Stephen M. Griffies

Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.

Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.

The extent to which coastal sea levels co‐vary with open‐ocean variability is explored through evaluation of a balanced dynamics framework relating sea level with pressure, density, and velocity along the continental shelf and slope seafloor. Using high‐resolution regional ocean model output, we develop and evaluate this relationship at 34 stations along the US East Coast. Variability in the extent of cross‐shore connectivity is explained through joint consideration of density and topography structure, an evaluation of steric setup. In the northern sector, reconstructed coastal sea levels have high skill over the continental shelf, while further south, skill increases are achieved when considering variability over the continental slope. These differences are attributed to differing seafloor hypsometries that shape density and along‐shore current structure. Results reveal regions of influence for points along the coast and establish a dynamical link to offshore changes in density classes associated with large scale ocean dynamics.

Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.

Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.

Coupled climate models generally simulate a decline in Antarctic sea ice extent (SIE) over the satellite period, failing to capture the observed near‐zero trend. Recent work has suggested that high‐resolution ocean models could ameliorate this issue via improved representation of mesoscale ocean processes, which may delay simulated Antarctic SIE decline. We investigate this hypothesis using two high‐resolution hierarchies of coupled climate models developed at GFDL and NCAR, which span atmospheric resolutions of 0.25–1 and ice‐ocean resolutions of 0.1–1, as well as three generations of Coupled Model Intercomparison Project (CMIP) models. Across the high‐resolution hierarchies and CMIP ensembles, we find no clear relationship between ocean and atmospheric resolution and Antarctic SIE trends over both the satellite period and the remainder of the 21st century. Climate models that reproduce observed trends in individual ensemble members are those that exhibit large amplitude multi‐decadal Southern Ocean climate variability.

The ongoing transformation of scholarly publishing has created new challenges for authors, reviewers, and editors in deciding which journals to engage with. As Editors in Chief of AGU Publications, we meet annually to reflect on the evolving editorial landscape, consider opportunities for improvement, and reach consensus on policies, priorities and responsibilities guiding AGU Publications. Here, we argue that publishing with AGU goes beyond the paper. It advances a set of shared, interconnected scientific purposes grounded in community stewardship, editorial independence, rigorous peer review, openness, equity, global participation, and long‐term scientific impact. As a not‐for‐profit global scientific society, AGU publication revenues are reinvested in science, education, professional development, and community engagement. Choosing where to publish is ultimately a choice about the scientific values, institutions, and communities we seek to sustain.

Climate models can be used to quantify the processes that contribute to a property's change through analysis of the tendency terms in the budget of the property. For example, a heat budget can be performed to understand the processes driving an increase in the ocean's heat content under climate change. However, conventional budget analyses in climate models are typically performed without proper consideration of their relationship to the property of interest. Specifically, most studies consider standard arithmetic time-averages of budget terms. For the tendency term, this average is related to the difference between two time instants (snapshots) of the property and thus is strongly impacted by rapid fluctuations, or "weather". Such an approach to budget analysis is thus less relevant to the "climate change" problem, that is, the difference between two long-term averages, or "epochs", of the property. Here we present a time-averaging method, referred to as the hat average, which exactly relates the processes contributing to a property's tendency budget to the epoch difference, or "climate change", of that property. We demonstrate the utility of this method by applying it to the horizontally integrated heat budget of a global ocean model. We find that the hat average removes high-frequency variability, typically associated with rapid and noisy advective processes, and so allows for a clear measure of how processes contribute to changes in climate.

Ocean circulations are strongly constrained by rotation and stratification, making potential vorticity (PV) a key dynamical property. Specifically, PV is conserved following water parcels in the absence of diabatic forcing and friction—a desirable feature for isopycnal ocean models. Here, we devise a finite‐volume discretization for PV fluxes in the vector‐invariant formulation of shallow‐water equations. The scheme adopts a seventh‐order weighted essentially non‐oscillatory reconstruction of PV on the Arakawa C‐grid while ensuring PV conservation. To prevent PV singularities arising from vanishing layer thickness—typical in isopycnal ocean models—we propose a thickness weighting for the PV reconstruction. The scheme is implemented in the Modular Ocean Model 6 (MOM6) dynamical core and tested through idealized simulations of baroclinic geostrophic turbulence and wind‐driven double gyres. Compared with existing discretizations in MOM6 that conserve energy and/or potential enstrophy, the new scheme not only substantially reduces dispersive errors but also yields a weak potential enstrophy dissipation near grid scales, and thus permits stable time integration without superfluous horizontal viscosity. Additionally, the scheme supports a strong inverse energy cascade and exhibits an elevated eddy kinetic energy. These properties in turn lead to more realistic ocean energetics in eddy‐permitting simulations for which the energy input by surface wind stress is dissipated through bottom drag. In terms of computational efficiency, the scheme leads to notable improvement in energy spectra in eddy‐permitting simulations with a minor increase in total cost by . Results here are promising for improving the simulation of ocean eddies and large‐scale circulations for climate applications.