Going electric: Incorporating marine ... - Wiley Online Library

Going electric: incorporating marine electromagnetism into ocean assimilation models N. R. Schnepf,

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1, 2

Department of Geological Sciences,

University of Colorado, Boulder, Colorado, USA. 2

Cooperative Institute for Research in

Environmental Sciences (CIRES), University of Colorado, Boulder, Colorado, USA.

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/2017MS001130

This article is protected by copyright. All rights reserved.

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Abstract.

SCHNEPF: OCEAN ASSIMILATIONS GO ELECTRIC

Having a clear multi-dimensional picture of Earth’s oceans is

crucial for understanding ocean processes and how they are evolving with climate change. Ocean knowledge is currently limited by relying on in situ data. A possible source of remote data is using the electromagnetic fields produced by the ocean and detected by geomagnetic satellites. Marine electromagnetic signals largely depend on three factors: oceanic transport, the local main magnetic field, and the electrical conductivity produced by the local salinity and temperature. Thus, how can marine electromagnetic signals be utilized to enhance the multi-dimensional picture of Earth’s ocean circulation and state?

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August 1, 2017, 3:46pm This article is protected by copyright. All rights reserved.

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1. Motivation Peering beneath the ocean’s surface is extremely challenging, but crucially important for improving our understanding of nature and creating realistic global ocean models. How does deep water mix with surface water and what is the timescale of this process? How well known are oceanic transports – especially in the regions considered crucial for the temperature- and salinity-dependent meridional-overturning circulation? Characterizing the ocean is challenging largely because oceanographers are mostly dependent on in situ measurements. While several aspects of the ocean may be remotely monitored, to develop a clear multi-dimensional picture of transport, temperature, salinity, or heat, oceanographers are dependent on in situ data collected either from buoys, moorings, or ships. Depending on instruments placed within the actual ocean limits greatly limits our knowledge. Many oceanic regions are too remote or dangerous for frequent travel, so there are several areas that completely lack data on how state properties vary with depth. Other regions are sporadically sampled causing each iteration of data collection to dramatically alter what is known of that area. Ideally, oceanographers would obtain a detailed picture of the state and circulation of the ocean by using in situ and remotely-sensed data. One possible avenue for obtaining remote oceanographic data is using the electromagnetic fields produced by the ocean and detected by geomagnetic satellites (illustrated in Figure 1). Geomagnetic satellite missions are designed to study Earth’s core dynamics, as well as the solid Earth and electric currents flowing in near-Earth space. However, the

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magnetic signals from oceanic lunar tides have been remotely detected and there is hope that the magnetic signals from ocean circulation will soon also be detected. Rather than wait for this data collection breakthrough, Irrgang et al. [2017] (recently published in JAMES) utilized synthetic satellite data to provide the first look at incorporating remote oceanic magnetic field data in general circulation data assimilation models. They also examine how incorporating remote marine magnetic data may help constrain conductivity-weighted and depth-integrated ocean velocities, as well as salinity and temperature. Their work provides an exciting introduction to examining how geomagnetic satellite data may be used to better understand Earth’s oceans.

2. Marine electromagnetism The ocean produces electromagnetic fields because salty seawater is a conducting fluid with a mean electrical conductivity of σsw = 3 − 4 S m−1 moving through the Earth’s main magnetic field (∼50 µT). While seawater’s electrical conductivity is seven orders of magnitude smaller than that of copper wires, it is still strong enough to induce significant electric fields, currents, and secondary magnetic fields. The electric current j produced by a given oceanic movement can be described as j = σ(U × Bm ). Thus, marine electromagnetic signals largely depend on three factors: transport (U), the local main magnetic field (Bm ), and the local sea water conductivity σ (which in turn depends on the local salinity S and temperature T ). Of these parameters, the local main magnetic field is already very well constrained by geomagneticians; obtaining the other parameters is the oceanographic goal. Electromagnetic (EM) fields recorded as time-varying data at observatories are the summation of all electromagnetic fields present at the observatory, no matter what source (eg.

