Maike Sonnewald

782 total citations · 1 hit paper
18 papers, 400 citations indexed

About

Maike Sonnewald is a scholar working on Oceanography, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Maike Sonnewald has authored 18 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Oceanography, 13 papers in Global and Planetary Change and 10 papers in Atmospheric Science. Recurrent topics in Maike Sonnewald's work include Climate variability and models (11 papers), Oceanographic and Atmospheric Processes (10 papers) and Meteorological Phenomena and Simulations (5 papers). Maike Sonnewald is often cited by papers focused on Climate variability and models (11 papers), Oceanographic and Atmospheric Processes (10 papers) and Meteorological Phenomena and Simulations (5 papers). Maike Sonnewald collaborates with scholars based in United States, United Kingdom and Germany. Maike Sonnewald's co-authors include Christopher Kadow, Elizabeth A. Barnes, Joanna Staneva, Jan Saynisch‐Wagner, Niklas Boers, Christopher Irrgang, Redouane Lguensat, Carl Wunsch, Patrick Heimbach and Stephanie Dutkiewicz and has published in prestigious journals such as Journal of Climate, Geophysical Research Letters and Science Advances.

In The Last Decade

Maike Sonnewald

18 papers receiving 392 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.

Towards neural Earth system modelling by integrating artificial intelligence in Earth system science

2021 160 citations Christopher Irrgang, Niklas Boers et al. Nature Machine Intelligence profile →

Peers

Maike Sonnewald

GPC

OCEAN

Stefan Liess United States

Christopher Irrgang Germany

Jan Saynisch‐Wagner Germany

G. Kivman Germany

Lesley C. Allison United Kingdom

Carlos Mejía France

Steve C. Hankin United States

Simone Cosoli Italy

Mathieu Hamon France

Stefan Liess United States

Maike Sonnewald

382 ×1.8

GPC

325 ×1.8

107 ×0.7

OCEAN

25 ×0.5

38 ×0.8

Citations per year, relative to Maike Sonnewald Maike Sonnewald (= 1×) peers Stefan Liess

Countries citing papers authored by Maike Sonnewald

Since Specialization

Citations

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

Fields of papers citing papers by Maike Sonnewald

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maike Sonnewald. 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 Maike Sonnewald. The network helps show where Maike Sonnewald may publish in the future.

Co-authorship network of co-authors of Maike Sonnewald

This figure shows the co-authorship network connecting the top 25 collaborators of Maike Sonnewald. A scholar is included among the top collaborators of Maike Sonnewald 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 Maike Sonnewald. Maike Sonnewald 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:

18 of 18 papers shown

Sonnewald, Maike, et al.. (2025). Unveiling 3D ocean biogeochemical provinces in the North Atlantic: A systematic comparison and validation of clustering methods. Ecological Informatics. 91. 103390–103390. 1 indexed citations

Griffies, Stephen M., Michele Buzzicotti, Hussein Aluie, et al.. (2024). A Scale‐Dependent Analysis of the Barotropic Vorticity Budget in a Global Ocean Simulation. Journal of Advances in Modeling Earth Systems. 16(6). 2 indexed citations

Lai, Ching‐Yao, Pedram Hassanzadeh, Aditi Sheshadri, et al.. (2024). Machine Learning for Climate Physics and Simulations. Annual Review of Condensed Matter Physics. 16(1). 343–365. 9 indexed citations

Jones, Daniel C., Maike Sonnewald, Ute Hausmann, et al.. (2023). Unsupervised classification identifies coherent thermohaline structures in the Weddell Gyre region. Ocean science. 19(3). 857–885. 2 indexed citations

Sonnewald, Maike, et al.. (2023). Digital Twins of the Ocean can foster a sustainable blue economy in a protected marine environment. The International Hydrographic Review. 29(1). 26–40. 20 indexed citations

Sonnewald, Maike, et al.. (2023). A Southern Ocean supergyre as a unifying dynamical framework identified by physics-informed machine learning. Communications Earth & Environment. 4(1). 7 indexed citations

Krasting, John P., et al.. (2022). Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning. Communications Earth & Environment. 3(1). 5 indexed citations

Sáenz, Juan A., et al.. (2022). Automated identification of dominant physical processes. Engineering Applications of Artificial Intelligence. 116. 105496–105496. 8 indexed citations

Clare, Mariana, Maike Sonnewald, Redouane Lguensat, Julie Deshayes, & V. Balaji. (2022). Explainable Artificial Intelligence for Bayesian Neural Networks: Toward Trustworthy Predictions of Ocean Dynamics. Journal of Advances in Modeling Earth Systems. 14(11). 26 indexed citations

10.

Sonnewald, Maike & Redouane Lguensat. (2021). Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning. Journal of Advances in Modeling Earth Systems. 13(8). 22 indexed citations

11.

Irrgang, Christopher, Niklas Boers, Maike Sonnewald, et al.. (2021). Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nature Machine Intelligence. 3(8). 667–674. 160 indexed citations breakdown →

12.

Sonnewald, Maike, Stephanie Dutkiewicz, Christopher Hill, & Gaël Forget. (2020). Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces. Science Advances. 6(22). eaay4740–eaay4740. 48 indexed citations

13.

Sonnewald, Maike, Carl Wunsch, & Patrick Heimbach. (2019). Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions. Earth and Space Science. 6(5). 784–794. 38 indexed citations

14.

Bras, Isabela Le, Maike Sonnewald, & John M. Toole. (2019). A Barotropic Vorticity Budget for the Subtropical North Atlantic Based on Observations. Journal of Physical Oceanography. 49(11). 2781–2797. 8 indexed citations

15.

Sonnewald, Maike, Carl Wunsch, & Patrick Heimbach. (2018). Linear Predictability: A Sea Surface Height Case Study. Journal of Climate. 31(7). 2599–2611. 9 indexed citations

16.

Bulczak, Anna, Sheldon Bacon, Alberto C. Naveira Garabato, et al.. (2014). Seasonal variability of sea surface height in the coastal waters and deep basins of the Nordic Seas. Geophysical Research Letters. 42(1). 113–120. 20 indexed citations

17.

Sonnewald, Maike, Joël Hirschi, & Robert Marsh. (2013). Oceanic dominance of interannual subtropical North Atlantic heat content variability. 4 indexed citations

18.

Sonnewald, Maike, Joël Hirschi, Robert Marsh, Elaine L. McDonagh, & Brian King. (2013). Atlantic meridional ocean heat transport at 26° N: impact on subtropical ocean heat content variability. Ocean science. 9(6). 1057–1069. 11 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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