scholarly journals Detection of Mesoscale Seasonal and Interannual Variation in the Vegetation of the Amazon Basin

2005 ◽  
Vol 9 (25) ◽  
pp. 1-16
Author(s):  
Miles G. Logsdon ◽  
Robin Weeks ◽  
Milton Smith ◽  
Jeffery E. Richey ◽  
Victoria Ballester ◽  
...  
Keyword(s):  
Time Series ◽  
El Niño ◽  
Amazon Basin ◽  
El Nino ◽  
Southern Oscillation ◽  
Mathematical Framework ◽  
Spectral Change ◽  
Range Analysis ◽  
Local Variance ◽  
Wide Range

Abstract In the Amazon basin, seasonal and interannual spectral changes measured by satellites result from anthropogenic disturbance and from the interaction between climate variation and the surface cover. Measurements of spectral change, and the characterization of that change, provide information concerning the physical processes evident at this mesoscale. A 17-yr sequence of daily Advanced Very High Resolution Radiometer (AVHRR) global area coverage (GAC) images were analyzed to produce a monthly record of surface spectral change encompassing El Niño–Southern Oscillation (ENSO) cycles. Monthly cloud-free composite images from daily AVHRR data were produced by linear filters that minimized the finescale spatial variance and allowed for a wide range analysis within a consistent mathematical framework. Here the use of a minimized local variance (MLV) filter that produced spatially smooth images in which major land-cover boundaries and spatial gradients are clearly represented is discussed. Changes in the configuration of these boundaries and the composition of the landscape elements they defined are described in terms of quantitative changes in landscape pattern. The time series produced with the MLV filter revealed a marked seasonal difference in the pattern of the landscape and structural differences over the length of the time series. Strikingly, the response of the region to drier El Niño years appears to be delayed in the MLV series, the maximum response being in the year following El Niño with little or no change seen during El Niño.

scholarly journals Global responses of terrestrial productivity to contemporary climatic oscillations

2008 ◽  
Vol 363 (1504) ◽  
pp. 2779-2785 ◽  
Author(s):  
F.I Woodward ◽  
M.R Lomas ◽  
T Quaife
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Gross Primary Production ◽  
Ecosystem Structure ◽  
Climatic Oscillations ◽  
Enso Events ◽  
The Pacific ◽  
Wide Range

The terrestrial biosphere is subjected to a wide range of natural climatic oscillations. Best known is the El Niño–southern oscillation (ENSO) that exerts globally extensive impacts on crops and natural vegetation. A 50-year time series of ENSO events has been analysed to determine those geographical areas that are reliably impacted by ENSO events. Most areas are impacted by changes in precipitation; however, the Pacific Northwest is warmed by El Niño events. Vegetation gross primary production (GPP) has been simulated for these areas, and tests well against independent satellite observations of the normalized difference vegetation index. Analyses of selected geographical areas indicate that changes in GPP often lead to significant changes in ecosystem structure and dynamics. The Pacific decadal oscillation (PDO) is another climatic oscillation that originates from the Pacific and exerts global impacts that are rather similar to ENSO events. However, the longer period of the PDO provided two phases in the time series with a cool phase from 1951 to 1976 and a warm phase from 1977 to 2002. It was notable that the cool phase of the PDO acted additively with cool ENSO phases to exacerbate drought in the earlier period for the southwest USA. By contrast in India, the cool phase of the PDO appears to reduce the negative impacts of warm ENSO events on crop production.

scholarly journals Analysis and quantification of ENSO-linked changes in the tropical Atlantic cloud vertical distribution using 14 years of MODIS observations

2019 ◽  
Vol 19 (21) ◽  
pp. 13535-13546
Author(s):  
Nils Madenach ◽  
Cintia Carbajal Henken ◽  
René Preusker ◽  
Odran Sourdeval ◽  
Jürgen Fischer
Keyword(s):  
Time Series ◽  
Vertical Distribution ◽  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Southern Oscillation ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Tropical Atlantic ◽  
High Cloud

