Multivariate ENSO Index Research Articles

Unravelling the Influence of ENSO and SAM Patterns on Skate Growth: The Case of the Shorttail Yellownose Skate in Patagonia, Argentina

ABSTRACTIn recent decades, the growth, physiology and distribution of many elasmobranch species have been altered as a result of environmental changes that affect prey abundance, availability and composition. Consequently, variations in nutrient input during climate events could manifest in the growth of their hard tissues. This study focuses on assessing the impact of the El Niño‐Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) on the growth of Shorttail Yellownose skate (Zearaja brevicaudata) in Patagonia, Argentina. To achieve this, vertebrae from 115 Z. brevicaudata specimens were analysed, and growth rings were dated and measured. By using cubic splines with varying flexibility, we constructed three standard chronologies. Generalized additive models (GAMs), the chronology with the best the R‐bar () and expressed population signal (EPS) were employed and values obtained from them linked to annual time series data of the Multivariate ENSO Index (MEI) and SAM, considering lags in the biological response. Surprisingly, no significant association was found with the MEI time series. However, a noteworthy positive association emerged between the chronology and the SAM time series lagged by 1 year, suggesting that SAM‐related climatic conditions could delay their transfer into the Patagonian marine ecosystem, subsequently impacting the growth of this ectothermic predator.

Unraveling the declining Indian ocean primary productivity and key drivers

Climate change severely affects the third-largest maritime regions, disrupting physical and biogeochemical processes and Ocean Net Primary Productivity (NPP), which affects the marine food web, fisheries, and livelihoods of surrounding regions. This study examined changes in NPP in the Indian Ocean and its drivers. The Ocean warming, equatorial and Pacific warm pools, stratification fluctuations, circulation changes, nutrient transport, monsoons, and different seasonal processes significantly influenced these microbial mats. The Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis data shows a high declining NPP trend in the Indian Ocean (-0.85 mgC/m3/day/yr). The high warming rate (0.01–0.02 °C/yr) primarily drives regional NPP alterations. The causal discovery analysis shows that sea surface temperature (SST) provides high negative feedback to NPP during all-time delays. Nitrate and phosphate show a positive association, reinforcing positive feedback, and Redfield stoichiometry holds paramount significance in fostering the NPP. Negative feedback is observed from the Western Pacific Index (WP), Oceanic Niño Index (ONI), Multivariate ENSO Index (MEI), and Pacific Decadal Oscillation (PDO). This climate pattern can modify mesoscale atmospheric circulation, influence currents, and change temperature and nutrient availability. The positive connection between the Southern Oscillation Index (SOI) and NPP underscores the complex interplay between climate patterns in the region. The Indian Ocean Dipole (IOD) negatively feedbacks the NPP. Various other climate patterns and events indirectly affect IO biogeochemical processes.

The variability of particulate organic carbon in the northern South China Sea during the 2009–2010 El Niño

The influences of El Niño on the changes of sea surface temperature (SST) and chlorophyll have been well studied, but its impact on changes of particulate organic carbon (POC) remains unclear. In this study, we analyzed the time series of POC and found that from January 2003 to December 2011, the spatially-averaged POC increased at a rate of 0.24 mg/m3 per year. However, during the 2009–2010 El Niño, the spatially-averaged POC increased at a rate of 1.78 mg/m3 per month. This indicated that the increase of POC during 2009–2010 may be related to El Niño. To investigate the relationship between POC and El Niño, we introduced the phase shift time between the POC and the Multivariate ENSO Index (MEI) to characterize the response of POC to El Niño, and calculated the correlation between the time series of the MEI and POC anomalies during El Niño to determine the phase shift time. The results of cross-correlation analysis indicated that changes of POC were delayed from the onset of El Niño by 4 months. Furthermore, a detailed analysis was presented based on a range of in situ observations and satellite-derived datasets available during the 2009–2010 El Niño. The correlation coefficients between POC and chlorophyll, as well as between POC and SST, are 0.9263 and −0.8315, respectively, and the constructed model indicated that chlorophyll and SST had a significant impact on POC. The spatial distribution of POC anomaly maximum occurred in the spring of 2010, because in the winter of 2009, the Rossby-type anomalies were reflected as upwelling Kelvin wave. However, the vertical POC anomaly maximum occurred in the spring of 2011, because the 2009–2010 El Niño experienced a fast phase transition from warm El Niño to cold La Niña, which was associated with oceanic upwelling.

