Multivariate ENSO Index Research Articles

A Place-Based Approach to Drought Forecasting in South-Central Oklahoma.

To assist water managers in south-central Oklahoma prepare for future drought, reliable place-based drought forecasts are produced. Past-, present-, and future-forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the periodJanuary 1901 to November 2021, a second-order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross-validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best-fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R-squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought.

Did Schwabe cycles 19–24 influence the ENSO events, PDO, and AMO indexes in the Pacific and Atlantic Oceans?

The sea surface temperature (SST), anomalies (SSTA), Oceanic Niño Index (ONI), and Multivariate ENSO (MEI) index in El Niño 1 + 2 and El Niño 3.4 regions, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO), as well as the meteorological parameter Southern Oscillation Index (SOI) were cross correlated to sunspot counts (SS) from cycles 19 to 24 (1954–2019). Over the 1954–2019 period, the SS or Schwabe cycles did not show strong cross-correlation coefficients (cc-ρ) with values falling between 0.063 and 0.100 ( p < 0.05). It seems that the Total Solar Irradiation (TSI) constant variability (±0.1%, ±1.361 W m −2 ) due to the SS cycles balanced out throughout the whole period. The cc-ρ coefficients for SST and SSTA versus each individual SS cycles were between 0.100 and 0.200 with lag times between maxima of each being from a few to 48 months. The ONI showed a cc-ρ < 0.1, while MEI reached up to 0.2 through all SS cycles. The slope of the cc-ρ changed from negative to positive over 6–12 months periods, with peaks in slope change occurring somewhere between 2 and 3 years. The SOI cc-ρ varied by around 0.200 through cycles 19–21, but for SS 22–24 it was not noticeable. Interdecadal indexes PDO and AMO showed cc-ρ of up to 0.283; with a possible association of 8%, with a lag time of 1–3 years. During the ascending and descending phases of each SS cycle, the cc-ρ were 0.288 and − 0.233, respectively for SST in El Niño 3.4 region, but in El Niño 1 + 2 were negligible. The ONI and MEI showed cc-ρ up to 0.448 and 0.480 respectively with lag times of 1–15 months in ascending phase of the SS cycle. The SS vs SOI had cc- ρ up to +0.420 to −0.567 in the ascending phases, while PDO and AMO registered cc-ρ up to 0.417 and − 0.491. AMO appeared systematically associated with SS cycles from 10.0 to 30.2% during descending and ascending phases, respectively. Compiled SS counts for all the ascending and descending phases of the SS cycles, gave a clear spectral coherence (quasi sine-function distribution); for SST and SSTA, lag times of 9–48 months were observed with inverse and direct linear relationship, and peaks of −0.38 and 0.39, respectively. The ONI and MEI with SS counts have similar cc-ρ values: 0.245 and 0.387. Around 6–15% of the ONI and MEI can be explained by SS during ascending and < 4% in descending phases. It seems that over the relatively short time scales of SS cycles, either on their initial ascending or final descending phases, the studied indexes seem to be influenced. Even though it was difficult to elucidate the physics behind the observed cross-correlations, these results can be used to help improve understanding and aid the predictions of ENSO, PDO and AMO oceanographic events in the Pacific and Atlantic oceans. • Sea surface temperature is affected by sun energy that varies in time due to astronomical factors and sun internal activity • The sun activity can be estimated by sun spots (SS) • Inter-annual El/La Niño/Niña as well as decadal processes should be affected by SS • It was found weak to moderate cross-correlation between SS and oceanographic indexes from 1954 to 2019 • This fact should be considered to improve skill modelling forecasts of such indexes

Drivers influencing climate of growing cities

Population growth and affordable housing have boosted realty sector and urban sprawl in India. Understanding the interrelation between urbanization and local climate, though complex, is the need of the hour and the focus of this study. An analysis of the Expert Team on Climate Change Detection and Indices (ETCCDI) on temperature and precipitation was carried out, and it confirms the change in the local urban climate. A Clausius-Clapeyron (CC) scaling relationship has been developed between the range of daily maximum temperature and precipitation for finding precipitation intensity, which is influenced by a rise in maximum temperature. Land use and land cover change derived for the period 1970–2017 from Landsat images were used to understand the effect of urbanization on average daily temperature and extreme precipitation. Multivariate ENSO Index and Global Temperature Anomalies were taken as global physical drivers. Urbanization growth rate anomalies, annual mean temperature anomalies, and summer mean temperature anomalies were taken as local physical drivers that affect one-day extreme precipitation. 22 combinations of these physical drivers were used as covariates to develop extreme value models. Models were evaluated with the L-R test and AIC. It is found that global average temperature and urbanization, individually as well as in combination with local summer mean temperature, were found to be influencing local extreme precipitation. Changes in precipitation patterns have a direct impact on urban water management.

