The long-term objectives of my research program are to use state-of-the-art field, modeling and data analysis methods to quantify the response of mountain glaciers to climate change on regional and global scales, and to narrow the uncertainties in projections of glacial contributions to regional streamflow as well as to global sea level rise.
Significant Past Contributions
World-wide melting of mountain glaciers and ice caps (i.e., excluding the Antarctic and Greenland ice sheets) is a well-established significant contributor to current and future sea-level rise. Early in my career (2004-2012), I developed and applied novel numerical and statistical methods to project the contribution of glacier melt to sea-level rise in response to future climate forcing scenarios from global climate models (GCMs). In Radic and Hock (2011), we provided the first detailed and regionally resolved projections of glacier mass change on a global scale.
Since 2012, the global inventory of glaciers has grown significantly and better climate projections from reliable GCMs have become available. To harness these, in Radic et al. (2014) I led a team of collaborators in a major initiative to update regionally resolved projections of glacier contribution to sea level rise. Our results featured in Chapter 13 (Sea Level Rise) of the Intergovernmental Panel on Climate Change Fifth Assessment Report. Critically, our updated results identify the regions with the largest potential contribution to future sea level rise (Arctic Canada, Alaska and Antarctic glaciers) and the regions most vulnerable to glacier wastage (European Alps, New Zealand, Caucasus, Western Canada). In particular, the latter are projected to lose more than 75% of their current ice volume by 2100.
Beyond future global sea level rise, deglaciation has major implications for regional hydrology. Changes in seasonal runoff impact agriculture, hydropower generation and freshwater ecosystems. As part of a long-term collaborative project, I contributed to the development of the Regional Glaciation Model (RGM) for western Canada. This was the first model for regional glacier evolution to couple surface mass-balance with an ice dynamics model of high complexity (Clarke et al., 2015). In this study, I developed a statistical downscaling method to convert coarsely-resolved climate fields from GCMs to finely-resolved ones that drive melt processes at the scale of individual glaciers.
Insufficiently resolved or incomplete data on glacier geometries as well as computational limitations prevent us from implementing an RGM-like model globally at present. However, I have shown that reliable projections of the global glacier response to climate change are possible with simplified models for the geometric evolution of glaciers (e.g. volume-area scaling models). In a recent study, we used a simplified glacier-dynamics model to assess sensitivity and response time of glaciers to climate perturbations on regional and global scales (Bach et al., 2018). We show that volume sensitivity is mainly driven by the glacier size distribution in a given region, as well by the distribution of surface slopes, while response time scales are dominated by the ablation gradient: mountain ranges with small glaciers and low slopes are the most vulnerable to volume loss, while those with high mass turnover (and in particular, high ablation rates) will adjust most rapidly to changes in climate.
My research on the projections of glacier mass changes on regional and global scales has identified three major sources of uncertainties in glacier melt models: (1) sensitivity to poorly constrained parameters in the semi-empirical models of surface mass balance; (2) sensitivity to the downscaling methods used to convert the climate variables at coarse spatial resolution (e.g. 100 x 100 km grid) to sub-kilometer spatial resolution; and (3) poor parameterization of turbulent heat fluxes, an important contributor to energy available for melting, at glacier surfaces. One strand of my research has aimed to narrow each of these uncertainties. To achieve this goal, we not only need better models and more observations, but also to test novel approaches (e.g. deep learning) in addressing 'old' problems. I further elaborate on the sources of uncertainty and give an overview of the ongoing research projects.
Models of Surface Energy Balance and Climate Downscaling
As part of regional and global mass balance models, the simulation of glacier melt is often based on semi-empirical temperature-index models that require calibration with available observations. Since the observations needed for calibration are available for fewer than 1% of mountain glaciers worldwide, these models cannot be adequately constrained for more than 99% of glaciers, making the projections of glacier melt and runoff highly uncertain. By contrast, physics-based models based directly on surface energy balance (SEB), are universally applicable at any glacier surface. To force these models, however, meteorological variables and energy fluxes are needed at or in the vicinity of the glaciers in question. In the absence of observations detailing these variables, the required forcing must be derived from downscaling the coarse-resolution output from GCMs and/or reanalysis datasets to the local glacier scale. Despite the advantage of physics-based modeling over the empirical models, it remains to be shown whether downscaled meteorological fields from GCMs can successfully resolve the local processes driving surface melting for the majority of glaciers. To address this question, we focus on evaluating the performance of an SEB model forced with dynamically downscaled meteorological fields. The first step in this analysis, presented in Mekdes Tessema's MSc thesis, evaluated the performance of Weather Research and Forecasting (WRF) model in downscaling the energy fluxes relevant for modeling surface melt at three glaciers in Canadian Rockies. Our ultimate goal is to develop a dynamical downscaling coupled with statistical downscaling (machine learning) approach to simulate spatially-resolved regional glacier melt based on SEB models (ongoing PhD project of Christina Draeger).
