Rainfall Prediction Using Teleconnection Patterns Through the Application of Artificial Neural Networks

Statistical models were developed to specify and predict the mean monthly sea level pressure (SLP) distribution over the central and eastern North Pacific Ocean from the mean monthly sea surface temperature (SST) distribution for the same area. These models were derived from data for the period 1947–71, with data from two additional periods (1933–41 and 1972–76) retained for independent testing. The earlier period SST data is taken from a new data set compiled at the National Climatic Center and processed for use in examining large-scale air-sea interactions. This procedure is described. Empirical orthogonal function (EOF) analysis was used to represent each field (SST and SLP) by a small number of composite variables. Regression analysis. was then used in which SST EOF amplitudes were the predictors and SLP EOF amplitudes were the predictands. The analyses were stratified by month, with lags from 0–3 months considered. Of the 84 models developed, 18 were statistically significant at the 10% level. The number of significant relationships was found to decrease with increasing tag, being greatest for SST contemporaneous with SLP. All statistically significant models involved SST's from the period June–January. Each of the significant models was tested on the independent samples, using the reduction of error (RE) statistic as the measure of skill. An adjustment was made to the 1933–41 SST data to remove a systematic bias, and the RE scores recomputed. Using the adjusted data, RE scores for the 1933–41 period improved, with 10 of 18 models demonstrating skill overall. Most of the skill, especially at longer lags, was associated with models using late autumn and early winter SST as predictors. Possible reasons for the seasonal distribution of the skillful relationships are discussed.

The El Niño-Southern Oscillation (ENSO) is the Earth's dominant inter-annual climate fluctuation, often accompanied by extreme weathers. The occurrence of drought, flood, heavy rainfall, hurricanes, extreme cold, and so on, may cause significant crops yield reduction or even no crop. Therefore, a timely ENSO forecast is critical to global agricultural production and food safety. The current mainstream ENSO indexes are based on monthly or quarterly data without a strong timeliness. This paper proposes a new ENSO index, named as Daily Niño Index (DNI), to study the ENSO events in daily temporal scale. To the best of our knowledge, there is no previous study defining ENSO events in daily temporal scale. High correlations between NOAA daily Optimum Interpolation Sea Surface Temperature (OISST) data set and some popular monthly Sea Surface Temperature (SST) data sets show the feasibility of using daily data to study ENSO events. Niño 3.4 SST region is selected as the computational domain for DNI in order to maximize the ENSO signal among SST-based indexes to identify the El Niño and La Niña events. Comparisons are made between DNI and some other ENSO forecast indexes. Experiment results illustrate that DNI is highly correlated with other indexes and advances the forecast time two to three months earlier.

The influence of El Niño–Southern Oscillation (ENSO) on Pacific decadal variability (PDV) is investigated by comparing two 500-yr simulations with the National Centers for Environmental Prediction (NCEP) Climate Forecast System coupled model. One simulation is a no-ENSO run, in which model daily sea surface temperature (SST) in the tropical Pacific Ocean is relaxed to the observed climatology. The other simulation is a fully coupled run and retains ENSO variability. The PDV considered in this study is the first two empirical orthogonal functions of monthly SST anomalies in the North Pacific: the Pacific decadal oscillation (PDO) and the North Pacific Gyre Oscillation (NPGO). The PDO in the no-ENSO run can be clearly identified. Without ENSO, the PDO displays relatively higher variance at the decadal time scale and no spectral peak at the interannual time scale. In the ENSO run, the PDO variability increases slightly. ENSO not only enhances the variability of the PDO at the interannual time scale, but also shifts the PDO to longer time scales—both consistent with observations. ENSO modulates the Aleutian low and associated surface wind over the North Pacific. The latter, in turn, helps establish a more persistent PDO in the ENSO run. The results also indicate a PDO modulation of global ENSO impacts and the linearity in the superposition of the ENSO-forced and PDO-related atmospheric anomalies. Compared to observations, the NPGO in both simulations lacks power at the time scale longer than 30 yr. On the decadal time scale, the variability of the NPGO is weaker in the ENSO run than in the no-ENSO run.

