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Increased extreme swings of Atlantic intertropical convergence zone in a warming climate

Nature Climate Change volume 12, pages 828–833 (2022)Cite this article

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Abstract

Interannual variability of the Atlantic intertropical convergence zone (ITCZ) affects hydrological cycles, extreme weather events, ecosystems, agriculture and livelihoods in Atlantic-rim countries. It can experience an interannual extreme swing, moving hundreds of kilometres northwards during boreal spring, causing severe droughts in central-eastern Amazon and floods in northern South America. How its interannual variability will respond to global warming remains unknown. Here using state-of-the-art climate models under a high-emission scenario, we project a more-than-doubling increase of extreme northward swings. This increase from one event per 20.4 years in the twentieth century to one per 9.3 years in the twenty-first century is underpinned by a mean state change of sea surface temperature, with faster warming north of the Equator. The warming differential facilitates the increased frequency of extreme swings, as the ITCZ follows the maximum sea surface temperature. Our finding suggests a substantial increase in ITCZ swing-induced severe droughts/floods in the Atlantic-rim countries.

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Fig. 1: Meridional swings of Atlantic ITCZ in the observations and CMIP6 multimodel ensemble.
Fig. 2: Multimodel projection of Atlantic ITCZ meridional swing events under global warming.
Fig. 3: Extreme swing-induced meteorological droughts/floods in most-affected regions.
Fig. 4: Mechanisms for the projected increase in extreme northward swing events under global warming.

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Data availability

Data relevant to the paper can be downloaded from the following websites: GPCP v.2.3 at https://psl.noaa.gov/data/gridded/data.gpcp.html; OISST v.2 at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html; ECMWF Reanalysis v.5 at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5; CMIP6 database at https://esgf-node.llnl.gov/search/cmip6/. Twenty-nine selected models are used in this study: ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CanESM5-CanOE, CESM2, CESM2-WACCM, CMCC-CM2-SR5, CMCC-ESM2, CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, E3SM-1-1, EC-Earth3-CC, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, INM-CM4-8, INM-CM5-0, KACE-1-0-G, MIROC6, MIROC-ES2L, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-LM, NorESM2-MM, TaiESM1, UKESM1-0-LL.

Code availability

Codes for generating all the results are available from the corresponding authors on request.

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Acknowledgements

This work is supported by China’s National Key Research and Development Projects (2018YFA0605704) and the National Natural Science Foundation of China (92058203 and 41806007) (Z.L.) and the National Natural Science Foundation of China (92058203 and 41925025) (X.L.). W.C. is supported by CSHOR, a joint research Centre for Southern Hemisphere Oceans Research between QNLM and CSIRO. Y.L. is also supported by the China Scholarship Council (202106330019). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access and the multiple funding agencies who support CMIP6 and ESGF.

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Authors and Affiliations

  1. Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China

    Yi Liu, Wenju Cai, Xiaopei Lin & Ziguang Li

  2. Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, China

    Yi Liu, Wenju Cai, Xiaopei Lin & Ziguang Li

  3. Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Wenju Cai

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Y.L., W.C., X.L. and Z.L. conceived this study. Y.L. performed all the analyses and wrote the initial manuscript with W.C. All authors contributed to interpreting results, discussion and improvement of this paper.

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Extended data

Extended Data Fig. 1 Seasonal cycle of the Atlantic ITCZ.

a, Observed seasonal climatology (December-February, DJF; March-May, MAM; June-August, JJA; September-November, SON) of total precipitation (mm day-1, color shading) over the tropical Atlantic based on data during the 1982–2020 period. Green lines indicate the 5 mm precipitation contours. b, Observed climatological monthly latitude position of Atlantic ITCZ based on data during the 1982–2020 period. Error-bars indicate the interannual variability during each month.

