Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model
Quantifying the mass balance of the Antarctic Ice Sheet (AIS), and the resulting sea level rise, requires an understanding of inter-annual variability and associated causal mechanisms. Very few studies have been exploring the influence of climate anomalies on the AIS and only a vague estimate of its...
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ftmdpi:oai:mdpi.com:/2072-4292/13/11/2199/ 2023-08-20T03:59:36+02:00 Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model Athul Kaitheri Anthony Mémin Frédérique Rémy 2021-06-04 application/pdf https://doi.org/10.3390/rs13112199 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13112199 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 11; Pages: 2199 Antarctic Ice Sheet GRACE Envisat RACMO2.3p2 firn densification model ENSO ACW Text 2021 ftmdpi https://doi.org/10.3390/rs13112199 2023-08-01T01:53:03Z Quantifying the mass balance of the Antarctic Ice Sheet (AIS), and the resulting sea level rise, requires an understanding of inter-annual variability and associated causal mechanisms. Very few studies have been exploring the influence of climate anomalies on the AIS and only a vague estimate of its impact is available. Changes to the ice sheet are quantified using observations from space-borne altimetry and gravimetry missions. We use data from Envisat (2002 to 2010) and Gravity Recovery And Climate Experiment (GRACE) (2002 to 2016) missions to estimate monthly elevation changes and mass changes, respectively. Similar estimates of the changes are made using weather variables (surface mass balance (SMB) and temperature) from a regional climate model (RACMO2.3p2) as inputs to a firn compaction (FC) model. Elevation changes estimated from different techniques are in good agreement with each other across the AIS especially in West Antarctica, Antarctic Peninsula, and along the coasts of East Antarctica. Inter-annual height change patterns are then extracted using for the first time an empirical mode decomposition followed by a principal component analysis to investigate for influences of climate anomalies on the AIS. Investigating the inter-annual signals in these regions revealed a sub-4-year periodic signal in the height change patterns. El Niño Southern Oscillation (ENSO) is a climate anomaly that alters, among other parameters, moisture transport, sea surface temperature, precipitation, in and around the AIS at similar frequency by alternating between warm and cold conditions. This periodic behavior in the height change patterns is altered in the Antarctic Pacific (AP) sector, possibly by the influence of multiple climate drivers, like the Amundsen Sea Low (ASL) and the Southern Annular Mode (SAM). Height change anomaly also appears to traverse eastwards from Coats Land to Pine Island Glacier (PIG) regions passing through Dronning Maud Land (DML) and Wilkes Land (WL) in 6 to 8 years. This is indicative of ... Text Amundsen Sea Antarc* Antarctic Antarctic Peninsula Antarctica DML Dronning Maud Land East Antarctica Ice Sheet Pine Island Pine Island Glacier West Antarctica Wilkes Land MDPI Open Access Publishing Antarctic The Antarctic Antarctic Peninsula East Antarctica Dronning Maud Land West Antarctica Amundsen Sea Pacific Wilkes Land ENVELOPE(120.000,120.000,-69.000,-69.000) Pine Island Glacier ENVELOPE(-101.000,-101.000,-75.000,-75.000) Coats Land ENVELOPE(-27.500,-27.500,-77.000,-77.000) Remote Sensing 13 11 2199 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Antarctic Ice Sheet GRACE Envisat RACMO2.3p2 firn densification model ENSO ACW |
spellingShingle |
Antarctic Ice Sheet GRACE Envisat RACMO2.3p2 firn densification model ENSO ACW Athul Kaitheri Anthony Mémin Frédérique Rémy Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
topic_facet |
Antarctic Ice Sheet GRACE Envisat RACMO2.3p2 firn densification model ENSO ACW |
description |
Quantifying the mass balance of the Antarctic Ice Sheet (AIS), and the resulting sea level rise, requires an understanding of inter-annual variability and associated causal mechanisms. Very few studies have been exploring the influence of climate anomalies on the AIS and only a vague estimate of its impact is available. Changes to the ice sheet are quantified using observations from space-borne altimetry and gravimetry missions. We use data from Envisat (2002 to 2010) and Gravity Recovery And Climate Experiment (GRACE) (2002 to 2016) missions to estimate monthly elevation changes and mass changes, respectively. Similar estimates of the changes are made using weather variables (surface mass balance (SMB) and temperature) from a regional climate model (RACMO2.3p2) as inputs to a firn compaction (FC) model. Elevation changes estimated from different techniques are in good agreement with each other across the AIS especially in West Antarctica, Antarctic Peninsula, and along the coasts of East Antarctica. Inter-annual height change patterns are then extracted using for the first time an empirical mode decomposition followed by a principal component analysis to investigate for influences of climate anomalies on the AIS. Investigating the inter-annual signals in these regions revealed a sub-4-year periodic signal in the height change patterns. El Niño Southern Oscillation (ENSO) is a climate anomaly that alters, among other parameters, moisture transport, sea surface temperature, precipitation, in and around the AIS at similar frequency by alternating between warm and cold conditions. This periodic behavior in the height change patterns is altered in the Antarctic Pacific (AP) sector, possibly by the influence of multiple climate drivers, like the Amundsen Sea Low (ASL) and the Southern Annular Mode (SAM). Height change anomaly also appears to traverse eastwards from Coats Land to Pine Island Glacier (PIG) regions passing through Dronning Maud Land (DML) and Wilkes Land (WL) in 6 to 8 years. This is indicative of ... |
format |
Text |
author |
Athul Kaitheri Anthony Mémin Frédérique Rémy |
author_facet |
Athul Kaitheri Anthony Mémin Frédérique Rémy |
author_sort |
Athul Kaitheri |
title |
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
title_short |
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
title_full |
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
title_fullStr |
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
title_full_unstemmed |
Inter-Annual Variability in the Antarctic Ice Sheets Using Geodetic Observations and a Climate Model |
title_sort |
inter-annual variability in the antarctic ice sheets using geodetic observations and a climate model |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13112199 |
long_lat |
ENVELOPE(120.000,120.000,-69.000,-69.000) ENVELOPE(-101.000,-101.000,-75.000,-75.000) ENVELOPE(-27.500,-27.500,-77.000,-77.000) |
geographic |
Antarctic The Antarctic Antarctic Peninsula East Antarctica Dronning Maud Land West Antarctica Amundsen Sea Pacific Wilkes Land Pine Island Glacier Coats Land |
geographic_facet |
Antarctic The Antarctic Antarctic Peninsula East Antarctica Dronning Maud Land West Antarctica Amundsen Sea Pacific Wilkes Land Pine Island Glacier Coats Land |
genre |
Amundsen Sea Antarc* Antarctic Antarctic Peninsula Antarctica DML Dronning Maud Land East Antarctica Ice Sheet Pine Island Pine Island Glacier West Antarctica Wilkes Land |
genre_facet |
Amundsen Sea Antarc* Antarctic Antarctic Peninsula Antarctica DML Dronning Maud Land East Antarctica Ice Sheet Pine Island Pine Island Glacier West Antarctica Wilkes Land |
op_source |
Remote Sensing; Volume 13; Issue 11; Pages: 2199 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs13112199 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13112199 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2199 |
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1774713740084641792 |