A Window to the Future: Why Greenland’s Continental Shelves Hold the Reins of Its Melting Glaciers

 By Josh Willis, Michael Wood and Ian Fenty

We were excited to learn that the Arctic Ocean Workshop will include the Sub-Arctic Oceans and the seas surrounding Greenland. Over the last two decades, researchers have established a clear connection between ocean conditions on the continental shelf and the behavior of Greenland’s more than 200 marine terminating glaciers. But there is still no comprehensive system for monitoring these changes over the long term—such as the one proposed by Straneo et al. (2019)—and it is desperately needed.

By 2050, as many as 350 million people could be affected by the rising oceans (Kulp and Strauss, 2019), and Greenland is currently the largest contributor to global sea level rise. Furthermore, there is now compelling evidence that the ocean conditions surrounding Greenland play a critical role in regulating the total ice loss (Wood et al., 2021). But despite the importance of these ocean waters in driving glacier retreat, historical observations of temperature and salinity on the continental shelf are quite sparse (see, for example, Figure 1 in the excellent blog post by Patrick Heimbach).

This picture changed dramatically in 2016, however, with the start of our NASA-funded airborne mission, Oceans Melting Greenland (OMG).  In addition to making widespread bathymetric surveys of the continental shelf and yearly surveys of the ice elevation at the edges of the glaciers, OMG collected approximately 250 temperature and salinity profiles each year, spread across the entire continental shelf (See Fenty et al, for a full overview of the mission).  OMG’s primary aim was to help establish a connection between wide-spread ocean conditions and glacier retreat. In this regard it was a success.  But it also made it clear that there is a critical need for ongoing measurements of the large-scale temperature and salinity changes on shelves surrounding the ice sheet.

On the continental shelf, warm, salty water of mostly Atlantic origin lies beneath a layer of colder, fresher water of mostly Artic origin (see Figure 1).   Because this warm water sits 100 to 200 m below the ocean surface, it is almost impossible to observe remotely.  This means that direct, in situ observations of waters on the shelf will continue to be critically important for explaining ongoing ice loss and projecting future sea level rise.

Figure 1.A cutaway diagram of a typical Greenland glacier in its fjord.  On the shelf, a layer of warm, salt water typically sits 100 to 200 meters below a layer of colder, fresher water. Most Greenland glaciers end in a vertical face like the one shown here. A plume of subsurface run off rises up the face of the glacier, pulling in the warm water and undercutting the glacier.

 Observations from OMG also show that large-scale patterns of ocean temperature change do occur on the continental shelf, and that these patterns can persist for several years.  Figure 2 shows locations of all the temperature and salinity profiles collected during a typical yearly ocean survey.  The survey is conducted by aircraft and typically takes 3-6 weeks to deploy approximately 250 expendable air-launched conductivity, temperature and depth sensors (AXCTDs).  The insets show average temperature profiles from three different survey years, over three different regions along the west coast, along with a one-standard error uncertainty bound.   The subsurface temperature maximum of approximately 2°C clearly shows the deep, warm layer.  And between 2016 and 2017, this layer cooled by almost 1°C in all three regions. This widespread cooling, likely driven by changes in the North Atlantic Oscillation, was shown to have a major impact on Greenland’s largest glacier (as ranked by discharge). After nearly two decades of thinning and retreat, Jakobshavn grew thicker for three years in a row when the cool water reached its fjord (Khazendar et al., 2019).

Figure 2. A map of the survey plan for the OMG ocean survey. Each yellow dot is the location where each temperature and salinity profile is collected (right panel). The insets show average temperature profiles in the regions shown for 2016 (blue), 2017 (green) and 2018 (red). The width of the curves is one standard error.  A 1°C cooling is visible in the warm deep layer along the entire west coast during these years.

And Jakboshavn was not alone in its reaction to these changes in ocean temperature. A comprehensive assessment of glacier retreat in Greenland was recently published by OMG investigator, Mike Wood (Wood et al., 2021). Figure 3 shows one of the central results from the work.  Using an ECCO ocean state estimate to extend the record of ocean temperature changes back to the mid-1990s, Mike and company showed that the average retreat among all of Greenland’s 226 marine terminating glaciers increased as ocean temperatures warmed, and decreased as they cooled down again.  They also found that including the full impact of ocean warming will increase current projections of sea level rise a factor of 2 or more.

Figure 3. Average glacier retreat (black) and depth-averaged ocean temperatures (red) from the ECCO ocean state estimate. The period of ocean warming coincides with faster average retreat across all of Greenland's 226 marine terminating glaciers.

This narrow strip of ocean on the continental shelf surrounding the Greenland Ice Sheet plays a key role in controlling ice loss.  But after 2021, the OMG experiment will end. And although a few key glaciers will continue to be measured along with yearly surveys in the southwest, the wide-spread measurements on the shelf will cease.  Given their importance, it seems clear to us that these regions must continue to be monitored.

And we are not alone.  The argument for sustained ocean observations has been made quite clearly for expansion of sustained observing systems that serve a variety of scientific and societal purposes (Weller et al., 2019). For Greenland ice loss this means, at the very least continuing the wide-spread collection of temperature and salinity observations around the continental shelf for decades to come, as the ice sheet continues to melt and drive sea levels higher around the globe.

