scholarly journals Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

2021 ◽  
Author(s):  
Peter A. Tuckett ◽  
Jeremy C. Ely ◽  
Andrew J. Sole ◽  
James M. Lea ◽  
Stephen J. Livingstone ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Climate Model ◽  
Google Earth ◽  
Katabatic Wind ◽  
Lake Area ◽  
Ice Shelf ◽  
Amery Ice Shelf ◽  
Surface Melt ◽  
The Antarctic

Abstract. Surface meltwater is widespread around the margin of the Antarctic Ice Sheet and has the potential to influence ice-shelf stability, ice-dynamic processes and ice-albedo feedbacks. Whilst the general spatial distribution of surface meltwater across the Antarctic continent is now relatively well known, our understanding of the seasonal and multi-year evolution of surface meltwater is limited. Attempts to generate robust time series of melt cover have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we implement an existing meltwater detection method alongside a novel method for calculating visibility metrics within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing us to automatically generate time series of surface melt area over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4,164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 km2 to 3898 km2 per melt season. However, by incorporating image visibility assessments into our results, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values, highlighting the importance of accounting for variations in image visibility when mapping lake areas. In a typical melt season, total lake area remains low throughout November and early December, before increasing, on average, by an order of magnitude during the second half of December. Peak lake area most commonly occurs during January, before decreasing during February as lakes freeze over. We show that modelled melt predictions from a regional climate model provides a good indication of lake cover in the Amery region, and that annual lake coverage is strongly associated with phases of the Southern Annular Mode (SAM); surface melt is typically highest in years with a negative austral summer SAM index. Furthermore, we suggest that melt-albedo feedbacks modulate the spatial distribution of meltwater in the region, with the exposure of blue ice from persistent katabatic wind scouring influencing the susceptibility of melt ponding. Results demonstrate how our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

scholarly journals Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

2021 ◽  
Vol 15 (12) ◽  
pp. 5785-5804
Author(s):  
Peter A. Tuckett ◽  
Jeremy C. Ely ◽  
Andrew J. Sole ◽  
James M. Lea ◽  
Stephen J. Livingstone ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Model ◽  
Regional Climate ◽  
Austral Summer ◽  
Image Data ◽  
Google Earth ◽  
Lake Area ◽  
Ice Shelf ◽  
Spatial And Temporal Scales ◽  
Amery Ice Shelf

Abstract. Surface meltwater is widespread around the Antarctic Ice Sheet margin and has the potential to influence ice shelf stability, ice flow and ice–albedo feedbacks. Our understanding of the seasonal and multi-year evolution of Antarctic surface meltwater is limited. Attempts to generate robust meltwater cover time series have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we add a novel method for calculating visibility metrics to an existing meltwater detection method within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing time series of surface meltwater area to be automatically generated over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 to 3898 km2 per melt season. By incorporating image visibility assessments, however, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values. We show that modelled melt predictions from a regional climate model provide a good indication of lake cover in the Amery region and that annual lake coverage is typically highest in years with a negative austral summer SAM index. Our results demonstrate that our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

scholarly journals Improving surface melt estimation over the Antarctic Ice Sheet using deep learning: a proof of concept over the Larsen Ice Shelf

2021 ◽  
Vol 15 (12) ◽  
pp. 5639-5658
Author(s):  
Zhongyang Hu ◽  
Peter Kuipers Munneke ◽  
Stef Lhermitte ◽  
Maaike Izeboud ◽  
Michiel van den Broeke
Keyword(s):  
Deep Learning ◽  
Climate Model ◽  
Ice Sheet ◽  
Katabatic Wind ◽  
Ice Shelf ◽  
Antarctic Ice Sheet ◽  
Surface Melt ◽  
Mlp Model ◽  
The Antarctic ◽  
Larsen Ice Shelf

