CR
Cryospheric Sciences

Remote sensing

Image of the Week – Ice-Spy: the launch of ICESat-2

The second generation Ice Cloud and land Elevation Satellite (ICESat-2) from NASA fires 10,000 pulses every second to take elevation measurements up to every 70 cm on-the-ground. This data will offer lots of opportunities for scientists to understand the changing cryosphere in more detail than ever before [Credit: NASA’s Goddard Space Flight Center].

On September 15th, 2018, at 18:02 local time, NASA launched its newest satellite – the second generation Ice, Cloud and land Elevation Satellite (ICESat-2). ICESat-2 only contains one instrument – a space laser that fires 10,000 pulses per second to Earth to measure elevation. Its primary purpose is for monitoring the ever changing cryosphere, so naturally there are plenty of ice enthusiasts that are excited for the data it will provide!


Blast off! ICESat-2 launches successfully from California, on the Delta II Rocket [Credit: NASA / Bill Ingalls].

Space laser?

The space laser is referred to more formally as an ‘altimeter’ (specifically, the Advanced Topographic Laser Altimeter System; ATLAS). Each of the 10,000 pulses per second contain around 20 trillion photons (the elementary unit that makes up light). The instrument works by measuring the time it takes for the photons to travel to Earth, reflect off the surface, and bounce their way back to the receiver. When the land is higher in elevation, there is less distance for the photons to travel, so they arrive back quicker and vice versa. The detector only lets in light at 532 nanometres in the visible spectrum. This means only the target photons are detected and sunlight is filtered out. An on-board clock measures time to a billionth of a second for maximum precision. The 10,000 pulses per second compares to just 40 per second in the original ICESat mission, giving us measurements every 70 cm on-the-ground. ICESat-2 repeats its orbit every 91 days, so we get elevation measurements for everywhere on Earth every 3 months.

What happened to the original ICESat?

ICESat launched in 2003 and lasted 7 years before its mission came to an end when its primary instrument stopped functioning. Its final task was to propel itself into Earth’s atmosphere and burn up on re-entry. In its lifetime, ICESat helped us to quantify decreasing Arctic sea-ice thickness, estimate global above ground biomass using forest canopy height and even find lakes beneath Antarctica. It was such a success that, since 2010, NASA have flown planes (Operation IceBridge) over the cryosphere with the same instruments to bridge the gap in the data loss between the two ICESat missions.

Operation IceBridge flies over ice sheets, ice shelves, glaciers and sea-ice to ensure there is no gap in data between the ICESat missions. You can see more of the stunning imagery collected from Operation IceBridge here! [Credit: NASA’s Goddard Space Flight Center]

What new science will we get from ICESat-2?

The primary purposes of the mission are to measure elevation change of ice sheets, glaciers, sea-ice and the subsequent impacts of sea-level rise. Whereas the original ICESat mission had a single laser beam with 40 pulses per second, ICESat-2 has 6 laser beams with 10,000 pulses per second, which gives an unprecedented level of detail. On the original mission, the orbit may have only provided a single track across a mountain glacier, but the new mission will have significantly more measurements. The higher spatial resolution of ICESat-2 means that the satellite can be used to identify and track icebergs that cross shipping lanes, provide extra measurements of sea-ice thickness for subsistence hunters and detect small topographic changes in potentially active volcanoes. There are many other potential applications of ICESat-2, including for non-cryospheric research, but there will also be many unforeseen applications of the new data that will come about with time.

I’m so excited! When will the first results start coming out?

Whenever satellites are launched with the purpose of earth observation, there is a long period of time when the instruments need to be checked to ensure they are working as intended. NASA will be calibrating ICESat-2 for a few months after launch to ensure the outputs are of the highest possible quality, so don’t expect any publicly available data until early 2019. It’s worth getting it right early in the mission because ICESat-2 has enough fuel on board to last 7 years, so mistakes early on can lead to delays or reduce the overall quality of data collected over the mission. If you can’t wait, however, you can see the first height measurements from Antarctica here!

The first data from NASA’s newest satellite – the second generation Ice Cloud and land Elevation Satellite (ICESat-2). ICESat-2 fires 10,000 pulses every second to take elevation measurements up to every 70 cm on-the-ground. This elevation data shows the first track across the Antarctic ice sheet. Who knows what new science we will discover during its mission! [Credit: NASA’s Goddard Space Flight] Center.

Find out more

Edited by Adam Bateson


Liam Taylor is a PhD student at the University of Leeds and Centre for Polar Observation and Monitoring. His research looks at identifying novel remote sensing methods to monitor mountain glaciers for water resource and hazard management. He is passionate about climate change and science communication to a global audience, as an educator on free online climate courses and through his personal blog. You can find Liam on Twitter.

 

Image of the Week – Breaking the ice: river ice as a marker of climate change

Figure 1. Dates of ice breakup on Alaskan river reaches wider than 150 m calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) data. [Credit: Wayana Dolan].

Common images associated with climate change include sad baby polar bears, a small Arctic sea ice extent, retreating glaciers, and increasing severe weather. Though slightly less well-known, river ice is a hydrological system which is directly influenced by air temperature and the amount and type of precipitation, both of which are changing under a warming climate. Ice impacts approximately 60 % of rivers in the Northern Hemisphere and therefore will be a clear indicator of climate change over the coming century.


