A document by Yiling Huo. Click on the document to view its contents.

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 sea- ice blooms and their contribution to the carbon budget in the Arctic Ocean. To address some of these questions, we use the fully-coupled, high-resolution Regional Arctic Sys- tem Model to simulate Arctic marine biogeochemistry over a thirty-year period. Our re- sults demonstrate the presence of massive under sea-ice blooms in the western Arctic not only in summer of 2011 but annually throughout the simulation period. In addition, sim- ilar blooms, yet of lower magnitude occur annually in the eastern Arctic. We investigate the constraints of nitrate concentration and photosynthetically available radiation (PAR) on the initiation, evolution and cessation of under sea-ice blooms. Our results show that increasing PAR reaching the ocean surface through the sea-ice in early summer, when the majority of ice-covered Arctic waters have sufficient surface nitrate levels, is criti- cal to bloom initiation. However, the duration and cessation of under sea-ice blooms is controlled by available nutrient concentrations as well as by the presence of sea-ice. Since modeled critical PAR level are consistently exceeded in summer only in the western Arc- tic, we therefore conclude that the eastern Arctic blooms shown in our simulations did not develop under sea ice, but were instead, at least in part, formed in open waters up- stream and subsequently advected by ocean currents beneath the sea ice.

In high-latitude environments such as the Arctic Ocean, phytoplankton growth is strongly constrained by light availability. Because light penetration into the upper ocean is attenuated by snow and ice cover, it was generally believed until recently that phytoplankton growth was limited to areas of open water, with negligible growth under the ice. However, under-ice phytoplankton blooms have been reported multiple times over the past several decades [e.g. Fukuchi et al. (1989); Legendre, Ingram, and Poulin (1989)]. In July 2011, Arrigo et al. (2012) observed a massive phytoplankton bloom beneath sea ice in the Chukchi Sea. Observational evidence suggests that this bloom was not an isolated case, and that under-ice blooms maybe widespread on Arctic continental shelves (Arrigo et al., 2014; Lowry, van Dijken, & Arrigo, 2014). Arrigo and van Dijken (2011) estimate the total primary production north of the Arctic Circle to be 438 +/- 21.5 Tg C yr -1. However, due to observational limitations, this estimate did not include under sea ice production. Therefore, an open question remains: How important are under-ice phytoplankton blooms to the total Arctic primary production? RASM is a high-resolution, fully-coupled, regional model with a domain encompassing the entire marine cryosphere of the Northern Hemisphere, including the major inflow and outflow pathways, with extensions into North Pacific and Atlantic oceans. The components of RASM include: atmosphere, sea ice, ocean, biogeochemical, and land hydrology (Maslowski et al. 2012, Roberts et al. 2015, DuVivier et al. 2016, Hamman et al. 2016, Hamman et al. 2017, Cassano et al. 2017). The ocean BGC component in RASM is a medium-complexity Nutrients-Phytoplankton-Zoo-plankton-Detritus (NPZD) model (Jin et al. 2018). The model has three phytoplankton categories: diatoms, small phytoplankton and diazotrophs. RASM results show that under-ice pelagic chl-a and primary production values can at times be very high, particularly during the spring and early summer. Our numerical model results produce a mean of 495 Tg C yr -1 north of the Arctic Circle during 1980-1998 (and 507 Tg C yr -1 during 1980-2018). We also see an increase in primary production over the last several decades. This increase is attributed to the reduced sea ice cover, which increases light availability to the upper ocean. We conclude that under-sea-ice pelagic primary production makes up a large fraction of the total production and cannot be considered negligible.

A set of decadal simulations has been completed and evaluated for gains using the Regional Arctic System Model (RASM) to dynamically downscale data from a global Earth system model (ESM) and two atmospheric reanalyses. RASM is a fully coupled atmosphere - land - ocean - sea ice regional Earth system model. Nudging to the forcing data is applied to approximately the top half of the atmosphere. RASM simulations were also completed with a modification to the atmospheric physics for evaluating changes to the modeling system. The results show that for the top half of the atmosphere, the RASM simulations follow closely to that of the forcing data, regardless of the forcing data. The results for the lower half of the atmosphere, as well as the surface, show a clustering of atmospheric state and surface fluxes based on the modeling system. At all levels of the atmosphere the imprint of the weather from the forcing data is present as indicated in the pattern of the monthly and annual means. Biases, in comparison to reanalyses, are evident in the ESM forced simulations for the top half of the atmosphere but are not present in the lower atmosphere. This suggests that bias correction is not needed for fully-coupled dynamical downscaling simulations. While the RASM simulations tended to go to the same mean state for the lower atmosphere, there is a different evolution of the weather across the ensemble of simulations. These differences in the weather result in variances in the sea ice and oceanic states.

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 Modelling and Observational Synthesis (FAMOS), the Sea Ice Rheology Experiment (SIREx) aims at evaluating current 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 I 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 forcing, 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 (LKFs) in a SIREx Part II companion paper.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project formed within the Forum of Arctic Modeling and Observational Synthesis (FAMOS) to collect and design skill metrics to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) and provide guidance for modeling small-scale deformation. 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 RADARSAT Geophysical Processor System (RGPS). 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 forcing 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.