Fig. 1. Causal diagram relating external forcings (F), internal
variability (IV), sea surface temperatures (SST), and Sahelian
precipitation (P) via directional causal arrows. Unobserved variables
and their causal effects are presented with dashed lines, while observed
variables are presented with solid lines.
Characterization of these path components has been controversial.
Firstly, separating the SST response to forcing (\(\overrightarrow{s}\))
from SST variability internal to the climate system
(\(\overrightarrow{o}\)) has proven difficult (top of diagram). In
particular, there is significant debate over whether observed AMV is a
response to external forcing (Booth et al.
2012; Chang et al. 2011;
Hua et al. 2019;
Menary et al. 2020;
Rotstayn and Lohmann 2002) or mainly an
expression of IV in the Atlantic Meridional Overturning Circulation
(AMOC, Han et al. 2016;
Knight et al. 2005;
Qin et al. 2020;
Rahmstorf et al. 2015;
Sutton and Hodson 2005;
Ting et al. 2009;
Yan et al. 2019;
Zhang 2017;
Zhang et al. 2016;
Zhang et al. 2013) that is underestimated
in models (Yan et al. 2018). This debate
has been hard to resolve partially because IV in AMOC and aerosol
forcing may have coincided by chance in the 20thcentury (Qin et al. 2020). Next, examine
the bottom of the diagram. The effect of the observed SST field on Sahel
precipitation \((\overrightarrow{t})\) can be directly estimated using
atmosphere-only simulations, but while these simulations capture the
pattern of observed Sahel precipitation variability, many fail to
capture its full magnitude (Biasutti 2019;
e.g. Hoerling et al. 2006;
Scaife et al. 2009). This could reflect
an underestimate in climate models of the strength of SST
teleconnections, which could be resolution dependent
(Vellinga et al. 2016), or of
land-climate feedbacks that amplify the teleconnections
(\(\overrightarrow{t}\)), such as vegetation changes
(Kucharski et al. 2013). But it could
also reflect a significant additional role in the observations for a
fast response to forcing (\(\overrightarrow{f}\)) that confounds the
SST-forced signal\([P\leftarrow F\rightarrow SST\rightarrow P\); see
Pearl et al. (2016) for notation] or
coincides with it by chance.
To examine the path components in coupled simulations, we need a
parsimonious characterization of the relationship between SST and Sahel
precipitation. Giannini et al. (2013) and
Giannini and Kaplan (2019, hereafter
GK19) identify the North Atlantic Relative Index (NARI), defined as the
difference between average SST in the North Atlantic (NA) and in the
Global Tropics (GT), as the dominant SST indicator of
20th century Sahel rainfall in observations and CMIP5
simulations. There are two main theories relating NARI to Sahelian
precipitation (see Biasutti 2019;
Hill 2019 for reviews of competing
theories of monsoon rainfall changes). In the first, the “local view”
(Giannini 2010), warming of GT causes
even stronger warming throughout the tropical upper troposphere
(Knutson and Manabe 1995;
Parhi et al. 2016;
Sobel et al. 2002), increasing
thermodynamic stability across the tropics and inhibiting convection in
an “upped ante” (Giannini and Kaplan
2019; Neelin et al. 2003) or
“tropospheric stabilization” (Giannini
et al. 2008; Lu 2009) mechanism. Warming
of NA, on the other hand, is expected to thermodynamically increase
moisture supply to the Sahel by increasing specific humidity over the
NA, and thus destabilize the atmospheric column from the bottom up
(GK19). The second theory interprets the relationship of Sahel
precipitation to NARI, or, similarly, to the Atlantic meridional
temperature gradient or the Interhemispheric Temperature Difference
(ITD), as the result of an energetically-driven shift in the
Intertropical Convergence Zone (ITCZ,
Donohoe et al. 2013;
Kang et al. 2009;
Kang et al. 2008;
Knight et al. 2006;
Schneider et al. 2014) and the African
rainbelt (e.g. Adam et al. 2016;
Biasutti et al. 2018;
Camberlin et al. 2001;
Caminade and Terray 2010;
Hoerling et al. 2006;
Hua et al. 2019;
Pomposi et al. 2015;
Westervelt et al. 2017). According to
both theories, an increase in NARI should wet the Sahel while a decrease
causes drying. Given the prominence of the NARI teleconnection in the
20th century and the assumption of linearity, we approximate the full
slow response as the product of the NARI response to external forcing
and the strength of the NARI-Sahel teleconnection.
This paper is organized as follows: Section 2 provides details on the
simulations and observational data used in this analysis while Section 3
discusses the methods. In Section 4.a, we update H20’s analysis to the
Coupled Model Intercomparison Project phase 6
(CMIP6, Eyring et al. 2016), examining
the total response to forcing (all paths from F to P) and internal
variability (all paths from IV to P). We then evaluate the performance
of the CMIP6 AMIP simulations, decomposing them into the path components
from the bottom half of Figure 1 \((\overrightarrow{t}\),\(\overrightarrow{f}\), and \(\overrightarrow{a}\)) in Section 4.b, and
focusing on the NARI teleconnection in Section 4.c. Section 4.d
decomposes coupled simulations of NARI into the path components from the
top half of Figure 1 (\(\overrightarrow{s}\) and\(\overrightarrow{o}\)), while Section 4.e evaluates the consistency of
the NARI teleconnection established in Section 4.c with coupled
simulations. Finally, in Section 4.f, we use simulated NARI and the
simulated NARI teleconnection to decompose the total response of Sahel
precipitation to external forcing in coupled simulations (examined in
Section 4.a) into fast and slow components. We discuss how our results
fit in with the existing literature in Section 5 before concluding in
Section 6.