Temporal and Spatial Variations in Rainfall Erosivity on Hainan Island and the Influence of the El Niño/Southern Oscillation

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies.

A Reconstruction of May–June Mean Temperature since 1775 for Conchos River Basin, Chihuahua, Mexico, Using Tree-Ring Width

Currently there are several precipitation reconstructions for northern Mexico; however, there is a lack of temperature reconstructions to understand past climate change, the impact on ecosystems and societies, etc. The central region of Chihuahua is located in a transition zone between the Sierra Madre Occidental and the Great Northern Plain, characterized by extreme temperatures and marked seasonal variability. The objectives of this study were (1) to generate a climatic association between variables from reanalysis models and the earlywood series for the center of Chihuahua, (2) to generate a reconstruction of mean temperature, (3) to determine extreme events, and (4) to identify the influence of ocean–atmosphere phenomena. Chronologies were downloaded from the International Tree-Ring Data Bank and climate information from the NLDAS-2 and ClimateNA reanalysis models. The response function was performed using climate models and regional dendrochronological series. A reconstruction of mean temperature was generated, and extreme periods were identified. The representativeness of the reconstruction was evaluated through spatial correlation, and low-frequency events were determined through multitaper spectral analysis and wavelet analysis. The influence of ocean–atmosphere phenomena on temperature reconstruction was analyzed using Pearson correlation, and the influence of ENSO was examined through wavelet coherence analysis. Highly significant correlations were found for maximum, minimum, and mean temperature, as well as for precipitation and relative humidity, before and after the growth year. However, the seasonal period with the highest correlation was found from May to June for mean temperature, which was used to generate the reconstruction from 1775 to 2022. The most extreme periods were 1775, 1801, 1805, 1860, 1892–1894, 1951, 1953–1954, and 2011–2012. Spectral analysis showed significant frequencies of 56.53 and 2.09 years, and wavelet analysis from 0 to 2 years from 1970 to 1980, from 8 to 11 years from 1890 to 1910, and from 30 to 70 years from 1860 to 2022. A significant association was found with the Multivariate ENSO Index phenomenon (r = 0.40; p = 0.009) and Pacific Decadal Oscillation (r = −0.38; p = 0.000). Regarding the ENSO phenomenon, an antiphase association of r = −0.34; p = 0.000 was found, with significant periods of 1 to 4 years from 1770 to 1800, 1845 to 1850, and 1860 to 1900, with periods of 6 to 10 years from 1875 to 1920, and from 6 to 8 years from 1990 to 2000. This study allowed a reconstruction of mean temperature through reanalysis data, as well as a historical characterization of temperature for central Chihuahua beyond the observed records.

Teleconnection Between Coastal Phytoplankton Blooms Phenomenon in Western North Pacific and El Niño–Southern Oscillation by Time‐Frequency Analysis

AbstractPhytoplankton blooms in coastal marine environments are regard as a double‐edged sword, offering benefits to coastal ecosystems while also posing significant environmental challenges. The occurrence and severity of these blooms are influenced by environmental factors, with climate oscillations playing a crucial role. In this study, we employed two time‐frequency analysis methods to investigate the teleconnection between phytoplankton blooms (denoted by bloom intensity, BI) and El Niño–Southern Oscillation (ENSO) from 2003 to 2020 in eight large marine ecosystems (LMEs) of Western North Pacific which are particularly sensitive to the impacts of climate oscillations and reflect well the impacts on marine ecosystems at different latitudes. These analytical methods unveiled these time series exhibited similar periodicity and synchronization patterns, which indicated the teleconnection between ENSO and phytoplankton blooms in the Western North Pacific. The Hilbert‐Huang Transformation classified the eight LMEs into two categories based on their similarity to BI and MEI in the time‐frequency domain. Specifically, one category is similar to MEI with energy concentrated at low frequencies, while the other category exhibits significant dissimilarity with dispersed energy. Wavelet analysis uncovered a robust coherence between the ENSO and BI across all LMEs, with a 57‐month periodicity. Furthermore, phytoplankton blooms in the Western North Pacific Ocean strengthen with increasing Multivariate ENSO Index on the large spatiotemporal scale due to a decrease in sea surface temperatures. The findings will be useful as a reference for improving understanding of the teleconnection between coastal phytoplankton blooms and climate oscillations in other coastal zones.

Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia.

