Detecting Changes in Large-Scale Metrics of Climate in Short
Integrations of a Global Storm-Resolving Model of the Atmosphere
Abstract
Recent advances have allowed for integration of global storm resolving
models (GSRMs) to a timescale of several years. These short simulations
are sufficient for studying characteristics and statistics of short- and
small-scale phenomena; however, it is questionable what we can learn
from these integrations about the large-scale climate response to
perturbations. To address this question, we use the response of X-SHiELD
(a GSRM) to uniform SST warming and CO$_2$ increase in a two-year
integration and compare it to similar CMIP6 experiments. Specifically,
we assess the statistical meaning of having two years in one model
outside the spread of another model or model ensemble. This is of
particular interest because X-SHiELD shows a distinct response of the
global mean precipitation to uniform warming, and the northern
hemisphere jet shift response to isolated CO$_2$ increase. We use the
CMIP6 models to estimate the probability of two years in one model being
more than one standard deviation away from another model (ensemble)
mean, knowing the mean of two models. For example, if two years in one
model are more than one standard deviation away from the other model’s
mean, we find that the chances for these models’ means to be within one
standard deviation are $\sim 25\%$. We
find that for some large-scale metrics, there is an important base-state
dependence that, when taken into account, can qualitatively change the
interpretation of the results. We note that a year-to-year comparison is
physically meaningful due to the use of prescribed
sea-surface-temperature simulations.