We propose a metric for measuring internal and forced variability in ensemble atmosphere, ocean, or climate models using information theory: Shannon entropy and mutual information. This metric differs from the standard ensemble-variance approaches. Information entropy quantifies variability by the size of the visited probability distribution, as opposed to variance that measures only its second moment. Shannon entropy and mutual information manage correlated fields, apply to any data, and are insensitive to outliers as well as a change of units or scale. Finally, we use an example featuring a highly skewed probability distribution (Arctic sea surface temperature) to show that the new metric is robust even with a sharp nonlinear cutoff (the freezing point). We apply these two metrics to quantify internal vs forced variability in (1) idealized Gaussian data, (2) an initial condition ensemble of a realistic coastal ocean model, (3) the Community Earth System Model large ensemble. Each case illustrates the advantages of the proposed metric over variance-based metrics. Furthermore, in the coastal ocean model, the new metric is adapted to further quantify the impact of different boundary forcing choices to aid in prioritizing model improvements–i.e., comparing different choices of extrinsic forcing. The metric can be applied to any ensemble of models where intrinsic and extrinsic factors compete to control variability and can be applied regardless of if the ensemble spread is Gaussian.

Sea ice is a heterogeneous, evolving mosaic comprised of many individual floes, which vary in spatial scales from meters to tens of kilometers. Both the internal dynamics of the floe mosaic (floe-floe interactions), and the evolution of floes under ocean and atmospheric forcing (floe-flow interactions), determine the exchange of heat, momentum, and tracers between the lower atmosphere and upper polar oceans. Climate models do not represent either of these highly variable interactions. We use a novel, high-resolution, discrete element modelling framework to examine the production of ice-ocean boundary layer (IOBL) turbulence within a domain approximately the size of a climate model grid. We show floe-scale effects cause a marked increase in IOBL turbulent production relative to continuum model approaches, and provide a method of representing that turbulence using bulk parameters related to the spatial variance of the ice and ocean: the floe size distribution and the ocean kinetic energy spectrum.

Ocean surface waves have been demonstrated to be an important component of coupled Earth System Models (ESMs), influencing atmosphere-ocean momentum transfer, ice floe breakage, CFC, carbon and energy uptake, and mixed-layer depth. Modest errors in sea state properties do not strongly affect the impacts of these parameterizations. The minimal data and accuracy needed contrast sharply with the computational costs of spectral wave models in next-generation ESMs. We establish an alternative, cost-efficient wave modeling framework for air-sea and ice-ocean interactions that enables the routine use of sea state-dependent air-sea coupling in ESMs. In contrast to spectral models, the Particle-in-Cell for Efficient Swell (PiCLES) wave model is constructed for coupled atmosphere-ocean-sea ice modeling. Combining Lagrangian wave growth solutions with the Particle-In-Cell method leads to a model that periodically projects onto any convenient grid and scales in an embarrassingly parallel manner. The set of equations solves for the growth and propagation of a parametric wave spectrum’s peak wavenumber and total wave energy, which reduces the state vector size by a factor of 50-200 compared to spectral models. We estimate PiCLES’s computational costs about 1-4 orders of magnitude faster than established wave models with sufficient accuracy for ESMs – rivaling that of spectral models in the open ocean. We evaluate PiCLES against WAVEWATCH III in efficiency and accuracy and discuss the advantages of future performance and planned extensions of its capability in ESMs.

As one kind of submesoscale instability, symmetric instability (SI) of the ocean surface mixed layer (SML) plays a significant role in modulating the SML energetics and material transport. The small spatial scales of SI, O(10 m ~ 1 km), are not resolved by current climate ocean models and most regional models. This paper describes comparisons in an idealized configuration of the SI parameterization scheme proposed by Bachman et al. (2017) (SI-parameterized) versus the K-Profile Parameterization (KPP) scheme (SI-neglected run) as compared to a SI-permitting model; all variants use the Coastal and Regional Ocean Community Model version of the Regional Ocean Modeling System (CROCO-ROMS) and this paper also serves to introduce the SI parameterization in that model. In both the SI-parameterized and SI-permitting model, the geostrophic shear production is enhanced and anticyclonic potential vorticity is reduced versus the SI-neglected model. A comprehensive comparison about the energetics (geostrophic shear production, vertical buoyancy flux), mixed layer thickness, potential vorticity, and tracer redistribution indicates that all these variables in the SI-parameterized case have structures closer to the SI-permitting case in contrast to the SI-neglected one, demonstrating that the SI scheme has a positive improvement to capture the impacts of SI. This work builds toward applying the SI scheme in a regional or climate model.

Projections of future sea-level change are characterized by both quantifiable uncertainty and by ambiguity. Both types of uncertainty are relevant to users of sea-level projections, particularly those making long-term investment and planning decisions with multigenerational consequences. Communicating information about both types is thus a central challenge faced by scientists who generate sea-level projections to support decision-making. Diverse approaches to communicating uncertainty in future sea-level projections have been taken over the last several decades, but the literature evaluating these approaches is limited and not systematic. Here, we review how the Intergovernmental Panel on Climate Change (IPCC) has approached uncertainty in sealevel projections in past assessment cycles and how this information has been interpreted by national and subnational assessments, as well as alternative approaches used by recent US subnational assessments. The evidence reviewed here generally supports the explicit approach to communicating both types of uncertainty adopted by the IPCC Sixth Assessment Report (AR6).

The Ocean State Ocean Model OSOM is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This paper discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare for coupling with biogeochemistry and fisheries models with widely disparate timescales, and to find the spin-up time needed to establish the climatological circulation of the region. Perturbed initial condition ensemble simulations are combined with metrics from information theory to quantify the predictability of the OSOM forecast system–i.e., how long anomalies from different initial conditions persist. The predictability timescale in this model agrees with readily estimable timescales such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) framework, indicating that the estuarine dynamics rather than chaotic transport is the dominant model behavior limiting predictions. The predictability of the OSOM is ~ 7 to 40 days, varying with parameters, region, and season.