Modeling of Ice Algal and Pelagic Production and Air-Sea CO2 Exchange in the Arctic Ocean

In the Arctic Ocean, both phytoplankton and sea ice algae are important contributors to the primary production and the arctic food web. Copepod in the arctic regions have developed their feeding habit depending on the timing between the ice algal bloom and the subsequent phytoplankton bloom. A mismatch of the timing due to climate changes could have dramatic consequences on the food web as shown by some regional observations. In this study, a global coupled ice- ocean-ecosystem model was used to assess the seasonality of the ice algal and phytoplankton blooms in the arctic. The ice-ocean ecosystem modules are fully coupled in the physical model POP-CICE (Parallel Ocean Program- Los Alamos Sea Ice Model). The model results are compared with various observations. The modeled ice and ocean carbon production were analyzed by regions and their linkage to the physical environment changes (such as changes of ice concentration and water temperature, and light intensity etc.) between low- and high-ice years. A model sensitivity run is conducted to investigate how sea ice permeability of gas may change the model bias in the arctic basin.Department of Energy, EPSCoR, JAMSTEC-IARC Research Agreement, NSF ARC-065283

Evaluation of Under Sea-ice Phytoplankton Blooms in the Fully-Coupled, High-Resolution Regional Arctic System Model

First posted online: Fri, 31 Jul 2020 09:53:38 | This content has not been peer reviewed.Manuscript submitted to JGR: OceansThe article of record as published may be found at https://doi.org/10.1002/essoar.10503749.1In July 2011, observations of a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 seaice 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 System Model to simulate Arctic marine biogeochemistry over a thirty-year period. Our results 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, similar 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 critical 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 Arctic, 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 upstream and subsequently advected by ocean currents beneath the sea ice.This research was partially supported by the following: Collaborative Research: Understanding Arctic Marine Biogeochemical Response to Climate Change for Seasonal to Decadal Prediction Using Regional and Global Climate Models, Award number IAA1417888, Program NSF ARCSS; High-Latitude Application and Testing of Earth System Models Phase II, Award number IAA89243019SSC000030, Program DOERGMA; Ministry of Science and Higher Education in Poland in the frame of co-financed international project agreement Award number 3808/FAO/2017/0 RASMer.This research was partially supported by the following: Collaborative Research: Understanding Arctic Marine Biogeochemical Response to Climate Change for Seasonal to Decadal Prediction Using Regional and Global Climate Models, Award number IAA1417888, Program NSF ARCSS; High-Latitude Application and Testing of Earth System Models Phase II, Award number IAA89243019SSC000030, Program DOERGMA; Ministry of Science and Higher Education in Poland in the frame of co-financed international project agreement Award number 3808/FAO/2017/0 RASMer

Improve ocean mixing caused by subgrid-scale brine rejection using multi-column ocean grid in a climate model

Heterogeneous ice pack with sporadic narrow but long leads in the polar oceans was unresolved in typical climate model grid. Although multi-category sea ice thickness distribution was used in one sea ice model grid to calculate separate heat, salt and tracer fluxes through each category, the ocean models use only single-column grid to communicate with the averaged fluxes from all categories. When the lead is resolved by the grid, the added salt at the sea surface will sink to the base of the mixed layer and then spread horizontally. When averaged at a climate-model grid size, this vertical distribution of added salt is lead-fraction dependent. When the lead is unresolved, the model errors were systematic leading to greater surface salinity and deeper mixed-layer depth (MLD). An empirical function was developed to revise the added-salt-related parameter n from being fixed to lead-fraction dependent. Application of this new scheme in climate model showed significant improvement in modeled wintertime salinity and MLD as compared to series of CTD data sets in 1997/1998 and 2006/2007. The results showed the most evident improvement in modeled MLD in the Arctic Basin, similar to that using a fixed n = 5, as recommended by the previous Arctic regional model study, in which the parameter n obtained is close to 5 due to the small lead fraction in the Arctic Basin in winter.This work was funded by NSF ARC-0652838, also supported by International Arctic Research Center through JAMSTEC-IARC Research Agreement

Ecosystem model intercomparison of under-ice and total primary production in the Arctic Ocean

