Drivers of Subseasonal To Seasonal Precipitation Over the Southwest U.S.: Potential Predictability, Case Studies and Identified Biases in Current Dynamical Models

Prof. Gudrun Magnusdottir

Department of Earth System Science

University of California Irvine

Monday November 20, 2023, 2 PM ET



The talk will focus on issues related to variability, potential predictability and hindcasts of Southwest US precipitation during the water year (cool months), highlighting work in my group at UC Irvine.

I will start by showing results from a study on potential predictability where we run a set of numerical experiments using the Whole Atmosphere Community Climate Model (WACCM).  In addition to prescribing observed sea surface temperature and sea ice concentration (AMIP-type experiments), observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or in the Arctic through nudging of wind and temperature (AMIP-plus experiments). These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the Southwest US, at various time scales.

Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated Southwest US seasonal precipitation. This is also the case at the subseasonal time scale.  When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions at high latitudes may also benefit prediction of Southwest US precipitation. However, subseasonal tropical variability (mostly the MJO), may interfere and weaken ENSO teleconnections with Southwest US precipitation.  This and the strong 2016 El Nino that only resulted in average precipitation, motivated a study of how non-ENSO Pacific teleconnections regulate precipitation in the region, which showed that in the CESM2 large ensemble experiment they are too ENSO-like.

I will then briefly describe current research that is focused on examining biases in the dynamical S2S models, focusing on the 3-6 week timescale, and experimenting with the AI weather forecast models, extending predictions into the subseasonal range.  Some preliminary results will be presented.



Professor Gudrun Magnusdottir got her BS in Physics from the University of Iceland, her MS in atmospheric science from Colorado State University following which she worked as a meteorologist in the Icelandic Meteorological Office.  She returned to Colorado State University for a PhD.  She was a postdoctoral researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge.  She joined the University of California Irvine in 1995, the year in which the program in Geoscience became the Department of Earth System Science (ESS).  She is a professor and past chair of ESS. She is a Fellow of the American Meteorological Society.



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Nov 20 2023


John Xun Yang