The climate system is a complex network with nonlinear interactions on
multiple spatial and temporal scales among multiple variables. Due to its
complexity, evaluating climate predictability, predicting climate changes,
and forewarning major climate events have been grand challenges
for a long time. Despite the rapid progress in dynamic models in recent
years, it is still challenging for the current generation of models to fully
capture many of the complex features of the climate system, thus
inducing uncertainties in climate prediction and early-warning techniques. In recent years, novel approaches from complex-systems science, dynamical
systems, and nonlinear dynamics as well as emerging machine
learning and artificial intelligence approaches have been shown to be
powerful with respect to estimating climate predictability and improving
predictive/early-warning skill regarding climate changes/climate events; however,
the extent to which these new approaches can compensate for the current
dynamic models and further enhance our understanding of the climate
system remains an open question.
In order to summarize the recent progress and promote the use of
novel approaches in climate predictability, prediction, and early-warning
studies, we would like to propose a special issue entitled Emerging
predictability, prediction, and early-warning approaches in climate
science
. The special issue is intended to bring together researchers
interested in complex-systems science, tipping points, and predictability.
All submissions within the scope of this special issue are welcome.