Empirical Orthogonal Function Method Research Articles

Comparing the Dominant Factors in Coastal Morphology: Inappropriate Infrastructure vs. Climate Change—A Case Study of the Hsinchu Fishery Harbor, Taiwan

: The construction of coastal infrastructure alters the natural hydrodynamics, leading to irreversible changes in coastal morphology. Furthermore, there has been an increasing concern about global climate change in recent years, which requires examining how climatic shifts impact the mechanisms that govern oceanic processes, the trends in morphological changes, the extent of the impacts, and the corresponding weightings assigned to coastal infrastructure. This study aims to assess the impacts of climate change on the wave distribution and coastal morphology around the two breakwaters perpendicular to the shoreline of Hsinchu Fishery Harbor in Taiwan at the end of the 21st century. The findings reveal that, by the end of the century, during extreme climatic events, such as typhoons (increasing the maximum wind speed and extreme sea levels), the wave heights around the Hsinchu Fishery Harbor, compared to the present day, may increase by 5.94% to 81.25%. Regarding the potential coastal morphological changes, erosion trends are evident around the harbor, but there is a pronounced tendency toward accretion in the sheltered area. The impact range and weightings of the Hsinchu Fishery Harbor breakwaters under climate change on coastal morphology were then determined based on historical bathymetric data and simulated bathymetric changes using the empirical orthogonal function (EOF) method. Under different-intensity climate change scenarios, the EOF analysis indicates no significant differences in the impact range and weightings on coastal morphological changes. In light of the analysis results, it is evident that structures have a much more substantial impact on coastal morphological change than climate change does.

A Method for Full-Depth Sound Speed Profile Reconstruction Based on Average Sound Speed Extrapolation

The speed of sound in seawater plays a crucial role in determining the accuracy of multibeam bathymetric measurements. In deep-sea multibeam measurements, the challenge of inadequate longitudinal coverage of sound speed profiles arises from variations in seafloor topography, meteorological conditions, measurement equipment, and operational efficiency, resulting in diminished measurement precision. Building upon the EOF (Empirical Orthogonal Function), a method employed to analyze spatiotemporal data such as sound speeds, this paper addresses the limitations of the EOF method caused by the shallowest sampling depth of the sound speed profile samples. It proposes two methods for EOF reconstruction of measured sound speed profiles extended to full water depth by splicing measured sound speed profiles at non-full water depths with historical average sound speed profiles of the surveyed sea area. Specially, Method 2 introduces the latest metaheuristic optimization algorithm, CPO (Crested Porcupine Optimizer), which exhibited superior performance on multiple standard test functions in 2024. The study reconstructs randomly sampled measured sound speed profiles using the two proposed methods and commonly employed substitution and splicing methods, followed by a comparative analysis of the experimental outcomes. At a sampling depth of 200 m, Method 2 demonstrates performance superior to other methods, with RMSE, MAE, MAPE, and R2 values of 0.9511 m/s, 0.8492 m/s, 0.0566%, and 0.9963, respectively. Method 1 yields corresponding values of 0.9594 m/s, 0.8492 m/s, 0.0568%, and 0.9962, respectively. Despite its slightly inferior performance compared with Method 2, it offers substantial advantages over the substitution and splicing methods. Varying the sampling depth of measured sound speed profiles reveals that Methods 1 and 2 exhibit inferior reconstruction performance in shallow water compared with the substitution and splicing methods. Nevertheless, when the sampling depth surpasses the depth range of initial spatial modes with abrupt variations, both methods achieve notably higher reconstruction accuracy compared with the substitution and splicing methods, reaching a stabilized state. Sound ray tracing reveals that the reconstructed sound speed profiles from both methods meet the stringent accuracy standards for bathymetric measurements, achieving an effective beam ratio of 100%. The proposed methods not only provide rapid reconstruction of sound speed profiles, thereby improving the efficiency of multibeam bathymetric surveys, but also provide references for the reasonable determination of sampling depths of sound speed profiles to ensure reconstruction accuracy, demonstrating practical application value.

