scholarly journals Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

2021 ◽  
Vol 13 (3) ◽  
pp. 480
Author(s):  
Jingang Zhan ◽  
Hongling Shi ◽  
Yong Wang ◽  
Yixin Yao
Keyword(s):  
Principal Component Analysis ◽  
Ice Sheet ◽  
Low Frequency ◽  
Principal Component ◽  
Equatorial Pacific ◽  
Mass Change ◽  
Frequency Variation ◽  
Periodic Signal ◽  
Time Frequency ◽  
The Antarctic

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation.

scholarly journals Response of Antarctic Ice Sheet Mass Balance to Climate Change

2018 ◽  
Author(s):  
Jingang Zhan ◽  
Hongling Shi ◽  
Yong Wang ◽  
Yixin Yao ◽  
Yongbin Wu
Keyword(s):  
Surface Temperature ◽  
Mass Balance ◽  
Ice Sheet ◽  
Low Frequency ◽  
Principal Component ◽  
Equatorial Pacific ◽  
Sea Surface ◽  
Mass Change ◽  
Antarctic Ice Sheet ◽  
The Antarctic

Abstract. The ice record should have recorded and will likely reflect information on environmental changes such as atmospheric circulation. In this paper, 153 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying gravity solutions were used to study the principal components of the Antarctic ice sheet mass change and their time-frequency variation. This assessment is based on complex principal component analysis and the wavelet amplitude-period spectrum method to reveal the main climatic factors that affect the change on the ice sheet. The complex principal component analysis results reveal the principal components that affect the mass change of the ice sheet; the wavelet analysis present the time-frequency variation of each component and the possible relationship between each principal component and different climatic factors. The results show that the specific climate factors represented by low-frequency signals with a period greater than 5 years dominate the changes of the Antarctic ice sheet mass balance. These climate factors are related to the abnormal sea surface temperature changes in the equatorial Pacific (Niño 1+2 region), the correlation between the low-frequency periodic signal of sea surface temperature anomalies in the equatorial Pacific and the first principal component of the ice sheet mass change in Antarctica is 0.65. The first principal component explains 85.45 % of the mass change in the ice sheet. The change in the meridional wind at 700 hPa in the South Pacific may be the key factor that determines the effect of sea surface temperature anomalies in the equatorial Pacific on the Antarctic ice sheet. The atmospheric temperature change in Antarctica is the second most important factor that affects the mass balance of the ice sheet in the area, and its contribution to the mass balance of the ice sheet is only 6.35 %. This result means that with the increase of low-frequency signals during the El Niño period, Antarctic ice sheet mass changes may intensify.

scholarly journals A Joint Inversion Estimate of Antarctic Ice Sheet Mass Balance Using Multi-Geodetic Data Sets

2019 ◽  
Vol 11 (6) ◽  
pp. 653 ◽  
Author(s):  
Chunchun Gao ◽  
Yang Lu ◽  
Zizhan Zhang ◽  
Hongling Shi
Keyword(s):  
Mass Balance ◽  
Joint Inversion ◽  
Ice Sheet ◽  
Mass Change ◽  
West Antarctica ◽  
Amundsen Sea ◽  
Antarctic Ice Sheet ◽  
Forward Models ◽  
Uplift Rates ◽  
The Antarctic

