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Cryospheric Sciences

Highlighted Paper

Image of the Week – A new way to compute ice dynamic changes

Fig. 1: Map of ice velocity from the NASA MEaSUREs Program showing the region of Enderby Land in East Antarctica [Credit: Fig. 1 from Kallenberg et al. (2017) ].

Up to now, ice sheet mass changes due to ice dynamics have been computed from satellite observations that suffer from sparse coverage in time and space. A new method allows us to compute these changes on much wider temporal and spatial scales. But how does this method work? Let us discover the different steps by having a look at Enderby Land in East Antarctica, for which ice velocities are shown in our Image of the Week…


Mass balance of ice sheets

The mass balance of an ice sheet is the difference between the mass gain of ice, primarily through snowfall, and the mass loss of ice, primarily via meltwater runoff and ice dynamic processes (e.g. iceberg calving, melting below ice shelves). When the mass gain is equal to the mass loss, the ice sheet is in balance. However, if one exceeds the other, the ice sheet either gains or loses mass.

Measuring mass balance changes of ice sheets is crucial due to their potential contribution to sea level rise (see previous post). You can have a look at this nice review for further details about the recent changes in the mass balance of the two biggest ice sheets on Earth, i.e. Antarctica and Greenland.

Ice mass changes from snowfall and meltwater runoff (what we call ‘surface mass balance’ changes) are reasonably well simulated by regional climate models, which give good agreement with observations (see this study for Antarctica and this one for Greenland). Mass changes from ice dynamics are more complex to obtain. They are commonly estimated by combining ice velocity and ice thickness. Ice velocity is measured via satellite radar interferometry, while ice thickness is obtained thanks to airborne radar. Unfortunately, these measurements have sparse temporal and spatial coverage, especially in Antarctica, which makes the computation of mass changes from ice dynamics challenging.

A new method to estimate ice dynamic changes

Kallenberg et al. (2017) conducted a study focussing on Enderby Land in East Antarctica (see our Image of the Week) in which they use a novel approach to estimate ice dynamic changes. This region of Antarctica has experienced a slightly positive mass balance in past years, meaning that the ice sheet has slightly thickened in this region.

Kallenberg et al. (2017) first used satellite observations to compute the total changes in ice sheet mass. They took advantage of two high-technology datasets. The first one, “Gravity Recovery And Climate Experiment” (GRACE), measures changes in the Earth’s gravity field, from which ice mass changes can be derived. A summary explaining how GRACE works can be found in this previous post. The second satellite dataset, “Ice, Cloud, and land Elevation Satellite” (ICESat), measures changes in ice surface elevation, from which changes in ice mass can be computed by using ice density.

However, Kallenberg et al. (2017) were not interested in the total ice mass changes, as obtained from GRACE and ICESat satellites, but rather in ice dynamic changes. They subtracted two quantities from the total mass changes in order to obtain the remaining dynamic changes:

  1. Surface mass balance changes: changes from processes happening at the surface of the ice sheet (e.g. snow accumulation, meltwater runoff). These changes were obtained from model simulations using the Regional Atmospheric Climate Model (RACMO2), for which details can be found in this previous post.
  2. Glacial Isostatic Adjustment: changes in land topography due to ice loading and unloading. These changes were computed from Glacial Isostatic Adjustment models.

What does this study tell us?

The results of this study show that it is possible to compute changes in ice mass resulting from ice dynamics with higher spatial and temporal coverage than before, using a combination of satellite observations and models.

Also, the use of two different satellite datasets (GRACE and ICESat) shows that they agree quite well with each other in the region of Enderby Land (see Fig. 2). This means that using one or the other dataset does not make a big difference.

Finally, this new method also shows that differences between GRACE and ICESat reduce when using the newer version of RACMO2 for computing surface mass balance changes. This tells us that comparing results of ice dynamics from both satellites with different models is a good way to identify which models correctly simulate surface processes and which models do not.

Fig. 2: Ice dynamic changes (dH/dt, where H is ice thickness and t is time) computed from (a) GRACE and (b) ICESat and expressed in meters per year [Credit: Fig. 5 from Kallenberg et al. (2017) ].

Further reading

Edited by Clara Burgard and Emma Smith


David Docquier is a post-doctoral researcher at the Earth and Life Institute of Université catholique de Louvain (UCL) in Belgium. He works on the development of processed-based sea-ice metrics in order to improve the evaluation of global climate models (GCMs). His study is embedded within the EU Horizon 2020 PRIMAVERA project, which aims at developing a new generation of high-resolution GCMs to better represent the climate.

Image of the Week — High altitudes slow down Antarctica’s warming

Elevations in Antarctica. The average altitude is about 2,500m. [Credit: subset of Fig 5 from Helm et al (2014)]

When it comes to climate change, the Arctic and the Antarctic are poles apart. At the north of the planet, temperatures are increasing twice as fast as in the rest of the globe, while warming in Antarctica has been milder. A recent study published in Earth System Dynamics shows that the high elevation of Antarctica might help explain why the two poles are warming at different speeds.


The Arctic vs the Antarctic

At and around the North Pole, in the Arctic, the ice is mostly frozen ocean water, also known as sea ice, which is only a few meters thick. In the Antarctic, however, the situation is very different: the ice forms not just over sea, but over a continental land mass with rugged terrain and high mountains. The average height in Antarctica is about 2,500 metres, with some mountains rising as high as 4,900 metres.

A flat Antarctica would warm faster

Mount Jackson in the Antarctic Peninsula reaches an altitude of 3,184 m  [Credit: euphro via Flickr]

Marc Salzmann, a scientist working at the University of Leipzig in Germany, decided to use a computer model to find out what would happen if the elevation in Antarctica was more like in the Arctic. He discovered that, if Antarctica were flat, there would be more warm air flowing from the equator to the poles, which would make the Antarctic warm faster.

