Decadal Variability In The Arctic Ocean: Greenland-Iceland-Norwegian Seas Ice-Ocean-Atmosphere Climate System

Thesis (Ph.D.) University of Alaska Fairbanks, 2003This study investigates the decadal variability of the Arctic Ocean---Greenland, Iceland, Norwegian Seas (GIN Sea) system and possible mechanisms driving variability. The theoretical foundation of this work is the theory of Proshutinsky & Johnson [1997] that two major climate states of the Arctic---Anticyclonic Circulation Regime (ACCR) and Cyclonic Circulation Regime (CCR)---are driven by variations in the freshwater contents of the Arctic Ocean and the GIN Sea. It is hypothesized that the Arctic Ocean and the GIN Sea form an auto-oscillatory ice-ocean-atmosphere climate system with a quasi-decadal period of interannual variability. The system is characterized by two stages: (1) cold Arctic (ACCR)---warm GIN Sea with weak interaction between the basins; (2) warm Arctic (CCR)---cold GIN Sea with intense interaction between the basins. Surface air temperature and dynamic height gradients between the basins drive the auto-oscillations. This study investigates interactions between the Arctic Ocean and the GIN Sea. To test the hypothesis, a simple model of the Arctic Ocean and Greenland Sea has been developed. The Arctic shelf processes have been parameterized in a box model coupled with an Arctic Ocean module. Both the Arctic Ocean and Greenland Sea modules are coupled with a thermodynamic ice model and atmospheric models. Several model experiments have been conducted to adjust the model and to reproduce the auto-oscillatory behavior of the climate system. One of the major results of this work is the simulation of auto-oscillatory behavior of the Arctic Ocean---GIN Sea climate system. Periodical solutions obtained with seasonally varying forcing for scenarios with high and low interaction between the regions reproduce major anomalies in the ocean thermohaline structure, sea ice volume, and fresh water fluxes attributed to ACCR and CCR regimes. According to the simulation results, the characteristic time scale of the Arctic Ocean---GIN Sea system variability reproduced in the model is about 10--15 years. This outcome is consistent with theory of Proshutinsky and Johnson [1997] and shows that the Arctic Ocean---GIN Sea can be viewed as a unique auto-oscillating system

Development of the CSOMIO Coupled Ocean-Oil-Sediment- Biology Model

The fate and dispersal of oil in the ocean is dependent upon ocean dynamics, as well as transformations resulting from the interaction with the microbial community and suspended particles. These interaction processes are parameterized in many models limiting their ability to accurately simulate the fate and dispersal of oil for subsurface oil spill events. This paper presents a coupled ocean-oil-biology-sediment modeling system developed by the Consortium for Simulation of Oil-Microbial Interactions in the Ocean (CSOMIO) project. A key objective of the CSOMIO project was to develop and evaluate a modeling framework for simulating oil in the marine environment, including its interaction with microbial food webs and sediments. The modeling system developed is based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport model (COAWST). Central to CSOMIO’s coupled modeling system is an oil plume model coupled to the hydrodynamic model (Regional Ocean Modeling System, ROMS). The oil plume model is based on a Lagrangian approach that describes the oil plume dynamics including advection and diffusion of individual Lagrangian elements, each representing a cluster of oil droplets. The chemical composition of oil is described in terms of three classes of compounds: saturates, aromatics, and heavy oil (resins and asphaltenes). The oil plume model simulates the rise of oil droplets based on ambient ocean flow and density fields, as well as the density and size of the oil droplets. The oil model also includes surface evaporation and surface wind drift. A novel component of the CSOMIO model is two-way Lagrangian-Eulerian mapping of the oil characteristics. This mapping is necessary for implementing interactions between the ocean-oil module and the Eulerian sediment and biogeochemical modules. The sediment module is a modification of the Community Sediment Transport Modeling System. The module simulates formation of oil-particle aggregates in the water column. The biogeochemical module simulates microbial communities adapted to the local environment and to elevated concentrations of oil components in the water column. The sediment and biogeochemical modules both reduce water column oil components. This paper provides an overview of the CSOMIO coupled modeling system components and demonstrates the capabilities of the modeling system in the test experiments

