Correlation Analysis of NINO3.4 SST and Inland Lake Level Variations Monitored with Satellite Altimetry: Case Studies of Lakes Hongze, Khanka, La-ang, Ulungur, Issyk-kul and Baikal

Guo, Jinyun; Sun, Jinquan; Chang, Xiaotao; Guo, Shuyan; Liu, Xin

doi:10.3319/tao.2010.09.17.01(tibxs)

Cited by 13 publications

(7 citation statements)

References 16 publications

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“…The relationships are reversed over the upper basin. Likewise, Guo et al [44] note that the Niño3.4 sea surface temperature has a lagged influence on the variation in lakes in East Asia. Here, we can observe that different archetypes are correlated with various Niño indices, suggesting that the extremal patterns are induced by different characteristics of the ENSO events.…”

Section: Discussion Of the Physical Mechanisms For Climate Teleconnecmentioning

confidence: 99%

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Hao

Yuan

et al. 2017

Water

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Abstract:Understanding the spatiotemporal variability of seasonal extreme precipitation and its linkage with climate patterns is of great importance for water resource management over the Yangtze River Basin. Hence, this study examined the spatiotemporal variability of seasonal extreme precipitation through the archetypal analysis (AA), by which observations were decomposed and characterized as several extreme modes. Six archetypes were identified and can obviously exhibit the features of events with above average or below average precipitation. Summer precipitation is the most variable compared to the winter, spring, and autumn precipitation through the trend analysis. It ranged from extremely dry (A6) to normal (A1 and A2) to extremely wet (A4). Climate teleconnections to the four archetypes for summer precipitation and relative importance of climate patterns were thus investigated. Results show that El Niño Southern Oscillation index is the strongest determinant of the ensuing archetypes representing the events with above average precipitation, while the Atlantic Multi-decadal Oscillation (AMO) contributes most to the events with below-average precipitation. A warm phase of the Pacific Decadal Oscillation (PDO) is significantly correlated with the above-average precipitation.

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Section: Discussion Of the Physical Mechanisms For Climate Teleconnecmentioning

confidence: 99%

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Hao

Yuan

et al. 2017

Water

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“…To study the periodic characteristics of sea level change in China seas and its vicinity, the 20-year SLA time series were analyzed with the wavelet analysis method (Guo et al, 2011), as shown in Figure 4. From Figure 4, we can find that there were two main cycles of sea level change over the China seas and its vicinity, which were 1 year and 9 years, respectively.…”

Section: Wavelet Analysis On Sea Level Changementioning

confidence: 99%

Sea Level Changes of China Seas and Neighboring Ocean Based on Satellite Altimetry Missions from 1993 to 2012

Guo

Zhibo

Wang

et al. 2015

Journal of Coastal Research

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Guo, J.; Hu, Z.; Wang, J.; Chang, X., and Li, G., 2015. Sea level changes of China seas and neighboring ocean based on satellite altimetry missions from 1993 toThe latest satellite altimetry data of TOPEX/Poseidon (T/P), Jason-1 and Jason-2 during the formation flight phase were analyzed. The average difference of sea level anomaly (SLA) was 9.72cm between Jason-1 and T/P, and -11.03 cm between Jason-2 and Jason-1 over the China seas and neighboring ocean. Then SLA time series from 1993 to 2012 were constructed and analyzed with the least squares method. In China seas and neighboring ocean, the average rising rate of sea surface height was about 4.64 mm/yr. The specific average rising rates were 4.44 mm/yr, 2.34 mm/yr, 3.02 mm/yr and 4.25 mm/yr in Bohai Sea, Yellow Sea, East China Sea and South China Sea, respectively. The cycles of sea level changes were also analyzed with wavelet analysis, and results showed that the most significant period of sea level change over each sea was 1 year. A quasi-nine-year cycle was also found over the South China Sea. ADDITIONAL INDEX WORDS:Satellite altimetry, china seas and neighboring ocean, sea level change, sea surface height.

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“…To date, the satellite radar altimeter and the tide gauge are the main techniques to collect SSHs (Fu and Cazenave, 2001;Guo et al, 2013). But SSHs at nadir can only be measured with the satellite altimeter and SSHs along coast line can be determined with the tide gauge point by point (Guo et al, 2011). More small spatial-temporal resolution of SSHs is needed to determine the mesoscale ocean currents and eddies.…”

Section: Introductionmentioning

confidence: 98%

Altimetric algorithm and errors of ocean altimetry using GNSS reflection signals

et al. 2015

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As a new remote sensing technology, the global navigation satellite system (GNSS) reflection signals can be used to collect the information of ocean surface wind, surface roughness and sea surface height. Ocean altimetry based on GNSS reflection technique is of low cost and it is easy to obtain large amounts of data thanks to the global navigation satellite constellation. We can estimate the sea surface height as well as the position of the specular reflection point. This paper focuses on the study of the algorithm to determine the specular reflection point and altimetry equations to estimate the sea surface height over the reflection region. We derive the error equation of sea surface height based on the error propagation theory. Effects of the Doppler shift and the size of the glistening zone on the altimetry are discussed and analyzed at the same time. Finally, we calculate the sea surface height based on the simulated GNSS data within the whole day and verify the sea surface height errors according to the satellite elevation angles. The results show that the sea surface height can reach the precision of 6 cm for elevation angles of 55° to 90°, and the theoretical error and the calculated error are in good agreement.

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Correlation Analysis of NINO3.4 SST and Inland Lake Level Variations Monitored with Satellite Altimetry: Case Studies of Lakes Hongze, Khanka, La-ang, Ulungur, Issyk-kul and Baikal

Cited by 13 publications

References 16 publications

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Spatiotemporal Variability of Extreme Summer Precipitation over the Yangtze River Basin and the Associations with Climate Patterns

Sea Level Changes of China Seas and Neighboring Ocean Based on Satellite Altimetry Missions from 1993 to 2012

Altimetric algorithm and errors of ocean altimetry using GNSS reflection signals

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