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Warm pool ocean heat content regulates ocean–continent moisture transport

Nature volume 612, pages 92–99 (2022)Cite this article

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Abstract

The Indo-Pacific Warm Pool (IPWP) exerts a dominant role in global climate by releasing huge amounts of water vapour and latent heat to the atmosphere and modulating upper ocean heat content (OHC), which has been implicated in modern climate change1. The long-term variations of IPWP OHC and their effect on monsoonal hydroclimate are, however, not fully explored. Here, by combining geochemical proxies and transient climate simulations, we show that changes of IPWP upper (0–200 m) OHC over the past 360,000 years exhibit dominant precession and weaker obliquity cycles and follow changes in meridional insolation gradients, and that only 30%–40% of the deglacial increases are related to changes in ice volume. On the precessional band, higher upper OHC correlates with oxygen isotope enrichments in IPWP surface water and concomitant depletion in East Asian precipitation as recorded in Chinese speleothems. Using an isotope-enabled air–sea coupled model, we suggest that on precessional timescales, variations in IPWP upper OHC, more than surface temperature, act to amplify the ocean–continent hydrological cycle via the convergence of moisture and latent heat. From an energetic viewpoint, the coupling of upper OHC and monsoon variations, both coordinated by insolation changes on orbital timescales, is critical for regulating the global hydroclimate.

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Fig. 1: Modern IPWP upper OHC and its influence on δ18Op.
Fig. 2: Precession and obliquity changes in the proxy reconstructed and modelled IPWP upper OHC.
Fig. 3: Precession-dominated changes in the indices of low-latitude hydrological cycle.
Fig. 4: Ocean-continent δ18O fractionation coupled with OHC and monsoon.

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Data availability

All data are presented in the main text and additional source data are stored in the Zenodo database (https://doi.org/10.5281/zenodo.6988959). The modern observed WOA2013, SODA and ERSST datasets are available at https://www.ncei.noaa.gov/data/oceans/woa/WOA13/DATAv2/, http://iridl.ldeo.columbia.edu/SOURCES/.CARTON-GIESE/.SODA/.v2p2p4/ and https://psl.noaa.gov/data/gridded/data.noaa.ersst.v4.html, respectively.

Code availability

Codes for GISS_ModelE2-R (version modelE2_AR_branch.2017.11.02_ 07.50.01) and CESM (version 1.0.4) are publicly available at https://simplex.giss.nasa.gov/snapshots/ and https://www.cesm.ucar.edu/models/cesm1.0/, respectively. Model outputs were processed using the NCAR Command Language (NCL, version 6.6.2, available at https://www.ncl.ucar.edu) and plotted using the Grid Analysis and Display System (GrADS, version 2.0.2, available at http://www.iges.org/grads/grads.html). Associated data processing scripts are available at https://doi.org/10.5281/zenodo.6988686.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grants 42188102, 91958208, 41976047 and 42176053) and the Shanghai Science and Technology Commission (no. 21NL2600200). For this research, we used samples provided by the cruises of Ocean Drilling Program, French R/V Marion Dufresne, German R/V Sonne and Chinese R/V Kexue-1. We thank P. Qiao, X. Cheng, X. Jiang, L. L. Hamady, D. Pak, G. Paradis, T. Guilderson and C. Zhou for lab support.

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Authors and Affiliations

  1. State Key Laboratory of Marine Geology, Tongji University, Shanghai, China

    Zhimin Jian, Yue Wang, Haowen Dang, Zhongfang Liu, Haiyan Jin & Xingxing Wang

  2. MARUM-Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany

    Mahyar Mohtadi

  3. Department of Marine and Coastal Science, Rutgers University, New Brunswick, NJ, USA

    Yair Rosenthal

  4. Department of Earth and Planetary Sciences, Rutgers University, New Brunswick, NJ, USA

    Yair Rosenthal

  5. Department of Earth Science, University of California, Santa Barbara, CA, USA

    David W. Lea

  6. Second Institute of Oceanography, Ministry of Natural Resource, Hangzhou, China

    Liming Ye

  7. Institute of Geosciences, Christian-Albrechts-Universität, Kiel, Germany

    Wolfgang Kuhnt

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  1. Zhimin Jian
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Contributions

Z.J. designed the research; Y.W. ran the numerical simulation; Z.J., H.D. and D.W.L. took the lead in the experiments, assisted by H.J., L.Y. and X.W.; M.M., Y.R., D.W.L., Z.L. and W.K. helped with the interpretation; Z.J., Y.W. and H.D. wrote the first draft, and all authors discussed and commented on the results and the manuscript.

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Correspondence to Zhimin Jian, Yue Wang or Haowen Dang.

