Diffusion-Based Joint Temperature and Precipitation Emulation of Earth
  System Models
Katie Christensen Lyric Otto Seth Bassetti Claudia Tebaldi Brian Hutchinson
Earth system models (ESMs) are the principal tools used in climate science to
generate future climate projections under various atmospheric emissions
scenarios on a global or regional scale. Generative deep learning approaches
are suitable for emulating these tools due to their computational efficiency
and ability, once trained, to generate realizations in a fraction of the time
required by ESMs. We extend previous work that used a generative probabilistic
diffusion model to emulate ESMs by targeting the joint emulation of multiple
variables, temperature and precipitation, by a single diffusion model. Joint
generation of multiple variables is critical to generate realistic samples of
phenomena resulting from the interplay of multiple variables. The diffusion
model emulator takes in the monthly mean-maps of temperature and precipitation
and produces the daily values of each of these variables that exhibit
statistical properties similar to those generated by ESMs. Our results show the
outputs from our extended model closely resemble those from ESMs on various
climate metrics including dry spells and hot streaks, and that the joint
distribution of temperature and precipitation in our sample closely matches
those of ESMs.