Analysis of gap filling techniques for GRACE/GRACE-FO terrestrial water storage anomalies in Canada

Terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) have become indispensable for hydrological monitoring since the first mission launched in 2002. Evaluation of basin dynamics is enabled through TWSA integration with independent hydrological and climate datasets such as precipitation, evapotranspiration, and surface runoff. With over 20 years of observations, long-term statistical analysis reveals water storage trends, provided the 11-month gap between the two missions is filled. This study compares four methods for reconstructing GRACE-like TWSA over Canada, namely: extreme gradient boosting, artificial neural networks, automated machine learning, and projection onto convex sets. The GRACE mascon product released by the Jet Propulsion Laboratory is used over the study region. Nine sets of hydrological and climate parameters are used as predictors for the machine learning models, namely GRACE average seasonal signals, TWSA from the GLDAS Catchment Land Surface Model, precipitation, air temperature, glacier surface mass balance, ocean tides, the North Atlantic Oscillation, the Multivariate ENSO Index, and sea surface temperature. For each of the four methods, 20 % of the GRACE TWSA data (the ‘testing data’) is omitted from the model training, and the model is used to fill the artificial gap. Root mean square error (RMSE) is computed using the difference between testing data and model predictions. Normalized RMSE expresses the RMSE as a proportion of the range of the GRACE TWSA data. The automated machine learning algorithm performs best in terms of mean normalized root mean square error, with 6 % (2.4 cm equivalent water height RMSE), followed by projection onto convex sets, extreme gradient boosting, and artificial neural networks. Inclusion of glacier surface mass balance models derived from the GMAO Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis and the Randolph Glacier Inventory improved the automated machine learning average root mean square error by 70 %. Results indicate that filling the gap between missions allows for more comprehensive analysis of terrestrial water storage changes, including basin-by-basin analysis, which is ongoing.

Spatial Patterns of Vegetation Activity Related to ENSO in Northern South America

AbstractInterannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the heterogeneous impacts of ENSO at regional scales. Particularly, ENSO has contrasting impacts on climate in northern South America (NSA) depending on the ENSO phase and geographical location, which in turn affect the activity of vegetation. Furthermore, changes of vegetation activity during multiple ENSO events are not well understood yet. In this study, we investigated the spatial and temporal differences in vegetation activity associated with ENSO variability and its three phases (El Niño, La Niña, Neutral) to identify hotspots of ENSO impacts in NSA, a region dominated by rainforest and savannas. To achieve this, we investigated time series of vegetation variables from 2001 to 2014 at moderate spatial resolution (0.0083°). Data were aggregated through dimensionality reduction analysis (i.e., Global Principal Component Analysis). The leading principal component served as a proxy of vegetation activity (VAC). We calculated the cross‐correlation between VAC and the multivariate ENSO index separately for each ENSO phase. Our results show that El Niño phase has a stronger impact on vegetation activity both in intensity and duration than La Niña phase. Moreover, seasonally dry ecoregions were more susceptible to El Niño impacts on vegetation activity. Understanding these differences is key for regional adaptation and differentiated management of ecosystems.

Long-term Hydrometeorological Time-series Analysis over the Central Highland of West Papua

This article presents an innovative data-driven approach for examining long-term temporal rainfall patterns in the central highlands of West Papua, Indonesia. We utilized wavelet transforms to identify signs of a negative temporal correlation between the El Niño-Southern Oscillation (ENSO) and the 12-month Standardized Precipitation Index (SPI-12). Based on this cause-and-effect relationship, we employed dynamic causality modeling using the Nonlinear Autoregressive with Exogenous input (NARX) model to predict SPI-12. The Multivariate ENSO Index (MEI) was used as an attribute variable in this predictive framework. Consequently, this dynamic neural network model effectively captured common patterns within the SPI-12 time series. The implications of this study are significant for advancing data-driven precipitation models in regions characterized by intricate topography within the Indonesian Maritime Continent (IMC).

Variability, cycles, and trends of mean air temperature north of Colombia

Climate variability is of global interest due to its socioeconomic and environmental effects on the world’s population. In Colombia, temperature changes affect food security, especially for the most vulnerable people in the Caribbean region. We analyzed monthly air temperature in northeastern Colombia (Cesar, La Guajira, and Magdalena departments). We reconstructed time series with missing data using nonlinear principal component analysis. Subsequently, temporal variability, associations with events of climatic variability, and temporal trends were evaluated. Periodicity analyses indicate the dominance of annual variability, although statistically significant associations with periods between 3 and 7 years show the influence of El Niño-Southern Oscillation (ENSO) events. The Spearman correlation coefficient with N = 360 and 95% significance shows a better association with the Multivariate ENSO Index (rsp mean = 0.38) and the Southern Oscillation Index (rsp mean = –0.32). The multi-year monthly analysis shows positive trends, with maximum values between March (1.04 ºC month–1), and June (1.07 ºC month–1) in the valley of the Cesar department, and a minimum in March, at the northernmost La Guajira (0.2 ºC month–1).

