Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

The regional climate model COSMOin Climate Limited-AreaMode (COSMO-CLM or CCLM) is used with a high resolution of 15km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 208C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice.Also, the 30-km version of theArctic SystemReanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 18C for the ocean and sea ice area. Thus,ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.58Cyr21 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 708N; for CCLM the warming amounts to an average of almost 58C for 2002/03–2011/12

Modelling investigation of interaction between Arctic sea ice and storms: insights from case studies and climatological hindcast simulations

Thesis (Ph.D.) University of Alaska Fairbanks, 2019The goal of this study is to improve understanding of atmosphere, sea ice, and ocean interactions in the context of Arctic storm activities. The reduction of Arctic sea ice extent, increase in ocean water temperatures, and changes of atmospheric circulation have been manifested in the Arctic Ocean along with the large surface air temperature increase during recent decades. All of these changes may change the way in which atmosphere, sea ice, and ocean interact, which may in turn feedback to Arctic surface air warming. To achieve the goal, we employed an integrative approach including analysis of modeling simulation results and conducting specifically designed model sensitivity experiments. The novelty of this study is linking synoptic scale storms to large-scale changes in sea ice and atmospheric circulation. The models were used in this study range from the regional fully coupled Arctic climate model HIRHAM-NAOSIM to the ocean-sea ice component model of the Community Earth System Model CESM and the Weather Research and Forecasting (WRF) model. Analysis of HIRHAM-NAOSIM simulation outputs shows regionally dependent variability of storm count with a higher number of storms over the Atlantic side than over the Pacific side. High-resolution simulations also reproduce higher number of storms than lower resolution reanalysis dataset. This is because the high-resolution model may capture more shallow and small size storms. As an integrated consequence, the composite analysis shows that more numerous intense storms produce low-pressure systems centered over the Barents-Kara-Laptev seas and the Chukchi-East Siberian seas, leading to anomalous cyclonic circulation over the Atlantic Arctic Ocean and Pacific Arctic Ocean. Correspondingly, anomalous sea ice transport occurs, enhancing sea ice outflow out of the Barents-Kara-Laptev sea ice and weakening sea ice inflow into the Chukchi-Beaufort seas from the thick ice area north of the Canadian Archipelago. This change in sea ice transport causes a decrease in sea ice concentration and thickness in these two areas. However, energy budget analysis exhibits a decrease in downward net sea ice heat fluxes, reducing sea ice melt, when more numerous intense storms occur. This decrease could be attributed to increased cloudiness and destabilized atmospheric boundary layer associated with intense storms, which can result in a decrease in downward shortwave radiation and an increase in upward turbulent heat fluxes. The sea ice-ocean component CICE-POP of Community Earth System Model (CESM) was used to conduct sensitivity experiment to examine impacts of two selected storms on sea ice. CICE-POP is generally able to simulate the observed spatial distribution of the Arctic sea-ice concentration, thickness, and motion, and interannual variability of the Arctic sea ice area for the period 1979 to 2011. However, some biases still exit, including overestimated sea-ice drift speeds, particularly in the Transpolar Drift Stream, and overestimated sea-ice concentration in the Atlantic Arctic but slightly underestimated sea ice concentration in the Pacific Arctic. Analysis of CICE-POP sensitivity experiments suggests that dynamic forcing associated with the storms plays more important driving role in causing sea ice changes than thermodynamics does in the case of storm in March 2011, while both thermodynamic and dynamic forcings have comparable impacts on sea ice decrease in the case of the August 2012. In case of March 2011 storm, increased surface winds caused the reduction of sea ice area in the Barents and Kara Seas by forcing sea ice to move eastward. Sea ice reduction was primarily driven by mechanical processes rather than ice melting. On the contrary, the case study of August 2012 storm, that occurred during the Arctic summer, exemplified the case of equal contribution of mechanical sea ice redistribution of sea ice in the Chukchi - East Siberian - Beaufort seas and melt in sea ice reduction. To understand the impacts of the changed Arctic environment on storm dynamics, we carried out WRF model simulations for a selected Arctic storm that occurred in March 2011. Model output highlight the importance of both increased surface turbulent heat fluxes due to sea ice retreat and self-enhanced warm and moist air advection from the North Atlantic into the Arctic. These external forcing factor and internal dynamic process sustain and even strengthen atmospheric baroclinicity, supporting the storm to develop and intensify. Additional sensitivity experiments further suggest that latent heat release resulting from condensation/precipitation within the storm enhances baroclinicity aloft and, in turn, causes a re-intensification of the storm from its decaying phase.NSF Grant #1023592 and ONR-Glable Grant #N62909-13-1-V219Chapter 1: Introduction and motivation -- 1.1: Arctic climate system: current state and mechanisms of ongoing change -- 1.2: Arctic climate system feedbacks -- 1.3: Motivations -- 1.4: Implications of Arctic climate change to socio-economic activity -- 1.5: Research goal and objectives -- 1.6: Methods and research approach -- 1.7: Thesis structure. Chapter 2: Climatology of Arctic cyclones and impacts on sea ice: results from regional fully coupled model hindcast simulations -- 2.1: Introduction -- 2.2: Data and methods -- 2.2.1: Model data -- 2.2.2: Cyclone identification algorithm -- 2.2.3: Composite analysis -- 2.3: Results and discussion -- 2.3.1: Arctic storm analysis -- 2.3.2: Impact on sea ice and ocean. Chapter 3: Processes associated with cyclone impacts on sea ice: a case study using sea ice-ocean model simulations -- 3.1: Introduction -- 3.1.1: Dynamic forcing of Arctic cyclones on sea ice -- 3.1.2: Thermodynamic impact of Arctic storms on sea ice -- 3.1.3: Sea ice momentum and mass balance -- 3.1.4: Sea ice surface heat budget -- 3.2: Data and methods -- 3.2.1: Model description -- 3.2.2: Forcing and initialization -- 3.2.3: Experimental design -- 3.2.4: Model validation -- 3.3: March 2011 cyclone -- 3.3.1: Dynamic vs thermodynamic forcing on sea ice -- 3.4: August 2012 cyclone -- 3.4.1: Dynamic and thermodynamic forcing on sea ice -- 3.5: Comparison of March 2011 and August 2012 storms. Chapter 4: Possible processes and forcing in Arctic cyclone development: a case study with WRF model simulations -- 4.1: Introduction -- 4.2: Synoptic analysis for March 16 - 22, 2011 -- 4.3: Model configuration -- 4.4: Model experimental design -- 4.5: Model validation -- 4.6: Results and discussions -- 4.6.1: Baroclinic instability -- 4.6.2: Sensitivity to decreased sea ice concentration and elevated SST -- 4.6.3: Sensitivity to latent heat release. Chapter 5: Conclusions and discussions -- References

Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

Changing state of Arctic sea ice across all seasons

The decline in the floating sea ice cover in the Arctic is one of the most striking manifestations of climate change. In this review, we examine this ongoing loss of Arctic sea ice across all seasons. Our analysis is based on satellite retrievals, atmospheric reanalysis, climate-model simulations and a literature review. We find that relative to the 1981-2010 reference period, recent anomalies in spring and winter sea ice coverage have been more significant than any observed drop in summer sea ice extent (SIE) throughout the satellite period. For example, the SIE in May and November 2016 was almost four standard deviations below the reference SIE in these months. Decadal ice loss during winter months has accelerated from -2.4%/decade from 1979 to 1999 to-3.4%/decade from 2000 onwards. We also examine regional ice loss and find that for any given region, the seasonal ice loss is larger the closer that region is to the seasonal outer edge of the ice cover. Finally, across all months, we identify a robust linear relationship between pan-Arctic SIE and total anthropogenic CO2 emissions. The annual cycle of Arctic sea ice loss per ton of CO2 emissions ranges from slightly above 1 m(2) throughout winter to more than 3 m(2) throughout summer. Based on a linear extrapolation of these trends, we find the Arctic Ocean will become sea-ice free throughout August and September for an additional 800 +/- 300 Gt of CO2 emissions, while it becomes ice free from July to October for an additional 1400 +/- 300Gt of CO2 emissions

