Lermusiaux, P.F.J., C.-S. Chiu, G.G. Gawarkiewicz, P. Abbot, A.R. Robinson, R.N. Miller, P.J. Haley, W.G. Leslie, S.J. Majumdar, A. Pang and F. Lekien, 2006. Quantifying Uncertainties in Ocean Predictions. Refereed invited manuscript. Oceanography, Special issue on "Advances in Computational Oceanography", T. Paluszkiewicz and S. Harper (Office of Naval Research), Eds., 19, 1, 92-105, doi: 10.5670/oceanog.2006.93.
A multitude of physical and biological processes
occur in the ocean over a wide range of temporal
and spatial scales. Many of these processes are nonlinear
and highly variable, and involve interactions
across several scales and oceanic disciplines. For
example, sound propagation is influenced by physical
and biological properties of the water column
and by the seabed. From observations and conservation
laws, ocean scientists formulate models that
aim to explain and predict dynamics of the sea.
This formulation is intricate because it is challenging
to observe the ocean on a sustained basis and to
transform basic laws into generic but usable models.
There are imperfections in both data and model
estimates. It is important to quantify such uncertainties
to understand limitations and identify the
research needed to increase accuracies, which will
lead to fundamental progress.
There are several sources of uncertainties in ocean
modeling. First, to simplify models (thereby reducing
computational expenses), explicit calculations are
only performed on a restricted range of spatial and
temporal scales (referred to as the “scale window”)
(Nihoul and Djenidi, 1998). Influences of scales outside
this window are neglected, parameterized, or
provided at boundaries. Such simplifications and
scale reductions are a source of error. Second, uncertainties
also arise from the limited knowledge of
processes within the scale window, which leads to
approximate representations or parameterizations.
Third, ocean data are required for model initialization
and parameter values; however, raw measurements
are limited in coverage and accuracy, and they
are often processed with the aim of extracting information
within a predetermined scale window. Initial
conditions and model parameters are thus inexact.
Fourth, models of interactions between the ocean
and Earth system are approximate and ocean boundary
conditions are inexact. For example, effects of
uncertain atmospheric fluxes can dominate oceanic
uncertainty. Fifth, miscalculations occur due to numerical
implementations. All of the above leads to
differences between the actual values (unknown) and
the measured or modeled values of physical, biological,
and geo-acoustical fields and properties.