New Greenland iceberg calving estimate

Posted by William Colgan on June 06, 2019
New Research / No Comments

We have a new – and long awaited! – open-access study out in the current issue of Earth Systems Science Data. In this study, we estimate the ice discharge – meaning transfer of land-ice into the ocean – at 276 tide-water glaciers around the Greenland Ice Sheet between 1986 and 2017. These individual glacier discharge records are now available online. We estimate that ice-sheet-wide discharge – or iceberg calving – increased from less than 450 Gt/yr in the 1980s and 1990s to closer to 500 Gt/yr at present. That increase of 50 Gt/yr is equivalent to an extra 1600 tonnes per second of icebergs – year-round – relative to the 1980s and 1990s.

Figure 1 – Time series of iceberg discharge from the Greenland Ice Sheet. Dots represent when observations occurred. The orange line is the annual average. Coverage denotes the percentage of glaciers from which total discharge is observed at any given time. Total discharge is “estimated”, rather than “observed”, when coverage is <100 %.

Dealing with unknown ice thickness or missing ice velocity data – in a transparent and reproducible fashion – was a huge aspect of making such a dense glacier discharge dataset. Perhaps the most novel aspect of this study is a sensitivity test to quantify just how precisely ice discharge from the entire ice sheet can be estimated at a single point in time. The result of this sensitivity test was a little surprising. We found that – using the same ice thickness and ice velocity information – assessed ice discharge can change tremendously just based on where we placed our “flux gates”.

We examined placing flux gates – meaning the virtual lines across every glacier through which we estimate ice discharge – between 1 and 9 kilometers up-glacier from the glacier tongue, and extending them laterally into minimum ice velocities of between 10 and 150 m/yr. These generally reasonable ranges can influence the apparent ice-sheet-wide discharge we estimate by around 50 Gt/yr. To place this flux gate uncertainty in perspective, we can say it is roughly equivalent to the total uncertainty in ice-sheet-wide discharge – from all sources of uncertainty – assigned in most previous studies. This flux gate uncertainty is also roughly equivalent to the change in ice-sheet-wide discharge since the 1980s.

Figure 2 – Sensitivity test of ice-sheet-wide discharge as a function of flux gate location. The vertical axis denotes the up-glacier distance of flux gates from the glacier tongue. The horizontal axis denotes the minimum ice velocity into which flux gates laterally extend.

A very cool thing about this study is that not only the data, but also the code, is open access. This code-sharing approach is part of the growing “open science” movement. The US National Academies – meaning Science, Engineering and Medicine – recently joined together to publish an open science mandate. Sharing code not only makes complex results reproducible, but also helps different teams move forward. For example, our ice-sheet-wide discharge is slightly different from previous studies. We are not entirely sure how much of this difference in ice discharge is due to differences in flux gate locations. But now – at least moving forward – future teams will be able to use precisely the same flux gates that we used.

Mankoff, K., W. Colgan, A. Solgaard, N. Karlsson, A. Ahlstrøm, D. van As, J. Box, S. Khan, K. Kjeldsen, J. Mouginot and R. Fausto. 2019. Greenland Ice Sheet solid ice discharge from 1986 through 2017. Earth System Science Data. 11: 769-786. https://doi.org/10.5194/essd-11-769-2019.

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Five Decades of Arctic Change

Posted by William Colgan on April 08, 2019
Climate Change, New Research / No Comments

We have just completed a study that assesses indicators of Arctic change since 1971. It is available open-access in the current issue of Environmental Research Letters. We assimilate nine hyper-diverse data types – air temperature, permafrost temperature, precipitation, river discharge, tundra greenness, wildfire area, snowcover duration and, of course, sea ice area and land ice loss – into standardized indices. The motivation of this study is to bring together almost a five-decade time-series of biophysical variables into a common open-data framework.

Figure 1 – Three of the nine types of Arctic indicators compiled in this study over the 1971-2017 period. These three biophysical indicators show that Arctic tundra is becoming greener in response to increasing Arctic temperature and precipitation.

If there is one climate variable to rule them all, it is air temperature. Increasing air temperature leads to an intensification of the hydrologic cycle, which is clearly evident as increases precipitation and river discharge. Increasing temperature also drives increasing land ice loss and decreasing fall sea ice area. Beyond just the physical climate system, changing air temperature is also driving changes in the biological ecosystem. There is a startlingly clear – more than 99.9 % certain – correspondence between air temperature and the greenness of Arctic-wide tundra. This means a warmer Arctic is a greener Arctic.

In addition to assessing the nine types of indicators, we also discuss some of the tremendous number of knock-on effects of these biophysical trends. These knock-on, or biophysical cascade, effects include decreased alignment of flowering and pollinating windows for plant; increased prevalence of wildfire ignition conditions; an acceleration of the CO2 cycle with increased uptake during growing season counterbalanced by increased emissions in spring and fall; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with biophysical implications not only within but beyond the Arctic.

