Comment on essd-2021-435

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records.

Long‐term ice phenology records quantify the effects of climate change on Northern Hemisphere lakes. This study uses lake ice phenological records across a gradient of lake sizes (0.1–31,967.8 km2 in lake surface area) obtained from community science networks. We compiled in situ ice phenological records for 2,499 lakes across 15 countries for an average of 30 years. These data revealed that for the last 50 years (1971–2020), the annual mean duration of lake ice cover decreased at a rate of 9 days per decade, with a regime shift in lake ice phenology in the late 1980s. We projected that at the end of the century (2070–2099), ice duration will decrease by an average of 10 days when compared to the historical time period (1971–2000) for the shared socioeconomic pathway (SSP) 1–2.6 climate scenario (SSP126), 23 days for SSP370, and 28 days for the SSP585. Impending human development can enhance or attenuate lake ice loss, as adaptation strategies can accelerate fossil fuel use, result in conflict, or seek strategies apart from fossil fuel development. These future pathways have critical implications for the future preservation of lake ice cover.

Fine-scale monitoring of lake ice phenology by synthesizing remote sensed and climatologic features based on high-resolution satellite constellation and modeling

Lake Ice phenology of small lakes: Impacts of climate variability in the Great Lakes region

Timely information on lake ice cover conditions is critical in support of commercial shipping, winter-road transportation, and winter leisure activities such as snowmobiling and ice fishing. Monitoring of lake ice extent and ice phenology (i.e. dates associated with freeze-up/break-up and ice duration) is also valuable for improving numerical weather prediction (NWP) and for climate monitoring. The availability of free synthetic aperture radar (SAR) data from the European Space Agency’s Sentinel-1 A/B constellation provides an unprecedented opportunity to develop operational algorithms for lake ice cover mapping at a temporal frequency not available until now from SAR missions (ca. every 1–5 days depending on latitude). For NWP, rapid and accurate mapping of ice cover and open water areas in lake-rich regions is required. However, the classification of SAR imagery using traditional machine learning (ML) approaches is challenging due to the large data volume generated by imaging systems such as Sentinel-1 SAR as well as the complexity of radar signatures as a function of sensor and target characteristics. For lakes specifically, radar backscatter can vary greatly with incidence angle, polarization and surface properties (e.g. calm and wind-roughed open water, new thin ice, surface roughness due to the presence of pressure ridges, ice type, melting of ice and on-ice snow). To address the challenge of large-scale lake ice mapping, we investigate the GPU-boosted deep neural network approach that is efficient at handling big complex data. In this paper, we design a novel maximum a posterior (MAP) approach combining a convolutional neural network (CNN) and conditional random field (CRF) for better addressing the challenges of operational lake ice mapping. The proposed approach is tested on 17 Sentinel-1 dual-polarization (VV and VH) SAR images, where eight are used for training and validation and nine used for out-of-sample testing. To identify the optimal network architecture, an independent validation set is used to evaluate the performance of a series of six CNNs with increasing model complexity. The best model overcomes the noise effect in Sentinel-1 SAR imagery and the lake ice signature ambiguity issue; it achieves average classification accuracies of 97.10% and 97.14% for open water and ice, respectively, on the validation set. Moreover, the best model outperforms the last-ranked model by about 2% in terms of mean overall accuracy (OA), demonstrating the improvement and importance of model selection. The use of CRF can consistently improve the CNN by reducing the artefacts and noise effect in ice maps, outperforming the CNN model when used alone by about 3% in terms of mean OA on the validation set. The proposed CNN-CRF approach also achieves high accuracy on the nine test scenes, achieving a mean OA of 93.10%, demonstrating strong generalization capability that is important for SAR lake ice mapping.

