Two types of El Niño and extratropical sea-level pressure variations

This study applies the nonlinear canonical correlation analysis (NLCCA) to explore the nonlinear relationship between the sea-level pressure (SLP) anomalies over the extratropical North Pacific and sea surface temperature (SST) anomalies in the tropical Pacific during 1985–2009. Our results suggest that the asymmetry between the warm eastern Pacific (EP) El Niño–Aleutian Low mode and the cool EP La Niña–anti-phase of the Aleutian Low mode is exhibited in the first NLCCA mode. Nonlinearity of the first NLCCA SST field is enhanced after 1998, and vice versa for the SLP field. The second NLCCA SST mode reveals weak nonlinearity representing the nonlinear central tropical Pacific (CP) El Niño–CP La Niña modes, while the second SLP field depicts the North Pacific Oscillation and anti-phase with the Aleutian Low phases. The nonlinearity of the second SST and SLP NLCCA modes is found to decrease gradually with time. During 1985–1997, the SST field exhibits linearity, while the SLP field shows weak nonlinearity. During 1997–2009, the SST and SLP fields both display weak linearity. Nonlinearity between the extratropical SLP and SST fields is further weakened from the first period. The Aleutian Low pattern could be excited by both EP and CP El Niños. Moreover, the CP El Niños have more connections with the North Pacific Oscillation state rather than the EP El Niños. Conclusively, this study reveals the asymmetric modes between the SLP and SST by the nonlinear method, and contributes to the understanding of the extratropical SLP variability response to two types El Niño events.     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Spatial and temporal variability of satellite-derived sea surface temperature in the Barents Sea

The Barents Sea (BS) is an important region for studying climate change. This sea is located on the main pathway of the heat transported from low to high latitudes. Since oceanic conditions in the BS may influence vast areas of the Arctic Ocean, it is important to continue to monitor this region and analyse the available oceanographic data sets. One of the important quantities that can be used to track climate change is the sea surface temperature (SST). In this study, we have analysed the 32 years, (1982–2013) National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation SST Version 2 data for the BS. Our results indicate that the regionally averaged SST trend in the BS (about 0.03°C year^–1) is greater than the global trend. This trend varies spatially with the lowest values north from 76° N and the highest values (about 0.06°C year^–1) in proximity of Svalbard and in coastal regions near the White Sea. The SST and 2 m air temperature (AT) trends are high in winter months in the open BS region located west from Novaya Zemlya. Such trends can be linked to a significant retreat of sea ice in this area in recent years. In this article, we also documented spatial patterns in the annual cycle of SST in the BS. We have shown that the interannual variability of SST is similar in different regions of the BS and well correlated with the interannual patterns in AT variability.     

Climate-driven latitudinal shift in fishing ground of jumbo flying squid (Dosidicus gigas) in the Southeast Pacific Ocean off Peru

The jumbo flying squid Dosidicus gigas is a pelagic squid species extensively distributed in the Eastern Pacific Ocean with climate-related geographical variability. An analysis was carried out to evaluate impacts of climatic and oceanographic variability on spatial distribution of D. gigas in the Southeast Pacific Ocean off Peru. Logbook data of the 2006–2013 Chinese squid-jigging fishery were used to determine latitudinal gravity centres (LATG) of fishing ground of D. gigas in relation to sea surface temperature (SST), chlorophyll-a (chl-a) concentration and sea surface height (SSH), coupled with the SST anomaly (SSTA) in the Niño 1 + 2 region. Results indicated that the SSTA in the Niño 1 + 2 region played crucial influences on SST, chl-a and SSH on the fishing ground of D. gigas. The LATG of D. gigas exhibited seasonal and interannual variability with closely associations with SST, chl-a, and SSH. Significantly positive relationships were found between monthly LATG and the average latitude of the most favourable contour lines of SST, chl-a, and SSH for D. gigas, with time lags at 0, 7, and 0 month, respectively. The spatial pattern of LATG largely responded to climate-induced oceanographic variability on the squid fishing ground: the Niño 1 + 2 SSTA became warm, the most favourable SST and SSH contour lines for D. gigas would move southward, resulting in a southward movement of the LATG; however, the Niño 1 + 2 SSTA shifted into cold episodes, the most favourable SST and SSH contour lines for D. gigas would shift northward, leading to a northward shift of the LATG. Our findings suggested that the SSTA in the Niño 1 + 2 region coupled with the most favourable contour lines of SST and SSH were the major drivers regulating the latitudinal movement of fishing ground of D. gigas in the Southeast Pacific Ocean off Peruvian waters.     