This study was developed within the Arctic Monitoring and Assessment Program (AMAP), with the ambition that high-level Arctic summary statistics are of interest to the forthcoming IPCC Sixth Assessment Report (AR6). Annual time-series of the nine types of Arctic indicators compiled for this study will soon be freely available for download at www.amap.no. For now, you can access them via this GoogleSheet.

Box, J., W. Colgan, T. Christensen, N. Schmidt, M. Lund, F.-J. Parmentier, R. Brown, U. Bhatt, E. Euskirchen, V. Romanovsky, J. Walsh, J. Overland, M. Wang, R. Corell, W. Meier, B. Wouters, S. Mernild, J. Mård, J. Pawlak and M. Olsen. 2018. Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters. 14: doi:10.1088/1748-9326/aafc1b.

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Lost Ice-Sheet Porosity and Sea-Level Buffering

Posted by William Colgan on March 12, 2019
New Research, Sea Level Rise / No Comments

We have a new open-access study that investigates the high-elevation firn plateau of the Greenland Ice Sheet in the current issue of The Cryosphere1. Firn is the relatively low density near-surface ice-sheet layer comprised of snow being compressed into ice. Firn is relatively porous, meaning that meltwater can percolate through it. The refreezing of meltwater within firn is a potential buffer against the ice-sheet sea-level contribution from surface melt; surface melt can refreeze within porous firn instead of running off into the ocean. Our study aims to assess how big this sea-level buffer might be, and how much sea-level buffer may have already been used.

We pull together a singularly unique dataset – 340 ice-core measurements of firn density collected over 65 years – to assess the near-surface density across the entire high-elevation firn plateau of the Greenland Ice Sheet. Many of these vertical firn density profiles were digitized and brought together for the first time from historical studies, but twenty are collected by our team and new to science. We analyze this ice-core dataset for empirical relations between firn density and accumulation or air temperature. This allows us to divide the ice sheet into three distinct firn areas, within each of which we can confidently predict the vertical profile of near-surface firn density.

Figure 1 – Left: Firn air content within the top 10 m (FAC10) estimated from ice-core measurements (denoted with ‘x’). The ice sheet is divided into three areas: the Dry Snow Area (DSA), the Low Accumulation Percolation Area (LAPA), and the High Accumulation Percolation Area (HAPA). Right: Change in top 10 m firn air content between 1998–2008 and 2010–2017 within Low Accumulation Percolation Area along the ice sheet’s western flank.

We find that the firn structure at the heart of the ice sheet – the highest, coldest and driest firn known as the Dry Snow Area – appears to have been stable since 1953. There is no trend in firn density within the Dry Snow Area. At lower elevations, however, we find significant changes in response to recent increases in surface melt due to climate change. The area we call the Low Accumulation Percolation Area – an elevation band of relatively low snowfall and high melt along the ice sheet’s west flank – has a marked increase in the firn densities measured pre- and post-2009. This firn density change is equivalent to a sea-level buffer loss of 1.5±1.2 mm sea-level equivalent (540±440 gigatonnes).

We compare the ice-sheet-wide firn density structure that we estimate from ice-core measurements with the firn density structure estimated from three regional climate models. The regional climate models suggest that the decrease in firn porosity initiated in the early 2000s and accelerated with post-2010 climate change. But we also find non-trivial differences between the firn porosities simulated by regional climate models, and that inferred from ice-core measurements, especially in what we call the High Accumulation Percolation Area. Here – the ice sheet’s low elevation southeast flank – modeled firn porosity can be biased the equivalent of between 3 and 7 meters of air distributed over the entire firn column depth.

Figure 2 – Left: Ice-sheet-wide firn air content within the top 10 m of firn (FAC10) simulated by three regional climate models (MAR, HIRHAM and RACMO) and derived from ice-core observations (this study) in different ice-sheet areas. Right: Same for firn air content over the entire depth of the firn column (FACtot).

This study highlights the importance of bringing together firn density measurements to document the response of ice-sheet firn – a non-trivial component of the sea-level budget – to recent climate change. The ice-sheet-wide firn porosity structure we infer from ice-core measurements can also serve as an independent evaluation target for the firn porosity structures simulated by regional climate models. This study also illustrates how new insight can be obtained from the synthesis and re-analysis of historical datasets. This emphasizes the tremendous value of open-access data within the scientific community. This work is part of the Retain project funded by the Danmarks Frie Forskningsfond (grant 4002-00234). The open-access publication is available via the hyperlink below.

