Assessment of errors and uncertainty patterns in GIA modeling

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

Abstract

During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one "preferred" GIA model has been used, without any consideration of the possible errors involved. Lacking a rigorous assessment of systematic errors in GIA modeling, the reliability of the results is uncertain. GIA sensitivity and uncertainties associated with the viscosity models have been explored in the literature. However, at least two major sources of errors remain. The first is associated with the ice models, spatial distribution of ice and history of melting (this is especially the case of Antarctica), the second with the numerical implementation of model features relevant to sea level modeling, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland due to GIA. Moreover GIA errors shows a sensitivity to different modeling parameters, and with a highly variable geographic distribution that can be exploited to select the most suitable sites to be used for point-like data comparison, i.e. GPS and relative sea level histories, in order to constrain GIA modeling. GIA errors are also important in the far field of previously glaciated areas and in the time evolution of global indicators. In this regard we also account for other possible errors sources which can impact global indicators like the sea level history related to GIA. The thermal expansion of the oceans and other sources of water during the deglaciation different from the one coming from ice-sheets account for further sea level rise since LGM, and the water stored in atmosphere, groundwater and lakes accounts for negative contribution to it. In this way we aim to assess which GIA modeling aspects give more variability at large and global scale and how to optimally constrain them.
Original languageEnglish
Publication date2011
Publication statusPublished - 2011
Event2011 AGU Fall Meeting - San Francisco, CA, United States
Duration: 5 Dec 20119 Dec 2011
http://fallmeeting.agu.org/2011/

Conference

Conference2011 AGU Fall Meeting
CountryUnited States
CitySan Francisco, CA
Period05/12/201109/12/2011
Internet address

Keywords

  • Geodesy and Gravity
  • Mass balance
  • Global change
  • Sea level change
  • Solid earth

Cite this

Barletta, V. R., & Spada, G. (2011). Assessment of errors and uncertainty patterns in GIA modeling. Abstract from 2011 AGU Fall Meeting, San Francisco, CA, United States.
Barletta, Valentina Roberta ; Spada, G. / Assessment of errors and uncertainty patterns in GIA modeling. Abstract from 2011 AGU Fall Meeting, San Francisco, CA, United States.
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keywords = "Geodesy and Gravity, Mass balance, Global change, Sea level change, Solid earth",
author = "Barletta, {Valentina Roberta} and G. Spada",
year = "2011",
language = "English",
note = "2011 AGU Fall Meeting, AGU 2011 ; Conference date: 05-12-2011 Through 09-12-2011",
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Barletta, VR & Spada, G 2011, 'Assessment of errors and uncertainty patterns in GIA modeling' 2011 AGU Fall Meeting, San Francisco, CA, United States, 05/12/2011 - 09/12/2011, .

Assessment of errors and uncertainty patterns in GIA modeling. / Barletta, Valentina Roberta; Spada, G.

2011. Abstract from 2011 AGU Fall Meeting, San Francisco, CA, United States.

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

TY - ABST

T1 - Assessment of errors and uncertainty patterns in GIA modeling

AU - Barletta, Valentina Roberta

AU - Spada, G.

PY - 2011

Y1 - 2011

N2 - During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one "preferred" GIA model has been used, without any consideration of the possible errors involved. Lacking a rigorous assessment of systematic errors in GIA modeling, the reliability of the results is uncertain. GIA sensitivity and uncertainties associated with the viscosity models have been explored in the literature. However, at least two major sources of errors remain. The first is associated with the ice models, spatial distribution of ice and history of melting (this is especially the case of Antarctica), the second with the numerical implementation of model features relevant to sea level modeling, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland due to GIA. Moreover GIA errors shows a sensitivity to different modeling parameters, and with a highly variable geographic distribution that can be exploited to select the most suitable sites to be used for point-like data comparison, i.e. GPS and relative sea level histories, in order to constrain GIA modeling. GIA errors are also important in the far field of previously glaciated areas and in the time evolution of global indicators. In this regard we also account for other possible errors sources which can impact global indicators like the sea level history related to GIA. The thermal expansion of the oceans and other sources of water during the deglaciation different from the one coming from ice-sheets account for further sea level rise since LGM, and the water stored in atmosphere, groundwater and lakes accounts for negative contribution to it. In this way we aim to assess which GIA modeling aspects give more variability at large and global scale and how to optimally constrain them.

AB - During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one "preferred" GIA model has been used, without any consideration of the possible errors involved. Lacking a rigorous assessment of systematic errors in GIA modeling, the reliability of the results is uncertain. GIA sensitivity and uncertainties associated with the viscosity models have been explored in the literature. However, at least two major sources of errors remain. The first is associated with the ice models, spatial distribution of ice and history of melting (this is especially the case of Antarctica), the second with the numerical implementation of model features relevant to sea level modeling, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland due to GIA. Moreover GIA errors shows a sensitivity to different modeling parameters, and with a highly variable geographic distribution that can be exploited to select the most suitable sites to be used for point-like data comparison, i.e. GPS and relative sea level histories, in order to constrain GIA modeling. GIA errors are also important in the far field of previously glaciated areas and in the time evolution of global indicators. In this regard we also account for other possible errors sources which can impact global indicators like the sea level history related to GIA. The thermal expansion of the oceans and other sources of water during the deglaciation different from the one coming from ice-sheets account for further sea level rise since LGM, and the water stored in atmosphere, groundwater and lakes accounts for negative contribution to it. In this way we aim to assess which GIA modeling aspects give more variability at large and global scale and how to optimally constrain them.

KW - Geodesy and Gravity

KW - Mass balance

KW - Global change

KW - Sea level change

KW - Solid earth

M3 - Conference abstract for conference

ER -

Barletta VR, Spada G. Assessment of errors and uncertainty patterns in GIA modeling. 2011. Abstract from 2011 AGU Fall Meeting, San Francisco, CA, United States.