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Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. 

Abstract. In sequential estimation methods often used in oceanic and general climatecalculations of the state and of forecasts, observations act mathematicallyand statistically as source or sink terms in conservation equations for heat, salt, mass, and momentum.These artificial terms obscure the inference of the system's variability or secular changes.Furthermore, for the purposes of calculating changes inimportant functions of state variables such as total mass and energy orvolumetric current transports, results of both filter and smoother-based estimates are sensitive to misrepresentationof a large variety of parameters, including initial conditions, prioruncertainty covariances, and systematic and random errors in observations.Here, toy models of a coupled mass–spring oscillator system and of a barotropic Rossby wave system are used todemonstrate many of the issues that arise from such misrepresentations.Results from Kalman filter estimates and those from finite intervalsmoothing are analyzed.In the filter (and prediction) problem, entry of data leads to violation ofconservation and other invariant rules.A finite interval smoothing method restores the conservation rules, butuncertainties in all such estimation results remain. Convincing trend andother time-dependent determinations in “reanalysis-like” estimates require a full understanding of models, observations, and underlying error structures. Application of smoother-type methods that are designed for optimal reconstruction purposes alleviate some of the issues.

 

The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction, and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.

 

Abstract. Use of an ocean parameter and state estimation framework – such as the Estimating the Circulation and Climate of the Ocean (ECCO) framework – could provide an opportunity to learn about the spatial distribution of the diapycnal diffusivity parameter (κρ) that observations alone cannot due to gaps in coverage. However, we show that the inclusion of misfits to observed physical variables – such as in situ temperature, salinity, and pressure – currently accounted for in ECCO is not sufficient, as κρ from ECCO does not agree closely with any observationally derived product. These observationally derived κρ products were inferred from microstructure measurements, derived from Argo and conductivity–temperature–depth (CTD) data using a strain-based parameterization of fine-scale hydrographic structure, or calculated from climatological and seafloor data using a parameterization of tidal mixing. The κρ products are in close agreement with one another but have both measurement and structural uncertainties, whereas tracers can have relatively small measurement uncertainties. With the ultimate goal being to jointly improve the ECCO state estimate and representation of κρ in ECCO, we investigate whether adjustments in κρ due to inclusion of misfits to a tracer – dissolved oxygen concentrations from an annual climatology – would be similar to those due to inclusion of misfits to observationally derived κρ products. We do this by performing sensitivity analyses with ECCO. We compare multiple adjoint sensitivity calculations: one configuration uses misfits to observationally derived κρ, and the other uses misfits to observed dissolved oxygen concentrations. We show that adjoint sensitivities of dissolved oxygen concentration misfits to the state estimate's control space typically direct κρ to improve relative to the observationally derived values. These results suggest that the inclusion of oxygen in ECCO's misfits will improve κρ in ECCO, particularly in (sub)tropical regions.