Effects of El Niño on spring phenology of the highest mountain in north-east Asia

Normalized difference vegetation index (NDVI) data on the highest mountain in north-east Asia were analysed to understand their temporal variability and response to large-scale El Niño–Southern Oscillation (ENSO) events. We demonstrated that El Niño events played an important role in determining the phenology conditions in the Mt Baekdu area in north-east Asia. The analysis confirmed that the onset of phenological spring was earlier during ENSO years. This was evident from a negative trend of about??16 days for each increase of 1 in the ENSO index in year-to-year variations in spring timing and those in ENSO magnitudes. Over two decades, the phenological phases were negatively correlated with air temperature variations under atmospheric warming at the mountain. However, such changes in NDVI are not likely to be affected by changes in local precipitation, as inferred from the analysis of forest types in this area. On the basis of NDVI changes during ENSO years, the results of this study emphasized the importance of the elevation effect and forest types on the ecological response. Moreover, we addressed a significant remote connection between local phenology at the highest mountain in north-east Asia and large-scale atmospheric and oceanic phenomena.     

Strong 2015–2016 El Niño and implication to global ecosystems from space data

During 2015, sea surface temperature (SST) in the central tropical Pacific (TP) was warmer than normal, what indicated about the potential for the development of El Niño Southern Oscillation (ENSO). By December 2015, El Niño intensified when SST anomaly in the Niño-3.4 tropical Pacific area reached +2.9 °C, which indicated about the strongest event of the past 36 years. El Niño normally impacts weather, ecosystems, and socioeconomics (agriculture, fisheries, energy, human health, water resource etc.) on all continents. However, the current El Niño is much stronger than the recent strong 1997–1998 event. Therefore, this paper investigates how the strength of El Niño impacts world ecosystems and which areas are affected. The vegetation health (VH) method and 36-year of its data have been used as the criteria of the impact. Specifically, the paper investigates VH-ENSO teleconnection, focusing on estimation of vegetation response to El Niño intensity and transition of the impact from boreal winter to spring and summer. Two types of ecosystem response were identified. In boreal winter, ecosystems of northern South America, southern Africa, eastern Australia, and Southeast Asia experienced strong vegetation stress, which will negatively affect agriculture, energy, and water resources. In Argentina, southeastern USA and the Horn of Africa ecosystem response is opposite. One of the worst disasters associated with ENSO is drought. The advantages of this study are in derivation of vegetation response to moisture, thermal, and combined conditions including an early detection of drought-related stress. For the first time, ENSO impact was evaluated based on all events with |SSTa|> 0.5 ºC and >2.0 ºC. The current strong El Niño has already triggered drought in Brazil, southern Africa, southeastern Asia, and eastern Australia during December–February. Such conditions will be transitioned from boreal winter to spring but not to summer 2016, except for two regions: northern Brazil and southeastern Asia.     

Remote sensing of the Indo-Pacific region: ocean colour,sea level,winds and sea surface temperatures

The 1997–1998 ENSO (El Niño-Southern Oscillation) was not only the largest event of the century but also the most comprehensively observed. Satellite data were employed for ocean colour, sea level, winds, sea surface temperature (SST), and outgoing longwave radiation (OLR) were used to describe the response of the surface marine ecosystem associated with the ENSO event. Some of the large-scale anomalies in ocean colour include elevated biological activity to the north of the Equator in the Pacific coincident with lower sea levels associated with the classic ENSO-horseshoe pattern ecosystem response to the anomalous upwelling in the eastern Indian Ocean caused by the 1997–1998 dipole event, and the dramatic eastward propagating feature in the Equatorial Pacific in response to the La Niña dynamics. Ocean general circulation model (OGCM) experiments show that capturing the high-frequency wind changes is crucial for simulating the La Niña and the coupled biological–physical model (OBGCM) runs clearly show that higher frequency winds are also important for capturing the mean upwelling and nutrient supply into the euphotic zone. Thus, the QuickSCAT winds are expected to play a major role in ecosystem modelling in the future. This study shows the utility of satellite data for understanding not only ocean circulation but also the coupled ecosystem variability. Morcover, it is also shown that spatio-temporal resolution of the satellite winds will directly affect the accuracy of oceanic and ecosystem simulations.     

