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Home » MAPP Webinar Series: Arctic Modeling: Improving Models and Predictions in the Arctic

MAPP Webinar Series: Arctic Modeling: Improving Models and Predictions in the Arctic


The NOAA CPO Modeling, Analysis, Prediction, and Projections (MAPP) program hosted a webinar on the topic of Arctic Modeling: Improving Models and Predictions in the Arctic on Tuesday, January 13, 2015. The announcement is provided below; you are invited to remotely join the session.

Date/Time Title
January 13, 2015
2:00 PM – 3:00 PM ET
Arctic Modeling: Improving Models and Predictions in the Arctic
  Speakers and Topics: Olga Sergienko (NOAA GFDL)
Outlet glaciers’ calving: observations and modeling

Hal Ritchie (Environment Canada)
Overview of an integrated marine Arctic prediction system for METAREAs

Wanqiu Wang (NOAA NCEP CPC)
The need of better initial conditions for improved seasonal sea ice predictions in CFS

Remote Access:   To view the slideshow:
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Watch Webcast:

(Right click and Save Link As) .wmv

ABSTRACTS: Olga Sergienko – Observations show that iceberg calving from tidewater and outlet glaciers is accompanied by the glaciers’ acceleration. However, it is unclear whether acceleration of glacier flow causes calving or iceberg calving triggers glacier acceleration. In order to gain deeper understanding of such glaciers’ behavior and account for it in large-scale ice-sheet models, we have been developing simplified parameterizations of the outlet and tidewater glaciers’ flow and analyzing available observations to test and validate them.

Hal Ritchie – In December 2007 Canada accepted official designation as the Issuing Service for meteorological Marine Safety Information in the form of forecasts / warnings and ice bulletins for METAREAs XVII and XVIII as part of the Global Maritime Distress and Safety System.  These areas are in the Arctic bordering on Canada.  An important part of Environment Canada’s involvement is the development of an integrated marine Arctic prediction system and satellite products in support of monitoring and warnings.  In particular, our group is working on the development, validation and implementation of marine forecasts using a regional high resolution coupled multi-component (atmosphere, land, snow, ice, ocean and wave) modelling and data assimilation system to predict near surface atmospheric conditions, sea ice (concentration, thickness, pressure, drift, ice edge), freezing spray, waves and ocean conditions (temperature and currents).  The core of the system consists of the GEM (Global Environmental Multi-scale) model as the atmospheric component coupled to the NEMO (Nucleus for European Modelling of the Ocean) ocean model, the CICE ice model and the WAVEWATCHIII® wave model. An ice-ocean data assimilation system is being developed in collaboration with Mercator-Océan using their system for ocean data assimilation together with the ice analysis system developed at Environment Canada.  The METAREAs research and development is a cornerstone activity within the Canadian Operational Network of Coupled Environmental PredicTion Systems (CONCEPTS).  This presentation will provide an overview of these activities, illustrate systems implemented and developments in progress as we approach the completion of the first phase of METAREAs, and discuss plans for future operational systems.

Wanqiu Wang – Improvements in the skill of long-range forecasts for Arctic sea ice can stem from many sources. One such source is the correct initialization of sea ice thickness (SIT). Despite its perceived importance, however, the influence of the observed information in initial SIT on the prediction is not well incorporated in the current generation of operational forecast systems.  As an example, the initial SIT in the current National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) is from the Climate Forecast System Reanalysis (CFSR) which contains substantial SIT errors.  The erroneous initial SIT used in CFS is an important factor limiting its sea ice prediction skill.   In this talk we will discuss some known SIT errors in the CFSR and their possible impacts on the seasonal sea ice prediction in the CFS. We will then present some preliminary results which show that CFS sea ice prediction can be improved by initializing SIT from the well validated Pan-arctic Ice/Ocean Modeling and Assimilation System (PIOMAS). These results indicate that a better initialization of SIT for CFS is required to enhance its sea ice predictions.

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