Snowmelt detection over the Greenland ice sheet from SSM/I brightness temperature daily variations

We propose a technique for monitoring snowmelt over the Greenland ice sheet between 1992 and 2005 based on the difference between ascending and descending brightness temperatures (DAV) measured either at 19.35- or 37- GHz by the Special Sensor Microwave Imager (SSM/I). Wet snow is detected when both brightness temperatures and DAV values exceed fixed thresholds. Differently from existing techniques, a multi-frequency approach allows detection of wet snow at different depths and intensities, providing a tool for improving climatological and hydrological applications. Air temperature values either recorded by ground based stations or derived from model are used for calibrating and validating the technique. Results are compared with those obtained using backscattering coefficients recorded by the NASA's Quick Scatterometer (QuikSCAT) during an extreme melting event occurring on June 2002. Long-term results show that snowmelt extent has been increasing at a rate of ∼40,000 Km² per year for the past 14 years

A Wavelet Melt Detection Algorithm Applied to Enhanced Resolution Scatterometer Data over Antarctica (2000-2009)

Melting is mapped over Antarctica at a high spatial resolution using a novel melt detection algorithm based on wavelets and multiscale analysis. The method is applied to Ku-band (13.4 GHz) normalized backscattering measured by SeaWinds onboard the satellite QuikSCAT and spatially enhanced on a 5 km grid over the operational life of the sensor (1999–2009). Wavelet-based estimates of melt spatial extent and duration are compared with those obtained by means of threshold-based detection methods, where melting is detected when the measured backscattering is 3 dB below the preceding winter mean value. Results from both methods are assessed by means of automatic weather station (AWS) air surface temperature records. The yearly melting index, the product of melted area and melting duration, found using a fixed threshold and wavelet-based melt algorithm are found to have a relative difference within 7% for all years. Most of the difference between melting records determined from QuikSCAT is related to short-duration backscatter changes identified as melting using the threshold methodology but not the wavelet-based method. The ability to classify melting based on relative persistence is a critical aspect of the wavelet-based algorithm. Compared with AWS air-temperature records, both methods show a relative agreement to within 10% based on estimated melt conditions, although the fixed threshold generally finds a greater agreement with AWS. Melting maps obtained with the wavelet-based approach are also compared with those obtained from spaceborne brightness temperatures recorded by the Special Sensor Microwave/Image (SSM/I). With respect to passive microwave records, we find a higher degree of agreement (9% relative difference) for the melting index using the wavelet-based approach than threshold-based methods (11% relative difference)

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer—Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration’s (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size

An updated Antarctic melt record through 2009 and its linkages to high-latitude and tropical climate variability

A 30-year minimum Antarctic snowmelt record occurred during austral summer 2008–2009 according to spaceborne microwave observations for 1980–2009. Strong positive phases of both the El-Niño Southern Oscillation (ENSO) and the Southern Hemisphere Annular Mode (SAM) were recorded during the months leading up to and including the 2008–2009 melt season. The 30-year record confirms that significant negative correlations exist at regional and continental scales between austral summer melting and both the ENSO and SAM indices for October–January. In particular, the strongest negative melting anomalies (such as those in 2008 and 2009) are related to amplified large-scale atmospheric forcing when both the SAM and ENSO are in positive phases. Our results suggest that enhanced snowmelt is likely to occur if recent positive summer SAM trends subside in conjunction with the projected recovery of stratospheric ozone levels, with subsequent impacts on ice sheet mass balance and sea level trends

Diagnosing the extreme surface melt event over southwestern Greenland in 2007

Abstract. Analysis of passive microwave brightness temperatures from the space-borne Special Sensor Microwave Imager (SSM/I) documents a record surface snowmelt over high elevations (above 2000 m) of the Greenland ice sheet during summer of 2007. To interpret this record, results from the SSM/I are examined in conjunction with fields from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis and output from a regional climate model. The record surface melt reflects unusually warm conditions, seen in positive summertime anomalies of surface air temperatures, downwelling longwave radiation, 1000–500 hPa atmospheric thickness, and the net surface energy flux, linked in turn to southerly airflow over the ice sheet. Low snow accumulation may have contributed to the record through promoting anomalously low surface albedo

How fast is the Greenland ice sheet melting?