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Earth’s core versus oceans) or process (eg. the geodynamo versus different types of marine electromagnetic induction) created the field. Isolating the EM signals corresponding to each source or process is possible using a variety of data processing techniques. In general, ocean magnetic signals are a ∼ 1 − 10 nT needle in a ∼ 50, 000 nT haystack. To isolate these marine signals ‘quiet’ data is used– i.e., data where the bulk size of the geomagnetic haystack is smaller due to calmer conditions in near-Earth space. Marine tidal magnetic signals are extracted by directly fitting for the tide’s period during spans of quiet, night-time magnetic data, whereas circulation signals have been isolated from seafloor electromagnetic data by removing other field sources from quiet data to reveal those most likely due to circulation (examples of both signals are shown in Figure 2). Both types of signals – those due to tides or those due to circulation – contain information on oceanic transport, salinity, and temperature. Furthermore, because each observed marine EM signal depends on those properties integrated throughout the water column, it does not matter if the observatory is within the ocean or above it. Of course, the further the observatory moves away from the ocean, the smaller the oceanic magnetic signals will be. Unlike ground observatories, satellites travel very quickly and their movement presents additional challenges for extracting relatively weak, spatially-small signals (such as those due to ocean circulation rather than ocean tides). Oceanic EM signals have been measured from both ground and satellite EM data. On the seafloor, telecommunication cables (both abandoned cables and active cables) can be used to examine marine electromagnetic signals because these cables effectively measure voltage variations between their two ends. Such “voltage cables” have been used to

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isolate tidal signals [Minami , 2017], as well as directly study oceanic transport [Larsen and Sanford , 1985; Larsen, 1991; Baringer and Larsen, 2001]. Meanwhile, in outer space, the CHAMP (CHAllenging Minisatellite Payload, Reigber et al. [2002]) mission used a Low Earth Orbit (LEO) satellite to collect vector magnetometer data from July 2000 to September 2010. The CHAMP mission was launched without much consideration of oceanic electromagnetic signals, nonetheless, its data enabled the first satellite detection of oceanic tidal magnetic signals [Tyler et al., 2003]. A follow-on mission, Swarm, is a constellation of three satellites flying in LEO known that was launched in November 2013 to collect vector magnetic field data. With Swarm, the magnetic signals of an additional oceanic tide has been remotely detected [Sabaka et al., 2016] and there is hope that detecting ocean circulation magnetic signals will soon follow [Friis-Christensen et al., 2006].

3. Incorporating marine electromagnetic data into data assimilations In preparation for the satellite-detection of ocean circulation magnetic signals, Irrgang et al. [2017] provide the first examination of using remotely-detected ocean magnetic signals in assimilated models. As previously discussed, marine electromagnetic signals largely depend on three factors: transport (U), the local main magnetic field (Bm ), and the electrical conductivity produced by the local salinity S and temperature T . The main question here is how can marine electromagnetic signals be used to improve our understanding of U , V , S and T across the world’s oceans? To answer this, Irrgang et al. focus on the electromagnetic signals induced by ocean circulation within a perfect model experiment. Their “true” model simulates the ocean state of January 2001, whereas an ensemble simulates the ocean state of January 2005. Assimilation of synthetic ocean

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circulation magnetic signals with an ensemble Kalman filter let Irrgang et al. demonstrate the potential value of such geomagnetic data. The synthetic ocean circulation magnetic signals were produced from the true model by applying electromagnetic induction code to the ocean state model’s output. This synthetic data is the only information from the true model used in the assimilation. The ocean state model used throughout their experiment was constrained by wind stress, surface pressure, heat flux, precipitation, and evaporation data. Thus, by comparing the results of their data assimilation ensemble to a January 2005 reference model, they test the influence of adding synthetic circulation magnetic signals to a data assimilation ensemble. Data assimilation using synthetic ocean circulation magnetic signals improved constraining transport. Conductivity-weighted and depth-integrated velocities were especially improved for the Kuroshio Current, the Gulf Stream region, and the Antarctic Circumpolar Current (ACC) near southeast Africa. In general, in the Northern Hemisphere they saw improvements constraining the transport– a promising result considering that Northern Hemisphere ocean currents induce weaker magnetic fields than the Southern Hemisphere. Unfortunately, adding geomagnetic observations degrades the simulation in some areas, especially in the Southern Hemisphere. Some of these regions are near the Southern magnetic pole, an area in which the ocean circulation signal is larger than anywhere else in the world, but unfortunately it is a slightly bigger needle in an especially huge haystack. The authors attribute the regional disparity to poor error covariances for the wind stress forcing in the Southern Ocean, rather than poor error covariances for the magnetic signal in this region. Indeed, this regional disparity in constraining transport needs to be investigated in future studies.

