Spatial Decorrelation Scales Research Articles

Spatial and temporal scales of sea surface salinity in the tropical Indian Ocean from SMOS, Aquarius and SMAP

The spatial and temporal decorrelation scales of sea surface salinity (SSS) have been calculated in the tropical Indian ocean from the satellite measurements including Soil Moisture and Ocean Salinity (SMOS), Aquarius, Soil Moisture Active Salinity (SMAP), and the model output data for the period of 2011–2017. The differences in spatial and temporal scales from different products are discussed and the physical interpretations of the scales of SSS variability are analysed. The results show that, despite the differences in spatial and temporal resolution, there is good agreement between the spatial and temporal scales of SSS field among all products. Large zonal scales (> 2000 km) and temporal scales with strong anisotropy appear in the central of the equatorial Indian Ocean (8° S–15 °S). In addition, the large meridional decorrelation lengths (~ 800 km) and temporal scales with low anisotropy are found in the southern region of the Arabian Sea (0–12 °N) for all products. The decorrelation scales of SSS in these two areas are mainly caused by freshwater flux and salinity advection, respectively. Our results provide new insights into the controlling mechanisms of SSS variability in different regions.

Large-Scale Structure in Wind Forcing over the California Current System in Summer

The wind that drives oceanic eastern boundary upwelling systems is highly variable. In many locations, the standard deviation of wind velocity on time scales of days to weeks is larger than the mean. In the ~1600-km-long California Current System (CCS), the spatial decorrelation scale of the wind fluctuations is ~400–800 km, suggesting wind fluctuations in the north and south ends of the system are not related. Yet, there is also the suggestion in the literature of a larger-scale structure in the fluctuations. Here, empirical orthogonal function (EOF) analysis of buoy and satellite wind velocities confirms the existence of that structure. This analysis covers a larger spatial domain than previous EOF studies in the CCS and, to allow for propagation of the wind fluctuations, includes an approach for calculating Hilbert EOFs from time series with gaps. The large-scale structure in the wind fluctuations is a quasi-dipole pattern spanning the coastline from Washington through California. It accounts for ~60% of the wind velocity variance on time scales of days to weeks. The time-mean wind velocity, showing a continuous zone of intensified wind along the coast, is deceptive. When the northern half of the CCS is in a relaxation state, the southern half often experiences intensified winds, and vice versa. There should be a resulting out-of-phase structure in oceanic upwelling. The out-of-phase wind fluctuations in the north and south parts of the CCS may affect the forcing of oceanic coastal-trapped waves, mesoscale eddy generation at capes, and offshore export of carbon.

Confronting weather and climate models with observational data from soil moisture networks over the United States.

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

Spatial and temporal scales of variability in Tropical Atlantic sea surface salinity from the SMOS and Aquarius satellite missions

Taking advantage of the spatially dense, multi-year time series of global Sea Surface Salinity (SSS) from two concurrent satellite missions, the spatial and temporal decorrelation scales of SSS in the Tropical Atlantic 30°N–30°S are quantified for the first time from SMOS and Aquarius observations. Given the dominance of the seasonal cycle in SSS variability in the region, the length scales are calculated both for the mean and anomaly (i.e. seasonal cycle removed) SSS fields. Different 7–10 days composite SSS products from the two missions are examined to explore the possible effects of varying resolution, bias corrections and averaging characteristics. With the seasonal cycle retained, the SSS field is characterized by strongly anisotropic spatial variability. Homogeneous SSS variations in the Tropics have the longest zonal scales of over ~ 2000 km and long temporal scales of up to ~ 70–80 days, as shown by both SMOS and Aquarius. The longest meridional scales, reaching over ~ 1000 km, are seen in the South Atlantic between ~ 10°–25°S, most discernible in Aquarius data. The longest temporal scales of SSS variability are reported by both satellites to occur in the North-West Atlantic region 15°–30°N, at the Southern end of the Sargasso Sea, with SSS persisting for up to 150–200 days. The removal of the seasonal cycle results in a noticeable decrease in the spatio-temporal decorrelation scales over most of the basin. Overall, with the exception of the differences in the South Atlantic, there is general agreement between the spatial and temporal scales of SSS from the two satellites and different products, despite differences in individual product calibration and resolution characteristics. These new estimates of spatio-temporal decorrelation scales of SSS improve our knowledge of the processes and mechanisms controlling the Tropical Atlantic SSS variability, and provide valuable information for a wide range of oceanographic and modelling applications.