1Vandecrux, B., MacFerrin, M., Machguth, H., Colgan, W., van As, D., Heilig, A., Stevens, C., Charalampidis, C., Fausto, R., Morris, E., Mosley-Thompson, E., Koenig, L., Montgomery, L., Miège, C., Simonsen, S., Ingeman-Nielsen, T., and Box, J. 2019. Firn data compilation reveals widespread decrease of firn air content in western Greenland. The Cryosphere. 13: 845-859. https://doi.org/10.5194/tc-13-845-2019.

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Eight trillion tonnes of Arctic ice lost since 1971

Posted by William Colgan on December 20, 2018
Climate Change, New Research, Sea Level Rise / 2 Comments

We have just completed a study that inventories Arctic land ice loss since 1971. It is available open-access in the current issue of Environmental Research Letters1. While we scientists have a pretty good idea of the health — or mass balance — of glaciers and ice sheets — or land ice — since the advent of satellite altimetry in the early 1990s, there is a need for better understanding of land ice health during the pre-satellite era. Our new study estimates the annual ice loss from all glacierized regions north of 55°N between 1971 and 2017.

We use in situ data – mass balance measurements from a handful of continuously monitored glaciers – as indicators for the health of land ice in seven Arctic regions. These hard-fought in situ data are scarce, they are only measured at between 20 and 44 Arctic glaciers every year. Extrapolating these data to entire regions is statistically challenging without additional information. Fortunately, independent estimates of regional mass balance are available from satellite gravimetry during the 2003 to 2015 period. This permits calibrating in situ and satellite-derived mass balance estimates during the satellite era. This makes our pre-satellite era estimates fairly robust.

During the 41 years assessed, we estimate that approximately 8,300 Gt of Arctic land ice was lost. It is difficult to contextualize this magnitude of ice loss. The flow of Niagara Falls – which is approximately 2400 m3 per second or about 75 km3 per year – is only equivalent to about half this volume (3500 km3) over the 1971-2017 period. The total Arctic land ice loss that we document represents 23 mm of sea-level rise since 1971. Greenland is by far the largest contributor (10.6 mm sea-level equivalent), followed by Alaska (5.7 mm sea-level equivalent) and then Arctic Canada (3.2 mm sea-level equivalent).

The UN Intergovernmental Panel on Climate Change (IPCC) now highlights two periods – the “recent past” (1986-2005) and “present day” (2005-2015) – as being of special interest in climate change studies. The Arctic land ice contribution to sea-level rise that we inventory increased from 0.4 to 1.1 mm sea-level equivalent between these periods. In terms of tonnes per second (5,000 to 14,000 t/s), both the magnitude – and the increase – are staggering.

Figure 1 – The cumulative sea-level rise contribution (in mm) from land ice in seven regions of the Arctic between 1971 and 2017. Analogous estimates from satellite gravimetry (GRACE) between 2003 and 2015 shown with open symbols.

The uncertainties associated with extrapolating sparse in situ data over large areas are undeniably large. But, the reality is that climate change was already gearing up as the global satellite observation network came online. So, in the absence of satellite data that can characterize the “pre-climate change” health of Arctic land ice, we need to leverage the extremely precious pre-satellite era observations that are available in creative ways. We hope that the ice loss estimates we present will be useful comparison targets for studies that estimate pre-satellite era mass balance in other ways.

The estimates of annual land ice mass balance — or health — in seven Arctic regions produced by this study are freely available for download here. This study was developed within the Arctic Monitoring and Assessment Program (AMAP) and International Arctic Science Committee (IASC) frameworks, as a direct contribution to the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC).

Figure 2 – Annual land ice mass balance — or health — in six Arctic regions between 1971 and 2017. Individual glacier mass balance records (blue lines) are combined into a regional composite (black line). Health is expressed both as a normalized score (left axis) and in gigatonnes per year (right axis). The numbers of glaciers comprising each composite is indicated in red text.

1Box, J., W. Colgan, B. Wouters, D. Burgess, S. O’Neel, L. Thomson and S. Mernild. 2018. Global sea-level contribution from Arctic land ice: 1971 to 2017. Environmental Research Letters.

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Changes in Ice-Sheet Density: How and Why?

Posted by William Colgan on October 25, 2018
Climate Change, Communicating Science, New Research, Sea Level Rise / No Comments

We investigate the high-elevation firn plateau of the Greenland Ice Sheet in a new open-access study in the current issue of Journal of Geophysical Research1. This study pulls together singularly unique – and hard fought – ice core observations and weather station data into a super-neat firn model. This relatively porous near-surface ice-sheet layer known as firn is being increasingly scrutinized for two main reasons.