Abstract. Accurately estimating the surface melt volume of the Antarctic Ice Sheet is challenging and has hitherto relied on climate modeling or observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations with a regional climate model by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs) and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations, (2) correcting Moderate Resolution Imaging Spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C ice shelves, Antarctica, cross-validation shows a high accuracy (root-mean-square error of 0.95 mm w.e. d−1, mean absolute error of 0.42 mm w.e. d−1, and a coefficient of determination of 0.95). Moreover, the deep MLP model outperforms conventional machine learning models and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18), the deep MLP model does not show improved agreement with AWS observations; this is likely because surface melt is largely driven by factors (e.g., air temperature, topography, katabatic wind) other than albedo within the corresponding coarse-resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. However, more work is required to refine the method, especially for complicated and heterogeneous terrains.

scholarly journals Surface melt magnitude retrieval over Ross Ice Shelf, Antarctica using coupled MODIS near-IR and thermal satellite measurements

2009 ◽  
Vol 3 (3) ◽  
pp. 1069-1107 ◽  
Author(s):  
D. J. Lampkin ◽  
C. C. Karmosky
Keyword(s):  
Meteorological Data ◽  
Temporal Variations ◽  
Katabatic Wind ◽  
Ice Shelf ◽  
Water Fraction ◽  
Ross Ice Shelf ◽  
Near Ir ◽  
Coarse Spatial Resolution ◽  
Surface Melt ◽  
The Antarctic

Abstract. Surface melt has been increasing over recent years, especially over the Antarctic Peninsula, contributing to disintegration of shelves such as Larsen. Unfortunately, we are not realistically able to quantify surface snowmelt from ground-based methods because there is sparse coverage of automatic weather stations. Satellite based assessments of melt from passive microwave systems are limited in that they only provide an indication of melt occurrence and have coarse spatial resolution. An algorithm was developed to retrieve surface melt magnitude using coupled near-IR/thermal surface measurements from MODIS were calibrated by estimates of liquid water fraction (LWF) in the upper 1 cm of the firn derived from a one-dimensional physical snowmelt model (SNTHERM89). For the modeling phase of this study, SNTHERM89 was forced by hourly meteorological data from automatic weather station data at reference sites spanning a range of melt conditions across the Ross Ice Shelf during a relatively intense melt season (2002). Effective melt magnitude or LWF<eff> were derived for satellite composite periods covering the Antarctic summer months at a 4 km resolution over the entire Ross Ice Shelf, ranging from 0–0.5% LWF<eff> in early December to areas along the coast with as much as 1% LWF<eff> during the time of peak surface melt. Spatial and temporal variations in the magnitude of surface melt are related to both katabatic wind strength and advection during onshore flow.

scholarly journals Surface meltwater drainage and ponding on Amery Ice Shelf, East Antarctica, 1973–2019

2021 ◽  
pp. 1-14
Author(s):  
Julian J. Spergel ◽  
Jonathan Kingslake ◽  
Timothy Creyts ◽  
Melchior van Wessem ◽  
Helen A. Fricker
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Drainage System ◽  
East Antarctica ◽  
Lake Area ◽  
Ice Shelf ◽  
Ice Shelves ◽  
Lake Volume ◽  
Amery Ice Shelf ◽  
Elevation Model

Abstract Surface melting on Amery Ice Shelf (AIS), East Antarctica, produces an extensive supraglacial drainage system consisting of hundreds of lakes connected by surface channels. This drainage system forms most summers on the southern portion of AIS, transporting meltwater large distances northward, toward the ice front and terminating in lakes. Here we use satellite imagery, Landsat (1, 4 and 8), MODIS multispectral and Sentinel-1 synthetic aperture radar to examine the seasonal and interannual evolution of the drainage system over nearly five decades (1972–2019). We estimate seasonal meltwater input to one lake by integrating output from the regional climate model [Regional Atmospheric Climate Model (RACMO 2.3p2)] over its catchment defined using the Reference Elevation Model of Antarctica. We find only weak positive relationships between modeled seasonal meltwater input and lake area and between meltwater input and lake volume. Consecutive years of extensive melting lead to year-on-year expansion of the drainage system, potentially through a link between melt production, refreezing in firn and the maximum extent of the lakes at the downstream termini of drainage. These mechanisms are important when evaluating the potential of drainage systems to grow in response to increased melting, delivering meltwater to areas of ice shelves vulnerable to hydrofracture.