동해 ARGO 플로트의 투하 전략

This study was carried out to determine the optimal number of ARGO floats in the East Sea in order to maximize their applications. The dominant spatio-temporal scale, size of the domain, and the typical float lifetimes in the East Sea were taken into consideration. The mean spatial de-correlation scale of temperature on isobaric surfaces reaches about 60 km. The minimum necessary number of floats is about 82 on average in order to secure independent ARGO profiles with the de-correlation scale. Considering the float lifetimes, about 27 floats per year should be deployed to maintain the 82 ARGO float array every year. To obtain spatially uniform distribution of ARGO float data, mean residence time and dispersion rate (basin area/residence time) of ARGO floats were evaluated in each basin of the East Sea. A faster (slower) dispersion rate requires more (less) ARGO floats to maintain the spatially uniform number of floats. According to the analysis, it is likely that the optimal ratio of the number of floats for each basin is 1:2:4 corresponding to Ulleung Basin:Yamato Basin:Japan Basin. In order to maintain relatively uniform ARGO observing networks, it is necessary to establish a long-term plan for deployment strategy based on float pathways and the dispersion rate parameters estimated by using currently active ARGO float trajectory data as well as reanalysis data.

Bayesian methodology for inverting satellite ocean-color data

The inverse ocean color problem, i.e., the retrieval of marine reflectance from top-of-atmosphere (TOA) reflectance, is examined in a Bayesian context. The solution is expressed as a probability distribution that measures the likelihood of encountering specific values of the marine reflectance given the observed TOA reflectance. This conditional distribution, the posterior distribution, allows the construction of reliable multi-dimensional confidence domains of the retrieved marine reflectance. The expectation and covariance of the posterior distribution are computed, which gives for each pixel an estimate of the marine reflectance and a measure of its uncertainty. Situations for which forward model and observation are incompatible are also identified. Prior distributions of the forward model parameters that are suitable for use at the global scale, as well as a noise model, are determined. Partition-based models are defined and implemented for SeaWiFS, to approximate numerically the expectation and covariance. The ill-posed nature of the inverse problem is illustrated, indicating that a large set of ocean and atmospheric states, or pre-images, may correspond to very close values of the satellite signal. Theoretical performance is good globally, i.e., on average over all the geometric and geophysical situations considered, with negligible biases and standard deviation decreasing from 0.004 at 412nm to 0.001 at 670nm. Errors are smaller for geometries that avoid Sun glint and minimize air mass and aerosol influence, and for small aerosol optical thickness and maritime aerosols. The estimated uncertainty is consistent with the inversion error. The theoretical concepts and inverse models are applied to actual SeaWiFS imagery, and comparisons are made with estimates from the SeaDAS standard atmospheric correction algorithm and in situ measurements. The Bayesian and SeaDAS marine reflectance fields exhibit resemblance in patterns of variability, but the Bayesian imagery is less noisy and characterized by different spatial de-correlation scales. Experimental errors obtained from match-up data are similar to the theoretical errors determined from simulated data. Regionalization of the inverse models is a natural development to improve retrieval accuracy, for example by including explicit knowledge of the space and time variability of atmospheric variables.

Complementary use of Wave Glider and satellite measurements: Description of spatial decorrelation scales in Chl-a fluorescence across the Pacific basin

A key challenge for ecosystem science in the 21st century is to characterize emerging trends in ecosystem productivity due to climate change and to better predict cycles in ecosystem variability. A first step toward this goal is to be able to characterize phytoplankton variability across a wide range of spatial and temporal scales. In this paper, 15 months of Wave Glider (WG) fluorometer measurements made across the Pacific Ocean were used to understand how WGs complement existing chlorophyll-a-based measurements of phytoplankton biomass from satellite platforms. Extensive analysis of the WG transects demonstrated that WG fluorometer readings reliably characterized similar large-scale variability in satellite Chl-a measurements in four distinct ecosystem types including coastal upwelling, transition zone, oligotrophic and equatorial upwelling regions. Complementary information provided by WG measurements included better resolution of coastal Chl-a patches and prominent diel cycles in the open ocean. The decorrelation scales computed from WG fluorometer measurements in this study provide necessary information for designing observing systems, process experiments, and data assimilation studies. We conclude this paper by suggesting how WGs can be used to anchor satellite measurements and to develop better predictive models.

