Multichannel Empirical Orthogonal Teleconnection Analysis: A Method for Space–Time Decomposition of Climate Variability

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.

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.

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.

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.

<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>

A nonlinear local Lyapunov exponent method based on monthly sea surface temperature data is employed to explore the predictability limit of warm El Niño–Southern Oscillation (ENSO) events. Results using observational data show an asymmetry of the predictability limit between the developing and decaying stages of the warm ENSO phase. To wit, predictability of the developing stage of warm ENSO events is found to approach a limit of 10 months, less than that of the mature and decaying stages. This asymmetrical predictability limit is also found in a long climate model simulation and may explain the asymmetry in operational forecast skill of warm ENSO events. Through exploring the error growth rate as represented by nonlinear local Lyapunov exponent and the instantaneous error growth rate, it is shown that error growth, especially during the first 8‐month lead forecasts, is the primary contributor to the asymmetry of the predictability limit of warm ENSO events.

Surface air temperature (SAT) variability is investigated for advancing our understanding of the climate patterns over the Kingdom of Saudi Arabia (KSA). SAT variability reveals significant warming trends, particularly from 1994 onward, as demonstrated by nonlinear and linear trend analysis. This warming is linked to global climate patterns, which serve as significant indicators for studying the effects of climate change on surface air temperature patterns across the KSA. The empirical orthogonal function (EOF) method is employed for analyzing SAT due to its effectiveness in extracting dominant patterns of variability during the winter (DJF) and summer (JJA) seasons. The first mode (EOF1) for both seasons shows positive variability across the KSA, explaining more than 45% of the variance. The second mode (EOF2) indicates negative variability in central and northern regions. The third mode (EOF3) describes positive variability but with lower variance over time. PC1 is used to describe the physical mechanism of SAT variability and correlations with global sea surface temperature (SST). The physical mechanism shows that the variability in Mediterranean troughs during the winter season and high pressure over the Indian Ocean and central Asia controls SAT variability over the KSA. The correlation coefficients (CCs) were calculated during the winter and summer season between the SAT of the KSA and six teleconnection indices, El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Atlantic Meridional Mode (AMM), Pacific Warm Pool (PWP), North Atlantic Oscillation (NAO), and Tropical North Atlantic (TNA) SST for the period from 1994 to 2022. ENSO shifts from positive to negative correlations with SAT from winter to summer. IOD shows a diminished correlation with SAT due to the absence of upper air dynamics. PWP consistently enhances surface warming in both seasons through upper air convergence during both seasons. AMM and NAO have a non-significant impact on SAT; however, TNA contributes warming over central and northern parts during winter and summer seasons. The seasonal SAT variations emphasize the significant role of ENSO, PWP, and TNA across the seasons. The findings of this study can be helpful for seasonal predictability in the KSA.

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

The El Niño-Southern Oscillation (ENSO) is a complex ocean-atmosphere interaction phenomenon that drives extreme weather events globally, with water vapor playing a crucial role in its evolution. Recently, Global Navigation Satellite Systems (GNSSs) have emerged as an effective tool for retrieving water vapor with high accuracy, high spatial and temporal resolution, and all-weather capability. However, GNSS-derived precipitable water vapor (PWV) has not been well researched for its potential in the study of ENSO, particularly regarding their lead-lag relationships. This study investigates the spatiotemporal response relationship between PWV variations and the Oceanic Niño Index (ONI) using 12 years of coastal GNSS-derived PWVdata. Multichannel singular spectrum analysis (MSSA) was employed to extract nonlinear trends of PWV anomalies, followed by Pearson correlation and lead-lag correlation analyses with ONI. The results reveal a moderate negative correlation in the western Pacific and positive correlations in the eastern Pacific and western Indian Ocean. Notably, stations closer to the Niño 3.4 region exhibited stronger correlations. Moreover, more than half of the stations showed absolute correlation coefficients exceeding 0.4 at optimal lag times, indicating that ENSO exerts a lagged influence on PWV at most stations. A case study at the COCO station near Indonesia (2015-2017) demonstrated that PWV and precipitation anomalies lagged ONI by approximately 8 months, consistent with the severe drought in 2015 and flooding in 2016. These findings suggest that GNSS-derived PWV serves as a valuable indicator for monitoring ENSO dynamics and could enhance early warning systems for ENSO-related drought and flood risks.

SUMMARYMeasurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter.In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability.We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements.Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

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.

In climate science, teleconnection analysis has a long standing history as a means for describing regions that exhibit above average capability of explaining variance over time within a certain spatial domain (e.g., global). The most prominent example of a global coupled ocean-atmosphere teleconnection is the El Nino Southern Oscillation. There are numerous signal decomposition methods for identifying such regions, the most widely used of which are (rotated) empirical orthogonal functions. First introduced by van den Dool, Saha, and Johansson (2000), empirical orthogonal teleconnections (EOT) denote a regression based approach that allows for straight-forward interpretation of the extracted modes. In this paper we present the R implementation of the original algorithm in the remote package. To highlight its usefulness, we provide three examples of potential usecase scenarios for the method including the replication of one of the original examples from van den Dool et al. (2000). Furthermore, we highlight the algorithm’s use for crosscorrelations between two different geographic fields (identifying sea surface temperature drivers for precipitation), as well as statistical downscaling from coarse to fine grids (using Normalized Difference Vegetation Index fields).

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.

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

<p>A complex system is a system composed of highly interconnected components in which the collective property of an underlying system cannot be described by dynamical behavior of the individual parts. Typically, complex systems are governed by nonlinear interactions and intricate fluctuations, thus to retrieve dynamics of a system, it is required to characterize and asses interactions between deterministic tendencies and random fluctuations. </p><p>For systems with large numbers of degrees of freedom, interacting across various time scales, deriving time-evolution equations from data is computationally expensive. A possible way to circumvent this problem is to isolate a small number of relatively slow degrees of freedom that may suffice to characterize the underlying dynamics and solve the governing motion equation for the reduced-dimension system in the framework of stochastic differential equations(SDEs).  For some specific example settings, we have studied the performance of three stochastic dimension-reduction methods (Langevin equation(LE), generalized Langevin Equation(GLE) and Empirical Model Reduction(EMR)) to model various synthetic and real-world time series. In this study corresponding numerical simulations of all models have been examined by probability distribution function(PDF) and Autocorrelation function(ACF) of the average simulated time series as statistical benchmarks for assessing the differnt models' performance. </p><p>First we reconstruct the Niño-3 monthly sea surface temperature (SST) indices averages across (5°N–5°S, 150°–90°W) from 1891 to 2015 using the three aforementioned stochastic models. We demonstrate that all these considered models can reproduce the same skewed and heavy-tailed distributions of Niño-3 SST, comparing ACFs, GLE exhibits a tendency towards achieving a higher accuracy than LE and EMR. A particular challenge for deriving the underlying dynamics of complex systems from data is given by situations of abrupt transitions between alternative states. We show how the Kramers-Moyal approach to derive drift and diffusion terms for LEs can help in such situations. A prominent example of such 'Tipping Events' is given by the Dansgaard-Oeschger events during previous glacial intervals. We attempt to obtain the statistical properties of high-resolution, 20yr average, δ<sup>18</sup>O and Ca<sup>+</sup><sup>2</sup> collected from the same ice core from the NGRIP on the GICC05 time scale. Through extensive analyses of various systems, our results signify that stochastic differential equation models considering memory effects are comparatively better approaches for understanding  complex systems.</p><p> </p>