A long-term system for observing these waters may look quite different than the OMG surveys of the past 5 years. The one proposed by Straneo et al. (2019) employs a wide variety of measurement systems and serves a variety of purposes. Expansion of the Argo Array of Profiling floats to cover marginal seas has long been discussed as a priority, but not yet funded in this region. OMG has tested Argo-like floats on the shelf, with some promising results that suggest floats of this type could be part of a viable solution. Gliders and other new systems may also play a key role. But regardless of the observing system we choose, the waters surrounding Greenland provide a window into the future of sea level rise, and we must not let that window close.

References

Fenty, I., Willis, J. K., Khazendar, A., DiNardo, S., Forsberg, R., Fukumori, I., et al. (2016), Oceans melting Greenland: Early results from NASA's ocean–ice mission in Greenland. Oceanography, 29(4), 72–83, https://doi.org/10.5670/oceanog.2016.100.

Khazendar, A., Fenty, I. G., Carroll, D., Gardner, A., Lee, C. M., Fukumori, I., Wang, O., Zhang, H., Seroussi, H., Moller, D., Noel, B. P. Y., Van Den Broeke, M. R., DiNardo, S., and Willis, J., (2019), Interruption of two decades of Jakobshavn Isbrae acceleration and thinning as regional ocean cools, Nat. Geosci., 12, 277–283, https://doi.org/10.1038/s41561-019-0329-3.  

Kulp, S.A., Strauss, B.H. (2019), New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding, Nat. Commun., 10, 4844 (2019), https://doi.org/10.1038/s41467-019-12808-z.

Straneo F, Sutherland DA, Stearns L, Catania G, Heimbach P, Moon T, Cape MR, Laidre KL, Barber D, Rysgaard S, Mottram R, Olsen S, Hopwood MJ and Meire L (2019), The Case for a Sustained Greenland Ice Sheet-Ocean Observing System (GrIOOS), Front. Mar. Sci., 6:138, https://doi.org/10.3389/fmars.2019.00138.

Weller RA, Baker DJ, Glackin MM, Roberts SJ, Schmitt RW, Twigg ES and Vimont DJ (2019), The Challenge of Sustaining Ocean Observations, Front. Mar. Sci., 6:105, https://doi.org/10.3389/fmars.2019.00105.

Wood, M. et al. (2021), Ocean forcing drives glacier retreat in Greenland, Science Advances, 01 Jan 2021: Vol. 7, no. 1, https://doi.org/10.1126/sciadv.aba7282. 

 

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State estimation and observing system design in support of Arctic Ocean Observing

By Patrick Heimbach, Nora Loose, An T. Nguyen, Helen Pillar, and Timothy Smith

https://crios-ut.github.io

 In previous posts Jamie Morison and Mary-Louise Timmermans provided concise overviews of our state of knowledge of the large-scale circulation of the Arctic Ocean (and sea ice). Their discussion reviewed the relationship of ocean circulation changes to dominant modes of atmospheric variability over the last few decades and the interplay between changes in surface atmospheric circulation, freshwater input (river runoff and land ice melt) and redistribution in regulating freshwater buildup in the Beaufort gyre. Deciphering the detailed physical processes underlying regional changes is hampered by the extreme sparsity of in-situ observations in parts of the Arctic, as Jamie noted. Fig. 1 gives an impression of the distribution of most in-situ observational assets between 2002 and 2017. Improved geophysical retrieval algorithms are beginning to enable us to fill certain gaps through satellite remote sensing of altimetry-derived sea surface height (SSH) anomalies from ICESat, ICESat-2, and CryoSat-2 (Armitage et al. 2018), gravimetry-derived ocean bottom pressure (OBP) anomalies from GRACE, GRACE-FO (Peralta-Ferriz et al. 2014), and more recently radiometry-derived sea surface salinity (SSS) from SMOS, Aquarius and SMAP (Fournier et al. 2020). 

Fig. 1: Distribution of in-situ observational assets between 2002 and 2017 used in the Arctic Subpolar gyre sTate Estimate (Nguyen et al. 2021)

Among the questions we may ask are 

1. how can we take maximum advantage of these sparse observations; 
2. how do we compare the heterogeneous data streams and disparate variables; and 
3. can we use quantitative methods to guide the deployment of new observations and/or the design of an “optimized” observing system? 

For example, SSH anomalies, combined with OBP anomalies, carry information about the ocean’s density, mass and circulation changes. Also, hydrographic observations taken at one point imply some knowledge of the surrounding state upstream and downstream (i.e., both backward and forward in time).

Arguably, one answer to these questions is to combine the knowledge reservoir offered by the available, yet incomplete observations with the knowledge reservoir that is encapsulated in the governing equations of motion, rendered in the form of numerical models (including conservation of mass, momentum, heat, and salt, constitutive laws for seawater and sea ice, and subgrid-scale parameterization of unresolved physical processes), but which themselves are subject to “model” uncertainties. The need to deal with sparse data is ubiquitous in the geosciences, fueling significant efforts in the development of inverse methods over many decades (see, e.g., Carrassi et al. 2018 and Wunsch 2019 for concise algorithmic and historical reviews). 