Multivehicle coverage control for a nonstationary spatiotemporal field

This paper presents a multivehicle sampling algorithm to generate trajectories for nonuniform coverage of a nonstationary spatiotemporal field characterized by spatial and temporal decorrelation scales that vary in space and time, respectively. The sampling algorithm described in this paper uses a nonlinear coordinate transformation that renders the field locally stationary so that existing multivehicle control algorithms can be used to provide uniform coverage. When transformed back to the original coordinates, the sampling trajectories are concentrated in regions of short spatial and temporal decorrelation scales. For fields with coupled spatial statistics, i.e., the spatial decorrelation scales are functions of both spatial dimensions, the coordinate transformation is implemented numerically, whereas for decoupled spatial statistics, the transformation is expressed analytically. We show that the analytical transformation results in vehicle motion that preserves the vehicle sampling speed (which is a measure of vehicle speed scaled by the ratio of the spatial and temporal decorrelation scales), in the original domain; the sampling speed determines the minimum number of vehicles needed to cover a spatiotemporal domain. Theoretical results are illustrated by numerical simulations.

Monitoring of harmful algal blooms in the era of diminishing resources: A case study of the U.S. West Coast

Spatial and temporal decorrelation scales in phytoplankton bloom magnitudes are reviewed with the goal of informing the design of efficient and informative observing networks for monitoring of potentially harmful algal blooms (HABs) along the U.S. West Coast. Our analysis of historic MODIS Fluorescent Line Height data shows that, unlike several previous studies, seasonal timing of phytoplankton blooms off the U.S. West Coast propagates from South to North. In situ data show that temporal decorrelation scales are shortest off Northern California (∼4 days) and longest in the Southern California Bight (∼17 days). In a cross-shore direction, we find that variability in the near-shore biomass is decoupled from biomass further offshore (∼2–4km).Our review of the cloud cover gap statistics suggests that satellite data provide reasonably inexpensive information about bloom events, particularly at seasonal to inter-annual scales, but is insufficient to capture many event-scale blooms. Absent adequate satellite data, in situ monitoring becomes essential. Existing networks of automated monitoring sites from piers and moorings off the California coast is insufficient to describe regional variability in blooms, but is likely informative of bloom magnitudes in the immediate proximity of observation stations. We suggest that a more effective network will have a combination of shore-based stations and a few (5–10) offshore moorings. Shore-based stations should be located in proximity to sensitive HAB targets. This would provide monitoring of existing conditions and guide decision-making about beach closures and aquaculture management practices. Offshore stations can serve as proxy for regional conditions and can be used to issue early warnings of potential HAB conditions developing in a specific region.

Spatial–temporal scales of synchrony in marine zooplankton biomass and abundance patterns: A world-wide comparison

Large scale synchrony in the fluctuations of abundance or biomass of marine fish populations in regions on opposite sides of an ocean basin or in different oceans have been viewed as externally forced by correlated environmental stochasticity (e.g., common external forcing), most often as atmospheric teleconnections of basin-to-global scale atmospheric forcing, such as the Arctic Oscillation, North Atlantic Oscillation or the Pacific Decadal Oscillation. Specific causal mechanisms have been difficult to unequivocally discover, but possible mechanisms include influences on habitat temperatures, productivity operating through bottom-up (trophodynamic) mechanisms or direct climate influence on the fish populations (top-down mechanisms). For small pelagic fishes (sardines and anchovies) in widely separated large marine ecosystems that lack obvious ocean interconnectivity, it has been argued that the teleconnections may be atmospheric, acting on the fishes directly and propagating to the ecosystem from the middle out (wasp-waist species). Zooplankton biomass or abundance time series data from >100 sites world-wide are used to examine the spatial scales of coherent temporal synchrony. If spatially correlated environmental factors (like climate) are important for creating synchrony in fish populations via bottom-up effects (trophic interactions involving fish prey, e.g., zooplankton), then we would expect to observe synchrony in fluctuations of zooplankton biomass/numbers at spatial scales similar to those found for fish species. Zooplankton biomass/abundance have 50% spatial decorrelation scales of ca. 700–1400km and scales of significant coherence that extend to separation distances of ca. 3000km. These are also the spatial scales of environmental (sea surface temperature) synchrony from our global analysis. These scales are slightly greater than the 50% decorrelation scales of ca. 150–700km for recruitment synchrony in Atlantic marine fish and survival and recruitment synchrony of Northeast Pacific salmonids (150–1000km depending on species). However, the spatial scales of synchrony of annual zooplankton biomass anomalies in the Humboldt Current, California Current and Kuroshio ecosystems of the Pacific are much too small (ca. 2000km) to be directly causal of the basin-scale (7000–15,000km) synchrony exhibited by sardine and/or anchovy populations in those ecosystems.