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Extended data figures and tables

Extended Data Fig. 1 Relationships of precipitation and δ18Op in East Asia with SST and upper OHC of the IPWP over 1970–2010.

a, Sea surface temperature anomaly (SSTA) of the annual mean ERSST (V4)86. b, Annual mean OHC anomaly above 20 °C isotherm depth over IPWP (15°S-15°N, 110°E-160°W), calculated from the SODA reanalysis dataset63. c,d, Annual mean anomalies of East Asian precipitation (black, d) and associated δ18Op (blue, c) (15°N-40°N, 85°E-125°E), based on the SWING2 GISS historical nudged simulation (over the years ~1970–2008)85. Time series in a–d are calculated by subtracting the temporal average of each variable. Vertical bars indicate the major periods of global warming hiatus. Dashed thick lines are long-term trends of 3-order polynomial fitting. e, Modern La Niña-associated annual mean OHC anomalies above 20 °C isotherm depth from SODA dataset, shown as regression coefficients against normalized time series of Southern Oscillation Index87 (SOI, grey line in b). f, As in e, but for La Niña-associated annual mean precipitation changes from the GISS historical simulation.

Extended Data Fig. 2 Age models of cores SO18480-3 and MD98-2162.

a, The age-model of core MD01-2378 based on its benthic foraminifera δ18O correlated to the global stacked benthic foraminifera δ18O (LR04 stack)49. b,c, Comparisons of the G. ruber δ18O of cores SO18480-3 (b) and MD98-2162 (c) to core MD01-2378. Arrows mark the AMS 14C dates performed on planktic foraminifera G. ruber, and crosses mark the δ18O-derived age control points.

Extended Data Fig. 3 Time series of original SSTs and TWTs.

a, SSTs for core ODP807 (purple) and KX21-2 (green). b, SSTs for MD10-3340 (blue) and SO18480-3 (red). c, SST for MD01-2162 (dark blue). d, TWTs for core ODP807 (purple) and KX21-2 (green). e, TWTs for MD10-3340 (blue) and SO18480-3 (red). The global stacked benthic foraminifera δ18O record (LR04 δ18Obenthic)49 and precession parameter are also given in a–c and in d,e for comparison, respectively. Vertical bars indicate glacial marine isotope stages.

Extended Data Fig. 4 Spectral amplitudes of original TWT time series.

a, ODP807; b, KX21-2; c, GeoB17426-3; d, MD06-3067; e, MD01-2386; f, MD10-3340; g, SO18480-3; h, MD01-2378; i, SO18460. The spectral amplitudes, calculated by the Redfit software (window = rectangle, segments = 9, oversample = 2 or 1), are shown as the ratio relative to theoretical red noise.

Extended Data Fig. 5 Modern observational constraints for proxy-based OHC calculation.

a, Climatological vertical profiles of annual mean temperature from WOA1361,62 (coloured lines) at locations of the nine sediment cores with paired SST and TWT records. Colour-filled dots mark the habitat depth of P. obliquiloculata for each core according to Mg/Ca-derived TWT averaged during the early–middle Holocene (6–10 ka), with a mean depth of 121 m and mean TWT of 23.2 °C (grey bars). Vertical dashed lines in a show the 20 °C and 26 °C isotherms, which range from ~70 to ~122 m and from ~110 to ~188 m in the IPWP (15°S-15°N, 110°E-160°W), respectively. b, Similar to a, but for potential density (coloured lines and dots), with an average of 23.8σθ (where 𝜎𝜃 denotes the potential density with reference to sea surface pressure, subtracting 1,000 for simplification) at the habitat depth of P. obliquiloculata. According to the shallowest 26 °C isotherm depth of these nine cores, dashed line in (b) marks the boundary between the mixed layer (0–70 m, mean density = 21.84σθ) and the upper thermocline layer (70-200 m, mean density = 23.64 σθ). c, Time series of the 26 °C isotherm depth (D26) and vertical temperature gradient between 0 m and 121 m (∆T0m-121m), calculated by averaging over the IPWP from SODA reanalysis dataset63. d, Linear regression relationship between D26 and ∆T0m-121m (r = −0.85), that is used for assessing past changes of IPWP D26 from those of palaeoceanographic proxy-based ∆T (∆T = SST − TWT).

Extended Data Fig. 6 Obliquity cycles in the IPWP upper OHC and Southern Hemisphere palaeoclimate records.

a, The obliquity (blue) and precession parameters (grey). b, IPWP OHC stack anomaly (black), compared with the surface air temperature from Antarctic EPICA Dome C ice core (EDC SAT; ice blue) and EDC noble-gas reconstructed global mean ocean temperature anomalies (∆MOT, blue squares with vertical bars showing the 1σ error)8,9. c, TWT stack of the Ontong Java Plateau sites (OJP, based on cores KX21-2 and GeoB17426, purple) and the Mg/Ca-SST record of the extratropical Southwest Pacific core MD97-2120 (MD2120 SST, green). In a–c, the Pmin and obliquity maxima are shown in brown and light purple vertical bars, respectively. d–f, Similar to those in Fig. 3 but for the spectral/cross spectral (relative to the obliquity maxima) results of IPWP OHC stack, OJP TWT stack, EDC SAT and MD2120 SST.

