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In July 2011, observations of a massive phytoplankton bloom in the ice‐covered waters of the western Chukchi Sea raised questions about the extent and frequency of under‐ice phytoplankton growth and its contribution to the carbon budget in the Arctic Ocean. To address some of these questions, we use the fully‐coupled, high‐resolution Regional Arctic System Model to simulate Arctic marine biogeochemistry over a 30‐year period. Our results demonstrate the presence of extensive under‐ice phytoplankton growth in the western Arctic (WA) in summer. In addition, similar growth, yet of lower magnitude, occurs annually in the eastern Arctic (EA). We investigate the critical levels of nitrate concentration and photosynthetically available radiation (PAR) that are necessary for under‐ice phytoplankton growth to occur. Our results show that while the majority of ice‐covered Arctic waters have sufficient surface nitrate levels to sustain growth, PAR reaching the ocean surface through the sea ice in early summer only exceeds critical levels in the WA. We therefore conclude that the EA high chlorophyll‐aconcentrations shown in our simulations did not develop under sea ice, but were instead, at least in part, formed in open waters upstream and subsequently advected by ocean currents beneath the sea ice. In July 2011, scientists conducting research in the western Arctic (WA) Ocean observed a large phytoplankton bloom under the sea ice. Traditionally, such blooms were believed to be rare. Using our state‐of‐the‐art Arctic system model, we were able to demonstrate that in fact, areas with high concentrations of under‐ice phytoplankton have been occurring annually for the past several decades. In the WA, under‐ice phytoplankton growth begins when sufficient sunlight penetrates through the sea ice to the ocean, and ends when nutrient concentrations become too low to sustain the phytoplankton. In the eastern Arctic (EA), our model shows that phytoplankton can still grow beneath the sea ice even when there is little sunlight penetrating the ice and entering the water column. We conclude that the EA high chlorophyll‐aconcentrations shown in our simulations did not develop under sea ice, but were instead, at least in part, formed in open waters upstream and subsequently advected by ocean currents beneath the sea ice. Model simulations show strong under‐ice phytoplankton growth similar to observations in summer 2011Under‐ice phytoplankton growth is common in parts of the western Arctic and is affected by light conditions and nutrient availabilityModel results suggest that high under‐ice chlorophyll‐aconcentrations in the eastern Arctic are at least partially a result of advection Model simulations show strong under‐ice phytoplankton growth similar to observations in summer 2011 Under‐ice phytoplankton growth is common in parts of the western Arctic and is affected by light conditions and nutrient availability Model results suggest that high under‐ice chlorophyll‐aconcentrations in the eastern Arctic are at least partially a result of advection

The current coarse‐resolution global Community Earth System Model (CESM) can reproduce major and large‐scale patterns but is still missing some key biogeochemical features in the Arctic Ocean, e.g., low surface nutrients in the Canada Basin. We incorporated the CESM Version 1 ocean biogeochemical code into the Regional Arctic System Model (RASM) and coupled it with a sea‐ice algal module to investigate model limitations. Four ice‐ocean hindcast cases are compared with various observations: two in a global 1° (40∼60 km in the Arctic) grid: G1deg and G1deg‐OLD with/without new sea‐ice processes incorporated; two on RASM's 1/12° (∼9 km) grid R9km and R9km‐NB with/without a subgrid scale brine rejection parameterization which improves ocean vertical mixing under sea ice. Higher‐resolution and new sea‐ice processes contributed to lower model errors in sea‐ice extent, ice thickness, and ice algae. In the Bering Sea shelf, only higher resolution contributed to lower model errors in salinity, nitrate (NO3), and chlorophyll‐a (Chl‐a). In the Arctic Basin, model errors in mixed layer depth (MLD) were reduced 36% by brine rejection parameterization, 20% by new sea‐ice processes, and 6% by higher resolution. The NO3concentration biases were caused by both MLD bias and coarse resolution, because of excessive horizontal mixing of high NO3from the Chukchi Sea into the Canada Basin in coarse resolution models. R9km showed improvements over G1deg on NO3, but not on Chl‐a, likely due to light limitation under snow and ice cover in the Arctic Basin. Model errors in mixed layer depth were reduced 36% by brine rejection parameterization, 20% by new ice processes, and 6% by higher resolutionThe modeled nitrate concentration biases in the Arctic Basin were caused by both mixed layer depth bias and coarse resolutionCoarse resolution models may introduce excessive horizontal mixing of high nitrate from the Chukchi Sea into the Canada Basin

