Forecasting and Uncertainties

GLOBEC Program

DiLorenzoBond

BalleriniBrodeurCollie

HastingsKimmel

RibicStrubWiebe

What we learned from GLOBEC

Improvement of physical/biological dynamical model (e.g. ROMS, FVCOM, NPZD, IBM)

Trained a generation of multi-disciplinary (e.g. from observationalist to modelers, from biologist to physical scientist)

Appreciation of the importance of forecast

What processes can we model? using Dynamical and Statistical models

1) Processes that we understand and model that can lead to forecast.

2) How to propagate uncertainties in current and future states of the physical/biological system, both observed and modeled.

3) Still limitations due to lack of observations to assemble statistics.

Dynamical and Ecosystem Regional Models

ROMS 3D circulation model

upwellingtransport dynamicschanges in property distributionvertical distribution and mix layerstratification

COAMPS, RSMupwelling windsboundary layer dynamics (e.g. fog)heat/fresh water fluxes Satellite products (winds, SST, SSH, CHL-a)

NPZD CHL-a distributionNutrient distributionsZooplanktonParameters uncertainty

FVCOM tidal and estuarine environmentsurface currents and transportbaroclinic eddy circulation

What is the role of the dynamical models in forecasting?

Large-scale variability: ENSO, NAO, SAM, PDO, NPGO, etc.

forecast the forecast the delayed delayed

ecosystem ecosystem responseresponse

dynamical model 20%

regression model

80%

nowcast of nowcast of unobservable unobservable

statesstates

What is the role of the dynamical models in forecasting?

Dynamical model can be used to compile statistics

and constrain the processes that we

understand

Statistical characterizations of things

we cannot model

Bayesian/Hybrid Modeling Frameworks

A possible approach

Need for specific examples of forecasting

Outcomes:

we learn from trying

relative merit of different approaches

synthesis activity in that it forces us to define what we really understand and model

Recommendation for present projects:

Obligation to assess uncertainties in models

Sources of error and measures of skill

Sources of uncertainties and relative importance

Summary of modeling applications

Future recommendations:

Pilot forecasting experiments with interdisciplinary team.

Real time basin-wide physical-biological model in assimilative mode to give a first order estimate of the states.

Continue the development of low-dimensional or simple models.

GLOBEC involved in IPCC assessments

END