Attributing weather patterns to Davao River extreme rainfall from Reanalysis and GCM

Acierto, Ralph Allen; Ushiyama, Tomoki; Koike, Toshio

doi:https://doi.org/10.5194/piahs-386-209-2024

Articles | Volume 386

https://doi.org/10.5194/piahs-386-209-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/piahs-386-209-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 386

Post-conference publication

|

19 Apr 2024

Post-conference publication |

| 19 Apr 2024

Attributing weather patterns to Davao River extreme rainfall from Reanalysis and GCM

Ralph Allen Acierto, Tomoki Ushiyama, and Toshio Koike

Model code and software

xarray (v2024.02.0) S. Hoyer et al. https://doi.org/10.5281/zenodo.598201

pandas-dev/pandas: Pandas (v2.2.1) The pandas development team https://doi.org/10.5281/zenodo.3509134

metpy/MetPy: 0.4 (v0.4.0) Ryan May et al. https://doi.org/10.5281/zenodo.160750

SciTools/cartopy: v0.22.0 (v0.22.0) Phil Elson et al. https://doi.org/10.5281/zenodo.1182735

ProPlot (v0.9.5) Luke L. B. Davis https://doi.org/10.5281/zenodo.3873878

Short summary

The study evaluated how well a GCM can reproduce the past weather patterns linked to the heavy rainfall events in Davao river basin by using raingauge and ERA5 data as reference. Our findings show that MRI-AGCM 3.2S reproduced similar weather patterns in JJA and DJF seasons as compared to ERA5 but due to small differences in configuration lead to biases in the local rainfall. This method can be also applied to other river basins and for evaluating how the future local rainfall will change.