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The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of the solar radiation than the underlying ocean. Hence, they are important for the global energy budget, and changes in the sea-ice cover can have a large impact on the Arctic climate and beyond. In the past 36 years the ice cover reduced significantly. The largest decline is observed in September, with a rate of more than 12% per decade. The negative trend is accompanied by large inter-annual sea-ice variability: in September the sea-ice extent varies by up to 27% between years. The processes controlling the large variability are not well understood. In this thesis the atmospheric contribution to the inter-annual sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice cover and sea-ice melt. Atmospheric reanalysis data are used to identify key processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found that in years with a very low September sea-ice extent more heat and moisture is transported in spring into the area that shows the largest ice variability. The increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies of water vapor and clouds. These alter the amount of downward radiation at the surface: positive cloud anomalies allow for more longwave radiation and less shortwave radiation. In spring, when the solar inclination is small, positive cloud anomalies result in an increased surface warming and an earlier seasonal melt onset. This reduces the ice cover early in the season and allows for an increased absorption of solar radiation by the surface during summer, which further accelerates the ice melt. The modeling experiments indicate that cloud anomalies of similar magnitude during other seasons than spring would likely not result in below-average September sea ice. Based on these results a simple statistical sea-ice prediction model is designed, that only takes into account the downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the importance of the spring atmosphere for the annual sea-ice evolution. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript.</p>

The Alaskan Arctic region is estimated to hold the largest undiscovered Arctic oil deposits---about 30 billion barrels. Realizing this immense potential, however, will not be easy, as firms face technical, political and regulatory barriers in their quest to explore and develop this frontier. To overcome these challenges, energy companies should adopt a comprehensive education and engagement strategy. This document formulates key elements of a strategy to help alleviate concerns of the stakeholders who have the power to thwart development. At a time of uncertainty over offshore oil and gas development in the U.S. Arctic, a combined education and engagement campaign promises to help interested parties protect and expand their license to operate in the region. / text

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Approved for public release; distribution is unlimited / Presently ice extent forecast models such as the U.S. Navy Polar Ice Prediction System (PIPS) neglect or treat small-scale thermodynamic processes and entrainment unrealistically. Incorporating better algorithms that include more complete physics of the mixed layer dynamics will allow for improved prediction of ice thickness and distribution, open water boundaries, polynyas, and deep-water formation in the polar seas. A one-dimensional mixed layer turbulent kinetic energy (TKE) budget model based on Garwood's NPS mixed layer model for deep convection (Garwood, 1991) was written in MATLAB. The model consisted of a system of ten equations derived by vertically integrating the budgets for heat, momentum, salinity, and turbulent kinetic energy between the sea-ice-air interface and the base of the turbulent mixed layer. The NPS mixed layer model was tested using atmospheric forcing and ocean profiles collected at the Surface Heat Budget of the Arctic Ocean Experiment (SHEBA) site. Sensitivity studies using ocean profiles of the Greenland Sea were also conducted to address thermodynamics and ocean profiles that enhance thermohaline circulation. Findings and results as well as recommendations for further study are addressed to extend the relationships determined from small 1-D scales to the larger 3-D scales suitable for improvements to current ice models. / Lieutenant, United States Navy

The purpose of this study is to investigate the evolution of the Arctic Ocean’s carbon uptake capacity and impacts on ocean acidification with the changing sea-ice scape. In particular, I study the influence on air-ice-sea fluxes of carbon with two major updates to commonly-used carbon cycle models I have included. One, incorporation of sea ice algae to the ecosystem, and two, modification of the sea-ice carbon pump, to transport brineassociated Dissolved Inorganic Carbon (DIC) and Total Alkalinity (TA) to the depth of the bottom of the mixed layer (as opposed to releasing it in the surface model layer). I developed the ice algal ecosystem model by adding a sympagic (ice-associated) ecosystem into a 1D coupled sea ice-ocean model. The 1D model was applied to Resolute Passage in the Canadian Arctic Archipelago and evaluated with observations from a field campaign during the spring of 2010. I then implemented an inorganic carbon system into the model. The carbon system includes effects on both DIC and TA due to the coupled ice-ocean ecosystem, ikaite precipitation and dissolution, ice-air and air-sea carbon exchange, and ice-sea DIC and TA exchange through a formulation for brine rejection to depth and freshwater dilution associated with ice growth and melt. The 1D simulated ecosystem was found to compare reasonably well with observations in terms of bloom onset and seasonal progression for both the sympagic and pelagic algae. In addition, the inorganic carbon system showed reasonable agreement between observations of upper water column DIC and TA content. The simulated average ocean carbon uptake during the period of open water was 10.2 mmol C m−2 day−1 ( 11 g C m−2 over the entire open-water season). Using the developments from the 1D model, a 3D biogeochemical model of the Arctic Ocean incorporating both sea ice and the water column was developed and tested, with a focus on the pan-Arctic oceanic uptake of carbon in the recent era of Arctic sea ice decline (1980 – 2015). The model suggests the total uptake of carbon for the Arctic Ocean (north of 66.5 N) increases from 110 Tg C yr−1 in the early eighties (1980 – 1985) to 140 Tg C yr−1 for 2010 – 2015, an increase of 30%. The rise in SST accounts for 10% of the increase in simulated pan-Arctic sea surface pCO2. A regional analysis indicated large variability between regions, with the Laptev Sea exhibiting low sea surface pH relative to the pan- Arctic domain mean and seasonal undersaturation of arag by the end of the standard run. Two sensitivity studies were performed to assess the effects of sea-ice algae and the sea-ice carbon pump in the pan-Arctic, with a focus on sea surface inorganic carbon properties. Excluding the sea ice-carbon-pump showed a marked decrease in seasonal variability of sea-surface DIC and TA averaged over the Arctic Ocean compared to the standard run, but only a small change in the net total carbon uptake (of 1% by the end of the no icecarbon-pump run). Neglecting the sea ice algae, on the other hand, exhibits only a small change in sea-surface DIC and TA averaged over the pan-Arctic Ocean, but a cumulative effect on the net total carbon uptake of the Arctic Ocean (reaching 5% less than that of the standard run by the end of the no-ice-algae run). / Graduate