Asymmetric responses of summer soil moisture anomalies to the large-scale teleconnections on the Loess Plateau between 2000 and 2020

Soil moisture is a key regulator of hydrological cycle and is closely related to vegetation growth and climate changes, which is mainly replenished by summer precipitation on the Loess Plateau. However, the response of soil moisture content (SMC) anomalies during summer on the Loess Plateau to large-scale teleconnection remains unclear. Here, using the empirical orthogonal function (EOF) method, this study extracted a homogenized area with significant variability of normalized difference vegetation index (NDVI) (p<0.01), which was called sensitive areas for vegetation restoration (SAVR) of the Loess Plateau. Based on 1 km daily soil moisture dataset named SMCI1.0 at top 1 m, we demonstrated that there was an interannual fluctuation of summer SMC from 2000 to 2020 over the SAVR of the Loess Plateau. Spatially, SMC during summer in the SAVR of the Loess Plateau exhibited an enhanced regional difference characterized by ‘dry to drier and wet to wetter’. Importantly, abnormal SMC during summer in the SAVR of the Loess Plateau can been attributed to distinct circulation pattern and anomalous water vapor transport, which showed asymmetrical distributions between positive and negative anomalous years of summer SMC. Due to the activities of western Pacific subtropical high (WPSH) and low-pressure system around the Lake Baikal (an anomalous anticyclone prevailed over the western north Pacific), the positive (negative) anomalous years of summer SMC witnessed net water vapor influx (outflux) anomalies to the Loess Plateau compared to the climatology, corresponding to anomalous upward (sinking) motions, further leading to more (less) SMC in summer over the SAVR of the Loess Plateau, respectively. The conclusions of this study have implications for excavating early warning information of SMC anomalies and natural restoration sustainable management on the Loess Plateau.

Early forecasting of corn yield and price variations using satellite vegetation products

Several approaches based on process-based, statistical or economic models have been developed and are currently used by market analysts and governmental agencies to forecast agricultural yields and commodity prices at the national and global scales. However, these models require input data that are often difficult to obtain before crop harvest and their predictions are not always reliable. Satellite data products provide easily accessible sources of information on vegetation characteristics, potentially relevant for predicting crop yield and commodity prices at a large scale, before harvest. This study presents and evaluates several methods for forecasting variations in US corn yield and global corn prices using regional vegetation satellite products. The spatial dimension of maps of vegetation indices derived from satellite products in the US Corn Belt is first reduced either by their spatial means or by principal components derived using the Empirical Orthogonal Function (EOF) method. Mean values and EOF principal components are then used as explanatory variables in generalized LASSO linear models to forecast corn price and yield changes at different time horizons, before harvest. A cross-validation method was implemented to assess the model performances using Area under the ROC curve (AUC), the Brier Skill Score (BSS) and the Matthew Correlation Coefficient (MCC). The results revealed that EOF principal components derived from satellite-based gross primary production (GPP) in July could be used to predict variations in US corn yields, with an AUC of 0.97 (95% CI 0.92-1), a BSS of 0.75 and an MCC of 0.83. The results also showed that the July spatial averages of satellite GPP data obtained in the US Corn Belt could be used to forecast the forthcoming increase or decrease of global corn prices as evidenced by an AUC reaching up to 0.91, a BSS up to 0.5, and an MCC up to 0.67. Finally, we compared our forecasts of yield and price variations to those derived from the WASDE forecasting method currently implemented in the US. This comparison showed that our approach was at least as accurate and often more accurate than WASDE. This study demonstrates how satellite data can be used to forecast variations in crop yields and corn prices several months before harvest.

Spatiotemporal Variations of Tropical- and Non-Tropical-Cyclone-Induced Rainfall over Southeast China and the Teleconnections to Climatic Indices

