The overarching and long-term objective of my research program is 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 and global sea level rise. To address these objectives, I draw on modern quantitative field, modeling and data analysis methods. Below, I summarize my past significant research contributions, as well as the most recent contributions I have made in collaboration with my graduate students.
Significant Past Contributions
World-wide melting of mountain glaciers and ice caps (i.e., excluding the Antarctic and Greenland ice sheets) has been identified as a significant contributor to current and future sea-level rise. During my PhD (2004-2008), 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. In Radic and Hock (2011), we provided the first detailed and regionally resolved projections of glacier mass change on a global scale. These projections were further updated in Radic et al. (2014), whose results feature in the Intergovernmental Panel on Climate Change Fourth Assessment Report, in the Chapter on 'Sea Level Rise'. In addition to projections of glacier mass loss on global scale, my model has identified 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). The latter are projected to lose more than 75% of their current ice volume by 2100.
Beyond having an effect on global sea level, regional deglaciation has major implications for regional hydrology, in particular for the seasonality of runoff and the availability of fresh water for irrigation and hydropower generation. My postdoctoral research at UBC (2008-2012) focused on a highly detailed study of glacier changes in Western Canada, where significant infrastructure investment in hydropower generation relies on future water resources. As part of the long-term collaborative project between several Canadian universities, I contributed to the development of the Regional Glaciation Model (RGM). The RGM, used to project high-resolution glacier mass and meltwater runoff changes on a regional scale, is the first regional modeling approach that couples a surface mass balance model with an ice dynamics model of high complexity (Clarke et al., 2015).
Models of glacier evolution, regardless of their complexity, rely on climate forcing from global climate models (GCMs). To date, data from more than fifty global climate models from different modeling centers are available from studies. To narrow the uncertainty range in future projections of glacier mass changes on regional scales, it is however necessary to know which global climate model (GCM) performs well in reproducing the relevant regional (rather than global) climatology. In particular, how well can a GCM reproduce the past climate forcing relevant for glacier mass balance in a region of interest? This question led me to develop statistical methods for evaluating the performance of GCMs over North America and Western Canada using a set of different performance metrics, one of which was a machine learning method known as Self-Organizing Maps (SOM). With the SOM method, I evaluated the performance of GCMs beyond their mean climatologies (Radic and Clarke, 2011). Specifically, I analyzed GCM performance in terms of how well GCMs simulate the occurrence of characteristic synoptic patterns over North America, thus focusing on interannual climate variability. The results, including the rankings of 22 GCMs, informed the selection of top-performing GCMs in the study of glacier evolution in Western Canada (Clarke et al., 2015) and the study of extreme precipitation events associated with atmospheric rivers in Western Canada (Radic et al., 2015).
My research on the projections of glacier mass changes on regional and global scales led me to identify three major sources of uncertainties in the current modeling approaches of glacier melt at these scales: (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 large-scale climate variables to sub-kilometer local-scale forcing at glacier surfaces; and (3) poor parameterization of turbulent heat fluxes, an important contributor to energy available for melting, at sloped glacier surfaces. One of my main research goal is to address and narrow these uncertainties. To achieve that goal, we not only need better models and more observations, but also 'a fresh look at an old problem'. I further elaborate on the sources of uncertainty in melt models below and summarize the ongoing research projects conducted with my graduate students.
Modeling glacier melt with downscaled meteorological fields
As part of regional and global mass balance models, the simulation of glacier melt is often based on temperature-index or semi-empirical models that require calibration with available observations (e.g. Radic et al., 2014, Clarke et al., 2014). Since the observations needed for the model calibration are available for fewer than 1% of mountain glaciers worldwide, model parameters for temperature-index or semi-empirical melt models cannot be adequately constrained for more than 99\% of glaciers, thus, making the projections of glacier melt and runoff highly uncertain. On the other hand, physics-based models of glacier melt, 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 is commonly derived by downscaling the coarse-resolution output from global climate models (GCMs) and/or climate reanalysis to a local (glacier) scale. Despite the advantage of physics-based modeling over the empirical models, it remains to be shown whether the downscaled meteorological fields from GCMs can successfully resolve the local processes driving surface melting for the full suite of glaciers. To address this question, the MSc project of Mekdes Tessema has been focused on evaluating the performance of an SEB model, forced with dynamically downscaled meteorological fields, at three glaciers in the interior of British Columbia. In particular, we evaluate the performance of the state-of-the-art regional climate model, known as the Weather Research and Forecasting (WRF) model.
Parametrization of turbulent heat fluxes at glacier surfaces
While turbulent heat fluxes are recognized as significant contributors to energy available for melting, their direct measurement is relatively rare because measuring fluxes is done using sophisticated instrumentation that requires continuous maintenance and is unsuitable for long-term operational purposes. A more common approach is to derive estimates of the turbulent heat fluxes through bulk aerodynamic methods, i.e., parameterization schemes that utilize observations of mean meteorological variables such as near-surface air temperature, wind, and relative humidity. While they are the simplest and most widely used, these bulk methods are based on assumptions that are rarely applicable at sloped glacier surfaces under prevailing katabatic flow conditions. In addition, bulk methods are highly sensitive to the choice of parameters, especially so-called surface roughness lengths that relate to the evolving small-scale geometry of the melting glacier surface. To investigate and potentially improve the theory behind these bulk methods, my team has conducted a series of field experiments to measure the turbulent fluxes and surface roughness lengths, in addition to the standard meteorological and SEB variables at glacier surfaces. As part of the Noel Fitzpatrick's PhD project, we collected measurements from a total of five stations at two mountain glaciers in the interior of British Columbia over the three summer seasons (2014-2016) and obtained one of the longest uninterrupted timeseries of eddy-covariance measurements at sloped glacier surfaces. As a first step towards our research objectives, we performed a detailed evaluation of the most commonly used bulk methods. Our results show that all bulk methods fail in the presence of katabatic flow, even when directly measured roughness lengths are specified in the methods (Fitzpatrick et al., 2017; Radic et al., 2017).
