Drivers of Low-Frequency Sahel Precipitation Variability: Comparing
CMIP5 and CMIP6 with Observations
Rebecca Jean Herman,a Michela
Biasutti,b Yochanan Kushnirb
a Department of Earth and Environmental Sciences of
Columbia University, New York, NY
b Lamont-Doherty Earth Observatory of Columbia
University, Palisades, NY
Corresponding author : Rebecca Herman, rebecca.herman@columbia.edu
ABSTRACT
We examine and contrast the simulation of Sahel rainfall in phases 5 and
6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). On
average, both ensembles grossly underestimate the magnitude of
low-frequency variability in Sahel rainfall. But while CMIP5 partially
matches the timing and pattern of observed multi-decadal rainfall swings
in its historical simulations, CMIP6 does not. To classify model
deficiency, we use the previously-established link between changes in
Sahelian precipitation and the North Atlantic Relative Index (NARI) for
sea surface temperature (SST) to partition all influences on Sahelian
precipitation into five components: (1) teleconnections to SST
variations; the effects of (2) atmospheric and (3) SST variability
internal to the climate system; (4) the SST response to external
radiative forcing; and (5) the “fast” response to forcing, which is
not mediated by SST. CMIP6 atmosphere-only simulations indicate that the
fast response to forcing plays only a small role relative to the
predominant effect of observed SST variability on low-frequency Sahel
precipitation variability, and that the strength of the NARI
teleconnection is consistent with observations. Applying the lessons of
atmosphere-only models to coupled settings, we imply that the failure of
coupled models in simulating 20th century Sahel
rainfall derives from their failure to simulate the observed combination
of forced and internal variability in SST. Yet differences between CMIP5
and CMIP6 Sahel precipitation do not mainly derive from differences in
NARI, but from either their fast response to forcing or the role of
other SST patterns.
1. Introduction
The semi-arid region bordering the North African Savanna and the Sahara
Desert, known as the Sahel, received much scientific attention since it
experienced unparalleled dramatic rainfall variability in the second
half of the 20th century. The importance of
teleconnections between Sahel precipitation and global sea surface
temperature (SST) was demonstrated in the early stages of Sahel climate
variability research (Folland et al.
1986; Giannini et al. 2003;
Knight et al. 2006;
Palmer 1986;
Zhang and Delworth 2006), and has been
further reinforced in more recent studies
(Okonkwo et al. 2015;
Parhi et al. 2016;
Park et al. 2016;
Pomposi et al. 2015;
Pomposi et al. 2016;
Rodríguez-Fonseca et al. 2015 and
references therein). But while the dominant role of SST in driving the
pacing (though not necessarily the full magnitude) of 20th century Sahel
rainfall variability is unquestioned
(Biasutti 2019), there is still debate on
whether the evolution of SST and the related Sahel precipitation
variability were externally forced
(Ackerley et al. 2011;
Biasutti 2013;
Biasutti and Giannini 2006;
Biasutti et al. 2008;
Bonfils et al. 2020;
Dong and Sutton 2015;
Giannini and Kaplan 2019;
Haarsma et al. 2005;
Haywood et al. 2013;
Held et al. 2005;
Hirasawa et al. 2020;
Hua et al. 2019;
Iles and Hegerl 2014;
Kawase et al. 2010;
Marvel et al. 2020;
Polson et al. 2014;
Undorf et al. 2018;
Westervelt et al. 2017) or the
manifestation of variability internal to the climate system (IV,
Sutton and Hodson 2005;
Ting et al. 2009;
Zhang and Delworth 2006).
Recently, Herman et al. (2020, hereafter
H20) investigated multi-model means (MMM) of historical simulations
from the Coupled Model Intercomparison Project phase 5
(CMIP5, Taylor et al. 2012), and found
that anthropogenic aerosols (AA) and volcanic aerosols (VA), but not
greenhouse gases (GHG), were responsible for forcing simulated Sahelian
precipitation that correlates well with observations, with AA alone
responsible for the low-frequency component of simulated variability.
This conclusion appeared consistent with previous claims that AA
emissions, which increased until the 1970s and then decreased in
response to clean air initiatives (Klimont
et al. 2013; Smith et al. 2011), caused
multi-decadal variability in Sahel precipitation via changes in Northern
Hemisphere surface temperature (Ackerley et
al. 2011; Haywood et al. 2013;
Hwang et al. 2013;
Undorf et al. 2018), or specifically via
multidecadal variability in North Atlantic SST (the Atlantic
Multidecadal Variability, AMV; Booth et
al. 2012; Hua et al. 2019). However, H20
also found that the simulated rainfall response to forcing has little
low-frequency power relative to observations, and that simulated IV is
unable to account for this difference.
H20 and most other attribution studies do not examine in depth the
pathways through which AA (and for that matter, IV and other external
forcing agents) affect Sahel precipitation. Thus, H20 did not determine
whether the discrepancy between CMIP5 simulations and observations
represents an underestimate of aerosol indirect effects and climate
feedbacks that amplify the simulated precipitation response to AA, or a
fundamental inability of the models to simulate aspects of the observed
climate response to forcing or observed modes of IV. Identifying the
deficiencies in model representation of the pathways by which external
forcing and IV influence the West African Monsoon and Sahel rainfall is
essential for attribution of 20th century changes and
also for prediction of this region’s climate future, as model
simulations don’t even agree on the sign of future precipitation changes
in the Sahel (Biasutti 2013).
Here, we use the well-established link between SST and Sahel
precipitation to decompose the effects of individual external forcing
agents (F) and internal variability (IV) on Sahel precipitation (P) into
five path components, presented in Figure 1: (1) teleconnections that
communicate variations in SST to variations in P (indicated by the arrow\(\overrightarrow{t}\)); (2) the “fast” atmospheric and land-mediated
effect of external forcing (F) on P (\(\overrightarrow{f}\)); (3) the
direct effect of atmospheric IV on P (\(\overrightarrow{a}\)); (4) the
effect of F on SST (\(\overrightarrow{s}\)); and (5) the impact of IV in
the coupled climate system on SST (\(\overrightarrow{o}\)). The path\(F\rightarrow SST\rightarrow P\) is the “slow,” SST-mediated effect
of F on P.