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State properties, such as temperature and salinity, are not as well constrained as transport. Temperature and salinity both influence the local electrical conductivity– a parameter which in turn influences the marine magnetic signal. However, the variability of electrical conductivity (3 − 4 S/m) is small in influencing marine magnetic signals when compared to geomagnetic field (∼ 20, 000 − 60, 000 nT) or transport (∼ 500Sm/s). The authors suggested a better estimate of temperature and salinity error covariances, and while improving error covariance terms is always useful, it may be more worthwhile to include additional magnetic signals (such as those caused by ocean tides) as data sources in the assimilation so there are more signals used to invert for T and S. Indeed, it would be very interesting for future studies to compare how well state properties can be constrained from data assimilations using oceanic magnetic signals from different oceanic processes, such as different lunar tides or oceanic circulation. Observations of the magnetic field are also better at constraining the velocity at depth than near the surface. It is not surprising that the magnetic signals of surface velocities (which are wind driven and relatively shallow) are out-weighed by those induced by larger scale transport occurring in underlying layers. Ideally, regardless of the signal source, oceanographers would have ample remote and in situ data for creating realistic models of the oceans. Investigating the influence of voltage data from seafloor telecommunication cables on regional data assimilation studies would be worthwhile. Such cables span swaths of the Pacific Ocean, as well as reach from the United Kingdom to the Northeastern United States. Irrgang et al. [2017] took an exciting plunge into uniting geomagnetism with physical oceanography to better understand Earth’s oceans. It seems promising to continue in-

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vestigating utilizing marine electromagnetic observations to estimate the ocean’s state. There is room for investigations to examine how constraining oceanic properties is influenced by the electromagnetic data source (i.e. seafloor versus remote) or signal (i.e. tidal vs circulation), as well as for examinations into the regional disparities in constraining transport. Using marine electromagnetic signals to help fill the void in oceanographer’s multi-dimensional view of the ocean depends on the continued exploration of incorporating marine electromagnetic data in assimilated models. Acknowledgments. This paper was enhanced by useful review and commentary from Editors Robert Pincus and Dick Dee. Discussions with Charles H. Greene, Robert H. Tyler, Manoj C. Nair, Adam Woods, and Arnaud Chulliat assisted in shaping the ideas discussed in this paper. The author is also grateful to the NASA Earth & Space Science Fellowship program and CIRES IRP 2017.

References Baringer, M. O., and J. C. Larsen (2001), Sixteen years of Florida Current Transport at 27N, Geophysical Research Letters, 28 (16), 3179–3182. Friis-Christensen, E., H. Luhr, and G. Hulot (2006), SWARM: A constellation to study the Earth’s magnetic field, Earth Planets Space, 58, 351–358. Irrgang, C., J. Saynisch, and M. Thomas (2017), Utilizing oceanic electromagnetic induction to constrain an ocean general circulation model: A data assimilation twin experiment, JAMES. Larsen, J. C. (1991), Transport measurements from in-service undersea telephone cables, IEEE Journal of Oceanic Engineering, 16 (4), 313–318, doi:10.1109/48.90893.

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Larsen, J. C., and T. B. Sanford (1985), Florida Current Volume Transports from Voltage Measurements, Science, 227 (4684), 302–304. Minami, T. (2017), Motional Induction by Tsunamis and Ocean Tides: 10 Years of Progress, Surveys in Geophysics, pp. 1–36, doi:10.1007/s10712-017-9417-3. Reigber, C., H. L¨ uhr, and P. Schwintzer (2002), Champ mission status, Advances in Space Research, 30 (2), 129–134. Sabaka, T. J., R. H. Tyler, and N. Olsen (2016), Extracting ocean-generated tidal magnetic signals from Swarm data through satellite gradiometry, Geophysical Research Letters, 43, 3237–3245, doi:10.1002/2016GL068180.Received. Tyler, R. H., S. Maus, and H. L¨ uhr (2003), Satellite observations of magnetic fields due to ocean tidal flow., Science, 299 (5604), 239–241, doi:10.1126/science.1078074.

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How to include satellite marine magnetic observations into data assimilation models to constrain U, V, S & T?

Figure 1.

Schematic explaining on marine electromagnetism and incorporating remote data

into data assimilations. Top left: seawater is an electrically conductive fluid. Due to local variations in salinity and temperature, seawater’s electrical conductivity varies across the globe. Right: as ocean currents move through Earth’s main magnetic field, they induced magnetic fields dependent on the depth-integrated transport and electric conductivity (i.e., the depth integrated salinity and temperature). Background: the Swarm mission has 3 satellites orbiting Earth to collect high quality magnetic field data. How to best incorporate remote marine magnetic signals into data assimilations?

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to a) ocean circulation magnetic induction and b) magnetic induction from the oceanic M2 lunar semi-diurnal tide. Satellite data measures both of these magnetic fields summed together with all of Earth’s other geomagnetic fields. The total amplitude of vertical geomagnetic fields at satellite height is illustrated in c).

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