Abstract. A total of 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data are used to quantify possible changes in the cloud vertical distribution over the tropical Atlantic. For the analysis multiple linear regression techniques are used. For the investigated time period significant linear changes were found in the domain-averaged cloud-top height (CTH) (−178 m per decade), the high-cloud fraction (HCF) (−0.0006 per decade), and the low-cloud amount (0.001 per decade). The interannual variability of the time series (especially CTH and HCF) is highly influenced by the El Niño–Southern Oscillation (ENSO). Separating the time series into two phases, we quantified the linear change associated with the transition from more La Niña-like conditions to a phase with El Niño conditions (Phase 2) and vice versa (Phase 1). The transition from negative to positive ENSO conditions was related to a decrease in total cloud fraction (TCF) (−0.018 per decade; not significant) due to a reduction in the high-cloud amount (−0.024 per decade; significant). Observed anomalies in the mean CTH were found to be mainly caused by changes in HCF rather than by anomalies in the height of cloud tops themselves. Using the large-scale vertical motion ω at 500 hPa (from ERA-Interim ECMWF reanalysis data), the observed anomalies were linked to ENSO-induced changes in the atmospheric large-scale dynamics. The most significant and largest changes were found in regions with strong large-scale upward movements near the Equator. Despite the fact that with passive imagers such as MODIS it is not possible to vertically resolve clouds, this study shows the great potential for large-scale analysis of possible changes in the cloud vertical distribution due to the changing climate by using vertically resolved cloud cover and linking those changes to large-scale dynamics using other observations or model data.

scholarly journals A skewed perspective of the Indian rainfall–El Niño–Southern Oscillation (ENSO) relationship

2020 ◽  
Vol 24 (11) ◽  
pp. 5473-5489 ◽  
Author(s):  
Justin Schulte ◽  
Frederick Policielli ◽  
Benjamin Zaitchik
Keyword(s):  
Time Series ◽  
Time Domain ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wavelet Coherence ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
The Time Domain ◽  
Time Domain Correlation

Abstract. Wavelet coherence is a method that is commonly used in hydrology to extract scale-dependent, nonstationary relationships between time series. However, we show that the method cannot always determine why the time-domain correlation between two time series changes in time. We show that, even for stationary coherence, the time-domain correlation between two time series weakens if at least one of the time series has changing skewness. To overcome this drawback, a nonlinear coherence method is proposed to quantify the cross-correlation between nonlinear modes embedded in the time series. It is shown that nonlinear coherence and auto-bicoherence spectra can provide additional insight into changing time-domain correlations. The new method is applied to the El Niño–Southern Oscillation (ENSO) and all-India rainfall (AIR), which is intricately linked to hydrological processes across the Indian subcontinent. The nonlinear coherence analysis showed that the skewness of AIR is weakly correlated with that of two ENSO time series after the 1970s, indicating that increases in ENSO skewness after the 1970s at least partially contributed to the weakening ENSO–AIR relationship in recent decades. The implication of this result is that the intensity of skewed El Niño events is likely to overestimate India's drought severity, which was the case in the 1997 monsoon season, a time point when the nonlinear wavelet coherence between AIR and ENSO reached its lowest value in the 1871–2016 period. We determined that the association between the weakening ENSO–AIR relationship and ENSO nonlinearity could reflect the contribution of different nonlinear ENSO modes to ENSO diversity.

scholarly journals A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship

2022 ◽  
Vol 29 (1) ◽  
pp. 1-15
Author(s):  
Justin Schulte ◽  
Frederick Policelli ◽  
Benjamin Zaitchik
Keyword(s):  
Time Series ◽  
El Niño ◽  
Indian Monsoon ◽  
El Nino ◽  
Southern Oscillation ◽  
Central Pacific ◽  
Central Pacific El Niño ◽  
Nonlinear Character ◽  
Indian Rainfall ◽  
Time Periods

