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High sensitivity of tropical precipitation to local sea-surface temperature

Abstract

Precipitation and atmospheric circulation are the coupled processes through which tropical ocean surface temperatures drive global weather and climate1–5. Local ocean surface warming tends to increase precipitation, but this local control is hard to disentangle from remote effects of conditions elsewhere. Such remote effects occur, for example, from El Niño Southern Oscillation (ENSO) events in the equatorial Pacific, which alter precipitation across the tropics. Atmospheric circulations associated with tropical precipitation are predominantly deep, extending up to the tropopause. Shallow atmospheric circulations6–8 impacting the lower troposphere also occur, but the importance of their interaction with precipitation is unclear. Uncertainty in precipitation observations9,10 and limited observations of shallow circulations11 further obstruct understanding of the ocean’s influence on weather and climate. Despite decades of research, persistent biases remain in many numerical model simulations12–18, including excessively wide tropical rainbands14,18, the ‘double-intertropical convergence zone (ITCZ) problem’12,16,17 and too-weak responses to ENSO15. These demonstrate stubborn gaps in our understanding, reducing confidence in forecasts and projections. Here we show that the real world has a high sensitivity of seasonal tropical precipitation to local sea-surface temperature. Our best observational estimate is 80% precipitation change per g/kg change in the saturation specific humidity (itself a function of the ocean surface temperature). This observed sensitivity is higher than in 43 of the 47 climate models studied, and is associated with strong shallow circulations. Models with more realistic sensitivity have smaller biases across a wide range of metrics. Our results apply to both temporal and spatial variation, over regions where climatological precipitation is around 1 millimetre per day or greater. Novel analysis of multiple independent observations, physical constraints and model data, underpin these findings. The spread in model behaviour is further linked to differences in shallow convection, providing a focus for accelerated research, to improve seasonal forecasts through multidecadal climate projections.

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Data availability

Datafiles with estimates of kqsat for models and observations, along with sample plotting code, are available from https://doi.org/10.5281/zenodo.3878691. Data from the integration of CNRM-CM6 with the CM5 convection scheme (denoted CNRM-CM6-conv5) are available from https://doi.org/10.5281/zenodo.3875005. Model and observational data is available at the following websites. CMIP5: https://cmip.llnl.gov/cmip5/; CMIP6: https://esgf-node.llnl.gov/projects/cmip6/; GTMBA: https://www.pmel.noaa.gov/gtmba/; TRMM: https://pmm.nasa.gov/data-access/downloads/trmm; GPCP and COBE: https://www.esrl.noaa.gov/psd/; HadISST: https://www.metoffice.gov.uk/hadobs/hadisst; ERSST: http://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v4/netcdf/.

Code availability

Python code for calculating kqsat, including the sortav regression routine, is available from https://doi.org/10.5281/zenodo.3878691.

Acknowledgements

This work was supported jointly by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra, and by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). S.S.R. was supported by the National Aeronautics and Space Administration Grant 80NSSC17K0227 and the Korean Meteorological Administration Research and Development Program under grant KMI2018-03110. We acknowledge the GTMBA Project Office of NOAA/PMEL for making the GTMBA data available. The QuikSCAT data were obtained from the NASA EOSDIS Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory, Pasadena, California (http://dx.doi.org/10.5067/GHGMR-4FJ01). We acknowledge NOAA/ESRL PSD for the wind profiler data. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Methods) for producing and making available their model output. For CMIP the US 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. GPCP data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/. COBE-SST2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/.

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P.G. conceived and designed the study and performed the analysis. All authors contributed to scientific interpretation and wrote the manuscript. R.R. performed the CNRM model simulations. P.G., R.C., C.E.H. and R.R. contributed understanding on physical processes. J.K. provided knowledge of SST observational uncertainty and datasets.

Corresponding author

Correspondence to Peter Good.

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The authors declare no competing interests.

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Good, P., Chadwick, R., Holloway, C.E. et al. High sensitivity of tropical precipitation to local sea-surface temperature. Nature (2020). https://doi.org/10.1038/s41586-020-2887-3

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