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Moist heatwaves intensified by entrainment of dry air that limits deep convection

Nature Geoscience volume 17, pages 837–844 (2024)Cite this article

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Abstract

Moist heatwaves in the tropics and subtropics pose substantial risks to society, yet the dynamics governing their intensity are not fully understood. The onset of deep convection arising from hot, moist near-surface air has been thought to limit the magnitude of moist heatwaves. Here we use reanalysis data, output from the Coupled Model Intercomparison Project Phase 6 and model entrainment perturbation experiments to show that entrainment of unsaturated air in the lower-free troposphere (roughly 1–3 km above the surface) limits deep convection, thereby allowing much higher near-surface moist heat. Regions with large-scale subsidence and a dry lower-free troposphere, such as coastal areas adjacent to hot and arid land, are thus particularly susceptible to moist heatwaves. Even in convective regions such as the northern Indian Plain, Southeast Asia and interior South America, the lower-free tropospheric dryness strongly affects the maximum surface wet-bulb temperature. As the climate warms, the dryness (relative to saturation) of the lower-free tropospheric air increases and this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heatwaves.

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Fig. 1: Spatial distribution of extreme moist heat days and the associated convective conditions.
Fig. 2: The joint frequency distribution between boundary-layer instability and lower-free tropospheric dryness measures, and its relationship to extreme WBTs.
Fig. 3: Large-scale dynamical conditions of the convective regime, the dry-inhibition regime and days with extreme WBT values.
Fig. 4: Climate model simulated instability/dryness joint frequency distribution and extreme moist heat days.
Fig. 5: Changes in the joint frequency distribution of the instability/dryness measures, and decomposing the increases in extreme MSE2m.

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Data availability

ERA5 hourly reanalysis data can be downloaded from the ECMWF Climate Data Store (https://cds.climate.copernicus.eu). CMIP6 model outputs used in this study can be downloaded from the CMIP6 data archive (https://esgf-node.llnl.gov/search/cmip6/). The CAM5 entrainment experiment data used for this study, and post-processed data underlying all figures, in both the main text and the extended data, have been deposited in Dryad (https://doi.org/10.5061/dryad.1ns1rn92v).

Code availability

A Python script to calculate the WBT with the Davies-Jones method can be found public at Dr. Xianxiang Li’s Github repository (https://github.com/smartlixx/WetBulb). The MetPy package can be installed following guidance from its website (https://unidata.github.io/MetPy/latest/index.html). Codes for conducting the analyses are available upon request.

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Acknowledgements

The authors acknowledge support from Department of Energy Regional and Global Model Analysis award DE-SC0023244, National Science Foundation awards AGS-1936810 (S.Q.D., F.A. and J.D.N.) and AGS-2225956 (F.A.) and National Oceanic and Atmospheric Administration award NA21OAR4310354 (S.Q.D., F.A. and J.D.N.). The authors appreciate European Centre for Medium-Range Weather Forecasts, climate modelling groups and the Earth System Grid Federation for producing, archiving and making available the ERA5 and CMIP6 model data.

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  1. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Suqin Q. Duan, Fiaz Ahmed & J. David Neelin

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  1. Suqin Q. Duan
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Contributions

All authors designed the study and contributed to the interpretation of results and writing of the manuscript. S.Q.D. conducted data analysis and visualization. The CAM5 entraining experiments were conducted by F.A.

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Correspondence to Suqin Q. Duan.

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Nature Geoscience thanks Larissa Back, Colin Raymond and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 The joint frequency distribution of the instability/dryness measures for land grid cells only.

Similar to Fig. 2, but the left panel is based on days over land grid cells only. Fig. 2b is repeated in the right panel for reference.

Extended Data Fig. 2 Mapping wet-bulb temperature values to the boundary-layer instability measure.

An illustration of the correspondence of wet-bulb temperature (WBT) values to the BL instability: frequency of non-raining (daily mean precipitation rate P < 0.5 mm/day; brown shadings) and raining (P ≥ 6 mm/day; green shadings) conditions, and the top 1% wet bulb temperature days (red dots), as a function of the boundary-layer instability and wet-bulb temperature, for (a) land plus coast, and (b) ocean in ERA5. The cyan stars show the mean for raining conditions, and the dark red stars show the mean for the top 1% WBT days.

