Alex J. Cannon

10.7k total citations · 2 hit papers
152 papers, 7.5k citations indexed

About

Alex J. Cannon is a scholar working on Global and Planetary Change, Atmospheric Science and Water Science and Technology. According to data from OpenAlex, Alex J. Cannon has authored 152 papers receiving a total of 7.5k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Global and Planetary Change, 82 papers in Atmospheric Science and 37 papers in Water Science and Technology. Recurrent topics in Alex J. Cannon's work include Climate variability and models (101 papers), Meteorological Phenomena and Simulations (48 papers) and Hydrology and Watershed Management Studies (37 papers). Alex J. Cannon is often cited by papers focused on Climate variability and models (101 papers), Meteorological Phenomena and Simulations (48 papers) and Hydrology and Watershed Management Studies (37 papers). Alex J. Cannon collaborates with scholars based in Canada, United States and China. Alex J. Cannon's co-authors include Trevor Q. Murdock, S. R. Sobie, Paul H. Whitfield, William W. Hsieh, Francis W. Zwiers, A. T. Werner, Aranildo R. Lima, Kabir Rasouli, Xuebin Zhang and Megan C. Kirchmeier‐Young and has published in prestigious journals such as Nature Communications, The Science of The Total Environment and Scientific Reports.

In The Last Decade

Alex J. Cannon

145 papers receiving 7.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?

2015 1.1k citations Alex J. Cannon, S. R. Sobie et al. Journal of Climate profile →
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

2017 428 citations Alex J. Cannon profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Alex J. Cannon Canada	42	5.0k	3.2k	2.4k	1.4k	556	152	7.5k
Hossein Tabari Belgium	51	6.7k 1.3×	2.0k 0.6×	2.9k 1.2×	2.0k 1.4×	873 1.6×	117	8.6k
Harald Kunstmann Germany	49	4.8k 1.0×	3.5k 1.1×	2.4k 1.0×	1.4k 1.0×	674 1.2×	296	7.2k
Qi Feng China	51	4.2k 0.8×	2.4k 0.7×	2.9k 1.2×	2.5k 1.8×	421 0.8×	406	9.7k
Hamid Moradkhani United States	64	7.2k 1.5×	3.9k 1.2×	5.6k 2.4×	3.4k 2.5×	612 1.1×	219	11.4k
Richard W. Katz United States	46	7.1k 1.4×	4.1k 1.3×	1.5k 0.6×	874 0.6×	946 1.7×	101	9.7k
Steven M. Quiring United States	41	3.5k 0.7×	2.1k 0.6×	901 0.4×	1.5k 1.1×	641 1.2×	133	6.0k
Mukand S. Babel Thailand	50	3.6k 0.7×	1.3k 0.4×	3.2k 1.3×	1.6k 1.1×	798 1.4×	205	6.8k
Quanxi Shao Australia	50	4.5k 0.9×	1.7k 0.5×	4.0k 1.7×	1.3k 1.0×	354 0.6×	179	6.9k
Bellie Sivakumar United States	52	5.0k 1.0×	1.5k 0.5×	3.5k 1.5×	2.7k 1.9×	387 0.7×	243	8.6k
David R. Legates United States	34	6.2k 1.2×	4.0k 1.2×	3.2k 1.4×	2.8k 2.0×	554 1.0×	77	11.1k

Countries citing papers authored by Alex J. Cannon

Since Specialization

Citations

This map shows the geographic impact of Alex J. Cannon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alex J. Cannon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex J. Cannon more than expected).

Fields of papers citing papers by Alex J. Cannon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alex J. Cannon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alex J. Cannon. The network helps show where Alex J. Cannon may publish in the future.

Co-authorship network of co-authors of Alex J. Cannon

This figure shows the co-authorship network connecting the top 25 collaborators of Alex J. Cannon. A scholar is included among the top collaborators of Alex J. Cannon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alex J. Cannon. Alex J. Cannon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Cannon, Alex J.. (2025). Twelve months at 1.5 °C signals earlier than expected breach of Paris Agreement threshold. Nature Climate Change. 15(3). 266–269. 13 indexed citations

Bohrer, Gil, Oliver Sonnentag, Bo Qu, et al.. (2025). Shrub Expansion Can Counteract Carbon Losses From Warming Tundra. Journal of Geophysical Research Biogeosciences. 130(8).

Menapace, Andrea, Konstantinos Kaffas, Alice Crespi, et al.. (2025). Review of bias correction methods for climate model outputs in hydrology. Journal of Hydrology. 660. 133213–133213. 1 indexed citations

Qu, Bo, Alexandre Roy, Joe R. Melton, et al.. (2023). A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). Environmental Research Letters. 18(8). 85002–85002. 3 indexed citations

Cannon, Alex J., et al.. (2023). Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales. 2(4). 9 indexed citations

Curasi, Salvatore R., Joe R. Melton, Elyn Humphreys, et al.. (2022). Evaluating the performance of the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) tailored to the pan-Canadian domain. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Meyer, Gesa, Elyn Humphreys, Joe R. Melton, Alex J. Cannon, & Peter M. Lafleur. (2021). 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. 18(11). 3263–3283. 11 indexed citations

Asong, Zilefac Elvis, Mohamed Elshamy, H. S. Wheater, et al.. (2020). High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin. Earth system science data. 12(1). 629–645. 27 indexed citations

Werner, A. T., Markus Schnorbus, Rajesh R. Shrestha, et al.. (2019). A long-term, temporally consistent, gridded daily meteorological dataset for northwestern North America. Scientific Data. 6(1). 180299–180299. 63 indexed citations

10.

Jeong, Dae Il, Alex J. Cannon, & Xuebin 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(4). 857–872. 39 indexed citations

11.

Innocenti, Silvia, Alain Mailhot, Anne Frigon, Alex J. Cannon, & Martin Leduc. (2019). Observed and Simulated Precipitation over Northeastern North America: How Do Daily and Subdaily Extremes Scale in Space and Time?. Journal of Climate. 32(24). 8563–8582. 14 indexed citations

12.

Cannon, Alex J. & Silvia 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(2). 421–440. 49 indexed citations

13.

Hsieh, William W., et al.. (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. 34 indexed citations

14.

Wang, Huimin, Jie Chen, Alex J. Cannon, Chong‐Yu Xu, & Hua Chen. (2018). Transferability of climate simulation uncertainty to hydrological climate change impacts. Biogeosciences (European Geosciences Union). 1 indexed citations

15.

Asong, Zilefac Elvis, H. S. Wheater, John W. Pomeroy, et al.. (2018). WFDEI-GEM-CaPA: A 38-year High-Resolution Meteorological Forcing Data Set for Land Surface Modeling in North America. Biogeosciences (European Geosciences Union). 3 indexed citations

16.

Wang, Huimin, Jie Chen, Alex J. Cannon, Chong‐Yu Xu, & Hua Chen. (2018). Transferability of climate simulation uncertainty to hydrological impacts. Hydrology and earth system sciences. 22(7). 3739–3759. 25 indexed citations

17.

Cannon, Alex J., S. R. Sobie, & Trevor Q. Murdock. (2015). Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?. Journal of Climate. 28(17). 6938–6959. 1050 indexed citations breakdown →

18.

Cannon, Alex 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(1). 217–229. 13 indexed citations

19.

Cannon, Alex J., D. Neilsen, & Bill 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. 6 indexed citations

20.

Cannon, Alex J.. (2011). Köppen versus the computer: an objective comparison between the Köppen-Geiger climate classification and a multivariate regression tree. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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