Abstract The spatiotemporal variations of annual tropical-cyclone-induced rainfall (TCR) and non-tropical-cyclone-induced rainfall (NTCR) during 1960–2017 in Southeast China are investigated in this study. The teleconnections to sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are examined. A significant decrease in annual TCR in the Pearl River basin was detected, while an increase in annual TCR in rainstorms was observed in the northeast of the Pearl River basin and south of the Yangtze River basin. A northward migration of a TCR belt was identified, which was also indicated by the pronounced anomalies of annual TCR. There was in general an increasing trend of non-tropical-cyclone-induced moderate rain, heavy rain, and rainstorms in Southeast China. Compared with the non-tropical-cyclone-induced heavy rain, the abnormal non-tropical-cyclone-induced rainstorms are more northerly. Both monthly TCR and NTCR were remarkably affected by the Arctic Oscillation, Southern Oscillation, and Indian Ocean dipole. TCR was more easily affected by the Arctic Oscillation compared to NTCR. Significance Statement Tropical-cyclone- and non-tropical-cyclone-induced rainfall (TCR and NTCR) prevails in Southeast China, and their characteristics of spatiotemporal variability are of significance in predicting rainfall over the study area. Therefore, this study aims to detect the degree to which rainfall varies in time and space, respectively, using the Mann–Kendall test and the empirical orthogonal function method. Moreover, to explore which climatic factor contributes the most to the spatiotemporal variability of TCR and NTCR, the teleconnections to the large-scale climatic indices including sea surface temperature, the Arctic Oscillation, the Southern Oscillation, and the Indian Ocean dipole are studied. The spatiotemporal variations of TCR and NTCR were affected by the sea surface temperature and the other three large-scale climatic indices. The findings in this study are expected to deepen the understanding of spatiotemporal variations of TCR and NTCR over Southeast China and the teleconnections to climatic indices.

Relationship between the Uncertainty of Empirical Orthogonal Function (EOF) Modes and Sampling Sizes in Climate Models

Abstract This study investigates the relationship between the uncertainty of empirical orthogonal function (EOF) modes and sampling size in climate models, using simulated results of preindustrial control (piControl) experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6), and taking the North Atlantic Oscillation (NAO) and El Niño–Southern Oscillation (ENSO) as examples. The results indicate that this relationship can be quantified by a concise fitting function [i.e., y = a/(x − b)]. Here, y is the 5%–95% confidence interval of congruence coefficient, x is the sampling size, and a and b are two parameters depending on models or observations. As compared with b, which modulates the sampling size in the fitting function, the parameter a scales the sampling size and thus plays a much more important role. Further analysis indicates that the parameter a, or the uncertainty of EOF1 mode, decreases dramatically with the increase of the difference between variance fractions of EOF1 and EOF2 modes, approximately in the form of a power function. The minimum sampling size to ensure a reliable EOF mode can also be estimated by the fitting function and shows a great diversity among models both for the NAO and ENSO. The diversity suggests the importance of estimating the minimum sampling size before model evaluations on climate variability modes and projections on the future change in modes, particularly when the EOF2 mode explains the variance close to EOF1 mode. Significance Statement Empirical orthogonal function (EOF) analysis, principal component analysis, or eigenvector analysis has been widely used in various research fields. However, it remains as an open question as to how large the sampling size is required to be to obtain reliable modes through the EOF method. In this study, we investigate the relationship between the uncertainty of EOF results and sampling size in current climate models, using adequately long simulated data, and we find that this relationship can be depicted by the fitting function y = a/(x − b). Here, y represents the uncertainty, x is the sampling size, and a and b are parameters. The parameter a is closely related to the difference between variance fractions of first and second EOF modes and plays a more important role in the fitting function. The minimum sampling sizes that are required to obtain reliable EOF modes can also be estimated by the fitting function and vary greatly from model to model. The results provide a basis for judging the reliability of EOF modes, particularly when the first and second EOF modes explain similar variance fractions.

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

A comparison of ionospheric TEC between the South Atlantic anomaly region and the Indian Ocean region based on TIEGCM simulations in 2002

Using the Thermosphere-Ionosphere-Electrodynamic General Circulation Model (TIEGCM), a comparison of the ionospheric total electron content (TEC) between the South Atlantic anomaly (SAA) and the Indian Ocean (IO) at solar maximum is performed in this study. The results show that the average total electron content in the SAA is greater than that in the Indian Ocean in general. In order to further analyze the difference between the two regions, the empirical orthogonal function (EOF) are used to investigate the temporal and spatial characteristics of TEC. The empirical orthogonal function method separate part of the global four-peak structure (an equatorial ionization anomaly structure, distributed in Southeast Asia, South America, Africa, and central Pacific) and spatial variations in both regions. Moreover, the first mode of EOF shows the different distribution of Equatorial ionization anomaly in South America and central Pacific caused by deviation of geomagnetic field and tides between two regions, and the enhancement of TEC in SAA region at dusk is emphasized, but the enhancement of TEC in IO region at dawn is emphasized. The second mode performs the distribution of EIA in Africa related to solar radiation and E×B drift. The third mode indicates the similar spatiotemporal variations from the geomagnetic field. Besides, the correlation between TEC and Dst in two regions indicate that there are some deficiencies in simulation to the specificity of SAA, and the deficiencies are likely caused by the model’s inaccurate simulation of the magnetic field and particle deposition in the SAA region.