The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

Eddy length scales in the North Atlantic Ocean

Eddy length scales are calculated from satellite altimeter products and in an eddy‐resolving model of the North Atlantic Ocean. Four different measures for eddy length scales are derived from kinetic energy densities in wave number space and spatial decorrelation scales. Observational estimates and model simulation agree well in all these measures near the surface. As found in previous studies, all length scales are, in general, decreasing with latitude. They are isotropic and proportional to the local first baroclinic Rossby radius (Lr) north of about 30°N, while south of 30°N (or for Lr > 30 km), zonal length scales tend to be larger than meridional ones, and (scalar) length scales show no clear relation to Lr anymore. Instead, they appear to be related to the local Rhines scale. In agreement with a recent theoretical prediction by Theiss [2004], the observed and simulated pattern of eddy length scales appears to be indicative of two different dynamical regimes in the North Atlantic: anisotropic turbulence in the subtropics and isotropic turbulence in the subpolar North Atlantic. Both regions can be roughly characterized by the ration between Lr and the Rhines scales (LR), with LR > Lr in the isotropic region and LR < Lr in the anisotropic region. The critical latitude that separates both regions, i.e., where LR = Lr, is about 30°N.

Seasonal Variation of Space/Time Statistics of Short-Term Sea Surface Temperature Variability in the Kuroshio Region

Spatial and temporal scales of sea surface temperature (SST) variations in the Kuroshio region have been investigated using a satellite-based one-year merged SST product. Targeting short-term variations with temporal scales of less than a year, decorrelation scales, which are defined as the e-folding scale of SST variability, have been derived as functions of regional positions and calendar months. We assumed that the autocorrelation function of SST has anisotropic Gaussian characteristics in the space-time domain. Resultant spatial and temporal decorrelation scales range from 1 to 3° and 2 to 3 days, respectively. They are strongly inhomogeneous, anisotropic and time-dependent. These characteristics are attributed to the oceanic and atmospheric disturbances. Spatial decorrelation scales are determined mainly by strong atmospheric forcing in the study region. In the area with dominant atmospheric forcing, the spatial scales are larger than those in the other regions. Those in the regions with dynamical oceanographic disturbances are as small as 1°. Signal-to-noise ratios are also large where the atmospheric forcing is strong, while they are small where the oceanic signals are active.

Seasonal circulation fields in the northern Gulf of Mexico calculated by assimilating current meter, shipboard ADCP, and drifter data simultaneously with the shallow water equations

Velocity measurements from current meter moorings, shipboard acoustic Doppler current profilers (ADCPs), and satellite-tracked drifters are used to estimate the seasonally averaged barotropic circulation throughout the continental shelf in the northern Gulf of Mexico in two regions: the Northeast Gulf of Mexico (NEGOM) shelf and the Louisiana–Texas (LATEX) shelf. A variational assimilation approach (weighted least squares) is used to combine the data simultaneously with a system of dynamical equations (time-independent shallow water equations), where the weights are based on the expected errors in the dynamical equations and measurements. Smoothing and boundary constraints are also included in this system to impose a spatial decorrelation scale and to restrict the component of flow that is perpendicular to the coastline, respectively. Seasonal solutions are therefore best fits to the weighted measurements, dynamics, and constraints, and thus do not fit any component exactly. Analysis of the solution deviations to the dynamical equations reveal where and during which seasons the dynamics and data are in disagreement. The largest deviations from the proposed dynamics occur at the head of the DeSoto Canyon on the NEGOM shelf and at the southwest corner of the LATEX shelf. Therefore, in these areas, there are processes occurring that are not described by the simplified barotropic dynamics. At the head of the DeSoto Canyon, the large source of error is believed to be due to deep-ocean water penetrating across the shelfbreak, whereas the process that occurs at the southwest corner of the LATEX shelf is proposed to be a result of a convergence zone caused by loop current eddies. Error analysis reveals that current meter data are more capable of constraining the solution than the other two data types due to its ability to more accurately observe barotropic currents. Separate experiments are conducted using the shipboard ADCP and drifter data sets individually to determine the relative observation capability. These experiments reveal that both data types provide reasonable estimations of seasonal flow fields. Results on the LATEX shelf suggest that shipboard ADCP data is more capable of observing the seasonal barotropic flow than drifter data. Whereas, on the NEGOM shelf results imply that drifter data are more capable. This result, however, may be skewed due to the shipboard ADCP and drifter data sets being collected during different years.

