Network-based approach to unravel sea-surface temperature and streamflow connectivity at different timescales

2021 ◽  
Author(s):  
Abinesh Ganapathy ◽  
Ravi Kumar Guntu ◽  
Ugur Ozturk ◽  
Bruno Merz ◽  
Ankit Agarwal
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Long Range ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Sea Surface ◽  
Degree Centrality ◽  
Link Distance ◽  
Decadal Scale ◽  
Streamflow Extremes

<p>Understanding the interactions between oceanic conditions and streamflow can deepen our knowledge on hydrological aspects. Most studies exploring this relationship only focus on seasonal or annual scales. However, various atmospheric and oceanic phenomena occur at different timescales and need to be accounted to attribute connectivity between sea-surface temperature and streamflow to specific oceanic and climate processes. In this study, we have investigated the influence of sea-surface temperature (SST) on German streamflow at timescales ranging from sub-seasonal to decadal. We apply wavelets' concepts to decompose the time series into multiple frequency signals and fed into complex networks to identify spatial connections. We employ degree centrality metric and average link distance concepts to interpret the outcomes of coupled SST-Streamflow networks. Our results indicate that the SST anomaly at North Atlantic Ocean region has a stable connection with German streamflow at shorter timescales up to annual scale. We also noticed scale-specific connections in the Pacific, Indian and Southern ocean regions at different timescales ranging from seasonal to decadal scale. Scale-specific connections exhibited by the streamflow stations at all timescales makes it difficult to cluster based on degree centrality. We observed that streamflow stations are influenced by short-range local connections at lower timescales and long-range teleconnections at higher time scale. Our preliminary analysis highlight that the low frequent streamflow extremes have long-range connections, usually not captured at the original scale, and geographical proximity plays a role in high-frequency streamflow signals, according to Tobler’s first law of geography. The results obtained from this study reconfirms reported existing streamflow influences and helped gain insights over other possible large-scale climatic influences.</p>

scholarly journals What Vertical Mode Does the Altimeter Reflect? On the Decomposition in Baroclinic Modes and on a Surface-Trapped Mode

2009 ◽  
Vol 39 (11) ◽  
pp. 2857-2874 ◽  
Author(s):  
Guillaume Lapeyre
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Sea Surface ◽  
Surface Mode ◽  
Trapped Mode ◽  
The North ◽  
Surface Buoyancy ◽  
Baroclinic Modes

Abstract This study is motivated by the ongoing debate on the dynamical properties of surface motions at mesoscales that are measured by altimetry [for sea surface height (SSH)] and microwave [for sea surface temperature (SST)]. The mesoscale signal obtained by the altimeter is often considered to be associated with the first baroclinic mode, but recent results indicate that SST spectra and surface kinetic energy spectra derived from SSH have the same slope, which is not consistent with this hypothesis. Moreover, baroclinic modes are associated by definition with vanishing buoyancy anomalies at the ocean surface, which is obviously not the case. Here a careful derivation of the vertical modes is done using the concepts of quasigeostrophic potential vorticity (QGPV) theory. Since the surface buoyancy can be interpreted as a Dirac function in PV, the traditional baroclinic modes have to be completed by a surface-trapped mode with no interior QGPV. The possible contribution of each mode is quantified in a realistic simulation of the North Atlantic Ocean. The surface mode is found to give the largest contribution in terms of surface energy in most of the Atlantic. Its relative importance compared to the other modes is determined at first order by the large-scale forcing of PV and surface buoyancy. These results emphasize the necessity for a new interpretation of satellite measurements of sea surface temperature or height.

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

Sign in / Sign up

Export Citation Format

Share Document