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative, sparse, and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Diverse spatiotemporal patterns of vapor pressure deficit and soil moisture across China

Study regionChina and the different climatic zones. Study focusVapor pressure deficit (VPD) and soil moisture (SM) are vital for the land-atmosphere hydrological cycle and vegetation growth. Understanding the spatiotemporal variations of VPD and SM is essential for exploring vegetation dynamics and ecosystem changes. However, our current understanding of the simultaneous variations of VPD and SM within specific regions remains limited. This study utilized Empirical Orthogonal Function (EOF) methods to analyze the spatiotemporal variability of VPD, surface SM (SMsurf), and root-zone SM (SMroot), respectively. We then investigated the synchronous and asynchronous variations of VPD and SM and examined their relationships with climatic factors. New hydrologic insights for the regionFrom 1980–2020, VPD exhibited a significant upward trend across China and in various climate zones, indicating an increase in atmospheric dryness. However, the trends of SMsurf and SMroot showed a slight upward trend across China but divergent patterns in different climate zones. In summary, approximately 43% of China experiences a significant simultaneous increase in both VPD and SM, mainly in semi-arid and arid regions. Conversely, about 4% of China shows contrasting changes in VPD and SM, primarily in humid tropical and subtropical regions. These findings enhance our understanding of VPD and SM patterns in various climates, emphasizing the significance of soil drought in humid and semi-humid regions.

Characterizing variability of spatial patterns of annual and seasonal precipitation of Turkey and identifying the probable driving factors including teleconnection patterns

Abstract This study aimed to identify primary modes of annual and seasonal precipitation in Turkey using a rotated Empirical Orthogonal Function (EOF) method, and the resulting patterns were described concerning the prevalent atmospheric circulations, orography, and continentality. The varimax rotation of the EOF determines modes that are more localized in space than the conventional EOF modes The first three EOFs accounted for approximately 67% and 62% of the total variance in the annual and wet season precipitation series, respectively, whereas only 50% of the variance was captured by the first three EOFs for dry season precipitation. Spatially different atmospheric circulation mechanisms are major drivers of variability in Turkish precipitation on annual and seasonal timescales. The spatial coherence of the highest negative and positive EOF1 loadings of the annual data was observed in the western and southern regions where westerly and northwesterly circulations prevailed during the wet season. A lower spatial coherency was observed with the dry season precipitation. The contribution of atmospheric moisture advection to precipitation variability diminishes in summer, whereas that of local land surface processes increases. Some regional teleconnection patterns, such as the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO), also contributed to the annual variability in precipitation.

Climatological characteristics and interannual variability of the leading mode of eastern African precipitation in January and February

AbstractThe climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA.

Study of Shoreline Changes Due to Tidal Flood at the Coastal City of Meulaboh

The coastline is constantly changing over time. The process of change that occurs continuously makes the coastline dynamic. Shorelines are vulnerable to changes caused by natural and human factors. One of the causes of shoreline changes due to natural factors is tidal flooding. Tidal floods cause abrasion of the coastline and create inundation on land. The coast of Meulaboh City has experienced tidal floods in 2020. The tidal floods that occur make the coastline abrasion and residential areas inundated. This study aims to determine the shoreline changes and the shoreline recovery process due to abrasion and accretion after tidal floods from 2020 to 2021. The shoreline in July 2019, 2020 and 2021 were analysed using DSAS tools to see horizontal shoreline changes. Measurement of the transverse profile of the beach was carried out 17 times in 12 transverse profiles. This measurement aims to map the profile condition after the tidal flood in July 2020. Measurement data for each profile was analysed using the EOF method to see vertical shoreline changes one year after the July 2020 tidal flood. Based on DSAS analysis, the shoreline conditions after tidal flooding (2019-2020) regressed with an average regression of 25.48 m and showed an accretion process after a year (2020-2021) with an average accretion of 14.38 m. Shoreline recovery due to tidal flooding can be seen well based on measurements for 1 (one) year and the results of Empirical Orthogonal Function (EOF) analysis. The recovery pattern is visible after 4 months of tidal flooding in July 2020. The beach profile tends to experience an accretion process in the 8th to 13th measurements and tends to experience abrasion in the 16th to 17th measurements. This shows that the accretion process on the transverse coastal profile is influenced by the east wind season, and the abrasion process generally occurs in the west wind season.

