OS
Ocean Sciences

Ocean Sciences

Satellite data for ocean reanalysis

Satellite data for ocean reanalysis

To understand the fundamental behaviour of the ocean, and any changes it’s undergoing, we need to know what the ocean is doing today, and on each day in the recent past.

We can do this by creating reanalyses, which use data assimilation to combine state-of-the-art models with observations. The focus is often on ocean physics, but we also need to know about the marine ecosystem and carbon cycle.

As well as the best possible models and data assimilation schemes, reanalyses need the best possible observations. These don’t just need to be plentiful and accurate, but for many climate applications they must be treated consistently through time. This is to avoid introducing artificial trends and variability which would simply be due to changes in the observing system, rather than changes in the ocean.

One programme set up to address this need for consistency and stability is the European Space Agency Climate Change Initiative (ESA CCI). For a selection of Essential Climate Variables that can be observed from space, climate-quality datasets have been produced covering the satellite era, complete with uncertainty estimates. For the ocean, products were initially produced for sea surface temperature (SST), sea level, sea ice, and ocean colour. These have now been joined by sea surface salinity and sea state, and all products continue to be updated.

‘Climate from Space’ demo. Credit: ESA/Planetary Visions; music: “View of the Greenland Sea north of Siglufjörður, 2” by The Gateless Gate, CC BY-NC-ND 4.0

In a recent paper for Ocean Science, I explored the role each variable of the initial set of marine Essential Climate Variables plays when assimilated into a global physical-biogeochemical ocean reanalysis, and the consistency of features between different variables. This work was done as part of the Climate Change Initiative Climate Modelling User Group project, which brings together the Earth observation, climate modelling, and reanalysis communities, to ensure a focus on the climate system is at the heart of the Climate Change Initiative.

There is a curious and as-yet-unsolved problem in ocean data assimilation. Assimilating physics data, while highly successful in improving model physics, can result in spurious vertical mixing which degrades biogeochemical fields. I didn’t directly address this issue in my paper, but was able to show that despite this, assimilating satellite SST, sea level and sea ice could improve the representation of spatial features and variability in both physical and biogeochemical systems.

Horizontal gradients in the Gulf Stream region during December 2010, for SST (left column), sea level anomaly (middle column), and surface log10(chlorophyll) (right column). Calculated from satellite observations (a-c), a model with no data assimilation (d-f), assimilating SST (g-i), assimilating sea level (j-l), assimilating ocean colour (m-o), and assimilating SST, sea level, ocean colour, and sea ice (p-r). Credit: Ford (2020).

As an example, the above figure plots surface gradients of three variables in the Gulf Stream region: SST, sea level anomaly (SLA), and log10(chlorophyll). The top row shows the satellite observations. The remaining rows show a set of model runs assimilating those observations individually or in combination. This allows us to see the impact of assimilating each Essential Climate Variable on how the Gulf Stream is represented by different variables.

In the observation fields (a-c), fronts of SST and chlorophyll (obtained from ocean colour) are largely collocated, and situated around eddies identified in the sea level products. This gives confidence that the different Climate Change Initiative satellite products are giving a consistent view of the ocean surface.

In the model without data assimilation (d-f), gradients broadly match the observations, but some features are missing, and sea level anomaly and chlorophyll gradients are much too weak. Assimilating SST on its own (g-i) is enough to improve the position and magnitude of gradients in all three fields, demonstrating the wide potential of satellite observations for improving many aspects of ocean reanalyses. Assimilating sea level anomaly on its own (j-l) improves sea level anomaly gradients, with some improvement to SST and chlorophyll. Assimilating ocean colour (m-o) can’t affect the physics in this model, because the physics-biogeochemistry coupling is currently one-way, but clearly improves the ecosystem variables. But it’s when all the variables are assimilated together (p-r) that the best representation of the Gulf Stream is seen in all fields.

Satellite observations are clearly of great importance for climate research, especially when combined with models, but remain insufficient. In situ measurements of temperature and salinity are vital for simulating the ocean circulation, and many Essential Ocean Variables, particularly for biogeochemistry, simply cannot be measured from space.

New technologies such as Biogeochemical-Argo floats and gliders will further transform oceanography, and are already starting to be assimilated into reanalyses. This will help us better understand the impact of climate change and other stresses on the marine ecosystem, the challenges of ocean acidification and eutrophication, and the role of the marine carbon cycle in global climate.

Carbon dioxide ocean–atmosphere exchange. Credit: ESA/Planetary Visions

 

References & Further reading:

Edited by Meriel Bittner

Eurec4a: Tales from the Tropics

Eurec4a: Tales from the Tropics

As many seagoing oceanographers find themselves on land for the foreseeable future, we’ve decided to share a tale of a research cruise to fill that ship-shaped void.