scholarly journals Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic

scholarly journals Modelling the response of the Lambert Glacier–Amery Ice Shelf system, East Antarctica, to uncertain climate forcing over the 21st and 22nd centuries

2014 ◽  
Vol 8 (3) ◽  
pp. 1057-1068 ◽  
Author(s):  
Y. Gong ◽  
S. L. Cornford ◽  
A. J. Payne
Keyword(s):  
Climate Model ◽  
Global Environmental Change ◽  
Ice Sheet ◽  
Adaptive Mesh ◽  
Ocean Model ◽  
East Antarctica ◽  
Ice Shelf ◽  
Amery Ice Shelf ◽  
Grounding Line ◽  
Lambert Glacier

Abstract. The interaction between the climate system and the large polar ice sheet regions is a key process in global environmental change. We carried out dynamic ice simulations of one of the largest drainage systems in East Antarctica: the Lambert Glacier–Amery Ice Shelf system, with an adaptive mesh ice sheet model. The ice sheet model is driven by surface accumulation and basal melt rates computed by the FESOM (Finite-Element Sea-Ice Ocean Model) ocean model and the RACMO2 (Regional Atmospheric Climate Model) and LMDZ4 (Laboratoire de Météorologie Dynamique Zoom) atmosphere models. The change of ice thickness and velocity in the ice shelf is mainly influenced by the basal melt distribution, but, although the ice shelf thins in most of the simulations, there is little grounding line retreat. We find that the Lambert Glacier grounding line can retreat as much as 40 km if there is sufficient thinning of the ice shelf south of Clemence Massif, but the ocean model does not provide sufficiently high melt rates in that region. Overall, the increased accumulation computed by the atmosphere models outweighs ice stream acceleration so that the net contribution to sea level rise is negative.

scholarly journals Evaluation of six geothermal heat flux maps for the Antarctic Lambert-Amery glacial system

2021 ◽  
Author(s):  
Haoran Kang ◽  
Liyun Zhao ◽  
Michael Wolovick ◽  
John C. Moore
Keyword(s):  
Heat Flux ◽  
Frictional Heating ◽  
Thermal Conditions ◽  
Ice Shelf ◽  
Geothermal Heat ◽  
Glacier System ◽  
Amery Ice Shelf ◽  
Ice Sheet Dynamics ◽  
Subglacial Lakes ◽  
The Antarctic

Abstract. Basal thermal conditions play an important role in ice sheet dynamics, and they are sensitive to geothermal heat flux (GHF). Here we estimate the basal thermal conditions, including basal temperature, basal melt rate, and friction heat underneath the Lambert-Amery glacier system in east Antarctica, using a combination of a forward model and an inversion from a 3D ice flow model. We assess the sensitivity and uncertainty of basal thermal conditions using six different GHFs. We evaluate the modelled results using all available observed subglacial lakes. There are very large differences in modelled spatial pattern of temperate basal conditions using the different GHFs. The two most-recent GHF fields inverted from aerial geomagnetic observations have higher values of GHF in the region, produce a larger warm-based area, and match the observed subglacial lakes better than the other GHFs. The fast flowing glacier region has a lower modelled basal friction coefficient, faster basal velocity, with higher basal frictional heating in the range of 50–2000 mW m−2 than the base under slower flowing glaciated areas. The modelled basal melt rate reaches ten to hundreds of mm per year locally in Lambert, Lepekhin and Kronshtadtskiy glaciers feeding the Amery ice shelf, and ranges from 0–5 mm yr−1 on the temperate base of the vast inland region.

Spatial distribution of Ice Shelf Water in front of the Amery Ice Shelf, Antarctica in summer

2011 ◽  
Vol 29 (6) ◽  
pp. 1325-1338 ◽  
Author(s):  
Shaojun Zheng ◽  
Jiuxin Shi ◽  
Yutian Jiao ◽  
Renfeng Ge
Keyword(s):  
Spatial Distribution ◽  
Shelf Water ◽  
Ice Shelf ◽  
Amery Ice Shelf ◽  
Ice Shelf Water

Simulations of Föhn in Antarctica with WRF for the Antarctic Mesoscale Prediction System AMPS