<div>Agriculture is a very important sector in Indramayu’s economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the </div><div>farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R</div><div>2</div><div> = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.Agriculture is a very important sector in Indramayu's economy. Approximately 53.52% of Indramayu residents are involved in agriculture. Indramayu is a center of rice production in Indonesia. Most of the farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic natural events, but not consider climate variability well. Climate variability has become a major obstacle to achieving a successful harvest, because it can affect the timing of planting and length of the growing season, which leads to drought and flood vulnerability. The planting date and growing season predicted by using monthly sea surface temperature anomalies (SSTa) in Nino 3.4. The August SSTa can describe the planting date better than the growing season, which are demonstrated best in Lohbener with R2 = 45% with forecast skill reach = 84% and 92% for advanced and delayed planting dates. Knowing the planting date and growing season length produce a more effective cropping calendar, which includes details such as when to prepare the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of climate variability by providing a more efficient cropping pattern and avoiding potential harvest failures.</div>

Effects of the ENSO on rainfall erosivity in the Fujian Province of southeast China

In this study we use the monthly mean sea surface temperature (SST) fields (1985-1997), calculated by the National Oceanic and Atmospheric Administration (NOAA) Pathfinder project analysis of advanced very high resolution radiometer (AVHRR) data, to investigate the temporal and spatial variability of warm/cool anomalies in the Levantine Basin (LB). Wind data from European Remote Sensing Satellite-1/2 (ERS-1/2) over the LB are analysed and surface wind stress and wind stress curls are calculated. First, we employed an optimal interpolation scheme to build up a dataset for 1985-1997 on an 18 km 2 18 km grid. An ensemble mean SST field ( T ) has been established with a rather weak horizontal gradient (19.4° C south-east of Rhodes to 22.4° C southeast of the LB). Second, we performed a composite analysis to obtain the monthly averaged SST anomaly field ( T ) deviation from the ensemble mean. Three areas of evident SST anomalies were found: south of Rhodes, south-east of Crete and north-east of Cyprus. During the spring-to-summer transition (March to May), a small low anomaly is formed south-east of Crete ( T <-1.4° C in May), and a small high anomaly is formed south of Rhodes ( T >-0.4° C in May). During the autumn-to-winter transition (October to November), a small high anomaly forms south-east of Crete ( T >1.2° C in November), and a small low anomaly forms between Rhodes and Antalia Bay ( T <-0.2° C in November). Third, we performed an empirical orthogonal function (EOF) analysis on the residual T relative to T + T in order to obtain transient and interannual variations of the SST fields. EOF1 explains 73% of the total variance and represents the warm/cool anomaly pattern, with a maximum value in the region between the south of Rhodes and Cyprus and a minimum value off the western part of the Egyptian coast. EOF2 explains 6% of the total variance. A strong LB warm anomaly (1° C warmer) appears during August 1988, May 1994 and September-October-November 1994. A strong cool anomaly (1° C cooler) occurs during May 1987, November 1988, August 1989 and May 1992. The cross-correlation coefficient between wind stress curl from ERS-1/2 over the LB in the period from January 1992 to December 1997 and the SST anomaly were calculated, and it reveals the air-sea interaction in the LB.

