Inter-annual variability of rainfalls in the Amazon basin and its vicinity

An analysis of the rainfall series (12-month running means) of the 5° × 5° gridded data in the Amazon river basin and its vicinity (15° N – 20° S, 30° - 80° W) indicated that the rainfalls were highly variable both from year to year and from region to region. Correlations with even nearby regions hardly exceeded 0.50, though correlations were better (up to 0.70) in the regions near the eastern coast of Brazil. Moderate relationship with ENSO indices was obtained for the Amazon river basin and the regions to its north, and for NE Brazil, while moderate relationship with South Atlantic SST was obtained for NE Brazil and the region immediately to its west. All other relationships (with 30 hPa wind, North Atlantic Oscillation Index, etc.) were obscure.

Precipitation Variability and its Teleconnection with the Global SST and ENSO Indices in the Food Insecure Rural Areas of Tigray

The impact of precipitation variability on food production is very significant. For food insecure rural areas, understanding the nature of precipitation variability and its teleconnection has paramount importance in guiding regional and local level decisions. In this study, we analyzed the monthly, seasonal and annual precipitation variability and the strength of its teleconnection with the global sea-surface temperature (SST) and El Niño Southern Oscillation (ENSO) indices in the food insecure rural areas of Tigray region, Ethiopia. The precipitation, SST, and ENSO indices data for the study were used from 1979 to 2019. A Summary of descriptive statistics and Mann Kendall test methods were applied to detect existence of trends; and Sen’s Slope and coefficient of variation are used to analyze the magnitude of the trend, and degree of variation in the trend of precipitation. Further, Pearson’s correlation is used to determine the effect of ENSO, and SST variations on the precipitation using the Canonical Correlation Analysis (CCA). The results revealed that the precipitation over the study areas is characterized by a distinctive bi-modal pattern with limited rains in March – May preceding the main rainy season June – September. The limited amount of precipitation, exacerbated by higher degree of variability, makes the food production in the study areas more uncertain. Besides, there was a very significant decline in the trend of March – May average precipitation and a significant decline in the trend of the annual average precipitation of Hintalo area. The SSTs of the central and eastern equatorial Pacific Ocean, and northeast and northwest equatorial Atlantic Ocean was strongly correlated with April’s average precipitation of the study areas. Further, the SST of south, west and southwest of equatorial Indian Ocean, and west equatorial Pacific Ocean were associated with July – September average precipitation with greater variation in strength among of the study areas. Moreover, July’s average precipitation of all the study areas, April’s average precipitation of Atsbi and Eirop, and May’s precipitation of Hintalo are found significantly associated with the ENSO indices of JFM, FMA, MJJ and MAM. Therefore, the task of achieving food security in the study areas should incorporate the design of informed food production strategies that can adapt the limited and variable precipitation based on these SST and ENSO indices.

Space- and time-varying associations between Bangladesh's seasonal rainfall and large-scale climate oscillations

Understanding teleconnections of a region's climate can be beneficial to seasonal outlooks and hydro-climate services. This study aims at analyzing the teleconnections of seasonal rainfall over Bangladesh with selected climate indices, including El Niño/Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) indices. Rainfall data spanning from 1965–2017 in the seven hydrological regions are used to derive three seasonal rains, namely the pre-monsoon (March–May), monsoon (June–September), and post-monsoon (October and November) rains, for correlation- and wavelet coherence (WC)-based teleconnection analyses. Among the three seasonal rains, the post-monsoon rain shows the negative correlations, strongest with the IOD and ENSO indices. Correlations between the pre-monsoon/monsoon rain and climate indices are subject to notable spatial and temporal variations. For instance, correlations between the pre-monsoon (monsoon) rain in the South Central (South West) region and the IOD (ENSO) index shift from negative to positive after the 1980s, whereas the comprehensive negative correlations of the post-monsoon rain with the IOD and ENSO indices further enhanced from the early to recent epochs. WC analysis not only corroborates the findings of correlation analysis at shorter time scales (e.g., 1–4 years), but also reveals significant coherence at longer time scales (e.g., 8–16 years). We find that the pre-monsoon and monsoon rains experience the phase change in WC from shorter to longer scales. In contrast, the post-monsoon rain shows the consistent anti-phase WC, more dominant at the longer time scale. Both correlation and WC analyses indicate that the association patterns of the PDO mimic those of ENSO. Lastly, the analysis results of the AMO suggest quite distinct and significant association between Bangladesh's rainfall and the Atlantic Ocean.

Teleconnection Between ENSO and Wind-Wave Climate Under the Influence of Monsoon in the Bay of Bengal Region

Wind-wave parameters like the significant wave height (SWH)impacts considerably deep ocean and maritime activities and lives of all those dwelling near the coast.Prediction of such a parameter has immense utility during extreme conditions. Teleconnection features are explored between the most widely studied climate mode, the El Niño-Southern Oscillation or ENSO and the SWHparameter in the Bay of Bengal (BB) region under the influence of monsoon in this study.In two separate experiments the SWH data of the BB region for the period 1958-2001 and the period 2006-2016 is subjected to empirical orthogonal function analysis to split the data into spatial and temporal parts.The temporal variations are of annual periodicity for both the data sets. On analysis teleconnection feature of lower (higher) SWH during El Niño (La Niña) episodes is observed in the BB region. Significant correlationis observed between SWH and the ENSO indices during the summer monsoon months.The continuous wavelet power spectrum is generated using the first principal component (PC1) extracted above. It exhibits significant regions in the 0.5-1 year band resembling the monsoon variability in the BB region. To determine how SWH is related to the ENSO indices wavelet coherence is applied for the BB region.The higher coherency regions are found in the 0.5-1 year band which maybe related to the monsoon oscillation having similar periodicity. Thus the SWH and ENSO relationship in the BB region is influenced by the monsoon significantly.