River ice terminology

First, I think it is important to get some quick vocabulary out of the way. There are three primary variables used to study large-scale trends in river ice:

  • Ice freeze-up: The process of ice accumulation on a river reach (a segment of a river), usually during the autumn or winter.
  • Ice breakup: The process of ice loss from a river segment. Breakup style is often related to a pulse of increased runoff from snow melt, known as the spring flood wave. Thermal breakup occurs when river ice melts prior to the arrival of the spring flood wave. It is a slow and relatively calm process. Alternatively, mechanical breakup occurs when ice on a river has not melted prior to the arrival of the spring flood wave. Mechanical breakups often cause severe ice jam floods, whereas thermal breakups are rarely associated with flooding events. You can observe an example of mechanical ice breakup and associated ice jam flooding in 2018 on the North Saskatchewan River at Petrofka Orchard on the video below [Credit: Planet Labs, Inc.].
  • Ice cover duration: The length of time a river segment is ice-covered between freeze-up and breakup.

Now that we know these key phrases, let’s get to the good stuff!

Why should you care about river ice?

Shifts in river ice cover duration can be used as an indicator for Arctic climate change due to its relationship with air temperature and precipitation (Prowse et al. 2002). Hotter air temperatures generally relate to earlier ice breakup, later ice freeze-up, and shorter ice cover duration. These trends in breakup and freeze-up have been observed over the past 150 years on multiple rivers in the Northern Hemisphere by Magnuson et al. 2000. Many arctic communities rely on ice roads, which often travel across frozen rivers, lakes, and wetlands. These roads are important for transporting food, fuel, and mining equipment, to predominately first nations people. They are also commonly used by people who live subsistence-based lifestyles for hunting and trapping during the winter months. If ice cover duration shortens, these roads will be stable for a shorter period each winter. Alternatively, longer ice-free seasons would allow for decreased shipping costs in many boreal and Arctic regions, which currently use ice breaking to clear shipping pathways (Prowse et al. 2011). Another trend observed in several large Arctic rivers is a shift from mechanical breakup to thermal breakup (Cooley & Pavelsky, 2016). While this change could lead to a decrease in ice jam flood damage to hydropower and other infrastructure, it could also cause a dramatic decrease in sediment and nutrient transport to near-river Arctic ecosystems such as floodplains and deltas.

Recent research has shown ice cover trends to be geographically complex and dependent upon variables such as air temperature, basin size, and precipitation (Bennett & Prowse, 2010; Prowse et al. 2002; Rokaya et al. 2018). However, many of these trends are poorly understood on a pan-Arctic scale.

How do we measure changes in river ice?

From the early-1980s through the mid-2000s, satellites missions such as Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) began allowing researchers to study ice on rivers in inaccessible areas. However, computing power limited the size and scale of rivers which could be observed. More recently, data processing through platforms like Google Earth Engine allow river ice to be studied on a much larger scale.

The University of North Carolina at Chapel Hill (UNC) has a working group which makes use of these new programs to study changes in pan-Arctic river and lake ice. My current project seeks to quantify historical river ice breakup and freeze-up using MODIS. We have developed an ice detection algorithm that has successfully been applied to all river reaches in Alaska wider than 150 m, limited by the 250 m spatial resolution of MODIS (Figure 1). Note that our detection algorithm can be applied to rivers which are slightly sub-pixel in width. I am currently working on calculating trends in this dataset and the expansion of the algorithm to pan-Arctic rivers, so that we can better identify which regions in the Arctic are changing the fastest. A quick glance at the dataset reveals that ice breakup is highly variable through time and space, even between upstream and downstream reaches of the same river. Internal variation in breakup dates within a given river may be caused by temperature gradients along the river profile, changes in elevation, as well as variation in the amount and type of precipitation. Additionally, preliminary work by UNC postdoctoral researcher Xiao Yang uses Google Earth Engine, Landsat, and MERRA-2 data to globally model river ice (Figure 2). This model can be applied to future climate change scenarios to see how river ice will change as the temperature warms. Keep an eye out for this paper in the next few months!

Figure 2. Preliminary results from modelling global river ice coverage using Landsat imagery, latitude, longitude, and surface air temperatures from MERRA-2. Colors refer to the percentage of the total river length in each area that is ice-covered each month (aggregated from 1984 to 2018) [Credit: Xiao Yang].

Future outlook

River ice cover duration is expected to shorten as the climate warms. Shifts in ice breakup and freeze-up processes can impact sediment and nutrient delivery, Arctic transportation and hunting, and ice-related hazards. However, our preliminary results show that river ice breakup varies both spatially and temporally throughout Alaska (Figure 1). Ongoing research at UNC will allow researchers to identify areas of the pan-Arctic which are most vulnerable to river ice-related change.

Further resources

Edited by Scott Watson


Wayana Dolan is a current M.S. student and future Ph.D. student at the University of North Carolina at Chapel Hill (USA) working with Dr. Tamlin Pavelsky. Her current research involves using remote sensing to study large-scale changes in river ice. She is passionate about any project that allows her to do Arctic fieldwork. Dolan also works with the WinSPIRE program – a summer research internship for female high school students in North Carolina. You can contact her by email or on twitter as @wayana_dolan.