Galapagos indicator of El Niño using monthly SST from NASA Giovanni system

One geographical point could be sufficient to track El Niño Southern Oscillation (ENSO). This paper describes an approach to such tracking based on environmental modeling of sea surface temperature (SST). The key model of the approach is the most anomalous indicator of SST as a point near Isabella Island, Galapagos. This point can be computed using monthly SST data from NASA Giovanni system. Comparison with most popular indices demonstrates that such Galapagos indicator can clear indicate both El Niño and La Niña events.     

Calculation of the sensible heat flux of the global ocean using satellite data

The sensible heat flux of the global ocean is derived using satellite data of Special Sensor Microwave/Imager (SSM/I) precipitable water, Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) and scatterometer wind speed. Prior to heat flux derivation, the air temperature over the sea surface in the global ocean is obtained with an iterative solving technique applied to a simplified equation that specifies the relationship among boundary-layer parameters. It is found that a bias exists between the calculated air temperature and the climatology data, which is corrected by a linear model based on the climatology of air temperature. Using the corrected air temperature and the bulk formula, we calculate the sensible heat flux from January 1992 to October 1998. The heat flux calculation is consistent with previous results. An error analysis suggests that based on the bulk formula, the uncertainty caused by the air temperature is comparable with the error components from the SST and wind. Empirical orthogonal function (EOF) analysis is used to extract the temporal and spatial characteristics of the calculated sensible heat flux. The first EOF is characterized by a winter and summer oscillation with an annual cycle, the second shows another annual cycle in spring and autumn oscillation, the third EOF is related to the El Niño and La Niña cycle, reflecting a certain relationship between El Niño Southern Oscillation (ENSO) events and the global ocean sensible heat flux, and the fourth EOF indicates an increasing trend with a quasi-biennial oscillation and atmospheric influences.     

Remote sensing of US cornbelt areas sensitive to the El Ni@o-Southern Oscillation

A 16-year time-series (1982-1997) of monthly maximum Normalized Difference Vegetation Index (NDVI) values derived from coarse-scale Advanced Very High Resolution Radiometer (AVHRR) satellite images was investigated for its application in identifying agricultural regions sensitive to El Ni@o-Southern Oscillation (ENSO) impacts. Cumulative NDVI values over the growing season were used to test the temporal and spatial sensitivity of rainfed agricultural regions in the US cornbelt to ENSO climate teleconnections. The correlation (R) between NDVI anomalies and yield anomalies aggregated to the Agricultural Statistics Division (ASD) level was 0.59 (significant at the 99% level). Sea surface temperatures (SSTs) from the NINO3 region (between +5.0° and m 5.0° latitude and 90.0° and 180.0° west longitude in the Pacific Ocean), an indicator of the ENSO phenomenon, were significantly but weakly correlated with growing season NDVI anomalies, precipitation anomalies, and yield anomalies. Two classification schemes for El Niño, La Niña and Neutral years related to the crop growing season were applied--one based on simultaneous Pacific NINO3 SSTs (spring-summer), and one based on following winter Pacific NINO3 SSTs. The strongest differences in Vegetation Condition Index (VCI) spatial patterns among the three ENSO categories were found using the following winter SST classification. Classification of ENSO years is a key issue in analysing ENSO impacts on agriculture as represented by the NDVI, because the regularity of annual agricultural seasons is not synchronous with the quasi-regular Pacific SST cycles.     