2021 ◽  
Author(s):  
Amélie Kirchgaessner ◽  
John King ◽  
Alan Gadian ◽  
Phil Anderson
Keyword(s):  
Weather Research And Forecasting ◽  
Mountain Range ◽  
Prediction System ◽  
Atmospheric Conditions ◽  
Ice Shelf ◽  
Cloud Properties ◽  
Surface Melt ◽  
Mixed Phase Clouds ◽  
The Antarctic

&lt;p&gt;We examine the representation of F&amp;#246;hn events across the Antarctic Peninsula Mountains during 2011 as they were observed in measurements by an Automatic Weather Station, and in simulations with the Weather Research and Forecasting Model (WRF) as run for the Antarctic Mesoscale Prediction System (AMPS). On the Larsen Ice Shelf (LIS) in the lee of this mountain range F&amp;#246;hn winds are thought to provide the atmospheric conditions for significant warming over the ice shelf thus leading to the initial firn densification and subsequently providing the melt water for hydrofracturing. This process has led to the dramatic collapse of huge parts of the LIS in 1995 and 2002 respectively.&lt;/p&gt;&lt;p&gt;Measurements obtained at a crest AWS on the Avery Plateau (AV), and the analysis of conditions upstream using the Froude number help to put observations at CP into a wider context. We find that, while the model generally simulates meteorological parameters very well, and shows good skills in capturing the occurrence, frequency and duration of F&amp;#246;hn events realistically, it underestimates the temperature increase and the humidity decrease during the F&amp;#246;hn significantly, and may thus underestimate the contribution of F&amp;#246;hn to driving surface melt on the LIS.&lt;/p&gt;&lt;p&gt;Our results indicate that the misrepresentation of cloud properties and particularly the absence of mixed phase clouds in AMPS, affects the quality of weather simulation under normal conditions to some extent, and to a larger extent the model&amp;#8217;s capability to simulate the strength of F&amp;#246;hn conditions - and thus their contribution to driving surface melt on the LIS - adequately. Most importantly our data show that F&amp;#246;hn conditions can raise the air temperature to above freezing, and thus trigger melt/sublimation even in winter.&lt;/p&gt;

Estimating Surface Melt on the Larsen Ice Shelf Using a Deep Neural Network: Opportunities and Challenges

2021 ◽  
Author(s):  
Zhongyang Hu ◽  
Peter Kuipers Munneke ◽  
Stef Lhermitte ◽  
Maaike Izeboud ◽  
Michiel van den Broeke
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Neural Network ◽  
Climate Model ◽  
Climate Modeling ◽  
Coefficient Of Determination ◽  
Ice Shelf ◽  
Heterogeneous Terrain ◽  
Surface Melt ◽  
Larsen Ice Shelf

&lt;p&gt;Presently, surface melt over Antarctica is estimated using climate modeling or remote sensing. However, accurately estimating surface melt remains challenging. Both climate modeling and remote sensing have limitations, particularly in the most crucial areas with intense surface melt.&amp;#160; The motivation of our study is to investigate the opportunities and challenges in improving the accuracy of surface melt estimation using a deep neural network. The trained deep neural network uses meteorological observations from automatic weather stations (AWS) and surface albedo observations from satellite imagery to improve surface melt simulations from the regional atmospheric climate model version 2.3p2 (RACMO2). Based on observations from three AWS at the Larsen B and C Ice Shelves, cross-validation shows a high accuracy (root mean square error = 0.898 mm.w.e.d&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, mean absolute error = 0.429 mm.w.e.d&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, and coefficient of determination = 0.958). The deep neural network also outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow neural network. To compute surface melt for the entire Larsen Ice Shelf, the deep neural network is applied to RACMO2 simulations. The resulting, corrected surface melt shows a better correlation with the AWS observations in AWS 14 and 17, but not in AWS 18. Also, the spatial pattern of the surface melt is improved compared to the original RACMO2 simulation. A possible explanation for the mismatch at AWS 18 is its complex geophysical setting. Even though our study shows an opportunity to improve surface melt simulations using a deep neural network, further study is needed to refine the method, especially for complicated, heterogeneous terrain.&lt;/p&gt;

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