Back in January 2020, four research vessels ventured out into the Tropical North Atlantic as part of the Eurec4a and ATOMIC campaigns. Eurec4a’s aim: to investigate the couplings between clouds, circulation and convection and how these feed into climate change. Within this, vast webs of interlinking research studies were constructed. By the campaign’s end in February 2020, we had gathered data from kilometres up in the atmosphere right down to the ocean floor.

I found myself stepping aboard the German R/V Meteor in the middle of January, ready for my first experience of life at sea. Four of our six weeks aboard were spent studying an area east of Barbados between roughly 14°30’N and 12°N, dubbed ‘the tradewind alley’. Whilst much scientific focus was dedicated to the sky and cloud systems, a few groups of scientists onboard focused instead on the ocean. Collectively, we studied the biology and physics of the ocean, utilising research equipment ranging from the conventional CTD to the less conventional autonomous vehicle.

Elizabeth Siddle setting up a Seaglider on the R/V Meteor. (Credit: Callum Rollo)

The autonomous vehicles deployed from the R/V Meteor were two Seagliders and five Argo floats, complemented by an AutoNaut and Seaglider launched from Barbados by the University of East Anglia.  The AutoNaut was equipped to carry and release the Seaglider from beneath it, to preserve the Seaglider’s battery. The AutoNaut and three Seagliders teamed up over 11 days to patrol a 10×10 km box in line with the HALO aircraft’s flight circle and the R/V Meteor’s meridional transect, giving us a wealth of data in a small area. At the end of our time in tradewind alley, we recovered the 3 Seagliders to the R/V Meteor and the AutoNaut returned to Barbados under its own power.

Seaglider – A buoyancy-powered autonomous underwater vehicle which pumps oil in and out of an external bladder to vary its density, thus buoyancy. Can profile down to 1000 m depth.
Argo float – A profiling float that drifts at 1000 m depth. Every 10 days it completes a cycle of sinking to 2000 m then returning to the sea surface to relay data via satellite.
AutoNaut – A wave-propelled unmanned autonomous surface vessel designed to take meteorological and oceanographic measurements at the air-sea interface, piloted over the Iridium satellite network.

Whilst the Eurec4a data analysis is in its very early stages, we hope this will, in time, provide vital information to feed into oceanographic and climate system research. Personally, I hope to use this data to investigate the fluxes of heat and momentum at the air-sea interface. These fluxes will help us quantify the heat budget of the upper ocean mixed layer, which can feed into improving climate models.

Overall, the experiments conducted from the R/V Meteor and within the wider Eurec4a campaign were a great success. A huge thank you for everyone involved in making Eurec4a possible, with a special mention for the amazing crew of the R/V Meteor!

If you are interested in reading more about the Eurec4a project, see below:

  • Eurec4a project blog
  • Bony S, Stevens B, Ament F, Bigorre S, Chazette P, Crewell S, Delanoë J, Emanuel K, Farrell D, Flamant C, Gross S, Hirsch L, Karstensen J, Mayer B, Nuijens L, Ruppert JH, Sandu I, Siebesma P, Speich S, Szczap F, Totems J, Vogel R, Wendisch M, Wirth M (2017) EUREC4A: A Field Campaign to Elucidate the Couplings Between Clouds, Convection and Circulation. Surv Geophys. doi:10.1007/s10712-017-9428-0

How Climate Models helped uncover the mechanisms behind the North Atlantic Warming Hole

How Climate Models helped uncover the mechanisms behind the North Atlantic Warming Hole

One of the only regions that have been observed to cool over the past century is the North Atlantic cold blob just south of Greenland.

In our recent paper, we analyse the cold blob or “warming hole” and the processes that contribute to its creation and evolution. While sea surface temperature has been reliably observed, the underlying mechanisms of changing ocean circulation are only sparsely measured. Therefore, we used climate models in different configurations to analyse the role of the ocean circulation in driving the warming hole and identify additional contributing processes. Overall, climate models do a pretty good job reproducing the warming hole (Menary & Wood 2018), albeit with some variation among them.

Climate in general and North Atlantic climate in particular are affected by the phenomenon of natural variability. North Atlantic sea surface temperatures and ocean currents show considerable variations on a decadal timescale, which can mask or strengthen a (until now) fairly weak global warming signal
(Jackson et al. 2016). Therefore, an important question to consider is to what degree the warming hole and its drivers are just natural variability. It is hard to tackle this question using observations alone, since there is only one observed reality and disentangling natural trends and from those forced by greenhouse gas emissions is not always possible.