As Antarctica warms and ice melts, it is actually getting flatter as time goes by, even if very slowly. As such, over the next few centuries or thousands of years, we could expect warming in the region to speed up.

Reference/further reading

planet_pressThis is modified version of a “planet press” article written by Bárbara Ferreira and originally published on 18 May 2017 on the EGU website
(a Serbian version is also available, why not considering adding a new language to the list? 🙂 )

Image of the Week – Ice lollies falling from the sky

Lolly in the sky. [Credit: Darwin Bell via flickr]

You have more than probably eaten many lollipops as a kid (and you might still enjoy them). The good thing is that you do not necessarily need to go to the candy shop to get them but you can simply wait for them to fall from the sky and eat them for free. Disclaimer: this kind of lollies might be slightly different from what you expect…


Are lollies really falling from the sky?

Eight years ago (in January 2009), a low-pressure weather system coming from the North Atlantic Ocean reached the UK and brought several rain events to the country. Nothing is really special about this phenomenon in Western Europe in the winter. However, a research flight started sampling the clouds in the warm front (transition zone where warm air replaces cold air) ahead of the low-pressure system and discovered hydrometeors (precipitation products, such as rain and snow) of an unusual kind. Researchers named them ‘ice lollies’ due to their characteristic shape and maybe due to their gluttony. The microphysical probes onboard the aircraft, combined with a radar system located in Southern England, allowed them to measure a wide range of hydrometeors, including these ice lollies that were observed for the first time with such concentration levels.

How do ice lollies form?

A recent study (Keppas et al, 2017) explains that ice lollies form when water droplets (size of 0.1 to 0.7 mm) collide with ice crystals with the form of a column (size of 0.25 to 1.4 mm) and freeze on top of them (see Fig. 2).

Fig 2: Formation of an ice lolly: water droplet (the circle) collides with an ice crystal (the column) [Credit: Fig. 1a from Keppas et al., (2017)].

Such ice lollies form in ‘mixed-phase clouds’, i.e. clouds made of water droplets and ice crystals and whose temperature is below the freezing point (0°C). At these temperatures, water droplets can be supercooled, meaning that they stay liquid below the freezing point.

Figure 3 below shows the processes and particles involved in the formation of ice lollies. Ice lollies are mainly found at temperatures between 0 and -6°C, in the vicinity of the warm conveyor belt, which represents the main source of warm moist air that feeds the low-pressure system. This warm conveyor belt brings water vapour that participates in the formation and growth of supercooled water droplets. Ice crystals formed near the cloud tops fall through the warm conveyor belt and collide with the water droplets to form ice lollies.

Fig 3: Processes involved with the formation of ice lollies, which mainly form under the warm conveyor belt [Credit: Fig 4 from Keppas et al., (2017)].

Are these ice lollies important?

Ice lollies were observed more recently (September 2016) during another aircraft mission over the northeast Atlantic Ocean but no radar coverage supported the observations. At the moment of writing this article, the lack of observations prevent us from determining the importance of these ice lollies in the climate system. However, future missions would provide more insight. In the meantime, we suggest you to enjoy a lollipop such as the one shown in the image of this week 🙂

This is a joint post, published together with the atmospheric division blog, given the interdisciplinarity of the topic.

Edited by Sophie Berger and Dasaraden Mauree

Reference/Further reading

Keppas, S. Ch., J. Crosier, T. W. Choularton, and K. N. Bower (2017), Ice lollies: An ice particle generated in supercooled conveyor belts, Geophys. Res. Lett., 44, doi:10.1002/2017GL073441

 


DavidDavid Docquier is a post-doctoral researcher at the Earth and Life Institute of Université catholique de Louvain (UCL) in Belgium. He works on the development of processed-based sea-ice metrics in order to improve the evaluation of global climate models (GCMs). His study is embedded within the EU Horizon 2020 PRIMAVERA project, which aims at developing a new generation of high-resolution GCMs to better represent the climate.

Image of the Week – Antarctica’s Flowing Ice, Year by Year

Fig 1: Map series of annual ice sheet speed from Mouginot et al. (2017). Speeds range from 0 (purple) to 1000+ (dark brown) m/yr. [Credit: George Roth]

Today’s Image of the Week shows annual ice flow velocity mosaics at 1km resolution from 2005 to 2016 for the Antarctic ice sheet. These mosaics, along with similar data for Greenland (see Fig.2), were published by Mouginot et al, (2017) last month as part of NASA’s MEaSUREs (Making Earth System Data Records for Use in Research Environments) program.


How were these images constructed?

The mosaics shown today (Fig 1 and 2) were built by combining optical imagery from the Landsat-8 satellite with radar (SAR) data from the Sentinel-1a/b, RADARSAT-2, ALOS PALSAR, ENVISAT ASAR, RADARSAT-1, TerraSAR-X, and TanDEM-X sensors.

Although the authors used the well-known techniques of feature and speckle tracking to produce their velocities from optical and radar images, respectively, the major novelty of their study lies in the automation and integration of the different datasets.

Fig.2: Mosaics of yearly velocity maps of the Greenland and Antarctic ice sheet for the period 2015-2016.Composite of satellite-derived yearly ice sheet speeds from 2005-2016 for both Greenland and Antarctica. [Credit: cover figure from Mouginot et al. (2017)]

How is this new dataset useful?

Previously, ice sheet modellers have used mosaics composed of satellite data from multiple years to cover the entire ice sheet. However, this new dataset is one of the first to provide an ice-sheet-wide geographic scale, a yearly temporal resolution, and a moderately high spatial resolution (1km). This means that modellers can now better examine how large parts of the Greenland and Antarctic ice sheets evolve over time. By linking the evolution of the ice sheets to the changes in weather and climate over those ice sheets during specific years, modellers can calibrate the response of those ice sheets’ outlet glaciers to different climate conditions. The changes in the speeds of these outlet glaciers have important consequences for the amount of sea level rise expected for a given amount of warming.

