Adjoint-based simultaneous state and parameter estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)

<p>With the presented work we aim to improve sea ice forecasts and our understanding of Arcitc sea ice formation though freeboard assimilation. Over the last years understanding Arctic sea ice changes and being able to make a reliable sea ice forecast has gained in importance. The central roll of Arctic sea ice extent in climate warming makes it a highly discussed topic in the climate research community. However a reliable Arctic sea ice forecast both on short term to seasonal time scales remains a challenge to be mastered, hinting that there are still many processes at play to be better understood. <br>One promising approach to improve forecasts has been to assimilate satellite sea ice data into numerical sea ice models. Mainly two parameters measured by satellites have been used for assimilation: Sea ice concentration, which is competitively easy to obtain from satellites measuring passive microwave emissions as for example obtained by the SMOS satellite, and sea ice thickness, which is not directly measured, but has to be calculated from surface elevation measurements, as for example obtained by Cryosat 2. Compering the skill, of assimilation products using sea ice thickness and sea ice concentration shows that sea ice thickness has a longer memory and is over all leading to a better performance then sea ice concentration assimilation. Knowing this, sea ice thickness assimilation is far from being straight forward. Surface elevation measurements, obtained from satellite altemitry measurements, have to be separated into snow and ice freeborad, by assuming a snow thickness, to derive sea ice thickness from. Most of the time this is done using a snow thickness climatology obtained from Soviet drift stations measuring snow over multi year ice during the period 1954-1991 with adaption over first year sea ice, where this climatology has proven to be overestimating snow thickness. The technique is widely used jet known to introduce an error. <br>To avoid errors caused by wrongly assumed snow covers the DMI and Aalborg University and DTU are at the moment collaborating on assimilating freebord instead of sea ice thickness into the CICE-NEMO modeling frame work using LARS NGen (LARS the Advanced Retracking System, Next Generation) sate of the art retracing software. In the presented work we will show first results of freeboard assimilation with a focus how this assimilation influences winter sea ice formation as well as the upper Arctic Ocean dynamics.</p>

The Arctic region has been warming at a rate significantly faster than the global average, leading to an accelerated decline in sea ice. This trend is expected to continue, potentially resulting in a "low-ice regime," which could make sea ice conditions more unpredictable. Anticipating changes in Arctic sea ice and climate states is therefore crucial for guiding various human activities, from natural resource management to risk assessment decisions. While global climate and Earth system models project continuous sea ice decline over decadal time scales, achieving reliable seasonal forecasts remains challenging. To address this, we apply dynamical downscaling with the state-of-the-art Regional Arctic System Model (RASM), which enables us to forecast Arctic sea ice on time scales ranging from weeks to six months. RASM is a fully coupled regional climate model that integrates components for the atmosphere, ocean, sea ice, and land, interconnected through the flux coupler of the Community Earth System Model. In our study, we simulate RASM at a horizontal resolution of 1/12 degree (approximately 9 km) for both the ocean and sea ice, with 45 vertical levels in the ocean and five thickness categories for sea ice. The atmosphere is configured on a 50-km grid with 40 vertical levels, dynamically downscaled from the NOAA/NCEP Climate Forecasting System version 2 (CFSv2) at 72-hour intervals for the upper half of the atmosphere. Monthly ensemble forecasts extending up to six months are generated using initial conditions derived from a fully-coupled RASM hindcast simulation without bias correction and assimilation. This presentation highlights results for September sea ice predictions initialized on April 1, May 1, June 1, July 1, August 1, and September 1, covering pan-Arctic and regional sea ice spatio-temporal conditions from 2012 to 2021. Specifically, we examine how lead time and initial conditions affect the quantitative skill of seasonal predictability for Arctic sea ice and demonstrate skillful predictions of September sea ice up to six months in advance. Overall, our study underscores that enhancing model physics and obtaining more realistic initial conditions are crucial for achieving skillful sub-seasonal to seasonal predictions.