 

Image of the Week — Orange is the new white

Figure 1. True color composite of a Sentinel-2 image showing the dust plume off the coast of Libya on 22-Mar-2018 (see also on the ESA website) [Credit: processed by S. Gascoin]

On 22 March 2018, large amounts of Saharan dust were blown off the Libyan coast to be further deposited in the Mediterranean, turning the usually white snow-capped Mountains of Turkey, Romania and even Caucasus into Martian landscapes.  As many people were struck by this peculiar color of the snow, they started documenting this event on social media using the “#orangesnow hashtag”. Instagram and twitter are fun, but satellite remote sensing is more convenient to use to track the orange snow across mountain ranges. In this new image of the week, we explore dusty snow with the Sentinel-2 satellites…

Марс атакует 🌔 #smurygins_family_trip

A post shared by Alina Smurygina (@sinyaya_ptiza) on


Sentinel-2: a great tool for observing dust deposition

Sentinel-2 is a satellite mission of the Copernicus programme and consists of two twin satellites (Sentinel-2A and 2B). Although the main application of Sentinel-2 is crop monitoring, it is also particularly well suited for characterizing the effect of dust deposition on the snowy mountains because:

  1. Sentinel-2A and 2B satellites provide high-resolution images with a pixel size of 10 m to 20 m (depending on the spectral band), which enables to detect dust on snow at the scale of hillslopes.
  2. Sentinel-2 has a high revisit capacity of 5 days which increases the probability to capture cloud-free  images shortly after the dust deposition.
  3. Sentinel-2 has many spectral bands in the visible and near infrared region of the light spectrum, making easy to separate the effect of dust on snow reflectance — i.e. the proportion of light reflected by snow — from other effects due to snow evolution. The dust particles mostly reduce snow reflectance in the visible, while coarsening of the snow by metamorphism (i.e. the change of microstructure due to transport of vapor at the micrometer scale) tends to reduce snow reflectance in the near infrared (Fig. 2).
  4. Sentinel-2 radiometric observations have high dynamic range and are accurate and well calibrated (in contrast to some trendy miniature satellites), hence they can be used to retrieve accurate surface dust concentration, provided that the influence of the atmosphere and the topography on surface reflectance are removed.

Figure 2: Diffuse reflectance for different types of snowpack. These spectra were computed with 10 nm resolution using the TARTES model (Libois et al, 2013) using the following parameters: snowpack density: 300 kg/m3, thickness: 2 m, fine snow specific surface area (SSA): 40 m2/kg, coarse snow SSA: 20 m2/kg, dust content: 100 μg/g. The optical properties of the dust are those of a sample of fine dust particles from Libya with a diameter of 2.5 μm or less (PM2.5) (Caponi et al, 2017). The Sentinel-2 spectral bands are indicated in grey. [Credit: S. Gascoin]

Dust on snow from Turkey to Spain

The region of Mount Artos in the Armenian Highlands (Turkey) was one of the first mountains to be imaged by Sentinel-2 after the dust event. Actually Sentinel-2 even captured the dust aloft on March 23, before its deposition (Fig. 3)

Figure 3: Time series of three Sentinel-2 images near Mount Artos in Turkey (true color composites of level 1C images, i.e. orthorectified products, without atmospheric correction). [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

Later in April another storm from the Sahara brought large amounts of dust in southwestern Europe.

Figure 4: Sentinel-2 images of the Sierra Nevada in Spain (true color composites of level 1C images). [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

This example in the spanish Sierra Nevada nicely illustrates the value of the Sentinel-2 mission since both images were captured only 5 days apart. The high resolution of Sentinel-2 is also important given the topographic variability of this mountain range. This is how it looks in MODIS images, having a 250 m resolution.

Figure 5: MODIS Terra (19) and Aqua (24) images of the Sierra Nevada in Spain. True Color composites of MODIS corrected reflectance. [Credit: NASA, processed by S. Gascoin]

Sentinel-2 satellites enable to track the small-scale variability of the dust concentration in surface snow, even at the scale of the ski runs as shown in Fig. 6.

Figure 6: Comparison of a true color Sentinel-2 image and a photograph of the Pradollano ski resort, Sierra Nevada. [Credit: photograph taken by J. Herrero / Contains modified Copernicus Sentinel data, processed by S. Gascoin]

A current limitation of Sentinel-2, however, is the relative shortness of the observation time series. Sentinel-2A was only launched in 2015 and Sentinel-2B in 2017. With three entire snow seasons, we can just start looking at interannual variability. An example in the Prokletije mountains in Albania is shown in Fig. 7.

Figure 7. Sentinel-2 images of the Prokletije mountains in Albania (true color composites of level 1C images) [Credit: Contains modified Copernicus Sentinel data, processed by S. Gascoin]

These images suggest that the dust event of March 2018 was not exceptional in this region, as 2016 also highlights a similar event. The Sentinel-2 archive will keep growing for many years since the EU Commission seems determined to support the continuity and development of Copernicus programme in the next decades. In the meantime to study the interannual variability the best option is to exploit the long-term records from other satellites like MODIS or Landsat.

Beyond the color of snow, the water resource

Dust on snow is important for water resource management since dust increases the amount of solar energy absorbed by the snowpack, thereby accelerating the melt. A recent study showed that dust controls springtime river flow in the Western USA (Painter et al, 2018).

