Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

This study presents an analysis of the spectral characteristics of remote sensing reflectance(Rrs)in northwestern South China Sea based on the in situ optical and water quality data for August 2018.Rrswas initially divided into four classes,classes A to D,using the max-classification algorithm,and the spectral properties of whole Rrs were characterized using the empirical orthogonal function(EOF)analysis.Subsequently,the dominant factors in each EOF mode were determined.The results indicated that more than 95%of the variances of Rrs are partly driven by the back-scattering characteristics of the suspended matter.The initial two EOF modes were well correlated with the total suspended matter and back-scattering coefficient.Furthermore,the first EOF modes of the four classes of Rrs(A-D Rrs-EOF1)significantly contributed to the total variances of each Rrs class.In addition,the correlation coefficients between the amplitude factors of class A-D Rrs-EOF1 and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones.The spectral shape of class ARrs-EOF1 was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter(CDOM).The spectral shape of class B Rrs-EOF1 was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM(ag(λ)),whereas the spectral shape of class C Rrs-EOF1 was governed by the back-scattering characteristics but not affected by the suspended matter.The spectral shape of class D Rrs-EOF1 exhibited a relatively good correlation with all the water quality parameters,which played a significant role in deciding its spectral shape.

A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial Ensemble Generation of an Ensemble Kalman Filter

The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly.

NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

ROTATED COMPLEX EMPIRICAL ORTHOGONAL FUNCTION(RCEOF)ANALYSIS—PART Ⅱ:THEORY AND EXAMPLES

The numerical solving and the program designing of the rotated complex empirical orthogonal function(RCEOF)are discussed.Some examples of RCEOF are also presented.

Empirical orthogonal function analysis of short time series of sectional data in the tropical western Pacific

In this paper,some short time series of pnserved data pm sectopm 18°20′N in the tropical western Pacificwere reorganized to give mixed depth-time series,and processed by means of means of empirical orthogonal fonction analysis. It is indicated that the original form of element distribution could be obtained by linear combination of several main canonical distribution functions, and the intrinsic structure of element distribution on a certain section and its variation propertiescould be reveled by canonical distribution function and profiles in corresponding periods.

Spatio-temporal variability of surface chlorophyll a in the Yellow Sea and the East China Sea based on reconstructions of satellite data of 2001-2020

Chlorophyll-a(Chl-a)concentration is a primary indicator for marine environmental monitoring.The spatio-temporal variations of sea surface Chl-a concentration in the Yellow Sea(YS)and the East China Sea(ECS)in 2001-2020 were investigated by reconstructing the MODIS Level 3 products with the data interpolation empirical orthogonal function(DINEOF)method.The reconstructed results by interpolating the combined MODIS daily+8-day datasets were found better than those merely by interpolating daily or 8-day data.Chl-a concentration in the YS and the ECS reached its maximum in spring,with blooms occurring,decreased in summer and autumn,and increased in late autumn and early winter.By performing empirical orthogonal function(EOF)decomposition of the reconstructed data fields and correlation analysis with several potential environmental factors,we found that the sea surface temperature(SST)plays a significant role in the seasonal variation of Chl a,especially during spring and summer.The increase of SST in spring and the upper-layer nutrients mixed up during the last winter might favor the occurrence of spring blooms.The high sea surface temperature(SST)throughout the summer would strengthen the vertical stratification and prevent nutrients supply from deep water,resulting in low surface Chl-a concentrations.The sea surface Chl-a concentration in the YS was found decreased significantly from 2012 to 2020,which was possibly related to the Pacific Decadal Oscillation(PDO).

Spatiotemporal variations of parameters of internal solitary waves in the northern South China Sea

The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.

Characteristics of the dynamic changes in active accumulated temperature in Sichuan,China in the last 51 years against the background of climate change