<p>Investigating the temporal and spatial changes in Arctic sea ice freeboard, thickness and volume is crucial for climate and environmental research. In this study, the freeboard, thickness and volume of the Arctic sea ice between 2011 and 2020 were acquired from CryoSat-2 data and it was compared with NASA Operation IceBridge and Alfred Wegener Institue AWI datasets. The effect of wind field and temperature and itscontribution on Arctic sea ice were also investigated. The main steps of our research are as follows. 1) Selecting data points above 66 degrees north latitude and filtering out sea ice by flag value and mask; Using OSI SAF sea ice concentration (SIC) data to further constrict the area; Interpolating the latest mean sea surface data DTU21 into CryoSat-2 data; Calculating the sea ice freeboard via spatial altimetry relationship. 2) Combining with snow density, snow depth, multi-year ice density and first-year ice density to estimate the sea ice thickness respectively from freeboard according to the assumption of hydrostatic equilibrium. 3) Using the area and extent provided by NSIDC to interpret the volume. 4) Utilizing Seasonal and Trend decomposition using Loess(STL) to analyse the seasonal and interannual variations of sea ice. 5) Dividing the Arctic Ocean by its marginal sea, coupled with the HY-2B microwave scatterometer data and NCEP/NCAR reanalysis data, the impact of the Beaufort Sea, the Chukchi Sea, the East Siberian Sea, the Laptev Sea, the Kara Sea, the Barents Sea, the Greenland Sea, and the Baffin Bay wind field on the sea ice were studied. 6) Exploring the correlation between the sea surface temperature and the sea ice freeboard, thickness and volume. The result indicated that (1) the freeboard and thickness was decreasing about 9.748% and 8.80% during 2011-2020,respectively; (2) there are interlunar variations in sea ice freeboard and thickness, the freeboard and thickness of sea ice reach the minimum in August to September each year and the maximum appears in March to April; (3) Arctic sea ice is affected by both thermodynamics and dynamics, the reduction of Arctic sea ice was largely due to the sea surface wind field, one of the dynamic factors is the wind field on the sea surface. The sea ice changes in the various sea areas of the Arctic Ocean are related to the wind field to varying degrees. The changes in the sea surface wind field in recent years have further promoted the reduction of sea ice, making it possible for the Arctic to open to navigation in the summer. Fully understanding the evolution of sea ice change in the Arctic Ocean is helpful for humans to better protect the earth.</p>

China launched its Arctic research program and organized the first Chinese National Arctic Research Expedition (CHINARE-Arctic) in 1999. By 2016, six further expeditions had been conducted using the R/V Xuelong. The main region of the expeditions has focused on the Pacific sector of the Arctic Ocean for sea ice observations. The expeditions have used icebreaker, helicopter, boat, floe, and buoy platforms to perform these observations. Some new technologies have been developed, in particular, the underway auto-observing system for sea ice thickness using an electromagnetic instrument. The long-term measurement systems, e.g., the sea ice mass balance buoy, allow observations to extend from summer to winter. Some international cooperation projects have been involved in CHINARE-Arctic, especially the “Developing Arctic Modeling and Observing Capabilities for Long-Term Environmental Studies” project funded by the European Union during the International Polar Year. Arctic sea ice observations have been used to verify remote sensing products, identify changes in Arctic sea ice, optimize the parameterizations of sea ice physical processes, and assess the accessibility of ice-covered waters, especially around the Northeast Passage. Recommendations are provided as guidance to future CHINARE-Arctic projects. For example, a standardized operation system of sea ice observations should be contracted, and the observations of sea ice dynamics should be enhanced. The upcoming launch of a new Chinese icebreaker will allow increased ship time in support of future CHINARE Arctic oceanographic investigations. Citation: Lei R B, Zhang Z H, Li Z J, et al. Review of research on Arctic sea ice physics based on the Chinese National Arctic Research Expedition. Adv Polar Sci, 2017, 28 (2): 100-110, doi:10.13679/j.advps.2017.2.00100

The seasonal cycle in Arctic sea ice extent is asymmetric. Its amplitude has grown in recent decades as the ice has retreated more rapidly in summer than in winter. These seasonal disparities have typically been attributed to different physical factors operating during different seasons. Here we show instead that the seasonal asymmetries in Arctic sea ice extent are a geometric consequence of the distribution of continents. Coastlines block southward ice extension during winter, thereby muting changes in ice extent, but they have relatively little effect at the time of summer minimum extent. We suggest that the latitude of the Arctic sea ice edge, averaged zonally over locations where it is free to migrate, is the most readily interpretable quantity to describe the Northern Hemisphere sea ice cover. We find that the zonal‐mean sea ice edge latitude during the 1978–present era of satellite measurements has been following an approximately sinusoidal seasonal cycle that has been migrating northward at an approximately annually constant rate of 8 km/year. These results suggest a change in perspective of the most critical quantities for understanding changes in Arctic sea ice.

