Alex Cannon

Adjunct Professor

Research Scientist - Environment and Climate Change Canada

Victoria, BC
(250) 363-8006

My primary position is as a Research Scientist with the Climate Research Division of Environment and Climate Change Canada. I am a part of the Climate Data and Analysis Section (CDAS) and am located in Victoria, BC at the Canadian Centre for Climate Modelling and Analysis (CCCma). I continue to be affiliated with Prof. William Hsieh's Climate Prediction Group at UBC.

My research collaborations with UBC deal mainly with the development and application of machine learning and statistical models to climate and weather analysis and prediction tasks, including:

  • estimation of hydroclimatological extremes; climate downscaling algorithms; climate model post-processing and bias correction; synoptic map-pattern classification and weather typing; assessing predictive uncertainty; and climate impacts on environmental systems

I'm one of the Editors-In-Chief of Atmosphere-Ocean and am on the editorial advisory/editorial boards of Stochastic Environmental Research and Risk Assessment and Computers & Geosciences. I'm a past member of the AMS Committee on Artificial Intelligence Applications to Environmental Science.

Google Scholar
ResearchGate profile

R packages: - Quantile regression neural network - Multivariate climate model bias correction - Gridded climate downscaling - Monotone multi-layer perceptron - Conditional density estimation network (CDEN) - Generalized extreme value CDEN

94. Snauffer, A., W.W. Hsieh, and A.J. Cannon, Machine learning estimates of snow water equivalent using gridded products, snow modeling and land covariates. Water Resources Research.
92. Asong, Z.E., H.W. Wheater, J.W. Pomeroy, A. Pietroniro, M. Elshamy, D.G. Princz, and A.J. Cannon, WFDEI-GEM-CaPA: A 38-year High-Resolution Meteorological Forcing Data Set for Land Surface Modeling in North America. Earth System Science Data Discussions.
91. Galmarini, S., A.J. Cannon, A. Ceglar, O. Christensen, [...], and M. Zampieri, Adjusting climate model bias for agricultural impact assessment: how to cut the mustard? Climate Services.
90. Meyer, J., I. Kohn, K. Stahl, K. Hakala, J. Seibert, and A.J. Cannon, Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments. Hydrology and Earth System Science Discussions. doi:10.5194/hess-2018-317
89. Qian, B., X. Zhang, W. Smith, B. Grant, Q. Jing, A.J. Cannon, D. Neilsen, B. McConkey, G. Li, B. Bonsal, H. Wan, and L. Xue. Climate impacts on Canadian yields of spring wheat, canola and maize for global warming levels of 1.5, 2.0 and 2.5°C.
In press / 2018:
88. Kirchmeier-Young, M.C., N.P. Gillett, F.W. Zwiers, A.J. Cannon, and F.S. Anslow, in press. Attribution of the influence of human-induced climate change on an extreme fire season. Earth's Future. doi:10.1029/2018EF001050
87. Werner, A.T., R.R. Shrestha, M.S. Schnorbus, A.J. Cannon, F.W. Zwiers, G. Dayon, and F. Anslow, in press. A long-term, temporally consistent, gridded daily meteorological dataset for northwest North America. Scientific Data.

86. Tam, B., K. Szeto, B. Bonsal, G. Flato, A.J. Cannon, and R. Rong, in press. CMIP5 projections of droughts in Canada based on the Standardized Precipitation Evapotranspiration Index. Canadian Water Resources Journal.
85. Mahony, C.R. and A.J. Cannon, 2018. Wetter summers can intensify departures from natural variability in a warming climate. Nature Communications​, 9:783. doi:10.1038/s41467-018-03132-z
84. Cannon, A.J., 2018. Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, 50(1-2):31-49. doi:10.1007/s00382-017-3580-6
Cannon, A.J., 2018. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes. Stochastic Environmental Research and Risk Assessment, 32(11):3207-3225. doi:10.1007/s00477-018-1573-6
82. Ouali, D. and A.J. Cannon, 2018. Estimation of rainfall Intensity-Duration-Frequency curves at ungauged locations using quantile regression methods. Stochastic Environmental Research and Risk Assessment, 32(10):2821-2836. doi:10.1007/s00477-018-1564-7
81. Neilsen, D., M. Bakker, T. Van der Gulik, S. Smith, A.J. Cannon, I. Losso, A. Warwick Sears, 2018. Landscape based agricultural water demand modeling - a tool for water management decision making in British Columbia, Canada. Frontiers in Environmental Science, 6:74. doi:10.3389/fenvs.2018.00074
80. Wang, H-., J. Chen, A.J. Cannon, Xu, C-., and H. Chen, 2018. Transferability of climate simulation uncertainty to hydrological climate change impacts. Hydrology and Earth System Sciences, 22:3739-3759. doi:10.5194/hess-22-3739-2018
79. Snauffer, A., W.W. Hsieh, A.J. Cannon, and M.A. Schnorbus, 2018. Improving gridded snow water equivalent products in British Columbia, Canada: multi-source data fusion by neural network models. The Cryosphere, 12(3):891-905. doi:10.5194/tc-12-891-2018
78. Hiebert, J., A.J. Cannon, T. Murdock, S. Sobie, and A. Werner, 2018. ClimDown: Climate Downscaling in R. The Journal of Open Source Software, 3(22):360. doi:10.21105/joss.00360
77. Li, G., X. Zhang, A.J. Cannon, T.Q. Murdock, S. Sobie, F.W. Zwiers, K. Anderson, and B. Qian, 2018. Indices of Canada's future climate for general and agricultural adaptation applications. Climatic Change, 148(1-2):249-263. doi:10.1007/s10584-018-2199-x
76. Stiff, H. W., K. D. Hyatt, M. M. Stockwell, and A. J. Cannon. 2018. Downscaled GCM Trends in Projected Air and Water Temperature to 2100 Due To Climate Variation in Six Sockeye Watersheds. Can. Tech. Rep. Fish. Aquat. Sci. 3259: vi + 83 p.

