Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions

Abstract

The ocean plays an important role in the climate system on time-scales of weeks to centuries. Despite improvements in ocean models, dynamical processes involving multiscale interactions remain poorly represented, leading to errors in forecasts. We present recent advances in understanding, quantifying, and representing physical and numerical sources of uncertainty in novel regional and global ocean ensembles at different horizontal resolutions. At coarse resolution, uncertainty in 21st century projections of the upper overturning cell in the Atlantic is mostly a result of buoyancy fluxes, while the uncertainty in projections of the bottom cell is driven equally by both wind and buoyancy flux uncertainty. In addition, freshwater and heat fluxes are the largest contributors to Atlantic Ocean heat content regional projections and their uncertainties, mostly as a result of uncertain ocean circulation projections. At both coarse and eddy-permitting resolutions, unresolved stochastic temperature and salinity fluctuations can lead to significant changes in large-scale density across the Gulf Stream front, therefore leading to major changes in large-scale transport. These perturbations can have an impact on the ensemble spread on monthly time-scales and subsequently interact nonlinearly with the dynamics of the flow, generating chaotic variability on multiannual time-scales. In the Gulf Stream region, the ratio of chaotic variability to atmospheric-forced variability in meridional heat transport is larger than 50% on time-scales shorter than 2 years, while between 40 and 48 • S the ratio exceeds 50% on on time-scales up to 28 years. Based on these simulations, we show that air-sea interaction and ocean subgrid eddies remain an important source of error for simulating and predicting ocean circulation, sea level, and heat uptake on a range of spatial and temporal scales. We discuss how further refinement of these ensembles can help us assess the relative importance of oceanic versus atmospheric uncertainty in weather and climate.

Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization

“…We have shown that neural networks can successfully represent the spatiotemporal variability of the eddy momentum forcing, implying potential for data‐driven oceanic turbulence closures in the future, as suggested by Zanna et al (). The generalization ability of the neural networks shows that only a limited amount of observations or high‐resolution model data may be needed to successfully represent subgrid‐scale processes.…”

Investigating the predictability of North Atlantic sea surface height

“…This area has already been the subject of many observational studies (e.g., Line W, Toole et al (2011)) and is well observed by altimetry (see Lillibridge and Mariano (2013) and references within). Furthermore, initialisation of GCM ensemble simulations with the optimal initial conditions could provide a better estimate of initial condition uncertainty in SSH prediction (Zanna et al 2018).…”

“…It also made use of a single model, and therefore the optimal initial conditions presented may be model specific. It would therefore be beneficial to examine the SSH predictability with a more extensive ensemble of model simulations, including those which isolate the effects of intrinsic processes (e.g., Sérazin et al (2015) and Zanna et al (2018)). Moreover, a comparison with altimetry or higher resolution model output may further elucidate the effects of the eddy field on interannual variability.…”

“…Assessing such uncertainty and associated mechanisms are crucial to provide reliable forecasts and potentially mitigate the effects of coastal flooding in certain regions. Furthermore, this can have implications for devising strategies on how best to design ocean observing systems and initialise climate models (Zanna et al 2018). Finally, more skilful SSH predictions could help improve predictions of other parts of the climate system, for example increased skill in predicting the Gulf Stream's meridional position could impact predictions of air-sea heat fluxes and atmospheric blocking (Scaife et al 2011).…”

Introduction to the special issue on “25 years of ensemble forecasting”

“…In this Special Issue, Zanna et al . () discusses the state of the art in ocean modelling and recent advances in the design of ocean ensembles, and Xian et al . () raises issues linked to the prediction of aerosol.…”