Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# 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 climate1,2,3,4,5. Local sea surface warming tends to increase precipitation, but this local control is difficult to disentangle from remote effects of conditions elsewhere. As an example of such a remote effect, El Niño Southern Oscillation (ENSO) events in the equatorial Pacific Ocean alter precipitation across the tropics. Atmospheric circulations associated with tropical precipitation are predominantly deep, extending up to the tropopause. Shallow atmospheric circulations6,7,8 affecting 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 our understanding of the ocean’s influence on weather and climate. Despite decades of research, persistent biases remain in many numerical model simulations12,13,14,15,16,17,18, including excessively wide tropical rainbands14,18, the ‘double-intertropical convergence zone problem’12,16,17 and too-weak responses to ENSO15. These biases demonstrate gaps in our understanding, reducing confidence in forecasts and projections. Here we use observations to show that seasonal tropical precipitation has a high sensitivity to local sea surface temperature. Our best observational estimate is an 80 per cent change in precipitation for every gram per kilogram change in the saturation specific humidity (itself a function of the sea 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 (closer to 80%) 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 about one millimetre per day or more. Our analyses 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, thus providing a focus for accelerated research to improve seasonal forecasts through multidecadal climate projections.

This is a preview of subscription content, access via your institution

## Relevant articles

• ### Revisiting mechanisms of the Mesoamerican Midsummer drought

Climate Dynamics Open Access 27 May 2022

## Access options

\$32.00

All prices are NET prices.

## 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 are available at the following websites: https://esgf-node.llnl.gov/projects/cmip5/; CMIP6: https://esgf-node.llnl.gov/projects/cmip6/; GTMBA: https://www.pmel.noaa.gov/gtmba/; TRMM: https://disc.gsfc.nasa.gov/datasets/TRMM_3B43_7/summary; GPCP data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA from their website at https://psl.noaa.gov/data/gridded/data.gpcp.html; COBE-SST2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA from their website at https://psl.noaa.gov/data/gridded/data.cobe2.html; HadISST: https://www.metoffice.gov.uk/hadobs/hadisst; ERSST: http://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v4/netcdf/. The QuikSCAT data were obtained from the NASA EOSDIS Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory, Pasadena, California (https://doi.org/10.5067/GHGMR-4FJ01). We acknowledge NOAA/ESRL PSD for the wind profiler data from https://psl.noaa.gov/psd3/boundary/MstToga/data_access.htmlSource data are provided with this paper.

## Code availability

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

## References

1. Philander, S. G. H. El-Nino Southern Oscillation phenomena. Nature 302, 295–301 (1983).

2. Pierrehumbert, R. T. Thermostats, radiator fins, and the local runaway greenhouse. J. Atmos. Sci. 52, 1784–1806 (1995).

3. Tian, B. J. Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias. Geophys. Res. Lett. 42, 4133–4141 (2015).

4. Back, L. E. & Bretherton, C. S. Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett. 33, L17810 (2006).

5. Sherwood, S. C., Bony, S. & Dufresne, J. L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

6. Trenberth, K. E., Stepaniak, D. P. & Caron, J. M. The global monsoon as seen through the divergent atmospheric circulation. J. Clim. 13, 3969–3993 (2000).

7. Back, L. E. & Bretherton, C. S. A simple model of climatological rainfall and vertical motion patterns over the tropical oceans. J. Clim. 22, 6477–6497 (2009).

8. Zhang, C. D., Nolan, D. S., Thorncroft, C. D. & Nguyen, H. Shallow meridional circulations in the tropical atmosphere. J. Clim. 21, 3453–3470 (2008).

9. Rui, L. & Fu, Y. Tropical precipitation estimated by GPCP and TRMM PR observations. Adv. Atmos. Sci. 22, 852–864 (2005).

10. Prakash, S., Mahesh, C. & Gairola, R. M. Comparison of TRMM Multi-satellite Precipitation Analysis (TMPA)-3B43 version 6 and 7 products with rain gauge data from ocean buoys. Remote Sens. Lett. 4, 677–685 (2013).

11. Bellon, G., Reitebuch, O. & Naumann, A. K. Shallow circulations: relevance and strategies for satellite observation. Surv. Geophys. 38, 1509–1528 (2017).

12. Li, G. & Xie, S. P. Tropical biases in CMIP5 multimodel ensemble: the excessive Equatorial Pacific cold tongue and double ITCZ problems. J. Clim. 27, 1765–1780 (2014).

13. Xie, S. P. et al. Towards predictive understanding of regional climate change. Nat. Clim. Chang. 5, 921–930 (2015).

14. Byrne, M. P. & Schneider, T. Energetic constraints on the width of the Intertropical Convergence Zone. J. Clim. 29, 4709–4721 (2016).

15. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: from CMIP3 to CMIP5. Clim. Dyn. 42, 1999–2018 (2014).

16. Zhang, X. X., Liu, H. L. & Zhang, M. H. Double ITCZ in coupled ocean-atmosphere models: from CMIP3 to CMIP5. Geophys. Res. Lett. 42, 8651–8659 (2015).

17. Hirota, N. & Takayabu, Y. N. Reproducibility of precipitation distribution over the tropical oceans in CMIP5 multi-climate models compared to CMIP3. Clim. Dyn. 41, 2909–2920 (2013).

18. Dixit, V., Geoffroy, O. & Sherwood, S. C. Control of ITCZ width by low-level radiative heating from upper-level clouds in aquaplanet simulations. Geophys. Res. Lett. 45, 5788–5797 (2018).

19. He, J. et al. Precipitation sensitivity to local variations in tropical sea surface temperature. J. Clim. 31, 9225–9238 (2018).

20. Rushley, S. S., Kim, D., Bretherton, C. S. & Ahn, M. S. Reexamining the nonlinear moisture-precipitation relationship over the tropical oceans. Geophys. Res. Lett. 45, 1133–1140 (2018).

21. Bretherton, C. S., Peters, M. E. & Back, L. E. Relationships between water vapor path and precipitation over the tropical oceans. J. Clim. 17, 1517–1528 (2004).

22. Kanemaru, K. & Masunaga, H. A satellite study of the relationship between sea surface temperature and column water vapor over tropical and subtropical oceans. J. Clim. 26, 4204–4218 (2013).

23. Tropical Rainfall Measuring Mission TRMM (TMPA/3B43) Rainfall Estimate L3 1 month 0.25 degree × 0.25 degree V7 (TRMM_3B43) https://disc.gsfc.nasa.gov/datasets/TRMM_3B43_7/summary (Goddard Earth Sciences Data and Information Services Center, 2011).

24. Huffman, G. J. et al. The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

25. Adler, R. F. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).

26. McPhaden, M. J. et al. The tropical ocean global atmosphere observing system: a decade of progress. J. Geophys. Res. 103, 14169–14240 (1998).

27. McPhaden, M. J. et al. RAMA: The Research Moored Array for African-Asian-Australian monsoon analysis and prediction. Bull. Am. Meteorol. Soc. 90, 459–480 (2009).

28. Bourles, B. et al. The PIRATA program: history, accomplishments, and future directions. Bull. Am. Meteorol. Soc. 89, https://doi.org/10.1175/2008BAMS2462.1 (2008).

29. Serra, Y. L., A’Hearn, P., Freitag, H. P. & McPhaden, M. J. ATLAS self-siphoning rain gauge error estimates. J. Atmos. Ocean. Technol. 18, 1989–2002 (2001).

30. Taylor, K. E., Williamson, D. & Zwiers, F. W. The sea surface temperature and sea-ice concentration boundary conditions for AMIP II simulations. In Progr. Clim. Model Diagnosis Intercomparison (PCMDI) report 60, https://pcmdi.llnl.gov/report/ab60.html (2000).

31. Allen, M. R. & Ingram, W. J. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 228–232 (2002).

32. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, https://doi.org/10.1029/2002JD002670 (2003).

33. Huang, B. Y. et al. Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and intercomparisons. J. Clim. 28, 911–930 (2015).

34. Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).

35. Biasutti, M., Sobel, A. H. & Kushnir, Y. AGCM precipitation biases in the tropical Atlantic. J. Clim. 19, 935–958 (2006).

36. Holloway, C. E. & Neelin, J. D. The convective cold top and quasi equilibrium. J. Atmos. Sci. 64, 1467–1487 (2007).

37. Nolan, D. S., Powell, S. W., Zhang, C. D. & Mapes, B. E. Idealized simulations of the Intertropical Convergence Zone and its multilevel flows. J. Atmos. Sci. 67, 4028–4053 (2010).

38. Knutson, T. R. & Manabe, S. Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean-atmosphere model. J. Clim. 8, 2181–2199 (1995).

39. Wu, Z. H. A shallow CISK, deep equilibrium mechanism for the interaction between large-scale convection and large-scale circulations in the tropics. J. Atmos. Sci. 60, 377–392 (2003).

40. Lin, Y. et al. TWP-ICE global atmospheric model intercomparison: convection responsiveness and resolution impact. J. Geophys. Res. Atmos. 117, https://doi.org/10.1029/2011JD017018 (2012).

41. Randall, D. A. Beyond deadlock. Geophys. Res. Lett. 40, 5970–5976 (2013).

42. Bony, S. & Dufresne, J. L. Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophys. Res. Lett. 32, https://doi.org/10.1029/2005GL023851 (2005).