The first reason is sea-level rise. These high regions of the Greenland ice sheet are normally preserved form intense melting, but this is changing, with more melt seen in recent years. Nevertheless, the porosity of the firn can provide a buffer against sea-level rise when meltwater refreezes within the firn instead of running off into the ocean. But exactly how much of this buffering capacity is available – and for how long – is not really understood.

The second reason is satellite altimetry. Repeat observation of ice thickness by satellite altimeter is a primary method by which ice-sheet mass balance – or overall health – is assessed. But since firn is porous, changes in elevation don’t always translate into changes in mass. For example, the firn layer can become thinner – making the ice-sheet appear thinner – when there’s actually just an increase in firn density rather than a change in mass.

Figure 1 – Locations of the four study sites on the Greenland Ice Sheet’s high-elevation firn plateau.

In this study, we were interested in teasing out the climatic controls of firn density: What makes firn porosity grow and shrink over time? So, we simulated the evolution of firn density – and therefore porosity – over time at four ice-sheet sites. These sites were carefully chosen as sites where both in-situ climate and firn measurements were available (Crawford Point, Dye-2, NASA-SE and Summit). The firn simulations used an updated version of the HIRHAM regional climate model’s firn model. At each site, we initiated simulations using firn density profiles observed from ice cores, and then ran the simulations forward in time using in-situ weather station records. We then ensured that simulated firn density also compared well with repeat firn density profiles observed again many years later. The simulations were between 11 and 15 years, depending on the data available at each site.

Figure 2 – Simulated firn density through time at the four study sites. At all sites, the relative depth of a given layer increases over time, as snowfall exceeds meltwater runoff.

A lot of recent ice-sheet research has focused on how increasing air temperatures and meltwater production are increasing firn density. And our simulations definitely confirmed that! But perhaps counterintuitively, we found that the leading driver of changes in firn density was actually year-to-year changes in amount of snowfall. Firn density decreases as snowfall increases, and vice versa. This study therefore highlights that if we want to project time-and-space variability in firn density we really need to project time-and-space variability in snowfall rates.

Figure 3 – Assessing the relative strength of four drivers of firn density change at the four study sites.

It was also satisfying to see that – given observed climate data – our simulations could reproduce the firn conditions as observed in the field. This gives confidence including this firn model in regional climate models. This finding is of course limited to the high-elevation firn plateau of the Greenland Ice Sheet, which admittedly does not experience tremendous melt. But, as the firn plateau covers over 80% of the ice-sheet area, understanding it plays a key role in tackling pressing satellite altimetry and sea-level buffering questions.

This work is part of the Retain project funded by the Danmarks Frie Forskningsfond (grant 4002-00234). The open-access publication is available via the hyperlink below.

1Vandecrux, B., R. Fausto, P. Langen, D. van As, M. MacFerrin, W. Colgan, T. Ingeman‐Nielsen, K. Steffen, N. Jensen, M. Møller and J. Box. 2018. Drivers of firn density on the Greenland ice sheet revealed by weather station observations and modeling. Journal of Geophysical Research: Earth Surface. 123: 10.1029/2017JF004597.

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Rapid Sampling of Ice-Sheet Temperatures

Posted by William Colgan on September 10, 2018
Applied Glaciology, New Research / 1 Comment

We are starting a new two-year project to design, build and deploy a new type of ice-drill to measure temperatures at the ice-bed interface of the Greenland Ice Sheet. Why? Because we are unsure whether the bed is frozen or thawed beneath about one third of the ice sheet. As the rate at which ice flows is dependent on ice temperature – and basal ice temperature in particular – this translates into uncertainty in simulations of how ice sheet form and flow will evolve over time.

We suspect that climate change is likely driving an expansion of the thawed-bedded portion of the ice sheet — eroding the frozen-bedded portion — over time. But in the last sixty years, direct temperature measurements of the ice-bed interface have only been made at six inland ice-sheet locations. These scarce, but tremendously valuable, basal ice temperatures have been measured at the sites of ice core deep-drilling projects. These deep-drilling projects take months or even years to create a 30 cm wide borehole to the ice-sheet bed from which to retrieve delicate ice core.

Figure 1 – Schematic of the HOTROD melt-tip and cross-section of the umbilical cord. The umbilical cord will both power the melt-tip as well as contain embedded ice-temperature sensors.

This project will design, build and deploy a drill for rapid sampling of ice-sheet basal temperatures. HOTROD will use an approximately 5KW electric melt-tip to open 3 cm wide access boreholes to depths of 500 m within days. The HOTROD umbilical cord will not only power the melt-tip, but also have embedded temperature sensors that — with the melt-tip — make a one-way trip to the ice-sheet bed. The heart of the melt-tip will be recently designed heating elements intended for rapid heating of energy-efficient domestic hot water supplies.

