Jie Sun

Area of research

Global and Planetary Change · Plant Science

Research interest

Research topics from publications: Changing rapid weather variability increases influenza epidemic risk in a warming climate; Pushing the boundary of seasonal prediction with the lever of varying annual cycles; Uncovering the Intrinsic Intensity–Size Relationship of Tropical Cyclones; A Comparative Study of Mathematical Models for the Tropical Cyclone Intensity–Size Relation; The Quasi-Linear Relation between Planetary Outgoing Longwave Radiation and Surface Temperature: A Climate Footprint of Radiative and Nonradiative Processes; Isolating spatiotemporally local mixed Rossby-gravity waves using multi-dimensional ensemble empirical mode decomposition; Global Warming and Topography Impact the Amplitude of the Synoptic-Scale Surface Temperature Variability across the US; Energy Gain Kernel for Climate Feedbacks. Part I: Formulation and Physical Understanding; Principle-based adept predictions of global warming from climate mean states; A Quasi-Linear Relationship between Planetary Outgoing Longwave Radiation and Surface Temperature in a Radiative-Convective-Transportive Climate Model of a Gray Atmosphere. Representative work: Abstract It is believed that the continuing change in the Earth’s climate will affect the viral activity and transmission of influenza over the coming decades. However, a consensus of the severity of the risk of an influenza epidemic in a warming climate has not been reached. It was previously reported that the warmer winter can reduce influenza epidemic caused mortality, but this relation cannot explain the deadly influenza epidemic in many countries over northern mid-latitudes in the winter of 2017–2018, one of the warmest winters in recent decades. Here, we reveal that the widely spread 2017–2018 influenza epidemic can be attributed to the abnormally strong rapid weather variability. We d Predicting climate anomalies months in advance is of tremendous socioeconomic value. Facing both theoretical and practical constraints, this realm of "seasonal prediction" progressed slowly in recent decades. Here we devise an innovative scheme that pushes the boundary of seasonal prediction by recognizing and isolating distinct spatiotemporal footprints left by modes of climate variability that cause varying annual cycles in response to the solar forcing. The predictive power harnessed from these spatiotemporal footprints results in a prediction skil