Mary Jo Nath

5.2k total citations
26 papers, 3.2k citations indexed

About

Mary Jo Nath is a scholar working on Global and Planetary Change, Oceanography and Atmospheric Science. According to data from OpenAlex, Mary Jo Nath has authored 26 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Global and Planetary Change, 18 papers in Oceanography and 18 papers in Atmospheric Science. Recurrent topics in Mary Jo Nath's work include Climate variability and models (26 papers), Oceanographic and Atmospheric Processes (18 papers) and Meteorological Phenomena and Simulations (12 papers). Mary Jo Nath is often cited by papers focused on Climate variability and models (26 papers), Oceanographic and Atmospheric Processes (18 papers) and Meteorological Phenomena and Simulations (12 papers). Mary Jo Nath collaborates with scholars based in United States. Mary Jo Nath's co-authors include Ngar‐Cheung Lau, S. George Philander, Ants Leetmaa, Keith W. Dixon, John R. Lanzante, R. C. Pacanowski, Hailan Wang, Carolyn E. Whitlock, Aparna Radhakrishnan and Anne M. K. Stoner and has published in prestigious journals such as Journal of Climate, Journal of the Atmospheric Sciences and Monthly Weather Review.

In The Last Decade

Mary Jo Nath

26 papers receiving 3.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mary Jo Nath United States 23 3.1k 2.5k 1.8k 140 108 26 3.2k
Hai Lin Canada 35 3.7k 1.2× 3.6k 1.4× 1.4k 0.8× 57 0.4× 141 1.3× 143 4.0k
Randall M. Dole United States 25 2.7k 0.9× 2.3k 0.9× 566 0.3× 150 1.1× 123 1.1× 39 2.9k
Pang‐Chi Hsu China 30 2.9k 1.0× 2.9k 1.1× 1.3k 0.7× 54 0.4× 85 0.8× 113 3.2k
Kyong‐Hwan Seo South Korea 30 2.2k 0.7× 2.1k 0.8× 820 0.5× 50 0.4× 60 0.6× 86 2.4k
Congwen Zhu China 29 2.3k 0.8× 2.1k 0.8× 850 0.5× 45 0.3× 86 0.8× 106 2.6k
Taiyi Xu United States 13 1.6k 0.5× 1.6k 0.6× 489 0.3× 117 0.8× 59 0.5× 17 1.9k
M. Blackburn United Kingdom 18 2.1k 0.7× 1.9k 0.8× 517 0.3× 104 0.7× 91 0.8× 29 2.3k
Rosie Eade United Kingdom 24 2.6k 0.9× 2.4k 0.9× 984 0.6× 27 0.2× 75 0.7× 40 2.8k
David Fereday United Kingdom 19 2.4k 0.8× 2.2k 0.9× 667 0.4× 47 0.3× 116 1.1× 28 2.7k
Wenjun Zhang China 29 2.7k 0.9× 2.3k 0.9× 1.4k 0.8× 29 0.2× 61 0.6× 110 3.0k

Countries citing papers authored by Mary Jo Nath

Since Specialization
Citations

This map shows the geographic impact of Mary Jo Nath'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 Mary Jo Nath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mary Jo Nath more than expected).

Fields of papers citing papers by Mary Jo Nath

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary Jo Nath. 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 Mary Jo Nath. The network helps show where Mary Jo Nath may publish in the future.