Abstract. Many geophysical time series possess nonlinear characteristics that reflect the underlying physics of the phenomena the time series describe. The nonlinear character of times series can change with time, so it is important to quantify time series nonlinearity without assuming stationarity. A common way of quantifying the time evolution of time series nonlinearity is to compute sliding skewness time series, but it is shown here that such an approach can be misleading when time series contain periodicities. To remedy this deficiency of skewness, a new waveform skewness index is proposed for quantifying local nonlinearities embedded in time series. A waveform skewness spectrum is proposed for determining the frequency components that are contributing to time series waveform skewness. The new methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to test a recently proposed hypothesis that states that changes in the ENSO–Indian monsoon relationship are related to ENSO nonlinearity. We show that the ENSO–Indian rainfall relationship weakens during time periods of high ENSO waveform skewness. The results from two different analyses suggest that the breakdown of the ENSO–Indian monsoon relationship during time periods of high ENSO waveform skewness is related to the more frequent occurrence of strong central Pacific El Niño events, supporting arguments that changes in the ENSO–Indian rainfall relationship are not solely related to noise.

scholarly journals Embedding reconstruction methodology for short time series – application to large El Niño events

2010 ◽  
Vol 17 (6) ◽  
pp. 753-764 ◽  
Author(s):  
H. F. Astudillo ◽  
F. A. Borotto ◽  
R. Abarca-del-Rio
Keyword(s):  
Time Series ◽  
El Niño ◽  
Time Delays ◽  
El Nino ◽  
Southern Oscillation ◽  
Dimensional Subspace ◽  
Reconstruction Method ◽  
Short Time Series ◽  
Long Time ◽  
Short Time

Abstract. We propose an alternative approach for the embedding space reconstruction method for short time series. An m-dimensional embedding space is reconstructed with a set of time delays including the relevant time scales characterizing the dynamical properties of the system. By using a maximal predictability criterion a d-dimensional subspace is selected with its associated set of time delays, in which a local nonlinear blind forecasting prediction performs the best reconstruction of a particular event of a time series. An locally unfolded d-dimensional embedding space is then obtained. The efficiency of the methodology, which is mathematically consistent with the fundamental definitions of the local nonlinear long time-scale predictability, was tested with a chaotic time series of the Lorenz system. When applied to the Southern Oscillation Index (SOI) (observational data associated with the El Niño-Southern Oscillation phenomena (ENSO)) an optimal set of embedding parameters exists, that allows constructing the main characteristics of the El Niño 1982–1983 and 1997–1998 events, directly from measurements up to 3 to 4 years in advance.

scholarly journals A Study of Historical Droughts in Southeastern Mexico

2006 ◽  
Vol 19 (12) ◽  
pp. 2916-2934 ◽  
Author(s):  
Blanca Mendoza ◽  
Victor Velasco ◽  
Ernesto Jáuregui
Keyword(s):  
Time Series ◽  
El Niño ◽  
Agricultural Sector ◽  
Historical Data ◽  
El Nino ◽  
Southern Oscillation ◽  
Time Lapse ◽  
Southeastern Part ◽  
Temperature Changes ◽  
Historical Series

Abstract A catalog containing an unprecedented amount of historical data in the southeastern part of Mexico covering almost four centuries (1502–1899) is used to construct a drought time series. The catalog records information of agricultural disasters and includes events associated with hydrometeorological phenomena or hazards whose effects were mainly felt in the agricultural sector, such as droughts. An analysis of the historical series of droughts in southeastern Mexico for the period 1502–1899 is performed. The highest drought frequency occurred around the years 1650, 1782, and 1884; no droughts were reported around 1540, between 1630 and 1640, along the largest time lapse of 1672–1714, and between 1740 and 1760. From 1760 until the end of the period of study droughts definitively occur more often than they did from ∼1550 to 1760. In addition, most droughts lasted for 1–2 yr. Analyzing the frequencies of the drought time series it is found that the most conspicuous cycles are ∼3–4 and 7 yr, although cycles of ∼12, 20, 43, and 70 yr are also evident. The relation between droughts and El Niño events indicates that 38% of droughts are associated with El Niño. Sea surface temperature changes, the Southern Oscillation index, and solar activity leave their signals in the southeastern part of Mexico, with the signs in Oaxaca clearer than in the Yucatan Peninsula. However, the dominance of some phenomena over others depends on the time scales considered.