Assessment of Turbulent Heat Fluxes under Katabatics
As part of the physics-based models of glacier melt, turbulent heat fluxes generated by wind drag and temperature gradients in the near-surface atmospheric layer can contribute significantly to the energy available for melt. These fluxes, however, are poorly understood as their direct measurements, which require specialized instrumentation and continuous maintenance, are hard to obtain at glacier surfaces. Current models of turbulent heat fluxes at glacier surfaces rely on simplified bulk or closure methods that use mean meteorological variables such as near-surface air temperature, wind, and relative humidity. Although these methods are widely used, they are based on assumptions such as a boundary layer flow driven primarily by wind in the free atmosphere, which is rarely the case near sloped glacier surfaces where katabatic flow is prevalent. Katabatic winds are negatively buoyant downslope winds that are characterized by a near surface wind speed maximum (< 50 m height above the surface). Since 2014, my team has conducted a series of field experiments measuring near-surface meteorological variables and turbulent fluxes using eddy-covariance technology at a total of seven stations at three mountain glaciers in western Canada. These measurements represent one of the longest uninterrupted time-series of eddy-covariance measurements at sloped glacier surfaces. Analysis of the data revealed that the bulk methods for turbulent fluxes fail in the presence of katabatic flow (Radic et al., 2017, Noel Fitzpatrick's PhD thesis). To improve the method’s performance, it is necessary to develop a katabatic flow model in which the two key parameters (eddy viscosity and diffusivity) are represented as functions of height, wind shear, and vertical temperature gradients (ongoing PhD project of Cole Lord-May).
Applications of Machine Learning Methods
A new way of thinking about big data and empirical models (machine learning) can make progress in ways that were previously not possible. Since 2013, I have been teaching a graduate course in data analysis and machine learning in the geosciences (EOSC510) and was able to contribute to a large number of studies led by grad students across the science and engineering communities at UBC. Unglert et al., (2016), for example, was the first study in volcano monitoring to develop an automated and unsupervised method for identifying patterns in seismicity potentially indicative of eruptive behavior. The ongoing PhD project of Sam Anderson is on detecting and projecting the changes in glacier-fed streamflows across western Canada to provide a first ever risk assessment of freshwater availability for the region. We have developed a novel framework for data analysis and machine learning in order to asses the local water resource vulnerability to rapid deglaciation in Canadian Rockies. Sam's ultimate goal is to use deep learning to model regional streamflow.
Associate Professor - University of British Columbia (2019 - )
Assistant Professor - University of British Columbia (2012 - 2019)
Post-doctoral fellow - University of British Columbia (2008 - 2012)
PhD - University of Alaska Fairbanks, Geophysical Institute, AK, USA (2007 - 2008) thesis pdf
Licentiate - Stockholm University, Department of Physical Geography and Quaternary Geology, Stockholm, Sweden (2004 - 2007)
MSc & BSc - University of Zagreb, Department of Geophysics, Zagreb, Croatia (1998 - 2004)
Meet the team
Sam Anderson, PhD student in Geophysics (Sep 2017 - present)
Research project: Streamflow in Alberta under climate change: Insights from data analysis and deep learning
Christina Draeger, PhD student in Geophysics (Sep 2019 - present)
Research interests: Application of dynamical climate downscaling and machine learning in modeling glacier mass changes on regional and global scales
Cole Lord-May, PhD student in Geophysics (Sep 2018 - present)
Research project: Quantification and numerical modeling of turbulent fluxes during katabatic winds over mountain glaciers
Jennifer Walker, Postdoctoral researcher (2018 - 2020)
Research project: Analysis of characteristic spatio-temporal patterns of ablation at Greenland Ice Sheet and their links with synoptic weather patterns
Mekdes Tessema, MSc in Geophysics (completed in Jun 2018)
Thesis: Evaluation of dynamically downscaled near-surface meteorological variables and energy fluxes at three mountain glaciers in British Columbia (PDF)
Noel Fitzpatrick, PhD student in Atmospheric Sciences (completed in Nov 2018)
Thesis: An investigation of surface energy balance and turbulent heat flux on mountain glaciers in British Columbia (PDF)
I am looking for highly motivated graduate students to join my team in tackling research problems on glacier mass changes and climate modeling. My long-term research objective is to develop a coupled atmosphere-glacier modeling approach, based on the blending of physics-based models and machine learning, and aimed at projecting glacier mass changes within a region of interest.