Sea surface temperature simulation and forecast

Unconventional computing of sea surface temperature (SST) was once featured by NASA as a unique merger of science and art. Our approach led to a discovery that just one geographical point could be sufficient to track global anomalies of SST based on El Niño Southern Oscillation (ENSO). Such single point in the Pacific Ocean off of the island of Isabella in the Galapagos Islands was named the Galapagos indicator. Now we show that a single point in the Baltic Sea off of the coast of Göteborg could be also sufficient to track ENSO. We propose to name it the Baltic indicator. We also demonstrate that two crisis falls of oil price in 2008 and 2014 followed just after the local maximums of Baltic indicator. However, Baltic and Galapagos indicators do not show any evident trend in settling the global warming from the beginning of this century.     

Spatio-temporal variability of surface chlorophyll-a in the Halmahera Sea and its relation to ENSO and the Indian Ocean Dipole

ABSTRACT

Long-term satellite data are used to investigate the variability of ocean surface chlorophyll-a (chl-a) concentration in the Halmahera Sea (HS) under influence of the Australian-Indonesian Monsoon (AIM), the El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). In this study, we first analysed the seasonal variability of chl-a, and then examine the relationship between surface chl-a, sea surface temperature (SST), and sea surface wind stress in the area. Our results suggest that prevailing southeasterly winds play a fundamental role in generating chl-a blooms in the HS. Particularly on a seasonal timescale, through the mechanism of Ekman mass transport, strengthening of southeasterly wind stress during the Southeast Monsoon season (June – August) produces enhanced chl-a concentrations associated with ocean surface cooling in the area of study. On the other hand, the chl-a bloom completely diminishes during the Northwest Monsoon season (December – February) due to weakening of wind stress and Ekman transport. On an interannual timescale, sea level pressure and wind stress are coherent with ENSO and IOD phases. During El Niño and positive IOD events (La Niña and negative IOD events), both sea level pressure and wind stress greatly increase (decrease) over the HS. These conditions cause an anomaly in southerly (northerly) wind stress, which is favourable to an enhancement (reduction) of the chl-a concentration in the region. This study demonstrates that sea level pressure and wind stress are the critical factors in determining the magnitude of chl-a bloom in the HS.     

Climatic oscillations in aerosol optical depth over tropical oceanic regions pertaining to genesis of the Indian Ocean Dipole

Data on aerosol optical depth (AOD) derived from the ocean colour sensor of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from September 1997 to December 2010 over the western tropical Indian Ocean (WTIO) (10° S to 10° N; 50° E to 70° E) and southeastern tropical Indian Ocean (SETIO) (10° S to equator; 90° E to 110° E) were analysed with a view to understanding its response to climatic oscillations in regard to the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study demonstrates the existence of a bimodal distribution pattern of AOD in the atmosphere over both WTIO and SETIO, with the highest values being around 1.1 during the period of primary maximum during August over the WTIO and during October over the SETIO. A secondary maximum (～0.9) appeared during March over both areas. In addition, the existence of a see-saw oscillation in the distribution of AOD between the atmospheric columns over the study regions was revealed, with higher values during August–December over the SETIO. AOD data over the SETIO captured very well the influence of these atmospheric modes, whereas the influence was not as significant over the WTIO. Stronger El Niño (Niño index > 0.80) events produced a significantly positive (more than +0.03) anomaly in AOD values over the SETIO during October, whereas the lone mode of IOD events and La Niña were not sufficient to induce any significant change in the aerosol distribution over the area. The mode of El Niño co-occurring with a positive IOD (PIOD) strengthens this anomalous behaviour. A significantly negative anomaly (≤0.03) in AOD was observed with concurrent La Niña (Niño index < ?1.1) and negative IOD (NIOD) (dipole mode index ≤ 1.1) events. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) winds were utilized to verify these observations.     