“It almost doesn’t matter how warm the spring is, it really just matters how dark the snow is.”

said snow hydrologist Jeff Deems in an interview about this study in Science Magazine. Little is known about how this applies to Europe…

Further reading

 Edited by Sophie Berger


Simon Gascoin is a CNRS researcher at Centre d’Etudes Spatiales de la Biosphère (CESBIO), in Toulouse. He obtained a PhD in hydrology from Sorbonne University in Paris and did a postdoc on snow and glacier hydrology at the Centro de Estudios Avanzados en Zonas Áridas (CEAZA) in Chile. His research is now focusing on the application of satellite remote sensing to snow hydrology. He tweets here and blog here.

 

 

Marie Dumont is a researcher, leading the snow processes, observations and modelling research team at the snow study centre (CNRM/CEN, Grenoble, France). Her research focuses on snow evolution mostly in alpine region using numerical modelling and optical remote sensing.

 

 

 

Ghislain Picard is a lecturer working at the Institute of Geosciences and Environment at the University Grenoble Alpes, in the climate and ice-sheets research group. His research focuses on snow evolution in polar regions in the context of climate change. Optical and microwave remote sensing is one of its main tools.

Mapping the bottom of the world — an Interview with Brad Herried, Antarctic Cartographer

Mapping the bottom of the world — an Interview with Brad Herried, Antarctic Cartographer

Mapping Earth’s most remote continent presents a number of unique challenges. Antarctic cartographers and scientists are using some of the most advanced mapping technologies available to get a clearer picture of the continent. We asked Brad Herried, a Cartographer and Web Developer at the Polar Geospatial Center at the University of Minnesota, a few questions about what it’s like to do this unique job both on and off the ice.


Before we go too much further… what is the Polar Geospatial Center, and what does it do for polar science and scientists?

The Polar Geospatial Center (PGC), founded in 2007 by Director Paul Morin, is a research group of about 20 staff and students at the University of Minnesota with a simple mission: solve geospatial problems at the poles (Antarctica and the Arctic). Because we are funded (primarily) through the U.S. National Science Foundation (NSF) and NASA Cryospheric Sciences, that is the community we support – other U.S.-funded polar researchers. We provide custom maps, high-resolution commercial satellite imagery, and Geographic Information System (GIS) support for researchers who would like to use the data for their research but may not have the expertise to do so.

Our primary service is providing high-resolution satellite imagery (i.e. from the DigitalGlobe, Inc. constellation) to U.S.-funded polar researchers – at no additional cost to their grants – through licensing agreements with the U.S. Government. It has proven beneficial to researchers to have a service so that we do the hard parts of data management, remote sensing, and automation of satellite imagery processing so that they don’t have to. So, a glaciologist or geomorphologist or wildlife ecologist studying at the poles may come to us and say: I would like to use satellite imagery to study phenomenon x or y. Some groups use it just for logistics (these are some of the least mapped places on Earth after all) to get to their site. Some groups’ entire research is done using remote sensing.

What kinds of data and resources do you use?

The PGC’s polar archive of high-resolution commercial imagery is absolutely astounding (like, in the thousands of terabytes). The imagery, although licensed to us by U.S. Government contracts, is collected by the DigitalGlobe, Inc. constellation of satellites (e.g. WorldView-2), much like the imagery where you can see your house/car in Google Earth. The benefit is that we can provide it at no cost to our users (researchers). That resource, along with the expertise of the staff at PGC, can provide solutions to users, whether it’s making a simple map of a remote research site or providing a time-series of satellite imagery for a researcher studying change detection (like, say for a glacier front in Greenland).

This also presents a challenge. How do we manage and effectively deliver that much data? We have relied on skilled staff, ingenuity, cheap storage, high-performance computing, and automation to become successful.

As the saying goes, automate or die.

What’s your role at the PGC? How did you find your way into a job like this?

I started at the PGC as a graduate student in 2008. I knew nothing about Antarctica or the Arctic, but my background and studies in GIS & cartography offered a wide range of jobs. After I graduated, I became a full-time employee as the lead cartographer of the (at the time, very small) group. Currently, I do a lot more GIS web application development and geospatial data management. We have recognized the need for more automated, “self-service” systems for our users to get the data they need in a timely manner, and less of asking a PGC employee for a custom product. As the saying goes, automate or die. But, of course, I still spend a fair bit of my times creating maps to keep my cartographic juices going.

Antarctica and the South Polar Regions. Map from the American explorer Richard Byrd’s second expedition in 1933. [Credit: Byrd Antarctic Expeditions]

What kind of work do PGC employees do in Antarctica?

The PGC staffs an office at the United States’ McMurdo Station annually from October to February, with 3-5 staff rotating throughout the field season. It is really an extension of our responsibilities, with a couple interesting twists, both good and bad. First, a majority of our users (NSF-funded researchers) come through McMurdo Station in preparation for their fieldwork. It’s a beneficial and unique experience to meet with them one-on-one and solve problems, ironically, faster than email exchanges back in the States. Second – and this is true of all of Antarctica – the internet bandwidth is very limited. So, we have to a) prepare more regarding what data/imagery we have on site and b) do more with less. That always proves to be a fun challenge because it is impossible to access our entire archive of imagery from down there.

How could I forget collecting Google Street View in Antarctica.

There have been several years, however, when we do get to go out into the field! In past years, we have conducted various field campaigns in the nearby McMurdo Dry Valleys to collect survey ground control to make our satellite imagery more accurate. And, how could I forget collecting Google Street View (with some custom builds of the typical car-camera system for snowmobiles, heavy-duty trucks, and backpacks). The Google Street View provides a window into the world of Antarctica – history, facilities, science, and of course its beautiful landscapes – to a wide audience who only dream of visiting Antarctica.