In 1971, thermal drilling was used to recover the top 372 m of ice core at Dye-3. The Dye-3 deep ice core was subsequently completed to 2037 m with electro-mechanical drilling in 1981. Thermal drilling technology was last used in Greenland in 1974, to recover a 403 m ice core at Crete, Greenland1. While there’s been numerous hot-water drilling projects since then, the working memory of thermal drilling is fading. The goal of this project is to successfully deploy a melt-tip thermal drill to measure a 500 m deep ice-sheet temperature profile with less than ten days of drilling. Initial field-testing activities will begin in 2019.

Figure 2 – The Dye-3 ice-drilling trench. In comparison to the multi-year logistical footprints of deep ice-coring projects, the HOTROD melt-tip drill will require trace logistics.

We hope that the advent of rapid melt-tip drilling will be a disruptive technology within the sphere of ice-sheet research now dominated by conventional electro-mechanical and hot-water drilling systems. A concerted effort to sample more temperatures at the ice-bed interface may potentially shift our understanding of ice-sheet basal temperatures and even ice-sheet sensitivity to climate change. This project is funded by Villum Experiment, a programme of Villum Foundation that funds science and engineering projects that challenge the norm and have the potential to transform traditional approaches2.

1Langway, C. 2008. The History of Early Polar Ice Cores. Cold Regions Research and Engineering Laboratory. Technical Report 08-1.

2Villum Foundation. 2018. 53 bold ideas receive funding from VILLUM FONDEN.

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Q-Transect: A Hotspot of Greenland ice loss

Posted by William Colgan on June 19, 2018
Climate Change, New Research / No Comments

We are introducing a rich trove of ice-sheet surface mass balance measurements in an open-access study in the current issue of Journal of Geophysical Research1. The Qagssimiut Lobe is among the most southern ice lobes of the Greenland Ice Sheet. The Q-transect – which runs up the heart of the Qagssimiut Lobe – has been home to automatic weather stations recording ice and climate measurements since 2000. In this study, we have compiled sixteen years of annual surface mass balance measurements and also added three hard-fought years of winter snow accumulation measurements. These data – spanning 300 to 1150 m elevation – now form an exceedingly unique record of ice-sheet health.

Herm_1

Figure 1. The Qagssimiut Lobe in South Greenland. Measurement locations are denoted with white dots. The Sermilik Glacier catchment is delineated with a black line. The ice-sheet margin is delineated with a white line. The background image was acquired by the ESA Sentinel-2 satellite on 28 August 2016 and clearly illustrates the bare ice area below equilibrium line altitude.

These comprehensive in situ measurements allowed us to evaluate the accuracy of the surface mass balance simulated by climate models. TO do this, we stacked our measurements against comparable simulations from three leading regional climate models (HIRHAM5, MAR and RACMO2). The climate models generally did well, but were never bang-on the measurements. One climate model consistently simulated more negative surface mass balances and lower equilibrium line altitudes than we measured. The other two model usually did the opposite, implying the ice sheet was healthier than in reality. These biases appear to stem from differences in simulated winter snow accumulation – which can vary by 200 % at low elevations – between models.

Herm_2

Figure 2. Elevation profiles of measured and simulated winter snow accumulation in (a) 2013/2014, (b) 2014/2015, and (c) 2016/2017. Shaded areas indicate uncertainty ranges. In (c), black lines illustrate the comparison of the model mean for 2000/2001 to 2015/2016 with the 2016/2017 observations.

Combining our knowledge of surface mass balance over the Qagssimiut Lobe with independent observations of iceberg calving rate at Sermilik Glacier – the main tidewater draining Qagssimiut Lobe – allowed us to calculate a total mass balance. We found that the relatively small Sermilik Glacier catchment is now losing up to 2.7 Gt of ice per year. That is a rather astounding – 20 times greater than the ice sheet average – the Sermilik Glacier catchment represents only about 0.03 % of ice-sheet area but is contributing about 0.61 % of ice-sheet mass loss. Its extreme southern location clearly makes Sermilik Glacier a hotspot of ice-sheet mass loss. Its rate of ice loss is more characteristic of lower latitude Andean glaciers than the vast majority of Greenland.

HERM_3

Figure 3. Left: Estimated total mass balance of Sermilik Glacier catchment between 2001 and 2012 in Gt/yr (uncertainty denoted by spread). Right: The Sermilik Glacier catchment overlaid on an ice velocity map derived from the ESA Sentinel-1 satellite. Thin lines indicate adjacent ice flow lines.

We hope that this study will be useful to climate modelers, as they further improve the accuracy with which their models simulate ice-sheet surface mass balance. We also hope that highlighting the Q-transect as a hotspot for both ice loss and in situ data availability will help inform future measurement campaigns seeking to improve our understanding of the physical processes influencing surface mass balance. All measurements of surface mass balance and winter snow accumulation are freely available in the study’s online material.

