课题基金 / 基金详情

Fingerprints of ice melting in geodetic GRACE and ocean modelling

Fingerprints of ice melting in geodetic GRACE and ocean modelling
大地测量 GRACE 和海洋建模中冰融化的指纹
批准号:
109076194
负责人:
Professor Dr.-Ing. Jürgen Kusche
金额:
$0.0万
依托单位国家:
德国
项目类别:
Priority Programmes
财政年份:
2009
资助国家:
德国
项目状态:
已结题
起止时间:
2008-12-31 至 2014-12-31

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中文摘要
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英文摘要
The general aim of this proposal is to improve the quantification of the Greenland/Antarctica ice sheet and land glacier contributions to present-day sea level rise. For this purpose fingerprints of uniform melting of Greenland or Antarctica have been used in the past in conjunction with tide gauge data. We will extend previous work towards the identification of fingerprints of more realistic melting patterns in GRACE and sea surface height in the open ocean, and include hydrological, steric and GIA-induced contributions to sea level in our analysis. Forward modelling of land deformations and geoid change as well as ocean surface topography in response to melting ice sheets will be performed. The combined use of GRACE and sea surface height measurements, from Altimetry and tide gauges, in an inverse scheme will be investigated. Individual contributions for Greenland and Antarctic ice loss and land glacier mass imbalance are estimated including the error variance/co-variance of our solution. In addition, hydrological, steric and GIA-induced contributions to sea level will be adjusted within the inversion. The non-uniform change of relative sea level for Europe will be calculated. Our final estimates of ice melting form the basis of an assessment and prediction experiment. The response of ocean transports and meridional overturning circulation to the additional fresh water input will be examined. Finally, we will extend our investigations towards to complementary data (gravimetry, ice altimetry) and assess the potential of future observing systems.
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会议论文
Developing an Ensemble Kalman Filter calibration and data assimilation (EnC/DA) approach for integrating geodetic and remote sensing data into a global hydrological model
Developing a Stabilized Ensemble Kalman Filter for integrating daily GRACE/GRACE-FO data into process models (S-ENKF)
Bayesian Methods in Geodetic Earth System Research
Lunar Reference Systems
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