Most common today in the ocean, atmosphere and climate science communities is the notion of data assimilation (DA) as developed for numerical weather prediction (Bauer et al. 2015). Before discussing its role in the context of the planned workshop, it is useful to sketch the process and goal of DA, with the following succinct description given by Kaminski et al (2015): “Ideally, all observational data streams are interpreted simultaneously with the process information provided by the model to yield a consistent picture of the state of the Arctic system that balances all the observational constraints, taking into account the respective uncertainty ranges.” DA more broadly serves different purposes: 

1. model calibration (parameter estimation); 
2. state estimation or reconstruction (interpolation or synthesis); 
3. model initialization for prediction (extrapolation); and 
4. observing system design (targeted observation). 

These diverse uses imply the use of different assimilation methods and the need to tackle distinct practical challenges. Here, we will focus on application of DA for reconstructing the time-evolving state of the Arctic over the past decades for the purpose of improved understanding, and the ability to do so more accurately in the future. In addition, we will discuss the related question of how to design a sustained Arctic Ocean observing system that would lead to a better understanding and monitoring of key indices or metrics (to be defined) pertinent to the Arctic Ocean as well as the coupled ocean, atmosphere, marine cryospheric, and biogeochemical system.

Most available Arctic Ocean (and sea-ice) multi-decadal reconstructions available to date are those obtained as part of the global ocean reanalysis efforts. The term “reanalysis” is mostly synonymous to “reconstruction” and derived from the process of “analysis” in the context of numerical weather prediction (e.g., Stammer et al. 2016). Arctic-focused assessments of these global and regional Ocean and sea ice ReAnalysis products (ORAs) have recently been conducted as part of the Polar ORA Intercomparison Project (Polar ORA-IP) under the joint auspices of the CLIVAR Global Synthesis and Observing Panel (GSOP) and the WMO GODAE OceanView program (called OceanPredict since 2021). The two main publications from these are by Chevallier et al. (2016) focusing on Artic sea ice cover, and by Uotila et al. (2018), focusing on Arctic Ocean hydrography and circulation.

It is worth noting that these reanalysis assessments are separate, albeit related to intercomparison efforts that have been previously conducted as part of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II) under the auspices of the CLIVAR Ocean Model Development Panel (OMDP). This led to Arctic-focused comparisons of simulated  sea ice and solid freshwater (Wang et al. 2016a), liquid freshwater (Wang et al. 2016b), hydrography and fluxes (Ilicak et al. 2016). A new generation of OMDP-led assessments (termed OMIP-2) have recently been conducted with a new atmospheric forcing data set, JRA55-do (Tsujino et al. 2020). Key differences between the Polar ORA-IP and CORE-II/OMIP-2 efforts are (i) CORE-II simulations do not perform any data assimilation; (ii) unlike ORA-IP, all CORE-II simulations use the same atmospheric forcing product and the same set of bulk formulae to compute air-sea fluxes from the atmospheric state over ice-free water; and (iii) unlike most ORA-IP members, CORE-II simulations are free of artificial interior sources or sinks of heat, salt, and momentum (which arise in many of the ORA-IP members from periodic “analysis increments”).

In addition to the global reanalyses, a regional Arctic Subpolar gyre sTate Estimate (ASTE) has recently been produced, supported by NSF, covering the early 21st century, 2002-2017 (Nguyen et al. 2021), which may be considered the northern sibling of the Southern Ocean State Estimate (SOSE; Mazloff et al. 2010).  ASTE seeks to combine all known observational data with the equations of motion encoded in a general circulation model by solving a giant least-squares optimization problem, based on the adjoint infrastructure developed by the ECCO group. It arguably represents the biggest effort undertaken to date with the aim of producing a dedicated Arctic ocean-sea ice estimate. What sets ASTE apart from most reanalysis products is that tracer and momentum tendencies are free of artificial sources or sinks, rendering the product dynamically and kinematically consistent (Wunsch 2019).  Nguyen et al. (2021) provide an initial assessment of the product. Fig. 2 summarizes estimated transports across main Arctic gateways (adapted from Østerhus et al. 2019).

Fig. 2: Time mean volume transports across main Arctic gateways during 2002-2017 (large blue), estimated by ASTE (along with standard deviations based on monthly values). Adapted from Østerhus et al. (2019).

ASTE is openly available to the research community through the Texas Advanced Computing Center (TACC).  The product also provides comprehensive output to conduct accurate budget analyses of tracers, as recommended by Griffies et al. (2016). Access to not only the data itself, but also to capabilities for conducting detailed, sometimes compute-intensive analyses is of increasing importance as the data volume grows. ASTE is exploring a new cloud-based capability enabled through TACC and hosted on Amazon Web Services (AWS) servers, see at http://aste-release1.s3-website.us-east-2.amazonaws.com/. Data access and analysis software support specifically tailored for ASTE are based on earlier development of the llcreader package led by R. Abernathey (2019), and enabled through a stack of open-source Python packages: xmitgcm (Abernathey et al. 2019) provides access to the data “on-the-fly” via dask (Dask 2016) in the form of a convenient xarray (Hoyer and Hamman 2017) dataset. Calculations and visualization are made easy for the ASTE grid topology with xgcm (Abernathey et al. 2020) and ECCOv4-py. A dedicated data portal is currently being established at the NSF Arctic Data Center, https://arcticdata.io. Enabling such cloud-based analytics capabilities with concurrent access to all available data (in-situ, satellite, and reanalyses/state estimates) represents a step from “Data as a Service” to “Analysis as a Service” (Schnase et al. 2016) and can be another discussion point of the workshop.