How can I start using this data?

The yearly MEaSUREs data is hosted at the NSIDC in NetCDF format. The maps shown in the animated image were made using Quantarctica/QGIS (for more information on Quantarctica, check out our previous post E). QGIS natively supports NetCDF files like these mosaics with no additional import steps. Users can quickly calculate new grids showing speed, changes in velocities between years, and more by using the QGIS Raster Calculator or gdal_calc.

References/ Further Reading

Mouginot, J., Rignot, E., Scheuchl, B., & Millan, R. (2017). Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data. Remote Sensing, 9(4), 364. http://dx.doi.org/10.3390/rs9040364

Image of the Week – Quantarctica: Mapping Antarctica has never been so easy!

Image of the Week – A high-resolution picture of Greenland’s surface mass balance

Written with help from Jelte van Oostsveen
Edited by Clara Burgard and Sophie Berger


George Roth is the Quantarctica Project Coordinator in the Glaciology group (@NPIglaciology) at the Norwegian Polar Institute. He has spent the last several years helping researchers with GIS, cartography, and remote sensing in both the Arctic and Antarctic.

Image of the Week — Hidden lakes in East Antarctica !

Image of the Week — Hidden lakes in East Antarctica !

Who would have guessed that such a beautiful picture could get you interviewed for the national news?! Certainly not me! And yet, the photo of this englacial lake (a lake trapped within the ice in Antarctica), or rather science behind it, managed to capture the media attention and brought me, one of the happy co-author of this study,  on the Belgian  television… But what do we see on the picture and why is that interesting?


Where was the picture taken?

The Image of this Week shows a 4m-deep meltwater lake trapped 4 m under the surface of the Roi Baudouin Ice Shelf (a coastal area in East Antarctica). To capture this shot, a team of scientists led by Stef Lhermitte (TU Delft) and Jan Lenaerts (Utrecht University) went to the Roi Baudouin ice shelf, drilled a hole and lowered a camera down (see video 1).

Video 1 : Camera lowered into borehole to show an englacial lake 4m below the surface. [Credit: S. Lhermitte]

How was the lake formed?

In this region of East Antarctica, the katabatic winds are very persistent and come down from the centre of the ice sheet towards the coast, that is the floating ice shelf (see animation below). The effect of the winds are two-fold:

  1. They warm the surface because the temperature of the air mass increases during its descent and the katabatic winds mix the very cold layer of air right above the surface with warmer layers that lie above.
  2. They sweep the very bright snow away, revealing darker snow/ice, which absorb more solar radiation

The combination leads to more melting of the ice/snow in the grounding zone — the boundary between the ice sheet and ice shelf — , which further darkens the surface and therefore increases the amount of solar radiation absorbed, leading to more melting, etc. (This vicious circle is very similar to the ice-albedo feedback presented in this previous post).

Animation showing the processes causing the warm micro-climate on the ice shelf. [Credit: S. Lhermitte]

All the melted ice flows downstream and collects in depressions to form (sub)surface lakes. Those lakes are moving towards the ocean with the surrounding ice and are progressively buried by snowfalls to become englacial lakes. Alternatively, the meltwater can also form surface streams that drain in moulins (see video 2).

Video 2 : Meltwater streams and moulins that drain the water on the Roi Baudouin ice shelf. [Credit: S. Lhermitte]

Why does it matter ?

So far we’ve seen pretty images but you might wonder what could possibly justify an appearance in the national news… Unlike in Greenland, ice loss by surface melting has  often been considered negligible in Antarctica. Meltwater can however threaten the structural integrity of ice shelves, which act as a plug of the grounded ice from upstream. Surface melting and ponding was indeed one of the triggers of the dramatic ice shelves collapses in the past decades, in the Antarctic Peninsula . For instance, the many surfaces lakes on the surface of the Larsen Ice shelf in January 2002, fractured and weakened the ice shelf until it finally broke up (see video 3), releasing more grounded ice to the ocean than it used to do.

Of course surface ponding is not the only precondition for an ice shelf to collapse : ice shelves in the Peninsula had progressively thinned and weakened for decades, prior their disintegration. Our study suggests however that surface processes in East Antarctica are more important than previously thought, which means that this part of the continent is probably more vulnerable to climate change than previously assumed. In the future, warmer climates will intensify melt, increasing the risk to destabilise the East Antarctic ice sheet.

Video 3 : MODIS images show Larsen-B collapse between January 31 and April 13, 2002. [Credit:NASA/Goddard Space Flight Center ]

Reference/Further reading

Edited by Nanna Karlsson

Katabatic winds – A load of hot (or cold) air?

Katabatic winds – A load of hot (or cold) air?

It might seem obvious that a warming world will lead to a reduction in glacial ice cover, but predicting the response of glaciers to climatic change is no simple task (even within the short term). One way to approach this problem is to come up with relationships which describe how glaciers interact with the world around them, for example, how the ice interacts with the air above it. Our post today delves into the world of ice-air interaction and describes some of the problems encountered by those who are investigating it, in particular the problem of modelling katabatic winds! Not sure what we are talking about…then read on to find out more! 


What are katabatic winds?

Anyone who has stood on, or in front of a glacier on a clear, sunny day has no doubt felt the bitter chill of a katabatic wind, forcing them to don a warm jacket and lose their chance at that lovely “glacier tan”. Katabatic winds (derived from the Greek word katabasis, meaning ‘downhill’) develop over snow and ice surfaces because the 0°C ice surface cools the air just above it. This cold, dense air then flows downhill under the force of gravity (Fig. 1 and Fig. 2). This is not recent news and such wind chill has no doubt punished glaciologists and explorers for the last century or more –  Mawson’s Description of the 1911-1914 Australian Antarctica Expedition is aptly named “The Home of the Blizzard“. However, despite being well known, this phenomenon still causes much uncertainty when it comes to modelling the melting of glacier ice surfaces around the world.

