An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters

Net primary productivity estimates and environmental variables in the Arctic Ocean; an assessment of coupled physical-biogeochemical models

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO (sub 3) ), mixed layer depth (MLD), euphotic layer depth (Z (sub eu) ), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO (sub 3) , MLD, and Z (sub eu) throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO (sub 3) was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modelin

A model for estimating the TMDL-related benefits of oyster reef restoration : Harris Creek, Maryland, USA

A user-friendly, web-accessible model has been developed that allows restoration practitioners and resource managers to easily estimate the TMDL-related benefits of oyster reef restoration per unit area, run restoration scenarios in Harris Creek, MD to optimize restoration planning and implementation, and calculate the benefits of the chosen plan. The model is rooted in scientifically defensible data and is readily transferable to systems throughout the Chesapeake Bay and Eastern Shore. The model operates in five vertically well-mixed boxes along the main axis of the creek. Exchanges among creeks are computed using a tidal prism approach and were compared to exchanges provided from a high resolution 3D hydrodynamic model. Watershed inputs for the model were obtained for the Harris Creek sub-watershed from the Phase V Chesapeake Bay Program Watershed Model. The base model simulates daily concentrations over an annual cycle of chlorophyll-a, dissolved inorganic nitrogen (N) and phosphorus (P), dissolved oxygen, total suspended solids, the biomass of benthic microalgae, and the water column and sediment pools of labile organic carbon (C) and associated N and P. Water quality data for model forcing and calibration were obtained from the Chesapeake Bay Program, the Choptank Riverkeeper, the University of Maryland Center for Environmental Science, and the Maryland Department of Natural Resources. An oyster sub-model has been coupled to this base model and computes the volume of water filtered, removal of phytoplankton, suspended solids, and associated nutrients via filtration, recycling of nutrients and consumption of oxygen by oyster respiration, production of feces, N and P accumulation in oyster tissues and shell, oyster-enhanced denitrification, and N and P burial associated with restored reefs. The completed model is served online and operates through a web browser, enabling users to conduct scenario analysis by entering box-specific values for acres restored, restored oyster density, and restored oyster size, as well as the economic value of associated N and P removal

Sensitivity of Arctic sea ice to variable model parameter space in Regional Arctic System Model simulations

The article of record as published may be found at https://doi.org/10.5194/egusphere-egu2020-13039EGU General Assembly 2020The Arctic climate system is very sensitive to the state of sea ice due to its role in controlling heat and momentum exchanges between the atmosphere and the ocean. However, the representation of sea ice state, its past variability and future projections in modern Earth system models (ESMs) vary widely. One of the reasons for that is strong sensitivity of ESMs to sea ice related varying parameter space. Based on limited observations, those parameters typically have a range of possible values and / or are not constant in space and time, which is a source of model uncertainties. The Regional Arctic System Model (RASM) is a limited-domain fully coupled climate model used in this study to investigate sensitivity of sea ice states to limited set of parameters. It includes the atmospheric (Weather Research and Forecasting; WRF) and land hydrology (Variable Inltration Capacity; VIC) components sharing a 50-km pan-Arctic grid. The sea ice (the version 6.0 of Los Alamos sea ice model, CICE) and ocean (Parallel Ocean Program, POP) components share a 1/12° pan-Arctic grid. In addition, a river routing scheme (RVIC) is used to represent the freshwater ux from land to ocean. All components are coupled at high frequency via the Community Earth System Model (CESM) coupler version CPL7. We have selected four parameters out of the set evaluated by Urrego-Blanco et al. (2016) and subject to their potential impact on sea ice and coupling across the atmosphere-sea ice- ocean interface. The total of 96 sensitivity simulations have been completed with fully coupled and forced RASM congurations, varying each parameter within its respective acceptable range. Using sea ice volume as a measure of sensitivity, the thermal conductivity of snow (ksno) parameter has produced the most sensitivity, in qualitative agreement with Urrego-Blanco et al. (2016). However, using dynamics related metrics, such as sea ice drift or deformation, other parameters, i.e. controlling the sea ice roughness and frictional energy dissipation, have been shown more important. Finally, dierent quantitative sensitivities to the same parameter have been diagnosed between fully-coupled and forced RASM simulations, as well as compared to the stand alone sea ice results

A Spatial Evaluation of Arctic Sea Ice and Regional Limitations in CMIP6 Historical Simulations

17 USC 105 interim-entered record; under review.The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-20-0491.1The Arctic sea ice response to a warming climate is assessed in a subset of models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice edge error, Brier score, and spatial probability score. We find that the CMIP6 multimodel mean captures the mean annual cycle and 1979–2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic subregions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.U.S. NavyDepartment of Energy (DOE)Regional and Global Model Analysis (RGMA)Office of Naval Research (ONR)Arctic and Global Prediction (AGP)National Science Foundation (NSF)Arctic System Science (ARCSS)Ministry of Science and Higher Education in PolandDOE: 89243019SSC0036DESC0014117ONR: N0001418WX00364NSF: IAA1417888IAA160360

On the variability of the Bering Sea Cold Pool and implications for the biophysical environment