Time-Dependent Spatial Wavenumber Spectra Derived from Merged Sea Surface Temperature Data

Monthly wavenumber spectra of sea surface temperatures (SST) have been estimated in two regions near the Kuroshio, in the recirculation and the Kuroshio Extension regions, using the merged SST product to determine the statistical parameter (spatial decorrelation scale) required for optimal interpolation of a high-resolution SST dataset. The two-dimensional wavelet transform was used for analysis. Estimates were made of daily mean and daily minimum SSTs. These do not significantly differ, which suggests that the same covariance matrix can be used for the daily mean and minimum in the merging procedure. The seasonality of wavenumber spectra is significant. There are also large differences between those in the recirculation region and in the Kuroshio Extension region. Therefore, it is recommended that the covariance matrix in the merging process for high-resolution SST dataset be defined as a function of time and space. Improvements of the merging methodology are discussed.

Japan Sea Thermohaline Structure and Circulation. Part III: Autocorrelation Functions

The autocorrelation functions of temperature and salinity in the three basins (Ulleung, Japan, and Yamato Basins) of the Japan/East Sea are computed using the U.S. Navy’s Master Oceanographic Observational Dataset for 1930‐97. After quality control the dataset consists of 93 810 temperature and 50 349 salinity profiles. The decorrelation scales of both temperature and salinity were obtained through fitting the autocorrelation function into the Gaussian function. The signal-to-noise ratios of temperature and salinity for the three basins are usually larger than 2. The signal-to-noise ratio of temperature is greater in summer than in winter. There is more noise in salinity than in temperature. This might be caused by fewer salinity than temperature observations. The autocorrelation functions of temperature for the three basins have evident seasonal variability at the surface: less spatial variability in the summer than in the winter. The temporal (spatial) decorrelation scale is shorter (longer) in the summer than in the winter. Such a strong seasonal variability at the surface may be caused by the seasonal variability of the net surface heat flux. The autocorrelation functions of salinity have weaker seasonal variability than those of the temperature field. The temporal and horizontal decorrelation scales obtained in this study are useful for designing an optimal observational network.

Spatial correlation patterns in coastal environmental variables and survival rates of salmon in the north‐east Pacific Ocean

We examined spatial correlations for three coastal variables [upwelling index, sea surface temperature (SST), and sea surface salinity (SSS)] that might affect juvenile salmon (Oncorhynchus spp.) during their early marine life. Observed correlation patterns in environmental variables were compared with those in survival rates of pink (O. gorbuscha), chum (O. keta), and sockeye (O. nerka) salmon stocks to help identify appropriate variables to include in models of salmon productivity. Both the upwelling index and coastal SST were characterized by strong positive correlations at short distances, which declined slowly with distance in the winter months, but much more rapidly in the summer. The SSS had much weaker and more variable correlations at all distances throughout the year. The distance at which stations were no longer correlated (spatial decorrelation scale) was largest for the upwelling index (> 1000 km), intermediate for SST (400–800 km in summer), and shortest for SSS (< 400 km). Survival rate indices of salmon showed moderate positive correlations among adjacent stocks that decreased to zero at larger distances. Spatial decorrelation scales ranged from approximately 500 km for sockeye salmon to approximately 1000 km for chum salmon. We conclude that variability in the coastal marine environment during summer, as well as variability in salmon survival rates, are dominated by regional scale variability of several hundred to 1000 km. The correlation scale for SST in the summer most closely matched the observed correlation scales for survival rates of salmon, suggesting that regional‐scale variations in coastal SST can help explain the observed regional‐scale covariation in survival rates among salmon stocks.

Reply to “Comment on ‘A parametric model for the Yellow Sea thermal variability’ by P. C. Chu et al.”

