The tropical Atlantic climate is characterized by prominent and correlated multidecadal variability in Atlantic sea surface temperatures (SSTs), Sahel rainfall and hurricane activity1,2,3,4. Owing to uncertainties in both the models and the observations, the origin of the physical relationships among these systems has remained controversial3,4,5,6,7. Here we show that the cross-equatorial gradient in tropical Atlantic SSTs—largely driven by radiative perturbations associated with anthropogenic emissions and volcanic aerosols since 19503,7—is a key determinant of Atlantic hurricane formation and Sahel rainfall. The relationship is obscured in a large ensemble of CMIP6 Earth system models, because the models overestimate long-term trends for warming in the Northern Hemisphere relative to the Southern Hemisphere from around 1950 as well as associated changes in atmospheric circulation and rainfall. When the overestimated trends are removed, correlations between SSTs and Atlantic hurricane formation and Sahel rainfall emerge as a response to radiative forcing, especially since 1950 when anthropogenic aerosol forcing has been high. Our findings establish that the tropical Atlantic SST gradient is a stronger determinant of tropical impacts than SSTs across the entire North Atlantic, because the gradient is more physically connected to tropical impacts via local atmospheric circulations8. Our findings highlight that Atlantic hurricane activity and Sahel rainfall variations can be predicted from radiative forcing driven by anthropogenic emissions and volcanism, but firmer predictions are limited by the signal-to-noise paradox9,10,11 and uncertainty in future climate forcings.
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Observed SST, rainfall and wind data were obtained from: ERSSTv5, https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html; COBE SST2, https://psl.noaa.gov/data/gridded/data.cobe2.html; HadISST, https://www.metoffice.gov.uk/hadobs/hadisst/; Climatic Research Unit precipitation data, https://crudata.uea.ac.uk/cru/data/hrg/; GPCC precipitation data, https://psl.noaa.gov/data/gridded/data.gpcc.html; University of Delaware precipitation data, http://climate.geog.udel.edu/~climate/html_pages/download.html; NCEP-NCAR Reanalysis 1 data, https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html; and NOAA 20th Century Reanalysis data, https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html. Hurricane frequency data were obtained from HURDAT: HURDAT2, https://www.nhc.noaa.gov/data/; and HURDAT, https://www.aoml.noaa.gov/hrd/hurdat/comparison_table.html. All model data were taken from the CMIP6 and DAMIP archives: https://esgf-node.llnl.gov/projects/cmip6/ and https://damip.lbl.gov.
The code for the dominance analysis can be found at: https://github.com/dominance-analysis/dominance-analysis. Other scripts to reproduce the results can be found at: https://doi.org/10.5281/zenodo.8098355.
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We acknowledge support for this work from the NOAA (Grant No. NA20OAR4310400), the Climate and Large-Scale Dynamics programme of the National Science Foundation (Grant Nos. AGS 1735245 and AGS 1650209) and the Paleo Perspectives on Climate Change programme of the National Science Foundation (Grant No. AGS 1703076). We also acknowledge high-performance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) carried on NCAR’s Computational and Information Systems Laboratory. We acknowledge climate modelling groups for producing model outputs and the Program for Climate Model Diagnosis and Intercomparison for maintaining the CMIP6 data archive.
The authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Model-data Northern Hemisphere rainfall difference due to inter-hemispheric SST contrast difference.
a, JJASO precipitation trend difference between CMIP6 and GPCC over land in 1950–2014. b, as in a but for the difference between CMIP6 and 20th reanalysis. c, regression of precipitation difference (CMIP6 – GPCC) on the inter-hemispheric SST contrast difference (gray curve in Fig. 1e). d, as in c but for precipitation difference (CMIP6 – 20th reanalysis). In d, the black curve is the location of maximum rainfall climatology in 20th reanalysis. Note that unlike in Fig. 1a–c, the precipitation time series here are not normalized. Regions of statistical significance at the 99% confidence level according to Student’s t test are hatched. Maps are plotted using the cartopy package in Python.