Changing state of Arctic sea ice across all seasons

The decline in the floating sea ice cover in the Arctic is one of the most striking manifestations of climate change. In this review, we examine this ongoing loss of Arctic sea ice across all seasons. Our analysis is based on satellite retrievals, atmospheric reanalysis, climate-model simulations and a literature review. We find that relative to the 1981–2010 reference period, recent anomalies in spring and winter sea ice coverage have been more significant than any observed drop in summer sea ice extent (SIE) throughout the satellite period. For example, the SIE in May and November 2016 was almost four standard deviations below the reference SIE in these months. Decadal ice loss during winter months has accelerated from −2.4 %/decade from 1979 to 1999 to −3.4%/decade from 2000 onwards. We also examine regional ice loss and find that for any given region, the seasonal ice loss is larger the closer that region is to the seasonal outer edge of the ice cover. Finally, across all months, we identify a robust linear relationship between pan-Arctic SIE and total anthropogenic CO₂ emissions. The annual cycle of Arctic sea ice loss per ton of CO₂ emissions ranges from slightly above 1 m² throughout winter to more than 3 m² throughout summer. Based on a linear extrapolation of these trends, we find the Arctic Ocean will become sea-ice free throughout August and September for an additional 800 ± 300 Gt of CO₂ emissions, while it becomes ice free from July to October for an additional 1400 ± 300 Gt of CO₂ emissions

An Evaluation of Trend and Anomalies of Arctic Sea Ice Concentration, 1979-2006

As a part of the Cyrosphere ecosystem, Arctic sea ice is one of the focal points when studying Arctic climate change. Arctic sea ice image has been documented by remotely sensed data since the 1970s. By examining these data, some climate patterns can be revealed. In this research, Arctic region is divided into 9 sections to analyze the regional differences of the ice coverage and variability. Data used are bootstrapped 1979 to 2006 SSM/I and SMMR images from NSIDC to perform a time series analysis to examine the sea ice trends and spatial/temporal anomalies detection by conducting a descending sort of sea ice coverage by years in the sub-regional scale. Then, the temporal mixture analysis developed by Piwowar & LeDrew is applied to the data to reveal the variability within each subregion. Fractional images produced by TMA highlight the temporal signature concentration in the entire Arctic region. And the color-mix image derived from TMA highlights and overlaps temporal signatures that have over 80% concentrations from highest to lowest. The color mix image can reveal the spatial distribution of similar temporal characteristics and the evolution of time series in the same area during the 30-year period. Through this analysis, the spatial and temporal variability of Arctic sea ice can be perceived that in the subpolar regions, Arctic sea ice has a higher seasonal pattern which varies a lot each other. The Arctic sea ice extent endures an overall decline trend, which the decline speed increases every ten years. But this trend is not statistically significant in every subregion. The spatial/temporal anomaly analysis reveals several patterns of Arctic sea ice variability. The seasonal variability of Arctic sea ice in the eastern and western side of the Arctic Basin resemble each other in the long term, which may coincide with the North Atlantic Oscillation. In addition, within a subregion, different areas may have significantly different temporal characteristics, such as the Greenland Sea and Seas of Okhotsk. Moreover, the temporal characteristics some areas in the Arctic region have changed through time significantly regarding early melt or late freeze. Hopefully this analysis will provide undiscovered temporal evolution through time and some new insights on the dynamics of the Arctic sea ice cover

Decadal changes in Arctic Ocean Chlorophyll a: Bridging ocean color observations from the 1980s to present time