Brad on a snowmobile collecting Google Street View imagery [Credit: Brad Herried]

What are some of the interesting projects PGC has worked on? What’s exciting at PGC right now?

The PGC does a lot to contribute to polar mapping. There’s not exactly a ton of geospatial data or maps for the polar regions, especially Antarctica. What data or maps there are, it is not often of very high quality. For example, there are regions of Antarctica (especially in inland East Antarctica) which have not been properly mapped or surveyed since the 1960s. Those maps offer little help if you’re trying to land an aircraft in the area. So, PGC has done a lot to improve that geospatial data including creating more accurate coastlines, improving geographic coordinates of named features (sometimes the location can be off by 10s of kilometers!), organizing historic aerial photography, and digitizing map collections. These are important to have, but it all changes when you can collect data 100 times more accurate with satellites…

There’s not exactly a ton of geospatial data or maps for the polar regions, especially Antarctica.

Where it gets really interesting is how we can apply our archive of satellite imagery to help researchers solve problems or come up with cutting-edge solutions with the data. One example is the ArcticDEM project. In a private-public collaboration, PGC is using high performance computing (HPC) to develop a pan-Arctic Digital Elevation Model (DEM) at a resolution 10 times better than what exists now. This project requires hundreds of thousands of stereoscopic satellite imagery pairs to be processed using photogrammetry techniques to build a three-dimensional model of the surface for the entire Arctic. There are countless more applications for the imagery and we’ll continue to push the limits of the technology to produce innovative products to help measure the Earth and solve really important research questions.

ArcticDEM hillshade in East Greenland. DEM(s) created by the Polar Geospatial Center from DigitalGlobe, Inc. imagery. [Credit: Brad Herried/ Polar Geospatial Center].

 

What resources can cryosphere researchers and other polar scientists without US funding get from PGC to enhance their research?

Our website provides a wealth of non-licensed data, freely available to download. That includes our polar map catalog (with over 2,000 historic maps of the polar regions), aerial photography, and elevation data. The ArcticDEM project I mentioned before is freely available (see https://www.pgc.umn.edu/data/arcticdem/), as are all DEMs created (derived) from the optical imagery. Moreover, we work with the international community on a regular basis to continue mapping efforts across both poles.

 

What advice do you have for students interested in a career in science or geospatial science?

This might be a little bit of a tangent, but learn to code. I was trained in cartography ten years ago and we hardly touched the command line. Now? You certainly don’t have to be an expert, say, Python programmer, but you’re behind if you don’t know how to automate some of your tasks, data processing, analysis, or other routine workflows. It allows you to focus on the things you’re actually an expert in (and, employers are most certainly looking for these skills).

ArcticDEM hillshade of Columbia Glacier, Alaska. DEM(s) created by the Polar Geospatial Center from DigitalGlobe, Inc. imagery. [Credit: Brad Herried/ Polar Geospatial Center].

Personally, what has been the highlight of your time at PGC so far?

I will never forget the first time I stepped off the plane landing in Antarctica as a graduate student. A surreal, breathtaking (literally), and completely foreign feeling. To be able to experience the most remote places on Earth first-hand naturally leads to a better understanding of them. So, the highlight for me is this: I find myself asking more questions, talking to the preeminent researchers and students about their work, and discovering the purpose of it all. I may be a small piece in the puzzle of understanding our Earth’s poles, but I’m humbled to be a part.

Interview and Editing by George Roth, Additional Editing by Sophie Berger

Image of the Week: Petermann Glacier

Figure 1: Satellite images showing the front of Petermann glacier from spring to autumn 2016 [Credit: LandSAT 8 (NASA) and L. Dyke]

Our image of the week shows the area around the calving front of Petermann Glacier through the spring, summer, and autumn of 2016. Petermann Glacier, in northern Greenland, is one of the largest glaciers of the Greenland Ice Sheet. It terminates in the huge Petermann Fjord, more than 10 km wide, surrounded by 1000 m cliffs and plunging to more than 1100 m below sea level at its deepest point. In 2010 and 2012, the glacier caught the world’s attention with two large events, which caused the glacier to retreat to a historically unprecedented position.


In Fig. 1 we see the changes happening through the season on Petermann glacier – and they are huge. The animated map highlights many different processes. As areas emerge from the Polar night at the start of spring, the shadows quickly shorten and the light levels become noticeably higher.  This is followed by the melting of the snow, first on south-facing slopes, and eventually to on the high-elevation areas in the mountains. As the sun returns, meltwater starts forming on the surface of the glacier, this is visible as vast turquoise lakes. Finally, the sea ice in the fjord succumbs to the seasonal warming of the ocean and atmosphere, it thins, and then completely disintegrates at around day 205.

The change in the glacier is perhaps the most interesting phenomenon. It is possible to observe the glacier flowing and advancing into the fjord. In addition, several large rifts near the front open through the course of the year. These will eventually spread across the front of the glacier and a new, huge iceberg will be born. These rifts are being closely monitored, and it is likely that when the iceberg calves it will bring cause Petermann Glacier to retreat to a new historical minimum.

The image above is an example of a new type of map, it takes cartography into the 4th dimension—time. Technological advances have only recently made it possible to create a map like this; with the launch of Landsat 8 and Sentinel-2 it is now possible to receive regular, high-resolution, and free satellite images of high latitude areas. These data have been projected onto a new, high quality digital elevation model (Howat et al., 2013) to create this map.

