Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3539-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Markov chain method for weighting climate model ensembles
Max Kulinich
CORRESPONDING AUTHOR
School of Mathematics and Statistics, UNSW Sydney, Australia
Yanan Fan
School of Mathematics and Statistics, UNSW Sydney, Australia
Spiridon Penev
School of Mathematics and Statistics, UNSW Sydney, Australia
Jason P. Evans
Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Australia
Roman Olson
Irreversible Climate Change Research Center, Yonsei University, South Korea
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Hydrol. Earth Syst. Sci., 29, 4491–4513, https://doi.org/10.5194/hess-29-4491-2025, https://doi.org/10.5194/hess-29-4491-2025, 2025
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Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Yinglin Mu, Jason Evans, Andrea Taschetto, and Chiara Holgate
EGUsphere, https://doi.org/10.5194/egusphere-2025-2833, https://doi.org/10.5194/egusphere-2025-2833, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Lagrangian approaches have been increasingly employed due to their suitability for extreme events and climatological studies in finding moisture sources of precipitation. However, as these approaches track independent air parcels carrying moisture—rather than simulate processes based on governing physical equations—they rely on several underlying assumptions. This study tests these assumptions and refines the approaches to enhance their broader applicability.
Rajesh Kumar Sahu, Hamza Kunhu Bangalath, Suleiman Mostamandi, Jason Evans, Paul A. Kucera, and Hylke E. Beck
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This study evaluates 36 microphysics (MP) and boundary layer (BL) scheme combinations in the Weather Research and Forecasting (WRF) model for extreme rainfall over Saudi Arabia. Using Kling-Gupta Efficiency (KGE), results show YSU (BL1) and Thompson (MP8) perform best, while Morrison-MYNN (MP10_BL6) ranks lowest. The mean temporal KGE is 0.37, and the spatial KGE is 0.26, highlighting spatial prediction challenges. Findings aid model evaluation and forecasting in arid regions.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
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Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
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In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
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Short summary
We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
We present a novel stochastic approach based on Markov chains to estimate climate model weights...