1Hermann, M., J. Box, R. Fausto, W. Colgan, P. Langen, R. Mottram, J. Wuite, B. Noel, M. van den Broeke and D. van As. 2018. Application of PROMICE Q-transect in situ accumulation and ablation measurements (2000-2017) to constrain mass balance at the southern tip of the Greenland ice sheet. Journal of Geophysical Research. 123: 10.1029/2017JF004408.

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What’s the density of snow on the Greenland Ice Sheet?

Posted by William Colgan on May 07, 2018
New Research / No Comments

We have a new open-access study in the current volume of Frontiers in Earth Science that tries to estimate snow density across the Greenland Ice Sheet1. Snow density might seem like an unexciting topic, but it is fundamental to blending ice-sheet thinning or thickening observations with surface mass balance simulations to assess ice-sheet health. Clearly, assuming a snow density of 400 kg/m3 makes a snowfall event observed by satellite altimeter twice as massive as assuming a snow density of 200 kg/m3 (and vice versa). There are several mathematical formulations presently being used to estimate snow density. These existing approaches generally estimate snow density as a function of more accessible geographic or climatic parameters.

RSF_figure1

Figure 1 – Locations of the surface snow density measurements collected in this public dataset. Contours lines indicate elevations in meters above sea level.

In this study, we assembled a large database of snow density measurements from the Greenland Ice Sheet. These measurements were collected from a variety of scientific expeditions going back to 1954, and provide the most complete spatial coverage of the ice sheet that is presently possible. Despite running a lot of statistics on this database, we could not find a compelling proxy for snow density. Our analysis indicates that snow density cannot be reliably predicted by common geographic (i.e. elevation, latitude or longitude) or climatic (i.e. air temperature or accumulation rate) variables. As existing approaches to estimate snow density rely on these common geographic and climatic variables, this was a somewhat unexpected finding.

RSF_figure2

Figure 2 – Snow density (0 to 10 cm depth) plotted against: (a) measurement year, (b) site latitude, (c) site longitude, (d) site elevation, (e) mean annual air temperature, and (f) accumulation rate.

Our study therefore recommends that the average measured density of 315 ± 44 kg/m3 (± standard deviation) is the most statistically defensible assumption for snow density. This recommendation of a constant, or zero-order approximation, differs from previous studies that have recommended estimating snow density as a second-order polynomial function of near-surface ice-sheet temperature. We show that these previous approaches may systematically overestimate snow density by 17 to 19 %. This is partially due to their mathematical formulations, but mainly due to previously considering measurement depths of up to 1 m as characteristic of “snow density”. As density increases with depth in the relatively porous near-surface layers of the ice sheet, we are instead careful to only include density measurements to a depth of 10 cm.

RSF_figure3

Figure 3 – Snow density (0-10 cm depth) versus mean annual air temperature. Solid line indicates the regression of this study, while the dotted and dashed lines indicate previously published temperature-dependent formulations for estimating snow density.

We hope that the approach of estimating snow density that we are proposing, which is mathematically less complex but statistically more robust, will be useful to researchers working with both surface mass balance simulations and satellite altimetry observations, as well as researchers modelling process-level studies of snow compaction and meltwater percolation in the near-surface ice-sheet layers. This study was supported by the Danish Research Council and the Programme for Monitoring of the Greenland Ice Sheet. Our database of 254 snow density measurements is freely available in the supplementary material of the study.

1Fausto R., J. Box, B. Vandecrux, D. van As, K. Steffen, M. MacFerrin, H. Machguth, W. Colgan, L. Koenig, D. McGrath, C. Charalampidis and R. Braithwaite. 2018. A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling. Frontiers in Earth Science 6:51. doi:10.3389/feart.2018.00051.

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Ice-Sheet Science by Charter Aircraft or Ground Traverse?

Posted by William Colgan on October 30, 2017
Commentary / No Comments

Charter aircraft are now ubiquitous in Greenland ice sheet research, but it wasn’t always that way. Overland traverses of specialized low-ground-pressure vehicles were the standard ice-sheet science platform until the early 1970s. The subsequent aircraft-era was born by the shifting nature of ice-sheet expeditions. Most expeditions are no longer months of diverse basic measurements, but rather weeks of highly-focused measurements. Small aircraft, like the ski-equipped Twin Otter in particular, have allowed ice-sheet expeditions to dart into any corner of Greenland with far more convenience than the lumbering ground vehicles used by larger traverse parties.

Logistics_figure2

Figure 1 – Left: Specialized ground traverse vehicle used by the French Polar Expedition in 1959. Right: Charter Twin Otter aircraft used by University of Colorado expedition in 2008.