 

Further Reading

Howat, I. M., Negrete, A., and Smith, B. E. (2014). The Greenland Ice Mapping Project (GIMP) land classification and surface elevation datasetsThe Cryosphere8 (1): 1509–1518

Edited by Nanna B. Karlsson


Laurence Dyke is a postdoctoral researcher at The Geological Survey of Denmark and Greenland (GEUS) in Copenhagen (DK). His work is primarily focussed on understanding the history of the Greenland Ice Sheet, from both marine and terrestrial perspectives. He works with marine sediment cores and surface exposure dating to investigate what triggered changes in glacier behaviour over the last 12 thousand years. Understanding the past is key to predicting the future! He tweets as @LaurenceDyke

Image of the Week – How geometry limits thinning in the interior of the Greenland Ice Sheet

Image of the Week –  How geometry limits thinning in the interior of the Greenland Ice Sheet

The Greenland ice sheet flows from the interior out to the margins, forming fast flowing, channelized rivers of ice that end in fjords along the coast. Glaciologists call these “outlet glaciers” and a large portion of the mass loss from the Greenland ice sheet is occurring because of changes to these glaciers. The end of the glacier that sits in the fjord is exposed to warm ocean water that can melt away at its face (a.k.a. its “terminus”) and force the glacier to retreat. As the glaciers retreat, they thin and this thinning can spread into the interior of the ice sheet along the glacier’s flow, causing glaciers to lose ice mass to the ocean as is shown in our Image of the Week. But how far inland can this thinning go?

Not all glaciers behave alike

NASA’s GRACE mission measures mass changes of the Earth and has been used to measure ice mass loss from the Greenland ice sheet (see Fig. 1a). The GRACE mission has been extremely valuable in showing us where the largest changes are occurring: around the edge of the ice sheet. To get a closer look, my colleagues and I use a technique called photogrammetry.

Using high-resolution satellite photos, we created digital elevation models of the present-day outlet glacier surfaces. The imagery was collected by the WorldView satellites and has a resolution of 50 cm per pixel! When we compared our present-day glacier surfaces with surfaces from 1985, with the help of an aerial photo survey of the ice sheet margin (Korsgaard et al., 2016), we found that glacier thinning was not very uniform in the West Greenland region (see our Image of the Week, Fig. 1b). Some glaciers thinned by over 150 meters at their termini but others remained stable and may have even thickened slightly! Another observation is that, of the glaciers that have thinned, some have thinned only 10 kilometers into the interior while others have thinned hundreds of kilometers inland (Felikson et al., 2017).

But atmospheric and ocean temperatures are changing on much larger scales – they can’t be the cause of these huge differences in thinning that we observe between glaciers. So what could be the cause of the differences in glacier behaviour? My colleagues and I used kinematic wave theory to help explain how each glacier’s unique shape (thickness and steepness) can control how far inland thinning can spread…

A kinematic wave of thinning

As a glacier’s terminus retreats, it thins and this thinning can spread upglacier, into the interior of the ice sheet, along the glacier’s flow. This spreading of thinning can be modeled as a diffusive kinematic wave (Nye, 1960). This means that the wave of thinning will diffuse in the upglacier direction while the flow of ice advects the thinning in the downglacier direction. An analogy for this process is the spreading of dye in a flowing stream. The dye will spread away from the source (diffusion) and it will also be transported downstream (advection) with the flow of water.

The relative rates of diffusion and advection are given by a non-dimensional value called the Peclet number. For glacier flow, the Peclet number is a function of the thickness of the ice and the surface slope of the ice. Where the ice is thick and flat, the Peclet number is low, and thinning will diffuse upglacier faster than it advects downglacier. Where the ice is thin and steep, the Peclet number is high, and thinning will advect downglacier faster than in diffuses upglacier.

Let’s take a look at an example, the Kangilerngata Sermia in West Greenland

Figure 2: Thinning along the centreline of Kangilerngata Sermia in West Greenland. (a) Glacier surface profile in 1985 (blue), present-day (red), and bed (black). (b) Dynamic thinning from 1985 to present along the profile with percent unit volume loss along this profile shown as colored line. (c) Peclet number along this profile calculated from the geometry in 1985 with Peclet number running maxima highlighted (red). [Credit: Denis Felikson]

There, dynamic thinning has spread from the terminus along the lowest 33 kilometers (see Fig. 2). At that location, the glacier flows over a bump in the bed, causing the ice to be thin and steep. The Peclet number is “high” in this location, meaning that any thinning here will advect downglacier faster than it can spread upglacier. Two important values are needed to further understand the relationship between volume loss and Peclet number. On the one hand, we compute the “percent unit volume loss”, which is the cumulative thinning from the terminus to each location normalized by the total cumulative thinning, to identify where most of the volume loss is taking place. On the other hand, we identify the “Peclet number running maxima” at the locations where the Peclet number is larger than all downglacier values. These locations are critical because if thinning has spread upglacier beyond a local maximum in the Peclet number, and accessed lower Peclet values, then thinning will continue to spread until it reaches a Peclet number that is “large enough” to prevent further spreading. But just how large does the Peclet number need to be to prevent thinning from spreading further upglacier?

