Reconstruction of Complex Dynamical Systems Using Stochastic Differential Equations

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.

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

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.

Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two- to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.

Climatic variability and trends in the surface waters of coastal British Columbia

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 & Sons, Ltd.

<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 temperature and food availability on adult body length in natural and laboratory populations of Paracerceis sculpta (Holmes), a Gulf of California isopod

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.

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.

A well-known atmospheric phenomenon is the Southern Oscillation (SO). The SO is an atmospheric see-saw process in the tropical Pacific sea level pressure between the eastern and western hemispheres associated with the El Nino and La Nina oceanographic features. The oscillation can be characterized by a simple index, the Southern Oscillation Index (SOI). (Kawamura et al., 1998). The Pacific Decadal Oscillation index (PDOI) is the leading principal component of monthly sea surface temperature (SST) anomalies in the North Pacific Ocean north of 20°N (Zhang et al., 1997; Mantua et al., 1997). Trenberth and Hurrell (1994) have defined the North Pacific Index (NPI) as the area-weighted sea level pressure over the region 30°N to 65°N, 160°E to 140°W to measure the decadal variations of atmosphere and ocean in the north Pacific.

Growth data for the New Zealand abalone Haliotis iris were collected from 30 sites around the New Zealand coast by tag–recapture methods. Most data were collected to provide input into abalone stock assessments within discrete management areas, but had not been examined to determine the nature or extent of any large-scale patterns that might be useful to fishery managers. Sites spanned more than 10° of latitude and were subject to a large range of wave energies and temperatures. Mean monthly sea surface temperature (SST) and wave energy were estimated for each site and a generalised linear regression model was used to examine the relationship between variables. Size-at-maturity was also examined at ten sites. Initial length of abalone explained 35% of the variation in incremental growth, and a further 19% was explained by maximum SST, which also explained 60% of the variation in asymptotic length. Fastest growth was generally in areas with lower mean monthly maximum SST, and sites with the slowest growth had the highest mean monthly maximum SST. Size-at-maturity decreased with increasing temperature. The implications of these broad patterns upon abalone fisheries management strategies are discussed.

As the western boundary current in the North Pacific Ocean, the Kuroshio plays a key role in the marine environment around Taiwan. Over the past decades, extensive research and in situ observations have identified a cold dome and cold eddy off the northeastern coast of Taiwan, presumably influenced by the interaction between the Kuroshio and the complex topography. Satellite images of monthly sea surface temperature have revealed a prominent cold water mass in summer. Previous studies have not yet explained clearly the formation mechanism of this cold dome. In this study, we used the Massachusetts Institute of Technology General Circulation Model to identify the mechanisms underlying the formation of this cold dome off the northeastern coast of Taiwan. Various scenarios were simulated to determine the primary causes of cold water upwelling in the cold dome region. Model results at 50-m depth revealed that a cold dome, 4 °C–5 °C cooler than the Kuroshio, was formed off the northeastern coast of Taiwan where the Kuroshio impinged. However, the model's temperature drop 1 °C in the surface layer falls short of field observations and satellite images, 5 °C. Including effects of wind stress in the model showed that the summer monsoon may increase the cold-dome area, but not the surface temperature drops. Including tides in the model accurately simulates the observed near surface temperature drop of 5°C–6°C. Model temperature exhibits periodic variations. Spectral analysis of model temperature revealed spectral peaks at diurnal, semidiurnal tidal and 14-day periods. • Two stages of upwelling contribute to the formation of the cold patch. • The stage of upwelling occurs when the Kuroshio is used to drive the model. • In the first stage, a cold dome forms below a depth of 50 m in the region. • Alternating flood and ebb tidal currents create an asymmetric flow field. • Tides lead to the second stage of upwelling, bringing water to the surface layer.

Optimal Proxy Indices for Annual Marine Heatwave Characteristics Using Monthly Sea Surface Temperature

Many corals depend upon the highly specialised and intricate relationship they form with Symbiodiniaceae algal symbionts. Porites lutea is a massive reef-building coral found throughout Southeast Asia that hosts these endosymbionts obligately. Yet despite the prevalence and importance of P. lutea as one of the most dominant corals here, its associated Symbiodiniaceae communities have not been precisely characterised. In this study, we used high-throughput DNA amplicon sequencing of the nuclear internal transcribed spacer 2 (ITS2) to characterise the diversity, community structure and biogeographic distribution of Symbiodiniaceae in P. lutea throughout Singapore and Peninsular Malaysia. Consistent with previous studies, we found that Cladocopium was the most dominant genus among all samples, and Cladocopium C15 was the most dominant type (or subclade) with 100% occurrence in all samples from every study site. Results also revealed numerous Symbiodiniaceae types associated with P. lutea that were previously undetected in Southeast Asia. Endosymbiont diversity and community variation are driven by a combination of site-specific mean monthly cloud cover and variance in monthly sea surface temperature. This study contributes baseline data toward understanding differences in Symbiodiniaceae assemblages hosted by P. lutea, shedding light on how they might be indicative of particular environmental conditions and coral responses.