Pinyin: JIALE LOU Simplified Chinese: 佳乐 娄 Traditional Chinese：佳樂 婁
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Jiale Lou
Jiale Lou
/dʒɑ'lə/ /loʊ/
Associate Research Scientist at University of Wyoming
I joined the University of Wyoming as an Associate Research Scientist in March 2026. My current research focuses on understanding the mechanisms and predictability of fire weather extremes using E3SM perturbed physics ensembles (PPE). By analyzing large ensembles of climate simulations with systematically varied model parameters, I aim to identify the physical processes and sources of uncertainty that control the occurrence and intensity of fire-conducive weather conditions. This work contributes to improving our understanding of climate-driven fire risk and its predictability across seasonal to decadal timescales.
Prior to joining the University of Wyoming, I worked at Princeton University and NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) beginning in April 2023. There, I served as an Associate Research Scholar, working closely with Dr. Andrew Wittenberg on understanding ENSO dynamics and sources of model biases. During my time at Princeton and GFDL, I also investigated the meterological drivers of fire activity in the western United States. This work examined the links between large-scale climate variability and fire-conducive atmospheric conditions. The project sparked my broader interest in understanding the causes and consequences of high-impact climate extremes, including wildfires, heatwaves, and droughts, and in exploring their predictability on seasonal to interannual timescales.
Before joining Princeton, I worked at the University of Colorado Boulder and NOAA’s Physical Sciences Laboratory (PSL), where I conducted research on multi-year ENSO prediction and predictability. My work focused on understanding the sources of predictability in the tropical Pacific and their implications for climate forecasts.
I received my Ph.D. in Marine Science from the University of Tasmania (UTAS), Australia, where I was supervised by Dr. Neil Holbrook and Dr. Terence O’Kane. My doctoral research examined the dynamics and predictability of South Pacific climate variability, particularly the interactions between ocean circulation and large-scale atmospheric variability.
Highlights of recent work
Vapor pressure deficit (VPD) Teleconnection
The observation-based analog apporach is applied. The analogous oceanic and atmospheric conditions (i.e., conditions where the analogs are drawn from) are also explored by showing the signal-to-noise ratio maps above.
Vapor pressure deficit (VPD) forecasts
A weighted model-analog technique can achieve VPD forecast skill comparable to that of complex dynamical models. This highlights the potential of data-driven approaches to deliver cost-effective seasonal forecasts—as long as we have high-quality training libraries and properly designed cost functions.
Model-analog ENSO hindcasts
By employing a simple pattern recognition tool known as model-analog techinque, we demonstrate ENSO forecast skills comparable to those of the state-of-the-art SEA5-20C hindcasts.
Big picture for South Pacific climate variability
A dynamical framework is proposed for understanding large-scale South Pacific climate variability, where the atmosphere interacts with the South Pacific ocean on multiple time scales.
Atmospheric drivers of South Pacific Ocean variability
We demonstrate that the atmospheric Pacific-South American patterns 1 and 2 (PSA1 and PSA2) serve as stochastic drivers for the South Pacific Ocean variability associated with the South Pacific Decadal Oscillation (SPDO) and South Pacific quadrupole pattern.
Temporal relationships between ENSO, and Pacific decadal variability
We investigate the South Pacific Decadal Oscillation (SPDO), defined as the leading sea surface temperature mode in the South Pacific, and explore its spatiotemporal relationships with ENSO and Pacific decadal variability.
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