Alex Cannon

Adjunct Professor

Research Scientist - Environment and Climate Change Canada

Victoria, BC
faculty

Google Scholar
ResearchGate profile
ORCID profile

R packages:
https://cran.r-project.org/package=qrnn - Quantile regression neural network
https://cran.r-project.org/package=MBC - Multivariate climate model bias correction
https://cran.r-project.org/package=ClimDown - Gridded climate downscaling
https://cran.r-project.org/package=monmlp - Monotone multi-layer perceptron
https://cran.r-project.org/package=CaDENCE - Conditional density estimation network (CDEN)
https://cran.r-project.org/package=GEVcdn - Generalized extreme value CDEN

Submitted:
115. Jeong, D.I., B. Yu, and A.J. Cannon, Links between atmospheric blocking and North American winter cold spells in two generations of Canadian Earth System Model large ensembles. Climate Dynamics.
114. Snauffer, A.M., W.W. Hsieh, and A.J. Cannon, Machine learning estimates of snow water equivalent using gridded products, snow modeling and land covariates. Atmosphere-Ocean.
113. Meyer, G., E.R. Humphreys, J.R. Melton, A.J. Cannon, and P.M Lafleur, Simulating shrubs and their energy and carbon dioxide fluxes in Canada's Low Arctic with the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC). Biogeosciences.
112. Qian, B., Q. Jing, A.J. Cannon, W. Smith, B. Grant, M. Semenov, Y-P. Xu, and D. Ma, Effectiveness of using representative subsets of global climate models in future crop yield projections. Environmental Research Letters.
111. Singh, H., M.R. Najafi, and A.J. Cannon, Evaluation and joint projection of temperature and precipitation extremes across Canada based on hierarchical Bayesian modelling and large ensembles of regional climate simulations. Weather and Climate Extremes.
 
In press / 2021:
110. Ma, D., Q. Jing, Y-P. Xu, A.J. Cannon, T. Dong, M.A. Semenov, and B. Qian, in press. Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator approach. Climate Research.
109. Jeong, D.I. and A.J. Cannon, 2021. Projected changes to risk of wind-driven rain on buildings in Canada under +0.5°C to +3.5°C global warming above the recent period. Climate Risk Management, 30:100261. doi:10.1016/j.crm.2020.100261
108. Shrestha, R.R., B. Bonsal, J.M. Bonnyman, A.J. Cannon, and M.R. Najafi, 2021. Heterogeneous snowpack response and snow drought occurrence over northwestern North America under 1.0 deg. C to 4.0 deg. C global warming. Climatic Change, 164(40). doi:10.1007/s10584-021-02968-7
107. Singh, H., M.R. Najafi, and A.J. Cannon, 2021. Characterizing non-stationary compound extreme events in a changing climate based on large-ensemble climate simulations. Climate Dynamics, 56:1389-1405. doi:10.1007/s00382-020-05538-2
 
2020:
106. Jeong, D.I, A.J. Cannon, and R.J. Morris, 2020. Projected changes to wind loads coinciding with rainfall for building design in Canada based on an ensemble of Canadian regional climate model simulations. Climatic Change, 162:821-835. doi:10.1007/s10584-020-02745-y
105. Francois, B., M. Vrac, A.J. Cannon, Y. Robin, and D. Allard, 2020. Multivariate bias corrections of climate simulations: Which benefits for which losses? Earth System Dynamics, 11:537-562. doi:10.5194/esd-11-537-2020
104. Qian, B., Q. Jing, W. Smith, B. Grant, A.J. Cannon, X. Zhang, 2020. Quantifying the uncertainty introduced by internal climate variability in projections of Canadian crop production. Environmental Research Letters, 15(7):074032. doi:10.1088/1748-9326/ab88fc
103. Cannon, A.J., 2020. Reductions in daily continental-scale atmospheric circulation biases between generations of Global Climate Models: CMIP5 to CMIP6. Environmental Research Letters, 15(6):064006. doi:10.1088/1748-9326/ab7e4f
102. Su, T., J. Chen, A.J. Cannon, P. Xie, and Q. Guo, 2020. Multisite bias correction of climate model outputs for hydro-meteorological impact studies: an application over a watershed in China. Hydrological Processes, 34: 2575-2598. doi:10.1002/hyp.13750
101. Lu, W., N.K. Newlands, O. Carisse, D.E. Atkinson, and A.J. Cannon, 2020. Disease risk forecasting with Bayesian networks: application to grape powdery mildew (Erysiphe necator) in vineyards. Agronomy, 10(5):622. doi:10.3390/agronomy10050622
100. Asong, Z.E., M.E. Elshamy, D. Princz, H.S. Wheater, J.W. Pomeroy, A. Pietroniro, and A.J. Cannon, 2020. High-Resolution Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis in Mackenzie River Basin. Earth System Science Data, 12:629-645. doi: 10.5194/essd-12-629-2020
99. Jeong, D.I. and A.J. Cannon, 2020. Projected changes to moisture loads for design and management of building exteriors over Canada. Building and Environment, 170:106609. doi:10.1016/j.buildenv.2019.106609
98. Cannon, A.J., C. Piani, and S. Sippel, 2020. Bias-correction of climate model output for impact models. pp. 77-104, Ch. 5, in: Sillmann, J., S. Sippel, S. Russo (Eds.), Climate Extremes and Their Implications for Impact and Risk Assessment. Elsevier, 360 pp. doi:10.1016/B978-0-12-814895-2.00005-7
 