The coastal streamflow flux from the Arctic drainage basin is an important driver of dynamics in the coupled ice‐ocean system. Comprising more than one third of the total freshwater flux into the Arctic Ocean, streamflow is a key component of the regional and global freshwater cycle. To better represent the coupling of the streamflow flux to the ocean, we have developed and applied the RVIC streamflow routing model within the Regional Arctic System Model (RASM). The RASM is a high‐resolution regional Earth System Model whose domain includes all of the Arctic drainage basin. In this paper, we introduce the RVIC streamflow routing model, detailing its application within RASM and its advancements in terms of representing high‐resolution streamflow processes. We evaluate model simulated streamflow relative to in situ observations and demonstrate a method for improving model performance using a simple optimization procedure. We also present a new, spatially and temporally consistent, high‐resolution data set of coastal freshwater fluxes for the Arctic drainage basin and surrounding areas that is based on a fully coupled RASM simulation and intended for use in Arctic Ocean modeling applications. This data set is evaluated relative to other coastal streamflow data sets commonly used by the ocean modeling community. We demonstrate that the RASM‐simulated streamflow flux better represents the annual cycle than existing data sets, especially in ungauged areas. Finally, we assess the impact that streamflow has on the coupled ice‐ocean system, finding that the presence of streamflow leads to reduced sea surface salinity, increased sea surface temperatures, and decreased sea ice thickness. Introduce the RVIC streamflow routing model and its application within RASMDemonstrate the role of the coastal streamflow flux within the coupled modelIntroduce a new, high‐resolution, coastal freshwater data set for use in ocean modeling applications

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. 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. Power law scaling and multi‐fractality of deformations in space and time can be achieved by both plastic and brittle sea‐ice rheologiesScaling statistics of simulated sea‐ice deformation fields depend on the model configuration and physical parameterizationsFinite‐difference plastic models need to be run at higher resolution than observations to agree with the observed deformation statistics 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

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state‐of‐the‐art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air‐ice‐ocean interaction processes. Winds and ocean currents continuously move and deform the sea ice cover of the Arctic Ocean. The deformation eventually breaks an initially closed ice cover into many individual floes, piles up floes, and creates open water. The distribution of ice floes and open water between them is important for climate research, because ice reflects more light and energy back to the atmosphere than open water, so that less ice and more open water leads to warmer oceans. Current climate models cannot simulate sea ice as individual floes. Instead, a variety of methods is used to represent the movement and deformation of the sea ice cover. The Sea Ice Rheology Experiment (SIREx) compares these different methods and assesses the deformation of sea ice in 36 numerical simulations. We identify and track deformation features in the ice cover, which are distinct narrow areas where the ice is breaking or piling up. Comparing specific spatial and temporal properties of these features, for example, the different amounts of fractured ice in specific regions, or the duration of individual deformation events, to satellite observations provides information about the realism of the simulations. From this comparison, we can learn how to improve sea ice models for more realistic simulations of sea ice deformation. All models simulate linear kinematic features (LKFs), but none accurately reproduces all LKF statisticsResolved LKFs are affected strongest by spatial and temporal resolution of model grid and atmospheric forcing and rheologyAccurate scaling of deformation rates is a proxy only for realistic LKF numbers but not for any other LKF static All models simulate linear kinematic features (LKFs), but none accurately reproduces all LKF statistics Resolved LKFs are affected strongest by spatial and temporal resolution of model grid and atmospheric forcing and rheology Accurate scaling of deformation rates is a proxy only for realistic LKF numbers but not for any other LKF static

To evaluate improvements in modelling Arctic sea ice, we compare results from two regional models at 1/12° horizontal resolution. The first is a coupled ice–ocean model of the Arctic Ocean, consisting of an ocean model (adapted from the Parallel Ocean Program, Los Alamos National Laboratory [LANL]) and the “old” sea ice model. The second model uses the same grid but consists of an improved “new” sea ice model (LANL/ CICE) with a simple ocean mixed layer. Both models are forced with European Centre for Medium-range Weather Forecasts reanalysis data for 1979–1993. A comparison of the two sea ice models focuses on the winter of 1987 to emphasize the internal ice stress and to minimize biases towards a particular Arctic climate regime. The “new” sea ice model gives improved ice deformation and drift fields. These improvements are associated at least in part with the multi-category representation of the ice thickness distribution and more realistic parameterization of the ice strength. Long, narrow features in ice divergence and shear fields resemble those observed in SAR imagery, except that their average width is overestimated, possibly due to insufficient horizontal resolution. We also compare the mean sea ice drift and its decadal variability in two “old” sea ice models at different horizontal resolutions: 18-km and 9-km. We find no significant change in ice drift between the two models, except in areas of significant ice–ocean interactions due to more realistic ocean currents and water mass properties in the 9-km model.