Soon gusts swept the tops of the rocky ridges, gradually descending to throw up the snow at a lower level. Then a volley raked the Hut, and within a few minutes we were once more enveloped in a sea of drifting snow, and the wind blew stronger than ever. – Mawson, 1915, The Home of the Blizzard

Figure 2: The view from the upper reaches of Tsanteleina Glacier in the western Italian Alps (Val d’Rhemes, Aosta). Katabatic winds generally flow in a down-glacier direction – here, from right to left [Credit: T Shaw].

Challenges for modelling

Air temperature is really important in determining how much a glacier melts and we need to know as much about it as possible to provide accurate predictions now and into the future. This is particularly relevant because the warmer it gets, the more energy is available to melt ice and seasonal snow. Unfortunately though, we don’t have an infinite supply of meteorological observations (e.g. air temperature, wind speed etc) at many locations we are interested in. As a result, we have to make simple assumptions about what the weather is doing at a remote, far away glacier. One such simple assumption is based upon the fact that air temperature typically decreases with increasing elevation, and so if we know the elevation of a location we are interested in, we can assume a ‘likely’ temperature. The rate of change in temperature with elevation is known as a ‘lapse rate’.

Air temperature is really important in determining how much a glacier melts…the warmer it gets, the more energy is available to melt ice and seasonal snow.

When predicting glacier melt, it is common practice to use a lapse rate which stays constant in time and space. This is convenient as we often don’t know the actual lapse rate at a given location, but this often ignores things happening at the surface of the Earth. An important example of this is when we have katabatic winds over glaciers!

When conditions are warm, and skies are clear, the cooling of the air above the ice surface, means that the application of a lapse rate is fairly useless, or close to it [Greuell and Böhm, 1998]! That is because the cooling from the surface continues as air flows down the glacier, typically creating colder temperatures at lower elevations, the opposite of the typical lapse rate assumption that models will apply.

‘Bow-shaped’ temperature vs. elevation relationships

To complicate matters for people trying to model the air temperature over glaciers, the effect of surface cooling is not just dependent on the amount of time an air parcel is in contact with the ice surface but also the characteristics of the ice surface it has been in contact with. In fact, after cooling on their descent down-glacier, air parcels have been documented to warm again, leaving interesting slightly “bow-shaped” curves to the temperature-elevation relationship. This effect has been found for the Swiss Haut Glacier d’Arolla and the Italian Tsanteleina Glacier (Fig. 3c,d). A new model approach to tackling this bow-shaped problem has been presented by recent research [Ayala et al., 2015] and offers a means of accounting for katabatic winds in glacier models. Nevertheless, more data and more work are still needed to generalise these models [Shaw et al., in review].

Figure 3: Relationship between elevation and air temperature on three different glaciers in the western Alps. Miage (Italy), Tsanteleina (Italy) and Arolla (Switzerland). Glaciers are represented using the mean of all data available (green), the top 10% of off-glacier temperatures (P90 – red) and the bottom 10% of off-glacier temperatures (P10 – blue), plus one standard deviation. The debris-covered Miage Glacier does not demonstrate a classic katabatic flow regime and therefore temperature corresponds well to elevation even under warm conditions [Credit: T Shaw, unpublished].

after cooling on their descent down-glacier, air parcels have been documented to warm again, leaving interesting slightly “bow-shaped” curves to the temperature-elevation relationship.

Air temperatures across debris-covered glaciers

As you may have read in our previous post on the topic, debris-covered glaciers behave in a different way to those with a clean ice surface. Detailed observations of air temperature across a debris-covered glacier show that the glacier responds to the heating of surface debris in the sunlight and a consequent warming of the lower atmosphere [Shaw et al., 2016]. Because of this, air temperature conforms very strongly to the elevation dependency that is assumed when using a lapse rate. Although very local variations of air temperature on other debris-covered glaciers cannot be well estimated by a lapse rate [Steiner and Pellicciotti, 2016], the insulating effect of thick debris cover means that the current approach to using simple lapse rates for estimating air temperature over debris-covered glaciers could be suitable.

Nevertheless, challenges for accurately representing air temperature above glaciers without debris cover remain. The fact that globally averaged temperatures are expected to rise over the current century (areas at high latitudes have shown a stronger warming trend) [Collins et al, 2013], the applicability of using lapse rates could further diminish. Recent patterns of warmer-than-average temperatures also suggest a difficulty of accurately estimating on-glacier temperatures in the short-term. For example, for the period of May 2015 – August 2016, every month beat the previously held record for warmest globally average temperature (GISTEMP). Imagine the bow-shaped problem to that!

Edited by Matt Westoby and Emma Smith


Thomas Shaw is a PhD student in the Department of Geography at Northumbria University, UK. His research is focused on the spatial and temporal variance in near-surface air temperature across debris-covered and debris-free glaciers in the western Italian Alps. As well as conducting research in the Alps, he is also very interested in glaciers and their processes on Svalbard (Norwegian Arctic) and has spent plenty of time studying above, or within (!), ice at high latitudes. Contact e-mail: thomas.shaw@northumbria.ac.uk

Image of the Week – For each tonne of CO2 emitted, Arctic sea ice shrinks by 3m² in summer

Image of the Week – For each tonne of CO2 emitted, Arctic sea ice shrinks by 3m² in summer

Declining sea ice in the Arctic is definitely one of the most iconic consequences of climate change. In a study recently published in Science, Dirk Notz and Julienne Stroeve find a linear relationship between carbon dioxide (CO2) emissions and loss of Arctic sea-ice area in summer. Our image of this week is based on these results and shows the area of September Arctic sea ice lost per inhabitant due to CO2 emissions in 2013.