A shared desire between reanalysis and state estimation efforts is the ability to derive estimates of time-mean and time-varying ocean mass, heat, salt and freshwater content, transport, divergences, and other derived processes from complete three-dimensional fields of the time-evolving ocean state. Such complete budget analyses provide dynamical insights into the causes and pathways of anomalies propagating throughout the Arctic. They enable quantification of, e.g., the relative role of local forcing (via air-sea fluxes) versus lateral advection, versus diffusive processes (e.g., Buckley et al. 2015). They complement or enhance purely observation-driven estimates, e.g., of time-evolving budgets of Arctic freshwater (Haine et al. 2015; Serreze et al. 2006; Solomon et al. 2021), volume (Østerhus et al. 2019), and heat (Schuckmann et al. 2020; Serreze et al. 2007).

Satisfying the desire to close budgets, however, is a challenging task. Despite the data constraints applied in the ORA-IP members, significant differences exist between the reanalyses for a range of metrics, including transports through key Arctic gateways, regional heat and salt content anomalies, hydrographic vertical profiles, mixed-layer depths, and circulation features. Deciphering the causes of these differences is made difficult by the fact that each reanalysis product is based on (i) a different underlying circulation model, (ii) different atmospheric forcing fields used (mostly atmospheric reanalyses), (iii) a different DA method, (iv) different observations used or different ways in which these observations are ingested (e.g., along-track vs. gridded), and (v) different error estimates (observation, representation and background errors). Overall, a valid expectation is that with increased observational coverage, model skill, and sophistication of the DA schemes, estimates in the ocean state should converge. Therefore, understanding and quantifying the observational needs to reduce the uncertainties among Arctic reanalyses should be an important point of discussion for the workshop. This leads to the issue of how to design an Arctic Ocean observing system. Important aspects for discussions in this regard are (i) what are the design criteria, i.e., what purposes should an observing system serve (e.g., Lee et al. 2019; Smith et al. 2019), and (ii) what quantitative methods exist to support observing system design using simulation-based approaches.

The field of computational science (Rüde et al. 2018) in general and computational oceanography (Haine et al. 2021) in particular, are emerging disciplines developing computational algorithm and physics-based simulation and inverse modeling capabilities to advance scientific discoveries where experimental approaches are severely limited, i.e., too costly, slow, dangerous, or infeasible. These limitations clearly fit the bill of Arctic Ocean observing, given its logistical and technological challenges of sensor design, deployment, and maintenance. A range of algorithms are being developed to support quantitative observing system design, mostly embedded within advanced data assimilation systems. Due to their computational cost and complexity, maturing these algorithms is itself an ongoing area of research, but one that is both highly active and promisingly transformative. It is the subject, among others, of a dedicated activity of the OceanPredict program (Fujii et al. 2019). Recent examples of Arctic and North Atlantic-focused efforts are the quantitative network design studies by Kaminski et al. (2015; 2018), Loose et al. (2020), and Loose and Heimbach (2021). Among the design aspects are the determination of relevant metrics (purpose and objectives of the observing system), determination of observational complementarity versus redundancy, and assessment of data utility as determined from signal-to-noise (or sensitivity-to-noise) ratios of possible acquisitions, along with numerous other practical aspects underlying the design, such as cost or logistical constraints. A workshop that brings together observationalists, modelers, data assimilation experts, and users, offers the prospect of discussing and developing concepts for co-designing a comprehensive yet efficient, targeted Arctic Ocean observing system. Because neither the model, nor the observing network, nor the assimilation systems are free of errors, improving each of these elements is of necessity a symbiotic, iterative process. Related efforts in numerical weather prediction (Bauer et al. 2021) suggest that such an endeavor will take time, resources and willingness to work across disciplines, but will have substantial benefits for the grand challenge of Arctic observing system design.

References

Abernathey, R. (2019) Petabytes of Ocean Data, Part 1: NASA ECCO Data Portal. https://medium.com/pangeo/petabytes-of-ocean-data-part-1-nasa-ecco-data-portal-81e3c5e077be

Abernathey, R. P., Busecke, J., Smith, T., Bot, S., Banihirwe, A., Zhang, C., . . . Rath, W. (2020, October). xgcm/xgcm: v0.5.1. Zenodo. Retrieved from https://doi.org/10.5281/zenodo.4097223 doi: 10.5281/zenodo.4097223 

Abernathey, R. P., Dussin, R., Smith, T., Bot, S., Cimatoribus, A., Doddridge, E., . . . Leskis, A. (2019, July). xgcm/xmitgcm: v0.4.1. Zenodo. Retrieved from https://doi.org/10.5281/zenodo.3332699 doi: 10.5281/zenodo.3332699 

Armitage, T. W. K., Bacon, S., & Kwok, R. (2018). Arctic Sea Level and Surface Circulation Response to the Arctic Oscillation. Geophysical Research Letters, 45(13), 6576–6584. http://doi.org/10.1029/2018GL078386

Bauer, P., Stevens, B., & Hazeleger, W. (2021). A digital twin of Earth for the green transition. Nature Climate Change, 5, 1–4. http://doi.org/10.1038/s41558-021-00986-y

Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. http://doi.org/10.1038/nature14956

Buckley, M. W., Ponte, R. M., Forget, G., & Heimbach, P. (2015). Determining the Origins of Advective Heat Transport Convergence Variability in the North Atlantic. Journal of Climate, 28(10), 3943–3956. http://doi.org/10.1175/JCLI-D-14-00579.1

Carrassi, A., Bocquet, M., Bertino, L., & Evensen, G. (2018). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9(5), e535–50. http://doi.org/10.1002/wcc.535

Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G., Fujii, Y., et al. (2016). Intercomparison of the Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP project. Climate Dynamics, 1–30. http://doi.org/10.1007/s00382-016-2985-y

Dask Development Team (2016). Dask: Library for dynamic task scheduling URL https://dask.org

Fournier, S., Lee, T., Wang, X., Armitage, T. W. K., Wang, O., Fukumori, I., & Kwok, R. (2020). Sea Surface Salinity as a Proxy for Arctic Ocean Freshwater Changes. Journal of Geophysical Research: Oceans, 125(7), 852. http://doi.org/10.1029/2020JC016110

Fujii, Y., Remy, E., Zuo, H., Oke, P., Halliwell, G., Gasparin, F., et al. (2019). Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks. Frontiers in Marine Science, 6, 1032–25. http://doi.org/10.3389/fmars.2019.00417

Griffies, S.M., et al., 2016: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project. Geosci. Model Dev., 9, 3231-3296, https://doi.org/10.5194/gmd-9-3231-2016

Haine, T., Gelderloos, R., Jimenez-Urias, M., Siddiqui, A., Lemson, G., Medvedev, D., et al. (n.d.). Computational Oceanography is Coming of Age. http://doi.org/10.1002/essoar.10504566.1

Haine, T. W. N., Curry, B., Gerdes, R., Hansen, E., Karcher, M., Lee, C., et al. (2015). Arctic freshwater export: Status, mechanisms, and prospects. Global and Planetary Change, 125(C), 13–35. http://doi.org/10.1016/j.gloplacha.2014.11.013 

Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: http://doi.org/10.5334/jors.148

Kaminski, T., Kauker, F., Eicken, H., & Karcher, M. (2015). Exploring the utility of quantitative network design in evaluating Arctic sea ice thickness sampling strategies. The Cryosphere, 9(4), 1721–1733. http://doi.org/10.5194/tc-9-1721-2015

Kaminski, T., Kauker, F., Toudal Pedersen, L., Vossbeck, M., Haak, H., Niederdrenk, L., et al. (2018). Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance. The Cryosphere, 12(8), 2569–2594. http://doi.org/10.5194/tc-12-2569-2018 

Lee, C. M., Starkweather, S., Eicken, H., Timmermans, M.-L., Wilkinson, J., Sandven, S., et al. (2019). A Framework for the Development, Design and Implementation of a Sustained Arctic Ocean Observing System. Frontiers in Marine Science, 6, 484. http://doi.org/10.3389/fmars.2019.00451

Loose, N., & Heimbach, P. (2021). Leveraging Uncertainty Quantification to Design Ocean Climate Observing Systems. Journal of Advances in Modeling Earth Systems, 1–29. http://doi.org/10.1029/2020MS002386

Loose, N., Heimbach, P., Pillar, H. R., & Nisancioglu, K. H. (2020). Quantifying Dynamical Proxy Potential Through Shared Adjustment Physics in the North Atlantic. Journal of Geophysical Research: Oceans, 125(9), e2019RG000654. http://doi.org/10.1029/2020JC016112

Mazloff, M. R., Heimbach, P., & Wunsch, C. (2010). An Eddy-Permitting Southern Ocean State Estimate. Journal of Physical Oceanography, 40(5), 880–899. http://doi.org/10.1175/2009JPO4236.1

Nguyen, A. T., Pillar, H., Ocaña, V., Bigdeli, A., Smith, T. A., & Heimbach, P. (2020). The Arctic Subpolar gyre sTate Estimate (ASTE): Description and assessment of a data-constrained, dynamically consistent ocean-sea ice estimate for 2002-2017. http://doi.org/10.1002/essoar.10504669.3

Østerhus, S., Woodgate, R., Valdimarsson, H., Turrell, B., de Steur, L., Quadfasel, D., et al. (2019). Arctic Mediterranean exchanges: a consistent volume budget and trends in transports from two decades of observations. Ocean Science, 15(2), 379–399. http://doi.org/10.5194/os-15-379-2019

Peralta-Ferriz, C., Morison, J. H., Wallace, J. M., Bonin, J. A., & Zhang, J. (2014). Arctic Ocean Circulation Patterns Revealed by GRACE. Journal of Climate, 27(4), 1445–1468. http://doi.org/10.1175/JCLI-D-13-00013.1

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Schuckmann, von, K., Cheng, L., Palmer, M. D., Hansen, J., Tassone, C., Aich, V., et al. (2020). Heat stored in the Earth system: where does the energy go? Earth System Science Data, 12(3), 2013–2041. http://doi.org/10.5194/essd-12-2013-2020