Altimeter observations of the MJO/ENSO connection through Kelvin waves

Altimeter‐derived Pacific Ocean sea surface height anomaly (SSHA) data are used to confirm the relationship between the Madden Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO), through Kelvin waves, previously identified in sea surface temperature (SST) data. The altimeter data, filtered for Kelvin waves, have been cross‐correlated with MJO indices, as an alternative way of exploring and quantifying the possible connection between the MJO and ENSO. The results of the cross‐correlations of the SSHA filtered data and the MJO indices generally show higher values in the periods preceding the three ENSO warm events and lower values during, and for a short time after, these events. The significance of this correlation pattern appears to link the MJO to the ENSO warm events, through Kelvin waves. Whether this can be used for predictive purposes needs to be confirmed by further studies.     

Forecasting ENSO with a smooth transition autoregressive model

This study examines the benefits of nonlinear time series modelling to improve forecast accuracy of the El Niño Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially smooth regime-dependent dynamics of the sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, which results in the superior out-of-sample forecast performance of STAR over the linear autoregressive models. The advantage of nonlinear models is especially apparent in short- and intermediate-term forecasts. These results are of interest to researchers and policy makers in the fields of climate dynamics, agricultural production, and environmental management.     

Seasonal evolution of the interannual variability of chlorophyll-a concentration and its forcing factors in the northwestern Pacific from 1998 to 2010

The northwestern Pacific Ocean is a complex region with significant biological spatial variations on a seasonal timescale. To investigate the joint variation patterns on both seasonal and interannual timescales, a season-reliant empirical orthogonal function (S-EOF) analysis was applied to seasonal mean chlorophyll-a concentration (chl-a) anomalies in the northwestern Pacific Ocean during the period 1998–2010. The first two dominant modes accounted for nearly 31% of the total interannual variance, with the second S-EOF mode (S-EOF2) lagging behind the first S-EOF mode (S-EOF1) by one year. S-EOF1 featured a strong variation pattern to the north of 30° N, with maximum chl-a in winter and minimum chl-a in summer. However, S-EOF2 indicated an opposite seasonally evolving pattern compared with S-EOF1, with chl-a increasing along the Kuroshio and extension current from boreal winter to autumn. Both these modes revealed significant relationships with climate-related indices. The two modes corresponded to the central Pacific (CP) La Niña developing episodes and the turnaround from eastern Pacific (EP) La Niña to CP El Niño, respectively. Both modes were associated with the cold phase of the Pacific Decadal Oscillation, which played an important role in prolonging the impact of the El Niño/Southern Oscillation on chl-a seasonal evolution from 1998 to 2010. In addition, we discuss the possible factors dominating chl-a seasonal variation, in terms of the subregions of the northwestern Pacific Ocean. In the subtropical northwestern Pacific Ocean (15° N – 30° N), the chl-a growth was primarily nutrient-limited, whereas in the mid-latitude northwestern Pacific Ocean (35° N – 50° N), the chl-a growth was mainly light-limited.     

Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests

Interannual variations in terrestrial carbon cycle over tropical rainforests affect the global carbon cycle. Terrestrial ecosystem models show the interannual relationship between climate changes due to El Niño‐Southern Oscillation (ENSO) and net primary production over tropical rainforests. However, we need an independent analysis using satellite‐based vegetation index and climate parameters. In the present study, we extracted the ENSO‐related interannual variations from time‐series in Normalized Difference Vegetation Index (NDVI) and climate data from 1981 to 2000, and analysed their relevance. We detected relationships among NDVI, ENSO, and climate parameters from long‐term data with negative NDVI–ENSO, NDVI–temperature, and positive NDVI–precipitation relations. These correlations suggest that interannual variability in vegetation activities over tropical rainforests could be extracted from NDVI time‐series despite noise components in NDVI data, and that interannual changes in precipitation and temperature caused by ENSO play a more important role in vegetation activities over tropical rainforests than in incoming surface solar radiation.     

