The well‐known fact that tropical sea level can be usefully simulated by linear wind driven models recommends it as a realistic test problem for data assimilation schemes. Here we report on an assimilation of monthly data for the period 1975–1992 from 34 tropical Pacific tide gauges into such a model using a Kalman filter. We present an approach to the Kalman filter that uses a reduced state space representation for the required error covariance matrices. This reduction makes the calculation highly feasible. We argue that a more complete representation will be of no value in typical oceanographic practice, that in principle it is unlikely to be helpful, and that it may even be harmful if the data coverage is sparse, the usual case in oceanography. This is in part a consequence of ignorance of the correct error statistics for the data and model, but only in part. The reduced state space is obtained from a truncated set of multivariate empirical orthogonal functions (EOFs) derived from a long model run without assimilation. The reduced state space filter is compared with a full grid point Kalman filter using the same dynamical model for the period 1979–1985, assimilating eight tide gauge stations and using an additional seven for verification [Miller et al., 1995]. Results are not inferior to the full grid point filter, even when the reduced filter retains only nine EOFs. Five sets of reduced space filter assimilations are run with all tide gauge data for the period 1975–1992. In each set a different number of EOFs is retained: 5, 9, 17, 32, and 93, accounting for 60, 70, 80, 90, and 99% of the model variance, respectively. Each set consists of 34 runs, in each of which one station is withheld for verification. Comparing each set to the nonassimilation run, the average rms error at the withheld stations decreases by more than 1 cm. The improvement is generally larger for the stations at lowest latitudes. Increasing the number of EOFs increases agreement with data at locations where data are assimilated; the added structures allow better fits locally. In contrast, results at withheld stations are almost insensitive to the number of EOFs retained. We also compare the Kalman filter theoretical error estimates with the actual errors of the assimilations. Features agree on average, but not in detail, a reminder of the fact that the quality of theoretical estimates is limited by the quality of error models they assume. We briefly discuss the implications of our work for future studies, including the application of the method to full ocean general circulation models and coupled models.
Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.
The intensity of the 1997 El Niño and the 8°C sudden drop in sea surface temperature (SST) around 0°–130°W during the turn into La Niña in 1998 were a surprise to the scientific community. This succession of warm and cold events was observed from start to finish with a comprehensive set of remotely sensed and in situ observations. In this study we employ space‐based observations to demonstrate, for the first time, their maturity in capturing the preconditioning, onset, evolution, and decay of the 1997 El Niño and its transition into the 1998 La Niña. An accumulation of warm water in the west and equatorial wave reflection on the western ocean boundary appeared favorable for the development of El Niño. However, the action of a series of westerly wind bursts from December 1996 to June 1997, notably in March 1997, was instrumental in setting up this huge El Niño. The westerly wind bursts excited equatorial downwelling Kelvin waves and advected the eastern edge of the warm pool eastward, which triggered a distinct warming over the central and eastern parts of the equatorial basin. Once these warmed regions joined, the coupling between the SST and surface winds was fully effective, and El Niño reached its mature phase. By that time much of the warm waters of the western equatorial Pacific was transferred toward the east by surface eastward currents. The demise of El Niño and its turn into La Niña in spring 1998 were due to the arrival in the east of various interrelated phenomena. Upwelling was brought from the west by favorable off‐equatorial wind stress curl and equatorial Kelvin waves generated by easterly winds and wave reflection on the western ocean boundary. Additional upwelling was brought from the east by equatorial Rossby waves generated by westerly winds. These various upwelling signals were added to the general uplifting of the thermocline because of the slow discharge of the upper layer of the equatorial basin by diverging surface currents. A series of equatorial Kelvin and Rossby waves, characterized by upwelling and opposite surface currents, led to the breakup of the warm waters, the surfacing of the thermocline, and the drastic drop in SST in May 1998 around 0°–130°W. With the arrival of cold water in the east the easterly winds expanded from the west, and La Niña turned into a growing mode. This view of the 1997–1998 El Niño–La Niña, afforded from space, enables the testing of various El Niño theories.
[1] El Niño-Southern Oscillation (ENSO) properties can be modulated by many factors; most previous studies have focused on physical aspects of the climate system in the tropical Pacific. Ocean biology-induced feedback (OBF) onto physics and bio-climate coupling have been the subject of much recent interest, revealing striking model dependence and even conflicting results. Current satellite data are able to resolve the space-time structure of oceanic signals both in biology and physics, providing an opportunity for quantifying their relationships. Here we use the biological signature from satellite ocean color data to estimate interannual variability of the attenuation depth of solar radiation (H p ), a field linking ocean biology and physics. We then apply a singular value decomposition (SVD) analysis to interannual H p and sea surface temperature (SST) anomaly fields to derive an empirical H p model which is incorporated in a hybrid coupled ocean-atmosphere model of the tropical Pacific to represent the OBF. It is shown that the OBF can have significant effects on ENSO behaviors, including its amplitude, oscillation periods and seasonal phase locking.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.