It is of utmost necessity to understand the dynamics of regional active accumulated temperature(AAT)to cope with the negative impacts of global warming on agroforestry development and food security and to provide a real-time and effective reference basis for regional agroforestry planning.The daily temperature data from 30 meteorological stations in Sichuan Province from 1970 to 2020,and sea surface temperature(SST)index data from the Atlantic Multiphase Oscillation(AMO)and Pacific Decadal Oscillation(PDO)were used for the study.Sichuan Province was divided into the western region(WS)and the eastern region(ES),considering 1000 m above sea level as the boundary.The spatiotemporal characteristics of≥0℃ and≥10℃ active accumulated temperature(AAT0,AAT10)in WS and ES were analyzed comprehensively using 5-day average sliding,empirical orthogonal function(EOF),ensemble empirical mode decomposition(EEMD),and multiple mutation tests.The results show that(1)AAT0 and AAT10 of WS ranged from 3034℃ to 3586℃ and 1971℃ to 2636℃,respectively,while the AAT0 and AAT10 of ES ranged from 5863℃ to 6513℃ and 4847℃ to 5875℃,respectively.The period around 1997 was a significant abrupt change,and the AAT in the province generally increased during the subsequent time period(2)AAT in the study area is mainly driven by the fluctuations of AMO,as reflected by the low-to-high variation of AAT coinciding with the jump of the cold-to-warm phase of AMO.Considering different time scale fluctuations in the past 51 years,the major cycle for both AAT0 and AAT10 in WS is 3.40 a,while the major cycles in ES are 3.64 a and 3.19 a,respectively with a sub-cycle of 7.29 a.AAT fluctuation has an insignificant periodic characteristic of 25.50 a on the interdecadal scale(3)The spatial heterogeneity of AAT in WS is prominent and is mainly reflected by the significantly warm conditions in the south of the WS region and relatively slight warm conditions in the north,as well as by the isolated cooling area in the form of"freezing point",i.e.,Xiaojin county.In contrast,the spatial variability of AAT in ES is more or less consistent,with the warming areas concentrated in the foothills of the western edge of the basin and a slight increase in AAT observed in the central part of the basin.

The Interannual Variability of Summer Rainfall in the Arid and Semiarid Regions of Northern China and Its Association with the Northern Hemisphere Circumglobal Teleconnection

Using the latest daily observational rainfall datasets for the period 1961–2008, the present study investigates the interannual variability of June–September （JJAS） mean rainfall in northern China. The regional characteristics of JJAS mean rainfall are revealed by a rotated empirical orthogonal function （REOF） analysis. The analysis identifies three regions of large interannual variability of JJAS rainfall： North China （NC）, Northeast China （NEC）, and the Taklimakan Desert in Northwest China （TDNWC）. Summer rainfall over NC is shown to have displayed a remarkable dry period from the late 1990s; while over NEC, decadal-scale variation with a significant decreasing trend in the last two decades is found, and over TDNWC, evidence of large interannual variability is revealed. Results also show that the interannual variability of JJAS rainfall in northern China is closely associated with the Northern Hemisphere circumglobal teleconnection （CGT）. Correlation coefficients between the CGT index and regional-averaged JJAS mean rainfall over NC and NEC were calculated, revealing values of up to 0.50 and 0.53, respectively, both of which exceeded the 99% confidence level.

Impact of Urbanization on Low-Temperature Precipitation in Beijing during 1960–2008

Daily precipitation and temperature records at 13 stations for the period 1960-2008 were analyzed to identify climatic change and possible effects of urbanization on low-temperature precipitation [LTP, precipitation of ≥ 0.1 mm d^-1 occurring under a daily minimum temperature （Tmin） of ≤ 0℃] in the greater Beijing region （B JR）, where a rapid process of urbaniza tion has taken place over the last few decades. The paper provides a climatological overview of LTP in B JR. LTP contributes 61.7% to the total amount of precipitation in B JR in the cold season （November-March）. There is a slight increasing trend [1.22 mm （10 yr）^-1] in the amount of total precipitation for the cold season during 1960-2008. In contrast, the amount of LTP decreases by 0.6 mm （10 yr）^-1. The warming rate of Train in B JR is 0.66℃ （10 yr）^-1. Correspondingly, the frequency of LTP decreases with increasing Tmin by -0.67 times per ℃. The seasonal frequency and amount of LTP in southeast B JR （mostly urban sites） are 17%-20% less than those in the northwestern （rural and montane sites）. The intensity of LTP for the urban sites and northeastern B JR exhibited significant enhancing trends [0.18 and 0.15 mm d^- 1 （10 yr）^- 1, respectively]. The frequency of slight LTP （〈0.2 mm d^-1） significantly decreased throughout B JR [by about -15.74% （10 yr）^-1 in the urban area and northeast B JR], while the contribution of the two heaviest LTP events to total LTP amount significantly increased by 3.2% （10 yr） ^-1.