scholarly journals On the progress of the 2015–2016 El Niño event

2016 ◽  
Vol 16 (4) ◽  
pp. 2007-2011 ◽  
Author(s):  
Costas A. Varotsos ◽  
Chris G. Tzanis ◽  
Nicholas V. Sarlis
Keyword(s):  
Time Series ◽  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Linear Dynamics ◽  
El Niño Event ◽  
Non Linear ◽  
El Niño Events ◽  
Index Time Series

Abstract. It has been recently reported that the current 2015–2016 El Niño could become "one of the strongest on record". To further explore this claim, we performed the new analysis described in detail in Varotsos et al. (2015) that allows the detection of precursory signals of the strong El Niño events by using a recently developed non-linear dynamics tool. In this context, the analysis of the Southern Oscillation Index time series for the period 1876–2015 shows that the running 2015–2016 El Niño would be rather a "moderate to strong" or even a "strong" event and not “one of the strongest on record", as that of 1997–1998.

scholarly journals Precipitation–fire linkages in Indonesia (1997–2015)

2017 ◽  
Vol 14 (18) ◽  
pp. 3995-4008 ◽  
Author(s):  
Thierry Fanin ◽  
Guido R. van der Werf
Keyword(s):  
Time Series ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Fire Season ◽  
Coefficient Of Determination ◽  
Fire Intensity ◽  
Rainfall Time Series ◽  
Fire Emissions ◽  
Active Fire

Abstract. Over the past decades, fires have burned annually in Indonesia, yet the strength of the fire season is for a large part modulated by the El Niño Southern Oscillation (ENSO). The two most recent very strong El Niño years were 2015 and 1997. Both years involved high incidences of fire in Indonesia. At present, there is no consistent satellite data stream spanning the full 19-year record, thereby complicating a comparison between these two fire seasons. We have investigated how various fire and precipitation datasets can be merged to better compare the fire dynamics in 1997 and 2015 as well as in intermediary years. We combined nighttime active fire detections from the Along Track Scanning Radiometer (ATSR) World Fire Atlas (WFA) available from 1997 until 2012 and the nighttime subset of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor from 2001 until now. For the overlapping period, MODIS detected about 4 times more fires than ATSR, but this ratio varied spatially. Although the reasons behind this spatial variability remain unclear, the coefficient of determination for the overlapping period was high (R2 = 0. 97, based on monthly data) and allowed for a consistent time series. We then constructed a rainfall time series based on the Global Precipitation Climatology Project (GPCP, 1997–2015) and the Tropical Rainfall Measurement Mission Project (TRMM, 1998–2015). Relations between antecedent rainfall and fire activity were not uniform in Indonesia. In southern Sumatra and Kalimantan, we found that 120 days of rainfall accumulation had the highest coefficient of determination with annual fire intensity. In northern Sumatra, this period was only 30 days. Thresholds of 200 and 305 mm average rainfall accumulation before each active fire were identified to generate a high-incidence fire year in southern Sumatra and southern Kalimantan, respectively. The number of active fires detected in 1997 was 2.2 times higher than in 2015. Assuming the ratio between nighttime and total active fires did not change, the 1997 season was thus about twice as severe as the one in 2015. Although large, the difference is smaller than found in fire emission estimates from the Global Fire Emissions Database (GFED). Besides different rainfall amounts and patterns, the two-fold difference between 1997 and 2015 may be attributed to a weaker El Niño and neutral Indian Ocean Dipole (IOD) conditions in the later year. The fraction of fires burning in peatlands was higher in 2015 compared to 1997 (61 and 45 %, respectively). Finally, we found that the non-linearity between rainfall and fire in Indonesia stems from longer periods without rain in extremely dry years.

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