<strong class="journal-contentHeaderColor">Abstract.</strong> The Arctic sea ice cover is strongly influenced by internal variability on decadal time scales, affecting both short-term trends and the timing of the first ice-free summer. Several mechanisms of variability have been proposed, but how these mechanisms manifest both spatially and temporally remains unclear. The relative contribution of internal variability to observed Arctic sea ice changes also remains poorly quantified. Here, we use a novel technique called low-frequency component analysis to identify the dominant patterns of winter and summer decadal Arctic sea-ice variability in the satellite record. The identified patterns account for most of the observed regional sea ice variability and trends, and thus help to disentangle the role of forced and internal sea ice changes over the satellite record. In particular, we identify a mode of decadal ocean-atmosphere-sea ice variability, characterized by an anomalous atmospheric circulation over the central Arctic, that accounts for approximately 30 % of the accelerated decline in pan-Arctic summer sea-ice area between 2000 and 2012. For winter sea ice, we find that internal variability has dominated decadal trends in the Bering Sea, but has contributed less to trends in the Barents and Kara Seas. These results, which detail the first purely observation-based estimate of the contribution of internal variability to Arctic sea ice trends, suggest a lower estimate of the contribution from internal variability than most model-based assessments.

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming. This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions. The results indicate that sea ice extent reduction during 1979–2013 is most significant in summer, following by that in autumn, winter and spring. In years with rich sea ice, sea ice extent anomaly with seasonal cycle removed changes with a period of 4–6 years. The year of 2003–2006 is the ice-rich period with diverse regional difference in this century. In years with poor sea ice, sea ice margin retreats further north in the Arctic. Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic. Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes, which shows an coincident change in the Arctic and sub regions. Since 2002, Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic, followed by C1 and C3. Sea ice changes in different regions show three relationships. The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high, suggesting good consistency of ice variation. In the Atlantic sector, sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea. Sea ice changes in the central Arctic are affected by surrounding seas.

Abstract. The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×106 km2, compared to 6.46×106 km2 for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×106 km2 for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 % yr−1) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Arctic sea ice variation in the Northwest Passage in 1979–2017 and its response to surface thermodynamics factors

