A New Perspective on Four Decades of Changes in Arctic Sea Ice from Satellite Observations

Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations—the extended AVHRR Polar Pathfinder (APP-x)—trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has become less ice-covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at a rate of −3.24 cm per year, resulting in an approximate 52% reduction in thickness from 2.35 m in 1982 to 1.13 m in 2020. Arctic sea ice volume has been decreasing at a rate of −467.7 km3 per year, resulting in an approximate 63% reduction in volume, from 27,590.4 km3 in 1982 to 10,305.5 km3 in 2020. These trends are further examined from a new perspective, where the Arctic Ocean is classified into open water, perennial, and seasonal sea ice-covered areas based on sea ice persistence. The loss of the perennial sea ice-covered area is a major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes in extent, concentration, and thickness continue, the Arctic is expected to have ice-free summers by the early 2060s.

The contribution of wind wave changes on diminishing ice period in Lake Pyhäjärvi during the last half-century

To address the contribution of long-term wind wave changes on diminishing ice period in Northern European lakes, an in situ observation of wind waves was conducted to calibrate a wind-wave numerical model for Lake Pyhäjärvi, which is the largest lake in southwest Finland. Using station-measured hydrometeorological data from 1963 to 2013 and model-simulated wind waves, correlation and regression analyses were conducted to assess the changing trend and main influences on ice period. Ice period in Lake Pyhäjärvi decreased significantly over 51 years (r = 0.47, P < 0.01). The analysis of main hydrometeorological factors to ice period showed that the significant air temperature rise is the main contributor for the diminishing of ice period in the lake. Besides air temperature, wind-induced waves can also weaken lake ice by increasing water mixing and lake ice breakage. The regression indicated that mean significant wave height in December and April was negatively related to ice period (r = - 0.48, P < 0.01). These results imply that long-term changes of wind waves related to climate change should be considered to fully understand the reduction of aquatic ice at high latitudes.

Modeling surface energy fluxes and thermal dynamics of a seasonally ice-covered hydroelectric reservoir

The thermal dynamics of human created northern reservoirs (e.g., water temperatures and ice cover dynamics) influence carbon processing and air-water gas exchange. Here, we developed a process-based one-dimensional model (Snow, Ice, WAater, and Sediment: SIWAS) to simulate a full year's surface energy fluxes and thermal dynamics for a moderately large (>500km(2)) boreal hydroelectric reservoir in northern Quebec, Canada. There is a lack of climate and weather data for most of the Canadian boreal so we designed SIWAS with a minimum of inputs and with a daily time step. The modeled surface energy fluxes were consistent with six years of observations from eddy covariance measurements taken in the middle of the reservoir. The simulated water temperature profiles agreed well with observations from over 100 sites across the reservoir. The model successfully captured the observed annual trend of ice cover timing, although the model overestimated the length of ice cover period (15days). Sensitivity analysis revealed that air temperature significantly affects the ice cover duration, water and sediment temperatures, but that dissolved organic carbon concentrations have little effect on the heat fluxes, and water and sediment temperatures. We conclude that the SIWAS model is capable of simulating surface energy fluxes and thermal dynamics for boreal reservoirs in regions where high temporal resolution climate data are not available. SIWAS is suitable for integration into biogeochemical models for simulating a reservoir's carbon cycle.