Extended Data Fig. 7 δ18O signatures of low-latitude hydrological cycle from the IPWP to Asian continent.

a, The IPWP δ18Osw stack (green, detrended), Chinese speleothem δ18Ocave (orange)31 and their differences (∆δ18Oocean–continent, black), in comparison with another measurement of the low-latitude hydrological cycle (namely, the Dole effect, ∆DE*, blue)33. b,c, Spectral powers of these time series (solid lines) shown as original spectral amplitude (b) and the ratio relative to theoretical red noise (c); dashed lines denote their 95% confidence levels. Note that for calculating ∆δ18Oocean–continent, the δ18Ocave is divided by a ratio of 7.85 according to the standard deviations of detrended δ18Osw (0.154) and δ18Ocave (1.209), which is assumed to stand for the ocean–continent δ18O fraction effect that will amplify the changes in Asian δ18Op.

Extended Data Fig. 8 Time series of hydroclimate indices of GISS_ghg experiment.

a, Regional averaged δ18Osw (green), δ18Op (blue) and evaporated water vapour δ18Oevapor (brown) over IPWP (5°S-15°N, 140°E-170°E). b, Similar to a but for the evaporation (brown) and precipitation (blue) rates over the IPWP. c, Regional averaged δ18Op (orange) and local hydrological balance (evaporation minus precipitation, E-P, black) over East Asia (15°N-40°N, 85°E-125°E), which are out-of-phase with the IPWP E-P (sapphire blue). Regions for IPWP and East Asia are shown in Extended Data Fig. 9. The unit of mm day−1 can be converted to latent heat by multiplying 9 × 108 J m−2 (≈latent heat of precipitation at 0 °C: 2.5 × 106 J kg−1, and multiplied by 360 days). d, Differences of hydrological balance (E-P, purple) and δ18O (∆δ18Oocean–continent, green) between IPWP and East Asia in the GISS simulation, in comparison with the proxy-reconstructed ∆δ18Oocean–continent (grey). e, Annual mean IPWP OHC from experiments CESM_ghg (red) and GISS_ghg (dark grey). All the GISS simulated time series are linearly detrended and 9-point or 18-point (only in b) smoothed. To calculate modelled ∆δ18Oocean–continent, the Asian δ18Op is divided by a ratio of 7.5 according to standard deviations of detrended IPWP δ18Osw (0.076) and Asian δ18Op (0.569) in a similar rationale as shown in Extended Data Fig. 7.

Extended Data Fig. 9 Multi-model comparison of the spatial patterns of precession-forced changes in different hydroclimate indices.

a,b, Boreal summer (JAS) SST (shading), air pressure at surface (PS, shading) and 850-hPa wind (vector) anomalies at Pmin from transient experiment CESM_ghg. c, JAS δ18Op differences between Pmin and Pmax from two equilibrium experiments of iCESM (Pmin minus Pmax)36. d–i, Outputs from transient experiment GISS_ghg, including JAS SST (d), PS & 850 hPa wind (e), evaporated vapour δ18O (δ18Oevap) (f), precipitation (g), vertical integrated atmospheric vapour content (h), and evaporation (i). Except for c, white shading denotes areas not significant at 95% confidence level of two-tail t-test. These patterns are shown as regression coefficients against the normalized time series of precession parameter, which are multiplied by −1 to demonstrate the effect of Pmin relative to Pmax. Yellow and black boxes are used to define those time series of IPWP (5°S-15°N, 140°E-170°E) and of East Asia (15°N-40°N, 85°E-125°E) in Extended Data Fig. 8, respectively.

Extended Data Fig. 10 Contributions of oceanic feedback to precession-forced ocean–continent moisture transport.

a, Boreal summer (JAS) air pressure at surface (PS) and 850 hPa wind differences between Pmin and Pmax from decoupled experiments with fixed modern SST (CESM-atm-alone experiments). b, The same as a, but for differences of atmospheric moisture transport flux divergence (indicated by P-E, negative values stand for moisture source). Variables in c and d are the same as a and b, respectively, but for differences of the Pmin-minus-Pmax differences (or experiments CESM-dyn-ocn minus CESM-atm-alone), highlighting the role of OHC associated oceanic feedback. e,f, Similar to c, but for evaporation and precipitation, respectively. Yellow and black boxes in a,c are defined by negative and positive anomalies of surface air pressure in c, respectively, and box-averaged values are shown as numbers to quantify the role of oceanic feedback. Similarly, those boxes in b,d–f) are defined by centres of anomalous atmospheric moisture transport flux divergences in d (yellow for source and black for sink). Yellow arrows and symbol in d show the schematic pathways of anomalous moisture transport from source to sink regions due to oceanic feedback, including the mean circulation sketched from vectors in c and contribution of tropical cyclones (similar to Fig. 4e). White shading denotes areas not significant at 95% confidence level of two-tail t-test.

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Jian, Z., Wang, Y., Dang, H. et al. Warm pool ocean heat content regulates ocean–continent moisture transport. Nature 612, 92–99 (2022). https://doi.org/10.1038/s41586-022-05302-y

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