Abstract
Abstract. The 2015–2016 El Niño event ranks as one of the most severe on record in terms of the magnitude and extent of sea surface temperature (SST) anomalies generated in the tropical Pacific Ocean. Corresponding global impacts on the climate were expected to rival, or even surpass, those of the 1997–1998 severe El Niño event, which had SST anomalies that were similar in size. However, the 2015–2016 event failed to meet expectations for hydrologic change in many areas, including those expected to receive well above normal precipitation. To better understand how climate anomalies during an El Niño event impact soil moisture, we investigate changes in soil moisture in the humid tropics (between ±25∘) during the three most recent super El Niño events of 1982–1983, 1997–1998 and 2015–2016, using data from the Global Land Data Assimilation System (GLDAS). First, we use in situ soil moisture observations obtained from 16 sites across five continents to validate and bias-correct estimates from GLDAS (r2=0.54). Next, we apply a k-means cluster analysis to the soil moisture estimates during the El Niño mature phase, resulting in four groups of clustered data. The strongest and most consistent decreases in soil moisture occur in the Amazon basin and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. In addition, we compare changes in soil moisture to both precipitation and evapotranspiration, which showed a lack of agreement in the direction of change between these variables and soil moisture most prominently in the southern Amazon basin, the Sahel and mainland southeastern Asia. Our results can be used to improve estimates of spatiotemporal differences in El Niño impacts on soil moisture in tropical hydrology and ecosystem models at multiple scales.
Highlights
The El Niño–Southern Oscillation (ENSO) is one of the major coupled ocean–atmosphere modes of variability internal to the Earth system operating on interannual timescales (Jones et al, 2001)
Depending on the directionally of the sea surface temperature (SST) deviation, ENSO events are classified in two modes: El Niño, or the warm mode, when unusually warm water exists in the eastern tropical Pacific Ocean off the South American coast and La Niña, or the cool mode, when anomalously cool water pools exist in approximately the same location (Trenberth, 1997)
Global Land Data Assimilation System (GLDAS) soil moisture data are used as the basis for this analysis because soil moisture estimates from the four individual GLDAS land surface models (LSMs) capture the range of variability in other similar global soil moisture data products at the locations of the in situ data that were used in this study and described in Table 1 (Fig. 1)
Summary
The El Niño–Southern Oscillation (ENSO) is one of the major coupled ocean–atmosphere modes of variability internal to the Earth system operating on interannual timescales (Jones et al, 2001). Depending on the directionally of the SST deviation, ENSO events are classified in two modes: El Niño, or the warm mode, when unusually warm water exists in the eastern tropical Pacific Ocean off the South American coast and La Niña, or the cool mode, when anomalously cool water pools exist in approximately the same location (Trenberth, 1997). Occurring at a much lower frequency than a non-super El Niño event, these events account for a disproportionately large amount of the global climate anomalies associated with El Niño. We use the definition put forth by Hong et al (2014) that defines a super El Niño as one with Niño-3 SST anomalies greater than one standard deviation above others in the instrumental record (Trenberth, 1997), coupled with a Southern Hemispheric transverse circulation that is robust relative to that of other El Niños. The 2015–2016 event fits the super El Niño classification using this definition (Huang et al, 2016; Chen et al, 2017)
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