Many recent mass balance estimates using the Gravity Recovery and Climate Experiment (GRACE) and satellite altimetry (including two kinds of sensors of radar and laser) show that the ice mass of the Antarctic ice sheet (AIS) is in overall decline. However, there are still large differences among previously published estimates of the total mass change, even in the same observed periods. The considerable error sources mainly arise from the forward models (e.g., glacial isostatic adjustment [GIA] and firn compaction) that may be uncertain but indispensable to simulate some processes not directly measured or obtained by these observations. To minimize the use of these forward models, we estimate the mass change of ice sheet and present-day GIA using multi-geodetic observations, including GRACE and Ice, Cloud and land Elevation Satellite (ICESat), as well as Global Positioning System (GPS), by an improved method of joint inversion estimate (JIE), which enables us to solve simultaneously for the Antarctic GIA and ice mass trends. The GIA uplift rates generated from our JIE method show a good agreement with the elastic-corrected GPS uplift rates, and the total GIA-induced mass change estimate for the AIS is 54 ± 27 Gt/yr, which is in line with many recent GPS calibrated GIA estimates. Our GIA result displays the presence of significant uplift rates in the Amundsen Sea Embayment of West Antarctica, where strong uplift has been observed by GPS. Over the period February 2003 to October 2009, the entire AIS changed in mass by −84 ± 31 Gt/yr (West Antarctica: −69 ± 24, East Antarctica: 12 ± 16 and the Antarctic Peninsula: −27 ± 8), greater than the GRACE-only estimates obtained from three Mascon solutions (CSR: −50 ± 30, JPL: −71 ± 30, and GSFC: −51 ± 33 Gt/yr) for the same period. This may imply that single GRACE data tend to underestimate ice mass loss due to the signal leakage and attenuation errors of ice discharge are often worse than that of surface mass balance over the AIS.

scholarly journals Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

2017 ◽  
Vol 36 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Shaojiang Dong ◽  
Tianhong Luo ◽  
Li Zhong ◽  
Lili Chen ◽  
Xiangyang Xu
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Frequency Domain Method ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Time Frequency

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

scholarly journals Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

2016 ◽  
Vol 30 (4) ◽  
pp. 431-445
Author(s):  
Angelica Durigon ◽  
Quirijn de Jong van Lier ◽  
Klaas Metselaar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Specific Humidity ◽  
Leaf Angle ◽  
Prediction Errors ◽  
Angle Distribution ◽  
Time Frequency ◽  
Soil Dryness

AbstractTo date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Agsmodel was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

scholarly journals Estimation of present-day glacial isostatic adjustment, ice mass change and elastic vertical crustal deformation over the Antarctic ice sheet

2017 ◽  
Vol 63 (240) ◽  
pp. 703-715 ◽  
Author(s):  
BAOJUN ZHANG ◽  
ZEMIN WANG ◽  
FEI LI ◽  
JIACHUN AN ◽  
YUANDE YANG ◽  
...  
Keyword(s):  
Antarctic Peninsula ◽  
Crustal Deformation ◽  
Ice Sheet ◽  
East Antarctica ◽  
Mass Change ◽  
Glacial Isostatic Adjustment ◽  
Antarctic Ice Sheet ◽  
Isostatic Adjustment ◽  
The Antarctic ◽  
Vertical Crustal Deformation

ABSTRACTThis study explores an iterative method for simultaneously estimating the present-day glacial isostatic adjustment (GIA), ice mass change and elastic vertical crustal deformation of the Antarctic ice sheet (AIS) for the period October 2003–October 2009. The estimations are derived by combining mass measurements of the GRACE mission and surface height observations of the ICESat mission under the constraint of GPS vertical crustal deformation rates in the spatial domain. The influence of active subglacial lakes on GIA estimates are mitigated for the first time through additional processing of ICESat data. The inferred GIA shows that the strongest uplift is found in the Amundsen Sea Embayment (ASE) sector and subsidence mostly occurs in Adelie Terre and the East Antarctica inland. The total GIA-related mass change estimates for the entire AIS, West Antarctica Ice Sheet (WAIS), East Antarctica Ice Sheet (EAIS), and Antarctic Peninsula Ice Sheet (APIS) are 43 ± 38, 53 ± 24, −23 ± 29 and 13 ± 6 Gt a−1, respectively. The overall ice mass change of the AIS is −46 ± 43 Gt a−1 (WAIS: −104 ± 25, EAIS: 77 ± 35, APIS: −20 ± 6). The most significant ice mass loss and most significant elastic vertical crustal deformations are concentrated in the ASE and northern Antarctic Peninsula.

scholarly journals Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

2016 ◽  
Vol 18 (4) ◽  
pp. 2167-2175 ◽  
Author(s):  
Radoslaw Zimroz ◽  
Jacek Wodecki ◽  
Pawel Stefaniak ◽  
Jakub Obuchowski ◽  
Agnieszka Wylomanska
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Time Frequency ◽  
Vibration Data
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