Arctic sea ice has declined rapidly. A record low of sea ice extent occurred in summer 2012 as observed over three decades of satellite data. The regime of the Arctic ice cover has shifted to a new state dominated by seasonal sea ice, as perennial sea ice has diminished drastically. Changes in the distribution of sea ice classes can precondition summer melt, alter the surface albedo, upset the Bowen ratio, and impact atmospheric photochemical processes. To investigate Arctic sea ice, environmental, and chemical changes, we conducted the Bromine, Ozone, and Mercury Experiment (BROMEX) around Barrow, Alaska, in March-April 2012, together with continuous measurements through the present. We used satellite active microwave scatterometer (AMS) and passive microwave radiometer (PMR) to observe the change in sea ice extent (SIE). A problem for sea ice observations in summer is that surface melt can impact PMR signatures of sea ice, causing large uncertainties in the measurement of SIE. We developed an AMS algorithm to determine SIE, primarily based on a quadruplet combination of relative AMS values rather than the absolute magnitudes of the signature and thus improving SIE accuracy. Then we took the SIE product from AMS in 2000–2009 to calibrate PMR results and reconstruct a SIE time series over three decades (1979–2013). To monitor sea surface temperature change caused by river discharge, we obtain results from two different algorithms using data acquired from the Moderate-resolution Imaging Spectroradiometer (MODIS) instruments that are currently in orbit on both the Terra and Aqua satellites, launched in 1999 and 2002, respectively. We present results from spring-summer 2012 over the Beaufort Sea where warm waters from the Mackenzie River surged onto the sea surface layer thereby accelerating sea ice melt. Additionally, we estimated the total annual heating power carried by 72 rivers into the Arctic Ocean. We found that Arctic rivers could provide an enormous amount of energy to melt sea ice. With their immense watersheds (several million square kilometers), the Arctic rivers are effective conduits that transport heat from the warm continents in summer to the Arctic Ocean, thus contributing to sea ice melt, and enhancing the sensitivity of Arctic sea ice to climate change. In stark contrast, Antarctica is a frozen continent without any warm rivers to affect sea ice in the Southern Ocean. Using satellite observations, we directly address the question: Why has Arctic sea ice decreased precipitously while Antarctic sea ice has been relatively stable? This topic draws tremendous attention from the cryospheric science community, and has been a challenging riddle in climate change research. The influence of river discharge into the Arctic Ocean helps to answer that puzzle.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic sea ice outflow to the Atlantic Ocean is essential to Arctic sea ice mass loss and the hydrographical and ecological environments in the Barents and Greenland Seas (BGS). In the context of the extremely positive Arctic Oscillation (AO) in January&ndash;March 2020, the impacts and feedback mechanisms on a seasonal scale of anomalies in Arctic sea ice outflow on winter&ndash;spring sea ice and other marine environmental conditions in the subsequent months until early summer in the BGS were investigated. The results reveal that the total sea ice area flux (SIAF) through the Fram Strait, the Svalbard-Franz Josef Land, and the Franz Josef Land-Novaya Zemlya passageways in January&ndash;March and June 2020 were higher than the 1988&ndash;2020 climatology, mainly through the Fram Strait (77.6 %). The interannual variability of this total SIAF was dominated by changes in ice motion speed (<em>R</em> = +0.86, <em>P</em> &lt; 0.001). The relatively high ice speed along the Transpolar Drift in January&ndash;June 2020 was related to the positive phases of winter (JFM) AO and the winter-spring air pressure gradient across the western and eastern Arctic Ocean. The abnormally high Arctic sea ice outflow led to an increased sea ice area and thickness in the BGS, which has been observed since March 2020, especially in May&ndash;June. In this region, the April sea ice area was significantly negatively correlated with synchronous sea surface temperature (SST) as well as the lagging SST of 1&ndash;3 months. High sea ice area in spring (AMJ) 2020 also inhibited phytoplankton bloom, with an extremely low <em>Chlorophyll-a</em> concentration observed over the BGS in April. Therefore, this study suggests that winter&ndash;spring Arctic sea ice outflow can be considered as a predictor of changes in sea ice and other marine environmental conditions in the BGS in the subsequent months, at least until early summer. The results increase our understanding of the physical connection between the central Arctic Ocean and the peripheral seas.

<strong class="journal-contentHeaderColor">Abstract.</strong> Arctic sea ice outflow to the Atlantic Ocean is essential to Arctic sea ice mass loss and the hydrographical and ecological environments in the Barents and Greenland Seas (BGS). In the context of the extremely positive Arctic Oscillation (AO) in January&ndash;March 2020, the impacts and feedback mechanisms on a seasonal scale of anomalies in Arctic sea ice outflow on winter&ndash;spring sea ice and other marine environmental conditions in the subsequent months until early summer in the BGS were investigated. The results reveal that the total sea ice area flux (SIAF) through the Fram Strait, the Svalbard-Franz Josef Land, and the Franz Josef Land-Novaya Zemlya passageways in January&ndash;March and June 2020 were higher than the 1988&ndash;2020 climatology, mainly through the Fram Strait (77.6 %). The interannual variability of this total SIAF was dominated by changes in ice motion speed (<em>R</em> = +0.86, <em>P</em> &lt; 0.001). The relatively high ice speed along the Transpolar Drift in January&ndash;June 2020 was related to the positive phases of winter (JFM) AO and the winter-spring air pressure gradient across the western and eastern Arctic Ocean. The abnormally high Arctic sea ice outflow led to an increased sea ice area and thickness in the BGS, which has been observed since March 2020, especially in May&ndash;June. In this region, the April sea ice area was significantly negatively correlated with synchronous sea surface temperature (SST) as well as the lagging SST of 1&ndash;3 months. High sea ice area in spring (AMJ) 2020 also inhibited phytoplankton bloom, with an extremely low <em>Chlorophyll-a</em> concentration observed over the BGS in April. Therefore, this study suggests that winter&ndash;spring Arctic sea ice outflow can be considered as a predictor of changes in sea ice and other marine environmental conditions in the BGS in the subsequent months, at least until early summer. The results increase our understanding of the physical connection between the central Arctic Ocean and the peripheral seas.