2. Data
We examine coupled “historical” simulations from CMIP5
(Taylor et al. 2012) and CMIP6
(Eyring et al. 2016) forced with four
sets of forcing agents—AA alone, natural forcing alone (NAT, which
includes VA as well as solar and orbital forcings), GHG alone, and all
three simultaneously (ALL)—as well as pre-Industrial control (piC)
simulations, in which all external forcing agents are held constant at
pre-Industrial levels. We additionally examine CMIP6 amip-piForcing
(amip-piF) simulations, in which atmospheric models are forced solely
with observed SST, and CMIP6 amip-hist simulations, which are forced
with observed SST and historical ALL radiative forcing. Calculations
with CMIP5 utilize the period between 1901 and 2003 while calculations
with CMIP6 extend to 2014.
In H20, we used all available institutions for each forcing subset.
Here, in order to provide a more stringent comparison of the effects of
different forcing agents, we exclude institutions from the coupled
ensemble that do not provide AA, GHG, and ALL simulations, and from the
AMIP ensemble if they do not provide both amip-piForcing and amip-hist
simulations. We additionally exclude piC simulations that are shorter
than the historical simulations as well as any simulations with data
quality issues. Tables S1-S3 enumerate the simulations used in this
analysis.
Precipitation observations are from the Global Precipitation Climatology
Center (GPCC, Becker et al. 2013)
version2018, and SST observations are from the National Oceanic and
Atmospheric Administration’s (NOAA) Extended Reconstructed Sea Surface
Temperature, Version 5 (ERSSTv5, Huang et
al. 2017).
We analyze precipitation over the Sahel (12°-18°N and 20°W-40°E) and the
SST indices of GK19: the North Atlantic (NA, 10°-40°N and 75°-15°W), the
Global Tropics (GT, ocean surface in the latitude band 20°S-20°N), and
the North Atlantic Relative index (NARI, the difference between NA and
GT). All indices are spatially- and seasonally-averaged for
July-September (JAS).
3. Methods
The multi-model mean (MMM) for a set of simulations consists of a
3-tiered weighted average over (1) individual simulations (runs) from
each model, (2) models from each research institution, and (3)
institutions in that ensemble. Details of the weighting are provided in
H20; the results are robust to differences in weighting. Time series are
not detrended, and anomalies are calculated relative to the period
1901-1950.
To evaluate the performance of the simulations relative to observations,
we compute correlations (r), which capture similarity in frequency and
phase, and root mean squared errors standardized by observed variance
(sRMSE), which measure yearly differences in magnitude between the
simulations and observations. An sRMSE of 0 represents a perfect match
between simulations and observations, and 1 would result from comparing
the observations with a constant time series.
To estimate uncertainty in the forced MMMs and associated metrics, we
apply a bootstrapping technique to the last tier of the MMM as described
in H20, yielding a probability distribution function (pdf) about the MMM
and each metric. Due to the finite number of simulations, these pdfs
underestimate the true magnitude of the uncertainty. We evaluate
significance by applying a randomized bootstrapping technique, which
increases the effective sample size, to the piC simulations with one
significant improvement over H20: instead of using just one subset of
each piC simulation at a random offset in the first tier of the MMM in
each bootstrapping iteration, we take enough subsets to match the number
of that model’s historical runs. Done this way, the confidence intervals
calculated using piC simulations accurately represent noise in the
forced MMMs. PiC pdfs from the same ensemble differ slightly because
many institutions provide a different number of simulations for
different subsets of forcing agents (see Table S2). Where the piC pdfs
and confidence intervals are similar enough, they are presented together
with a single grey dotted curve and dashed line; when they differ, they
are presented in the colors associated with the relevant forcings.
We perform a residual consistency test, which compares the power spectra
(PS) of individual simulations to that of observations, with one
significant modification over H20: we calculate the PS using the
multi-taper method. Confidence intervals for the PS for observations and
MMMs are given by the multi-taper method, without accounting for the
uncertainty in the MMMs themselves. Mean PS by model are colored by
climatological rainfall bias given by those simulations. The multi-model
mean of these PS, or the “tiered mean”, is calculated using the three
tiers from the definition of the MMM, but without weights, since
spectral power is not attenuated when averaging PS.
4. Results
a. Changes in CMIP6: Total
Precipitation Response to Forcing and Internal Variability
If Sahelian precipitation is a linear combination of IV in the coupled
climate system and variability forced by external agents, then the MMM
over coupled simulations with differing initial conditions filters out
atmospheric and oceanic IV (\(\overrightarrow{a}\) and\(\overrightarrow{o}\)), leaving the fast and slow precipitation
responses to external radiative forcing (\(\overrightarrow{f}\) and\(F\rightarrow SST\rightarrow P\)). Figure 2 compares observed
Sahelian precipitation anomalies (black, left ordinates) to the MMM
anomalies of simulated Sahelian precipitation (right, amplified colored
ordinates) in CMIP5 (dotted curves) and CMIP6 (solid curves) for four
sets of forcing agents: ALL (a, blue), AA (b, magenta), natural forcing
(c, “NAT,” brown and red), and GHG (d, green). The figure also
presents the bootstrapping 95% confidence intervals of the forced CMIP6
MMMs (blue, magenta, brown, and green shaded areas) and of MMMs over the
CMIP6 piC simulations (yellow shaded areas) on the right ordinates. The
width of the yellow shaded areas represents the magnitude of noise
deriving from coincident IV in the MMMs. Differences in its width
between panels arise from varying numbers of simulations for the
different forcing subsets (see Methods and Table S2).