Satellite data, Empirical Orthogonal Functions, and the 1997-1998 El Niño off California

The features of the 1997-1998 El Niño event were analyzed by Empirical Orthogonal Functions (EOF) statistical methods applied to the remotely sensed sea surface temperature anomalies (SSTA) measured by AVHRR radiometers; anomalies of water circulation derived from sea surface height anomalies (SSHA) measured by TOPEX/Poseidon radar altimeter; and meteorological information (air temperature, upwelling index, and wind stress curl). EOF statistics demonstrated the features of an El Niño event during the second half of 1997 and the first half of 1998, with sea level elevated along the coast and with SSHA gradients, indicating a retarding of both the equatorward California Current and the alongshore poleward Southern California Countercurrent. The positive SST anomaly developed first in the Southern California Bight and then in the zone of upwelling to the north of Point Conception. The anomalies of upwelling index and the wind stress curl pattern also changed during the El Niño event, but these changes occurred later than hydrographic variations and were too weak to explain the observed changes in SSTA and SSHA. We conclude that off central and southern California oceanic teleconnection (i.e., the consequences of propagation northward of coastally trapped downwelling waves) was responsible for the 1997-1998 El Niño event.     

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.     

Provincial nature of chlorophyll and sea surface temperature observed by satellite

Monthly composite SeaWiFS derived chlorophyll, National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived sea surface temperature (SST) and QuikScat derived wind data of 2003–2005 (January–December) were analysed to study the provincial nature of chlorophyll‐a (chl‐a), SST and wind speed in the Arabian sea and Bay of Bengal. The study was confined to five provinces, three in the Arabian Sea and two in the Bay of Bengal. Results indicate provincial variability in the SST‐chl‐a relation. It suggests that the correlation between chlorophyll and SST is not always negative. A negative correlation was observed in provinces 1, 2 and 3 for all the seasons, whereas, except for the month of January–February, it was positively correlated for province 4. Analysis shows the provincial specific nature of chlorophyll variability to physical forcing and suggests that treatment of such a problem should not be undertaken on the basin scale.     

North American vegetation dynamics observed with multi-resolution satellite data

We investigated normalized difference vegetation index data from the NOAA series of Advanced Very High Resolution Radiometers and found regions in North America that experienced marked increases in annual photosynthetic capacity at various times from 1982 to 2005. Inspection of these anomalous areas with multi-resolution data from Landsat, Ikonos, aerial photography, and ancillary data revealed a range of causes for the NDVI increases: climatic influences; severe drought and subsequent recovery; irrigated agriculture expansion; insect outbreaks followed by logging and subsequent regeneration; and forest fires with subsequent regeneration. Vegetation in areas in the high Northern Latitudes appear to be solely impacted by climatic influences. In other areas examined, the impact of anthropogenic effects is more direct. The pattern of NDVI anomalies over longer time periods appear to be driven by long-term climate change but most appear to be associated with climate variability on decadal and shorter time scales along with direct anthropogenic land cover conversions. The local variability of drivers of change demonstrates the difficulty in interpreting changes in NDVI and indicates the complex nature of changes in the carbon cycle within North America. Coarse scale analysis of changes could well fail to identify the important local scale drivers controlling the carbon cycle and to identify the relative roles of disturbance and climate change. Our results document regional land cover land use change and climatic influences that have altered continental scale vegetation dynamics in North America.     

On some aspects of precipitation over the tropical Indian Ocean using satellite data

