Glaciers play a major role to society by simultaneously serving as an important water supply to lower-lying areas while also being a major contributor to sea level rise. Due to their importance, there is wide scientific interest in understanding how glaciers evolve over time. A recent article published in Reviews of Geophysics explores glacial evolution models that incorporate ice dynamics. Here, we asked the lead author about glacier evolution, recent advances in modeling, and what knowledge gaps remain.
In simple terms, why do glaciers change over time and why does it matter?
At present, there are about 200,000 glaciers on Earth, most of which are strongly out of balance with respect to the climate. To reduce this imbalance, glaciers adapt their geometry by thinning and retreating to higher elevations. As scientists, it is crucial for us to understand and to be able to simulate the processes that drive these glacier changes.
This is important because glaciers are major contributors to sea-level rise: about 25% of the present sea-level rise originates from glacier loss. Additionally, glaciers are also unique freshwater resources that provide water to millions of people living in their valleys, are known to cause natural hazards when undergoing strong changes, and have an important role in the ecosystem of mountains.
What different methods do scientists use to measure change in glaciers over time?
Two main types of methods are used to measure glacier changes over time. The first method consists of going into the field to measure glacier changes directly. This can for instance be realized by planting stakes on the glacier from which the mass changes at the surface can be derived after a revisit. Such measurements allow for high-precision observations but typically come with high logistical and financial costs. Less than 1 per cent of all glaciers on Earth are monitored with such detailed field measurements.
An alternative approach is to measure glacier changes via remote sensing techniques such as aerial surveys or satellite observations. Recently, it has even become possible to derive glacier elevation changes from remote sensing products, which is important data to calibrate and/or evaluate glacier evolution models.
What are ice-dynamical glacier evolution studies (IDGES) and what kinds of data or results do they produce?
In ice-dynamical glacier evolution studies the temporal evolution of glaciers is modeled by explicitly accounting for ice flow processes. This is important since ice flow processes determine the mass transfer within a glacier, which in combination with the processes adding and removing mass at the glacier (the mass balance processes), determine the glacier evolution over time. Ice-dynamical glacier evolution studies thus produce future estimates on the evolution of glaciers, which are of direct relevance for studies focusing on future sea-level rise and the changing role that glaciers will play in providing water to downstream environments.
On what kind of time scales can ice dynamical models predict change and how accurate are they?
Most ice-dynamical glacier evolution studies simulate the future evolution of glaciers where the focus is often on twenty-first century, coinciding with the time scale over which most climate change projections are available.
To assess the accuracy of glacier evolution models, an important part of the work typically consists of using past (dating back to several years up to maximum a few decades back) and present glacier observations to which some model parameters can be calibrated. Ideally, other – independent – observations are then used to evaluate the performance of the ice-dynamical glacier model (a kind of “quality control”), after which the model can be used to perform future projections.
At present, there is thus typically a discrepancy between the time scales used for model calibration and evaluation (multi-annual to decadal), and the time scales over which glacier projections are performed (multi-decadal to centennial). Some recent and ongoing studies now also focus on longer time scales for model calibration and evaluation, which has the potential to reduce uncertainties in simulated future glacier evolution.
How have IDGES advanced our understanding of glacial evolution?
Ice-dynamical glacier evolution studies allow us to better represent glacier evolution, thereby enhancing our understanding of past glacier changes. This is particularly important since glaciers react with a lag to changing climatic conditions due to their slow response time. As the glacier response time strongly depends on the glacier geometry, even two adjacent glaciers can respond in a very different way to changing climatic conditions. Ice-dynamical glacier evolution studies allow us to understand and quantify these differences.
For predicting future glacier evolution, the inclusion of ice flow processes is a major advantage compared to simplified methods in which these processes are not included, as they provide a closer representation of ‘real’ glacier behavior, thereby reducing uncertainties in future glacier projections.
What are the major unsolved questions or knowledge gaps where additional research, data or modeling efforts are needed?
An important issue when modeling future glacier changes originates from the fact these modeled changes strongly depend on the (calibrated) model parameters. Therefore, ideally, it is important to quantify how sensitive the modeled glacier evolution is to the model parameters. However, to date, only a few ice-dynamical glacier models have focused on this. A new series of studies aim to tackle this problem, including some that rely on Bayesian techniques that can be used to quantify these uncertainties and provide future glacier evolution not as a single answer, but as a distribution of possible future glacier evolutions. Such techniques need to be refined and to be further applied in future studies.
Another important knowledge gap resides in translating the techniques that have been developed and used to model the evolution of single glaciers to large ensemble of glaciers (for example for regional- to global-scale projections). This is now becoming increasingly feasible since many remote sensing observations and derived products provide information that is crucial to model glaciers in detail. In the coming years, we can thus expect to see techniques that were used to model individual glaciers (including 3-D approaches) increasingly being applied for regional- to global-scale applications.
Editor’s Note: It is the policy of AGU Publications to invite the authors of articles published in Reviews of Geophysics to write a summary for Eos Editors’ Vox.