To overcome this, we used a so-called large ensemble of a climate model, in our case the MPI-ESM-1.1 Grand Ensemble (Maher et al. 2019), and investigated 100 simulations of the historical period. The simulations were run with the same boundary conditions but different initial conditions, resulting in 100 different realisations of the past climate. In these simulations we find that the warming hole varies in strength and position, but is consistently simulated in some form across all ensemble members (Fig. 2). Further, averaging the surface temperature trend over all ensemble members produces a clear cooling patch.

Simulated sea surface temperatures in the North Atlantic region for the first 24 ensemble members of the Grand Ensemble with historical forcing. Shown are trends in K per decade over the period 1870-2005. (Fig. 2; Credit: Paul Keil)

This means that the warming hole can indeed be attributed to global warming and is not just an expression of natural variability. Furthermore, examining a second set of 100 simulations with stronger CO2 forcing, we find that the warming hole is likely to strengthen and to emerge more distinctly from natural variability in the future.
We can use a similar approach to examine the driving processes of the warming hole. A change in the ocean circulation which is coupled to the heat transport into the region is likely responsible for a large part of the cooling (Rahmstorf et al. 2015, Menary and Wood 2018). It was thought that the incoming heat transport from the south associated with the AMOC (Atlantic meridional overturning circulation) is the crucial driver of the warming hole. However, in our large ensemble of the historical period, we find that the heat transport south of the warming hole, mainly associated with the AMOC, does show a decreasing trend in some simulations, there is no coherent trend over all 100 simulations (Fig. 3b).

While the AMOC is projected to slow down, and does so in our second set of simulations with stronger CO2 forcing (Fig. 3a), here it does not emerge from natural variability. Instead, the heat transport north of the warming hole shows a significant positive trend over all simulations, which means an increase of exported heat to the Arctic, rather than a decrease of heat import from the south. We are further able to demonstrate that this increase in heat transport into higher latitudes is mainly associated with a heat transport from the horizontal gyre circulation. Thus, for the formation of the warming hole, the role of the subpolar gyre heat transport is crucial (illustrated as blue line in Figure 1). For our observed reality, of which there only is one, this means that the high latitude heat transport changes are likely causing the observed warming hole, especially in its early stages. Nevertheless, due to its large natural variability, the AMOC might also be contributing to it. As global warming progresses, the impact of the AMOC on the warming hole will increase.

North Atlantic Ocean heat transport (OHT) changes in the Grand ensemble.
a, Evolution of the Atlantic heat transport anomalies relative to the preindustrial control simulation at 26.25° N (black solid line) and 63.75° N (black dashed line) in the 1pctCO2 experiment. Shading represents the 5th and 95th percentiles of the ensemble.
b, Ensemble mean linear trends of the Atlantic OHT in the historical ensemble from 1850 to 2005. Shading represents the 5th and 95th percentiles of the total heat transport trend (black line) of the ensemble. Linear trends of the AMOC and gyre components are shown in red and blue, respectively. (Fig. 3; Credit: Keil et al. 2020)

Climate models are far from perfect and continue to misrepresent some aspects of climate, and show considerable differences to the observations and amongst themselves. In our case for example, we have excluded a change in local vertical mixing as a driver for the warming hole, but this process plays an important role for the warming hole in some climate models (Menary and Wood 2018). Nevertheless, climate models can be a unique tool that helps us to understand the climate. We can even switch off the clouds and thereby erase any possible effect they might have on the warming hole (for more details see Keil et al. 2020).

The climate model helped us understand the mechanisms of changing ocean circulation, which are still measured sparsely over longer timescales. However, even if we had perfect measurements of the warming hole and its associated ocean circulation changes, we would still have trouble separating the climate change signal form the ever-present natural variability. With the 100 alternatives, but equally probable realities produced by the Grand Ensemble we were able to overcome this problem. This is just one aspect of how climate models let us construct artificial worlds, with which we explore the underlying mechanisms behind climate change.

 

References & Further reading:

Jackson LC, Peterson KA, Roberts CD, Wood RA (2016) Recent slowing of Atlantic overturning circulation as a recovery from earlier strengthening. Nat Geosci. doi:10.1038/ngeo2715

Keil P, Mauritsen T, Jungclaus J, Hedemann C, Olonscheck D, Ghosh R (2020) Multiple drivers of the North Atlantic warming hole. Nat Clim Chang. doi:10.1038/s41558-020-0819-8

Maher N, Milinski S, Suarez-Gutierrez L, Botzet M, Dobrynin M, Kornblueh L, Kröger J, Takano Y, Ghosh R, Hedemann C, Li C, Li H, Manzini E, Notz D, Putrasahan D, Boysen L, Claussen M, Ilyina T, Olonscheck D, Raddatz T, Stevens B, Marotzke J (2019) The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability. J Adv Model Earth Syst. doi:10.1029/2019MS001639

Menary MB, Wood RA (2018) An anatomy of the projected North Atlantic warming hole in CMIP5 models. Clim Dyn. doi:10.1007/s00382-017-3793-8

Rahmstorf S, Box JE, Feulner G, Mann ME, Robinson A, Rutherford S, Schaffernicht EJ (2015) Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nat Clim Chang. doi:10.1038/nclimate2554

Carbon Brief article by Robert McSweeney (2020) Scientists shed light on human causes of North Atlantic’s ‘cold blob’.