Whether sea‐ice loss or lapse‐rate feedback dominates the Arctic amplification (AA) remains an open question. Analysis of data sets based upon observations reveals a 1.11 K per decade surface warming trend in the Arctic (70°–90°N) during 1979–2020 cold season (October–February) that is five times higher than the corresponding global mean. Based on surface energy budget analysis, we show that the largest contribution (∼82%) to this cold season warming trend is attributed to changes in clear‐sky downward longwave radiation. In contrast to that in Arctic summer and over tropics, a reduction in lower‐tropospheric inversions plays a unique role in explaining the reduction of the downward longwave radiation associated with atmospheric nonuniform temperature and corresponding moisture changes. Our analyses also suggest that Arctic lower‐tropospheric stability should be considered in conjunction with sea‐ice decline during the preceding warm season to explain AA. Observations and climate models have consistently shown a stronger surface warming in the Arctic than the global mean, a.k.a. Arctic amplification (AA), which has a strong asymmetry between the cold season and warm season. Previous studies suggested that key contributors to AA are the positive surface‐albedo feedback and lapse‐rate feedback. However, the lapse‐rate feedback itself depends on temperature profiles and sea‐ice loss. Whether sea‐ice loss or lapse‐rate feedback dominates AA remains an open question. Here, by analyzing the latest generation of observationally‐based reanalysis data (1979–2020), we present a unique role of lower‐tropospheric temperature inversion changes in representing the contribution of vertically inhomogeneous atmospheric temperature and associated moisture changes to clear‐sky downward longwave radiation during the cold season. This unique role is not found either in the tropics or during Arctic summertime. We further link the inversion during the cold season to sea‐ice loss during the preceding warm season. These results reinforce previous findings not only that lapse‐rate feedback and sea‐ice loss play a key role in AA but also that lapse‐rate feedback in the cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season. An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trendReduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changesLapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season An increase in clear‐sky downward longwave radiation (rldsclr) explains 82% of the recent cold season Arctic surface warming trend Reduced low inversion largely explains the Arctic rldsclr reduction due to vertical nonuniform temperature and related moisture changes Lapse‐rate feedback in the Arctic cold season is likely a consequence of sea‐ice albedo feedback during the preceding warm season

Recent observations suggest that substantial phytoplankton blooms occur under sea ice on Arctic continental shelves during June and July. This is opposed to the traditional view that no significant biomass is produced in sea‐ice covered waters. However, no observational estimates are available on the Arctic‐wide primary production beneath sea ice. Here, using a fully coupled Arctic system model, we estimate that 63%/41% of the total primary production in the central Arctic occurs in waters covered by sea ice that is ≥50%/≥85% concentration. The total primary production there is increasing at a rate of 5.2% per decade during 1980–2018. Increased light transmission, due to the removal of sea ice, more extensive melt ponds, and thinner sea ice, is implicated as the main cause of increasing trends in primary production. At present, there are no Arctic‐wide observations and estimates of primary production beneath Arctic sea ice because it cannot be measured by satellite and is difficult to access via ship. Utilizing our state‐of‐the‐art Arctic system model, we have quantified the primary production beneath Arctic sea ice and our findings show that the majority of production occurs in waters covered by at least 50% ice. This analysis represents a major revision to the traditional view that no significant biomass is produced in sea‐ice covered waters. Our results are consistent with recent and very limited (in space and time) observational studies; however, we provide information for the entire Arctic Ocean, including increasing trends in production over the time period of 1980–2018. The majority of modeled primary production in the central Arctic is found beneath sea iceThe annual cycle of primary production in the central Arctic peaks in June due to the under‐ice fraction of productionPrimary production and photosynthetically active radiation are increasing in the central Arctic over the period 1980–2018