Reconstruction of Large-Scale Sea Surface Temperature and Salinity Fields Using Sub-Regional EOF Patterns From Models

Jüri Elken,Mihhail Zujev,Priidik Lagemaa,Jun She

doi:10.3389/feart.2019.00232

Abstract

A method for reconstruction of gridded fields of sea surface variables from time-dependent observations, using sub-regional EOF (Empirical Orthogonal Functions) patterns from models, is presented and tested. Covariance fields, calculated from the model results over long enough time span, are used to find EOF modes. The gravest “observational” amplitudes and their first temporal derivatives are determined from the least-square minimization of fitting errors in relation to the observed values. The field is reconstructed by superposition of continuous model-based mode patterns multiplied by observational amplitudes that meet adopted statistical limits. If the observational amplitude exceeds the limits, gridded fields for this and higher modes are not produced. We applied the method in the northeastern Baltic over the model time series 2010-2015. Daily averages of sea surface temperature (SST) and salinity (SSS) from the high-resolution (grid step 0.5 nautical miles) sub-regional HBM model were spatially averaged over bins of 5x5 nautical miles. Three first modes cover 99% of variance of temperature and 61.4% of salinity. As shown by experiments with pseudo-observations (model values at these points reconstructed to the model grid and then compared with the original model data), reconstruction performance depends on the configuration of the observation points in the model domain. Still, a few first modes usually produce acceptable results. When removing the SST seasonal cycle prior to EOF analysis, spatial patterns of leading modes remained practically unchanged, share of variance of the three first modes was reduced to 88,6% and reconstruction errors were reduced by about 25%. Sufficient spatial data coverage of the larger basin with ship-born observations usually takes quite long time – of the order of month; therefore, time correction of the amplitudes using the found temporal derivatives improves the accuracy of reconstruction. The method is compared with the Optimal Interpolation (OI) by using the pseudo-observations. Results show that, for SST reconstruction, the OI method is significantly worse than the EOF method. For SSS, OI is slightly better than EOF. The superiority of EOF is that the remote correlation patterns can be used in the reconstruction, which is important when the observations are sparse.

Highlights

Many oceanographic tasks require appropriate reconstruction of gridded fields from different observational data: shipborne monitoring, coastal stations, offshore buoy stations, FerryBoxes, gliders and remote sensing
While our sea surface salinity (SSS) map is close to the yearly climatological map (Janssen et al, 1999) sea surface temperature (SST) is in the Gulf of Finland higher by 1–1.5◦C and in the Gulf of Riga by 0.5–1◦C
In the smaller sea regions, which are affected by the same large-scale forcing patterns, the Empirical Orthogonal Functions (EOFs) patterns have obvious physical interpretations and their shape does not depend very much on the selection of boundaries

Summary

Introduction

Many oceanographic tasks require appropriate reconstruction of gridded fields from different observational data: shipborne monitoring, coastal stations, offshore buoy stations, FerryBoxes, gliders and remote sensing. Meteorology and oceanography share the same theoretical foundations of interpolation and data assimilation (Ghil and Malanotte-Rizzoli, 1991; Ide et al, 1997) Their practical implementation is, rather different (Ghil, 1989), owing to the nature of governing processes (landlocked basins, shallow areas and wind driving characterize oceans; atmosphere is unbounded, “deep” and self-driving by polar-tropical gradients), and of techniques and amount/density of observations. Marginal seas and/or their subbasins which have typical lateral dimensions less than 1000 km (typical Rd in the atmosphere), are forced by the same or neighboring weather patterns This causes for example coherent upwelling/downwelling patterns (Lehmann et al, 2012) on the left-hand/right-hand coasts from the direction of weathergenerated wind. Considering faster heating or cooling of shallow coastal areas compared to the deeper offshore regions (Legrand et al, 2015), and freshwater spreading patterns due to the dynamics of river plumes (Soosaar et al, 2016), there could be significant covariance of sea surface temperature (SST) and sea surface salinity (SSS) in marginal seas over large distances, mainly stretched along the topography isolines and/or coasts (Fu et al, 2011)

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Results

Discussion

Conclusion