Projecting future change in the Arctic Ocean

  • Dr Johan Faust, ChAOS
  • Dr Johan Faust, ChAOS
  • Dr Johan Faust, ChAOS

The main goal of the Changing Arctic Ocean Programme is to understand how the Arctic Ocean functions in a quantitative way. This information feeds into models that determine how the Arctic Ocean will react to change. These future projections are important because they help refine decision-making processes.

The Arctic Ocean is a vastly complex system with poorly understood interactions between the physical environment, defined by the presence of sea-ice and extreme annual seasonal cycles, and the ecosystem and biogeochemical cycles. To model this system in any meaningful way necessitates a solid understanding of key aspects of the ecosystem and the biogeochemical cycles that operate in the Arctic Ocean. This is an inherent component of the 16 projects in the Programme, which are generating the data to test and develop the computer models further.

The models used in the programme are diverse in terms of the spatial and temporal scales they cover, the components of the marine system they are aiming to replicate, and the research questions they address. A summary of the models is presented below.

More information about the models applied in the individual projects is available in the project pages.

ModelTypeSpatial domainProjectContact
BFM-SI (Biogeochemical Flux Model – Sea Ice)Coupled on-dimensional sea ice physics-algal productivity modelSingle-point modelDiatom-ARCTICLetitzia Tedesco
BRNSDiagenetic Model (benthic reaction-transport model) Barents Sea shelf and slopeChAOSSandra Arndt
“BRNSlight”Numerically efficient encapsulation (ANN) of the BRNS for regional applications/ coupling to pelagic modelsBarents Sea shelf and slope, and planned Pan-ArcticChAOSSandra Arndt
ColtraneIndividual-based, trait-based representation of Calanus complexPan-ArcticDIAPODNeil
Dynamic life history model for C. finmarchicus and T. inermisDynamic life history modelRange of C. finmarchicus and T. inermisCHASEGeraint Tarling
ERSEMBiogeochemical model of lower trophic levels of both benthic and pelagic environmentsLena and Kolyma River estuaries and near shore (Russia Arctic and East Siberian Shelf)

0D process studies in PETRA, but can be run in 3D at all scales






Ricardo Tores




Yuri Artioli

EwE (Ecopath and Ecosim)EwE is a marine ecosystem modelling software suiteCoastal and pelagic surface water ecosystemEISPACSilvana Birchenough
FESOM (Finite Element Sea ice Ocean Model)Coupled sea-ice–ocean modelGlobalEco-LightGiulia Castellani
FRUITSBayesian diet and foodweb estimation modelIcelandLOMVIAThomas Larsen
FVCOMUnstructured grid finite volume hydrodynamic modelLena and Kolyma River estuaries and near shore (Russia Arctic and East Siberian Shelf)CACOONRicardo Tores
GFR (General Functional Response)Resource selection function IcelandLOMVIAJason Matthiopoulos
Individual based model for C. finmarchicus and T. inermisIndividual based modelRange of C. finmarchicus and T. inermisCHASEJürgen Groeneveld and Bettina Meyer
MEDUSA-2Biogeochemical; component of UK Earth System Model and coupled OGCMsVariable (global, regional, 1-D)APEAR ARISE
Katya Popova
Clive Trueman
Andrew Yool
NEMO-MEDUSA-2Global coupled OGCM with Bio-Geo-ChemistryGlobal and Pan-ArcticAPEARYevgeny Aksenov
Katya Popova
Andrew Yool
OSB-EcoM (Optimality Size Based Ecosystem Model)1D (vertically resolved) lower trophic food-web model with biogeochemistry; focus on linking physiological- and ecological processes with biogeochemical element cycling of C and N (possibly P); forcing and physical variables from NEMOEastern part of Fram Strait, at the location of the Alfred Wegener Institute’s time series station “Hausgarten”Micro-ARCMarkus Schartau and
Ben Ward
PISCESComplex biogeochemical model, focus on isoscapeGlobal/Pan-ArcticARISEAlessandro Tagliabue
PRIZE Phytoplankton modelNPZD (biogeochemical, focus on plankton)Lagrangian testbeds on Barents, Bering, Chukchi shelvesPRIZE
REcoM (Regulated Ecosystem Model)Ocean biogeochemical model, cell-quota Pan-Arctic, applied to Beaufort GyreEco-LightGiulia Castellani
Seal population modelsIndividual-based, statisticalEast Greenland, Barents ShelfARISESophie Smout
StrathCalSpatial demography of a copepod populationRange of C. finnmarchicusDIAPODDougie Speirs
StrathE2EFunctional group nitrogen mass dynamics –  nutrients, detritus, bacteria to mammals food web modelTwo domains: Barents Sea, Fram Strait, each with coarse vertical and horizontal resolutionMiMeMoMike Heath
SCHISM-ECOSMO E2EFully coupled Physical-Biogeochemical model including functional groups for P,Z,Fish and Macrobenthos Atlantic-Arctic (including Barents Sea, Fram Strait)MiMeMo Ute Daewel
SIMBA (Sea Ice Model for Bottom Algae)Sea ice biogeochemical model, NPD, RedfieldianPan-Arctic, applied to Beaufort GyreEco-LightGiulia Castellani


Synthesis analysisSpatial domainProjectContact
Calanus lipid pump estimationNorth Atlantic – Arctic BasinDIAPODMike Heath and
Sigrún Jónasdóttir
Horizontal and vertical carbon fluxes through the planktonTBDChAOS
Christian März
Neil Banas
Isoscape models – statistical and mechanistic models of CNS isotopic variabilityBarents SeaColdfishClive Trueman
Spatial models of ecosystem metrics (benthic pelagic coupling strength, food chain length, trophic niche)Barents SeaColdfishClive Truman