Back in 2008, the US National Science Foundation signaled that the economics of large-scale Greenland ice sheet logistics were shifting, when it initiated the quasi-bi-annual Greenland Inland Traverse from Thule Air Base to Summit1,2. The round-trip 70-day traverses use modified farm vehicles. Each traverse phases out more than 100 tonnes of Summit Station re-supply that was previously performed by ski-equipped C-130 Hercules aircraft.

If you spend an inordinate amount of time looking at the logistics for ice-sheet expeditions, it might seem like ground vehicles are now starting to edge into the realm of financial possibility for compact expeditions as well. Especially since the last Greenland-based Twin Otter was transferred out-of-country c. 2012, forcing ice-sheet researchers to ferry Twin Otters from Canada or Iceland on an as-needed basis.

Imagine a hypothetical expedition of six researchers for two weeks to survey a 100 km transect in the middle of South Greenland. My guess is that it would take around USD 127,000 to get in and out with a chartered Twin Otter. If the same expedition took an extra week to commute using two specialized ground traverse vehicles, my guess is that it would take around USD 203,000 (Table 1). If you relocate to North Greenland, and assume 50% more flight burden, the charter aircraft option increases to USD 179,000, while the cost of traversing from the nearest suitable settlement effectively remains the same.

Table 1 – Estimated expenses of transportation logistics associated with two-week charter aircraft and three-week specialized ground traverse vehicle expeditions (in USD). The charter aircraft scenario assumes deploying four ancillary snowmobiles and the ground traverse assumes two specialized vehicles.

I think this is a fair zero-order analysis. USD 250,000 buys a pretty specialized traverse vehicle, and assuming a minimum of 105 days of use (five 21-day seasons) is on the aggressive end of amortization scenarios. Sure, there are indirect costs associated with owning a traverse vehicle, like shipping it to/from Greenland, but these are small in comparison to the capital cost. The reality of expedition logistics is that large unexpected costs can drown planned costs: an aircraft recalled to its home-base or a broken drive-train can ring up USD 40,000 overnight.

If the economic advantage of charter aircraft over traverse vehicles is within 12% in North Greenland, perhaps it is worth looking to look at another metric: carbon. Ice-sheet researchers generally fall into the “climate aware” crowd; the consequences of anthropogenic carbon dioxide emissions motivate much of our climate change research niche. The Paris Agreement alone provides a strong motivation to understand the climate impacts of our climate change research activities.

Table 2 – Estimated direct carbon emissions associated with ice-sheet expeditions under the logistical support scenarios of a single charter aircraft versus two specialized ground traverse vehicles (Table 1). This does not include non-trivial indirect emissions.

Perhaps it is no surprise that the slow-and-steady ground traverse scenario has a smaller direct carbon footprint than the fast-and-convenient aircraft scenarios. But what may be surprising, is just how much smaller: four to six times less carbon dioxide emitted! Now, this is just the direct carbon footprint, the carbon dioxide associated with burning fueling during expedition activities, and does not include the non-trivial indirect emissions associated with aircraft or vehicle manufacture. Full life cycle carbon accounting is a huge issue: an aircraft can easily have a 100x more working days in its life than a specialized ground vehicle.

So, why even spend time on rough estimates of the cost and carbon associated with ice-sheet logistics? Well, earlier this month, the US National Science Foundation issued a “Request for Information on Mid-scale Research Infrastructure”. The NSF asks for this type of input once in a literal blue moon to develop multi-year strategy to address the big-picture infrastructure needs of the science and engineering community. Not just the cryospheric community, but the entire research community. These sorts of calls are the time for researchers to team-up and simply ask for the chance to ask for big-ticket items, things that cannot be financed through smaller individual grants.

Vehicles

Figure 2 – Sampling of contemporary ice-sheet vehicles (Left to Right): Custom Toyota Land Cruiser, Hagglunds Arctic Tracks, Tucker Sno Cat, and Arctic Truck Toyota Hilux.

Researchers teaming up is critical to chasing the widespread adoption of lower-carbon ground traverses; the finances only make sense at group-scale. Higher-carbon charter aircraft still provide cheaper one-time ice-sheet access than lower-carbon ground vehicles. Even if a single research project can afford the cost of one or two amortized ground traverse expeditions, virtually no research group has sufficiently committed funding to fully amortize vehicle purchases over 5+ expeditions. This seems to be a clear and present opportunity for a research consortium to own Greenland traverse vehicles and lease them to individual science teams.