The annual and inter‐annual variability of precipitation over the tropical Indian Ocean is studied for the period 1979–1997, using satellite data from a variety of sensors. The Climate Prediction Center Merged Analysis Precipitation (CMAP), Microwave Sounding Unit (MSU) estimates of rainfall had better correlation with the island rainfall data than the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NRA) estimates. A comparison of the mean annual rainfall by different estimates (CMAP, MSU, NRA and GPCP (Global Precipitation Climatology Programme)) showed significant differences with the CMAP, GPCP and MSU estimates depicting maximum off the Indonesian Islands whilst the NRA exhibited maximum in the southern part of the Bay of Bengal and equatorial Indian Ocean. A study of the inter‐annual variability of the monsoon rainfall using the monthly CMAP data over the tropical Indian Ocean for different study areas, namely, Arabian Sea (AS), Bay of Bengal (BB), south Indian Ocean (SIO) and Indian Ocean (IO) showed significant differences during deficit years (1979, 1982, 1986 and 1987), excess monsoon years (1983 and 1988) and also during El Nino Southern Oscillation (ENSO) years (1982, 1987, 1992 and 1997). An analysis of the rainfall anomalies showed positive and negative anomalies in the north‐eastern Bay of Bengal during the summer season of deficit (1986) and excess (1988) monsoon years respectively, whilst the eastern equatorial Indian Ocean showed large positive and negative rainfall anomalies during the autumn season of El Niño years, 1987 (deficit monsoon) and 1997 (normal monsoon) respectively.     

Vegetation dynamics of Zimbabwe investigated using NOAA-AVHRR NDVI from 1982 to 2006: a principal component analysis

The dominant modes of vegetation variability over Zimbabwe are investigated using principal component analysis (PCA) on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) normalized difference vegetation index (NDVI) monthly imagery from 1982 to 2006. Spectral analysis is also used to determine the periodicities of the component loadings. NDVI PCA-1 corresponds to the major vegetation types of Zimbabwe, and we demonstrated that grasslands and dry savannah have the strongest relationship with mean annual precipitation. Furthermore, the March–April loadings showed the highest correlation (r?=?0.73) with mean annual precipitation. NDVI PCA-1 sheds some light on the land reform challenge in Zimbabwe. NDVI PCA-2 is highly correlated (r?=?0.87) with the mean annual relative variability of the rainfall map indicating a southeast/north mode of anomalies associated with the convectional rainfall-bearing systems over Zimbabwe. NDVI PCA-2 is also highly correlated (r?=?0.86) with precipitation PCA-2. NDVI PCA-3 shows a southeast/west mode and is highly correlated (r?=?0.87) with precipitation PCA-3. A high correlation (r?=?0.66) is also noted between NDVI PCA-4 and the elevation map. Spectral analysis of the PCA loadings revealed several periodicities corresponding to those found in tropical sea surface temperatures (SSTs).     

Interannual variability of NDVI over Africa and its relation to El Niño/Southern Oscillation

The array of Normalized Difference Vegetation Index (NDVI) products now being derived from satellite imagery open up new opportunities for the study of short and long-term variability in climate. Using a time series analysis procedure based on the Principal Components transform, and a sequence of monthly Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI imagery from 1986 through 1990, we examine trends in variability of vegetation greenness for Africa for evidence of climatic trends. In addition to the anticipated seasonal trends, we identify signals of interannual variability. The most readily identified is one that periodically affects Southern Africa. It is shown that the temporal loadings for this component exhibit a very strong relationship with the El Nino/Southern Oscillation (ENSO) Index derived from atmospheric pressure patterns in the Pacific, Pacific sea surface temperature (SST) anomalies, and with anomalous Outgoing Longwave Radiation (OLR). However, we have also detected a second interannual variation, affecting most particularly East Africa and the Sahel, that does not exhibit a consistent ENSO relationship. The results show the teleconnection patterns between climatic conditions in the Pacific Ocean basin and vegetation conditions at specific regional locations over Africa. The comprehensive spatial character and high temporal resolution of these data offer exciting prospects for deriving a land surface index of ENSO and mapping the impacts of ENSO activity at continental scale. This study illustrates that vegetation reflectance data derived from polar orbiting satellites can serve as good proxy for the study of interannual climate variability.     

Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data

This research explores the relationship between El Nino/Southern Oscillation (ENSO), captured by equatorial Pacific Ocean Sea Surface Temperature (SST), and interannual variation in vegetation vigour in the southeast USA, captured by Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI), for the period 1982-1992. The moving average and 'baseline' methods (anomaly from the long term mean) were used to extract interannual patterns in the NDVI signature for croplands, deciduous forests and evergreen forests. The ENSO cycle was measured using mean SST anomalies and the percentage of SST cells above certain threshold values (e.g. 1.0° C above the long term mean). The baseline method indicated a weak, yet persistent, negative correlation between ENSO warm phase events and vegetation vigour in the south-east USA. The moving average method yielded similar results but produced higher correlation values (-0.45 to-0.76, significant at the 0.01 level). Use of the 2.0° C threshold SST anomaly was found to yield the highest correlation values as it captures not only the presence but also the intensity of ENSO warm phase events. These results indicate that there is a clear and recognizable, though inconsistent, relationship between ENSO and vegetation vigour in the south-east USA.     

