density

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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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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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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