Anomaly feature of seasonal frozen soil variationson the Qinghai-Tibet Plateau

The seasonal frozen soil on the Qinghai-Tibet Plateau has strong response to climate change, and its freezing-thawing process also affects East Asia climate. In this paper, the freezing soil maximum depth of 46 stations covering 1961–1999 on the plateau is analyzed by rotated experience orthogonal function (REOF). The results show that there are four main frozen anomaly regions on the plateau, i.e., the northeastern, southeastern and southern parts of the plateau and Qaidam Basin. The freezing soil depths of the annual anomaly regions in the above representative stations show that there are different changing trends. The main trend, except for the Qaidam Basin, has been decreasing since the 1980s, a sign of the climate warming. Compared with the 1980s, on the average, the maximum soil depth decreased by about 0.02 m, 0.05 m and 0.14 m in the northeastern, southeastern and southern parts of the plateau, but increased by about 0.57 m in the Qaidam Basin during the 1990s. It means there are different responses to climate system in the above areas. The spectrum analysis reveals different change cycles: in higher frequency there is an about 2-year long cycle in Qaidam Basin and southern part of the plateau in the four representative areas whereas in lower frequency there is an about 14-year long cycle in all the four representative areas due to the combined influence of different soil textures and solutes in four areas.

The low-frequency variance of the ocean surface wave field in the area of the Antarctic Circumpolar Current

The low-frequency variance of the surface wave in the area of the Antarctic Circumpolar Current （ACC） and its correlation with the antarctic circumpolar wave （ACW） are focused on. The analysis of the series of 44 a significant wave height （SWH） interannual anomalies reveals that the SWH anomalies have a strong periodicity of about 4-5 a and this signal propagates eastward obviously from 1985 to 1995, which needs about 8 a to complete a mimacircle around the earth. The method of empirical orthogonal function （EOF） is used to analyze the filtered monthly SWH anomalies to study the spatio-temporal distributions and the propagation characteristics of the low-frequency signals in the wave field. Both the dominant wavenumber- 2 pattern in space and the propagation feature in the south Pacific, the south Atlantic and the south Indian ocean show strong consistency with the ACW. So it is reasonable to conclude that the ACW signal also exists in the wave field. The ACW is important for the climate in the Southern Ocean, so it is worth to pay more attention to the large- scale effect of the surface wave, which may also be important for climate studies.

Seasonal Evolution of Dominant Modes in South Pacific SST and Relationship with ENSO

A season-reliant empirical orthogonal function （S-EOF） analysis was applied to the seasonal mean SST anomalies （SSTAs） based on the HadISST1 dataset with linear trend removed at every grid point in the South Pacific （60.5°-19.5°S, 139.5°E-60.5°W） during the period 1979-2009. The spatiotemporal characteristics of the dominant modes and their relationships with ENSO were analyzed. The results show that there are two seasonally evolving dominant modes of SSTAs in the South Pacific with interannual and interdeeadal variations; they account for nearly 40% of the total variance. Although the seasonal evolution of spatial patterns of the first S-EOF mode （S-EOF1） did not show remarkable propagation, it decays with season remarkably. The second S-EOF mode （S-EOF2） showed significant seasonal evolution and intensified with season, with distinct characteristics of eastward propagation of the negative SSTAs in southern New Zealand and positive SSTAs southeast of Australia. Both of these two modes have significant relationships with ENSO. These two modes correspond to the post-ENSO and ENSO turnabout years, respectively. The S- EOF1 mode associated with the decay of the eastern Pacific （EP） and the central Pacific （CP） types of ENSO exhibited a more significant relationship with the EP/CP type of E1 Nifio than that with the EP/CP type of La Nifia. The S-EOF2 mode contacted with the EP type of E1 Nifio changing into the EP/CP type of La Nifia showed a more significant connection with the EP/CP type of La Nifia.

Notes on Extended-Range Atmospheric Prediction in the Northern Hemisphere Winter

We examined the characteristic feature and predictability of low frequency variability (LFV) of the atmosphere in the Northern Hemisphere winter (January and February) by using the empirical orthogonal functions (EOFs) of the geopotential height at 500 hPa. In the discussion, we used the EOFs for geostrophic zonal wind (Uznl) and the height deviation from the zonal mean (Zeddy). The set of EOFs for Uznl and Zeddy was denoted as Uznl-1, Uznl-2, ..., Zeddy-1, Zeddy-2, ..., respectively. We used the data samples of 396 pentads derived from 33 years of NMC, ECMWF and JMA analyses, from January 1963 to 1995. From the calculated scores for Uznl-1, Uznl-2, Zeddy-1, Zeddy-2 and so on we found that Uznl-1 and Zeddy-1 were statistically stable and their scores were more persistent than those of the other EOFs. A close relationship existed between the scores of Uznl-1 and those of Zeddy-1. 30-day forecast experiments were carried out with the medium resolution version of JMA global spectral model for 20 cases in January and February for the period of 1984-1992. Results showed that Zeddy-1 was more predictable than the other EOFs for Zeddy. Considering these results, we argued that prediction of the Zeddy-1 was to be one of the main target of extended-range forecasting.