Some tips for prospective candidates:
Scholarships available in Canada and at UBC are typically awarded competitively, so a strong performance in your last degree is essential. For Canadian applicants and permanent residents, note that NSERC funding applications for graduate scholarships have to be submitted in October of the year before you plan to start your studies. To get full consideration for internal scholarships at UBC, your application to EOAS has to be complete with references by the start of January. EOAS typically requires you to have the equivalent of a thesis-based MSc before acceptance into the PhD program. With satisfactory progress, you may be permitted to transfer from the EOAS MSc program into the PhD program without completing the MSc (note however that there are no internal scholarships for MSc students at UBC). There turns out to be some logic to the MSc-before-PhD requirement - committing to a four year PhD without prior graduate research experience is a risky thing to do.
For work in my group, strong mathematics and physics skills are essential. You should have fluency in calculus, linear algebra, data analysis and statistics. Supervision through the Institute of Applied Mathematics is possible: there is a strong fluid dynamics presence across campus.
For the fieldwork- and/or data-oriented side of research in my group, experience with instrumentation, experimental work and/ or practical engineering is important, as are strong quantitative skills in the physical sciences in general. You need to have a good grasp of physics and university-level mathematics. The ultimate aim of my fieldwork on glaciers is to generate high-quality data that can be used to test and further develop quantitative models of glaciological and/or meteorological phenomena, so you will need to understand these models. Above all, being a team player or being keen to become one, is a 'must' skill to have.
- 2021 -
Lumnitz S., Devisscher T., Mayaud J.R., Radić V., Coops N.C. and V.C. Griess (2021), Mapping trees along urban street networks with deep learning and street-level imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 175, 144-157, https://doi.org/10.1016/j.isprsjprs.2021.01.016.
Edwards, T.L., Nowicki, S., Marzeion, B. et al. (2021) Projected land ice contributions to twenty-first-century sea level rise, Nature, 593, 74–82. https://doi.org/10.1038/s41586-021-03302-y
Hassan M. A., Radić V., Buckrell E., Chartrand S. M. and C. McDowell (2021) Pool‐riffle adjustment due to changes in flow and sediment supply, Water Resources Research, 57(2), 2020WR028048, https://doi.org/10.1029/2020WR028048
- 2020 -
Pelto B., Maussion F., Menounos B., Radić V. and M. Zeuner (2020) Bias-corrected estimates of glacier thickness in the Columbia River Basin, Canada, J. Glaciol., 1–13, https://doi.org/10.1017/jog.2020.75
Anderson S. and V. Radić (2020) Identification of local water resource vulnerability to rapid deglaciation in Alberta. Nat. Clim. Chang. https://doi.org/10.1038/s41558-020-0863-4
Marzeion B., Hock R., Anderson B., Bliss A., Champollion N., Fujita K., Huss M., Immerzeel W., Kraaijenbrink P., Malles J.-H., Maussion F., Radić V., Rounce D.R., Sakai A., Shannon S., van de Wal R. and H. Zekollari (2020) Partitioning the uncertainty of ensemble projections of global glacier mass change, Earth’s Future, 8, e2019EF001470. https://doi.org/10.1029/2019EF001470
Pitman K.J., Moore J.W., Sloat M.R., Beaudreau A.H., Bidlack A.L., Brenner R.E., Hood E.W., Pess G.R., Mantua N.J., Milner A.M., Radić V., Reeves G.H., Schindler D.E. and D.C. Whited (2020) Glacier Retreat and Pacific Salmon, BioScience, 70(3), 220-236, https://doi.org/10.1093/biosci/biaa015
