Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1909-2023
https://doi.org/10.5194/hess-27-1909-2023
Research article
 | 
15 May 2023
Research article |  | 15 May 2023

Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US

Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung

Related authors

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024,https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025,https://doi.org/10.5194/hess-29-2551-2025, 2025
Short summary
Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025,https://doi.org/10.5194/hess-29-2393-2025, 2025
Short summary
Towards a robust hydrologic data assimilation system for hurricane-induced river flow forecasting
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025,https://doi.org/10.5194/hess-29-2407-2025, 2025
Short summary
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025,https://doi.org/10.5194/hess-29-2133-2025, 2025
Short summary
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025,https://doi.org/10.5194/hess-29-2023-2025, 2025
Short summary

Cited articles

Agel, L., Barlow, M., Qian, J.-H., Colby, F., Douglas, E., and Eichler, T.: Climatology of daily precipitation and extreme precipitation events in the northeast United States, J. Hydrometeorol., 16, 2537–2557, 2015. a
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019. a
Beven, J.: Abbreviated Tropical Cyclone Report: Subtropical Depression Twenty-Two 8–10 October 2005, Tech. rep., National Hurricane Center, https://web.archive.org/web/20060929185404/http://www.mountwashington.org/weather/f6/2005/10.pdf (last access: 5 January 2022), 2006. a
Blumen, W.: Geostrophic adjustment, Rev. Geophys., 10, 485–528, 1972. a
Bosart, L. F.: New England coastal frontogenesis, Q. J. Roy. Meteorol. Soc., 101, 957–978, 1975. a
Download
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Share