Ocean-Sea Ice Sub-project

Last modified by s2s_wiki on 2020/05/21 10:34

Scientific Objectives

The key science questions to be addressed are:

  • What are the key ocean-atmosphere interactions that directly influence sub-seasonal variability and prediction skill? 
  • What mechanisms affect extreme ocean weather (e.g., heat waves and bleaching events) and their predictability? 
  • What are the key processes driving sea ice variability at S2S timescales

The key modeling and prediction questions to be addressed are:

  • How is S2S predictability influenced by pre-existing ocean state, both intraseasonally varying and at lower frequency associated with e.g., ENSO and climate change? 
  • How well are ocean-atmosphere interactions depicted in coupled S2S forecast models? What role does ocean model resolution, both horizontal and vertical, play?
  • How well is the intraseasonally varying component of the ocean initialed in the S2S models?  How dependent is S2S forecast skill on assimilated ocean observations?
  • How does ocean mean state drift impact S2S predictability? 
  • What is the current capability to make sub-seasonal prediction of sea ice that can be assessed with the S2S hindcast datasets? What is the sensitivity of forecast skill and predictability to initial state? What are the key processes driving sub-seasonal variations of sea ice and how are these key processes represented in the S2S models? 

These will be addressed using available ocean data in the S2S data base (at this point only SST), by developing a plan and protocol for archiving ocean outputs into the S2S database, and by coordinated case studies.

Requirements and Needs

Assessing the evolution of the predicted ocean and sea ice  state is imperative for addressing these questions.  This assessment requires several key ocean variables:

Single level fields include SST, MLD, SSH, SSS, H300, Z20, surface currents, and sea ice extent and thickness.

Multilevel fields include upper ocean 3-d currents, temperature, and salinity.  Because the data volume of multilevel fields generated from retrospective hindcasts is prohibitively large for S2S Project resources, alternative strategies could be considered.  One approach would involve storing only those profiles collocated with moorings.  A second alternative would limit full ocean output to only a select number of case studies that target specific, well-observed phenomena or periods of high impact ocean weather.  Case study selection for these “virtual field campaigns” would likely benefit from input from the broader community, including modeling centers, to select cases that are both scientifically interesting and likely to reveal strengths and weaknesses of participating forecast systems.

Activities report for 2019

The major activities for 2019 have been:

  • Addition of nine ocean and sea ice variables to the S2S database (an action item from last year’s report) .   The majority of this effort was focused on the development Grib data tags for ocean output.  Thanks to substantial efforts by ECMWF, the following variables are now being archived in the S2S database for the ECMWF model (output from several other models is forthcoming):
    • Sea surface salinity
    • Depth of the 20C isotherm
    • Heat content in the top 300 m
    • Salinity in the top 300 m
    • Zonal surface current
    • Meridional surface current
    • Sea surface height
    • Mixed-layer depth
    • Sea ice thickness
  • Creation of the “S2S-oceans” e-mail list discussion forum.  The list, which is managed by S2S Oceans sub-project co-chair Charlotte DeMott, currently has 117 members representing modeling centers, university researchers, students and post docs, and water and fisheries managers.
  • Analysis of tropical SST drift and SST anomaly prediction skill in a subset of S2S models and their impact on MJO prediction skill (by C. DeMott; an action item from last year’s report).  S2S models exhibit regions of both positive and negative SST drift, which imprint onto column water vapor drift and affect predictions of MJO propagation through the horizontal moisture advection.  Contributions of SST drift from net surface heating and estimated ocean dynamics contributions reveal that both processes contribute to SST drift. 

Proposed Activities for 2020

  • Develop protocol for coordinated case studies that can be conducted by centres doing S2S prediction of  i)specific ocean extreme events and ii) air-sea interaction (e.g. onset of ENSO). Examples could include predictability study of prominent coral bleaching event in 2017, and the intraseasonal air-sea interaction at the onset of the 2015-16 El Nino.  This was a proposed activity for 2019 that we anticipate receiving more attention now that ocean output variables are available through the S2S database.  We also anticipate that recent efforts to update the Tropical Pacific observing system (i.e., TPOS2020) may focus attention and resources on this activity.
  • Advocate for the analysis of S2S ocean output variables.  This advocacy will take the form of informal or “grass roots” promotion through the S2S-ocean mailing list, seeking more formal recommendations from US CLIVAR, and communicating the benefits of such analyses to US funding agencies.  US CLIVAR, which already has a firm appreciation of the importance of oceanic processes on a variety of atmospheric processes and is increasingly focused on S2S prediction, is well-positioned to promote such activities.  S2S ocean sub-project co-chair C. DeMott will encourage US CLIVAR’s focus on this activity through her membership on the US CLIVAR Science Steering Committee.  


We mainly work through collaboration with WGNE and participating modeling centers with support from externally funded research projects.  

Linkages with WCRP/WWRP WGs & projects 

This sub-project will work in coordination with WGSIP, DAOS, TPOS2020, YMC, and PDEF (Predictability, Dynamics, Ensemble Forecasting) Working Groups to promote improved sub-seasonal predictions though improved initialization of the ocean-sea ice state and depiction of key ocean and sea ice processes that provide predictability at sub-seasonal times scales.

Created by Administrator on 2019/11/28 16:49
This wiki is licensed under a Creative Commons 2.0 license
XWiki Enterprise 6.2.2 - Documentation