Assessment of drought time-frequency relationships with local atmospheric-land conditions and large-scale climatic factors in a tropical Andean basin

This study analyzed in the Tomebamba subbasin the relationships between droughts, the condition of the vegetation, global scale climatic indices and local atmospheric variables. The variability of monthly meteorological and hydrological droughts, as well as the condition of the vegetation, were characterized using the standardized precipitation index (SPI), the standardized streamflow index (SSI), and the vegetation condition index (VCI). The wavelet coherence method was used to establish time-frequency relationships. The results indicate that the vegetation in good condition reduces the impact of hydrological drought events when there are no sudden precipitation impacts. The atmospheric variables at the local scale with the highest significant correlation are cloud cover, relative humidity, specific humidity, temperature and specific rainwater content between 500 and 750 hPa. The Multivariate ENSO Index (MEI), ENSO 3, the Southern Oscillation Index (SOI), the Pacific Decadal Oscillation (PDO), and the Pacific North American Index (PNA) are strongly linked to SPI and the ENSO 4 index to SSI. Vegetation does not show strong relationships with any climate indices, obtaining the best relationship with the PNA. The results provide a basis for the analysis of variability and propagation of the droughts in Andean basins, their relationships with local and large-scale factors, and the influence of drought on vegetation.

Satellite-Observed Chlorophyll-a Concentration Variability in the East Sea (Japan Sea): Seasonal Cycle, Long-Term Trend, and Response to Climate Index

In this study, to determine the spatiotemporal variability of satellite-observed chlorophyll-a(Chl-a) concentrations in the East Sea (Japan Sea, EJS), monthly composite images were generated via noise processing using Level-2 MODIS Chl-adata from 2003 to 2020. Harmonic analysis was performed on time-series Chl-adata to present the spatial distribution of seasonal and intraseasonal variability with 1–4 cycles per year. In the EJS, seasonal cycles contributed less than approximately 30% to the total variance in Chl-avariability, indicating the existence of dominant interannual variability. Analysis of the temporal trend in Chl-aconcentrations showed that they increased (&amp;lt; 0.06 mg m–3yr–1) in most of the EJS over almost two decades (2003–2020). In recent years, in the areas showing positive trends in Chl-a, it tended to increase with time, especially in the northern part of the EJS. As a result of examining the trend associated with the physical environment that affects the long-term trend in Chl-aconcentrations, sea surface temperature (SST) trends were mostly increased. The wind speeds showed a characteristic strengthening trend in the northeastern part of the EJS and the North Korean coast. Long-term changes in wind direction indicated strengthening of the northerly wind components on the Russian coast and the westerly components on the eastern coast of the Korean Peninsula. These wind changes were closely related to the Arctic Oscillation (AO) index variability in relation to the recent warming of the Arctic Ocean. When the AO index was greater than 1, the wind speed tended to be weakened and the SSTs showed a tendency to increase. This led to general increasing responses in Chl-aconcentrations during positive AO. The summer SST anomaly revealed an inverse relationship between higher positive values during the La Niña period and lower ones during the El Niño period. When the amplitude of MEI (Multi-variate ENSO Index) was high (| MEI| &amp;gt; 1), the SST anomaly indicated an inverse correlation with the Chl-aconcentration anomaly in the EJS. This study demonstrated the regional effects of climate change on Chl-avariability in the EJS in response to tropical–subtropical and arctic–subarctic interactions between ocean and atmospheric variations.