75. Shrestha, R.R., A.J. Cannon, M.A. Schnorbus, and F.W. Zwiers, 2017. Projecting future nonstationary extreme streamflow for the Fraser River, Canada. Climatic Change, 145(3-4):289-303. doi:10.1007/s10584-017-2098-6
74. Kirchmeier-Young, M.C., F.W. Zwiers, N.P. Gillett, and A.J. Cannon, 2017. Attributing extreme fire risk in western Canada to human emissions. Climatic Change, 144(2):365-379. doi:10.1007/s10584-017-2030-0
73. Lima, A.R., W.W. Hsieh, and A.J. Cannon, 2017.  Variable complexity online sequential extreme learning machine, with application to streamflow prediction. Journal of Hydrology, 555:983-994. doi:10.1016/j.jhydrol.2017.10.037

72. Zhang, X., F.W. Zwiers, G. Li, H. Wan, and A.J. Cannon, 2017. Complexity in estimating past and future extreme short-duration rainfall. Nature Geoscience, 10:255-259. doi:10.1038/NGEO2911
71. Mahony, C., A.J. Cannon, T. Wang, and S. Aitken, 2017. A closer look at novel climates: new method and insights at continental to landscape scales. Global Change Biology, 23:3934-3955. doi:10.1111/gcb.13645
70. Eum, H.I., A.J. Cannon, and T.Q. Murdock, 2017. Intercomparison of multiple statistical downscaling methods: Application of multi-criteria decision making to a model selection procedure. Stochastic Environmental Research and Risk Assessment, 31(3):683–703. doi:10.1007/s00477-016-1312-9
69. Eum, H.I. and A.J. Cannon, 2017. Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble. International Journal of Climatology, 37(8):3381-3397. doi:10.1002/joc.4924
68. Peng, H., A.R. Lima, A. Teakles, J. Jin, A.J. Cannon, and W.W. Hsieh, 2017. Forecasting hourly air quality concentration in Canada using updatable machine learning methods. Air Quality, Atmosphere and Health, 10(2):195-211. doi:10.1007/s11869-016-0414-3
67. Neilsen, D., S. Smith, G. Bourgeois, B. Qian, A.J. Cannon, G. Neilsen, and I. Losso, 2017. Modelling changing suitability for tree fruits in complex terrain. Acta Horticulturae (ISHS)
1160:207-214. doi:10.17660/ActaHortic.2017.1160.30