Extended Data Fig. 3 The joint frequency distribution of the instability/dryness measures in individual climate models.

Frequency of raining/non-raining conditions (green/drown shading) as a function of the boundary-layer instability measure and the lower-free tropospheric (LFT) dryness measure, similar to Fig. 2 but for the base climate in individual models. The first six panels show the distribution for land plus coast, and the latter six panels show the distribution for ocean.

Extended Data Fig. 4 Changes in the joint frequency distribution of the instability/dryness measures between the warm and the base climate states in individual climate models.

Changes in the joint frequency distribution of the boundary-layer instability measure and the lower-free tropospheric (LFT) dryness measure between the 4 × CO2 and the base climate states. Similar to Fig. 5a–b but for individual models. The first six panels show the distribution for land plus coast, and the latter six panels show the distribution for ocean.

Extended Data Fig. 5 The joint frequency distribution of the instability/dryness measures when only grid cells within 20∘S–20∘N are included.

Sensitivity test (see Methods M1) for comparison to Fig. 2a–b, but only including grid cells within 20∘S–20∘N. The frequency distributions show generally similar patterns: raining samples align along the entraining QE line and a large number of non-raining samples exceeding the idealized QE–WTG limit form the dry-inhibition regime. The dry-inhibition regime extends slightly less far towards very subsaturated LFT, that is, the most subsaturated points occur outside 20∘S–20∘N. For land plus coast, a density center on the stable (left) side of the idealized QE–WTG limit is weaker in this figure since many of those conditions are over the subtropical desert. As in Fig. 2, the top 1% wet-bulb temperature days occur at the highest values of the BL instability measure and are distributed in both the convective and the dry-inhibition regimes. The similar patterns confirm that the behavior regimes we show in the main text are robust to the inclusion of subtropical latitudes 20-30∘S and N.

Extended Data Fig. 6 The joint frequency distribution of the instability/dryness measures when not considering the weak temperature gradient (WTG) component.

Sensitivity test (see Methods M1) for comparison to Fig. 2a–b, but without including the WTG component. When not considering WTG, Subsat850 compensates for the excessive MSE2m to the local column \({{{{\rm{MSE}}}}}_{500}^{* }\) to maintain convective neutrality. The frequency distributions in this figure (a, land plus coast; b, ocean) show similar patterns compared with Fig. 2a,b, indicating that deviations from WTG play a minor role compared with the LFT entrainment effect in explaining samples exceeding the idealized QE constraint.

Extended Data Fig. 7 Cross validation of wet-bulb temperatures calculated by two methods.

A comparison of wet bulb temperatures (WBTs) calculated using the Davies-Jones method (WBT_DJ) versus the MetPy package (WBT_MetPy). Each dot represents one day at one grid cell during 1980–1989 warm season in ERA5. The gray line shows the 1:1 for reference.

Extended Data Fig. 8 An overview of samples used from the ERA5 reanalysis data.

Overview of sample counts (left panel) and percentages (right panel) for different categories of precipitation rate over land, ocean and coast in ERA5. The total sample pool for each bar consists of 10 years × 150 warm-season days/year × the number of land, ocean and coast grid cells.

Extended Data Fig. 9 The absolute sample counts for the frequency distribution of the instability/dryness measures.

The same frequency distributions as in Fig. 2a–b, but with two contours marking the absolute counts 10 and 100 in each bin for raining and non-raining conditions respectively. The edges of the frequency distribution with the lowest count values for raining conditions (green shadings) are masked for sake of clear visualization; the unmasked distribution cover 99% of the raining samples.

Extended Data Fig. 10 An overview of samples used from the CESM2 model data.

Overview of sample counts (left panel) and percentages (right panel) for different categories of precipitation rate over land, ocean and coast in CESM2 for the control and the 4 × CO2 experiments. The total sample pool for each bar consists of 10 years × 150 warm-season days/year × the number of land, ocean and coast grid cells.

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Duan, S.Q., Ahmed, F. & Neelin, J.D. Moist heatwaves intensified by entrainment of dry air that limits deep convection. Nat. Geosci. 17, 837–844 (2024). https://doi.org/10.1038/s41561-024-01498-y

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