Serreze, M. C., Barrett, A. P., Slater, A. G., Woodgate, R. A., Aagaard, K., Lammers, R. B., et al. (2006). The large-scale freshwater cycle of the Arctic. Journal of Geophysical Research, 111(C11). http://doi.org/10.1029/2005JC003424

Serreze, M. C., Barrett, A. P., Slater, A. G., Steele, M., Zhang, J., & Trenberth, K. E. (2007). The large-scale energy budget of the Arctic. Journal of Geophysical Research, 112(D11), D11122. http://doi.org/10.1029/2006JD008230

Smith, G. C., Allard, R., Babin, M., Bertino, L., Chevallier, M., Corlett, G., et al. (2019). Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season. Frontiers in Marine Science, 6, 373. http://doi.org/10.3389/fmars.2019.00429

Solomon, A., Heuzé, C., Rabe, B., Bacon, S., Bertino, L., Heimbach, P., et al. (2020). Freshwater in the Arctic Ocean 2010–2019. Ocean Science Discussions, 1–28. http://doi.org/10.5194/os-2020-113

Stammer, D., Balmaseda, M., Heimbach, P., Köhl, A., & Weaver, A. (2016). Ocean Data Assimilation in Support of Climate Applications: Status and Perspectives. Annual Review of Marine Science, 8(1), 491–518. http://doi.org/10.1146/annurev-marine-122414-034113 

Tsujino, H., Urakawa, L. S., Griffies, S. M., Danabasoglu, G., Adcroft, A. J., Amaral, A. E., et al. (2020). Evaluation of global ocean–sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2). Geoscientific Model Development, 13(8), 3643–3708. http://doi.org/10.5194/gmd-13-3643-2020

Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A., Bricaud, C., et al. (2018). An assessment of ten ocean reanalyses in the polar regions. Climate Dynamics, 94(C10), 1–38. http://doi.org/10.1007/s00382-018-4242-z

Wunsch, C. (2019). Inverse Problems, Inverse Methods, and Inverse Models. In Encyclopedia of Ocean Sciences (pp. 502–512). Elsevier. http://doi.org/10.1016/B978-0-12-409548-9.11262-X

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The Arctic Ocean's Beaufort Gyre

Jamie set us up in the last blog describing the sense of the Arctic Ocean surface circulation since the 1990s. He takes the perspective that the overall circulation is best characterized by the strength and extent of a sea-surface height trough and the associated cyclonic circulation in the Eurasian Basin, instead of a characterization of the overall circulation that centers on the state of the Beaufort Gyre in the Canadian Basin. I look forward to discussing this perspective in the workshop, and I hope the debate will help us to converge on a framework that appreciates relationships and feedbacks between flow regimes in different sectors of the Arctic Ocean. This is particularly important to the extent that the metrics and characteristics we use for describing how the surface circulation of the Arctic Ocean works, and how it evolves on interannual and decadal timescales, guide our observing focus and strategies. To prepare for these important discussions, it might be helpful to review the changes that are presently underway in the Beaufort Gyre, under the influence of the atmospheric Beaufort High.

The anticyclonic (clockwise) Beaufort Gyre dominates the Canadian Basin circulation with a diameter of around 800 km and typical speeds near the ocean surface of several centimeters per second. The seawater that constitutes the gyre is fresher overall than anywhere else in the Arctic Ocean; the Beaufort Gyre is the Arctic's largest freshwater reservoir. Freshwater is accumulated in the gyre by the influence of the wind forcing associated with the Beaufort High. The winds drive surface convergence of freshwater from river discharge, ice melt, net precipitation and Pacific Water inflows. The amount of freshwater accumulated depends on the availability of freshwater from the various sources during times when wind forcing is most amenable to convergence. Since the early 2000s, we have been able to gain a better understanding of these processes through observations collected under the NSF-funded Beaufort Gyre Observing System (BGOS, Figure 1a). BGOS was established in 2003 as a collaborative program between US scientists and scientists from Fisheries and Oceans, Canada. Observations in the Beaufort Gyre region derive from moorings, yearly hydrographic surveys and drifting Ice-Tethered Profilers.

Observations indicate an almost 40% increase in Beaufort Gyre freshwater since the 1970s (from around 17 × 10³ km³ to 23.5 × 10³ km³ in 2018) (Proshutinsky et al., 2019, 2020), see Figures 1b and 2. These increases are attributed to a strengthening of the Beaufort Gyre circulation in response to anticyclonic wind forcing during a time of increased freshwater entering through Bering Strait, more freshwater available from sea-ice melt, and freshwater discharge from the Mackenzie River having more influence.

Figure 1 (from Proshutinsky et al., 2020): (a) Beaufort Gyre region with Beaufort Gyre Observational System (BGOS) mooring locations (stars), and hydrographic station locations where observations have been made from 2003-2020. (b) Time‐averaged summer freshwater content (meters, relative to reference salinity 34.8; colors and contours) in the Beaufort Gyre region for 1950s-1980s and 2013-2018.

Figure 2 (from Proshutinsky et al., 2019): Annual freshwater content in the Beaufort Gyre region inferred from satellite SSH (yellow bars), and calculated using Ice‐Tethered Profiler (ITP) data (blue bars) and BGOS mooring data (red bars). The black dotted, black dashed, and red dashed lines indicate linear freshwater content trends from ITP (455±232 km³/year), SSH (524±256 km³/year), and mooring data (534±153 km³/year), respectively. All trends are positive and statistically significant. Freshwater content estimated from mooring data does not include the upper 65 m of the water column.