A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

Under variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are, therefore, developed by fitting non-stationary distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) are considered as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared. The study also concentrated on the trivariate and the Pairwise Copula Construction (PCC) models to estimate the drought return periods. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. This study showed that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

Recent changes in temperature extremes in subtropical climate region and the role of large-scale atmospheric oscillation patterns

Understanding the recent variations in temperature extremes is crucial to anticipate the forthcoming incidences of extreme phenomena. However, knowledge of temperature extremes’ spatial and temporal patterns, as well as their links to atmospheric oscillation and topography, is scarce in Bangladesh. To this end, this research intends to analyze the spatial and temporal trends in recent extreme temperatures and their relationships with oscillation indices and the topography of Bangladesh. Daily temperature data obtained from 20 meteorological stations for 1980–2017 were employed for this purpose. Results revealed increasing trends in summer days (SU25), tropical nights (TR20), warm days (TX90p), warmest days (TXx), and warm nights (TN90p), while decline in the coldest days (TNn), cold days (TX10p), and cold nights (TN10p) was observed in Bangladesh. Spatial distribution of trends revealed an increase in SU25 and TN90p by 1.9–2.38, 2.33–2.90 days/decade, and a decrease in TX10p and TN10p by 1.7–3.3 days/decade in most regions. Besides, TR20 showed an increase of 3.22–4.17 days/decade in all sub-regions. The temperature extremes of Bangladesh showed a significant connection with multivariate ENSO index (MEI) and Sea Surface Temperature (SST). Besides, the extremes in most regions of the country showed a significant connection with Southern Oscillation Index (SOI) and Indian Ocean Dipole (IOD). The influence of atmospheric oscillation indices was more evident on cold days/nights than on warm days/nights. TN10p and SU25 also showed a significant correlation with elevation, which suggests an increase in cold night and summer day temperature with the increase in elevation in Bangladesh. Large-scale climate mode reanalysis revealed that a strong (weak) wind speed, enhancing (decreasing) geopotential height, and fast warming (cooling) over the northwestern (southeast) region have attributed to the variations in extreme temperature in Bangladesh to several extents. These findings will assist the policymakers in disaster mitigation and climate change adaptation in Bangladesh.

Forecasting Scrub Typhus Cases in Eight High-Risk Counties in China: Evaluation of Time-Series Model Performance

Scrub typhus (ST) is expanding its geographical distribution in China and in many regions worldwide raising significant public health concerns. Accurate ST time-series modeling including uncovering the role of environmental determinants is of great importance to guide disease control purposes. This study evaluated the performance of three competing time-series modeling approaches at forecasting ST cases during 2012–2020 in eight high-risk counties in China. We evaluated the performance of a seasonal autoregressive-integrated moving average (SARIMA) model, a SARIMA model with exogenous variables (SARIMAX), and the long–short term memory (LSTM) model to depict temporal variations in ST cases. In our investigation, we considered eight environmental variables known to be associated with ST landscape epidemiology, including the normalized difference vegetation index (NDVI), temperature, precipitation, atmospheric pressure, sunshine duration, relative humidity, wind speed, and multivariate El Niño/Southern Oscillation index (MEI). The first 8-year data and the last year data were used to fit the models and forecast ST cases, respectively. Our results showed that the inclusion of exogenous variables in the SARIMAX model generally outperformed the SARIMA model. Our results also indicate that the role of exogenous variables with various temporal lags varies between counties, suggesting that ST cases are temporally non-stationary. In conclusion, our study demonstrates that the approach to forecast ST cases needed to take into consideration local conditions in that time-series model performance differed between high-risk areas under investigation. Furthermore, the introduction of time-series models, especially LSTM, has enriched the ability of local public health authorities in ST high-risk areas to anticipate and respond to ST outbreaks, such as setting up an early warning system and forecasting ST precisely.

Does ENSO Significantly Affect Rice Production In Indonesia? A Preliminary Study Using Computational Time-Series Approach

ENSO is a phenomenon that is suspected to influence rice production in Indonesia. In this study, we try to find direct correlation between ENSO and rice production in this region by using various latest computational time series methods, such as Dynamic Time Warping, Wavelet Coherence, and Bayesian Structural Time Series to quantify the statistical relationship between the Multivariate ENSO Index on annual rice production in 1961- 2019. We did not find a direct correlation between these two variables, which may be due to the local influence of ENSO on different rice production areas in Indonesia. This study would also point out the importance of shifting the theme of research in Indonesia from mapping to monitoring and freely share the data. This step would bring science to progress further and faster.