Credible modeling, tools, and guidance, regarding the changing Laurentian Great Lakes and the climatic impacts, are needed by local decision-makers to inform their management and planning. The present study addresses this need through a model evaluation study of the representation of lake–atmosphere interactions and resulting lake-effect snowfall in the Great Lakes region. Analysis focuses on an extensive ensemble of 74 historical simulations generated by 23 high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP). The model assessment addresses the modeling treatment of the Great Lakes, the spatial distribution and seasonality of climatological snowfall, the seasonal cycle of lake-surface temperatures and overlake turbulent fluxes, and the lake-effect ratio between upwind and downwind precipitation. A deeper understanding of model performance and biases is achieved by partitioning results between HighResMIP GCMs that are 1) coupled to 1D lake models versus GCMs that exclude lake models, 2) between prescribed-ocean model configurations versus fully coupled configurations, and 3) between deep Lake Superior versus relatively shallow Lake Erie. While the HighResMIP GCMs represent the Great Lakes by a spectrum of approaches that include land grid cells, ocean grid cells (with lake surface temperature and ice cover boundary conditions provided by the Met Office Hadley Center Sea Ice and Sea Surface Temperature Dataset), and 1D lake models, the current investigation demonstrates that none of these rudimentary approaches adequately represent the complex nature of seasonal lake temperature and ice cover evolution and its impact on lake–atmosphere interactions and lake-effect precipitation in the Great Lakes region. Significance Statement The purpose of this study is to evaluate the capability of high-resolution global climate models to simulate lake–atmosphere interactions and lake-effect snowfall in the Great Lakes region, given the critical influence of the lakes on regional climate and vast societal and environmental impacts of lake-effect snowfall. It is determined that the models inadequately represent lake temperatures and ice cover, often leading to insufficient annual snowfall in the lake-effect zones. More advanced, three-dimensional lake models need to be coupled to climate models to support greater credibility in regional lake and climate simulations and future climate projections.

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

Lake ice phenology of Nam Co, Central Tibetan Plateau, China, derived from multiple MODIS data products

Changes in ice phenology on polish lakes from 1961 to 2010 related to location and morphometry

Lakes sensitively respond to global and regional climate change, especially in arid areas. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the lake ice phenology of 23 lakes in the Xinjiang Uygur Autonomous Region of China from 2001 to 2018 was extracted based on thresholds of ice/water pixel numbers, and their change trends over 18 years were calculated. The results of MODIS-derived lake ice phenology showed consistent variations with existing ice phenology data sets derived from passive microwave data. Generally, lakes in Xinjiang begin to freeze from October to December every year, and their ice cover periods end from March to June. The average ice cover duration for the 23 lakes is 167 days, of which 16 lakes have an average shortening rate of − 1.08 days/year and seven lakes have an average extending rate of 1.18 days/year. The majority of lakes experienced later freeze-up (17 lakes) and earlier break-up (18 lakes) from 2001 to 2018. Lake ice phenology is affected by both climatic factors and lake physicochemical characteristics, in which freeze-up dates are more easily affected by lake-specific factors such as lake area (r = 0.535), while climatic factors especially water surface temperature have greater impacts on lake break-up dates (r = − 0.874). Compared to air temperature, water surface temperature changes have a more direct influence on the variations in lake ice phenology, 1° increase in water surface temperature may cause the ice cover duration to decrease by 12 days, while precipitation changes have almost no effect on the lakes in Xinjiang. In some cases, lake changes such as changes to the area and mineralization may also have dominant impacts on lake ice phenology.