In the paper by Chu et al. [1997a] (hereinafter referred to as CETAL) we developed a thermal parametric model that was capable of estimating physical parameters from historical profile data. The output parameters included sea surface temperature (SST), mixed-layer depth (MLD), thermocline depth (THD), thermocline temperature difference (TTD), and deep layer stratification. On the basis of the U.S. Navy's Master Oceanographic Observation Data Set (MOODS) that was taken in the Yellow Sea (YS) from 1950 to 1988, we computed the thermal structure functions and spatial decorrelation scales of the water properties in the Yellow Sea [Chu et al., 1997b]. The results shown in our papers were derived from •35,658 profiles, which have provided us with the statistical seasonal variations of the thermal structure in this region. To our knowledge, these issues have not been addressed in previous observational and modeling studies in the Yellow Sea. We appreciate Lie's [1999] (hereinafter referred to as L99) comments on our two papers. The points he raised, to a certain extent, resulted from an unclear explanation of some aspects of our analysis and from a limitation on access to Asian (especially Korean and Chinese) journals. We were unaware of previous studies on the YS thermal features reported in the Asian literature, such as Kang [1985], Lie [1984], Lie et al. [1986], Nakao [1977], Seung et al. [1990], and Zhao [1989]. On the other hand, most previous work was based on individual hydrographic surveys. Those studies did provide some insights into the seasonal pattern of the thermal structure in the YS for particular years, but they lacked statistical meaning. MOODS has included more than 39 years of observational data in the YS, and our study was the first to provide a statistical pattern of the seasonal variation of the thermal structure in this region. Detailed responses and explanations to L99's comments are given as follows.

Temporal and spatial scales of the Yellow Sea thermal variability

This paper presents an analysis on the space/time statistical thermal structure in the Yellow Sea from the Navy's Master Observation Oceanography Data Set during 1929–1991. This analysis is for the establishment of an Optimum Thermal Interpolation System of the Yellow Sea (a shallow sea), for the assimilation of observational data into coastal σ coordinate ocean prediction models (e.g., the Princeton Ocean Model), and for the design of an optimum observational network. After quality control the data set consists of 35,658 profiles. Sea surface temperatures at 50% and 80% water depths are presented here as representing the thermal structure of surface, middepth, and near‐bottom layers. In the Yellow Sea shelf the temporal and spatial signals fluctuate according to the Asian monsoon. Variation of surface forcing from winter to summer monsoon season causes the change of the thermal structure, including the decorrelation scales. Our computation shows that the seasonal variation of the surface horizontal decorrelation scale is around 90 km from 158 km in winter to 251 km in summer and the seasonal variation of the surface temporal decorrelation scale is around 2.4 days from 14.7 days in winter to 12.3 days in summer. The temporal decorrelation scale increases with depth in both summer (evident) and winter (slight). The near‐bottom water (σ=0.8) has the longest temporal scale in summer, which could be directly related to the existence of the Yellow Sea Cold Water throughout the summer in the middle of the Yellow Sea. The temporal and spatial decorrelation scales obtained in this study are useful for running optimum interpolation models and for designing an optimum observational network. The minimum sampling density required to detect thermal variability in the Yellow Sea shelf would be 50–80 km and 4–6 day intervals per temperature measurement with the knowledge that the subsurface features will also be adequately sampled.

Scales of spatial and temporal variability in the Southern Ocean

Spatial and temporal variability in Geosat altimeter data is used to consider whether the Antarctic Circumpolar Current (ACC) responds on the large scale to global changes in wind forcing or whether its variability is primarily mesoscale. The Geosat data indicate a spatial decorrelation scale of 85 km and a temporal e‐folding scale of 34 days. Using these scales to define autocovariance functions, the sea surface height variability is objectively mapped. The resulting maps indicate substantial evidence of mesoscale eddy activity. Over 17‐day time intervals, meanders of the Polar Front and Subantarctic Front appear to elongate, break off as rings, and propagate. Statistical analysis of ACC variability from altimeter data is conducted using empirical orthogonal functions (EOFs). The first‐mode EOF describes 16% of the variance in total sea surface height across the ACC; reducing the domain into basin scales does not significantly increase the variance represented by the first EOF, suggesting that the scales of motion are relatively short and may be determined by local instability mechanisms rather than larger basin‐scale processes. Likewise, neither complex nor extended EOFs indicate statistically significant traveling wave modes.