This article describes a spatial prediction method that predicts the monthly sea surface temperature (SST) anomaly field from the land only data. The land data are from the Global Historical Climatology Network (GHCN). The prediction period is 1880–1999 and the prediction ocean domain extends from 60°S to 60°N with a spatial resolution 5°×5°. The prediction method is a regression over the basis of empirical orthogonal functions (EOFs). The EOFs are computed from the following data sets: (a) the Climate Prediction Center's optimally interpolated sea surface temperature (OI/SST) data (1982–1999); (b) the National Climatic Data Center's blended product of land‐surface air temperature (1992–1999) produced from combining the Special Satellite Microwave Imager and GHCN; and (c) the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis data (1982–1999). The optimal prediction method minimizes the first‐M‐mode mean square error between the true and predicted anomalies over both land and ocean. In the optimization process, the data errors of the GHCN boxes are used, and their contribution to the prediction error is taken into account. The area‐averaged root mean square error of prediction is calculated. Numerical experiments demonstrate that this EOF prediction method can accurately recover the global SST anomalies during some circulation patterns and add value to the SST bias correction in the early history of SST observations and the validation of general circulation models. Our results show that (i) the land only data can accurately predict the SST anomaly in the El Nino months when the temperature anomaly structure has very large correlation scales, and (ii) the predictions for La Nina, neutral, or transient months require more EOF modes because of the presence of the small scale structures in the anomaly field. Copyright © 2004 John Wiley &amp; Sons, Ltd.

While significant improvements have been made in understanding how the El Nino–Southern Oscillation (ENSO) impacts both North American and Asian climate, its relationship with the North Atlantic Oscillation (NAO) remains less clear. Observations indicate that ENSO exhibits a highly complex relationship with the NAO-associated atmospheric circulation. One critical contribution to this ambiguous ENSO/NAO relationship originates from ENSO’s diversity in its spatial structure. In general, both eastern (EP) and central Pacific (CP) El Nino events tend to be accompanied by a negative NAO-like atmospheric response. However, for two different types of La Nina the NAO response is almost opposite. Thus, the NAO responses for the CP ENSO are mostly linear, while nonlinear NAO responses dominate for the EP ENSO. These contrasting extra-tropical atmospheric responses are mainly attributed to nonlinear air-sea interactions in the tropical eastern Pacific. The local atmospheric response to the CP ENSO sea surface temperature (SST) anomalies is highly linear since the air-sea action center is located within the Pacific warm pool, characterized by relatively high climatological SSTs. In contrast, the EP ENSO SST anomalies are located in an area of relatively low climatological SSTs in the eastern equatorial Pacific. Here only sufficiently high positive SST anomalies during EP El Nino events are able to overcome the SST threshold for deep convection, while hardly any anomalous convection is associated with EP La Nina SSTs that are below this threshold. This ENSO/NAO relationship has important implications for NAO seasonal prediction and places a higher requirement on models in reproducing the full diversity of ENSO.

Sea surface temperature and rainfall anomaly over the Bay of Bengal during the El Niño-Southern Oscillation and the extreme Indian Ocean Dipole events between 2002 and 2016

This paper addresses analysis of the global monthly sea surface temperatures using a reconstructed dataset that goes back to 1884. We use fractional integration methods to examine features such as persistence, seasonality, and time trends in the data. The results show that seasonality is a relevant issue, finding evidence of seasonal unit roots. With the seasonal component removed, persistence is also very significant, and, when looking at the data month by month, evidence of significant linear trends is detected in all cases. According to these results, monthly sea surface temperatures increase by between 0.07° and 0.11°C every 100 years.

Seasonal variability in sea surface oceanographic conditions in the Aegean Sea (Eastern Mediterranean): an overview

Correlations between summer Sahel rainfall and Southern Oscillation Index has increased during the last thirty years. At high frequency time scale (periods lower than 8 years), an intertropical Atlantic zonal divergent circulation anomaly is forced by the difference of sea surface temperature (SST) anomalies between the eastern equatorial parts of Pacific and Atlantic. This zonal connection worked well during most of the El Nino/Southern Oscillation (ENSO) events occurring after 1970; positive/negative SST anomalies in the eastern Pacific/Atlantic led to rainfall deficits over the whole West Africa. At low frequency time scale (periods greater than 8 years), positive SST anomalies in the Indian ocean and in equatorial Pacific existing after 1970 have been associated with decreasing rainfall intensity over West Africa through another zonal divergent circulation. These different time scales remote SST forcings are combined to provide a global zonal divergent circulation anomaly pattern which could explain the strong association between Sahel drought and ENSO dynamics after 1970.