Predicting the fate of Beaufort Gyre freshwater as it relates to continued sea-ice losses is a priority for future climate projections. At present, the Beaufort Gyre is controlled by sustained wind forcing, with both ocean eddy fluxes and stresses at the ice-ocean interface playing a role in balancing the wind forcing and regulating the gyre’s freshwater content (see e.g., Meneghello et al., 2020). The role of ice-ocean stresses will be greatly diminished in a future, seasonally ice-free Beaufort Gyre having a thinner, more mobile winter sea-ice cover. It is unclear whether this is already a factor in the recent build-up of Beaufort Gyre freshwater.

 Returning to the topic of Jamie's blog, changes in the strength of the Beaufort Gyre circulation, and its capacity to accumulate freshwater bear a clear relationship to the general circulation of the entire Arctic Ocean. Understanding this general circulation relies on our ability to predict the prevailing wind forcing, namely the two main atmospheric centers of action - the Beaufort High and the Icelandic Low. Will ongoing Arctic warming and sea-ice losses lead to a reduced Beaufort High (favoring freshwater release), and an intensified Icelandic Low (e.g., Moore et al., 2018)? In a warming Arctic, it is unclear which atmospheric circulation patterns will dominate and how these will influence the ocean dynamics and freshwater of the Beaufort Gyre and the Arctic as a whole. Our workshop will be most productive and stimulating if atmospheric dynamicists are among the participants deliberating anticipated changes in atmospheric forcing. Finally, we will need to consider how best to characterize ocean circulation and modes of variability as these metrics relate to decisions surrounding observing strategies. This is a topic for a future blog post.

References:

Meneghello, G., Doddridge, E., Marshall, J., Scott, J., & Campin, J.-M. (2020). Exploring the role of the “ice–ocean governor” and mesoscale eddies in the equilibration of the Beaufort Gyre: Lessons from observations. Journal of Physical Oceanography, 50(1), 269–277.

Moore, G., Schweiger, A., Zhang, J., & Steele, M. (2018). Collapse of the 2017 winter Beaufort High: A response to thinning sea ice? Geophysical Research Letters, 45, 2860–2869. https://doi.org/10.1002/2017GL076446.

Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M.-L.,Williams,W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T.W.K., Dukhovskoy, D., Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T., Nishino, S., Itoh, M., Kang, S.-H., Cho, K.-H., Tateyama, K., & Zhao, J. (2019). Analysis of the Beaufort Gyre freshwater content in 2003-2018. Journal of Geophysical Research: Oceans, 124, 9658–9689. https://doi.org/10.1029/2019JC015281

Proshutinsky, A., Krishfield, R., & Timmermans, M.-L. (2020). Introduction to special collection on Arctic Ocean Modeling and Observational Synthesis (FAMOS) 2: Beaufort Gyre phenomenon. Journal of Geophysical Research: Oceans, 125, e2019JC015400. https://doi.org/10.1029/2019JC015400.

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Workshop on Observing, Modeling, and Understanding the Circulation of the Arctic Ocean and Sub-Arctic Seas

        I am grateful to US CLIVAR for organizing the Workshop on Observing, Modeling, and Understanding the Circulation of the Arctic Ocean and Sub-Arctic Seas. Next summer, we look forward to meeting in person and getting the perspectives of many oceanographers and atmospheric scientists, including early career scientists and students.

        For now I am excited for the chance to start things off, albeit in this online way necessitated by the pandemic. Since before the beginning of the Study of Environmental Arctic Change (SEARCH) in the late 1990s, through the development of all the new ocean observing components of the Arctic Observing Network (AON), and the emergence of an amazing array of remote sensing instruments over the last two decades, it has been a great time to study the Arctic Ocean environment. Our community has learned a tremendous amount about changes in the Arctic Ocean and how these relate to global change. The Workshop is an opportunity to assess what we have learned, refine our questions, and talk about new directions forward. Consequently the Workshop will be divided into three parts assessing: 1) State of Knowledge, 2) State of the Observing System, and 3) Overcoming Challenges to Observation. We anticipate vigorous debate in all three parts in person at the Workshop next summer, but we will kick things off with a series of blog posts of various opinions on the subjects of the workshop. Here is my take on 1) through 3):

1) I think that since the early 1990s, Arctic Ocean near surface circulation has been in a relatively cyclonic state. The mean state of surface circulation dominated, by the Beaufort Gyre (BG), tends to be anticyclonic, but in situ and more recently remote sensing observations show variations in circulation to be dominated not by variations in the strength of the Beaufort Gyre, but by a trough in sea surface height and associated cyclonic circulation along the Russian side of the Arctic Ocean. In its positive phase set against the mean circulation, this cyclonic mode presents a dipole pattern with an intensified but smaller BG opposite an extensive cyclonic pattern. Notable examples of this shift to the cyclonic mode in the early 1990s [1] and in 2007-08 [2] have followed increases in the Arctic Oscillation (AO) index, the principal component of the primary EOF of variation of atmospheric pressure north of 20°N. Since 1989 the AO has averaged about one standard deviation above the average before 1989 [2, 3], and as a result, Arctic Ocean circulation has tended to be in the cyclonic mode since that time. This is important to climate because: a) the AO is a hemispheric climate index that arguably increases with global warming [4, 5], b) the cyclonic mode deflects Eurasian runoff to the Canada Basin [2] and thus weakens the cold halocline layer that isolates sea ice from Atlantic Water heat [6, 7], and c) a positive AO and cyclonic circulation reduce ice extent in the following summer [3, 8] ,  enhance the export of ice and near-surface freshwater [9], which increase the stratification of the sub-Arctic seas and potentially throttle convective overturning [10].