a, JJASO SST trend in runs forced by anthropogenic aerosols from DAMIP. b, as in a but for greenhouse gases. c, as in a but for natural forcings. d, the sum of a–c. e, as in a but for all forcing from CMIP6. f, as in e but for observation. Regions of statistical significance at the 99% confidence level according to Student’s t test are hatched in a–f. Maps are plotted using the cartopy package in Python.
a, hurricane track density in positive AMV. b, as in a but for negative AMV. c, the difference between a and b. d, hurricane genesis density in positive AMV. e, as in d but for negative AMV. f, the difference between d and e. g, normalized hurricane frequency (shading) as in Fig. 2c and ratio difference between hurricane generated over main development region [10-20°N, 80-20°W] and over Bermuda Sea [24-32°N, 90-60°W] (green curve, right y-axis). In f, the main development region is marked as gray box, and the Bermuda Sea is pink box. Maps are plotted using the cartopy package in Python.
a, correlation distribution between simulated AMVs and the observed AMV. The dark blue line is the mean of the distribution (MOE). The light blue line is the correlation between the forced AMV (EM) and observation. b–d, as in a but for Sahel rainfall, VWS, and AMM.
Extended Data Fig. 5 Correlations between AMV/AMM and Sahel rainfall and VWS in preindustrial and historical runs (1950–2014).
a, joint distribution of correlations between the AMV and VWS (y-axis) and Sahel rainfall (x-axis) in preindustrial runs (N = 3100) and observation (red star). b, as in a but for AMM. c, as in b but for historical runs (with forced response included in single realizations). Comparing panel a and b shows the AMM has a better correlation with Sahel rainfall and VWS than AMV. Similar conclusion could also be drawn by comparing panel c and Fig. 2e. Comparing panel c and Fig. 4b shows the shift of observation before and after 1950. The small purple stars under the big red star are correlations calculated using different datasets, so the red start is the average of the purple stars. See methods how the correlations are calculated.
Extended Data Fig. 6 Distribution of regression coefficients by regressing detrended and lowpassed all-forcing run on single-forcing runs in bootstrap.
a, AMV. b, Sahel rainfall. c, VWS. d, AMM.
a, greenhouse gases. b, c, as in a but for anthropogenic aerosols and natural forcings. Regions of statistical significance at the 99% confidence level according to Student’s t test are hatched. Maps are plotted using the cartopy package in Python.
a, 200hPa geopotential height and circulation. b, as in a but for 850hPa. c, precipitation. The 200hPa (850hPa) response is averaged between 150–250hPa (700–900hPa) on native hybrid-pressure model level. Maps are plotted using the cartopy package in Python.
a, distribution of the post-1950 AMV variance for ensemble member (gray), forced response (EM, blue), total response of model (MOE, black), and observation (red). b, c, d as in a, but for VWS, Sahel rainfall, and AMM. e, ensemble mean of AMV in models with strong aerosol-cloud interaction (blue) and weak aerosol-cloud interaction (red). f, g, h as in e, bur for VWS, Sahel rainfall, and AMM. i, distribution of the post-1950 AMV variance in models with strong aerosol-cloud interaction (blue) and weak aerosol-cloud interaction (red). j, k, l as in i, but for VWS, Sahel rainfall, and AMM. In e-l, models are divided into two composites based on the strength of aerosol forcings19. Models with strong aerosol forcings are represented in blue and they are: TaiESM1, CESM2-FV2, SAM0-UNICON, CESM2-WACCM, CESM2-WACCM-FV2, CESM2, NorESM2-LM, NorESM2-MM, ACCESS-CM2, CNRM-CM6-1, MIROC6. Models with weak aerosol forcings are represented in red, and they are: GFDL-ESM4, MIROC-ES2L, BCC-CSM2-MR, CNRM-ESM2-1, GFDL-CM4, CanESM5, EC-Earth3-Veg, IPSL-CM6A-LR, BCC-ESM1, FGOALS-g3, MPI-ESM1-2-HR, MPI-ESM1-2-LR, INM-CM4-8, CAMS-CSM1-0.
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He, C., Clement, A.C., Kramer, S.M. et al. Tropical Atlantic multidecadal variability is dominated by external forcing. Nature (2023). https://doi.org/10.1038/s41586-023-06489-4