Climate Dynamics and Predictability

I am a postdoctoal researcher at NOAA/GFDL and Princeton University working with Dr. Nat Johnson. Currently, my research interests are the prediction and predictability of extreme weather from a dynamical system perspective. I am also interested in numerical modeling (e.g., GPU dynamical core, a.k.a. making climate models simpler and go faster), artificial intelligence (e.g., Deep Learning) and statistical method. Detailed information can be referred to my “Publications”.

I got my PhD in June 2019 from Colorado State University, where my advisors were Dr. Elizabeth Barnes and Dr. Eric Maloney.

In summer 2022, I will start an Assistant Professorship in the Department of Atmospheric Science, National Taiwan Unviersity (teaching applied math, dynamical system analysis and predictability).

Recent Work

Tropical-extratropical Interaction

The dynamics of tropical-extratropical teleconnection has been well-studied since 1980 through the lens of linear Rossby wave theory. However, it's connection with predictability of extratropical weather and ensemble spread remain uncertain. In our research, we demonstrate the spread of a dynamical system (such as hydrological extreme or extratropical geopotential height) can be explained through linear wave dynamics, which bridges the gap of deterministic forecast and probabilistic forecast link

S2S prediction with Fluctuation Dissipation Theorem (FDT)

The use of fluctuation dissipation theorem can be traced back to Annus mirabilis in 1905. The year that Albert Einstein published 5 legendary paper which changed how we observe the world. Fluctuation Dissipation Theorem is one of them which transfers Newton's Law from a deterministic perspective to a probabilistic perspective. In this study, a linear inverse model (LIM, or FDT) is applied to study how the tropical forcing (i.e., Madden-Julian oscillation) and the stochastic extratropical internal modes trigger the large-scale extratropical teleconnections. link

Atmospheric Rivers in a warmer climate

Atmospheric river is characterized by strong moisture flux, which brings both water resource and disastrous flooding when it makes landfall. As climate warms, it is anticipated that the air can hold more moisture and lead to more extreme precipitation. It is therefore important to understand how the ARs will change in a warmer climate. In this research, we use large ensemble simulations generated by GFDL next generation climate model and identify when will humanity notice its influence on atmospheric rivers. In addtion, we also provide a more computationally efficient approach, where we derive a analytical solution to quantify the uncertainty in the foreced reponse caused by the internal climate variability(manuscript submitted)

Machine Learning and Climate Science

Machine Learning is a powerful tool which enables scientists to deal with non-Gaussian data. In climate research, non-Gaussian data exists almost everywhere including cumulus process or in any kind of nonlinear feedback. However, it has been dubbed as black box since its development. In our study, we demonstrate the potential of leveraging machine learning to the extended prediction and make it "physically more interpretable". link

Recent Publications


Tseng K.-C., and co-authors: The multiseasonal forecast of CONUS tornado activity and the optimal environment for severe weather (in preparation, to be submitted)

Tseng K.-C., and co-authors: When will humanity notice its influences on atmospheric rivers?(submitted to JGR-Atmosphere)

Jia L.,and co-authors: Skillful seasonal prediction of North American summertime hot extremes(accepted, in press)

Bushuk B., and co-authors: Mechanisms of Regional Artic Sea Ice Predictability in Dynamical Seasonal Forecast Systems (accepted, in press)


Chen Y.-L.,and co-authors: Effect of the MJO on East Asian winter rainfall as revealed by a SVD analysis J. Clim., 34(25), 9729--9746 link

Zhang L.,and co-authors: Using large ensembles to elucidate the possible roles of Southern Ocean meridional overturning circulation in the Southern Ocean 36-yr SST trend (J. Clim, in press)

Tseng K.-C., and co-authors: Are multiseasonal forecasts of atmospheric rivers possible? Geophys. Res. Lett 48 e2021GL094000. link (NOAA-GFDL and Princeton CIMES Research highligh)

Bushuk B., and co-authors: Seasonal prediction and predictability of regional Antarctic sea ice J. Clim., 34(15), 6207--6233 link

Zhang G., and co-authors: Seasonal Predictability of Baroclinic Wave Activity npj Clim Atmos Sci 4(50) link

Tseng K.-C., N. C. Johnson., E. D. Maloney, E. A. Barnes, and S. B. Kapnick: Mapping Large-scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal-to-Seasonal prediction J. Clim., 34(11), 4207--4225 link

Tseng K.-C., E. A. Barnes, and E. D. Maloney: The important role of the MJO for extratropical variability in observations and the CMIP5 climate models (submitted to JGR-Atmosphere)