Previous observational studies have found increasing primary production (PP) in response to declining sea ice cover in the Arctic Ocean. In this study, under-ice PP was assessed based on three coupled ice-ocean-ecosystem models participating in the Forum for Arctic Modeling and Observational Synthesis (FAMOS) project. All models showed good agreement with under-ice measurements of surface chlorophyll-a concentration and vertically integrated PP rates during the main under-ice production period, from mid-May to September. Further, modeled 30-year (1980–2009) mean values and spatial patterns of sea ice concentration compared well with remote sensing data. Under-ice PP was higher in the Arctic shelf seas than in the Arctic Basin, but ratios of under-ice PP over total PP were spatially correlated with annual mean sea ice concentration, with higher ratios in higher ice concentration regions. Decreases in sea ice from 1980 to 2009 were correlated significantly with increases in total PP and decreases in the under-ice PP/total PP ratio for most of the Arctic, but nonsignificantly related to under-ice PP, especially in marginal ice zones. Total PP within the Arctic Circle increased at an annual rate of between 3.2 and 8.0 Tg C/yr from 1980 to 2009. This increase in total PP was due mainly to a PP increase in open water, including increases in both open water area and PP rate per unit area, and therefore much stronger than the changes in under-ice PP. All models suggested that, on a pan-Arctic scale, the fraction of under-ice PP declined with declining sea ice cover over the last three decades

Arctic Ecosystem Changes from Gloal Community Earthc System Model (CESM) and Regional Arctic System Model (RASM)

The Arctic Ocean is currently experiencing rapid and large environmental changes related to global warming. Many small scale physical processes, such as mesoscale eddies, mixed layer dynamics, ocean boundary and coastal currents, varying sea ice edges, upwelling can influence nutrient transport, light availability and ocean stratification, thus are critical for understanding marine primary production and carbon cycling in the Arctic Ocean. A high-resolution pan-Arctic regional earth system model (RASM) was developed to investigate the ecosystem response to climate changes in seasonal to decadal scales. Here we show some initial results from the high resolution ecosystem model and comparison with results from coarse resolution global community earth system model. Both models include coupled ice algal submodel at the bottom of sea ice and intermediate NPZD pelagic ecosystem submodel in water column

シベリア側北極海の沿岸-陸棚-海盆域に及ぶ生物地球化学過程の変化

北極海の中でもシベリア側北極海は、地球温暖化に伴う海底永久凍土の融解や海岸浸食、そして海氷激減が引き金となり生物地球化学的な変化が最もダイナミックに起きている海域である。海底永久凍土の融解は温暖化ガスであるメタンを大気中に放出させ (Shakhova et al., 2010a, b)、さらなる温暖化を招く恐れがある。海岸浸食は炭素・栄養塩・微量金属・粒子態及び溶存態有機物等を北極海に供給し (e.g., Semiletov et al., 2011, 2012, 2013)、CO2の大気への放出や基礎生産、微生物生産の増減を左右する可能性がある。また、海氷減少は水塊構造や海洋循環の変化を伴い栄養塩分布を変化させ (Nishino et al., 2011, 2013)、その結果、基礎生産や生物ポンプにも影響すると考えられる。しかし、この海域はロシアEEZ内、或いはそれに近接しているため、利用できるデータが非常に限られており、生物地球化学的な変化の定量的な評価はもちろん、基本的な物質循環像さえ、ほとんど分かっていない。 　本研究では、ロシアEEZ海域を含むシベリア側北極海の船舶観測を中心として、氷上キャンプやセジメントトラップ・係留系による観測、さらに衛星データや数値モデルを駆使して、シベリア側北極海で起きているダイナミックな生物地球化学的変化を把握するとともに、それが環北極海域、そして全球の生態系・気候システムに与える影響について評価する。発表資料, 北極環境研究コンソーシアム (JCAR) 長期計画ワークショッ

Competitive ligand exchange voltammetric determination of iron organic complexation in seawater in two-ligand case: Examination of accuracy using computer simulation and elimination of artifacts using iterative non-linear multiple regression

Accuracy of the Scatchard linearization data processing method for competitive ligand exchange (CLE)–cathodic stripping voltammetry (CSV) measurements of seawater Fe-organic complexation in two-ligand case is examined with idealized Fe titrations data sets that are simulated using preset values of ligand parameters (conditional binding constants and total ligand concentrations). The results reveal substantial inherent artifacts for this method. An examination of patterns by which these artifacts vary with changes in Fe-binding strength of natural ligands relative to that of the added ligand suggests that the artifacts result not only from underestimated voltammetric sensitivity, but also from inadequate separation of individual ligand's contribution to Fe complexation at each titration point. For idealized simulated titration data, these artifacts can be eliminated by a procedure that combines non-linear regression with Turoczy and Sherwood's iteration. The method is demonstrated by reproducing preset values of ligand parameters over a diverse range of organic ligand alpha coefficients and by modeling titration data determined by CLE–CSV for seawater samples collected from the Eastern Bering Sea. Error analysis suggests that the sensitivity of model-derived ligand parameters to the error in the titration data is a strongly non-linear function of ligand parameters. Accurate measurement of titration data is thus required to use this method for accurate CSV measurements of seawater Fe-organic complexation