Task Team 1: AMOC Observing System Implementation and Evaluation

The team is charged with the design and implementation of an AMOC monitoring system. AMOC monitoring in the US is currently accomplished by a collection of in-situ field programs and large-scale observations including: ARGO, the Global Drifter Array, and collection of satellites returning ocean surface and meteorological information.   

View TT1 Near- and Long-Term Priorities


US AMOC Task Team 1 Members
M. Femke de Jong, chair Duke University
Magdalena Andres, vice-chair Woods Hole Oceanographic Institution
Molly Baringer NOAA Atlantic Oceanographic and Meteorological Laboratory
Kathleen Donohue University of Rhode Island
Charles Eriksen University of Washington
Gustavo Goni NOAA Atlantic Oceanographic and Meteorological Laboratory
George Halliwell NOAA Atlantic Oceanographic and Meteorological Laboratory
Patrick Heimbach University of Texas at Austin
Bill Johns University of Miami
Felix Landerer Caltech/NASA Jet Propulsion Laboratory
Craig Lee University of Washington
Susan Lozier Duke University
Chris Meinen NOAA Atlantic Oceanographic and Meteorological Laboratory
Renellys Perez University of Miami/NOAA Atlantic Oceanographic and Meteorological Laboratory
Thomas Rossby University of Rhode Island
Uwe Send UCSD/Scripps Institution of Oceanography
Bill Smethie Columbia University
John Toole Woods Hole Oceanographic Institution

Near-term priorities

  • Improving understanding of the meridional coherence (and/or lack thereof) of the AMOC and the mechanisms that control AMOC changes continues to be a high near-term priority. The development of dynamically consistent model-data synthesis methods to combine the heterogeneous observational pieces will also play an important role in achieving this priority.
  • Seeking new potential funding mechanisms to sustain key elements of the US AMOC observational networks is a new near-term priority.
  • Expansion of the existing observing system to better capture the deep ocean and to better quantify the role of deep temperature and salinity signals in contributing to AMOC variability continues to be a priority. Enhancements such as Deep Argo, Deepgliders, and enhanced moored observations should be evaluated in the context of a full-depth observing system.
  • Ensuring that AMOC estimates are being made available in widely recognized locations such as the World Ocean Database, OceanSITES, the National Center for Environmental Information (NCEI), etc. These estimates should be accompanied by their key underlying measurements collected as part of the AMOC estimates as well as their error estimates on applicable time scales (days, weeks, months, and years) to provide the necessary precision information for analyses, inter-array comparisons, and numerical model studies.
  • Improving communication between different US AMOC observing system groups, particularly between more established observing system groups and newer groups becoming involved at the national and international levels, continues to be a recommended activity. 

Long-term priorities

  • Finding and/or developing new technologies and methods for studying the AMOC and its key components will be necessary moving forward in order to address the overall observing goals for AMOC in a world of finite resources.  
  • Development of plans to observe and study the shallow and deep pathways of the AMOC through the basin at locations away from the places of the few trans-basin arrays will be important in the long-term. This may involve future Lagrangian studies in the South Atlantic and/or tropical Atlantic regions similar to the ongoing work in the high-latitude North Atlantic, or it may involve the development of new technologies and/or techniques.  
  • Rigorous testing of data assimilation schemes is needed in order to better understand how the systems are using the data collected. Better communication is needed between the US AMOC community and the data assimilation community to test and potentially expand the set of collected observations that are assimilated into models.