Abstract
Abstract. Precipitation is a crucial component of the global water
cycle. Rainfall features (e.g., strength or frequency) strongly affect
societal activities and are closely associated with the functioning of
terrestrial ecosystems. Hence, predicting global and gridded precipitation
under different emission scenarios is an essential output of climate change
research, enabling a better understanding of future interactions between
land biomes and climate change. Some current lower-complexity models (LCMs)
are designed to emulate precipitation in a computationally effective way.
However, for precipitation in particular, they are known to have large
errors due to their simpler linear scaling of precipitation changes against
global warming (e.g., IMOGEN; Zelazowski et al., 2018). Here, to reduce the
errors in emulating precipitation, we provide a data-calibrated
precipitation emulator (PREMU), offering a convenient and computationally
effective way to estimate and represent precipitation well, as simulated by
different Earth system models (ESMs) and under different user-prescribed
emission scenarios. We construct the relationship between global and local
precipitation and modes of global gridded temperature and find that the
emulator shows good performance in predicting historically observed
precipitation from Global Soil Wetness Project
Phase 3 (GSWP3). The ESM-specific emulator also estimates well the
simulated precipitation of nine ESMs and under four dissimilar future
scenarios of atmospheric greenhouse gases (GHGs). Our ESM-specific
emulator also reproduced well interannual fluctuations (R=0.82–0.93, p<0.001) of global land average precipitation (GLAP) simulated by
the nine ESMs, as well as their trends and spatial patterns. The default
configuration of our emulator only requires gridded temperature, also
available from lower-complexity models such as IMOGEN (Zelazowski et al.,
2018) and MESMER (Beusch et al., 2022; Nath et al., 2022), which themselves
are calibrated against ESMs. Therefore, our precipitation emulator can be
directly coupled within other LCMs, improving on, for instance, the current
emulations of precipitation implicit in IMOGEN. The PREMU model has the
opportunity to provide the driving conditions to model well the hydrological
cycle, ecological processes and their interactions with climate change.
Critically, the efficiency of LCMs allows them to make projections for many
more potential future trajectories in atmospheric GHG concentrations than is
possible with full ESMs due to the high computational requirement of the
latter. By coupling with PREMU, LCMs will have the ability to emulate
gridded precipitation; thus, they can be widely coupled with hydrological
models or land surface models.
Funder
National Natural Science Foundation of China
Cited by
2 articles.
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