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High sensitivity of tropical precipitation to local sea surface temperature

Nature volume 589, pages 408–414 (2021)Cite this article

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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.

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Fig. 1: Validating observations of ln(Precipitation) from satellites.
Fig. 2: Model precipitation biases.
Fig. 3: The region of applicability of kqsat.
Fig. 4: High sensitivity of precipitation to SST, and strong shallow circulations, in the real world.
Fig. 5: Linking kqsat to shallow circulations.

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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 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.html. Source 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.

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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.

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Authors and Affiliations

  1. MetOffice Hadley Centre, Exeter, UK

    Peter Good, Robin Chadwick, John Kennedy & Jason A. Lowe

  2. Global Systems Institute, University of Exeter, Exeter, UK

    Robin Chadwick

  3. Department of Meteorology, University of Reading, Reading, UK

    Christopher E. Holloway

  4. Priestley International Centre for Climate, University of Leeds, Leeds, UK

    Jason A. Lowe

  5. CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

    Romain Roehrig

  6. Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA

    Stephanie S. Rushley

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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.

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Correspondence to Peter Good.

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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.

a–d, 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 a–f, high-kqsat models; g–l, mid-kqsat models; m–r, 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). c–f, 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 (a–e: 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.

a–c, 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. d–h, 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.

Extended Data Table 1 Column-integrated DSE budget for the descent region, August–October, averaged over the high-, mid- and low-kqsat groups of CMIP5 models
Full size table
Extended Data Table 2 Supporting results for observational estimate of the kqsat lower bound
Full size table

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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

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