Analysis of the relationship between dust aerosol and precipitation in spring over East Asia using EOF and SVD methods

This study utilized the Empirical Orthogonal Function (EOF) and Singular Value Decomposition (SVD) methods to investigate the spatial and temporal patterns and trends of dust aerosol and precipitation, and to identify the coupled modes between them. The research employed MODIS and CALIPSO retrieved dust aerosol optical depth (DAOD) data to represent dust aerosol information and CMORPH data to provide precipitation information. The results indicated that specific dust source regions were associated with the primary modes of spring dust in East Asia, while atmospheric circulation and land-sea monsoon were closely related to the primary modes of spring precipitation. Additionally, the study revealed that the impact of dust aerosol on precipitation varied based on the source region within the coupled modes. The first coupled mode, with dust sources in the Mongolian Gobi and Taklamakan Desert, demonstrated a pattern of increased dust aerosol and reduced precipitation in most regions. The second coupled mode, with dust sources focused on the Mongolian Gobi, exhibited a consistent pattern of increased dust aerosol and a significant increase in precipitation in northern China. This study highlights the significance of considering dust source regions when examining the relationship between dust aerosol and precipitation, providing new insights into the potential impact of dust aerosol on precipitation in East Asia during the spring.

Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China.

Climate modulation of external forcing factors on air quality change in Eastern China: Implications for PM2.5 seasonal prediction

Meteorological conditions significantly influence the frequency and duration of air pollution events, making the prediction of seasonal variations of PM2.5 concentration crucial for air quality control. This study analyzed the spatiotemporal variations of PM2.5 concentration anomalies over the past 39 years (1980–2018) in winter (November to January) over eastern China based on the empirical orthogonal function (EOF) method. Regression analysis is conducted on external forcing factors such as sea ice, sea temperature, and snow cover in the pre-autumn (September to October) using the time series of the first three modes. Nine key factors were selected, which further led to establishing a model for predicting winter PM2.5 concentration in eastern China using the long short-term memory deep learning algorithm (LSTM). Independent verification revealed that the predicted and observed PM2.5 concentration distributions were consistent, with the absolute value of deviation within 15 μg·m−3 between 2016 and 2018. The correlation coefficients between the predicted and observed values were between 0.42 and 0.93 over eight key cities in the past 10 years (2009–2018). The contribution rates of the nine factors to PM2.5 concentration were calculated to explore their impact on PM2.5 concentration during winter. The Arctic sea ice (ASI) was found to be the key contributor to the winter PM2.5 concentration in eastern China. The predictors can be monitored in real time; hence, the model provides a real-time predictive tool, improving the prospects of predicting seasonal PM2.5 pollution, especially in vulnerable regions such as eastern China.

Relationship between Large-Scale Variability of North Pacific Waves and El Niño-Southern Oscillation

Ocean waves are crucial for driving various oceanic processes. In this study, the spatial and temporal distribution of significant wave height (SWH) in the North Pacific (NP) is analyzed using the 42-year ERA5 reanalysis dataset from European Centre for Medium-Range Weather Forecasts (ECMWF). The presence of an ENSO signal is confirmed in wave fields of the North Pacific. Furthermore, the spatial distributions of swells and wind waves are analyzed using the Empirical Orthogonal Function (EOF) method, revealing that waves can transport large-scale signals from the NP to lower latitudes through swells. In addition, our research reveals a relationship between ENSO and Stokes drift in the NP. Stokes drift contributes positively to the maintenance of stable sea surface temperatures (SSTs) by transporting more (less) water towards the equatorial Pacific during El Niño (La Niña year) years. It is further noted that during strong ENSO events, the strength of the Stokes drift anomaly intensifies accordingly, which implies a strong link between wave-induced transport and ENSO.