Spatial, seasonal, and climatic variability in mesozooplankton size spectra along a coastal-to-open ocean transect in the subarctic Northeast Pacific

Biomass size spectra (i.e., the distribution of biomass by body size) are increasingly being used to describe energy flows through pelagic communities. We relate spatial and temporal variability in mesozooplankton normalized biomass size spectra (NBSS) to environmental drivers to understand how climatic variability impacts these energy flows. Seasonally-resolved mesozooplankton samples, collected between 1997 and 2019 along a subarctic Northeast Pacific coastal-to-open ocean transect using a 236 µm Bongo net, were analyzed using microscopy and a laboratory laser optic particle counter to obtain NBSS. NBSS intercepts decreased and slopes flattened along the transect until about 400 km offshore, after which they remained relatively similar. NBSS showed significant seasonality, with steeper slopes and lower intercepts during the winter, and the strongest seasonality near the coast. While there was weak evidence of long-term trends in NBSS, no evidence of gelatification was detected. Seasonality was also evident in the environmental parameters, along with some long-term trends. Stepwise regression analysis suggested that changes in NBSS slope were driven by surface chlorophyll-a concentration, depth-integrated nitrate + nitrite, multivariate ENSO index, number of species, species diversity, and water content (i.e., gelatinous contribution). Surface chlorophyll-a concentration had the greatest effect on NBSS slope, suggesting that future studies may employ satellite-derived estimates of chlorophyll-a to infer changes in mesozooplankton NBSS. This study demonstrates how NBSS may be used to compare and understand the broad implications of climate-induced environmental changes on zooplankton in ecosystems with differing species composition.

Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

The increasing contribution of potential evapotranspiration to severe droughts in the Yellow River basin

Under global warming, droughts have become more severe and more frequent. In this context, it is widely accepted that both water supply (e.g., precipitation, PRE) and demand (e.g., potential evapotranspiration, PET) should be considered in drought assessments due to increasing evapotranspiration. China’s Yellow River basin is located mainly in arid and semi-arid regions that suffer severe droughts. Analyzing the temporal and spatial patterns of droughts in this area and exploring the relative effects of supply and demand on drought occurrence and variations are thus necessary to adapt to increased droughts. Here, we compared three kinds of drought indices, including the standardized precipitation evapotranspiration index (SPEI), the standardized precipitation index (SPI), and the standardized evapotranspiration deficit Index (SEDI), and used SPEI to identify droughts in the Yellow River basin during the six decades from 1956 to 2016. We identified droughts and described drought dynamics based on drought duration, intensity, frequency, and spatial extent. We then evaluated the relative contributions of PRE and PET to droughts of varying severity and long-term trend, as well as the relationships between ocean signals and droughts. Our results show that most of the basin has undergone progressively drier conditions since the 1950 s. This increased drought occurrence has manifested as longer, more frequent droughts affecting a larger spatial extent. PRE was the dominant driver (64%) of drought occurrence in an area of ∼ 86% of the basin, whereas PET was the main driver (55%) in the remaining 14% of the basin. However, changes in PET contributed more than PRE to drought variation, i.e., long-term SPEI trends, in about ∼ 60% of the area. And effects of PET increased during severer droughts due to the intensive interaction between soil moisture and atmospheric water demand in highly arid conditions. Physically, droughts were closely related to several ocean signals, such as the Multivariate ENSO Index (MEI), the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation index (AMO), and the Arctic Oscillation index (AO), at different time scales. Understandings of drought dynamics, their physical mechanisms, and the effects of PRE and PET on droughts of varying severity are essential for water resource management, especially in arid regions.

El Niño Southern Oscillation (1896 to 2016): Quantifying Effects on Winter Precipitation and Temperature in Southwest Ohio, USA

Continental-scale studies of North America suggest that the El Niño Southern Oscillation (ENSO) can cause winters to be warmer, with less precipitation, during El Niño conditions and colder, with more precipitation, during La Niña conditions in the Midwest United States. Two sources of historical records of precipitation and temperature in southwest Ohio from 1896 to 2016 were analyzed. Three statistical methodologies were used to test the hypothesis that anomalies in winter temperature and precipitation occurred in relation to ENSO phases. Eighty percent of El Niño winters had below-average winter precipitation; the average anomaly was −5 cm. Precipitation decreased with increase in El Niño strength as measured by the Multivariate ENSO Index (MEI). These results were statistically significant beyond the 95% level. However, variation in MEI only accounted for 3% of the overall variability in winter precipitation. Many of the drier winters on record, including the extrema, occurred during neutral winters. During La Niña winters precipitation was not statistically significantly different from that in neutral winters. Winter temperature was not statistically significantly different during El Niño and La Niña winters within the century of record. The results were consistent between separate analyses of data from the 2 different sources.