The article of record as published may be found at http://dx.doi.org/10.1371/ journal.pone.0266180The Bering Sea experiences a seasonal sea ice cover, which is important to the biophysical environment found there. A pool of cold bottom water (<2 ?C) is formed on the shelf each winter as a result of cooling and vertical mixing due to brine rejection during the predominately local sea ice growth. The extent and distribution of this Cold Pool (CP) is largely controlled by the winter extent of sea ice in the Bering Sea, which can vary considerably and recently has been much lower than average. The cold bottom water of the CP is important for food security because it delineates the boundary between arctic and subarctic demersal fish species. A northward retreat of the CP will likely be associated with migration of subarctic species toward the Chukchi Sea. We use the fully-coupled Regional Arctic System Model (RASM) to examine variability of the extent and distribution of the CP and its relation to change in the sea ice cover in the Bering Sea during the period 1980–2018. RASM results confirm the direct correlation between the extent of sea ice and the CP and show a smaller CP as a consequence of realistically simulated recent declines of the sea ice cover in the Bering Sea. In fact, the area of the CP was found to be only 31% of the long-term mean in July of 2018. In addition, we also find that a low ice year is followed by a later diatom bloom, while a heavy ice year is followed by an early diatom bloom. Finally, the RASM probabilistic intra-annual forecast capability is reviewed, based on 31-member ensembles for 2019– 2021, for its potential use for prediction of the winter sea ice cover and the subsequent summer CP area in the Bering Sea.This work was supported by the US National Science Foundation (GEO/PLR ARCSS IAA1417888 and IAA1603602), the US Department of Energy (DOE) Regional and Global Model Analysis (RGMA) (89243019SSC0036 and DESC0014117), and the Office of Naval Research (ONR) Arctic and Global Prediction (AGP) (N0001418WX00364). The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources.This work was supported by the US National Science Foundation (GEO/PLR ARCSS IAA1417888 and IAA1603602), the US Department of Energy (DOE) Regional and Global Model Analysis (RGMA) (89243019SSC0036 and DESC0014117), and the Office of Naval Research (ONR) Arctic and Global Prediction (AGP) (N0001418WX00364). The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources

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

On the circulation, water mass distribution, and nutrient concentrations of the western Chukchi Sea

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.5194/os-18-29-2022Substantial amounts of nutrients and carbon enter the Arctic Ocean from the Pacific Ocean through the Bering Strait, distributed over three main pathways. Water with low salinities and nutrient concentrations takes an eastern route along the Alaskan coast, as Alaskan Coastal Water. A central pathway exhibits intermediate salinity and nutrient concentrations, while the most nutrient-rich water enters the Bering Strait on its western side. Towards the Arctic Ocean, the flow of these water masses is subject to strong topographic steering within the Chukchi Sea with volume trans port modulated by the wind field. In this contribution, we use data from several sections crossing Herald Canyon collected in 2008 and 2014 together with numerical modelling to investigate the circulation and transport in the western part of the Chukchi Sea. We find that a substantial fraction of water from the Chukchi Sea enters the East Siberian Sea south of Wrangel Island and circulates in an anticyclonic direction around the island. This water then contributes to the high nutrient waters of Herald Canyon. The bottom of the canyon has the highest nutrient concentrations, likely as a result of addition from the degradation of organic matter at the sediment surface in the East Siberian Sea. The flux of nutrients (nitrate, phosphate, and silicate) and dissolved inorganic carbon in Bering Summer Water and Winter Water is computed by combining hydrographic and nutrient observations with geostrophic transport referenced to lowered acoustic Doppler current profiler (LADCP) and surface drift data. Even if there are some general similarities between the years, there are differences in both the temperature–salinity and nutrient characteristics. To assess these differences, and also to get a wider temporal and spatial view, numerical modelling results are applied. According to model results, high-frequency variability dominates the flow in Herald Canyon. This leads us to conclude that this region needs to be monitored over a longer time frame to deduce the temporal variability and potential trends.The science was financially supported by: US National Science Foundation (Grant Number: GEO/PLR ARCSS 575 IAA#1417888), the Department of Energy (DOE) Regional and Global Model Analysis (RGMA), the Swedish Research Council Formas (contract no. 2018-01398), and the Swedish Research Council (contract nos. 621-2006-3240, 621-2010-4084, and 2012-1680). This work was carried out with logistic support from the Knut and Alice Wallenberg Foundation and from Swedish Polar Research Secretariat. The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources. This study was also supported by the Russian Scientific Foundation (grant no. # 21-77-580 30001).The science was financially supported by: US National Science Foundation (Grant Number: GEO/PLR ARCSS 575 IAA#1417888), the Department of Energy (DOE) Regional and Global Model Analysis (RGMA), the Swedish Re search Council Formas (contract no. 2018-01398), and the Swedish Research Council (contract nos. 621-2006-3240, 621-2010-4084, and 2012-1680). This work was carried out with logistic support from the Knut and Alice Wallenberg Foundation and from Swedish Polar Research Secretariat. The Department of Defense (DOD) High Performance Computer Modernization Program (HPCMP) provided computer resources. This study was also supported by the Russian Scientific Foundation (grant no. # 21-77-580 30001)

Modeling and Prediction of Arctic Climate Using the Regional Arctic System Model

EGU General Assembly 2019The Regional Arctic System Model (RASM) has been developed and used to investigate critical processes controlling the evolution of the Arctic climate system under a diminishing sea ice cover. RASM is a fully coupled limited-domain ice-ocean-atmosphere-land hydrology model. Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid. The ocean and sea ice components are configured on rotated sphere meshes with four configuration options: 1/12o (∼9.3km) or 1/48o (∼2.4km) in the horizontal space and with 45 or 60 vertical layers. As a regional climate model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which are derived either from global atmospheric reanalyses for simulations of the past to present or from Earth System models (ESMs) for climate projections. This allow comparison of RASM results with observations in place and time, which is a unique capability not available in global ESMs. Several examples of key physical processes and coupling between different model components will be presented, that improve the representation of the past and present Arctic climate system. The impact of such processes and feedbacks will be discussed with regard to improving model physics and reducing biases in the representation of its initial state for prediction of Arctic climate change at time scales from synoptic to decadal

Hidden Production: On the Importance of Pelagic Phytoplankton Blooms Beneath Arctic Sea Ice

The article of record as published may be found at https://doi.org/10.1029/2020JC016211Recent 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