 

Edited by Meriel Bittner

Research Cruise in times of COVID-19

Research Cruise in times of COVID-19

While the whole world is on stand-by we are in the middle of the North Atlantic and I want to explain you, why!

During those crazy times cruise planning also slightly changes. The original plan was to depart and arrive back in Reykjavik which would have only included a short transit to our research area, the OSNAP East section. In the end we had a 9 day transit from our homeport Texel, Netherlands to the southernmost tip of Greenland, Cape Farewell, where we finally started our work. This also included a self-quarantine of two weeks before boarding the Dutch Research Vessel Pelagia for both crew and scientists. The scientific party was also reduced from 12 to 6 people only. On board we had to keep the 1.5 m distance during the first two weeks. For the first test CTD with everyone we had to wear face masks and most of the labs were for two people at a time only.

This cruise is part of OSNAP, the Overturning in the subpolar North Atlantic program. OSNAP started in 2014 measuring the heat, volume and freshwater transport across the subpolar North Atlantic, and with that the status of the Atlantic Meridional Overturning Circulation (AMOC). Different countries installed mooring arrays from the western Labrador Sea towards Greenland and from Greenland to Scotland. So, this project is a huge international effort which is probably the reason why even in those times all OSNAP moorings will be serviced this year.

 

A mooring consists of a cable that is anchored to the sea floor and stands in the water column with instruments attached to it. Depending on the project, you can measure different parameters. Our instruments measure temperature, salinity and velocities. A drifter floats at the oceans surface along with the flow and can either send GPS position only or also measure temperature and/or salinity.

 

Our cruise focused on OSNAP East and especially on the NIOZ (Netherlands Institute for Sea Research) moorings in the Irminger Sea on the western side of the Reykjanes Ridge. Our program was completed by drifter deployments at Cape Farewell to investigate the freshwater pathways of the East Greenland Current towards deep convection regions (EGC-DrIFT: East Greenland Current – Drifter Investigation of Freshwater Transport).

Nora Fried with an Aquadopp to be redeployed. An Aquadopp measures velocities at a predefined depth of the mooring.

I can already say that we successfully recovered and redeployed all five moorings in the Irminger Current. Also 30 drifters are on their way. Both mooring and drifter recovery as well as deployment days were quite intense with the reduced number of people. But with a working schedule we managed reading out and redeploying instruments with CTDs happening in between. We even had time for a visit of the US R/V Neil Armstrong which was on its way back after intense weeks of mooring work in the western Irminger Sea. Our days were enlightened by groups of pilot whales coming by once in a while and also by a fin whale. Apart from that we mainly saw water, fog and some sunshine in between.

The NIOZ moorings are part of my PhD project about the Irminger Current. As a continuation of the North Atlantic Current the Irminger Current is responsible for northward heat and salt transport at the surface. At the same time overflow water from the Iceland-Scotland ridge is transport at depth. Finding out about the Irminger Current’s mean state, variability, its pathways along the ridge and its contribution to the AMOC is part of my PhD project. Meaning I am emotionally invested in the cruise and with every mooring suddenly appearing in the water you could see my face light up. Recovering a mooring has fascinated me since my first research cruise and I’m thankful that my PhD position gives me the opportunity to enjoy more of those unique moments.

Nora Fried with the top buoys during the recovery of IC1.

Out in the ocean and after the two weeks of social distancing on board, life feels normal although we all know that we are just a small floating island while the world is holding its breath. Just yesterday we finished the last mooring and we will end our cruise with a CTD section towards the east until we finally head back to Texel.

Thanks a lot already to the crew of the R/V Pelagia who made our stay so nice and who are always there to help. And finally, a shout-out to our three master students who are very motivated to help even if it means spooling up cable for two hours or cleaning the instruments with a dishwashing brush.

Although we don’t know yet, to which world we will return we hope that we will be out here in two years to service the OSNAP moorings again.

Greetings from the Iceland Basin @ 58 37.32 N, 29 49.36 W!

Further Reading

Edited by Meriel Bittner


Nora Fried is a second-year PhD student in Physical Oceanography at the Royal Netherlands Institute for Sea Research (NIOZ) on Texel. She is a sea-going oceanographer with interest in changes of the Atlantic Meridional Overturning Circulation. Her project focusses on mooring data from five deep-reaching moorings in the subpolar North Atlantic combined with hydrographic sections and altimetry data.