The relationship between tibetan snow depth,ENSO, river discharge and the monsoons of Bangladesh

Using satellite estimates of snow depth, we examine the interannual variability of the monsoon rains of Bangladesh, an area greatly affected by land surface hydrological processes including Himalayan snowpack size, snowmelt river flooding, and Bay of Bengal storm surge. For the twentieth century, we found Bangladesh monsoon rainfall (BMR) to be uncorrelated with the All‐Indian Monsoon Index. This result is consistent with previous findings for shorter time records. We next used a short 9‐year record of satellite estimates of April snow depth for the Himalayan region and concurrent seasonal El Niño–Southern Oscillation (ENSO) conditions in the equatorial Pacific to develop an empirical model that explains a high percentage of BMR interannual variability. Inclusion of late spring river discharge levels further improves the empirical model representation of BMR for June–September. These results, though with a limited length satellite record, suggest that BMR interannual variability is constrained by concurrent ENSO conditions, spring Himalayan snowpack size and land surface flooding. The same results could not be obtained from analyses using satellite estimates of snow cover. These findings stress the need for development of a quality longer record of satellite estimated snow depth. The twentieth‐century analysis also indicates that BMR should be considered independently of Indian monsoon rainfall.     

Interannual variability of convective activity over the tropical Indian Ocean during the El Niño/La Niña events

Convection over the tropical Indian Ocean is important to the global and regional climate. This study presents the monthly climatology of convection, inferred from the outgoing longwave radiation (OLR), over the tropical Indian Ocean. We also examine the impact of El Niño/La Niña events on the convection pattern and how variations in convection over the domain influence the spatial rainfall distribution over India. We used 35 recent years (1974–2008) of satellite-derived OLR over the area, the occurrence of El Niño/La Niña events and high resolution grid point rainfall data over India. The most prominent feature of the annual cycle of OLR over the domain is the movements of convection from south-east to north and north-west during the winter to the summer monsoon season. This feature represents the movement of the inter-tropical convergence zone (ITCZ). The climatology of OLR during the winter months (December–February) over the domain is characterized by high subsidence over central India with a decrease of OLR values towards the north and south. Moderate convection is also seen over the Himalayan Range and the south-east Indian Ocean. In contrast, during the summer (June–September) the OLR pattern indicates deep convection along the monsoon trough and over central India, with subsidence over the extreme north-west desert region. The annual march of convection over the Arabian Sea and Bay of Bengal sector shows that the Arabian Sea has a limited role, compared to the Bay of Bengal, in the annual cycle of the convection over the tropical Indian Ocean. The composite OLR anomalies for the El Niño cases during the summer monsoon season show suppressed convection over all of India and moderate convection over the central equatorial Indian Ocean and over the northern part of the Bay of Bengal. Meanwhile in La Niña events the OLR pattern is nearly opposite to the El Niño case, with deep convection over entire Indian region and adjoining seas and subsidence over the northern Bay of Bengal and extreme north-west region. The spatial variability of the 1°?×?1° summer monsoon rainfall data over India is also examined during El Niño/La Niña events. The results show that rainfall of the summer monsoon season over the southern peninsular of India and some parts of central India are badly affected during El Niño cases, while the region lying along the monsoon trough and the west coast of India have received good amounts of rainfall. This spatial seasonal summer monsoon rainfall distribution pattern seems to average out the influence of El Niño events on total summer monsoon rainfall over India. It seems that, in El Niño events, the convection pattern over the Bay of Bengal remains unaffected during summer monsoon months and thus this region plays an important role in giving good summer monsoon rainfall over the northern part of India, which dilutes the influence of El Niño on seasonal scale summer monsoon rainfall over India. These results are also confirmed by using a monthly bias-corrected OLR dataset.     