'A fresh look on an old problem'
In my research, whether it is focused on evaluating a physics-based model, calibrating an empirical model or improving parameterizations in a semi-empirical model, the application of data is crucial. In fact, the accelerating growth of data archives from remote sensing and climate simulations exceeds the capacity of data analysis methods that are based on manual identification of characteristic patterns and/or multi-variable correlation analysis. On the other hand, machine learning algorithms 'feed' on this data growth, and have no limitations associated with for instance assumed linearity, as is the case with classical data analysis (e.g. multiple-linear regression analysis, principal component analysis). Rather than focusing on the expected data patterns that can either approve or disprove the existing theories or models, an alternative approach is to allow for an unsupervised recognition of patterns in the data. By this I mean that one allows a machine learning algorithm to identify a set of characteristic patterns unconstrained by the user's expectations of the size of this set and the nature of the patterns. My expertise in the application of the Self-Organizing Maps (SOMs) method got me invited to contribute in multiple inter-disciplinary projects across my department and UBC. Unglert et al., (2016), for example, was the first study in volcano monitoring to develop an automated method for identification of seismic patterns indicative of upcoming eruptions in volcanic-tremor spectra. We found that a method based on the principal component analysis (PCA) coupled with hierarchical clustering algorithm can successfully identify characteristic patterns in the spectrograms. This combined PCA-hierarchical clustering method holds great potential for near-real time applications, and thus ultimately for eruption forecasting.
Motivated by the success of pattern recognition methods, I have started incorporating these novel data analysis techniques in my glaciological research projects. One of these projects, led by my PhD student, Sam Anderson, is on analyzing and projecting the mountain glacier contribution to runoff for the purpose of quantifying the changes in freshwater availability within Western Canada. As mountain glaciers retreat across the world, many studies have focused on quantifying and projecting glacier contribution to regional runoff, but failed to resolve the complex patterns these projections might have within a region of interest. In our study, we aim to shed light on these complex patterns and their drivers at the level of detail necessary to inform local policy makers and user communities. To that end, we analyze all available long-term timeseries of measured river streamflow in Alberta and British Columbia, with a goal of identifying the patterns of 'glacier-fed' streamflow and investigate the evolution of these patterns in space and over time.
Another project motivated by the use of the pattern recognition methods, is led by my postdoctoral researcher, Jennifer Walker, and is focused on the observed ablation patterns across the Greenland ice sheet (GrIS). Despite the relatively large number of studies on mass balance and ice-dynamics of the GrIS, none provide a detailed analysis of observed spatio-temporal patterns of surface ablation. The goal of this project is to identify the most characteristic spatio-temporal patterns of ablation at GrIS and investigate their links with the synoptic weather patterns from climate reanalysis data. In particular, we aim to quantify the effect of synoptic storms, associated with heavy rainfall and anomalously high rates of sensible and latent heat fluxes and incoming longwave radiation, on the ablation patterns.
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)
PDFs and RAs
Meet the team
Sam Anderson, PhD student in Geophysics
Research project: Analysis of the characteristic spatio-temporal patterns of glacier-fed stream-flow for the purpose of quantifying the changes in freshwater availability within Western Canada
Cole Lord-May, MSc student in Geophysics
Research interests: modeling of energy and mass fluxes at glacier surfaces; katabatic flow models
Jennifer Walker, Postdoctoral researcher
Research project: Analysis of the characteristic spatio-temporal patterns of ablation at Greenland Ice Sheet and their links with the synoptic weather patterns
Mekdes Tessema, MSc in Geophysics
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
Thesis: An investigation of surface energy balance and turbulent heat flux on mountain glaciers in British Columbia (PDF)
I am actively looking for highly motivated prospective graduate students to join my team in tackling the cutting edge research on water resources and climate modeling in alpine and polar environments. My long term research objective is to develop a fully coupled atmosphere-land modeling approach, based on the blending of physics-based models and machine learning, and aimed at projecting spatially resolved water availability within a region of interest. More specifically, I envision a two-way coupling between a regional climate model, on one end, and models of surface processes including glacier, snow and hydrological models, on the other.
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 that I am aware of). 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. See below.
Expected work ethics:
The most important quality in a graduate student is the ability to be fully engaged with a research project for several years, and the desire - compulsion, really - to keep learning and get to the heart of whatever you're doing, no matter how frustrating. Especially when it gets frustrating. If you've never felt truly challenged in your previous degree, that may not be a good thing: everyone meets their match in research, sooner or later. Likewise, if you've never felt compelled to take a project or assignment further than what the homework script asked for, you might want to ask yourself why you want to do graduate studies. If your main reason is that you want to continue your undergraduate experience, then graduate school is definitely not for you - there are no neat, definitely do-able assignments, everything is open-ended to an extent, and "low-hanging fruit" is likely to be few and far between. You also have to work working days like the rest of the population, and may spend more than the 9 till 5 period doing it. And you have to be organized and disciplined about your work. Lastly, if your primary motivation for wanting to come to UBC is outdoor recreation, please consider a different way of moving here. Someone out there is paying their taxes to support our research, and hence to pay your way in grad school.
For work in my group, strong mathematics and physics skills are essential. You should have fluency in calculus, linear algebra, and be very familiar with differential equations, 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 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 an absolute must.
- 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., accepted
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