Natural vegetation responses to warming climates in Qaidam Basin 1982–2003

In this paper, we quantified vegetation variations in the Qaidam Basin from 1982 to 2003 by using growing-season NDVI sequences, which were defined as the summation of monthly NDVI values from May to September, and were calculated pixel-by-pixel from a successive 8-km NDVI dataset. We adopt linear regressions to examine the trends in growing-season NDVI and the trends in climate (temperature, precipitation and sunshine duration) during this period in an attempt to depict their temporal and spatial variability. Our results indicate that climate in the Qaidam Basin has homogeneously warmed at a rate of about 0.6°C/decade during the study period, with significant trends in monthly mean temperatures in April–September. However, there were no statistically significant trends observed in precipitation and sunshine duration. We found positive growing-season NDVI trends in 31.6% of the vegetated lands in 1982–2003 and in 24.1% over the first half period, 1982–1992. In addition, few areas were shown to have negative trends during these periods. In 1993–2003, however, the percentage of land with a positive trend decreased to 13.1%, and the percentage of vegetated land with a negative trend increased to 10.2%. Growing-season NDVI trends show both temporal and spatial variability. Areas with negative trends are distributed mostly at lower elevations and near oasis boundaries, and areas with positive trends at higher elevations. Using correlation analyses we estimated the relationship between growing-season NDVI and the climatic factors with the consideration of duration and lagging effects. The results suggest that growing-season NDVI trends are more correlated to temperature increases in growing-season months when compared to variations in precipitation and sunshine duration; however increased precipitation amounts within May–August can also facilitate vegetation growth in some of this arid basin. However, we found no significant correlations between growing-season NDVI and temperature in the non-trend areas, which account for the majority of the vegetated land. We suggest that the variability in vegetation responses to the observed warming climates results from the differences in background thermal condition and moisture availability, which depend on elevation and other factors, such as hydrological conditions.     

Intraseasonal signals in the daily high resolution blended Reynolds sea surface temperature product over the tropical Indian Ocean and their validation

National Oceanic and Atmospheric Administration daily sea surface temperature (SST) products based on Advanced Microwave Scanning Radiometer (AMSR) and Advanced Very High Resolution Radiometer (AVHRR) have been used to understand the variability in the tropical Indian Ocean SST. These products are comparable with the deep sea moored buoy observations and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) SST in the tropical Indian Ocean. However considerable difference is noticed between these satellite SST products and deep sea buoys, especially at the intraseasonal time scale. Further the first Complex Empirical Orthogonal Function (CEOF) mode of TMI and AVHRR SST explains respectively 46.49% and 46.19% of the total variance. The second CEOF mode of TMI and AVHRR SST explains respectively 23.19% and 18.94% of the total SST variance in the tropical Indian Ocean. The AVHRR SST product is important because this daily product has been available since 1985. The analysis shows that AMSR measurements are contributing considerably to the understanding of the tropical Indian Ocean SST variability. Though satellite SST products are able to capture the observed intraseasonal variability reasonably well, more accurate satellite SST products are therefore necessary to understand the climatologically important Indian Ocean region and its air–sea interaction processes.     

Eddy-advective effects on the temperature and wind speed of the sea surface in the Northwest Pacific Subtropical Countercurrent area from satellite observations

In this study, satellite microwave and altimeter data from 1998 to 2007 are used to quantify the eddy-induced meridional heat advection (EMHA) in the Northwest Pacific Subtropical Countercurrent area. Generally, from March to May, the robust EMHA is formed at the point where meridional currents of eddies cross a zonal front of climatological background sea surface temperature (SST). The EMHA shifts westwards with eddies and varies seasonally with the SST front. It warms (cools) the sea surface west of anticyclonic (cyclonic) eddies, inducing noticeable SST anomalies (SSTAs), which are westwardly phase shifted from the eddy-induced sea surface height anomalies by about 90°. Surface wind subsequently varies with the induced SSTAs: it blows faster (slower) over the warm (cold) SST regions than the surroundings. The spatial variations of SST and sea surface wind due to the EMHA shift westwards with eddy motion. These findings from satellite observations give us the possibility of studying the role of oceanic eddies in ocean–atmosphere interaction at the timescale of weather systems in an open ocean.     