- 2019 -
Wang R., Liu S., Shangguan D., Radić V. and Y. Zhang (2019) Spatial heterogeneity in glacier mass-balance sensitivity across High Mountain Asia. Water, 11(4), 776, https://doi.org/10.3390/w11040776
Fitzpatrick N., Radić V. and B. Menounos (2019) A multi-season investigation of glacier surface roughness lengths through in situ and remote observation. The Cryosphere, 13, 1051-1071, https://doi.org/10.5194/tc-13-1051-2019
Hock, R., Marzeion B., Bliss A., Giesen R., Hirabayashi Y., Huss M., Radić, V. and A. Slangen (2019). GlacierMIP - A model intercomparison of global-scale glacier mass-balance models and projections. J. Glaciol., 65(251), 453-467, https://doi.org/10.1017/jog.2019.22
Mayaud J., Anderson, S., Tran M. and V. Radić (2019) Insights from self-organizing maps for predicting accessibility demand for healthcare infrastructure. Urban Sci., 3(1), 33, https://doi.org/10.3390/urbansci3010033
- 2018 -
Pulwicki A., Flowers, G., Radić, V. and D. Bingham (2018) Uncertainties in estimating winter balance from direct measurements of snow depth and density on alpine glaciers. J. Glaciol., 64(247), 781-795, doi:10.1017/jog.2018.68
Foroozand H., Radić V. and S. V. Weijs (2018) Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures. Entropy, 20 (207), doi:10.3390/e20030207
Bach E., Radić V. and C. Schoof (2018), How sensitive are mountain glaciers to climate change? Insights from a block model. J. Glaciol., 64(244), 247-258. doi:10.1017/jog.2018.15
- 2017 -
Radić V., Menounos B., Shea J., Fitzpatrick N., Tessema M. A. and S. J. Déry (2017) Evaluation of different methods to model near-surface turbulent fluxes for a mountain glacier in the Cariboo Mountains, BC, Canada. The Cryosphere, 11, 2897-2918, https://doi.org/10.5194/tc-11-2897-2017
Fitzpatrick N., Radić V. and B. Menounos (2017) Surface Energy Balance Closure and Turbulent Flux Parameterization on a Mid-Latitude Mountain Glacier, Purcell Mountains, Canada. Front. Earth Sci., 5:67, doi: 10.3389/feart.2017.00067
Gilbert A., Flowers G. E., Miller G. H., Refsnider K., Young N. E. and V. Radić (2017) The projected demise of Barnes Ice Cap: evidence of an unusually warm 21st century Arctic. Geophys. Res. Lett., 44, doi: 10.1002/2016GL072394
- 2016 -
Aubry T. J., Jellinek A. M., Degruyter W., Bonadonna C., Radić V., Clynne M. and A. Quainoo (2016) Impact of global warming on the rise of volcanic plumes and implications for future volcanic aerosol forcing. J. Geophys. Res. Atmos., 121(22): 13326-13351, doi:10.1002/2016JD025405
Schannwell C., Barrand N. E. and V. Radić (2016) Future sea-level rise from tidewater and ice-shelf tributary glaciers of the Antarctic Peninsula. Earth and Planetary Science Letters, 453: 161-170, https://doi.org/10.1016/j.epsl.2016.07.054
Unglert K., Radić V. and A. M. Jellinek (2016) Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra. Journal of Volcanology and Geothermal Research, 320: 58-74, doi:10.1016/j.jvolgeores.2016.04.014
- 2015 -
Schannwell C., Barrand N. E. and V. Radić (2015) Modeling ice dynamic contributions to sea level rise from the Antarctic Peninsula. J. Geophys. Res. Earth Surface, 120 (11): 2374-2392, doi:10.1002/2015JF003667
Chartrand S. M., Hassan M. A. and V. Radić (2015) Pool-riffle sedimentation and surface texture trends in a gravel bed stream. Water Resour. Res., 51, doi:10.1002/2015WR017840
Radić V., Cannon A. J., Menounos B. and N. Gi (2015) Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models. J. Geophys. Res. Atmos., 120, doi:10.1002/2015JD023279
