Abstract
The ocean is central to climate, ecosystems, food production, transport, and
coastal industries, yet much of its variability remains difficult to observe directly.
This limits our ability to describe present conditions, understand how
the system evolves, and provide reliable information for forecasting and coastal
decision-making. Variability unfolds acrossmultiple spatial and temporal scales,
from basin circulation to coastal fronts and fjord exchange, and much of it occurs
below the surface. No single observing system can resolve this full range.
The observational pyramid provides one useful way to frame the problem, with
satellites offering broad synoptic coverage, mobile and autonomous platforms
resolving evolving regional features, and fixed or depth-resolving systems constraining
local structure and continuity. Numerical ocean and ecosystem models
provide the dynamical link between these observational levels, but they
are also uncertain. This challenge is addressed here through data assimilation,
where observations are combined with model predictions to estimate the evolving
ocean state, and through observation-design methods that help determine
where measurements are most informative.
The Ensemble Kalman Filter is implemented in SINMOD, an ocean model
developed and maintained at SINTEF Ocean, to combine heterogeneous observations
with a coupled ocean and ecosystem model and to represent uncertainty
through ensembles on high-performance computing clusters. The work
shows how observations can constrain both observed and unobserved system
states, including dynamical model variables and static parameters, and it identifies
practical requirements for stable estimation such as localization, perturbation
design, and treatment of bounded ecosystem variables. The same framework
is then used to study how analysis corrections propagate from regional
mother domains into nested coastal domains. The results show that this impact
depends strongly on physical connectivity and is most persistent where
corrected water masses enter through inflow.
The framework is also applied in two observing contexts. A two-month operational
field campaign demonstrates closed-loop monitoring in which chlorophylla
observations from the HYPSO-1 satellite and the AutoNaut autonomous surface
vehicle are assimilated into operational nested SINMOD forecasts, while
those forecasts provide feedback to adaptive sampling under practical constraints
related to latency, communication, weather, computing, and model
bias. A complementary observation-design study in the Trondheim Fjord uses
a large high-resolution ensemble to optimize platform type and placement by
expected posterior uncertainty reduction within a region of interest, and compares
the predicted impact patterns with an assimilation case based on the
OceanLab observing system. Overall, the work links multiscale observing, coupled
modelling, data assimilation, adaptive monitoring, and observing-system
design within one coherent framework for coastal and fjord environments.