Forecasting river basin yield using information of large-scale coupled atmospheric–oceanic circulation and local outgoing longwave radiation

ABSTRACT Global and local climate parameters influence the distribution of precipitation over continents. The spatio-temporal distribution of rainfall and the depth of rainfall over a river basin influence the river basin yield. This study deals with the prediction of the river basin scale yield of the ‘Upper Bhima River basin’ from the Maharashtra State of India. The information on large-scale circulation patterns El Nino-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO) index, Multivariate ENSO Index (MEI), and local meteorological input viz. Outgoing longwave radiation (OLR) has been used to predict the river basin scale yield. The Artificial Intelligence (AI) tool – Genetic Programming (GP) – is used for developing prediction models. Ten different combinations of input variables are attempted for the development of monthly river basin yield models to arrive at the best input variable combination for the best predictions with varying lead times. Also, three combinations of input variables were tested for the prediction of ‘Seasonal Yield’. The findings of this research work indicate that GP-derived monthly River Basin Scale Yield forecasting models are successful in the prediction of yield with a correlation coefficient of 0.83. The seasonal yields could be predicted with a correlation coefficient of 0.75.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971.

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971–2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen’s slope estimator, and the Mann–Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

A Moments View of Climatology and Variability of the Asian Summer Monsoon Anticyclone

AbstractA comprehensive investigation of the climatology of and interannual variability and trends in the Asian summer monsoon anticyclone (ASMA) is presented, based on a novel area and moments analysis. Moments include centroid location, aspect ratio, angle, and “excess kurtosis” (measuring how far the shape is from elliptical) for an equivalent ellipse with the same area as the ASMA. Key results are robust among the three modern reanalyses studied. The climatological ASMA is nearly elliptical, with its major axis aligned along its centroid latitude and a typical aspect ratio of ~5–8. The ASMA centroid shifts northward with height, northward and westward during development, and in the opposite direction as it weakens. New evidence finding no obvious climatological bimodality in the ASMA reinforces similar suggestions from previous studies using modern reanalyses. Most trends in ASMA moments are not statistically significant. ASMA area and duration, however, increased significantly during 1979–2018; the 1958–2018 record analyzed for one reanalysis suggests that these trends may have accelerated in recent decades. ASMA centroid latitude is significantly positively (negatively) correlated with subtropical jet-core latitude (altitude), and significantly negatively correlated with concurrent ENSO; these results are consistent with and extend previous work relating monsoon intensity, ENSO, and jet shifts. ASMA area is significantly positively correlated with the multivariate ENSO index 2 months previously. These results improve our understanding of the ASMA using consistently defined diagnostics of its size, geometry, interannual variability, and trends that have not previously been analyzed.

Climate anomalies influence tooth growth patterns of South American sea lions

AbstractEl Niño‐Southern Oscillation (ENSO) and Southern Annular Mode (SAM) indirectly affect tooth growth of marine mammals, through changes in prey abundance, availability, and composition. This is reflected as variation in the dentine deposition, which determines the formation of annual layers, called growth layer groups (GLGs). The aim of this work was to study potential effects of ENSO and SAM on South American sea lion (Otaria byronia) of Patagonia, by examining interannual variability of GLG width. Adapting dendrochronological techniques, we analyzed 97 individuals (60 females and 37 males) and built six chronologies (three for each sex) by using cubic splines with different rigidity. Chronologies were correlated with MEI (Multivariate ENSO Index) and SAM index time series, considering lags in the biological response. The chronologies of females spanned 39 years and only two of these chronologies showed negative correlation with 1 year lagged SAM events. The chronologies of males spanned 23 years, but none showed significant correlation with these indexes. Our results did not indicate any ENSO effects on this species, while positive SAM events could reduce the abundance or availability of South American sea lion's prey, affecting the nutritional status of females and generating thinner GLGs during the next year.