What did we know about the Arctic sea ice before this study?

Since the late 1970s, sea ice has been dramatically shrinking in the Arctic, losing 3.8% of its area per decade. Sea-ice area is at its minimum in September, at the end of the melting season.

The main cause of this loss is the increase in surface temperature over the recent years (Mahlstein and Knutti, 2012), which has been more pronounced in the Arctic compared to other regions on Earth (Cohen et al., 2014). The use of statistical methods involving both observations and climate models shows that the recent warming in the Arctic can be attributed to human activity, i.e. mainly greenhouse gas emissions (Gillett et al., 2008). This suggests a direct link between human activity and Arctic sea-ice loss, which is confirmed in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).

How exactly is sea-ice loss related to CO2 emissions ?

Notz and Stroeve (2016) relate the Arctic sea-ice decline to cumulative CO2 emissions since 1850 (i.e. the total amount of CO2 that has been emitted since 1850) for both observations and climate models. Cumulative CO2 emissions constitute a robust indicator of the recent man-made global warming (IPCC, 2014).

The two quantities are clearly linearly related (see Figure 2). From 1953 to 2015, about 3.5 million km² of Arctic sea ice have been lost in September while 1200 gigatonnes (1 Gt = 10e9 tonnes) of CO2 have been emitted to the atmosphere. This means that for each tonne of CO2 released into the atmosphere, the Arctic loses 3 m² of sea ice.

Fig 2: Monthly mean September Arctic sea-ice area against cumulative CO2 emissions since 1850 for the period 1953-2015. Grey circles and diamonds show the results from in-situ (1953-1978) and satellite (1979-2015) observations, respectively. The thick red line shows the 30-year running mean and the dotted red line represents the trend of 3 m² sea-ice area loss per tonne of CO2 emitted. [Credit: D. Notz, National Snow and Ice Data Center ]

Starting from the relationship between cumulative CO2 emissions and sea-ice area, it is then easy to attribute to each country in the world their own contribution to sea-ice loss based on their CO2 emissions per capita. The countries that stand out in the map are thus the countries emitting the most in relation to their population.

Could the Arctic be ice-free in the future?

If this relationship holds in the future (in other words, if we extend the red dotted line to zero sea-ice area in Figure 2), adding 1000 Gt of CO2 in the atmosphere would free the Arctic of sea ice in September. Since we are currently emitting about 35 Gt CO2 per year, it would take less than 30 years to have the Arctic free of sea ice in the summer (which confirms previous model studies (e.g. Massonnet et al., 2012)).

Edited by Clara Burgard and Sophie Berger

Further reading

DavidDavid Docquier is a post-doctoral researcher at the Earth and Life Institute of Université catholique de Louvain (UCL) in Belgium. He works on the development of processed-based sea-ice metrics in order to improve the evaluation of global climate models (GCMs). His study is embedded within the EU Horizon 2020 PRIMAVERA project, which aims at developing a new generation of high-resolution GCMs to better represent the climate.

Black Carbon: the dark side of warming in the Arctic

Black Carbon: the dark side of warming in the Arctic

When it comes to global warming, greenhouse gases – and more specifically CO2 – are the most often pointed out. Fewer people know however that tiny atmospheric particles called ‘black carbon’ also contribute to the current warming. This post presents a paper my colleague and I recently published in nature communications. Our study sheds more light into the chemical make-up of black carbon, passing through the Arctic.


Black Carbon warms the climate

 Figure 1: Global radiative forcing of CO2 (green) compared to black carbon (blue). The colored bars show the mean change in radiative forcing due to the concentration of CO2 and BC in the atmosphere. The estimated range for the expected radiative forcing is everything between the white lines, which show the 90% confidence interval. (Data according to Boucher et al. 2013 (IPCC 5th AR) and Bond et al. 2013). [Credit: Patrik Winiger]

Figure 1: Global radiative forcing of CO2 (green) compared to black carbon (blue). The colored bars show the mean change in radiative forcing due to the concentration of CO2 and BC in the atmosphere. The estimated range for the expected radiative forcing is everything between the white lines, which show the 90% confidence interval. (Data according to Boucher et al. 2013 (IPCC 5th AR) and Bond et al. 2013). [Credit: Patrik Winiger]

Black Carbon (BC) originates from incomplete combustion caused by either natural (e.g., wild fires) or human (e.g., diesel car emissions) activities. As the name suggests, BC is a dark particle which absorbs sunlight very efficiently. In scientific terms we call this a strong positive radiative forcing, which means that the presence of BC in the atmosphere is helping to heat the planet. Some estimates put its radiative forcing in second place, only after CO2 (Figure 1). The significant thing about BC is that it has a short atmospheric lifetime (days to weeks), meaning we could quickly avoid some climate warming by getting rid of its emissions. Currently global emissions are increasing year by year and on snow and ice, the dark particles have a longer lasting effect due to the freeze and thaw cycle, where BC can re-surface, before it is washed away. It is important however to note, that our main focus on emission reduction should target (fossil-fuel) CO2 emissions, because they will affect the climate long after (several centuries) they have been emitted.

Arctic amplification: strongest warming in the North Pole

The Arctic is warming faster than the rest of our planet. Back in 1896, the Swede Arrhenius, (better known for his works: in chemistry), calculated, that a change in atmospheric CO2 – which at that time was a good 100 ppm lower than today – would change the temperature at higher latitudes (towards the poles) more than at lower latitudes.

Figure 2: Observation based global surface temperature anomalies for Jan-Mar (2016) in °C with respect to a 1961-1990 base year. Credit: GISTEMP Team, 2016: GISS Surface Temperature Analysis (GISTEMP). NASA Goddard Institute for Space Studies. Dataset accessed 2016-10-15 at http://data.giss.nasa.gov/gistemp/ [Hansen et al., 2010].

