Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Abstract

Abstract. Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

“…Colosio et al. (2021), showed that melt extent derived from the enhanced‐resolution SSMIS observations is consistent well with the MAR simulations (Colosio et al., 2021). The overestimate of baer ice albedo would be one of the reasons that MAR underestimates melt flux in summer (Fettweis et al., 2017).…”

“…Surface freeze/thaw state is determined according to Colosio et al. (2021), and is used as a mask for the BNN‐derived melt flux (Text S2 in Supporting Information ). Results from SSMIS observations indicate that the GrIS surface melt shows obvious seasonal variations.…”

“…Earlier microwave radiometers, including Scanning Multi‐channel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I), have been utilized in the study of ice sheet surface melt since the late 1970s (Colosio et al., 2021; Mote, 2007; Steffen et al., 1993; Tedesco, 2007). Long time series of melt occurrence has been mapped based on a cross‐calibration of the data acquired by the different sensors.…”

“…In particular, SSMIS underestimates daily melt flux at the QAS_U station in South‐West Basin where the annual melt flux from satellite is lower than that from RCMs. The melt detection method is based on a thresholding method in which melt was recognized only when volume liquid water content reaches 0.2% (Colosio et al., 2021), and thus may miss the weak melt events (Figure S8 in Supporting Information ). The agreement between annual melt flux from SSMIS, MAR and RACMO results from compensating errors across space and time.…”

“…The melt flux retrieval algorithm utilized in this study can be described as: $$$M=g\left(T{b}_{\mathrm{E}19\mathrm{V}},T{b}_{\mathrm{M}19\mathrm{V}},T{b}_{\mathrm{E}19\mathrm{H}},T{b}_{\mathrm{M}19\mathrm{H}},T{b}_{\mathrm{E}37\mathrm{V}},T{b}_{\mathrm{M}37\mathrm{V}},T{b}_{\mathrm{E}37\mathrm{H}},T{b}_{\mathrm{M}37\mathrm{H}}\text{,DOY,}\,H\right)+\varepsilon $$$ where g is the estimation function, and ε is residual error. The BNN input matrix is built mainly based on observations at K‐ and Ka‐band which have been widely used in the detection of surface melt (Colosio et al., 2021; Mote, 2007). Both the morning and evening passes are utilized considering the diurnal Tb amplitude variations are very sensitive to surface melt (Tedesco, 2007).…”