66. Cannon, A.J., 2016. Multivariate bias correction of climate model output: matching marginal distributions and inter-variable dependence structure. Journal of Climate, 29(19):7045-7064. doi:10.1175/JCLI-D-15-0679.1
65. Snauffer, A., W.W. Hsieh, and, A.J. Cannon, 2016. Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. Journal of Hydrology, 541(Part B):714-726. doi:10.1016/j.jhydrol.2016.07.027
64. Werner, A.T. and A.J. Cannon, 2016. Hydrologic extremes – An intercomparison of multiple gridded statistical downscaling methods. Hydrology and Earth System Sciences, 20: 1483-1508. doi:10.5194/hess-20-1483-2016
63. Lima, A.R., A.J. Cannon, and W.W. Hsieh, 2016. Forecasting daily streamflow using online sequential extreme learning machines. Journal of Hydrology, 537: 431-443. doi:10.1016/j.jhydrol.2016.03.017
62. Johnson, M.D., W.W. Hsieh, A.J. Cannon, A. Davidson, F. Bedard, 2016. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods. Agricultural and Forest Meteorology, 218-219: 74-84. doi:10.1016/j.agrformet.2015.11.003
61. Cannon, A.J., 2015. Revisiting the nonlinear relationship between ENSO and winter extreme station precipitation in North America. International Journal of Climatology, 35:4001-4014. doi: 10.1002/joc.4263
60. Radić, V., A.J. Cannon, B. Menounos, and C. Gi, 2015. Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models. Journal of Geophysical Research: Atmospheres, 120(18):9279-9302. doi:10.1002/2015JD023279
59. Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015. Bias correction of simulated precipitation by quantile mapping: how well do methods preserve relative changes in quantiles and extremes? Journal of Climate, 28(17):6938-6959. doi:10.1175/JCLI-D-14-00754.1
58. Lima, A.R., A.J. Cannon, and W.W. Hsieh, 2015. Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation. Environmental Modelling & Software, 73: 175-188. doi:10.1016/j.envsoft.2015.08.002
57. Bennett, K.E., A.J. Cannon, and L. Hinzmann, 2015. Historical trends and extremes in boreal Alaska river basins. Journal of Hydrology, 527: 590-607. doi:10.1016/j.jhydrol.2015.04.065
56. Shrestha, R.R., M.A. Schnorbus, and A.J. Cannon, 2015. A dynamical climate model-driven hydrologic prediction system for the Fraser River, Canada. Journal of Hydrometeorology, 16(3): 1273-1292. doi:10.1175/JHM-D-14-0167.1
55. Cannon, A.J., 2015. Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. Journal of Climate, 28(3): 1260-1267. doi:10.1175/JCLI-D-14-00636.1
54. Cannon, A.J., 2015. An intercomparison of regional and at-site rainfall extreme value analyses in southern British Columbia, Canada. Canadian Journal of Civil Engineering, 42(2): 107-119. doi:10.1139/cjce-2014-0361
53. Matsumura, K., C.F. Gaitan, K. Sugimoto, A.J. Cannon, and W.W. Hsieh, 2015. Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science (Cambridge), 153(3): 399-410. doi:10.1017/S0021859614000392
52. Neilsen, D., S. Smith, T. Van Der Gulik, B. Taylor, A.J. Cannon, 2015. Modeling regional water demand for current and future climate in the Okanagan basin, British Columbia, Canada. Acta Horticulturae (ISHS), 1068: 211-218. doi:10.17660/ActaHortic.2015.1068.26
51. Farajzadeh, M., R. Oji, A.J. Cannon, Y. Ghavidel, and A.R. Massah, 2015. An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran. Theoretical and Applied Climatology, 120(1-2): 377-390. doi:10.1007/s00704-014-1157-4
50. Schnorbus, M.A. and A.J. Cannon, 2014. Statistical emulation of streamflow projections from a distributed hydrological model: application to CMIP3 and CMIP5 climate projections for British Columbia, Canada. Water Resources Research, 50(11):8907-8926. doi:10.1002/2014WR015279
49. Gaitan, C.F., W.W. Hsieh, and A.J. Cannon, 2014. Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada. Climate Dynamics, 43(12):3201-3217.  doi:10.1007/s00382-014-2098-4
48. Gaitan, C.F., W.W. Hsieh, A.J. Cannon, and P. Gachon, 2014. Validation of linear and nonlinear downscaling methods in terms of weather and climate indices: Surface temperature in Southern Ontario and Quebec. Atmosphere-Ocean, 52(3): 211-221. doi:10.1080/07055900.2013.857639
47. Bürger, G., S.R. Sobie, A.J. Cannon, A.T. Werner, and T.Q. Murdock, 2013. Downscaling extremes - an intercomparison of multiple methods for future climate. Journal of Climate, 26: 3429-3449. doi:10.1175/JCLI-D-12-00249.1