China launched a new marine satellite HaiYang (HY)-2B satellite on October 24, 2018 at 22:57 (UTC time), which provides full-time and all-weather observations with a design life of five years. It can cover 90% of the world’s oceans and obtain marine dynamic environment data. The Scanning Microwave Radiometer (SMR) is the main payload onboard the HY-2B, which can provide near-real-time observations. The instrument is a 5-frequency 9-channel microwave observing instrument with the central frequencies set to 6.925, 10.7, 18.7, 23.8, and 37 GHz. Except for 23.8 GHz, which is vertical polarization only, other four frequencies have both vertical and horizontal polarization detection channels. For the past forty years, passive microwave brightness temperature data from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), Special Sensor Microwave Imager/Sounder (SSMI/S), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Microwave Scanning Radiometer 2 (AMSR2), Microwave Radiation Imager (MWRI) and the microwave imager of Global Precipitation Measurement mission (GMI) have provided considerable information on geophysical and climatic research. In order to expand the use of SMR observations on the earth’s land surface, in this study, SMR is compared with other on-orbit radiometers (SSMI/S, AMSR2, MWRI and GMI), and is conducted the cross-sensor calibration in land surface, which is critical foundation for the inversion of Earth surface geophysical parameters and climate researches in the near future.

A preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demonstrates the need for an intercalibration procedure. Several different scenarios based on filtering were tested, and the impact on the soil moisture retrievals was evaluated against two independent reference soil moisture datasets (reanalysis and in situ soil moisture) that cover both individual observation periods of the AMSR-E and AMSR2 sensors. Results show a high degree of consistency between both satellite products and two independent reference products for the soil moisture products retrieved from X-band observations. Care should be taken in the interpretation of the presented soil moisture products, and future research is needed to further align the AMSR2 and AMSR-E sensor calibrations.

More than 50% of global lakes periodically freeze, and their lake ice phenology is sensitive to climate change. However, spatially detailed quantification of the changes in lake ice at the global scale is not available. Here, we map ice cover in >33,000 lakes throughout the North Temperate Zone (23.5°–66.5°N) using 0.55 million Landsat images from 1985 to 2020. Over this period, we found a remarkable reduction in median ice cover occurrence (ICO) (61% to 43%), which was strongly related to warming terrestrial mean surface temperatures (R2 = 0.94, p < 0.05). Lakes in Europe showed the most pronounced ice loss (median ICO decreased from 50% to 24%), and extensive lake ice losses were also detected in the northern US, and central and eastern Asia. An overall increase in ice cover was identified from P2 (1999–2006) to P3 (2007–2014) due to regional decreased temperatures associated with the “global warming hiatus.” Thehigh‐resolution mapping of lake ice here provides essentialbaseline information whichcan be used to elucidate ice loss‐induced environmental and societal impacts.

On 26 August, Arctic sea ice cover dipped to 4.1 million square kilometers, 70,000 square kilometers below the 2007 mark, which was previously the lowest extent of ice cover in the satellite record, according to an analysis of satellite data by scientists with NASA and the National Snow and Ice Data Center (NSIDC) of the University of Colorado, Boulder. “By itself it's just a number, and occasionally records are going to get set. But in the context of what's happened in the last several years and throughout the satellite record, it's an indication that the Arctic sea ice cover is fundamentally changing,” said NSIDC scientist Walt Meier. “The persistent loss of perennial ice cover—ice that survives the melt season—led to this year's record summertime retreat. Unlike 2007, temperatures were not unusually warm in the Arctic this summer,” said Joey Comiso, senior research scientist at NASA's Goddard Space Flight Center, in Greenbelt, Md. The six lowest ice extents in the satellite record have occurred in the last 6 years, according to NSIDC.

Ice cover on the Great Lakes plays an important role in regional climate, supports tourism and recreation, and provides ecological habitat. As the climate warms, ice cover in the Great Lakes is expected to decline, which in turn will create more lake effect precipitation, reduce ice cover for recreation, and alter habitat for aquatic species. While it is important to understand the historical ice patterns to better understand past distributions of aquatic species and improve the accuracy of forecasts for future ice cover on the lakes, Great Lakes ice cover data prior to 1973 is scarce, due to the limited routine satellite observations. We used weather station data around the Great Lakes to compile daily air temperature, calculate cumulative freezing degree-days and net melting degree-days from 1897-2023, and develop raster layers estimating ice duration and variability spatially during the historical period from 1897-1960.