Figure 2: Surface temperature anomalies (in °C) for Jan-Mar (2016) with respect to a 1961-1990 baseline. [ Credit: NASA — GISTEMP (accessed 2016-10-15) and Hansen et al., 2010].

The problem with his calculations – as accurate and impressive they might have been – was, that he ignored the earth’s geography and seemed unaware of the big heat capacity of the oceans. On the southern half of our planet there is a lot more water, which can take up more heat, as compared to the northern half with more land surface. Thus, in reality the latitudes on the southern hemisphere have not heated as much as their northern counterparts and this effect came to be known as Arctic amplification.

Dark particles on bright snow and ice

Figure 3: Welcome to the Greenland Ice Sheet everybody. Probably an extreme case of ice covered in cryoconite, captured in August 2014 [Credit: Jason Box, (LINK: http://darksnow.org/)].

Figure 3: Ice covered in cryoconite, Greenland Ice Sheet, in August 2014 [Credit: Jason Box — Dark Snow project].

Greenhouse gases and BC are not the only reasons for the increase in temperature change and earlier onset of the melting season in the Arctic. Besides BC, there are other ‘light absorbing impurities’ such as dust, microorganisms, or a mixture of all of the above, better known as cryoconite. They all absorb solar radiation and thus decrease the albedo – the amount of solar energy reflected back to space – of the underlying white surface. This starts a vicious cycle by which these impurities melt the snow or ice and eventually uncover the usually much darker surface (e.g., rock or open sea water), leading to more solar absorption and the cycle continues. The effect and composition of these impurities are currently intensively studied on the Greenland ice sheet (check out the Black and Bloom, as well as the Dark Snow projects).

 Black Carbon effect on climate is highly uncertain

One of the reasons for the high uncertainty of BC’s climate effects is the big range in effects it has (see white line on Figure 1), when it interacts with snow and ice (or clouds and the atmosphere).

Another source of uncertainty is probably the big estimated range in the global, and especially in the regional emissions of BC in the Arctic. For example, the emission inventory we work with (ECLIPSE), is based on international and national statistics that indicate how much of a certain fuel (diesel, coal, gas, wood, etc.) is used, and in which way it is used (vehicle sizes, machine type and age, operating conditions, etc.). These numbers can vary a lot. If we, for example, line up different emission inventories of man-made emissions (Figure 4), by comparing the two different fractions of BC (fossil fuels vs. biomass burning) at different latitudes, then we see that the closer we get to the North pole, the more these emission inventories disagree. And this is still ignoring atmospheric transport or emissions of natural sources, such as wildfires.

Computer models, necessary to calculate global climate change, are partly based on input from these emission inventories. Models used for the calculation of the transport of these tiny particles have vastly improved in recent years, but still struggle at accurately mimicking the seasonality or extent of the observed BC concentrations. To some extent this is also due to the range of parametrization in the model, mainly the lifetime of BC, including its removal from the atmosphere by wet scavenging (e.g., rain). So to better understand black carbon effects on climate, more model calculations are necessary, for which the emission inventory estimates need to be verified by observations.

Figure 4: Fraction biomass burning of BC (fbb) at different latitudes North, estimated by three different emission inventories. The green line shows the GAINS emission inventory, which was the precursor to the ECLIPSE inventory (Klimont et al. 2016) [Credit: Patrik Winiger]

Figure 4: Fraction biomass burning of BC (fbb) at different latitudes North, from three different emission inventories. The green line shows the GAINS emission inventory, which was the precursor to the ECLIPSE inventory (Klimont et al. 2016) [Credit: Patrik Winiger]

How do we trace the origin of black carbon?

This is where the science of my colleagues and me comes in. By looking at BC’s isotopic ratio of stable-carbon (12C/13C) and its radiocarbon (14C) content we were able to deduce information about the combustion sources (Figure 5).

Plants (trees) take up contemporary radiocarbon, naturally present in the atmosphere, by photosynthesis of atmospheric CO2. All living organisms have thus more or less the same relative amount of radiocarbon atoms, we talk of a similar isotopic fingerprint. BC from biomass (wood) burning thereby has a contemporary radiocarbon fingerprint.

When they die, organisms stop incorporating contemporary carbon and the radiocarbon atoms are left to decay. Radiocarbon atoms have a relative short (at least on geological time-scales) half-life of 5730 years, which means that fossils and consequentially BC from fossil fuels are completely depleted of radiocarbon. This is how the measured radiocarbon content of a BC sample gives us information on the relative contributions of fossil fuels vs. biomass burning.

The stable carbon isotopic ratio gives information on the type of combustion sources (liquid fossil fuels, coal, gas flaring or biomass burning). Depending on how a certain material is formed (e.g., geological formation of coal), it has a specific isotopic ratio (of 12C/13C), like a fingerprint. Sometimes isotopic fingerprints can be altered during transport (because of chemical reactions or physical processes like condensation and evaporation). However, BC particles are very resistant to reactions and change only very little. Hence, we expect to see the same fingerprints at the observation site and at the source, only that the isotopic signal at the observation site will be a mixture of different source fingerprints.

Figure 5/ Carbon isotopic signatures of different BC sources, summarized by E.N. Kirillova (2013).

Figure 5: Carbon isotopic signatures of different BC sources, summarized by E.N. Kirillova (2013). To give information about the isotopic fingerprint, the delta-notation is used (small delta for 12C/13C, and big delta for 12C/14C). The isotopic values show how much a certain sample is different, on a per mil scale, from an international agreed isotopic standard value (or ratio) for carbon isotopes. [Credit: fig 1 from  Kirillova (2013)]

Where does the black carbon in European Arctic come from?

In our study (Winiger et al, 2016), we observed the concentrations and isotopic sources of tiny particles in airborne BC for over a year, in the European Arctic (Abisko, Sweden), and eventually compared these observations to model results, using the freely available atmospheric transport model FLEXPART and emission inventories for natural and man-made BC emissions.