43. Voldoire, A. et al. Evaluation of CMIP6 DECK experiments with CNRM-CM6–1. J. Adv. Model. Earth Syst. (2019).

44. Nie, J., Boos, W. R. & Kuang, Z. Observational evaluation of a convective quasi-equilibrium view of monsoons. J. Clim. 23, 4416–4428 (2010).

45. Stevens, B. & Bony, S. What are climate models missing? Science 340, 1053–1054 (2013).

46. Oueslati, B. & Bellon, G. Convective entrainment and large-scale organization of tropical precipitation: sensitivity of the CNRM-CM5 hierarchy of models. J. Clim. 26, 2931–2946 (2013).

47. Vial, J., Bony, S., Stevens, B. & Vogel, R. Mechanisms and model diversity of trade-wind shallow cumulus cloud feedbacks: a review. Surv. Geophys. 38, 1331–1353 (2017).

48. Fläschner, D., Mauritsen, T., Stevens, B. & Bony, S. The signature of shallow circulations, not cloud radiative effects, in the spatial distribution of tropical precipitation. J. Clim. 31, 9489–9505 (2018).

49. NASA SeaWinds on QuikSCAT Level 3 Surface Northward Wind for Climate Model Comparison (SNW-CMIP1) Version 1, https://doi.org/10.5067/QSSNW-CMIP1 (Physical Oceanography Distributed Active Archive Center, 2012).

50. Gage, K. S., Williams, C. R. & Ecklund, W. L. UHF wind profilers: a new tool for diagnosing tropical convective cloud systems. Bull. Am. Meteorol. Soc. 75, 2289–2294 (1994).

51. Hutcheon, J. A., Chiolero, A. & Hanley, J. A. Random measurement error and regression dilution bias. Br. Med. J. 340, c2289 (2010).

## 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. 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 the 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.

## Author information

Authors

### Contributions

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 of physical processes. J.K. provided knowledge of SST observational uncertainty and datasets.

### Corresponding author

Correspondence to Peter Good.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

Peer review information Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Extended data figures and tables

### Extended Data Fig. 1 Effect of low spatial resolution in GPCP satellite observations of ln(Precipitation).

The y axis is the regression gradient in validation against GTMBA rain-gauge data (that is, gradients in Fig. 1 for light blue and red symbols). The x axis is the horizontal grid dimension relative to TRMM (for example, the TRMM resolution is 0.25°, ten times smaller than the GPCP resolution of 2.5°, so the red symbol is placed at x = 10). Dark blue symbols represent results when TRMM data are regridded (by area averaging) to coarser grids. The coarser grids are chosen so that the grid box edges overlap the edges of the native TRMM grid. To give the errors context, the dash-dotted line marks the ratio between the largest and smallest model values of kqsat (2.5). The solid black line is a quadratic least-squares best-fit line through the TRMM-based data. The intercept of the TRMM best-fit curve at x = 0 (that is, infinitely fine grid) is very close to the value estimated on the TRMM native grid (light blue symbol), indicating that the TRMM grid is sufficiently fine for comparison with the rain-gauge data on seasonal timescales.

### Extended Data Fig. 2 Testing the method of estimating kqsat.

ad, Example results of the sortav method for TRMM precipitation and HadISST SST, for different seasons: mean vectors of anomalies in ln(Precipitation) (y axis) and qsat (x axis); kqsat is given by the gradient of the blue best-fit regression line. ASO, August–October; NDJ, November–January; FMA, February–April; MJJ, May–June. e, The y axis shows kqsat calculated after excluding the 9 years with the largest absolute value of the Niño3.4 index; the x axis shows the default kqsat (one symbol per model); kqsat is on average 6% lower when ENSO years are excluded, owing to a small sensitivity to the ENSO characteristic spatial pattern; but the model ranking is largely unchanged (r = 0.99). f, kqsat calculated for individual seasons versus the annual mean value. g, kqsat using only years 1995–1999 versus the full 25-year estimate. h, Variability (due to internal variability in SST patterns) in kqsat estimated from 25 years of data: for each coupled ocean–atmosphere model, kqsat is estimated both for the full historical run, and for all 25-year chunks. The cumulative distribution function of absolute percentage differences between the 25-year estimates and the full estimates (95% of samples are within 8% of the long-term value from the full historical run) is shown. Results for two methods of estimating kqsat are shown: our ‘sortav’ method (as used throughout the paper), and standard ordinary least-squares (OLS) regression between seasonal anomalies in ln(Precipitation) and qsat. i, comparing $${k}_{{\rm{qsat}}}^{{\rm{spatial}}-{\rm{temporal}}}$$ with kqsat; each cross represents one CMIP5 model. j, k, Cumulative distribution functions of climatological mean precipitation (j) and log(Climatological mean precipitation) (k), from HadGEM2-A, May–July season (the same picture is seen in other seasons).