46. Gaitan, C.F. and A.J. Cannon, 2013. Validation of historical and future statistically downscaled pseudo-observed surface wind speeds in terms of annual climate indices and daily variability. Renewable Energy, 51: 489-496. doi:10.1016/j.renene.2012.10.001
45. Lima, A.R., A.J. Cannon, and W.W. Hsieh, 2013. Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy. Computers & Geosciences, 50: 136-144. doi:10.1016/j.cageo.2012.06.023
44. Neilsen, D., G. Neilsen, S. Smith, and I. Losso, B. Taylor, A.J. Cannon, T. Van der Gulik, 2013. Assessing risks from climate change and variability in perennial horticultural crops. Acta Horticulturae (ISHS), 984: 87-100.
43. Wu, M.R., B.J. Snyder, R. Mo., A.J. Cannon, and P.I. Joe, 2013. Classification and conceptual models for heavy snowfall events over East Vancouver Island, British Columbia, Canada. Weather and Forecasting, 28(5): 1219-1240. doi: 10.1175/WAF-D-12-00100.1
42. Bürger, G., T.Q. Murdock, A.T. Werner, S.R. Sobie, and A.J. Cannon, 2012. Downscaling extremes - an intercomparison of multiple statistical methods for present climate. Journal of Climate, 25:4366-4388. doi:10.1175/JCLI-D-11-00408.1
41. Cannon, A.J., 2012. Regression-guided clustering: a semisupervised method for circulation-to-environment synoptic classification. Journal of Applied Meteorology and Climatology, 51(2): 185-190. doi:10.1175/JAMC-D-11-0155.1
40. Cannon, A.J., 2012. Semi-supervised multivariate regression trees: putting the "circulation" back into a "circulation-to-environment" synoptic classifier. International Journal of Climatology, 32:2251-2254. doi:10.1002/joc.2417 
39. Cannon, A.J., 2012. Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation & Evaluation (CaDENCE) in R. Computers & Geosciences, 41:126-135. doi:10.1016/j.cageo.2011.08.023
38. Cannon, A.J., 2012. Köppen versus the computer: comparing Köppen-Geiger and multivariate regression tree climate classifications in terms of climate homogeneity. Hydrology and Earth System Sciences, 16: 217-229. doi:10.5194/hess-16-217-2012
37. Cannon, A.J., D. Neilsen, and W.G. Taylor, 2012. Lapse rate adjustments of gridded surface temperature normals in an area of complex terrain: atmospheric reanalysis versus statistical up-sampling. Atmosphere-Ocean, 50(1): 9-16. doi:10.1080/07055900.2011.649035
36. Cohen, S., S. Sheppard, A. Shaw, D. Flanders, S. Burch, B. Taylor, D. Hutchinson, A.J. Cannon, S. Hamilton, B. Burton, and J. Carmichael, 2012. Downscaling and visioning of mountain snow packs and other climate change implications in North Vancouver, British Columbia. Mitigation and Adaptation Strategies for Global Change, 17(1): 25-49. doi:10.1007/s11027-011-9307-9
35. Pellatt, M.G., S. Goring, K.M. Bodtker, and A.J. Cannon, 2012. Using a down-scaled bioclimate envelope model to determine long-term temporal connectivity of Garry oak (Quercus garryana) habitat in western North America: implications for protected area planning. Environmental Management, 49(4): 802-815. doi:10.1007/s00267-012-9815-8
34. Rasouli, K., W.W. Hsieh, and A.J. Cannon, 2012. Daily streamflow forecasting by machine learning methods with weather and climate inputs. Journal of Hydrology, 414-415: 284-293. doi:10.1016/j.jhydrol.2011.10.039
33. Jenkner, J., W.W. Hsieh, and A.J. Cannon, 2011. Seasonal modulations of the active MJO cycle characterized by nonlinear principal component analysis. Monthly Weather Review, 139(7):2259-2275. doi:10.1175/2010MWR3562.1
32. Cannon, A.J., 2011. Quantile regression neural networks: implementation in R and application to precipitation downscaling. Computers & Geosciences, 37: 1277-1284, doi:10.1016/j.cageo.2010.07.005
31. Cannon, A.J., 2011. GEVcdn: an R package for nonstationary extreme value analysis by generalized extreme value conditional density estimation network. Computers & Geosciences, 37:1532-1533. doi:10.1016/j.cageo.2011.03.005
2010 and prior:
30. Allen, D.M., A.J. Cannon, M.W. Toews, and J. Scibek, 2010. Variability in simulated recharge using different GCMs. Water Resources Research, 46: W00F03, doi:10.1029/2009WR008932
28. Quamme, H.A., A.J. Cannon, D. Neilsen, J.M. Caprio, and W.G. Taylor, 2010. The potential impact of climate change on the occurrence of winter freeze events in six fruit crops grown in the Okanagan Valley. Canadian Journal of Plant Science, 90(1): 85-93.