Reconstruction of daily chlorophyll-a concentrations in the transit of severe tropical cyclone Hudhud using the ExDINEOF method

Tropical regions experience a diverse range of dense clouds, posing challenges for the daily reconstruction of chlorophyll-a concentration data. This underscores the pressing need for a practical method to reconstruct daily-scale chlorophyll-a concentrations in such regions. While traditional data reconstruction methods focus on single variables and rely on specific factors to infer missing data at specific locations, these single-variable methods may falter when applied to tropical oceans due to the scarcity of available data. Fortunately, all oceanographic variables undergo similar atmospheric and marine dynamic processes, creating internal relationships between them. This allows for the reconstruction of missing data through correlations between variables. Thus, this study introduces a multivariate reconstruction approach using the extended data interpolating empirical orthogonal function (ExDINEOF) method to reconstruct missing daily-scale chlorophyll-a concentration data. The ExDINEOF method considers the simultaneous relationships among multiple variables for data reconstruction in tropical oceans. To verify the method’s robustness, missing data were reconstructed during the formation and passage of severe tropical cyclone Hudhud through the Bay of Bengal. The results demonstrate that ExDINEOF outperforms traditional data reconstruction methods, exhibiting favorable spatial distribution and enhanced accuracy within the dynamic tropical marine environment. Furthermore, an assessment of marine physical environmental factors associated with chlorophyll-a concentration data provides additional evidence for the ExDINEOF method’s accuracy. Notably, the ExDINEOF method offers comprehensive spatial distribution aligned with underlying physical mechanisms governing phytoplankton distribution patterns, detailed phytoplankton growth, bloom, extinction variations in time series, satisfactory accuracy, and comprehensive local-level details.

Relationship between the North Atlantic sea surface temperature and the summer extreme high temperature in the Beijing-Tianjin-Hebei region, China

Climate change influence on the economy and environment in the Beijing–Tianjin–Hebei (BTH) region. This study examined the variations of extreme high temperature days (EHDs) over the BTH region in summer during 1982 to 2021. The trend and frequency of EHDs has increased significantly during the last 40 years. The spatial and temporal variations of EHDs also indicate a significant increase by using the rotated empirical orthogonal functions (REOF) method. Furthermore, the singular value decomposition (SVD) method and correlation analysis are used to indicate the relationship between the EHDs in June–July-August (JJA) in the BTH region and North Atlantic sea surface temperature anomalies (SSTA) in the March–April-May (MAM) during 1982–2021. The result shows that there was positive relationship between the North Atlantic SSTA in MAM and the EHDs in JJA in the BTH region. Composite analysis suggests that relationship between EHDs and SSTA are connected by associated atmospheric circulation through the wave activity flux.

Assessing the impact factors and corresponding weights affecting the coastal morphology of Hsinchu Coast, Taiwan

This study investigates the long-term morphological pattern of the Hsinchu coast in northwestern Taiwan using three different methods: long-term isobath and shoreline change analysis, bathymetric volume change analysis, and the empirical orthogonal function (EOF) method. Our objectives include analyzing and understanding the effects of anthropogenic structures, natural processes, and their combined impacts on coastal morphological change. The shoreline analysis and bathymetric volume analysis briefly reveal that the morphological pattern of Hsinchu coast. Then, EOF analysis is applied to identify the dominant impact factors affecting the coastal morphology near the harbor, determine the weights of the impact factors, and infer the impact range of anthropogenic structures. Moreover, the EOF analysis is divided into two-time segments—namely, the periods 2005–2010 and 2017–2021—based on different bathymetric surveys and the corresponding values of impact factors. The primary and secondary EOF modes are strongly linked to the “interaction between wave and structure” and “interaction between river discharges and structure”, respectively. The spatial components of the EOF modes demonstrate that the impact range influenced by river discharges was mainly located in the northern part of the harbor, while the minimum impact range of the structures on coastal morphological interaction changes under natural factors was 3.5 km south of Hsinchu Fishing Harbor.