge Discovery - Continued Deborah McGuinness and Joanne Luciano with.
Imagery.
Ocean Team Summary Chuck McClain MODIS Science Team Meeting July 13-15, 2004.
Lecture 5 The Climate System and the Biosphere. One significant way the ocean can influence climate is through formation of sea ice. Sea ice is much more.
U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis U.S. ECoS Science Team* ABSTRACT. The U.S. Eastern Continental.
Melting glaciers help fuel productivity hotspots around Antarctica
What is the key science driver for using Ocean Colour Radiometry (OCR) for research and applications? What is OCR, and what does it provide? Examples of.
Optical Water Mass Classification for Interpretation of Coastal Carbon Flux Processes R.W. Gould, Jr. & R.A. Arnone Naval Research Laboratory, Code 7333,
Rick Reynolds and Dariusz Stramski Measurements of IOPs and Characterization of Particle Assemblages for Monterey Bay Experiment Marine Physical Laboratory.
Dariusz Stramski Marine Physical Laboratory Scripps Institution of Oceanography University of California, San Diego OCEAN OPTICS SCIENCE IN SUPPORT OF.
U.S. ECoS U.S. Eastern Continental Shelf Carbon Budget: Modeling, Data Assimilation, and Analysis A project of the NASA Earth System Enterprise Interdisciplinary.
NASA Ocean Color Research Team Meeting, Silver Spring, Maryland 5-7 May 2014 II. Objectives Establish a high-quality long-term observational time series.
1) Canadian Airborne and Microwave Radiometer and Snow Survey campaigns in Support of International Polar Year. 2) New sea ice algorithm Does not use a.
2006 OCRT Meeting, Providence Assessment of River Margin Air-Sea CO 2 Fluxes Steven E. Lohrenz, Wei-Jun Cai, Xiaogang Chen, Merritt Tuel, and Feizhou Chen.
Department of Environmental Earth System Science Stanford University
Yvette H. Spitz Oregon State University, CEOAS Carin J. Ashjian (1), Robert G. Campbell (2), Michael Steele (3) and Jinlun Zhang (3) (1) Woods Hole Oceanographic.
Impact of Watershed Characteristics on Surface Water Transport of Terrestrial Matter into Coastal Waters and the Resulting Optical Variability:An example.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 The Influences of Changes.
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.
Progresses in IMaRS Caiyun Zhang Sept. 28, SST validation over Florida Keys 2. Potential application of ocean color remote sensing on deriving.
1 Melting glaciers help fuel productivity hotspots around Antarctica Kevin R. Arrigo Gert van Dijken Stanford University Melting glaciers help fuel productivity.
OCB Scoping Workshop Observing biogeochemical cycles at global scales with floats and gliders April 2009, Moss Landing, CA
A semi-analytical ocean color inherent optical property model: approach and application. Tim Smyth, Gerald Moore, Takafumi Hirata and Jim Aiken Plymouth.
Science Enabled by New Hyperspectral Observations Related to Physiology and Functional Types (HyspIRI) Dar Roberts, Frank Muller-Karger Reiterate Break.
ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING Julia Uitz, Dariusz Stramski, and Rick A. Reynolds Scripps Institution.
Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M. Sosik Hui Feng In Situ Time Series for Validation.
Collaborative Research on Sunlight and the Arctic Atmosphere-Ice-Ocean System (AIOS) Hajo Eicken Univ. of Alaska Fairbanks Kathy Jones CRREL Bonnie Light.
Global Ice Coverage Claire L. Parkinson NASA Goddard Space Flight Center Presentation to the Earth Ambassador program, meeting at NASA Goddard Space Flight.
Coupling a Bio-Geo-Chemistry module to HYCOM within the NASA-GISS climate model Anastasia Romanou, Columbia U. and NASA-GISS Rainer Bleck, NASA-GISS Watson.
Filling the Gap in the Ocean Color Record Watson Gregg and Nancy Casey NASA/Global Modeling and Assimilation Office ABSTRACT A critical.
SeaWiFS Highlights July 2002 SeaWiFS Celebrates 5th Anniversary with the Fourth Global Reprocessing The SeaWiFS Project has just completed the reprocessing.
A Net Primary Productivity Algorithm Round Robin (PPARR) for the Arctic Ocean: A brief introduction to the PPARR 5 adventure Patricia Matrai 1, Yoonjoo.
Distributed Biological Observatory (DBO) 2010 Pilot Program, Data Plans, and Future December 2010 National Institute of Polar Research (NIPR) Tokyo,
Eddies in the Beaufort Sea & its impact to ecosystem Watanabe (2011, JGR) & Nishino et al. (2011, GRL) 2) Preliminary results of XCTD measurement during.
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.
Michael Steele Polar Science Center / APL University of Washington Jan 14, 2009 AOMIP WHOI Mechanisms of Upper Ocean Warming in the Arctic and the Effect.
OBJECTIVES Develop an understanding of variability in the relationships between particulate organic carbon (POC), light scattering, and ocean color Develop.
Jian Wang, Ph.D IMCS Rutgers University
UW: Jinlun Zhang, Mike Steele
Presentation transcript:

