Fig. 4. PS of observed Sahelian precipitation (solid black curve) and
the residual of observations and the ALL MMM (dotted-dashed black curve)
and associated 95% confidence intervals (grey shading), compared to the
average PS by model of piC simulations (brown to turquoise). Mean piC PS
are colored by the average yearly piC precipitation by model, where
brown simulations are drier than observed, and turquoise simulations are
wetter than observed.
We must conclude that no linear combination of the simulated forced
signal (which correlates poorly with observations) and simulated IV
(which has insufficient low-frequency variance) in coupled CMIP6
simulations can explain observed Sahel variability during the
20th century. Thus, model deficiency cannot be blamed
solely on the simulation of climate feedbacks: the CMIP6 ensemble
displays a fundamental inability to simulate the observed fast and slow
Sahelian precipitation responses to forcing, observed low-frequency IV,
or both. To identify the proximate cause of this failure, in the next
three sections we examine each causal path component identified in
Figure 1.
b.
AMIP
simulations: the Response to SST, Atmospheric Internal Variability, and
the Fast Response to Forcing (\(\overrightarrow{t}\),\(\overrightarrow{a}\), and \(\overrightarrow{f}\))
To isolate the effect of SST on the Sahel (\(\overrightarrow{t}\)), we
examine precipitation in the CMIP6 amip-piForcing simulations, which
force atmosphere-only models with the observed SST history (containing
both internal, \(\overrightarrow{o}\), and forced,\(\overrightarrow{s}\), oceanic variability) and constant preindustrial
external radiative forcing (no \(\overrightarrow{f}\)). The MMM of
simulated Sahel precipitation filters out atmospheric IV
(\(\overrightarrow{a}\)), leaving the precipitation response to the
entire observed SST field. It is displayed in Figure 5a (orange) and
compared to observations (black) on the same ordinates. Overall, the
performance of the amip-piF MMM is much better than that of the coupled
simulations: it achieves a high correlation (r = 0.60) and a low sRMSE
(0.81, see orange curves in Figure 3). The good match with observations
is achieved mostly at low frequencies: though it doesn’t accurately
capture many interannual episodes—notably including the precipitation
minimum in 1984—the MMM appears to capture the magnitude of
low-frequency variability, even including wetting in the 50s and early
60s, which is missing from the coupled MMM. This can be seen more
quantitatively by spectral analysis. In Figure 6a, the PS of the
amip-piF MMM (dashed orange curve) and its 95% confidence interval
(orange shaded areas), are compared to those of observations (black).
Unlike previous generations of AMIP experiments
(e.g. Scaife et al. 2009), the PS of the
simulated MMM is roughly consistent with observations.