Remotely-sensed Ocean color data offer a unique opportunity for studying variations of bio-optical properties which is especially valuable in the Arctic Ocean (AO) where in situ data are sparse. In this study, we re-processed the raw data from the Sea-viewing Wide Field-of-View (SeaWiFS, 1998–2010) and the MODerate resolution Imaging Spectroradiometer (MODIS, 2003–2016) ocean-color sensors to ensure compatibility with the first ocean color sensor, namely, the Coastal Zone Color Scanner (CZCS, 1979–1986). Based on a bio-regional approach, this study assesses the quality of this new homogeneous pan-Arctic Chl a dataset, which provides the longest (but non-continuous) ocean color time-series ever produced for the AO (37 years long between 1979 and 2016). We show that despite the temporal gaps between 1986 and 1998 due to the absence of ocean color satellite, the time series is suitable to establish a baseline of phytoplankton biomass for the early 1980s, before sea-ice loss accelerated in the AO. More importantly, it provides the opportunity to quantify decadal changes over the AO revealing for instance the continuous Chl a increase in the inflow shelves such as the Barents Sea since the CZCS era

Moisture Flux Estimates Derived from EOS Aqua Data in the Arctic

The Arctic sea ice acts as a barrier between the ocean and atmosphere inhibiting the exchange of heat, momentum, and moisture. Recently, the ice pack has been decreasing in area and concentration. This diminished sea ice coverage could potentially allow for larger moisture fluxes that affect surface energy budgets, the occurrence of clouds, and the near-surface humidity and temperature. Currently, reanalyses are known to produce large errors and biases in the Arctic, warranting improved moisture flux algorithms and input data. Using the Monin-Obukhov similarity theory, with adjustments made to better suit the conditions of the Arctic, and observations from NASA's EOS Aqua satellite, specifically the AIRS and AMSR-E instruments, the daily moisture flux is calculated from 2003-2011. The moisture flux is studied for a series of North Water polynya events between 2003-2009 to test the accuracy of the Aqua products and our algorithm. Using in situ data we validated moisture flux results, finding an error of 20.3%, improving the moisture flux accuracy compared to other climate models. The moisture flux for the entire Arctic was studied to look for inter-annual variations and was compared to changes in the sea ice. Instead of an expected increase in the moisture flux due to a declining sea ice pack, there has been a 15% decrease. On a regional scale and based on their average moisture flux, the Chukchi/Beaufort Seas, Laptev/E. Siberian Seas, Canadian Archipelago and Central Arctic are increasing, between 2.1 and 4.8 %/yr. Increases are due to the changes in the ice concentration, which allows for the surface temperatures to increase substantially in the fall and winter months when the amount of moisture exchanged is highest. The Kara/Barents Seas, E. Greenland Sea and Baffin Bay are decreasing, between 0.53 and 9.2 %/yr. These regions have areas of open water year round, and their exchanges of moisture are due mostly to smaller differences in surface and 2 m specific humidities. The contribution of the sea ice zone to the total moisture flux (from the open ocean and sea ice zone) has increased by 3.6% because the amount of open water within the sea ice zone has increased by 4.3%

A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p

Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends

Sea ice leads in the Arctic are important features that give rise to strong localized atmospheric heating; they provide the opportunity for vigorous biological primary production, and predicting leads may be of relevance for Arctic shipping. It is commonly believed that traditional sea ice models that employ elastic-viscous-plastic (EVP) rheologies are not capable of properly simulating sea ice deformation, including lead formation, and thus, new formulations for sea ice rheologies have been suggested. Here we show that classical sea ice models have skill in simulating the spatial and temporal variation of lead area fraction in the Arctic when horizontal resolution is increased (here 4.5 km in the Arctic) and when numerical convergence in sea ice solvers is considered, which is frequently neglected. The model results are consistent with satellite remote sensing data and discussed in terms of variability and trends of Arctic sea ice leads. It is found, for example, that wintertime lead area fraction during the last three decades has not undergone significant trends