Using timing of ice retreat to predict timing of fall freeze-up in the Arctic

Reliable forecasts of the timing of sea ice advance are needed in order to reduce risks associatedwith operating in the Arctic as well as planning of human and environmental emergencies. This studyinvestigates the use of a simple statistical model relating the timing of ice retreat to the timing of ice advance,taking advantage of the inherent predictive power supplied by the seasonal ice-albedo feedback and oceanheat uptake. Results show that using the last retreat date to predict the first advance date is applicable insome regions, such as Baffin Bay and the Laptev and East Siberian seas, where a predictive skill is found evenafter accounting for the long-term trend in both variables. Elsewhere, in the Arctic, there is some predictive skillsdepending on the year (e.g., Kara and Beaufort seas), but none in regions such as the Barents and Bering seas orthe Sea of Okhotsk. While there is some suggestion that the relationship is strengthening over time, this mayreflect that higher correlations are expected during periods when the underlying trend is strong

A linear mixed effects model for seasonal forecasts of Arctic sea ice retreat

With sea ice cover declining in recent years, access to open Arctic waters has become a growing interest to numerous stakeholders. Access requires time for planning and preparation, which creates the need for accurate seasonal forecasts of summer sea ice characteristics. One important attribute is the timing of sea ice retreat, of which current statistical and dynamic sea ice models struggle to make accurate seasonal forecasts. We develop a linear mixed effects model to provide forecast of sea ice retreat over five major regions of the Arctic – Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas. In this, the fixed effect – i.e. the mean influence of the atmosphere on sea ice retreat – is modeled using predictors that directly influence the dynamics or thermodynamics of sea ice, and random effects are grouped regionally to capture the local-scale effects on sea ice. The model exhibits very good skill in forecast of sea ice retreat at lead times of up to half a year over these regions

Arctic sea ice melt onset favored by an atmospheric pressure pattern reminiscent of the North American-Eurasian Arctic pattern

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively

Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery

Melt ponds on sea ice play an important role in the seasonal evolution of the summer ice cover. In this study we present two machine learning algorithms, one (multi-layer neural network) for the retrieval of melt pond binary classification and another (multinomial logistic regression) for melt pond fraction using moderate resolution visible satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). To minimize the impact of the anisotropic reflectance characteristics of sea ice and melt ponds, normalized MODIS band reflectance differences from top-of-the-atmosphere (TOA) measured reflectances were used. The training samples for the machine learning were based on MODIS reflectances extracted for sea ice, melt ponds and open water classifications based on high resolution (~2 m) WorldView (WV) data. The accuracy assessment for melt pond binary classification and fraction is further evaluated against WV imagery, showing mean overall accuracy (85.5%), average mean difference (0.09), and mean RMSE (0.18). In addition to cross-validation with WV, retrieved melt pond data are validated against melt pond fractions from satellite and ship-based observations, showing average mean differences (MD), root-mean-square-error (RMSE), and correlation coefficients (R) of 0.05, 0.12, and 0.41, respectively. We further investigate a case study of the spectral characteristics of melt ponds and ice during refreezing, and demonstrate an approach to mask out refrozen pixels by using yearly maps of melt onset and freeze-up data together with ice surface temperatures (IST). Finally, an example of monthly mean pan-Arctic melt pond binary classification and fraction are shown for July 2001, 2004, 2007, 2010, 2013, 2016, and 2019. Bulk processing of the entire 20 years of MODIS data will provide the science community with a much needed pan-Arctic melt pond data set

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1‐ to 7‐month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability

Causes and evolution of winter polynyas north of Greenland

During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.</p

Simulated Ka-and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka-and Ku-band frequencies, and the impact on the Ka-and Ku-band radar scattering horizon or the "track point"(i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka-and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka-and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka-and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka-and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (> 0:1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka-and Ku-band dualfrequency CRISTAL radar altimeter missions

Atmospheric Forcing Drives the Winter Sea Ice Thickness Asymmetry of Hudson Bay

Recently, we highlighted the presence of a strong west‐east asymmetry in sea ice thickness across Hudson Bay that is driven by cyclonic circulation. Building on this work, we use satellite altimetry and a unique set of in situ observations of ice thickness from three moored upward looking sonars to examine the role of atmospherically driven ice dynamics in producing contrasting regional ice thickness patterns. Ultimately, north‐northwesterly winds coupled with numerous reversals during winter 2016/2017 led to thicker ice in southern Hudson Bay, while enhanced west‐northwesterly winds during winter 2017/2018 led to thicker ice in eastern Hudson Bay that delayed breakup and onset of the summer shipping season to coastal communities. Extending the analysis over the 40‐year satellite observation period, we find that these two different patterns of atmospheric forcing alter the timing of breakup by 30 days in eastern Hudson Bay and offer some skill in seasonal predictions of breakup

Extending the Arctic Sea Ice Freeboard and Sea Level Record with the Sentinel-3 Radar Altimeters

In February 2016 and April 2018 the European Space Agency launched the Sentinel-3A and 3B satellites respectively, as part of the European Commission’s multi-satellite Copernicus Programme. Here we process Sentinel-3A waveform data to estimate Arctic sea level anomaly and radar freeboard from November 2017 to April 2018. We compare our results to those from the CryoSat-2 satellite, and find an intermission bias on sea-level anomaly of 2 cm. We also find a mean radar freeboard difference of 1 cm, which we attribute to the use of empirical retrackers to retrieve lead and floe elevations. Ahead of Sentinel-3B waveform data being made available, we use orbit files to estimate the improvement in sampling resolution afforded by the addition of Sentinel-3A and 3B data to the CryoSat-2 dataset. By combining data from the three satellites, grid resolution or time-sampling can be almost tripled compared with using CryoSat-2 data alone

On the existence of stable seasonally varying Arctic sea ice in simple models

Within the framework of lower order thermodynamic theories for the climatic evolution of Arctic sea ice we isolate the conditions required for the existence of stable seasonally-varying solutions, in which ice forms each winter and melts away each summer. This is done by constructing a two-season model from the continuously evolving theory of Eisenman and Wettlaufer (2009) and showing that seasonally-varying states are unstable under constant annual average short-wave radiative forcing. However, dividing the summer season into two intervals (ice covered and ice free) provides sufficient freedom to stabilize seasonal ice. Simple perturbation theory shows that the condition for stability is determined by when the ice vanishes in summer and hence the relative magnitudes of the summer heat flux over the ocean versus over the ice. This scenario is examined within the context of greenhouse gas warming, as a function of which stability conditions are discerned.Comment: 11 pages, 6 figures, 1 tabl