Tweedie models for Malaysia rainfall simulations with seasonal variabilities

Abstract This study aims to evaluate the suitability of the Tweedie generalised linear model for characterising monthly rainfall patterns across 18 meteorological stations in Peninsular Malaysia. It incorporates harmonic functions consisting of sine and cosine functions as seasonal predictors and El Niño Southern Oscillation (ENSO) indices as climatic predictors. Results indicate that three harmonic functions are essential to accurately portray rainfall dynamics in the southwestern and northwestern regions, while two suffice for the inland and western regions. However, incorporating four harmonic functions is the most optimal representation of the eastern region. An additional 1-month lag in ENSO indices is introduced to the optimal seasonal predictor model. Based on the findings, the southern oscillation index notably impacts monthly rainfall significantly in eastern and inland areas, while meteorological stations in the western and northwestern areas fit better with the multivariate ENSO index. Strikingly, no substantial impact of climate predictors is observed on the monthly rainfall within the southwestern region. Thus, the influence of climate indices is very much influenced by the geographical locations of the regions. Importantly, generating simulated data through the Tweedie model contributes to a more accurate representation of the statistical properties inherent in rainfall analysis.

ENSO Forcing of Rainfall Erosivity in the Wuding River Basin

Rainfall erosivity (RE) is an important indicator of the ability of rainfall to cause soil erosion and is linked to ENSO through the transport of rainfall. Accurate assessment of RE and improved understanding of RE are essential for soil erosion prediction, optimization of soil and water conservation measures, and ecological management and restoration. Therefore, the Wuding River Basin, a typical ecologically fragile area, is selected as the research area. The erosivity model based on daily rainfall was first used to calculate RE, and the temporal and periodic characteristics of RE are studied. Then the effects of the Southern Oscillation Index (SOI), the Surface Temperature of the Central East Equatorial Pacific Ocean (SST), and the Multivariate ENSO Index (MEI) on RE are explored by using cross wavelet technique. Results indicated that: (1) the concentration of rainfall leads to the largest RE values in summer compared with other seasons, accumulating about 69% of the annual RE; (2) The overall trend of increasing RE in the Wuding River basin is not significant, but shows an abrupt change in 2015; (3) the influence of the SOI and SST indices of ENSO events on RE is significant, which is shown to be a statistically significant correlation (95% confidence level), indicating that ENSO has a strong influence on the changing pattern of RE. These findings are helpful in predicting soil erosion and are significant for developing further erosion control measures.

Impacts of large-scale circulation patterns on the temperature extremes in the cold regions of China with global warming

The cold regions of China (CRC) are important and vulnerable freshwater recharge areas on land, and any changes in them are related to the survival of millions of people in East Asia. However, for nearly half a century, in cold regions, the extreme temperature response to global warming is still poorly understood. In this study, we systematically studied the temperature extreme changes in cold regions of China since 1961 and discussed the possible circulation factors in detail. The results showed that 1) the warming magnitudes in cold nights and warm nights are greater than those in cold days and warm days, and decreases in cold nights and cold days and increases in warm days and warm nights appeared in almost all of cold regions of China. Most of the temperature indices displayed the largest magnitudes of warming in winter. 2) Spatially, for most of the temperature extremes, the stations located at Qinghai–Tibet Plateau (TPC) and Northwest China (NWC) showed a larger warming trend than that shown by the station at Northeast China (NEC). 3) The responses of temperature extremes at different cold regions to each circulation index are variable. Atlantic Multidecadal Oscillation (AMO) has a significant relationship with almost all the indices in cold regions of China. Almost all the temperature extremes of TPC and NWC showed closely relationship with the North Atlantic Oscillation (NAO), especially for diurnal temperature range (DTR), daily maximum temperature, and the cold extremes. Multivariate ENSO Index (MEI) is significantly related to most the temperature indices of Northwest China and Northeast China. However, MEI has a significant impact on only TPC’s diurnal temperature range and warmest night (TNx). 4) Atlantic Multidecadal Oscillation displayed significant relationships with most the temperature extremes in every season in cold regions of China. However, the summer and winter MEI and the summer and winter North Atlantic Oscillation showed significant impacts on only diurnal temperature range, daily minimum temperatures (TNm), and TNx.