Seeing our results we were first of all surprised at how well the model agreed with our observations. We saw a clear seasonality of the BC concentrations, like it has been reported in the literature before, and the model was able to reproduce this. Elevated concentrations were found in the winter, which is sometimes referred to as Arctic haze. The combustion sources showed a strong seasonality as well. The radiocarbon data showed, that fossil fuel combustion dominated in the winter and (wood) biomass burning during the low BC-burden periods in the summer. With a combination of the stable isotope fingerprints and Bayesian statistics we further concluded, that the major fossil fuel emissions came from liquid fossil fuels (most likely diesel). The model predicted a vast majority of all these BC emissions to be of European origin. Hence, we concluded, that the European emissions in the model had to be well constrained and the model parametrization of BC lifetime and wet-scavenging had to be fairly accurate for the observed region and period. Our hope is now that our work will be implemented in future models of BC effects and taken into account for future BC mitigation scenarios.

Figure 6: This is an example from the model calculations, showing where the (man-made) BC came from in January 2012. Abisko's position is marked as a blue star. The darker (red) spots show sources of higher BC contribution. This winter example was among the three highest observed (in terms of BC concentration) and the sources were ~50% wood burning, ~20% liquid fossil fuels (diesel) and ~30% coal. Some of the darkest spots can clearly be attributed to European cities.

Figure 6: Example from the model calculations, showing where the (man-made) BC came from in January 2012. Abisko’s position is marked as a blue star. The darker (red) spots show sources of higher BC contribution. This winter example was among the three highest observed (in terms of BC concentration) and the sources were ~50% wood burning, ~20% liquid fossil fuels (diesel) and ~30% coal. Some of the darkest spots can clearly be attributed to European cities. [Credit: fig4b from Winiger et al (2016)]

References

  • Anderson, T. R., E. Hawkins, and P. D. Jones (2016), CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today’s Earth System Models, Endeavour, in press, doi:10.1016/j.endeavour.2016.07.002.
  • Arrhenius, S. (1896), On the influence of carbonic acid in the air upon the temperature of the ground., Philos. Mag. J. Sci., 41(August), 239–276, doi:10.1080/14786449608620846.
  • Hansen, J., R. Ruedy, M. Sato, and K. Lo (2010), Global surface temperature change, Rev. Geophys., 48(4), RG4004, doi:10.1029/2010RG000345.
  • Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P., Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of particulate matter including black carbon, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-880, in review, 2016.
  • Kirillova, Elena N. “Dual isotope (13C-14C) Studies of Water-Soluble Organic Carbon (WSOC) Aerosols in South and East Asia.” (2013). ISBN 978-91-7447-696-5 pp. 1-37
  • Winiger, P., Andersson, A., Eckhardt, S., Stohl, A., & Gustafsson, Ö. (2016). The sources of atmospheric black carbon at a European gateway to the Arctic. Nature Communications, 7.

Edited by Sophie Berger, Dasaraden Mauree and  Emma Smith
This is joint post with the Atmospheric Division , given the interdisciplinarity of the topic featured.


portraitPatrik Winiger is a PhD student at the Department of Environmental Science and Analytical Chemistry and the Bolin Centre for Climate Research, at Stockholm University. His research interest focuses on impact and mitigation of Short Lived Climate Pollutants and anthropogenic CO2 emissions. Currently he investigates the sources of black carbon aerosols in the Arctic. He tweets as @PatrikWiniger

 

Image of the Week – Satellite Measurements of Arctic Sea Ice

Image of the Week – Satellite Measurements of Arctic Sea Ice

Sea ice is an important part of the Earth’s climate system. When sea ice forms, it releases heat and salt. When sea ice melts, it takes up heat and adds freshwater to the salty ocean water. It is also important for the exchange of energy between the atmosphere and the ocean surface, and for the ocean currents that transport warm and cold water from the equator to the poles and back.

The main route of sea ice moving south from the Arctic Ocean is through Fram Strait. This is the passage between  Greenland and Svalbard around 75-80°N latitude (see map). Today’s “Image of the Week” shows the amount of sea ice that flows  through this pathway each day (known as the mean observed volume flux) for October-November 2006.


What is sea ice volume flux and how is it computed?

Sea ice volume flux sounds a bit wordy – so what is it and why do we use it? Let’s start with the sea ice volume  – this simply tells us how much sea ice there is. In order to calculate the volume, you need to know not only the concentration of sea ice (i.e. the fraction of ocean covered by ice) but also its thickness. The flux parts comes in when we want to talk about the amount of sea ice moving through an area (e.g. Fram Strait). In order to calculate sea ice volume flux, you need to know both sea ice volume and drift. The latter is the displacement of sea ice over a certain period of time, typically measured in kilometres per day. So the volume flux is basically a measure of the volume of ice that moves through a certain area over a given time! Simple!?

An aerial view of the helicopter taking data of the sea ice below.

An aerial view of the helicopter taking data of the sea ice below. Photo credit: Alice O’Connor.

What does the “Image of the Week” show?

The  map was constructed by Spreen et al. (2009) from satellite data and provided one of the first estimates of  sea ice volume flux through Fram Strait exclusively using satellites.  However, it is  challenging to accurately measure sea ice thickness in the summer using satellites because of the  melt ponds (pools of open water forming on sea ice when temperatures are higher, i.e. summer) that interfere with the satellite signals. A few measurements of sea ice thickness have been collected in the summer from British and US submarines in the 1980s and 1990s but only in the region near the pole.  A recent study presents a way to compute sea ice volume fluxes through Fram Strait in the summer using a new dataset of ground-based and airborne electromagnetic ice thickness measurements (Krumpen et al., 2016).