Extended Data Fig. 2 Observed precipitation variations and its relationship with Atlantic ITCZ position.

a, Time-series of precipitation first principal component (PC1) in the boreal spring in the observational record. b, Linear relationship between precipitation PC1 and latitude position of the Atlantic ITCZ in the boreal spring. The correlation (R) and its P-value are indicated. Red dot and blue dots in a and b indicate the years with an extreme northward swing event (1983) and six moderate northward swing events (1990, 1992, 1993, 1998, 2010, and 2012), respectively. The highly-correlated relationship between precipitation PC1 and ITCZ position suggests that the first EOF mode of precipitation represents the interannual springtime meridional swings of the Atlantic ITCZ.

Extended Data Fig. 3 Precipitation and surface wind anomalies of each observed extreme/moderate northward swing event.

MAM-averaged precipitation (mm day-1, color shading) and surface wind (m s-1, vectors) anomalies for observed extreme and moderate events in a 1983, b 1990, c 1992, d 1993, e 1998, f 2010, and g 2012. Specially, the 1983 extreme event caused extreme droughts across the equatorial Atlantic basin, extending from central-eastern Amazon to the west coast of Africa, together with strong cross-equatorial winds. Other moderate events caused relatively small droughts in scale and intensity, but severe droughts in the north-east Brazil.

Extended Data Fig. 4 Convection patterns of each observed extreme/moderate northward swing event.

MAM-averaged SST anomalies (°C, color shading) referenced to the SST threshold for tropical convection, defined as the MAM SST averaged from 20°S to 20°N over the whole tropics, for observed extreme and moderate events in a 1983, b 1990, c 1992, d 1993, e 1998, f 2010, and g 2012. Areas with SST lower than the convection threshold are masked out. A large northward extension of convection boundary in the western tropical Atlantic is seen during the 1983 extreme event, together with diminishing convection to the south of equator in the central-eastern tropical Atlantic.

Extended Data Fig. 5 Model selection, based on the relationship of precipitation PC1 with ITCZ meridional swings.

a, Inter-model relationship between correlation of precipitation PC1 with ITCZ meridional swings with climatological position of ITCZ during boreal spring. Models with a stronger ITCZ south bias produce a weaker PC1-ITCZ correlation. The large black dot indicates the observed value. The 29 models that produce PC1-ITCZ correlation greater than half of the observed value are denoted by colored dots and referred as ‘selected’ (located in the blue area); the other 11 models that produce a PC1-ITCZ correlation lower than half of the observed value are denoted by colored stars and referred as ‘non-selected’ (located out of the blue area). The correlation (R) and P-value of this inter-model relationship are indicated. b, c, Relationship between the normalized latitude position anomalies of the Atlantic ITCZ and precipitation PC1 during boreal spring for b, ‘selected’ and c, ‘non-selected’ models. The correlation (R) is indicated. d, e, The leading EOF mode of MAM-averaged precipitation anomalies in the tropical Atlantic for d, ‘selected’ and e, ‘non-selected’ models. Stippled areas indicate where the mean values exceed 1 SD.

Extended Data Fig. 6 Observed and simulated relationship of SST and wind anomalies with precipitation PC1.

a, MAM-averaged SST (°C, color shading) and surface wind (m s-1, vectors) anomalies regressed onto the precipitation first principal component (PC1) in the observations. Only the grid-points where regression coefficients are statistically significant above the 95% confidence level are shown. The meridional SSTA gradient is defined as difference of SST anomalies between 40°W-20°W, 5°N-10°N and 25°W-5°W, 2.5°S-2.5°N, as indicated by the two green boxes. b, Observed meridional distribution of wind coefficient, defined as regression coefficient of MAM-averaged tropical Atlantic meridional wind anomalies (50°W-0°) onto the precipitation PC1, over the range of 30°S-30°N. c, Observed linear relationship between precipitation PC1 and meridional SSTA gradient, as defined in a. The correlation (R) and its P-value are indicated. Red dot and blue dots indicate the years of one extreme event (1983) and six moderate events (1990, 1992, 1993, 1998, 2010, and 2012), respectively. d, e, Same as a, b, but for the multi-model ensemble mean. f, Same as c, but for the multi-model ensemble. Springtime meridional swings of the Atlantic ITCZ are affected by the meridional SSTA gradient and the associated cross-equatorial wind anomalies near to the equator.

