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Peer-Reviewed Publications Eyring, V., W.D. Collins, P. Gentine, E.A. Barnes, M. Barreiro, T. Beucler, M. Bocquet, C.S. Bretherton, H.M. Christensen, K. Dagon, et al. (2024), Pushing the frontiers in climate modelling and analysis with machine learning, Nature Climate Change, 14, 916-928, doi:10.1038/s41558-024-02095-y. PDF Molina, M.J., T.A. O'Brien, G. Anderson, M. Ashfaq, K.E. Bennett, W.D. Collins, K. Dagon, J.M. Restrepo, and P.A. Ullrich (2023), A review of recent and emerging machine learning applications for climate variability and weather phenomena, Artificial Intelligence for the Earth Systems, 2, 220086, doi:10.1175/AIES-D-22-0086.1. Touma, D., J.W. Hurrell, M. Tye, and K. Dagon (2023), The impact of stratospheric aerosol injection on extreme fire weather risk, Earth's Future, 11, e2023EF003626, doi:10.1029/2023EF003626. Molina, M.J., J.H. Richter, A.A. Glanville, K. Dagon, J. Berner, A. Hu, and G.A. Meehl (2023), Subseasonal representation and predictability of North American weather regimes using cluster analysis, Artificial Intelligence for the Earth Systems, 2, e220051, doi:10.1175/AIES-D-22-0051.1. Cheng, Y., K.N. Musselman, S. Swenson, D. Lawrence, J. Hamman, K. Dagon, D. Kennedy, and A.J. Newman (2023), Moving land models towards more actionable science: A novel application of the Community Terrestrial Systems Model across Alaska and the Yukon River Basin, Water Resources Research, 59, e2022WR032204, doi:10.1029/2022WR032204. Dagon, K., J. Truesdale, J.C. Biard, K.E. Kunkel, G.A. Meehl, and M.J. Molina (2022), Machine learning-based detection of weather fronts and associated extreme precipitation in historical and future climates, Journal of Geophysical Research: Atmospheres, 127, e2022JD037038, doi:10.1029/2022JD037038. PDF Tye, M.R., K. Dagon, M.J. Molina, J.H. Richter, D. Visioni, B. Kravitz, and S. Tilmes (2022), Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention, Earth System Dynamics, 13, 1233-1257, doi:10.5194/esd-13-1233-2022. Ali, A.A., Y. Fan, M.D. Corre, M.M. Kotowska, E. Preuss-Hassler, A.N. Cahyo, F.E. Moyano, C. Stiegler, A. Röll, A. Meijide, A. Olchev, A. Ringeler, C. Leuschner, R. Ariani, T. June, S. Tarigan, H. Kreft, D. Hölscher, C. Xu, C.D. Koven, K. Dagon, R.A. Fisher, E. Veldkamp, and A. Knohl (2022), Implementing a new rubber plant functional type in the Community Land Model (CLM5) improves accuracy of carbon and water flux estimation, Land, 11, 183, doi:10.3390/land11020183. Prabhat, K. Kashinath, M. Mudigonda, S. Kim, L. Kapp-Schwoerer, A. Graubner, E. Karaismailoglu, L. von Kleist, T. Kurth, A. Greiner, K. Yang, C. Lewis, J. Chen, A. Lou, S. Chandran, B. Toms, W. Chapman, K. Dagon, C.A. Shields, T. O'Brien, M. Wehner, and W. Collins (2021), ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather, Geoscientific Model Development, 14, 107-124, doi:10.5194/gmd-14-107-2021. Dagon, K., B.M. Sanderson, R.A. Fisher, D.M. Lawrence (2020), A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Advances in Statistical Climatology, Meteorology and Oceanography, 6, 223-244, doi:10.5194/ascmo-6-223-2020. PDF Xu, Y., L. Lin, S. Tilmes, K. Dagon, L. Xia, C. Diao, W. Cheng, Z. Wang, I. Simpson, and L. Burnell (2020), Climate engineering to mitigate the projected 21st-century terrestrial drying of the Americas: a direct comparison of carbon capture and sulfur injection, Earth System Dynamics, 11, 673-695, doi:10.5194/esd-11-673-2020. Cheng, W., D.G. MacMartin, K. Dagon, B. Kravitz, S. Tilmes, J.H. Richter, M.J. Mills, and I.R. Simpson (2019), Soil moisture and other hydrological changes in a stratospheric aerosol geoengineering large ensemble, Journal of Geophysical Research: Atmospheres, 124, 12773-12793, doi:10.1029/2018JD030237. Kravitz, B., D.G. MacMartin, S. Tilmes, J.H. Richter, M.J. Mills, W. Cheng, K. Dagon, A.S. Glanville, J.- F. Lamarque, I.R. Simpson, J.J. Tribbia, and F. Vitt (2019), Comparing surface and stratospheric impacts of geoengineering with different SO2 injection strategies, Journal of Geophysical Research: Atmospheres, 124, 7900-7918, doi:10.1029/2019JD030329. Dagon, K., and D.P. Schrag (2019), Quantifying the effects of solar geoengineering on vegetation, Climatic Change, 153, 235-251, doi:10.1007/s10584-019-02387-9. PDF Dagon, K., and D.P. Schrag (2017), Regional climate variability under model simulations of solar geoengineering, Journal of Geophysical Research: Atmospheres, 122, 12106-12121, doi:10.1002/2017JD027110. PDF Dagon, K., and D.P. Schrag (2016), Exploring the effects of solar radiation management on water cycling in a coupled land-atmosphere model, Journal of Climate, 29, 2635-2650, doi:10.1175/JCLI-D-15-0472.1. PDF Tobias, S.M., K. Dagon, and J.B. Marston (2011), Astrophysical fluid dynamics via direct statistical simulation, The Astrophysical Journal, 727, 127, doi:10.1088/0004-637X/727/2/127. PDF Non Peer-Reviewed Publications Mayer, K.J., K. Dagon, and M.J. Molina (2023), Identifying Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability with Explainable Neural Networks, Subseasonal-to-Seasonal Prediction Project Newsletter, No. 23, PDF. Molina, M.J., T.A. O'Brien, G. Anderson, M. Ashfaq, K.E. Bennett, W. Collins, S. Collis, K. Dagon, S. Klein, J.M. Restrepo, and P.A. Ullrich (2022), Climate Variability and Extremes, in Hickmon, N.L., C. Varadharajan, F.M. Hoffman, S. Collis, and H.M. Wainwright (Eds.), Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop Report, doi:10.2172/1888810. Dagon, K., M.J. Molina, et al. (2021), Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system, DOE EESSD White Paper on AI4ESP, doi:10.2172/1769744. |