In this new study, the concentration of sea ice is found from satellite measurements (passive microwave sensors to be specific). The sea ice drift can be found by using both satellite data and observations from buoys. Finally, the thickness of the sea ice comes from surveys using radar instruments on the ground or from an aircraft. This data was collected during 5 summers, by combining the three of these measurements, Krumpen et al., (2016) calculated the sea ice volume flux during the summer!

In the figure below, the red lines show the sea ice area flux using the concentration and drift only through Fram Strait in July and August from 1980 to 2012. Positive values mean that sea ice moves out of the Arctic Basin, while negative values represent sea ice that goes into the Arctic Basin. The figure shows that there are large variations in sea ice area flux from year to year.

The black dots are the volume fluxes (area flux times thickness) for the 5 summer seasons where the ice thickness was measured. The dots show that in some summers sea ice volume flux is negative, meaning that sea ice moves into the Arctic Basin. In other summers, for example in 2010, sea ice moves out of the Arctic Basin.

July (black, gray shading for uncertainty) and August (red, light red shading for uncertainty) sea ice area fluxes through Fram Strait (left axis). Black dots show sea ice volume flux for the 5 campaigns during which ice thickness is computed using electromagnetic measurements (right axis). Positive values mean that sea ice is moving out out of the Arctic Ocean while negative values mean that sea ice is flowing into the Arctic Ocean. [Figure 8 of Krumpen et al. (2016)]

July (black, gray shading for uncertainty) and August (red, light red shading for uncertainty) sea ice area fluxes through Fram Strait (left axis). Black dots show sea ice volume flux for the 5 campaigns during which ice thickness is computed using electromagnetic measurements (right axis). Positive values mean that sea ice is moving out out of the Arctic Ocean while negative values mean that sea ice is flowing into the Arctic Ocean. [Figure 8 of Krumpen et al. (2016)]

What comes next…

If field campaigns in the future continue to measure sea ice thickness in the summer, the calculations can be continued and we will learn more about the volume of sea ice that is transported through Fram Strait in the summer. This is important if we are to measure the balance of sea ice in the Arctic Ocean.

Edited by Nanna B. Karlsson


DavidDavid Docquier is a post-doctoral researcher at the Earth and Life Institute of Université catholique de Louvain (UCL) in Belgium. He works on the development of processed-based sea-ice metrics in order to improve the evaluation of global climate models (GCMs). His study is embedded within the EU Horizon 2020 PRIMAVERA project, which aims at developing a new generation of high-resolution GCMs to better represent the climate.

Image of the week – The winds of summer (and surface fluxes of winter)

Image of the week – The winds of summer (and surface fluxes of winter)

Antarctica is separated from the deep Southern Ocean by a shallow continental shelf. Waters are exchanged between the deep ocean and the shallow shelf, forming the Antarctic cross-shelf circulation:

  • Very dense waters leave the shelf as Antarctic Bottom Water (AABW) that will then flow at the bottom of all oceans.
  • Meanwhile, relatively warm water from the Southern Ocean, Modified Circumpolar Deep Water (MCDW*) comes on the continental shelf and brings heat to the ice shelves.

That is, Antarctic cross-shelf circulation influences the water mass that transports heat, carbon and nutrient all around the globe in very large volumes (Purkey and Johnson 2013), and the basal melting of Antarctic floating ice (Hellmer et al. 2012), hence the stability of the whole Antarctic ice sheet.

Although critical for both the ocean and the cryosphere, very little is known about the mechanisms behind cross-shelf circulation. We know that the mechanisms that control it vary on a seasonal time scale (Snow et al. 2016b). However, most hydrographic observations around Antarctica are taken in summer, when there is less sea ice and when the Southern Ocean is the least stormy. This means that we have very few measurements of the seasonal variations of the cross-shelf circulation itself.

Why does it matter that the cross-shelf circulation varies between summer and winter?

Three words: sea level rise.
Nearly half of the world’s population lives in coastal areas (
UN report). Antarctica contains enough ice to raise the sea level by 60 m, and although a total melting is very unlikely, current rates could raise the sea level by 1m by 2100 (read more about it on AntarcticGlaciers.org). To project future sea level rise and design relevant coastal defences, we need models to predict when and where the Antarctic ice will melt.

However, models are only as good as the observations that were used to constrain them. Having only summer observations in an area of Antarctica that has notable differences between summer and winter ocean circulation means that until now, models could not represent accurately the transfer of heat from the ocean to the ice shelves

A better observation strategy is needed if we want our models to correctly represent Antarctic basal melting and the global ocean circulation.

Antarctic cross-shelf circulation: summer vs winter

In summer, the circulation is mostly controlled by the strong katabatic winds blowing from the interior of the Antarctic continent towards the ocean. All the surface water masses go in the same direction, simply following the Antarctic coastal current. Nothing really happens at depth.

In winter, the circulation is also controlled by buoyancy forcings, that is changes in temperature or salinity at the surface of the ocean. Here, these mostly occur in a polynya (a hole in the sea ice cover) where the “warm” ocean is cooled by the very cold atmosphere, and where the surface becomes very salty as sea ice reforms (a process called “brine rejection”: salt is expelled from the new ice as water freezes). These buoyancy forcings form dense water (DSW), which sinks to the abyss and off the shelf as AABW. Mass conservation means that something else (here MCDW*) needs to come to the shelf to compensate for that outflow. You can notice that MCDW now flows in the opposite direction than it did in summer.

Take home message

Summer data is better than no data. But always be aware of the limitations of your model if you don’t have the datasets to test it– you may have a surprise when you do!

Reference

Snow, K., B. M. Sloyan, S. R. Rintoul, A. McC. Hogg, and S. M. Downes (2016), Controls on circulation, cross-shelf exchange, and dense water formation in an Antarctic polynya, Geophys. Res. Lett., 43, doi:10.1002/2016GL069479.

Edited by Sophie Berger and Emma Smith

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