The conductive heat flux (CHF) from the ocean to the snow/sea ice‐atmosphere interface through sea ice is a crucial component of the surface energy budget over the sea ice‐covered Arctic Ocean. The CHF is influenced by surface skin temperature, sea ice thickness, and depth of snow on sea ice. This study uses monthly mean surface skin temperature from ERA5 reanalysis, and sea ice thickness and snow depth from the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System to derive the means and changes of CHF over the Arctic Ocean from 1979 to 2022. The findings reveal that CHFs are generally positive (from flux to the surface from below) from November to March and negative from June to August. CHFs are large in peripheral seas with thin sea ice and low snow depth, and small over pack ice with thick sea ice and snow. The thinning of sea ice and snow on sea ice contributes to increasing CHF, while rising surface skin temperatures lead to decreasing CHF. Overall, CHF increases from 1979 to 2022, primarily driven by the thinning of sea ice. The magnitude of CHF changes during this period is comparable to or higher than those in sensible heat flux, latent heat flux, and net longwave/thermal radiation flux at the surface from November to March over the peripheral seas. A better representation of sea ice thickness and snow depth over sea ice in the reanalysis would possibly improve the depiction of CHF and surface temperature changes.

&amp;lt;p&amp;gt;Arctic sea ice is steadily retreating due to climate warming, but regional and seasonal variations in Arctic sea ice are important. This study aims in understanding how the winter atmospheric circulation affects the sea ice drift and how ice motion contributes to regional sea ice concentration and thickness anomalies. Sea ice conditions in late winter and spring are crucial for predicting summer sea ice. Understanding the mechanism that affect the spring sea ice concentration and thickness have potential to improve sea ice predictions year-round. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Atmospheric pressure patterns affect the thermodynamic vertical structure of the atmosphere, heat and moisture transport to the Arctic, and radiative and turbulent fluxes at the surface (eg. Nyg&amp;amp;#229;rd et al 2021). Circulation types also control surface wind speed and direction, and are closely linked to ice drift speed (eg. Mallett et al 2021). Winter atmospheric circulation can precondition spring sea ice anomalies and summer melt by ice dynamics (eg. sea ice transport to lower latitudes where it is more vulnerable to melt) and thermodynamics (eg. positive surface energy balance anomalies prohibiting ice growth in winter leading to thinner ice in spring).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study we present a Self Organizing Maps (SOM) clustering of the winter (December-March) mean sea level pressure to detect the typical circulation patterns. The SOM-analysis covers the period from December 2000 to March 2021. The circulation patterns, or SOM nodes, are linked to atmospheric conditions (surface energy balance and wind speed) and sea ice conditions (concentration, drift speed and thickness). We use data from ERA5, ORAS5, and PIOMAS reanalyses, and Polar Pathfinder Sea Ice Motion and Cryosat2-SMOS ice thickness remote sensing products. We show how the circulation types are linked to near surface wind speed and direction, and consequent sea ice drift. &amp;amp;#160;As a result, we analyze if the circulation patterns can be linked to sea ice anomalies thought sea ice dynamics or thermodynamics.&amp;lt;/p&amp;gt;

Experiments with an atmospheric GCM are used to determine the effect of anomalous Arctic sea ice thickness on the atmospheric circulation. Ice thickness data are taken from a hindcast simulation with an ocean‐sea ice model under NCAR/NCEP forcing. Ice conditions from 1964–1966 and 1994–1996 represent extreme cases of largest and smallest ice volume, respectively, during the last 50 years. The atmospheric response to the 1990s thinning of Arctic sea ice comprises a reduction in sea level pressure in the central Arctic and over the Nordic Seas. High pressure anomalies evolve over the subtropical North Atlantic and over the subpolar North Pacific. Similar signals occur at 500 hPa. Realistic sea ice thickness changes can induce atmospheric signals that are of similar magnitude as those due to changes in sea ice cover.