Association between ENSO and extremes in total ozone content over northern India

Using daily station total ozone column (TOC) data from the Total Ozone Mapping Spectrometer (TOMS) onboard the Nimbus-7 satellite, an association between the El Niño–Southern Oscillation (ENSO) and extremes in TOC content has been revealed during the period 1979–1993 over northern India in the winter season. From lag-simultaneous correlations of extremes in ozone with Niño 3.4 sea surface temperatures (SSTs), it is seen that, during this season, the highest TOC values show a strong positive relationship at the beginning of the preceding year with the occurrence of the highest values for all the stations. A weak relationship is observed up to the month of July and its sign is then reversed. The negative but weak relationship continues until the occurrence of the event, becoming positive again afterwards. On the contrary, the occurrence of the lowest values shows opposite features. The analysis indicates that the increase in SSTs during the first half of the preceding year is favourable for an increase in the highest values occurring over different stations while the increase in SSTs during the latter half of the preceding year is favourable for an increase in the lowest values of ozone. The lag-simultaneous correlations of the low/high ozone days and the mean TOC values occurring during the winter season also suggest a significant positive relationship for the frequency of the high ozone days at the beginning of the preceding year, becoming weaker as time progresses. Although both features show that the relationship is statistically significant for only a few months of the preceding year, it gives a broad indication of the association between ENSO and the extremes in the TOC amount in addition to local/geographical factors.     

ENSO drought onset prediction in northeast Brazil using NDVI

El Niño Southern Oscillation (ENSO) indices and satellite-recorded Normalized Difference Vegetation Index (NDVI) were used to construct a drought onset prediction model for northeast Brazil (NEB) using a multiple linear regression technique. Monthly NDVI and ENSO indices anomaly data for the period January 1981 to December 1993 were used to develop the model, while those of 1951 to 1998 were used to simulate the NDVI anomaly time series for model validation. Three different regression models were constructed using the NDVI anomaly as dependent variable and various ENSO indices anomalies including: Sea Surface Temperature in the Pacific Ocean area (5°N-5°S and 120°W-170°W, called Niño3.4), Southern Oscillation Index (SOI), North Atlantic Sea Surface Temperature (NATL), South Atlantic Sea Surface Temperature (SATL) and Dipole 2 (DIP2=SATL-NATL), as independent variables. Model 1 was constructed using 12-month NDVI data while Models 2 and 3 used data from only four months (September to December). The results showed that R 2 values of 0.38, 0.62 and 0.79 at a significance level of 1% were obtained for Model 1, Model 2 and Model 3 respectively. Simulated NDVI anomaly values agreed quite well with observed values for all three models but Model 3 had a better intensity estimate. The simulated dynamic evolution of the NDVI anomaly of 1951 to 1998 showed that the predicted NDVI anomalies coincided with historical ENSO induced drought events reported in the literature. It is concluded that the use of satellite-recorded NDVI instead of rainfall data improved the correlation with ENSO indices. Drought onset Model 3, based on the dataset with high anomaly values of NDVI and ENSO indices, predicted drought onset in NEB four months before its occurrence with reasonable success (68%). Combined use of ENSO indices and NDVI inferred drought may provide a better alternative to the construction of an ENSO drought onset prediction model for other regions. Further studies will be carried out to investigate the ENSO drought and flood onsets in the southeastern South America.     

Analysis of collocated AIRS and MODIS data: a global investigation of correlations between clouds and atmosphere in 2004–2012

We used collocated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) to investigate correlations between cloud parameters and atmospheric stability. We focus on low clouds and specifically investigate the cloud parameters cloud cover and cloud optical thickness from MODIS. The selected atmospheric parameters from AIRS are maximum relative humidity (MRH), lower tropospheric stability (LTS), and water vapour gradient (QTS). The correlations were tested for temporal and regional variation on a global scale and over a time frame of 10 years. Cloud cover and MRH show weak correlations and strong variations on both the temporal and spatial scales. However, cloud cover and lower tropospheric stability show a high correlation in areas with low maritime clouds. The correlation is relatively stable, but slightly increased for the years 2009–2012. Correlations between cloud cover and QTS show a similar behaviour, but slightly stronger variations on the spatial and temporal scales, with better correlations in the East Pacific and from 2004 to 2012. The correlations with cloud optical thickness are weaker in all three cases. A more detailed analysis of the Southeast Pacific shows the influence of El Niño Southern Oscillation (ENSO) on most parameters, but a relatively stable behaviour for the connection of cloud fraction and LTS. Based on the analysis, we suggest that relative humidity is an insufficient approach to link atmospheric properties and low cloud cover. However, we find good correlations with respect to LTS and QTS. LTS in particular indicates low temporal fluctuations, even in the case of influence by ENSO.     