Co-authorship network of co-authors of Mary Jo Nath

This figure shows the co-authorship network connecting the top 25 collaborators of Mary Jo Nath. A scholar is included among the top collaborators of Mary Jo Nath 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 Mary Jo Nath. Mary Jo Nath is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lanzante, John R., et al.. (2021). Evaluation of some distributional downscaling methods as applied to daily precipitation with an eye towards extremes. International Journal of Climatology. 41(5). 3186–3202. 9 indexed citations
2.
Lanzante, John R., et al.. (2018). Evaluation and improvement of tail behaviour in the cumulative distribution function transform downscaling method. International Journal of Climatology. 39(4). 2449–2460. 20 indexed citations
3.
Lanzante, John R., et al.. (2017). Some Pitfalls in Statistical Downscaling of Future Climate. Bulletin of the American Meteorological Society. 99(4). 791–803. 84 indexed citations
4.
Dixon, Keith W., John R. Lanzante, Mary Jo Nath, et al.. (2016). Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?. Climatic Change. 135(3-4). 395–408. 116 indexed citations
5.
Lau, Ngar‐Cheung & Mary Jo Nath. (2014). Model Simulation and Projection of European Heat Waves in Present-Day and Future Climates. Journal of Climate. 27(10). 3713–3730. 133 indexed citations
6.
7.
8.
Lau, Ngar‐Cheung, Ants Leetmaa, & Mary Jo Nath. (2008). Interactions between the Responses of North American Climate to El Niño–La Niña and to the Secular Warming Trend in the Indian–Western Pacific Oceans. Journal of Climate. 21(3). 476–494. 29 indexed citations
9.
Lau, Ngar‐Cheung, Ants Leetmaa, Mary Jo Nath, & Hailan Wang. (2005). Influences of ENSO-Induced Indo–Western Pacific SST Anomalies on Extratropical Atmospheric Variability during the Boreal Summer. Journal of Climate. 18(15). 2922–2942. 99 indexed citations
10.
Lau, Ngar‐Cheung & Mary Jo Nath. (2004). Coupled GCM Simulation of Atmosphere–Ocean Variability Associated with Zonally Asymmetric SST Changes in the Tropical Indian Ocean. Journal of Climate. 17(2). 245–265. 133 indexed citations
11.
Lau, Ngar‐Cheung & Mary Jo Nath. (2003). Atmosphere–Ocean Variations in the Indo-Pacific Sector during ENSO Episodes. Journal of Climate. 16(1). 3–20. 391 indexed citations
12.
Lau, Ngar‐Cheung & Mary Jo Nath. (2001). Impact of ENSO on SST Variability in the North Pacific and North Atlantic: Seasonal Dependence and Role of Extratropical Sea–Air Coupling. Journal of Climate. 14(13). 2846–2866. 96 indexed citations
13.
Lau, Ngar‐Cheung & Mary Jo Nath. (2000). Impact of ENSO on the Variability of the Asian–Australian Monsoons as Simulated in GCM Experiments. Journal of Climate. 13(24). 4287–4309. 430 indexed citations
14.
Broccoli, Anthony J., Ngar‐Cheung Lau, & Mary Jo Nath. (1998). The Cold Ocean–Warm Land Pattern: Model Simulation and Relevance to Climate Change Detection. Journal of Climate. 11(11). 2743–2763. 44 indexed citations
15.
Lau, Ngar‐Cheung & Mary Jo Nath. (1996). The Role of the “Atmospheric Bridge” in Linking Tropical Pacific ENSO Events to Extratropical SST Anomalies. Journal of Climate. 9(9). 2036–2057. 398 indexed citations
16.
Philander, S. George, R. C. Pacanowski, Ngar‐Cheung Lau, & Mary Jo Nath. (1992). Simulation of ENSO with a Global Atmospheric GCM Coupled to a High-Resolution, Tropical Pacific Ocean GCM. Journal of Climate. 5(4). 308–329. 98 indexed citations
17.
Lau, Ngar‐Cheung, S. George Philander, & Mary Jo Nath. (1992). Simulation of ENSO-like Phenomena with a Law-Resolution Coupled GCM of the Global Ocean and Atmosphere. Journal of Climate. 5(4). 284–307. 73 indexed citations
18.
Lau, Ngar‐Cheung & Mary Jo Nath. (1990). A General Circulation Model Study of the Atmospheric Response to Extratropical SST Anomalies Observed in 1950–79. Journal of Climate. 3(9). 965–989. 116 indexed citations
19.
Philander, S. George, Ngar‐Cheung Lau, R. C. Pacanowski, & Mary Jo Nath. (1989). Two different simulations of the Southern Oscillation and El Niño with coupled ocean-atmosphere general circulation models. Philosophical Transactions of the Royal Society of London Series A Mathematical and Physical Sciences. 329(1604). 167–178. 25 indexed citations
20.
Lau, Ngar‐Cheung & Mary Jo Nath. (1987). Frequency Dependence of the Structure and Temporal Development of Wintertime Tropospheric Fluctuations—Comparison of a GCM Simulation with Observations. Monthly Weather Review. 115(1). 251–271. 26 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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