2) The present in situ observing system is nearly blind to the cyclonic mode of circulation change. Figure 1 from Kwok and Morison [2011] [11] illustrates this and the cyclonic mode pattern. Dynamic heights from IPY hydrographic sampling a year after a positive shift in the AO (Figure 1a) reveal an intense Beaufort Gyre that dominates the region sampled by IPY. However, dynamic ocean topography (DOT) from ICESat (Figure 1b) shows that virtually the whole rest of the Arctic Ocean without in situ observations is a cyclonic trough spread along the length of the Russian side of the Arctic Ocean. Comparisons among hydrography, ICESat DOT, and GRACE ocean bottom pressure (OBP) reveal fresh water increase in the Beaufort Gyre is almost completely balanced by freshwater decrease in the rest of the Arctic Ocean [2].

 A lack of in situ data outside the Beaufort Sea remains a critical shortcoming. Arctic Ocean hydrographic observations in the first part of 2019 (Figure 2) including moorings, aircraft sections, Ice Tethered Profiler and UpTempO drifting buoys were all concentrated in the Beaufort Sea. Meanwhile, ICESat-2 sea surface height relative to CryoSat-2 mean sea surface height, 2011-2015 (Figure 3) show the strong depression on the Russian side of the ocean characteristic of the cyclonic mode, completely unseen by the in situ observations.

3) The challenges to measuring the cyclonic mode are formidable. The center of action of the cyclonic mode along the Russian margins, unlike the BG, is difficult to reach. Buoys drifting with the ice tend to converge in an anticyclonic gyre like the BG and diverge out of a cyclonic feature like that shown over most of the Arctic Ocean in Figure 2b. And the shortest most convenient cruise tracks to the Pole tend to line up with the Transpolar Drift and Front and bypass the region of cyclonic circulation. Integrated remote sensing and in situ observing approaches, and international cooperation will be required to overcome these problems. 

 

References

1.    Morison, J.H., K. Aagaard, and M. Steele, Recent environmental changes in the Arctic: A review. Arctic, 2000.53(4): p. 359-371.

2.    Morison, J.H., et al., Changing Arctic Ocean freshwater pathways. Nature, 2012. 481(7379): p. 66-70.

3.    Williams, J., et al., Dynamic Preconditioning of the Minimum September Sea-Ice Extent. Journal of Climate, 2016. 29(16): p. 5879-5891.

4.    Fyfe, J.C., G.J. Boer, and G.M. Flato, The Arctic and Antarctic Oscillations and their projected changes under global warming,. Geophysical. Research Letters, 1999. 26: p. 1601–1604.

5.    Gillett, N.P., M.R. Allen, and K.D. Williams, The role of stratospheric resolution in simulating the Arctic Oscillation response to greenhouse gases. Geophysical Research Letters, 2002. 29(10).

6.    Steele, M. and T. Boyd, Retreat of the cold halocline layer in the Arctic Ocean. Journal of Geophysical Research-Oceans, 1998. 103(C5): p. 10419-10435.

7.    Polyakov, I.V., et al., Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin of the Arctic Ocean.Science, 2017. 356(6335): p. 285.

8.    Rigor, I.G., J.M. Wallace, and R.L. Colony, Response of sea ice to the Arctic oscillation. Journal of Climate, 2002. 15(18): p. 2648-2663.

9.    Hilmer, M. and T. Jung, Evidence for a recent change in the link between the North Atlantic Oscillation and Arctic Sea ice export. Geophysical Research Letters, 2000. 27(7): p. 989-992.

10.  Dickson, R.R., et al., The Great Salinity Anomaly in the Northern North-Atlantic 1968-1982. Progress in Oceanography, 1988. 20(2): p. 103-151.

11.  Kwok, R. and J. Morison, Dynamic topography of the ice-covered Arctic Ocean from ICESat. Geophysical Research Letters, 2011. 38(L02501): p. L02501.

Figures

Figure 1. Spring 2008 dynamic height from Arctic Ocean hydrography (a) and dynamic ocean topography from ICESat (b). The hydrographic stations include individual CTD (NPEO, Switchyard, and BGEP) and Aircraft eXpendable CTD (AXCTD) profiles, and 10-day averaged Ice-Tethered Profiler (ITP) data.  From Kwok and Morison [2011])

Figure 2. Arctic Ocean IABP drifting buoy observations in March 2019 plus sections and moorings of the Bering Strait mooring program, Beaufort Gyre Exploration Project (BGEP) and Seasonal Ice Zone Reconnaissance Surveys (SIZRS). Figure courtesy of Ignatius Rigor, 2019. 

Figure 3. Monthly ICESat-2 sea surface height anomaly December 2018 through March 2019 relative to the CryoSat-2 mean sea surface.

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