### Extended Data Fig. 3 Model biases for the high, mid-range and low-kqsat models separately.

As in Fig. 2, for af, high-kqsat models; gl, mid-kqsat models; mr, low-kqsat models.

### Extended Data Fig. 4 Testing potential errors in the satellite validation against GTMBA.

a, b, Testing for regression dilution bias from error in TRMM observations: as in Fig. 1, but for TRMM versus GPCP (both interpolated to GTMBA sites and masked as in Fig. 1) (a) and GPCP versus TRMM (b). cf, Testing for effects of SST uncertainty on the binning: as in Fig. 1, but using ERSST (c, d) and COBE (e, f) SST datasets to bin the data.

### Extended Data Fig. 5 Regions where models are most sensitive to kqsat.

For each latitude of each region: the y axis shows Pearson correlation coefficients (r) between the 28 different CMIP5 model values kqsat, and the 28 CMIP5 model values of the logarithm of the precipitation ratio for that latitude and region (that is, the logarithm of the grey lines in Fig. 2a–f). Green bands mark the latitude intervals chosen to estimate the observational constraints on kqsat (ae: intervals chosen where |r|> 0.6; f, a band of most-negative r is chosen). Coefficients close to zero near 8° N in the Atlantic and East Pacific spatial patterns correspond to the latitude of the precipitation peak in most models (the model spread in the precipitation peak is scaled out; coefficients are not exactly zero because there is a small model spread in the latitude of the precipitation peak).

### Extended Data Fig. 6 Scatter plots underpinning the central observational estimate of kqsat.

a–g, Precipitation error index versus kqsat for each of the seven latitude intervals highlighted in Fig. 2. The y axes show the logarithm of the precipitation ratio, averaged over each latitude band, minus the equivalent value for TRMM observations, for CMIP5 (black) and CMIP6 (red) models. The dotted lines are linear least-squares fits (using CMIP5 data only). The vertical black line is the kqsat estimate for each latitude interval, from the intercept of the green line with zero error index (dotted line). h, Mean precipitation error index versus kqsat: the mean error index is averaged over the seven indices in the other panels (after the signs of the five indices with negative best-fit slopes were changed, to ensure a positive correlation with kqsat).

### Extended Data Fig. 7 Supporting results for observational estimate of the kqsat lower bound.

Estimating error, from internal variability, owing to the fact that the TRMM operational period only partly overlaps the time period simulated by the AMIP SST-forced models. Error magnitudes are estimated from the coupled ocean-atmosphere simulations, using differences between kqsat estimated from all possible overlapping 17-year (TRMM-like) and 25-year (AMIP-like) periods (with the same overlap as TRMM and the 25-year SST-forced model simulations). Results are given for two methods of estimating kqsat: our ‘sortav’ method (as used throughout the manuscript), and standard OLS regression between seasonal anomalies in ln(Precipitation) and qsat.

### Extended Data Fig. 8 Atmospheric circulation measures in CMIP5 and CMIP6 models.

ac, Thick lines are CMIP5 composite means, for the high-kqsat subset (magenta); low-kqsat (blue) subset and intermediate kqsat (gold). Thin grey lines are individual models (CMIP5 and CMIP6). Descent (5° S–1° N), mid (1°–7° N) and ascent (7°–13° N) regions are marked by vertical dotted lines in Fig. 5c–e. dh, Each symbol represents one CMIP5 (black) or CMIP6 (red) model. Title gives Pearson correlation coefficient. d, Shallow descent versus kqsat; the vertical line marks our best estimate of kqsat. e, Shallow ascent versus shallow descent. f, Shallow meridional return flow versus shallow descent. g, Shallow versus very-shallow meridional wind, over the Galápagos Islands: the negligible correlation indicates different physical processes at these two levels. h, Deep versus shallow ascent. i, Standard deviation, across models, of the pressure velocity (wap) at each pressure level.

## Rights and permissions

Reprints and Permissions

Good, P., Chadwick, R., Holloway, C.E. et al. High sensitivity of tropical precipitation to local sea surface temperature. Nature 589, 408–414 (2021). https://doi.org/10.1038/s41586-020-2887-3

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41586-020-2887-3

• ### Revisiting mechanisms of the Mesoamerican Midsummer drought

• J. L. García-Franco

Climate Dynamics (2023)

• ### Thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects

• Jiabo Yin
• Shenglian Guo
• Yao Zhang

Science China Earth Sciences (2023)

• ### Projection of droughts and their socioeconomic exposures based on terrestrial water storage anomaly over China

• Jiabo Yin
• Shenglian Guo
• Jinghua Xiong

Science China Earth Sciences (2022)

• ### A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 2: global performance and future changes

• Kuniko Yamazaki
• David M. H. Sexton
• Glen R. Harris

Climate Dynamics (2021)