Arctic circulation regimes

Between 1948 and 1996, mean annual environmental parameters in the Arctic experienced a well-pronounced decadal variability with two basic circulation patterns: cyclonic and anticyclonic alternating at 5 to 7 year intervals. During cyclonic regimes, low sea-level atmospheric pressure (SLP) dominated over the Arctic Ocean driving sea ice and the upper ocean counterclockwise; the Arctic atmosphere was relatively warm and humid, and freshwater flux from the Arctic Ocean towards the subarctic seas was intensified. By contrast, during anticylonic circulation regimes, high SLP dominated driving sea ice and the upper ocean clockwise. Meanwhile, the atmosphere was cold and dry and the freshwater flux from the Arctic to the subarctic seas was reduced. Since 1997, however, the Arctic system has been under the influence of an anticyclonic circulation regime (17 years) with a set of environmental parameters that are atypical for this regime. We discuss a hypothesis explaining the causes and mechanisms regulating the intensity and duration of Arctic circulation regimes, and speculate how changes in freshwater fluxes from the Arctic Ocean and Greenland impact environmental conditions and interrupt their decadal variability

Skill metrics for evaluation and comparison of sea ice models

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 5910–5931, doi:10.1002/2015JC010989.Five quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root-Mean-Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two-dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three-dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two-dimensional (ice contour) and three-dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two-dimensional and three-dimensional sea ice characteristics across data sets.U.S. National Science Foundation (NSF) Grant Number: PLR-0804017, NASA JPL OVWST, Bureau of Ocean Energy Management (BOEM), FSU Grant Number: M12PC00003, NSF Grant Numbers: projects PLR-0804010 , PLR-1313614 , PLR-1203720, BP/The Gulf of Mexico Research Initiative Grant Number: SA12-12, GoMRI-008, DoD High Performance Computing Modernization Progra

Time scales of the Greenland freshwater anomaly in the subpolar North Atlantic

Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 34(22), (2021): 8971–8987, https://doi.org/10.1175/JCLI-D-20-0610.1.The impact of increasing Greenland freshwater discharge on the subpolar North Atlantic (SPNA) remains unknown as there are uncertainties associated with the time scales of the Greenland freshwater anomaly (GFWA) in the SPNA. Results from numerical simulations tracking GFWA and an analytical approach are employed to estimate the response time, suggesting that a decadal time scale (13 years) is required for the SPNA to adjust for increasing GFWA. Analytical solutions obtained for a long-lasting increase of freshwater discharge show a non-steady-state response of the SPNA with increasing content of the GFWA. In contrast, solutions for a short-lived pulse of freshwater demonstrate different responses of the SPNA with a rapid increase of freshwater in the domain followed by an exponential decay after the pulse has passed. The derived theoretical relation between time scales shows that residence time scales are time dependent for a non-steady-state case and asymptote the response time scale with time. The residence time of the GFWA deduced from Lagrangian experiments is close to and smaller than the response time, in agreement with the theory. The Lagrangian analysis shows dependence of the residence time on the entrance route of the GFWA and on the depth. The fraction of the GFWA exported through Davis Strait has limited impact on the interior basins, whereas the fraction entering the SPNA from the southwest Greenland shelf spreads into the interior regions. In both cases, the residence time of the GFWA increases with depth demonstrating long persistence of the freshwater anomaly in the subsurface layers.D. S. Dukhovskoy and E. P. Chassignet were funded by the DOE (Award DE-SC0014378) and HYCOM NOPP (Award N00014-19-1-2674). The HYCOM-CICE simulations were supported by a grant of computer time from the DoD High-Performance Computing Modernization Program at NRL SSC. G. Platov was funded by the RSF N19-17-00154. P. G. Myers was funded by an NSERC Discovery Grant (Grant RGPIN 04357). A. Proshutinsky was funded by FAMOS project (NSF Grant NSF 14-584)

Arctic decadal variability from an idealized atmosphere-ice-ocean model : 2. Simulation of decadal oscillations

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 111 (2006): C06029, doi:10.1029/2004JC002820.A simple model of the Arctic Ocean and Greenland Sea, coupled to a thermodynamic sea ice model and an atmospheric model, has been used to study decadal variability of the Arctic ice-ocean-atmosphere climate system. The motivating hypothesis is that the behavior of the modeled and ultimately the real climate system is auto-oscillatory with a quasi-decadal periodicity. This system oscillates between two circulation regimes: the Anticyclonic Circulation Regime (ACCR) and the Cyclonic Circulation Regime (CCR). The regimes are controlled by the atmospheric heat flux from the Greenland Sea and the freshwater flux from the Arctic Ocean. A switch regulating the intensity of the fluxes between the Arctic Ocean and Greenland Sea that depends on the inter-basin gradient of dynamic height is implemented as a delay mechanism in the model. This mechanism allows the model system to accumulate the “perturbation” over several years. After the perturbation has been released, the system returns to its initial state. Solutions obtained from numerical simulations with seasonally varying forcing, for scenarios with high and low interaction between the regions, reproduced the major anomalies in the ocean thermohaline structure, sea ice volume, and fresh water fluxes attributed to the ACCR and CCR.This publication is the result of research sponsored by Alaska Sea Grant with funds from the National Oceanic and Atmospheric Administration Office of Sea Grant, Department of Commerce, under grant no. NA 86RG0050 (project no. GC/01-02), and from the University of Alaska with funds appropriated by the state. This research has also been supported by the National Science Foundation and by the International Arctic Research Center, University of Alaska Fairbanks, under auspices of the United States National Science Foundation

