Fig. 10. Compares the fast Sahelian precipitation response to forcing in AMIP simulations (purple, as in Figure 5c) to the estimated fast component of the precipitation MMMs in coupled CMIP6 simulations (precipitation – 0.87*NARI; the difference between the colored and light blue curves in the left column of Figure 9) forced with ALL (a, blue), AA (b, magenta), NAT (c, brown), and GHG (d, green). Similar to Figure 2, the colored shaded areas denote the bootstrapping confidence interval of this difference, and the yellow shaded areas, which represent the magnitude of noise in the fast MMMs, are the confidence intervals of the MMM of randomized bootstrapped differences between precipitation and 0.87*NARI in piC simulations. Panel (a) additionally shows a 20-year running mean of the sum of the AA, NAT, and GHG fast MMMs (burgundy dashed curve). The label shows the number of institutions used for each CMIP6 MMM (N), the correlation of the fast MMM with the AMIP fast response (r), and the standardized root mean squared error of the CMIP6 MMM with observations (sRMSE).
Though NARI in the GHG simulations differs between CMIP5 and CMIP6, most of the difference in simulated forced precipitation between CMIP5 and CMIP6 is not mediated by a linear relationship with NARI, and can be attributed to the fact that the GHG- and AA-induced drying in CMIP5 is replaced with AA- and GHG-induced wetting in CMIP6. Whether the GHG-induced drying in CMIP5 is a fast response to forcing or a response mediated by SST in ocean basins other than the Atlantic cannot be firmly established by this analysis, but we offer our perspective below.
5. Discussion
Using SST (and specifically NARI) as a mediator, we have established that the failure of CMIP coupled models to simulate observed Sahel rainfall stems from their inability to simulate observed SST, especially NA, and that the differences in simulation of Sahel rainfall between CMIP5 and CMIP6 stem from differences in mechanisms not mediated by a linear teleconnection with NARI. (Let’s denote the difference between simulated precipitation and scaled NARI as PnonNARI). We initially suggested that PnonNARI provides a good measure of the fast (non-SST-related) response to forcing because of the prominence of the NARI-Sahel teleconnection in observations and AMIP-style simulations of the 20th century. But without examining further mediators, we cannot decisively rule out the possibility that PnonNARI captures teleconnections with other ocean basins or nonlinearities in the NARI teleconnection. Which explanation is most likely?
The PnonNARI indices in CMIP5 and CMIP6 are nearly opposite. If we assume that both represent a fast response to forcing, we need to conclude that increasing GHG (or reducing AA) lead to fast wetting in CMIP6, but drying in CMIP5.
The interpretation of PnonNARI in CMIP6 as a fast response is more consistent with theory. First, increasing rainfall is consistent with theory linking reduced aerosol concentrations to fast surface warming and decreasing optical depth of the atmosphere (Allen and Ingram 2002; Rosenfeld et al. 2008), although a couple highly non-linear simulations suggest the fast precipitation response of the Sahel to changing AA in the 20th century was drying whether AA forcing was increasing or decreasing (Hirasawa et al. 2020). Second, it is generally accepted that the fast response of the Sahel to GHG is wetting (e.g. Biasutti 2013; Gaetani et al. 2017; Giannini 2010; Haarsma et al. 2005). The good match in the estimated fast response between coupled CMIP6 simulations and the amip-hist simulations increases our confidence that the deviations from the NARI-mediated slow response to forcing in CMIP6 really reflect a fast response to forcing. The same cannot be said for CMIP5.
We noted in Section 4.c that NARI only explains 36% of simulated SST-forced variability in the amip-piF simulations, leaving room for the influence of other ocean basins or SST indices on Sahel precipitation. Indeed, this is consistent with GK19: while they argue that NARI is the primary indicator for 20th century Sahel rainfall, they also argue that p1, which is approximately (NA+GT)/2 and is intended to capture the effects of uniform global warming, plays a secondary—but important—role in the 20th century and a dominant role in the future. In CMIP5, PnonNARImay capture not the fast responses to forcing, but slow drying in response to uniform global warming, consistent with previous literature (e.g. Gaetani et al. 2017). In this read, the differences in simulation of Sahel rainfall between CMIP5 and CMIP6 are due to a combination of changes in the fast response to forcing and the influence of SST patterns not captured by NARI.
6. Summary and Conclusions
In this paper, we decompose simulated Sahelian precipitation into (1) teleconnections with SST, (2) fast, atmospheric- and land-mediated responses to forcing, (3) atmospheric noise, (4) forced SST variability, and (5) internal SST variability, in order to determine why the 5th and 6th generations of CMIP differ in their simulation of Sahel rainfall, and why both ensembles are inconsistent with observed Sahel precipitation variability.