Extended Data Fig. 7 Multi-model consistency of one-century separate and two-century merged EOF analysis, and mean state change of precipitation.

Multi-model relationship of between PC1 variability (standard deviation of PC1) derived from separate and merged EOF during a, 20th century; b, 21st century, and c, changes (‘21st century’ minus ‘20th century’) in PC1 variability. Here the PC1 is unnormalized for comparison. A total of 23 out of 29 models project an increase in PC1 variability under separate and merged EOF. Red asterisks denote the multi-model ensemble mean values. The correlation (R) and P-value are indicated. d, Multi-model ensemble mean changes (‘21st century’ minus ‘20th century’) of MAM-averaged raw precipitation in the whole tropics. Stippled areas indicate where changes are statistically significant above the 95% confidence level according to a two-tailed Student’s t-test. There is no inter-model agreement on mean state change of precipitation in the tropical Atlantic, in contrast to the Pacific and Indian basins. e, Same as d, but for changes after grid-point precipitation is quadratically-detrended. The detrending process removes any mean precipitation trends before the EOF analysis.

Extended Data Fig. 8 Multi-model ensemble average of mean state changes.

Changes (‘21st century’ minus ‘20th century’) of ensemble average of MAM-averaged mean state for a, surface wind speed (m s-1) and b, latent heat flux (W m-2, positive values indicate an upward transfer of energy). Stippled areas indicate where changes are statistically significant above the 95% confidence level according to a two-tailed Student’s t-test. A reduced surface wind speed over the TNA region suppresses the upward latent heat flux here via WES feedback, leading to a faster TNA SST warming than the south.

Extended Data Fig. 9 Multi-model projection of variability of precipitation PC1 and TNA.

a, Variability of MAM-averaged precipitation PC1 among 29 selected models during the 20th century (blue bars) and 21st century (red bars) climate. A total of 23 out of 29 models (79%) generate an increase in precipitation PC1 variability under global warming. The multi-model ensemble mean (MMEM) values of variability during each period are shown in the last bars, superimposed with the error-bars indicating the 95% confidence level determined by a bootstrap test (see the ‘Statistical significance test’ section in the Methods). b, Same as a, but for variability of MAM-averaged TNA SST, which is defined as the amplitude of first principal component of EOF mode of the SST anomalies over the tropical north Atlantic domain (80°W-20°E, 0°-30°N; see the ‘Data, model outputs, EOF analysis and ITCZ definition’ section in the Methods). A total of 19 out of 29 models (66%) generate an increase in TNA variability under global warming. c, Linear relationship between changes (‘21st century’ minus ‘20th century’) in variability of precipitation PC1 and changes in variability of TNA SST, with their correlation (R) and its P-value indicated. Both the changes in precipitation PC1 and TNA variability are scaled by the increase in global mean SST of each model to enhance the inter-model comparability. Model with a greater increase in TNA variability tends to produce a greater increase in precipitation PC1 variability, leading to more occurrences of extreme meridional swings of Atlantic ITCZ.

Extended Data Fig. 10 Multi-model projection of extreme southward swings of the Atlantic ITCZ under global warming.

As in Fig. 2, but for extreme southward swings (red dots) and moderate southward swings (blue dots) during a, the 20th century (1900–1999) and b, the 21st century (2000–2099). There is nearly no change in frequency of extreme southward swing events under global warming, suggesting an asymmetric response of extreme meridional swings of the Atlantic ITCZ to global warming.

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Liu, Y., Cai, W., Lin, X. et al. Increased extreme swings of Atlantic intertropical convergence zone in a warming climate. Nat. Clim. Chang. 12, 828–833 (2022). https://doi.org/10.1038/s41558-022-01445-y

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