Figure 3: (a) Percent unit volume loss against Peclet number running maximum for 12 thinning glaciers in West Greenland. (b) Distances from the termini along glacier flow where the Peclet number first crosses 3. Abbreviations represent glacier names [Credit: Denis Felikson]

If we now look at the percent unit volume loss versus Peclet number running maxima for not only one but twelve thinning glaciers in the region, we see a clear pattern: as the Peclet number increases, more of the volume loss is occurring downglacier (see Fig. 3). By calculating the medians of the glacier values, we find that 94% of unit volume loss has occurred downglacier of where the Peclet number first crosses three. All glaciers follow this pattern but, because of differences in glacier geometry, this threshold may be crossed very close to the glacier terminus or very far inland. This helps explaining the differences in glacier thinning that we’ve observed along the coast of West Greenland. Also, it shows that the Peclet number can be a useful tool in predicting changes for glaciers that have not yet retreated and thinned.

Further reading

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 — The ice blue eye of the Arctic

Image of the Week — The ice blue eye of the Arctic

Positive feedback” is a term that regularly pops up when talking about climate change. It does not mean good news, but rather that climate change causes a phenomenon which it turns exacerbates climate change. The image of this week shows a beautiful melt pond in the Arctic sea ice, which is an example of such positive feedback.


What is a melt pond?

The Arctic sea ice is typically non-smooth, and covered in snow. When, after the long polar night, the sun shines again on the sea ice, a series of events happen (e.g. Fetterer and Untersteiner, 1998):

  • the snow layer melts;

  • the melted snow collects in depressions at the surface of the sea ice to form ponds;

  • these ponds of melted water are darker than the surrounding ice, i.e. they have a lower albedo. As a result they absorb more heat from the Sun, which melts more ice and deepens the pond. Melt ponds are typically 5 to 10 m wide and 15 to 50 cm deep (Perovich et al., 2009);

  • eventually, the water from the ponds ends up in the ocean: either by percolation through the whole sea-ice column or because the bottom of the pond reaches the ocean. Sometimes, it can also simply refreeze, as the air temperatures drop again (Polashenski et al., 2012).

Melt ponds cover 50-60% of the Arctic sea ice each summer (Eicken et al., 2004), and up to 90% of the first year ice (Perovich al., 2011). How do we know these percentages? Mostly, thanks to satellites.

Monitoring melt ponds by satellites

Like most phenomena that we discuss on this blog, continuous in-situ measurements are not feasible at the scale of the whole Arctic, so scientists rely on satellites instead. For melt ponds, spectro-radiometer data are used (Rösel et al., 2012). These measure the surface reflectance of the Earth i.e. the proportion of energy reflected by the surface for wavelengths in the visible and infrared (0.4 to 14.4 μm). The idea is that different types of surfaces reflect the sunlight differently, and we can use these data to then map the types of surfaces over a region.

In particular for the Arctic, sea ice, open ocean and any stage in-between all reflect the sunlight differently (i.e. have different albedos). The way that the albedo changes with the wavelength is also different for each surface, which is why radiometer measurements are taken for a range of wavelengths. With these measurements, not only can we locate the melt ponds in the Arctic, but even assess how mature the pond is (i.e. how long ago it formed) and how deep it extends. These values are key for climate change predictions.

Fig. 2: Melt pond seen by a camera below the sea ice. (The pond is the lighter area) [Credit: NOAA’s climate.gov]

Melt ponds and the climate

Let’s come back to the positive feedback mentioned in the introduction. Solar radiation and warm air temperature create melt ponds. The darker melt ponds have a higher albedo than the white sea ice, so they absorb more heat, and further warm our climate. This extra heat is also transferred to the ocean, so melt pond-covered sea ice melts three times more from below than bare ice (Flocco et al., 2012). This vicious circle heat – less sea ice – more heat absorbed – even less sea ice…, is called the ice-albedo feedback. It is one of the processes responsible for the polar amplification of global warming, i.e. the fact that poles warm way faster than the rest of the world (see also this post for more explanation).

The ice-albedo feedback is one of the processes responsible for the polar amplification of global warming

But it’s not all doom and gloom. For one thing, melt ponds are associated with algae bloom. The sun light can penetrate deeper through the ocean under a melt pond than under bare ice (see Fig. 2), which means that life can develop more easily. And now that we understand better how melt ponds form, and how much area they cover in the Arctic, efforts are being made to include more realistic sea-ice properties and pond parametrisation in climate models (e.g. Holland et al., 2012). That way, we can study more precisely their impact on future climate, and the demise of the Arctic sea ice.

Edited by Sophie Berger

Further reading

Image of the Week – Icelandic glaciers monitored from space!

Image of the Week – Icelandic glaciers monitored from space!

Located in the North Atlantic Ocean, just south of the polar circle, Iceland is a highly fascinating land. Covered by some of the largest glaciers in Europe and hosting active volcanoes, geothermal sites and subglacial lakes, it is extremely dynamic in nature and ever changing. With this Image of the Week we will tell you a bit about the changing ice caps of Iceland and how we can monitor them from space!


Icelandic ice caps since the mid-1990s

Iceland enjoys a mild and moist climate because of the relatively warm and saline Irminger current transporting heat to its southern coast, although the cold East Greenland and East Icelandic currents may cause sea ice to form to the north. Iceland’s ice caps, which receive abundant precipitation from North Atlantic cyclones, cover about 11% of the land, and contain ~3600 km2 of ice. If they completely melted they would contribute 1 cm to Sea Level Rise (SLR).