Small-angle fragmentation of carbon ions at 0.6 GeV/n: a comparison with models of ion-ion interactions

Momentum distributions of hydrogen and helium isotopes from 12C fragmentation at 3.5° were measured at 0.6 GeV/nucleon in the FRAGM experiment at ITEP TWA heavy ion accelerator. The fragments were selected by correlated time of flight and dE/dx measurements with a magnetic spectrometer with scintillation counters. The main attention was drawn to the high momentum region where the fragment velocity exceeds the velocity of the projectile nucleus. The momentum spectra of fragments span the region of the fragmentation peak as well as the cumulative region. The differential cross sections cover six orders of magnitude. The distributions measured are compared to the predictions of three ion-ion interaction models: BC, QMD and LAQGSM03.03. The kinetic energy spectra of fragments in the projectile rest frame have an exponential shape with two temperatures, being defined by their slope parameters

Arctic decadal variability from an idealized atmosphere-ice-ocean model: 1. Model description, calibration, and validation

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 111 (2006): C06028, doi:10.1029/2004JC002821.This paper describes a simple “multibox” model of the Arctic atmosphere-ice-ocean system. The model consists of two major modules (an Arctic module and a Greenland Sea module) and several sub-modules. The Arctic module includes a shelf box model coupled with a thermodynamic sea ice model, and an Arctic Ocean model coupled with a sea ice model and an atmospheric box model. The Greenland Sea module includes an oceanic model coupled with a sea ice model and a statistical model of surface air temperature over the Greenland Sea. The full model is forced by daily solar radiation, wind stress, river runoff, and Pacific Water inflow through Bering Strait. For validation purposes, results from model experiments reproducing seasonal variability of the major system parameters are analyzed and compared with observations and other models. The model reproduces the seasonal variability of the Arctic system reasonably well and is used to investigate decadal Arctic climate variability in Part 2 of this publication.This publication is the result of research sponsored by Alaska Sea Grant with funds from the National Oceanic and Atmospheric Administration Office of Sea Grant, Department of Commerce, under grant no. NA 86RG0050 (project no. GC/01-02), and from the University of Alaska with funds appropriated by the state. This research has also been supported by the National Science Foundation and by the International Arctic Research Center, University of Alaska Fairbanks, under auspices of the United States National Science Foundation

Greenland freshwater pathways in the sub-Arctic Seas from model experiments with passive tracers

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 121 (2016): 877–907, doi:10.1002/2015JC011290.Accelerating since the early 1990s, the Greenland Ice Sheet mass loss exerts a significant impact on thermohaline processes in the sub-Arctic seas. Surplus freshwater discharge from Greenland since the 1990s, comparable in volume to the amount of freshwater present during the Great Salinity Anomaly events, could spread and accumulate in the sub-Arctic seas, influencing convective processes there. However, hydrographic observations in the Labrador Sea and the Nordic Seas, where the Greenland freshening signal might be expected to propagate, do not show a persistent freshening in the upper ocean during last two decades. This raises the question of where the surplus Greenland freshwater has propagated. In order to investigate the fate, pathways, and propagation rate of Greenland meltwater in the sub-Arctic seas, several numerical experiments using a passive tracer to track the spreading of Greenland freshwater have been conducted as a part of the Forum for Arctic Ocean Modeling and Observational Synthesis effort. The models show that Greenland freshwater propagates and accumulates in the sub-Arctic seas, although the models disagree on the amount of tracer propagation into the convective regions. Results highlight the differences in simulated physical mechanisms at play in different models and underscore the continued importance of intercomparison studies. It is estimated that surplus Greenland freshwater flux should have caused a salinity decrease by 0.06–0.08 in the sub-Arctic seas in contradiction with the recently observed salinification (by 0.15–0.2) in the region. It is surmised that the increasing salinity of Atlantic Water has obscured the freshening signal.NSERC. Grant Numbers RGPIN 227438-09, RGPIN 04357 and RGPCC 433898; RFBR. Grant Number 13-05-00480, 14-05-00730, and 15-05-02457; NSF Grant Number: PLR-0804010, PLR-1313614, and PLR-12037202016-07-2