A dipole mode at thermocline layer in the tropical Indian Ocean

Temperature data at different layers of the past 45 years were studied and we found adiploe mode in the thermocline layer (DMT): anomalously cold sea temperature off the coast of Sumatra and warm sea temperature in the western Indian Ocean. First, we analyzed the temperature and the temperature anomaly (TA) along the equatorial Indian Ocean in different layers. This shows that stronger cold and warm TA signals appeared at subsurface than at the surface in the tropical Indian O-cean. This result shows that there may be a strong dipole mode pattern in the subsurface tropical Indian Ocean. Secondly we used Empirical Orthogonal Functions (EOF) to analyze the TA at thermocline layer. The first EOF pattern was a dipole mode pattern. Finally we analyzed the correlations between DMT and surface tropical dipole mode (SDM), DMT and Nino 3 SSTA, etc. and these correlations are strong.

Early warning signals of abrupt temperature change in different regions of China over the past 50 years

In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann-Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5-10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.

Bias correction of sea surface temperature retrospective forecasts in the South China Sea

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.

Performance of a data-driven technique applied to changes in wave height and its effect on beach response

In this study the medium-term response of beach profiles was investigated at two sites： a gently sloping sandy beach and a steeper mixed sand and gravel beach. The former is the Duck site in North Carolina, on the east coast of the USA, which is exposed to Atlantic Ocean swells and storm waves, and the latter is the Milford-on-Sea site at Christchurch Bay, on the south coast of England, which is partially sheltered from Atlantic swells but has a directionally bimodal wave exposure. The data sets comprise detailed bathymetric surveys of beach profiles covering a period of more than 25 years for the Duck site and over 18 years for the Milford-on-Sea site. The structure of the data sets and the data-driven methods are described. Canonical correlation analysis （CCA） was used to find linkages between the wave characteristics and beach profiles. The sensitivity of the linkages was investigated by deploying a wave height threshold to filter out the smaller waves incrementally. The results of the analysis indicate that, for the gently sloping sandy beach, waves of all heights are important to the morphological response. For the mixed sand and gravel beach, filtering the smaller waves improves the statistical fit and it suggests that low-height waves do not play a primary role in the medium-term morohological resoonse, which is primarily driven by the intermittent larger storm waves.

Monitoring recent changes in snow cover in Central Asia using improved MODIS snow-cover products

Snow cover plays an important role in the fields of climatology and cryospheric science. Remotely-sensed data have been proven to be effective in monitoring snow covers. Improved methods to process the 8-day snow-cover products derived from MODIS Terra/Aqua data can dramatically increase the data quality and reduce noise. A five-step algorithm for removing cloud effects was designed to improve the quality of MODIS snow products, and the overall accuracy of the MODIS snow data without cloud（defined as cloud-free snow-cover dataset） was enhanced by more than 90% based on direct and indirect validation methods. The snow-cover frequency（SCF） and snow-cover rate（SCR） of Central Asia were analyzed from 2000 to 2015 using trend analysis and empirical orthogonal functions（EOFs）. Over the plain regions, the SCF displayed a significant north-south declining trend with a rate of 0.03 per degree of latitude, and the SCR showed a similar north-south gradient. In the mountainous areas, the SCF significantly increased with altitude by 0.12 per kilometer. Within the study area, the SCF in 65% of the study area experienced an increasing trend, but only 4.3% of the SCF-increasing pixels passed a significance test. The remaining 35% of the area underwent a decreasing trend of SCF, but only 5.2% of the SCF-decreasing pixels passed a significance test. For the entire Central Asia, the inter-annual variations of snow-cover presented a slight and insignificant increase trend from 2000 to 2015. However, the change trends of snow cover are different between the plain and mountainous regions. That is, the annual mean SCR in the plain areas displayed an increasing trend, but a decreasing trend was found in the mountainous areas.

Temporal-spatial oceanic variation in relation with the three typical Kuroshio paths south of Japan

Empirical orthogonal function(EOF)analysis was applied to a 50-year long time series of monthly mean positions of the Kuroshio path south of Japan from a regional reanalysis.Three leading EOF modes characterize the contributions from three typical paths of the Kuroshio meander:the typical large meander path,the offshore nonlarge meander path,and the nearshore non-large meander path,respectively.Accordingly,the spatial variation characteristics of oceanic anomaly fields can be depicted by their regression fields upon the associated three leading principal components(PCs),which are well-matched with the results of composite analysis corresponding to each period of the three typical Kuroshio paths.A new index for the typical large meander is defined by using the second leading PC,which is highly correlated with the Kushimoto-Uragami index.Spectral analysis of this new index series shows variability of the Kuroshio path south of Japan at time scales of about 7–8 years and 20 years.