In the period 1995-2010, Icelandic glaciers shrank every year and lost mass at an average rate of 9.5±1.5 Gton a-1 – generally reflecting higher summer temperatures and longer melting seasons than in the early 1990s (Björnsson et al., 2013). Importantly, in recent decades Iceland has been the second largest source of glacier meltwater to the North Atlantic after Greenland and its peripheral glaciers. Furthermore, surge-type outlet glaciers – which have unpredictable dynamics – are present in all Icelandic ice caps and represent as much as 75% of the area of Vatnajökull (Bjornsson et al., 2003), the largest ice cap in Europe by volume. Therefore, it is important to continuously monitor Icelandic ice caps (>90% of the whole glaciated area) at high spatial resolution. Glaciological field surveys can yield accurate measurements and are routinely performed in Iceland on all ice caps and most glaciers. However, it is not always feasible to use field methods, depending on the remoteness and size of the glacier (e.g. several glaciers and ice caps in the Arctic). Continuous monitoring of such hardly accessible areas can be achieved from space at high spatial resolution.

Continuous health check from space

Since 2010, the ESA CryoSat-2 (CS2) mission has been fundamental in retrieving ice elevation data over glacial terrain characterised by complex topography and steep slopes – notoriously hard to monitor via satellite. CS2’s radar altimeter provides the elevation of the Point-Of-Closest-Approach (POCA) – the point at the surface closest to the satellite on a straight line – every ~400 m along the flight track. The main novelty of this mission is the use of a second antenna, which allows the use of interferometry across-track to accurately infer the location of a surface reflection in presence of a slope (read more about it here). Additionally, a new and exciting application of CS2 interferometric capabilities is that we can exploit the echos after the POCA, i.e. the reflections coming from the sloping surface moments after the first one. This approach generates a swath of elevations every ~400 m and provides up to two orders of magnitude more elevation data than with conventional POCA processing (Fig. 2; Gray et al., 2013, Foresta et al., 2016).

Since 2010, the ESA CryoSat-2 (CS2) mission has been fundamental in retrieving ice elevation data

Figure 2: Example of the improved elevation data using CS2 swath-processing. CS2 swath data (colors) and conventional (circles) heights over the Austfonna ice cap (Svalbard) for two satellite passes. Swath processing delivers up to two orders of magnitude more elevation data. [Credit: Dr. N. Gourmelen,University of Edinburgh, School of GeoSciences]

This rich dataset can be used to generate maps of surface elevation change rates at sub-kilometer resolution (Figs. 1 and 3). These maps show extensive thinning of up to -10 m a-1 in marginal areas of Iceland’s ice caps, while patterns of change are more variable in their interior. Fig. 3 shows the difference in spatial coverage between the POCA and Swath approaches, with the former sampling preferentially along topographic highs (see for example the Langjökull ice cap in Fig. 3). Using these high resolution maps, it is possible to independently infer the mass balance of each ice cap purely from satellite altimetry data. Based on CS2 swath-processed elevations, between glaciological years 2010/11 and 2014/15 Iceland has lost mass at an average rate of 5.8±0.7 Gton a-1 contributing 0.016±0.002 mm a-1 to SLR (Foresta et al., 2016). The rate of mass loss is ~40% less than during the preceding 15 years, partly caused by Vatnajökull (63% of the total mass loss) having had positive mass balance during the glaciological year 2014/15 due to anomalously high precipitation. Langjökull, with widespread thinning up to the ice divide (Figs. 1 and 3), is the fastest changing ice cap in terms of mass loss per unit area.

between glaciological years 2010/11 and 2014/15 Iceland has lost mass at an average rate of 5.8±0.7 Gton a-1 contributing 0.016±0.002 mm a-1 to SLR

Beside estimating mass change at the ice cap scale, the novel swath approach demonstrates the capability to observe glaciological processes at a sub-catchment scale. Different accumulation and thinning patterns over Vatnajökull and Langjökull, for example, are directly related to past surges or subglacial volcanic eruptions, some of which happened decades ago. Their long term lingering effects on the ice cap topography are now visible from space and as the satellite data record extends we will be able to gain an increased understanding of how these effects evolve over time.

Figure 3 – Comparison between swath-processed (Swath) and conventional (POCA) surface elevation change rates over the six largest ice caps in Iceland, representing 90% of the glaciated area. V (Vatnajökull), L (Langjökull),H(Hofsjökull),M(Mýrdalsjökull), D (Drangajökull), and E (Eyjafjallajökull). The inset shows the location of individual elevation measurements by using Swath and POCA approaches over Langjökull. [Credit: After Foresta et al. (2016).]

Edited by Emma Smith


Luca Foresta is a PhD student in the Glaciology and Cryosphere Research Group at the University of Edinburgh (@EdinGlaciology), and his research focuses on improving CryoSat-2 processing as well as exploiting swath-processed CryoSat-2 data to quantify surface, volume and mass changes over ice caps.

 

Image of the Week – Sea Ice Floes!

Image of the Week – Sea Ice Floes!

The polar regions are covered by a thin sheet of sea ice – frozen water that forms out of the same ocean water it floats on. Often, portrayals of Earth’s sea ice cover show it as a great, white, sheet. Looking more closely, however reveals the sea ice cover to be a varied and jumbled collection of floating pieces of ice, known as floes. The distribution and size of these floes is vitally important for understanding how the sea ice will interact with its environment in the future. [Read More]