This is not a new idea among folks who organize Greenland ice sheet expeditions. But, perhaps it is time for us to organize the kind of bona fide “evidence of research community support” that any funding agency wants to see before supporting a transformative infrastructure shift. So, if you are into ice-sheet research and interested in exploring the possibility of shared ice-sheet traverse vehicles in Greenland, then I have started an email listserve to connect and exchange ideas on this rather quirky topic. Otherwise, if you are just taking a moment to peer into the void of ice-sheet logistics, I hope you can slightly better appreciate the practicalities of exploring lower-carbon solutions to ice-sheet transportation!

1Lever, J. and J. Weale. 2011. Mobility and Economic Feasibility of the Greenland Inland Traverse. Cold Regions Research and Engineering Laboratory. Technical Report 11-9.

2Lever, J. et al. 2016. Economic Analysis of the Greenland Inland Traverse. Cold Regions Research and Engineering Laboratory. Special Report 16-2.

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Draining the Ice-Sheet: Crevasses vs Moulins

Posted by William Colgan on March 22, 2017
Climate Change, New Research / No Comments

We have an open-access study that explores the various ways by which meltwater drains from the Greenland ice sheet in the current volume of Journal of Glaciology1. While surface meltwater rivers and lakes are a conspicuous feature of the Greenland ice sheet, virtually all meltwater that drains from the ice-sheet margin is flowing either within or beneath the ice sheet, rather than on its surface. Where and how meltwater reaches the ice-sheet bed can have implications on ice dynamics. For example, the sharp pulses of meltwater transmitted to the bed by near-vertical conduits called moulins are believed to cause more sliding at the ice-bed interface than the subdued pulses transmitted by crevasses.

In the study, we simulated a portion of the ice sheet in West Greenland known as Paakitsoq, using a computer model. The model used the measured ice sheet topography, as well as observed locations of crevasses and moulins, to simulate how meltwater was produced and flowed across the ice sheet surface under the climate conditions of the 2009 average intensity melt season and the 2012 extreme intensity melt season. The model could also simulate catastrophic lake drainage events, known as hydrofracture events, when a surface lake becomes sufficiently deep that its pressure fractures the underlying ice and creates a new moulin.

Figure 1 – Location of the Paakitsoq study area. Black outline denotes the model domain. Blue dots denote moulin locations identified in satellite imagery. Black contours denote surface elevations. Base image is Landsat-8 from 4 August 2014.

Our simulations suggested that, during an average intensity melt season, crevasses drain almost half (47 %) of ice-sheet meltwater. The hydrofracture of surface lakes drained about 24 % of ice-sheet meltwater, the majority of which resulted from drainage into new moulins following hydrofracture events, rather than the hydrofracture events themselves. Previously existing moulins drained an additional 15 % of meltwater. (The remaining meltwater either reached the ice-sheet margin, remained stored within the model area, or drained to ice-sheet areas North or South of the model area.)

While our simulations suggest that crevasses now drain more meltwater from the ice sheet at Paakitsoq (47 %) than previously existing and newly hydrofractured moulins together (39 %), our 2012 extreme intensity melt season simulation suggests that this ratio may change. The proportion of meltwater drainage via moulins increases, and the proportion of drainage via crevasses decreases, in the 2012 extreme intensity melt season simulation, which may be characteristic of a warmer future climate. This increase in both relative and absolute moulin drainage under warmer conditions is due to an increase in moulins created by hydrofracture events, as meltwater production moves to higher elevations.

Figure 2 – Partitioning surface meltwater drainage into eight categories under the average melt (2009; R1) and extreme melt (2012; R11) simulations. “Lake” is the volume remaining in lakes at season end. “Remaining Flow” is the volume in transit at season end. “Lateral Outflow” is the volume that drains through North and South model domain boundaries. “Ice Margin” is the volume that reaches the ice-sheet edge. The volumes captured in “Crevasses” and “Moulins” are denoted. The volumes drained by lake hydrofracture induced surface-to-bed connections are “Lake Hydrofracture Lake” (LHL), while the subsequent drainage into the new surface-to-bed connection is “Lake Hydrofracture Moulin” (LHM).

Overall, our study provided insight on the space-time variation of pathways by which the vast majority of ice-sheet meltwater descends to the ice-sheet bed prior to reaching the ice-sheet margin. A better understanding of how meltwater travels through the ice-sheet can help improve scientific understanding of not only the ice-sheet mass loss caused by runoff, but also the implications of increasing meltwater production on the mass loss caused ice dynamics. Our simulations also suggest there is value in ice-sheet wide mapping of surface hydrology features like crevasses, rivers, lakes, and moulins, as computer models can use this knowledge to improve simulations of meltwater routing.

1Koziol, C., N. Arnold, A. Pope and W. Colgan. 2017. Quantifying supraglacial meltwater pathways in the Paakitsoq region, West Greenland. Journal of Glaciology. 1-13. doi:10.1017/jog.2017.5

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