Influence of El Niño‐Southern Oscillation on bigeye and yellowfin tuna longline catch per unit effort in the equatorial Pacific

AbstractBigeye tuna (BET) and yellowfin tuna (YFT) are economically important target species of pelagic fisheries worldwide, especially for tropical Pacific nations whose economies and food sources are heavily affected by commercial and sustenance tuna fishing. The El Niño‐Southern Oscillation (ENSO) has a strong effect on the oceanographic conditions in the equatorial Pacific, including BET and YFT equatorial habitat and fishing grounds. For optimal fisheries management, the effects of environmental variability such as ENSO on the stocks and on the performance of fisheries must be known and predictable. However, besides some model predictions, the effects of ENSO on these two tuna species are not well understood. In this study, I investigate the statistical relationships between past ENSO conditions and equatorial fisheries using the Multivariate ENSO Index, sea surface temperature (SST), and catch and effort records from the longline fisheries in the region. Results of this study indicate that El Niño events have both delayed and concurrent positive effects on BET and YFT catch per unit effort (CPUE). The delayed positive ENSO effect on CPUE is hypothesized to be the result of enhanced recruitment acting via different mechanisms in the west than in the east. The concurrent positive effects on CPUE could be due to catchability, abundance, and/or vertical distribution of BET and YFT relative to fishing gear and require further investigation. Further exploration of the mechanisms that may underlie the results presented here could lead to predictability of CPUE of these two tuna species.

Temporal oceanographic variation using satellite imagery data in the central Mexican Pacific convergence zone

The oceanographic variation of the central Mexican Pacific (CMP) is mainly driven by the California Current and the Mexican Coastal Current, which generate a convergence zone. Little information is available regarding changes in oceanographic variables at the mesoscale level in the CMP. Therefore, this study characterized 6 oceanographic variables (sea surface temperature, chlorophyll concentration [Chl], primary productivity [PP], diffuse attenuation coefficient [K490], and particulate inorganic/organic carbon concentration [PIC, POC]) in the CMP from 2010 to 2017 and their relationships with El Niño/Southern Oscillation (ENSO). The variables were standardized to monthly pixel values of 0.08 latitude degrees, and the study area covered 48,846.48 km2. Friedman tests were used to compare the temporal variation in the variables, while Spearman correlations were used to evaluate the relationship between each variable and the Multivariate ENSO Index (MEI). A cross-correlation analysis was performed to determine the temporal lag between the oceanographic variables and the MEI. The cyclicity of the variation in the CMP was determined by spectral analysis. All variables showed significant differences between months and years. Two seasons defined by temperature were also detected: a cold season (December–June), in which high values of these variables were observed, and a warm season (July–November), in which low values of these variables were observed. No variables were correlated with the MEI; however, a 4-month time lag was identified between the variables and the MEI. The cyclicity of the variables corresponded to the cold and warm seasons. The cold phase of ENSO increased the values of PP, Chl, and K490 up to 4 times compared to those of other years. Taken together, the observed variation in oceanographic conditions makes the CMP one of the most dynamic coastal regions of the Mexican Pacific.

Unravelling the influence of teleconnection patterns on monsoon extreme precipitation indices over the Sikkim Himalayas and West Bengal