Application of spectra-temporal analysis methods to detect common signals in length of day, global sea level rise, global temperature data, and ENSO indices

<p>It is already well known that intra-seasonal oscillations in the Earth’s global temperature are driven by ENSO (El Niño Southern Oscillation) events. ENSO signal is also present in length of day and global sea level rise, because during El Niño the increase of the length of day and global sea level rise can be noticed. To detect common oscillations in length of day, global sea level rise, global temperature data and ENSO indices the wavelet-based semblance filtering method was used. This method, however, seeks the signals with a good phase agreement of oscillations in two time series thus, no phase agreement results in very small amplitudes of the common signals. The spectra-temporal semblance functions allow detecting the similarity of two time series in spectral bands in which the amplitudes and phases of the oscillations are consistent with each other. The amplitudes of oscillations in the considered data vary in time and in order to detect the signals with similar amplitude variations between pairs of time series the normalized Morlet wavelet transform (NMWT) and the combination of the Fourier transform bandpass filter with the Hilbert transform (FTBPF+HT) were used. These two methods enable computation of the instantaneous amplitudes and phases of oscillations in two real-valued time series. In order to detect oscillations with similar amplitude variations in two time series correlation coefficients between the amplitude variations as a function of oscillation frequencies were computed.</p>

Wavelet coherence between ENSO indices and two precipitation database for the Andes region of Colombia

The influence of El Niño Southern Oscillation (ENSO) on Colombia's hydrological variables has been shown in different studies. Most of the methodologies implemented have identified linear relationships and have associated the warm (cold) phase called El Niño (La Niña) with negative (positive) rainfall and streamflow anomalies. One of the most adverse impacts founded is the reduction in water supply during the warm phase. Therefore, it is necessary to study the linkage between ENSO and precipitation variability for efficient management of water resources. Consequently, the present paper has two purposes. The first one is to explore nonlinear correlations of the ENSO-precipitation relationship, particularly for specific regions where the freshwater resources have been significantly reduced during El Niño events. The second one is to identify which indices will enable in improving the predictability of hydro-climatological variables. The research was based on the wavelet coherence analysis of monthly precipitation time series from 1981-2016 and the ENSO indices for the same period. The results show that ENSO events influence the precipitation as periods of rainfall deficit or excess. Also, precipitation is organized in bands and that the 2–8-year scales explain most of their variance. The most significant sectors are those that cover El Niño events. In contrast, sectors are smaller when La Niña episodes. Then impacts on precipitation tend to be greater for warm events. Results also allowed to identify that El Niño 3, Niño 3,4, ONI, and BEST indices can be good indicators for forecasting work in these specific places. The use of two kinds of data, one in situ and the other from CHIRPS program, allows to establish the feasibility of using data from satellite origin in regions without enough information; the results showed that CHIRPS data tend to report fewer anomalies than data in situ. However, the coherence structure is similar, but in periods between 36 and 48 months, there were discrepancies of pi/4 in the phase difference, that is, between 3 and 6 months of difference in lags calculated with each database.

Relationships of Rainy Season Precipitation and Temperature to Climate Indices in California: Long-Term Variability and Extreme Events

To better understand the change in California’s climate over the past century, the long-term variability and extreme events of precipitation as well as minimum, mean, and maximum temperatures during the rainy season (from November to March) are investigated using observations. Their relationships to 28 rainy season average climate indices with and without time lags are also studied. The precipitation variability is found to be highly correlated with the tropical/Northern Hemisphere pattern (TNH) index at zero time lag with the highest correlation in Northern California and the Sierra and the correlation decreasing southward. This is an important finding because there have been no conclusive studies on the dominant climate modes that modulate precipitation variability in Northern California. It is found that the TNH modulates California precipitation variability through the development of a positive (negative) height anomaly and its associated low-level moisture fluxes over the northeast Pacific Ocean during the positive (negative) TNH phase. Temperature fields, especially minimum temperature, are found to be primarily modulated by the east Pacific/North Pacific pattern, Pacific decadal oscillation, North Pacific pattern, and Pacific–North American pattern at zero time lag via changes in the lower-tropospheric temperature advections. Regression analysis suggests a combination of important climate indices would improve predictability for precipitation and minimum temperature statewide and subregionally compared to the use of a single climate index. While California’s precipitation currently is primarily projected by ENSO, this study suggests that using the combination of the TNH and ENSO indices results in better predictability than using ENSO indices only.

Revisiting the ENSO–SSW Relationship

Stratospheric sudden warming (SSW) events exhibit pronounced interannual variability. Based on zonal wind reversals at 60°N and 10 hPa, it has been suggested that SSW events occur more preferentially during El Niño–Southern Oscillation (ENSO) winters (both El Niño and La Niña winters) than during ENSO-neutral winters. This relationship is reevaluated here by considering seven different SSW definitions. For all definitions, SSW events are detected more frequently during El Niño winters than during ENSO-neutral winters, in agreement with a strengthened planetary-scale wave activity. However, such a systematic relationship is not found during La Niña winters. While three SSW definitions, including the wind-reversal definition, show a higher SSW frequency during La Niña winters than during ENSO-neutral winters, other definitions show no difference or even lower SSW frequency during La Niña winters. This result, which is qualitatively insensitive to the choice of reanalysis datasets, ENSO indices, and SST datasets, indicates that the reported ENSO–SSW relationship is dependent on the details of the SSW definition. This result is interpreted in terms of different background wind, latitudinal extent of wind reversal, and planetary-scale wave activity during El Niño and La Niña winter SSW events.