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 researcher scholar at Princeton University and GFDL NOAA
I have been with Princeton University and NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) since April 2023. I am currently an Associate Research Scholar working closely with Dr. Andrew Wittenberg on maintaining and developing the CLIVAR ENSO metrics package for diagnosing GFDL model outputs.
Prior to this role, I spent two years at Princeton and GFDL investigating the dynamical drivers of fire activity in the western United States, in collaboration with Dr. Thomas Delworth and Dr. Youngji Joh. This project sparked my broader interest in understanding the causes and consequences of high-impact weather and climate extremes—such as wildfires, heatwaves, and droughts—and 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. I hold a Ph.D. in Marine Science from the University of Tasmania (UTAS), Australia. My doctoral research, supervised by Dr. Neil Holbrook and Dr. Terence O'Kane, focused on the dynamics and predictability of South Pacific Ocean climate variability.
Highlights of recent work
Vapor pressure dificit (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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