The influence of major climate indices of tropical seas and equatorial oceans on the variability of monsoon (June to September) extreme precipitation indices (EPIs) is explored for observed (1961–2017) period over two meteorological subdivisions of India. The strength and span of covariate relations for each EPI and the Indian Ocean Dipole (IOD), the Multivariate El Niño-Southern Oscillation Index (MEI), the Pacific Decadal Oscillation (PDO), the Southern Oscillation Index (SOI), and sea surface temperature (SST) anomaly of the Bay of Bengal (SST-BoB) as well as the Arabian Sea (SST-AS) are evaluated by linear and nonlinear functions. The periodicities and coherent patterns of each EPI and the climate indices are investigated in time–frequency space using wavelet power spectra. This is to identify the periodic behaviour, strength of nonlinear linkages and major climatic factors involved in the variability of monsoon precipitation extremes. EPIs show significant negative association for MEI and SST-AS. Consecutive dry days (CDD) develop significant positive (negative) correlation with MEI (SST-BoB) over Bihar, GWB, Orissa, Jharkhand and Sub-Himalayan West Bengal (SWB). The duration, intensity and absolute indices over the Sikkim (SIK) Himalayas exhibit significant negative correlation with the SST-BoB. The EPIs develop significant positive relation with PDO phases across the regions. EPIs show asymmetric correlation pattern for SST-BoB and SST-AS with the changes in latitudes and altitudes. Interannual periodic bands from low to medium frequency regions are observed in EPIs. CDD shows in-phase coherency with MEI, and a complex periodic oscillation with SST-BoB. Consecutive wet days (CWD) develop strong consistent decadal coherency with IOD. Monsoon total precipitation (PRCPTOT) is dominantly influenced by MEI and SST-BoB. Heavy precipitation events show strong response to the MEI and SST-AS over GWB. Absolute indices show regional variability in power spectra with intermittent breaks. For absolute indices, major controlling factors considerably vary at both subdivisions and timescales. This study reveals corresponding changes in active- and break-spells, drought and flood events, regional availability of water resources and its possible consequences.

Spatiotemporal variation of daily precipitation concentration and its potential relationship with climatic factors and land use types in the Haihe River basin, China

AbstractPrecipitation concentration is a key climatic factor in the hydrologic system. The purpose of this study was to explore the spatiotemporal variation in precipitation concentration and its possible relationship with large‐scale atmospheric circulation and land use types. Based on daily precipitation recorded at 254 stations during 1961–2019 throughout the Haihe River basin (HRB), linear slope and Manner–Kendall trend analysis were used to analyse the spatial variations and trends of the annual daily precipitation concentration index (CI). The Pearson correlation coefficient and cross wavelet analysis were used to evaluate potential correlations between CI and eight climatic factors. Meteorological stations were classified into three land use types: urban type (UT), farmland type (FT), and natural type (NT) using hierarchical clustering, and the impacts of land use types on CI variations and trends were investigated. Annual CI in HRB showed a significant downward trend with a linear rate of −0.0058/decade, and about 210 of the 254 stations had a downtrend, in which 42 stations were at the significance level of 0.05 but did not reach the significance level of 0.01, and 52 stations are at the 0.01 significance level. The East Asian monsoon index (EASMI), South Asian monsoon index (SASMI), South China Sea monsoon index (SCSMI), and Southern Oscillation Index (SOI) were positively correlated with CI, whereas the Pacific Decadal Oscillation (PDO), Multivariate ENSO index (MEI), and Western Pacific index (WP) were negatively correlated. The Sunspot index (SS) had a significant resonance period of 9–14‐year with CI. EASMI and ENSO events were the dominant factors driving CI trends. The negative CI trends of UT were more significant than those of FT and NT, with linear slope gaps of −0.0041/decade (UT versus FT) and −0.0064/decade (UT vs. NT), respectively.

Responses of GNSS ZTD Variations to ENSO Events and Prediction Model Based on FFT-LSTME

The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO events effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time-frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (-1.12,1.92) for the tropical monsoon zone, (-1.12,1.61) for the subtropical monsoon zone, (-1.19,1.62) for the temperate monsoon zone, (-1.26,1.64) for the temperate continental zone, and (-1.22,1.72) for the mountain plateau zone. The ENSO event causes the amplitude of the 9-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed with integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.

Fire Dynamics in an Emerging Deforestation Frontier in Southwestern Amazonia, Brazil

Land management and deforestation in tropical regions cause wildfires and forest degradation, leading to a loss of ecosystem services and global climate regulation. The objective of the study was to provide a comprehensive assessment of the spatial extent and patterns of burned areas in a new deforestation frontier in the Amazonas state. The methodology applied cross-referenced burned area data from 2003 to 2019 with climate, land cover, private properties and Protected Areas information and performed a series of statistical tests. The influence of the Multivariate ENSO Index (MEI) contributed to a decreasing rainfall anomalies trend and increasing temperature anomalies trend. This process intensified the dry season and increased the extent of annual natural vegetation affected by fires, reaching a peak of 681 km2 in 2019. The results showed that the increased deforestation trend occurred mostly in public lands, mainly after the new forest code, leading to an increase in fires from 66 to 84% in 2019. The methods developed here could identify fire extent, trends, and relationship with land cover change and climate, thus pointing to priority areas for preservation. The conclusion presented that policy decisions affecting the Amazon Forest must include estimates of fire risk and impact under current and projected future climates.