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.

Model Type Spatial domain Project Contact
BFM-SI (Biogeochemical Flux Model – Sea Ice) Coupled one-dimensional sea ice thermodynamics-biogeochemical model  Single-point model Diatom-ARCTIC Letitzia Tedesco
BRNS Diagenetic Model (benthic reaction-transport model) Barents Sea shelf and slope ChAOS Sandra Arndt
“BRNSlight” Numerically efficient encapsulation (ANN) of the BRNS for regional applications/ coupling to pelagic models Barents Sea shelf and slope, and planned Pan-Arctic ChAOS Sandra Arndt
Coltrane Individual-based, trait-based representation of Calanus complex Pan-Arctic DIAPOD Neil
Dynamic life history model for C. finmarchicus  Dynamic life history model Norwegian and Greenland Seas CHASE Geraint Tarling, Jennifer Freer
ERSEM Biogeochemical model of lower trophic levels of both benthic and pelagic environments Lena 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 suite Coastal and pelagic surface water ecosystem EISPAC Silvana Birchenough
FESOM (Finite Element Sea ice Ocean Model) Coupled sea-ice–ocean model Global Eco-Light Giulia Castellani
FRUITS Bayesian diet and foodweb estimation model Iceland LOMVIA Thomas Larsen
FVCOM Unstructured grid finite volume hydrodynamic model Lena and Kolyma River estuaries and near shore (Russia Arctic and East Siberian Shelf) CACOON Ricardo Tores
GFR (General Functional Response) Resource selection function Iceland LOMVIA Jason Matthiopoulos
Individual based model for C. finmarchicus and T. inermis Individual based model Range of C. finmarchicus and T. inermis CHASE Jürgen Groeneveld and Bettina Meyer
MEDUSA-2 Biogeochemical; component of UK Earth System Model and coupled OGCMs Variable (global, regional, 1-D) APEAR ARISE
Katya Popova
Clive Trueman
Andrew Yool
NEMO-MEDUSA-2 Global coupled OGCM with Bio-Geo-Chemistry Global and Pan-Arctic APEAR Yevgeny Aksenov
Katya Popova
Andrew Yool
NEMO-PISCES Complex biogeochemical model, focus on isoscape Global/Pan-Arctic ARISE Alessandro Tagliabue
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 NEMO Fram Strait Micro-ARC Markus Schartau and
Ben Ward
CLADACH (Consensus model of Light and Algal Dynamics under Arctic Change) NPZD (biogeochemical, focus on plankton) Lagrangian testbeds on Barents, Bering, Chukchi shelves PRIZE
and Fabian Groβe
REcoM (Regulated Ecosystem Model) Ocean biogeochemical model, cell-quota Pan-Arctic, applied to Beaufort Gyre Eco-Light Giulia Castellani
Seal population models Individual-based, statistical East Greenland, Barents Shelf ARISE Sophie Smout
StrathCal Spatial demography of a copepod population Range of C. finnmarchicus DIAPOD Dougie Speirs
StrathE2EPolar Functional group nitrogen mass dynamics –  nutrients, detritus, bacteria to mammals food web model Two domains: Barents Sea, and East Greenland Shelf, each with coarse vertical and horizontal resolution MiMeMo Mike Heath
SCHISM-ECOSMO E2E Fully 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, Redfieldian Pan-Arctic, applied to Beaufort Gyre Eco-Light Giulia Castellani


Synthesis analysis Spatial domain Project Contact
Calanus lipid pump estimation North Atlantic – Arctic Basin DIAPOD Mike Heath and
Sigrún Jónasdóttir
Horizontal and vertical carbon fluxes through the plankton TBD ChAOS
Christian März
Neil Banas
Isoscape models – statistical and mechanistic models of CNS isotopic variability Barents Sea Coldfish Clive Trueman
Spatial models of ecosystem metrics (benthic pelagic coupling strength, food chain length, trophic niche) Barents Sea Coldfish Clive Trueman