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 dynamics of tropical-extratropical teleconnections and its relationship with extreme weather predictability from a dynamical system perspective. I am also interested in numerical modeling (e.g., GPU dynamical core and idealized model), 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.

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. Similar idea can be applied to atmospheric science research...(manuscript to be submitted)

Atmospheric Rivers in a warmer climate

Atmospheric rivers are characterized by strong moisture flux, which brings high societal costs through the disastrous flooding that often accompanies their occurrence. Since previous research has identified the competing roles of moisture and circulation. It is therefore important to understand the change in both dynamics and thermodynamics over different climate states especially for hydrological extreme. In this research, we use large ensemble simulations of GFDL new generation climate model and identify the dependence of two competing processes on geographical locations and the signals of emergence. (manuscript in preparation)

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 any kind of nonlinear feedback. However, it has been dubbed as black box since its development. In our study, we not only leverage machine learning to the extended prediction but also use it to advance our physical interpretation for a given dataset. link

Recent Publications

2020

Tseng K.-C., and co-authors: The response of atmospheric river to a warmer climate and time of signal emergence (in preparation)

Tseng K.-C., and co-authors: Seasonal Prediction of western North America Atmospheric Rivers (to be submitted)

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 (submitted to J. Clim)

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)

Tseng K.-C., E. D. Maloney and E. A. Barnes, 2020: The consistency of MJO teleconnection patterns on interannual timescales J. Clim., 33, 3471–-3486 link

Tseng K.-C., E. A. Barnes, and E. D. Maloney, 2020: The importance of past MJO activity in determining the future state of midlatitude circulation J. Clim., 33, 2131–-2147 link