2019:
97. Innocenti, S., A. Mailhot, A. Frigon, A.J. Cannon, and M. Leduc, 2019. Observed and simulated precipitation over northeastern North America: how do daily and sub-daily extremes scale in space and time. Journal of Climate, 32:8563-8582. doi:10.1175/JCLI-D-19-0021.1
96. Innocenti, S., A. Mailhot, M. Leduc, A.J. Cannon, and A. Frigon, 2019. Projected changes in the probability distributions, seasonality, and spatiotemporal scaling of daily and sub-daily extreme precipitation simulated by a 50-member ensemble over northeastern North America. Journal of Geophysical Research - Atmospheres, 124(19):10427-10449. doi:10.1029/2019JD031210
95. Shrestha, R.R., A.J. Cannon, M.S. Schnorbus, and H. Alford, 2019. Climatic controls on future hydrologic changes in a subarctic river basin in Canada. Journal of Hydrometeorology, 20:1757-1778. doi:10.1175/JHM-D-18-0262.1
94. Jeong, D.I., A.J. Cannon, and X. Zhang, 2019. Projected changes to extreme freezing precipitation and design ice loads over North America based on a large ensemble of Canadian regional climate model simulations. Natural Hazards and Earth System Sciences, 19:857-872. doi:10.5194/nhess-2018-395
93. Qian, B., X. Zhang, W. Smith, B. Grant, Q. Jing, A.J. Cannon, D. Neilsen, B. McConkey, G. Li, B. Bonsal, H. Wan, L. Xue, and J. Zhao, 2019. Climate change impacts on Canadian yields of spring wheat, canola and maize for global warming levels of 1.5, 2.0, 2.5 and 3.0°C. Environmental Research Letters, 14(7):074005. doi:10.1088/1748-9326/ab17fb
92. Meyer, J., I. Kohn, K. Stahl, K. Hakala, J. Seibert, and A.J. Cannon, 2019. Effects of univariate and multivariate bias correction on hydrological impact projections in alpine catchments. Hydrology and Earth System Sciences, 23(3):1339-1354. doi:10.5194/hess-23-1339-2019
91. Cannon, A.J. and S. Innocenti, 2019. Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: Implications for future Intensity-Duration-Frequency curves. Natural Hazards and Earth System Sciences, 19:421-440. doi:10.5194/nhess-19-421-2019
90. Galmarini, S., A.J. Cannon, A. Ceglar, O. Christensen, [...], and M. Zampieri, 2019. Adjusting climate model bias for agricultural impact assessment: how to cut the mustard. Climate Services, 13:65-69. doi:10.1016/j.cliser.2019.01.004
89. Kirchmeier-Young, M.C., N.P. Gillett, F.W. Zwiers, A.J. Cannon, and F.S. Anslow, 2019. Attribution of the influence of human-induced climate change on an extreme fire season. Earth's Future, 7(1):2-10. doi:10.1029/2018EF001050
88. Werner, A.T., R.R. Shrestha, M.S. Schnorbus, A.J. Cannon, F.W. Zwiers, G. Dayon, and F. Anslow, 2019. A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Scientific Data, 6:180299. doi:10.1038/sdata.2018.299

87. Tam, B., K. Szeto, B. Bonsal, G. Flato, A.J. Cannon, and R. Rong, 2019. CMIP5 projections of droughts in Canada based on the Standardized Precipitation Evapotranspiration Index. Canadian Water Resources Journal, 44(1):90-107. doi:10.1080/07011784.2018.1537812
86. Farjad, B., A. Gupta, H. Sartipizadeh, and A.J. Cannon, 2019. A novel approach for selecting extreme climate change scenarios for climate change impact studies. Science of the Total Environment, 678:476-485. doi:10.1016/j.scitotenv.2019.04.218

2018:
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
83. 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.

2017:
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

2016:
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
 
2015:
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
 
2014:
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
 
2013:
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
 
2012:
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
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
 
2011:
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.

I'm a Research Scientist in the Climate Data and Analysis Section of Climate Research Division, Environment and Climate Change Canada. I work on the University of Victoria campus in Victoria, BC and am co-located with the Canadian Centre for Climate Modelling and Analysis.

Since moving to Victoria, I no longer co-supervise students at UBC, but do continue to serve on graduate committees and collaborate with UBC researchers. Research collaborations have dealt mainly with the development and application of machine learning and statistical models for 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

More broadly, I'm involved in activities that contribute to understanding of the state, trends, variability, extremes, and future projections of climate at both global and regional scales, with an emphasis on societally-relevant climatic variables.

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