Fine-Resolution Mapping of Pan-Arctic Lake Ice-Off Phenology Based on Dense Sentinel-2 Time Series Data

The timing of lake ice-off regulates biotic and abiotic processes in Arctic ecosystems. Due to the coarse spatial and temporal resolution of available satellite data, previous studies mainly focused on lake-scale investigations of melting/freezing, hindering the detection of subtle patterns within heterogeneous landscapes. To fill this knowledge gap, we developed a new approach for fine-resolution mapping of Pan-Arctic lake ice-off phenology. Using the Scene Classification Layer data derived from dense Sentinel-2 time series images, we estimated the pixel-by-pixel ice break-up end date information by seeking the transition time point when the pixel is completely free of ice. Applying this approach on the Google Earth Engine platform, we mapped the spatial distribution of the break-up end date for 45,532 lakes across the entire Arctic (except for Greenland) for the year 2019. The evaluation results suggested that our estimations matched well with both in situ measurements and an existing lake ice phenology product. Based on the generated map, we estimated that the average break-up end time of Pan-Arctic lakes is 172 ± 13.4 (measured in day of year) for the year 2019. The mapped lake ice-off phenology exhibits a latitudinal gradient, with a linear slope of 1.02 days per degree from 55°N onward. We also demonstrated the importance of lake and landscape characteristics in affecting spring lake ice melting. The proposed approach offers new possibilities for monitoring the seasonal Arctic lake ice freeze–thaw cycle, benefiting the ongoing efforts of combating and adapting to climate change.

Inter-annual variations and large-scale atmospheric forcing on ice thickness and composition during the last decade in an Arctic lake

<p>A thermistor-string-based Snow and Ice Mass Balance Apparatus (SIMBA) was deployed in an Arctic lake Orajärvi in northern Finland (67.36°N, 26.83°E) during winter seasons 2011/2012 - 2019/2020. The snow depth and ice thickness (total and separately for congelation ice and granular ice) were retrieved from SIMBA temperature measurements. The average maximum ice thickness was 72 cm with a standard deviation of 10 cm. The interannual variability of lake ice composition was large. In the past 3 ice seasons, the granular ice dominated the total ice thickness. For example, granular ice accounted 80% of the total ice thickness in May 2020. A high-resolution thermodynamic snow/ice model was applied to simulate ice mass balance, with special attention to the lake ice composition. Local weather station data and ECMWF reanalysis products were used as model forcing.</p><p> </p><p>The increase of granular ice formation is a result of more snow precipitation during the ice season, increased variability of seasonal air temperature, and a warming trend. The observed snow thickness on land showed a high correlation with snow-ice thickness on top of lake ice. The relationships between the ratio of snow-ice to total ice thickness and the large-scale atmospheric circulation indexes were investigated. Precipitation and, consequently, snow ice thickness on Lake Orajärvi correlated with the phase of the Pacific Decadal Oscillation, which is in line with previous results for precipitation and ice conditions in northern Finland, but an eventual causal teleconnection still requires further studies.</p><p> </p>

Harmonizing heterogeneous multi-proxy data from Arctic lake sediment records

<p>Lake systems play a central role in broadening our knowledge about future trends in the Arctic, as their sediments store information on interactions between climate change, lake ontogeny, external abiotic sediment input, and biodiversity changes. In order to make reliable statements about future lake trajectories, we need sound multi-proxy data from different lakes across the Arctic. Various studies using data from repositories already showed the effectiveness of multi-proxy, multi-site investigations (e.g., Kaufman et al., 2020; PAGES 2k Consortium, 2017). However, there are still datasets from past coring expeditions to Arctic lake systems that are neither included in any of these repositories nor subject to any particular standard. When working with such data from heterogeneous sources, we face the challenge of dealing with data of different format, type, and structure. It is therefore necessary to transform such data into a uniform format to ensure semantic and syntactic comparability. In this talk, we present an interdisciplinary approach by transforming research data from different lake sediment cores into a coherent framework. Our approach adapts methods from the database field, such as developing entity-relationship (ER) diagrams, to understand the conceptual structure of the data independently of the source. Based on this knowledge, we developed a conceptual data model that allows scientists to integrate heterogeneous data into a common database. During the talk, we present further steps to prepare datasets for multi-site statistical investigation. To test our approach, we compiled and transformed a collection of published and unpublished paleolimnological data of Arctic lake systems into our proposed format. Additionally, we show our results from conducting a comparative analysis on a set of acquired data, hereby focusing on comparing total organic carbon and bromine content. We conclude that our harmonized dataset enables numerical inter-proxy and inter-lake comparison despite strong initial heterogeneity.</p><p> </p><p>[1]   D. S. Kaufman et al., “A global database of Holocene paleotemperature records,” Sci. Data, vol. 7, no. 115, pp. 1–34, 2020.</p><p>[2]   PAGES 2k Consortium, “A global multiproxy database for temperature reconstructions of the Common Era,” Sci. Data, vol. 4, no. 170088, pp. 1–33, 2017.</p>

Lake Tarfala, N-Sweden – first results from a natural observatory mimicking future changes in glacier-fed Arctic lakes

<p>Lake Tarfala is an up to 50 m deep glacier-proximal Arctic lake in the Kebnekaise Mountains, northern Sweden (~67°55' N, ~18°35' E, 1162 m asl) in direct vicinity to the Tarfala Research Station run by Stockholm University, and to the glacier Storglaciären for which the world’s longest glacier mass balance record is kept since 1946. The neighboring Kebnepakte Glacier drains directly into Lake Tarfala. The site provides a unique an easily accessible natural observatory to study the impacts of climate and environmental change in an Arctic lake linked to a melting glacier.</p><p>As other Arctic lakes, Lake Tarfala is exposed to accelerated atmospheric warming in recent decades leading to increasingly shorter periods of lake freeze-over. Recent warming has also led to a widespread mass loss from glaciers with so for unclear implications for glacier-fed lakes which may receive larger amounts of meltwater and sediments from shrinking glaciers.</p><p>General atmospheric warming on the one hand and in response an increased influx of cold glacial meltwater to glacier-fed lakes on the other hand thus cause two competing processes determining the thermal state of a lake. Understanding (changing) lake thermal states and associated lake mixing dynamics is important because it has ramifications for a multitude of lake ecological, biological, and geochemical processes.</p><p>Here, we present the first continuous 3-year water temperature record from the deepest part of Lake Tarfala, acquired between 2016 and 2019. The record shows that Lake Tarfala is dimictic with overturning during spring and fall with substantial interannual variability concerning the timing, duration and intensity of mixing processes, as well as of summer and winter stratification. Particularly cold lake winter states appear to be related to elevated influx of cold glacial meltwater.</p><p>The projected high mass loss of Scandinavian glaciers with up to more than 80% of their volume under RCP8.5 until 2100 AD relative to 2015 renders Lake Tarfala a natural observatory where changes in processes, inherent timescales and impacts in response to competing drivers can be studied before they occur at other glacial lake sites where glaciers melt at a slower place.</p>

The Influence of Snow and Ice Albedo towards Improved Lake Ice Simulations

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in ice composition between northern and temperate region lake ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of High Arctic lake ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.