Chlorophyll variability in the northeastern Gulf of Mexico

Changes in chlorophyll concentration distribution in surface waters of the northeastern Gulf of Mexico (NEGOM) were examined using satellite and in situ data collected between November 1997 and August 2000. The patterns of chlorophyll distribution derived from in situ data consistently matched the satellite observations, even though the satellite-derived concentrations in coastal and offshore waters influenced by rivers were overestimated by the standard satellite data processing algorithms. River discharge and wind-driven upwelling were the major factors influencing surface chlorophyll-a variability for inshore regions. High in situ chlorophyll-a concentrations (≥1 mg m^?3) occurred inshore and particularly near major river mouths during the summer seasons of 1998, 1999 and 2000. Plumes of Mississippi River water extended offshore to the southeast of the delta over distances >500 km from the river delta for maximum periods of 14 weeks between May and September every year and could reach the Florida Keys in certain years. The offshore transport of the plume was initiated by eastward or southeastward winds and then by separate anticyclonic eddies located southeast of the Mississippi delta and nearby shelf every year. Chlorophyll concentrations during the winter to spring transition in 1998 off Escambia, Choctawhatchee, Apalachicola and Suwannee Rivers and off Tampa Bay were up to 4 times higher than during the same periods in 1999 and 2000. This was related to higher freshwater discharge during the 1997–1998 winter–spring transition, coinciding with an El Niño–Southern Oscillation event, and to the unusually strong upwelling observed along the coast in spring 1998.     

Spatiotemporal association patterns of multiple parameters in the northwestern Pacific Ocean and their relationships with ENSO

The northwestern Pacific Ocean (NWPO) is a region sensitive to global climate change and regional sea–air interactions. A number of remote-sensing images from the past three decades were used to define sensitive marine regions, which were then applied to determine the spatiotemporal association patterns of abnormal variations in marine environmental parameters using a quantitative association rule-mining method. The NWPO object 1 (NWPO-obj1) region (130°–150° E, 2°–15° N) and NWPO object 2 (NWPO-obj2) region (170°–180° E, 0°–8° N) showed more pronounced changes than elsewhere, and the monthly anomaly of sea-surface temperature (SSTA), monthly anomaly of sea-surface chlorophyll-a (chl-a), monthly anomaly of sea-level anomaly (SLAA), and El Niño Southern Oscillation (ENSO) events were closely related to one another in these two regions. In NWPO-obj1, the relation between SLAA and chl-a yields a correlation coefficient of ?0.79 and the abnormal drop in SLAA was the principal factor controlling the chl-a bloom. In NWPO-obj2, the SSTA is anti-correlated with chl-a (correlation coefficient of ?0.83), and the abnormal increase in SSTA might be one of the main factors leading to the extinction of chl-a. Comparing the two regions, abnormal increases in chl-a and decreases in SSTA in NWPO-obj2 were indicators of abnormal increases in SLAA in NWPO-obj1 (positive and negative correlation coefficients of 0.60 and ?0.61, respectively), and the abnormal decrease of SLAA in NWPO-obj1 is correlated with the abnormal decrease of SSTA in NWPO-obj2 (correlation coefficient of 0.86), although ahead by one year. In addition, the abnormal decrease of SLAA in NWPO-obj1 was the only factor influenced by El Niño, while La Niña events had an impact on the abnormal increase of SLAA in NWPO-obj1 and the abnormal decrease of SSTA in NWPO-obj2, and also dominated their interrelationships.