Functional Data Visualization and Outlier Detection on the Anomaly of El Niño Southern Oscillation

The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of the ENSO phenomenon has become a main research interest. Statistical methods, including linear and nonlinear models, have intensively been used in modeling the ENSO index. However, these models are unable to capture sufficient information on ENSO index variability, particularly on its temporal aspects. Hence, this study adopted functional data analysis theory by representing a multivariate ENSO index (MEI) as functional data in climate applications. This study included the functional principal component, which is purposefully designed to find new functions that reveal the most important type of variation in the MEI curve. Simultaneously, graphical methods were also used to visualize functional data and capture outliers that may not have been apparent from the original data plot. The findings suggest that the outliers obtained from the functional plot are then related to the El Niño and La Niña phenomena. In conclusion, the functional framework was found to be more flexible in representing the climate phenomenon as a whole.

Climatic Regulation of Vegetation Phenology in Protected Areas along Western South America

Using 19 years of remotely sensed Enhanced Vegetation Index (EVI), we examined the effects of climatic variability on terrestrial vegetation of six protected areas along southwestern South America, from the semiarid edge of the Atacama desert to southern Patagonia (30°S–51°S). The relationship between satellite phenology and climate indices, namely MEI (Multivariate ENSO Index), PDO (Pacific Decadal Oscillation) and SAM (Southern Annular Mode) were established using statistical analyses for non-stationary patterns. The annual mode of phenological activity fluctuated in strength through time from the semiarid region to the border of southern Patagonia. Concomitantly, enhanced synchrony between EVI and climatic oscillations appeared over interannual cycles. Cross correlations revealed that variability in MEI was the lead predictor of EVI fluctuations over scales shorter than 4 months at lower latitudes and for the most poleward study site. The PDO was correlated with EVI over lags longer than 4 months at low latitude sites, while the SAM showed relationships with EVI only for sites located around 40°S. Our results indicate that the long-term phenological variability of the vegetation within protected areas along southwestern South America is controlled by processes linked to climate indices and that their influence varies latitudinally. Further studies over longer time scales will be needed to improve our understanding the impacts of climate change on vegetation condition and its effect over phenological variability.

Wind speed and mesoscale features drive net autotrophy in the South Atlantic Ocean

A comprehensive in situ dataset of chlorophyll a (Chl a; N = 18,001), net primary production (NPP; N = 165) and net community production (NCP; N = 95), were used to evaluate the performance of Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A) algorithms for these parameters, in the South Atlantic Ocean, to facilitate the accurate generation of satellite NCP time series. For Chl a, five algorithms were tested using MODIS-A data, and OC3-CI performed best, which was subsequently used to compute NPP. Of three NPP algorithms tested, a Wavelength Resolved Model (WRM) was the most accurate, and was therefore used to estimate NCP with an empirical relationship between NCP with NPP and sea surface temperature (SST). A perturbation analysis was deployed to quantify the range of uncertainties introduced in satellite NCP from input parameters. The largest reductions in the uncertainty of satellite NCP came from MODIS-A derived NPP using the WRM (40%) and MODIS-A Chl a using OC3-CI (22%).The most accurate NCP algorithm, was used to generate a 16 year time series (2002 to 2018) from MODIS-A to assess climate and environmental drivers of NCP across the South Atlantic basin. Positive correlations between wind speed anomalies and NCP anomalies were observed in the central South Atlantic Gyre (SATL), and the Benguela Upwelling (BENG), indicating that autotrophic conditions may be fuelled by local wind-induced nutrient inputs to the mixed layer. Sea Level Height Anomalies (SLHA), used as an indicator of mesoscale eddies, were negatively correlated with NCP anomalies offshore of the BENG upwelling fronts into the SATL, suggesting autotrophic conditions are driven by mesoscale features. The Agulhas bank and Brazil-Malvinas confluence regions also had a strong negative correlation between SLHA and NCP anomalies, similarly indicating that NCP is forced by mesoscale eddy generation in this region. Positive correlations between SST anomalies and the Multivariate ENSO Index (MEI) in the SATL, indicated the influence of El Niño events on the South Atlantic Ocean, however the plankton community response was less clear.