CMIP6 atmospheric simulations forced with observed SST alone capture observed Sahel precipitation quite well (r=0.6), and, in combination with atmospheric white noise, are able to reproduce the power of observed low-frequency variability. This is a welcome improvement from previous generations of climate models. Including radiative forcing alongside observed SST barely changes simulated precipitation, suggesting that the fast response is small and plays a secondary role to SST-forced precipitation variability. We summarize the Sahel teleconnections with global SST as a linear relationship with an index of the warming of the North Atlantic relative to the global Tropics (NARI), which explains about 36% of the simulated precipitation response to observed SST. The simulated NARI teleconnection is measured as \(0.87\pm 0.26\frac{\text{mm}}{day*C}\), consistent with the strength of the observed teleconnection. We conclude that the observed SST history and simulated teleconnections in atmospheric simulations are together necessary and sufficient to capture the timing and magnitude of the low-frequency droughts and pluvials in 20thcentury Sahel rainfall.
In coupled simulations, the NARI-Sahel teleconnection is consistent with AMIP simulations, but NARI’s variability – which mostly comes from North Atlantic SST (NA) – differs from the observed. In simulations, AA cause a cooling trend and GHG cause a warming trend with magnitudes comprable to the observed, but no combination of forcing agents produces a decadal-scale oscillation in NA in either CMIP5 or CMIP6, and only three CMIP6 models (out of 25 CMIP5 and 30 CMIP6 models) are able to generate internal SST variability commensurate to the residual (the difference between total and radiatively forced) low-frequency variability. How do we reconcile our results with those claiming that the observed Atlantic Multidecadal Variability (AMV) is externally forced (mainly by AA; Bellomo et al. 2018; Booth et al. 2012; Hirasawa et al. 2020; Hua et al. 2019; Murphy et al. 2017)? The discrepancy can be explained because these studies examine only one or two models (Booth et al. 2012; Hirasawa et al. 2020) or subtract a linear trend from simulated NA before comparing to observations (Bellomo et al. 2018; Hua et al. 2019; Murphy et al. 2017), thus inducing low-frequency variability in the simulated monotonic decreasing step function. Moreover, a prominent role for internal variability cannot yet be dismissed, as suggested by Yan et al. (2018), who, consistent with our analysis, find that most models do not capture observed AMOC variability. The NARI-mediated slow response to external radiative forcing is to dry the Sahel slightly in the 60s and to wet it immediately afterwards; this does not, in isolation, explain the timing or magnitude of the observed drought or recovery. Furthermore, forced NARI variability is small in the first half of the century. We are led to conclude that either the pattern of the simulated SST response to forcing in coupled models is incorrect or the Sahelian precipitation response to internal SST variability overshadowed the response to external radiative forcing in the 20thcentury, at least up to the mid-1960s.
While we can ascribe the deficiency of 20th century Sahel rainfall simulations in both CMIP5 and CMIP6 coupled models to their simulations of SST, NARI is not the main explanation for the differences in forced Sahel rainfall between the two ensembles, since it is quite similar in CMIP5 and CMIP6 ALL simulations. The difference, rather, is in PnonNARI: the component of Sahel rainfall that comes either from the influence of other SST patterns or from the fast response to forcing. CMIP6 underperforms relative to CMIP5 because PnonNARI includes substantial fast wetting responses to increasing GHG and decreasing AA, comparable in magnitude to the NARI-related component. In contrast, PnonNARI in CMIP5 is drying, likely in response to uniform SST warming. Sahel drying in response to uniform warming is strong in models that simulate a deeper ascent profile, but weak otherwise (Hill et al 2017), so it is possible that newer parameterizations and higher resolution have changed the sensitivity to this forcing in the latest generation of models.
This work has shown that, while there has been progress in the simulation of the Sahel’s response to global SST, much remains uncertain in the simulation of the pathways of Sahel multi-decadal variability, especially in the amplitude and timing of forced and natural SST anomalies in the Atlantic and in the fast and slow response of rainfall to GHG forcing. Differing mechanisms can lead to similar time evolutions in observations and simulations; to avoid this pitfall, future work should focus on evaluating in more detail the hypothesized pathways of the Sahel response to anthropogenic emissions and oceanic internal variability in order to further categorize model performance and improve predictions of the future.
Acknowledgments.
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output (listed in Tables S1-S3 of this paper). For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. The CMIP6 data used in this study are available in Google cloud storage (https://console.cloud.google.com/storage/browser/cmip6) as a result of a grant to the Pangeo project (https://pangeo.io/). We thank Haibo Liu for preparing the CMIP5 data for use, and Naomi Henderson for transferring needed CMIP6 simulations to the cloud, and for aiding in data access and general technical support throughout the project. We additionally thank Alessandra Giannini for her guidance throughout the project. This research was supported by the U.S. National Science Foundation Grant AGS-1612904.
Data Availability Statement.
Observational data from the Global Precipitation Climatology Center (GPCC, Becker et al. 2013) and the National Oceanic and Atmospheric Administration’s (NOAA) Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5, Huang et al. 2017) are freely available online (see https://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html and https://www.ncei.noaa.gov/products/extended-reconstructed-sst, respectively). CMIP5 (CMIP5, Taylor et al. 2012) and CMIP6 (Eyring et al. 2016) model data is freely available through the Earth System Grid (see https://esgf-node.llnl.gov/projects/esgf-llnl/).
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