Retrieving and broadcasting near-real-time biophysical parameters from MODIS and SEVIRI receiving stations at the global change unit of the University of Valencia

We present here the automatic processing chains implemented at the Global Change Unit of the University of Valencia. These allow for a near-real-time retrieval of various biophysical parameters from both Sun-synchronous TERRA/AQUA Moderate Resolution Imaging Spectroradiometer (MODIS) and geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI) sensors. Retrieved parameters, namely sea and land surface temperatures (SST and LST, respectively), normalized difference vegetation index (NDVI), and vegetation condition index (VCI), are similar for both sensors, and specific approaches have been developed and implemented for near-real-time parameter retrievals: <2 hours for MODIS and <5 min for MSG SEVIRI. Bidirectional reflectance distribution function (BRDF) correction is still required to be implemented in both processing chains, while more advanced parameters are already retrieved (hot spot detection and MSG SEVIRI phenology), in good agreement with independent ground observations. Validation of the retrieved products is underway and the above-mentioned products are available for downloading at http://ceosspain.lpi.uv.es.     

Early cotton yield assessment by the use of the NOAA/AVHRR derived Vegetation Condition Index (VCI) in Greece

Satellite data can significantly contribute to agricultural monitoring. The reflected radiation, as recorded by satellite sensors, provides an indication of the type, density and condition of canopy. A widely used index for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI) derived from the National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data provided in high temporal resolution. An extension of the NDVI is the Vegetation Condition Index (VCI). VCI is a tool for monitoring agrometeorological conditions, providing a quantitative estimation of weather impact to vegetation. The primary objective of this paper is the quantitative assessment of the cotton yield before the end of the growing season by examining the weather effects as they are depicted by the VCI. The study area comprises several cotton producing areas in Greece. Ten-day NDVI maximum value composites (MVC) are initially utilized for the period 1982–1999. The correlation between VCI images as extracted from NDVI and the 10-day intervals during the growing season is examined to identify the critical periods associated mostly with the yield. Empirical relationships between VCI and yield are developed. The models are tested on an independent dataset. The results show that an early estimation of the cotton yield trend is feasible by the use of the VCI.     

Interannual variations in global SST deviations through SMMR from 1978 to 1987

We study variability of global sea surface temperature (SST) utilizing the data of scanning multichannel microwave radiometer (SMMR) on board the NASA Nimbus 7 satellite from 1978 to 1987. First, we model, and then remove from the SMMR SST data, the seasonal cycle by using an intercept, a trend and first five harmonics of the annual cycle to fit the data at each grid point by the method of least squares. A general negative nine‐year trend was observed. In order to analyse the deviations in the global SSTs, we calculate and remove zonally averaged temperatures. We then show Hoffmueller diagrams for the deviations along paths in different oceanic regions over the globe. These paths include a quadrangle in the south Pacific and paths in the north Pacific, Atlantic and along the equatorial Pacific. Both 1983 and 1987 El Niño events as well as the 1984–85 La Niña event are clearly depicted. During these events, the SSTs in the equatorial Pacific and Atlantic are completely out of phase. We also demonstrate spatial propagation of SST waves over interannual scales. In particular, a wave of a period of about 3–4 years following the North Pacific Current will be shown.     

Interannual variability of vegetation over the Indian sub-continent and its relation to the different meteorological parameters

The dynamic nature of climate over Indian sub-continent is well known which influences Indian monsoon. Such dynamic variability of climate factors can also have significant implications for the vegetation and agricultural productivity of this region. Using empirical orthogonal function (EOF) and wavelet decomposition techniques, normalized difference vegetation index (NDVI) monthly data over Indian sub-continent for 18 years from 1982 to 2000 have been used to study the variability of vegetation. The present study shows that the monsoon precipitation and land surface temperature over the Indian sub-continent landmass have significant impact on the distribution of vegetation. Tropospheric aerosols exert a strong influence too, albeit secondary to monsoon precipitation and prove to be a powerful governing factor. Local climate anomaly is seen to be more effective in determining the vegetation change than any global teleconnection effects. The study documents the dominating influence of monsoon precipitation and highlights the importance of aerosols on the vegetation and necessitates the need for remedial measures. The present study and an earlier one point towards a possible global teleconnection pattern of ENSO as it is seen to affect a particular mode of vegetation worldwide.     

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.