A part of the large-scale thermodynamic forcing of the upper ocean is examined by relating monthly anomalous latent and sensible heat flux to changes in sea surface temperature (SST) anomalies over the North Atlantic and North Pacific. The fluxes are estimated using bulk formulas from a set of about four decades of marine observations from the COADS dataset from 1946 to 1986. Monthly anomalies are constructed by removing the long-term monthly means. The latent and sensible flux anomalies are strongly correlated over most of the ocean, so they are considered together as a sum. The heat flux estimates contain large spatial-scale anomalies consistent with both atmospheric circulation anomalies and with month-to-month changes (tendencies) in monthly SST anomalies. The monthly flux anomalies and the SST anomaly tendency are significantly correlated over much of the oceans, with anomalous positive/negative fluxes associated with anomalous cooling/warming. The connection between the flux and the SST ten...

The winter sea surface temperature (SST) anomalies in the Kuroshio and adjacent regions (KAR), which greatly influence the East Asian&amp;#8211;North Pacific&amp;#8211;North American climate, are closely related to El Ni&amp;#241;o&amp;#8211;Southern Oscillation (ENSO). This SST relationship between the KAR and the equatorial eastern-central Pacific is widely assumed to be symmetric between El Ni&amp;#241;o and La Ni&amp;#241;a. Compared to previous studies indicating the significant and strong KAR warming during El Ni&amp;#241;o winters, this study indicates weakly negative KAR SST anomalies in the composite analysis for all La Ni&amp;#241;a events. Positive winter KAR SST anomalies unexpectedly appear in approximately half of La Ni&amp;#241;a events, which counteract negative SST anomalies in the rest of La Ni&amp;#241;a events. Further analysis suggests that the impact of La Ni&amp;#241;a on KAR SST anomalies is modulated by the East Asian winter monsoon (EAWM) during early winter. The weaker-than-normal EAWM offsets the anomalous northeasterly winds in the KAR induced by La Ni&amp;#241;a and then reinforces the KAR warming through warm oceanic advection. As for strong EAWM, it enhances the northeasterly winds to the west of an anomalous Philippine Sea cyclone associated with La Ni&amp;#241;a, leading to KAR cooling with more latent heat flux loss from the ocean and anomalous cold oceanic advection. Additionally, when the EAWM is independent of ENSO and is associated with the western Pacific pattern, it also can exhibit a pronounced influence on the KAR SST anomalies via the major processes of surface latent flux and horizontal heat advection in the ocean, accompanied by a change in Kuroshio transport.

El Niño Southern Oscillation (ENSO) impacts the supply of and, to some extent, the demand for primary commodities. But what are the consequences of ENSO shocks for these commodity price dynamics? I consider monthly series of 43 primary commodity prices and sea surface temperature (SST) anomalies in the Niño3.4 region from January 1980 to December 2016. The SST anomalies serve as a proxy for ENSO, as persistent positive (negative) SST deviations from their historical mean are associated with El Niño (La Niña) events. I apply a time‐varying smooth transition autoregressive modeling framework to account for potentially complex dynamic relationships between SST anomalies and prices. The estimated models reveal more amplified price responses during El Niño events, and at the onset of the ENSO cycle. I find statistically significant linkages between SST anomalies and a subset of agricultural commodity prices, which manifests in a better price forecasting for the commodities produced in the tropics. While I also find some in‐sample evidence for prices of selected nonagricultural commodities, for example, timber and metals, the ability of SST anomalies to predict these commodity prices in an out‐of‐sample setting is lacking. These findings carry important welfare implications, especially for developing economies that have been historically linked to the behavior of primary commodity prices, and offer valuable insights to policy makers working in areas related to economic growth and foreign aid programs, as well as those concerned with issues of farm income and rural poverty.