Impacts of Climate on EcoSystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) Kevin R. Arrigo Stanford University

Background

Given ongoing changes in the Arctic Ocean… How has primary production changed in recent years?

Changes in Arctic Productivity Between 1998 and 2009, annual primary production increased by 134 Tg C (statistically significant trend) A 38% increase over the last twelve years Unexpected given presumed nutrient limitation Largest increases on continental shelf

Changes in Arctic Productivity Annual production highly correlated to open water area

Changes in Arctic Productivity Increase in production also related to increase in open water season Open water season has lengthened by an average of 3.8 days yr-1 over last 12 years 45 days longer in 2009 than in 1998 Length of Open Water Season Days open water >5 x 106 km2

Annual Primary Productivity (1998-2009) Tg C yr-1 27 Regional variability Atlantic sectors most productive >50% of Arctic primary production Low sea ice cover 26 23 40 29 50 109 123

Percent yr-1 Change in Primary Productivity 1998-2009 Largest annual increase: East Siberian Sea Smallest: Greenland Sea Significant values in white 4.4 10 3.8 4.8 0.2 4.7 2.3 -0.3

Changes in Arctic Productivity What is responsible for this increase? Lower ice cover and longer growing seasons play a role Increased nutrient supply also must be important - Greater shelf-break upwelling as sea ice retreats? - Increased eddy activity? - Intensified advection of nutrients from Bering Strait?

ICESCAPE Central science question: What is the impact of climate change (natural and anthropogenic) on the biogeochemistry and ecology of the Chukchi and Beaufort seas?

ICESCAPE

ICESCAPE

ICESCAPE

ICESCAPE

ICESCAPE

ICESCAPE When? June 15 - July 21, 2010 & September 2011 Where? Start in Dutch Harbor, AK Cruise to Bering Strait Beaufort/Chukchi Sea - Continental shelf - Canada Basin Sea ice sampling Back through Bering Strait End In Seward, AK

ICESCAPE Physical Oceanography: Bob Pickart – XBTs, ADCP, eddies Jim Swift – CTD, O2, salinity Mike Steele – ARGO floats Jinlun Zhang – 3D coupled physical-chemical-biological ice-ocean modeling

Eddy formation from dense water outflow Pickart/Arrigo 2-D transect through a cold-core eddy in 2002 Mid-depth, cold-core eddy We hope to conduct a three-dimensional survey of a cold-core eddy, with the full suite of biological measurements.

ICESCAPE Biological Oceanography: Kevin Arrigo and Greg Mitchell – Primary production, microalgal abundance (ice and water column) and physiology Sam Laney – Phytoplankton community composition Eva Ortega-Retuerta - Bacterial production Jinlun Zhang – 3D coupled physical-chemical-biological ice-ocean modeling

Monitoring Climate-Driven Changes in Arctic Algal Assemblages PIs: Sam Laney & Heidi Sosik Biology Dept., Woods Hole Oceanographic Institution Use Imaging FlowCytobot technology to assess spatial & vertical variability in Arctic algal assemblages. Combines flow cytometry, individual cell imaging, & automated image classification to assess the composition of microalgal assemblages. Quantifies cells ml-1 of different algal taxa Instrument images ~500 cells per mL in ~ 4 mins. Uses a computer & classifier algorithm to sort images by “taxon” Chaetoceros Ceratium Cylindrotheca & ~25 other categories currently… Olson et al. 2003; Olson & Sosik 2007; Sosik & Olson 2007

