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The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean’s role in Earth’s climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements.

As the sea‐ice modeling community is shifting to advanced numerical frameworks, developing new sea‐ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea‐ice models. Initiated at the Forum for Arctic Modeling and Observational Synthesis, the Sea Ice Rheology Experiment (SIREx) aims at evaluating state‐of‐the‐art sea‐ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea‐ice physics (rheology) and by model configuration. Part 1 of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea‐ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the viscous‐plastic (and the elastic‐viscous‐plastic variant), elastic‐anisotropic‐plastic, and Maxwell‐elasto‐brittle rheologies are compared in a single study. We find that both plastic and brittle sea‐ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi‐fractality. Model configuration (e.g., numerical convergence, atmospheric representation, spatial resolution) and physical parameterizations (e.g., ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea‐ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features in a SIREx Part 2 companion paper. Plain Language Summary: The ice in the Arctic Ocean is not continuous: it is broken into individual pieces of ice (floes). As the winds and ocean currents continually move these ice floes, they get piled up together or pushed away from each other, forming regions of increased ice thickness (ridges) or regions of open water (leads). These leads and ridges (ice deformations) are important features of the Arctic pack ice because they control the amount of energy that can be exchanged between the atmosphere and the ocean. Current climate models cannot simulate individual ice floes and their deformations. Instead, various methods are used to represent the movement and deformation of the Arctic sea‐ice cover. The goal of the Sea Ice Rheology Experiment (SIREx) is to compare these different methods and evaluate the ability of a large number of sea‐ice models to reproduce observed sea‐ice deformations from satellite imagery. SIREx is divided in two parts. In Part 1 (this study), we evaluate how the intensity of ice deformations varies in space and time. In Part 2 (companion paper), we track and evaluate the occurrence of specific deformation features. With this work, we show how to improve sea‐ice models for realistic simulations of sea‐ice deformations. Key Points: Power law scaling and multi‐fractality of deformations in space and time can be achieved by both plastic and brittle sea‐ice rheologies Scaling statistics of simulated sea‐ice deformation fields depend on the model configuration and physical parameterizations Finite‐difference plastic models need to be run at higher resolution than observations to agree with the observed deformation statistics

Increasing Greenland discharge has contributed more than 5000 km3 of surplus fresh water to the Subpolar North Atlantic since the early 1990s. The volume of this freshwater anomaly is projected to cause freshening in the North Atlantic leading to changes in the intensity of deep convection and thermohaline circulation in the subpolar North Atlantic. This is roughly half of the freshwater volume of the Great Salinity Anomaly of the 1970s that caused notable freshening in the Subpolar North Atlantic. In analogy with the Great Salinity Anomaly, it has been proposed that, over the years, this additional Greenland freshwater discharge might have a great impact on convection driving thermohaline circulation in the North Atlantic with consequent impact on climate. Previous numerical studies demonstrate that roughly half of this Greenland freshwater anomaly accumulates in the Subpolar Gyre. However, time scales over which the Greenland freshwater anomaly can accumulate in the subpolar basins is not known. This study estimates the residence time of the Greenland freshwater anomaly in the Subpolar Gyre by approximating the process of the anomaly accumulation in the study domain with a first order autonomous dynamical system forced by the Greenland freshwater anomaly discharge. General solutions are obtained for two types of the forcing function. First, the Greenland freshwater anomaly discharge is a constant function imposed as a step function. Second, the surplus discharge is a linearly increasing function. The solutions are deduced by utilizing results from the numerical experiments that tracked spreading of the Greenland fresh water with a passive tracer. The residence time of the freshwater anomaly is estimated to be about 10–15 years. The main differences in the solutions is that under the linearly increasing discharge rate, the volume of the accumulated Greenland freshwater anomaly in the Subpolar Gyre does not reach a steady state. By contrast, solution for the constant discharge rate reaches a steady state quickly asymptoting the new steady state value for time exceeding the residence time. Estimated residence time is compared with the numerical experiments and observations.