26. Hao, P., A.J. Cannon, P.H.  Whitfield, and H. Lu, 2009. Pentad average temperature changes of Inner Mongolia during recent 40 years. Journal of Applied Meteorological Science, 20(4): 443-450.
25. Quamme, H.A., D. Neilsen, J.M. Caprio, A.J. Cannon and W.G. Taylor, 2009. The occurrence of winter freezes in fruit crops grown in the Okanagan Valley and the potential impact of climate change. Chapter 19 in Gusta, L., Wisniewski, M. and Tanino, K. (eds.), Plant Cold Hardiness: From the Laboratory to the Field. pp. 190-197, CAB International, Wallingford, Oxon, UK. 
23. Cannon, A.J. and W.W. Hsieh, 2008. Robust nonlinear canonical correlation analysis: Application to seasonal climate forecasting. Nonlinear Processes in Geophysics, 15: 221-232.
22. Stahl, K., R.D. Moore, J.M. Shea, D. Hutchinson, and A.J. Cannon, 2008. Coupled modelling of glacier and streamflow response to future climate scenarios. Water Resources Research, 44: W02422, doi:10.1029/2007WR005956
21. Hsieh, W.W. and A.J. Cannon, 2008. Towards robust nonlinear multivariate analysis by neural network methods. Lecture Notes in Earth Sciences, 12:97-124. doi:10.1007/978-3-540-78938-3_6
19. Scibek, J., D.M. Allen, A.J. Cannon, and P.H. Whitfield, 2007. Groundwater-surface water interaction under scenarios of climate change using a high-resolution transient groundwater model. Journal of Hydrology, 333: 165-181.
18. Song, L., A.J. Cannon, and P.H. Whitfield, 2007. Changes in seasonal patterns of temperature and precipitation in China during 1971-2000. Advances in Atmospheric Science, 24(3): 459-473.
17. Cannon, A.J., 2006. Nonlinear principal predictor analysis: application to the Lorenz system. Journal of Climate, 19(4): 579-589.
16. Wang, J.Y., P.H. Whitfield, and A.J. Cannon, 2006. Influence of Pacific climate patterns on low-flows in British Columbia and Yukon, Canada. Canadian Water Resources Journal, 31(1): 25-40.
15. Hall, A.W., P.H. Whitfield, and A.J. Cannon, 2006. Recent variations in temperature, precipitation, and streamflow in the Rio Grande and Pecos River Basins of New Mexico nd Colorado. Reviews in Fisheries Science, 14(1-2): 51-78.
14. Cannon, A.J., 2005. Defining climatological seasons using radially constrained clustering. Geophysical Research Letters, 32: L14706, doi:10.1029/2005GL023410
13. Whitfield, P.H., A.W. Hall, and A.J. Cannon, 2004. Changes in the seasonal cycle in the circumpolar Arctic, 1976-1995: Temperature and precipitation. Arctic, 57(1): 80-93.
12. Whitfield, P.H., J.Y. Wang, and A.J. Cannon, 2003. Modelling future streamflow extremes - Floods and low flows in Georgia Basin, British Columbia. Canadian Water Resources Journal, 28(4):633-656.
11. Whitfield, P.H., A.J. Cannon, J.Y. Wang, and C.J. Reynolds, 2003. Modelling streamflows in present and future climates - Examples from rainfall/snowmelt streams in coastal British Columbia. Hydrological Science & Technology, 19(1-4): 41-56.
10. Whitfield, P.H., C.J. Reynolds, and A.J. Cannon, 2002. Modelling streamflow in present and future climates - Examples from the Georgia Basin, British Columbia. Canadian Water Resources Journal, 27(4): 427-456.
9. Cannon, A.J., P.H. Whitfield, and E.R. Lord, 2002. Synoptic map-pattern classification using recursive partitioning and principal component analysis. Monthly Weather Review, 130(5): 1187-1206.
8. Cannon, A.J. and P.H. Whitfield, 2002. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. Journal of Hydrology, 259: 136-151.
6. Whitfield, P.H., K. Bodtker, and A.J. Cannon, 2002. Recent variations in seasonality of temperature and precipitation in Canada - 1976-1995. International Journal of Climatology, 22: 1617-1644.
5. Cannon, A.J. and P.H. Whitfield, 2001. Modeling transient pH depressions in coastal streams of British Columbia using neural networks. Journal of the American Water Resources Association, 37(1): 73-89.
4. Whitfield, P.H. and A.J. Cannon, 2000. Recent variations in climate and hydrology in Canada. Canadian Water Resources Journal, 25(1): 19-65.
3. Whitfield, P.H. and A.J. Cannon, 2000. Polar plotting of seasonal hydrologic and climatic data. Northwest Science, 74(1):76-80.
2. Cannon, A.J. and E.R. Lord, 2000. Forecasting summertime surface level ozone concentrations in the lower Fraser Valley of British Columbia: An ensemble neural network approach. Journal of the Air & Waste Management Association, 50: 322-339.