ICESCAPE Chemical Oceanography: Nick Bates – Carbon cycle measurements (e.g. DIC, alkalinity) Jim Swift – Nutrients (e.g. NO3, NO2, NH4, PO4 , SiO3), O2, salinity Jinlun Zhang – 3D coupled physical-chemical-biological ice-ocean modeling

STS/ODF will collect water samples from a 12-place rosette with 30-liter bottles. Rosette will be outfitted with: - SeaBird 911+ CTD w/ dual C/T sensor - Oxygen sensor, - Flourometer - Tranmissometer - CDOM fluorometer On-board seawater analysis equipment will include: - Salinometers - Oxygen autotitration rig - 5-channel nutrient autoanalyzer (NO3, NO2, NH4, PO4, and SiO3).

ICESCAPE Optical Oceanography: Stan Hooker and Greg Mitchell – Spectral Lu, Ed, AOPs, IOPs Rick Reynolds and Dariusz Stramski – Particle size distribution, bb, volume scattering, SPM Atsushi Matsuoka – aCDOM Robert Frouin – Atmospheric correction algorithm

Mitchell Group High latitudes are unique Chl a (mg m-3) Southern Ocean High latitudes are unique Make detailed measurements of ap, ad, CDOM and backscatter to better understand Arctic Ocean bio-optical properties to Improve Chl a and productivity algorithms required for ecological modeling. In polar regions, standard NASA algorithms based on low latitude data underestimate Chl a (Mitchell 1992, Arrigo et al. 1998). Will collect data to determine relationships between reflectance, Chl a and IOPs.

Mitchell Group aph440 adg440 bbp4400000 A B C Inversion of the bio-optical properties in Mackenzie Bay, Beaufort Sea using MODIS-Aqua data of 7/6/2008 and QAAv5 algorithm of Lee et al. A. aph = phytoplankton absorption B. adg = CDOM + detritus C. bbp = particle backscatter. Will determine details of absorption and backscattering to improve algorithms and retrievals for key biogeochemical properties Will provide near-real time delivery of satellite imagery to the ship for cruise planning.

Improving Existing Satellite Color Observations of the Chukchi and Beaufort Seas for Biogeochemical Modeling PI: Robert Frouin, SIO/UCSD; Co-I: P.-Y. Deschamps, LOA/U. Lille; Co-I: B. Pelletier, I3M/U. Montpellier Theme Improved atmospheric correction of satellite ocean-color imagery in the presence of snow/ice and clouds. Objective Generate, for the Chukchi and Beaufort seas, a daily time series of satellite-derived marine reflectance and chlorophyll concentration at 4.63 km resolution. Approach - Use of multiple ocean-color sensors (MERIS, SeaWiFS, MODIS). - Use of appropriate atmospheric correction algorithm (POLYMER). - Reconstruction of missing data.

MERIS image of the Chukchi Sea showing that water reflectance is correctly retrieved by the POLYMER algorithm, but not by the MEGS algorithm in the presence of a large semi-transparent cloud.

ICESCAPE Sea Ice: Don Perovich – Concentration, thickness, salinity, snow cover, optical properties Kevin Arrigo – Primary production, microalgal abundance, and physiology Karen Frey – CDOM, DOC, O2 isotopes Jinlun Zhang – 3D coupled physical-chemical-biological ice-ocean modeling

Planktonic Ecosystem Response to Changing Sea Ice and Upper Ocean Physics in the Chukchi and Beaufort Seas: Modeling, Satellite and In Situ Observations Jinlun Zhang, Carin Ashjian, Robert Campbell, Victoria Hill, Yvette Spitz, and Mike Steele Biology/Ice/Ocean Modeling and Assimilation System (BIOMAS) Synthesis and modeling of the integrated system of sea ice, the upper ocean, and the plankton ecosystem in the Chukchi and Beaufort seas

BIOMAS grid configuration and ecosystem model

ICESCAPE Data Policy: Preliminary data will be staged at an ftp site at Stanford http://ocean.stanford.edu/ICECAPS All data collected will be subject to the standard NASA Earth Science data policy (http://nasascience.nasa.gov/earth-science/earth-science-data-centers/data-and-information-policy/). Data collected are required to be submitted to the NASA SeaBASS archive (http://seabass.gsfc.nasa.gov/) within one year of collection.

ICESCAPE Thank You