Clarke G. K. C., Jarosch A. H., Anslow F. S., Radić V. and B. Menounos (2015) Projected deglaciation of Western Canada in the 21st century. Nature Geosci., 8: 372-377, doi:10.1038/ngeo2407
- 2014 -
Pfeffer W. T., Arendt A., Bliss A., Bolch T., Cogley J. G., Gardner A., Hagen J., Hock R., Kaser G., Kienholz C., Miles E., Moholdt G., Mölg N., Paul F., Radić V., Rastner P., Raup B., Rich J. and M. Sharp (2014) The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol., 60(221): 537-552, doi:10.3189/2014JoG13J176
Bliss A., Hock R. and V. Radić (2014) Global response of glacier runoff to twenty-first century climate change. J. Geophys. Res. Earth Surf., 119: 717-730, doi:10.1002/2013JF002931
Radić V. and R. Hock (2014) Glaciers in the Earth's hydrological cycle: assessments of glacier mass and runoff changes on global and regional scales. Surv. Geophys., 35: 813-837, doi: 10.1007/s10712-013-9262-y
Radić V., Bliss A., Beedlow A. C., Hock R., Miles E. and J. G. Cogley (2014) Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models. Clim. Dyn., 42(1-2): 37-58, doi:10.1007/s00382-013-1719-7
- 2013 -
Mernild S. H., Lipscomb W. H., Bahr D. B., Radić V. and M. Zemp (2013) Global glacier changes: a revised assessment of committed mass losses and sampling uncertainties. The Cryosphere, 7: 1565-1577, doi: 10.5194/tc-7-1565-2013
Levermann A., Clark P. U., Marzeion B., Milne G. A., Pollard D., Radić V. and A. Robinson (2013) The multimillennial sea-level commitment of global warming. PNAS, doi: 10.1073/pnas.1219414110
- 2012 -
Clarke G. K. C., Anslow F. S., Jarosch A. H., Radić V., Menounos B., Bolch T. and E. Berthier (2012) Ice volume and subglacial topography for western Canadian glaciers from mass balance fields, thinning rates, and a bed stress model. J. Climate, e-View, doi: 10.1175/JCLI-D-12-00513.1
Bahr D. B. and V. Radić (2012) Significant contribution to total mass from very small glaciers. The Cryosphere, 6: 763-770, doi: 10.5194/tc-6-763-2012
- 2011 -
Radić V. and G. K. C. Clarke (2011) Evaluation of IPCC models performance in simulating late 20th century climatologies and weather patterns over North America. J. Climate, 24: 5257-5274, https://doi.org/10.1175/JCLI-D-11-00011.1
Radić V. and R. Hock (2011) Regionally differentiated contribution of mountain glaciers and ice caps to future sea-level rise. Nature Geosci., 4: 91-94, doi:10.1038/NGEO1052
- 2010 -
Radić V. and R. Hock (2010) Regional and global volumes of glaciers derived from statistical upscaling of glacier inventory data. J. Geophys. Res., 115, F01010, doi:10.1029/2009JF001373
- 2009 -
Hock R., de Woul M., Radić V. and M. Dyurgerov (2009) Mountain glaciers and ice caps around Antarctica make a large sea-level rise contribution. Geophys. Res. Lett., 36, L07501, doi:10.1029/2008GL037020
- 2008 -
Radić V., Hock R. and J. Oerlemans (2008) Analysis of scaling methods in deriving future volume evolutions of valley glaciers. J. Glaciol., 54(187): 601-612, https://doi.org/10.3189/002214308786570809
- 2007 -
Radić V., Hock R. and J. Oerlemans (2007) Volume-area scaling vs flowline modelling in glacier volume projections. Ann. Glaciol., 46: 234-240, https://doi.org/10.3189/172756407782871288
Hock R., Radić V. and M. de Woul (2007) Climate sensitivity of Storglaciären - An intercomparison of mass balance models using ERA-40 reanalysis and regional climate model data. Ann. Glaciol., 46: 342-348, https://doi.org/10.3189/172756407782871503
- 2006 -
Radić V. and R. Hock (2006) Modeling future glacier mass balance and volume changes using ERA-40 reanalysis and climate models: A sensitivity study at Storglaciären, Sweden. J. Geophys. Res., 111, F03003, doi:10.1029/2005JF000440