The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.

As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are “good” or “bad.” This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like “season” or “variability,” and that simple decompositions can be combined to represent more complex concepts, like “seasonal variability,” creating an expressive language through which to interrogate models and data.

The mean absolute error (MAE) and root mean squared error (RMSE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide. Some of this confusion arises from a recent debate between Willmott and Draxler (2005) and Chai and Draxler (2014), in which either side presents their arguments for one metric over the other. Neither side was completely correct; however, because neither metric is inherently better: MAE is optimal for Laplacian errors, and RMSE is optimal for normal (Gaussian) errors. When errors deviate from these distributions, other metrics are superior.

Ocean‐driven melting of ice shelves is a primary mechanism for ice loss from Antarctica. However, due to the difficulty in accessing the sub‐ice‐shelf ocean cavity, the relationship between ice‐shelf melting and ocean conditions is poorly understood, particularly near the grounding zone, where the ice transitions from grounded to floating. We present the first borehole oceanographic observations from the grounding zone of the Ross Ice Shelf, Antarctica's largest ice shelf by area. Contrary to predictions that tidal currents near grounding zones mix the water column, we found that Ross Ice Shelf waters were vertically stratified. Current velocities at mid‐depth in the ocean cavity did not change significantly over measurement periods at two different parts of the tidal cycle. The observed stratification resulted in low melt rates near this portion of the grounding zone, inferred from phase‐sensitive radar observations. These melt rates were generally <10 cm yr^‐1, which is lower than average for the Ross Ice Shelf (~20 cm yr^‐1). Melt rates may be higher at portions of the grounding zone that experience higher subglacial discharge or stronger tidal mixing. Stratification in the cavity at the borehole site was prone to diffusive convection as a result of ice‐shelf melting. Since diffusive convection influences vertical heat and salt fluxes differently than shear‐driven turbulence, this process may affect ice‐shelf melting and merits further consideration in ocean models of sub‐ice‐shelf circulation.

Ice shelves surround ∼ 75% of Antarctica's coastline and are highly sensitive to climate change; several have recently collapsed and others are predicted to in the near future. Marine waters beneath ice shelves harbor active ecosystems, while adjacent seas can be important areas of bottom water formation. Despite their oceanographic significance, logistical constraints have resulted in few opportunities to directly sample sub‐ice shelf cavities. Here, we present the first data on microbial diversity and biogeochemistry beneath the McMurdo Ice Shelf (MIS) near Ross Island, Antarctica. Physicochemical profiles obtained via a 56 m deep borehole through the MIS revealed three vertically layered water masses (Antarctic Surface Water [AASW], Ice Shelf Water [ISW], and modified High Salinity Shelf Water [mHSSW]). Metabolically active, moderately diverse (Shannon diversity from 2.06 to 5.74) microbial communities were detected in the AASW and mHSSW. Heterotrophic bacterial production and dissolved organic matter concentrations were higher (12–37% and 24%, respectively) in mHSSW relative to AASW. Chemoautotrophic production was 5.3 nmol C L⁻¹ d⁻¹ and 6.0 nmol C L⁻¹ d⁻¹ in the AASW and mHSSW, respectively. Phytoplankton cells were more abundant and larger in the mHSSW sample relative to the AASW, which indicates sinking of phytoplankton produced in surface waters and, together with southerly flowing currents (0.09–0.16 m s⁻¹), horizontal advection of phytoplankton from McMurdo Sound. Advected phytoplankton carbon together with in situ chemoautotrophic production provide important sources of organic matter and other reduced compounds to support ecosystem processes in the dark waters in the ice shelf cavity.