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scambos, T., Straneo, F., & Tedesco, M. How fast is the Greenland ice sheet melting? Arctic Antarctic and Alpine Research, 53(1), (2021): 221–222, https://doi.org/10.1080/15230430.2021.1946241.THE ISSUE The Greenland Ice Sheet and the glacier-covered areas of Alaska and other Arctic lands are losing ice at an accelerating rate, contributing billions of tons of water to sea level rise. WHY IT MATTERS Ice loss from the ice sheets contributes directly to sea level rise. These losses are likely to increase rapidly as warming in the Arctic continues. Surface melt and runoff is now increasing more quickly than all other factors driving Greenland’s ice loss, although faster glacier outflow remains important. Increased ice loss from Alaska’s glaciers is also due mainly to surface melting. Given these trends, and the rapid warming in the Arctic (twice the global rate of warming), the Arctic is poised to lose ice even more rapidly and raise sea level. STATE OF KNOWLEDGE Since 2000, the net loss of ice from the Greenland Ice Sheet has increased five-fold, from 50 billion to about 250 billion tons per year1,2 (362 billion tons is equal to 1 mm in sea level rise). Ice losses in the Gulf of Alaska region have risen from about 40 to 70 billion tons per year3. These trends are confirmed by three independent satellite methods, using gravitational changes, elevation changes, and changes in the mass budget (the net difference between snowfall and the combination of glacier outflow and runoff)1. In total, the Arctic currently contributes approximately 350 billion tons (~1 mm) to sea level each year, primarily from Greenland, Alaska, and Arctic Canada. Recent measurements of the rate of sea level rise are 3.0 mm per year, with the additional rise coming from other glaciers and Antarctica (~0.4. mm) and expansion of the oceans due to warming (~1.7 mm)4. Slightly cooler summer seasons for Greenland in 2013 and 2014, and again in 2017 and 2018, temporarily reduced the rate of ice loss. Ocean temperatures cooled in some places along the western Greenland coast, slowing glacier outflow there5. However, strong melting in 2015, 2016 and 2019 again contributed large amounts of runoff to the ocean2. Because surface melt is closely tied to seasonal weather conditions, it is more variable than ice loss due to increased glacier outflow. Despite this variability, the overall warming trend of Arctic air and ocean has driven greatly increased melting and ice loss in Greenland and Alaska in the past two decades. As spring and summer temperatures have increased, net runoff of meltwater has grown dramatically (Figure 1). Ice loss due to faster glacier flow has remained stable overall and is unlikely to accelerate as rapidly as melting. Current increases in surface melt runoff rate are about twice that of ice loss due to increased ice flow speed1. As intense summer melt seasons like 2012, 2016, and 2019 become more common, further increases in melt runoff are inevitable.This work was supported by the Office of Polar Programs, National Science Foundation, and NSF’s Study of Environmental Arctic Change

Persistent surface snowmelt over Antarctica (1987–2006) from 19.35 GHz brightness temperatures

[1] Persistent melting (e.g., continuing for more than three days or for one consecutive day and night) is mapped in Antarctica (1987–2006) using night- and day-time Special Sensor Microwave Imager brightness temperatures (Tb) at 19.35 GHz, horizontal polarization. Snowmelt is indicated when Tb and relative daily difference exceed threshold values, respectively Tc and ΔT, computed for each pixel and year, or when both daytime and nighttime Tb exceed Tc. Results from an electromagnetic model suggest that the minimum detectable liquid water content ranges between 0.2 and 0.5%, in volume. We find that melting areas have been moving inland since 1987. A first-time extensive melting (1987–2006) is detected over the Transantarctic Mountains on January 2005, 875 Km inland and 2000 m above sea level. Melting extent and index have been decreasing over Antarctica, since 1987, although either positive and negative trends are observed from a sub-continental scale analysis

L-band ice-sheet brightness temperatures at Dome C, Antarctica: spectral emission modelling, temporal stability and impact of the ionosphere

The temporal thermal stability, size, structure and spatial uniformity of Antarctic Plateau regions such as Dome C make them ideal candidates for external calibration and validation, or radiometric performance monitoring of planned space-borne L-band radiometers such as the Soil Moisture and Ocean Salinity (SMOS) radiometer. As experimental observations do not exist at this frequency, a combination of historical Ka-, X- and C-band data are used together with electromagnetic and ionospheric models to extrapolate to this longer wavelength. Dome C is demonstrated to exhibit negligible seasonal variability (<0.1 K) and may prove to be a suitable candidate, provided the effects of atmospheric and ionospheric perturbations can be mitigated

Asymmetric Total Synthesis of Illisimonin A

The discovery of illisimonin A in 2017 extended the structural repertoire of the Illicium sesquiterpenoids─a class of natural products known for their high oxidation levels and neurotrophic properties─with a new carbon backbone combining the strained trans-pentalene and norbornane substructures. We report an asymmetric total synthesis of (−)-illisimonin A that traces its tricyclic carbon framework back to a spirocyclic precursor, generated by a tandem-Nazarov/ene cyclization. As crucial link between the spirocyclic key intermediate and illisimonin A, a novel approach for the synthesis of tricyclo[5.2.1.01,5]decanes via radical cyclization was explored. This approach was applied in a two-stage strategy consisting of Ti(III)-mediated cyclization and semipinacol rearrangement to access the natural product’s carbon backbone. These key steps were combined with carefully orchestrated C–H oxidations to establish the dense oxidation pattern