# Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection

## Cite this dataset

Duan, Suqin Q.; Ahmed, Fiaz; Neelin, J. David (2024). Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection [Dataset]. Dryad. https://doi.org/10.5061/dryad.1ns1rn92v

## 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 heat waves. Here, we use reanalysis data, and 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 heat waves. 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; this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heat waves.

## README: Data associated with the manuscript "Moist heatwaves intensified by entrainment of dry air that limits deep convection"

https://doi.org/10.5061/dryad.1ns1rn92v

Archived here are the Community Atmospheric Model version 5 (CAM5) entrainment experiment data and the post-processed data underlying all the figures, in the manuscript "Moist heatwaves intensified by entrainment of dry air that limits deep convection".

Please cite this dataset (and the manuscript) if you are using the data for creating other products. Data were processed by Suqin Duan (sqduan@ucla.edu), and the CAM5 entrainment experiments were conducted by Fiaz Ahmed (fiaz@ucla.edu). Questions and comments can be addressed to both sqduan@ucla.edu and fiaz@ucla.edu.

### Description of the data and file structure

##### CAM5 entrainment experiment data

The following two files contain the original 3-hourly model output of temperature (T), specific humidity (Q), and geopotential (Z) at the reference level, and specific pressure levels (T500, Z500, T850, Q850, Z850), surface precipitation rate (Pr), surface pressure (Ps), and a few post-processed variables including MSE2m, Subsat850, MSEsat500, and MSE2m - MSEsat500, during 150 days in the warm season and in 30S--30N, from the CAM5 non-entraining and default entraining convection experiments.

MSEmeasures_150dayLat30_Entr0.nc

MSEmeasures_150dayLat30_Entr1.nc

List of variables in the data files:

* T500 [K]: temperature at 500 hPa

* Z500 [m]: geopotential height at 500 hPa

* T850 [K]: temperature at 850 hPa

* Q850 [kg/kg]: specific humidity at 850 hPa

* Z850 [m]: geopotential height at 850 hPa

* T [K]: temperature at the reference height (2m)

* Q [kg/kg]: specific humidity at the reference height (2m)

* Z [m]: height above the sea surface at the reference height (2m)

* Pr [m/s]: surface precipitation rate (times 1e3 x 24 x 3600 to convert to units of mm/day)

* fMSE [J/kg]: moist static energy at the reference height, MSE2m. Moist static energy is defined as MSE = c_p T + L_v q + gz, where c_p is the specific heat of dry air, T is temperature, L_v is the latent heat of vaporization, q is specific humidity, g is the gravitational acceleration, and z is the height above the surface

* fMSEsat500 [J/kg]: saturated moist static energy at 500 hPa, MSEsat500. Saturated moist static energy, defined as MSEsat = c_p T + L_v qsat + gz, where qsat is saturated specific humidity.

* diff_MSEMSEsat_5002m [J/kg]: local column MSE2m - MSEsat500. This is used to calculate the boundary-layer (BL) instability which also includes a term representing local deviations from WTG.

* SubSat850 [J/kg]: saturation deficit at 850 hPa

* Ps [Pa]: surface pressure

##### Post-processed data underlying the Figures

Note: values of MSE, Subsat, BL instability measure (named 'fMSEdev...'), and LFT dryness measure (named 'SubSatPlus...') in the datasets are in units of J/kg. In the manuscript figures, we present data in units of kJ/kg. Also note that, for the joint distribution plots in the manuscript, (x10^3) beside the color bar label "Fractional frequency" means Fractional frequency x 10^3 is the value shown in the color bar.

**Main Text:**

Figure 1:

- MappedTop1ST_ERA5_MSEmeasures_150dayLat30.nc

Variables in the data files (variables are sampled over [land, ocean, coast, landcoast]; switch "land" for other types of surfaces):

* saveLon_pi_top1ST_land: Longitude for the mapped top 1% WBT

* saveLat_pi_top1ST_land: Latitude for the mapped top 1% WBT

* saveCountMapped_pi_top1ST_land: Count for the mapped top 1% WBT

* saveFracCountMapped_pi_top1ST_land: Fractional count for the mapped top 1% WBT; the 'fraction' is relative to the total days*gridcells of the sampled top 1%

* savefMSEdevMapped_pi_top1ST_land: Contitional BL instability for the mapped top 1% WBT; the 'fraction' is relative to the total days*gridcells of the sampled top 1%

* saveSubSatPlusMapped_pi_top1ST_land: Conditional LFT dryness measure for the mapped top 1% WBT; the 'fraction' is relative to the total days*gridcells of the sampled top 1%

* saveLon_pi_top1STMSE2m_land: Longitude for the mapped top 1% MSE2m

* saveLat_pi_top1STMSE2m_land: Latitude for the mapped top 1% MSE2m

* saveCountMapped_pi_top1STMSE2m_land: Count for the mapped top 1% MSE2m

* saveFracCountMapped_pi_top1STMSE2m_land: Fractional count for the mapped top 1% MSE2m; the 'fraction' is relative to the total days*gridcells of the sampled top 1%

Figure 2: involves data from both ERA5 and the CAM5 entrainment perturbation experiments

Panels a--b: HistN2D_ERA5_MSEmeasures_150dayLat30.nc

Panels c--d: HistN2D_Entr0_MSEmeasures_150dayLat30.nc; HistN2D_Entr1_MSEmeasures_150dayLat30.nc

Variables in the data files (switch "landcoast", i.e. land+coast, to one of ["land", "ocean", "coast", "landcoast"]):

* FracHistN2D_XY_pi_Pr00_landcoast: fractional frequency [frequency/(days*gridcells)] of the instability/dryness joint distribution for non-raining conditions (P <= 0.5 mm/day). In plotting, fold this value by 10^3 to use the color bar range (0, 1) as in the paper.

* FracHistN2D_XY_pi_Pr6_landcoast: fractional frequency [frequency/(days*gridcells)] of the instability/dryness joint distribution for raining conditions (P > 6 mm/day). In plotting, fold this value by 10^3 to use the color bar range (0, 1) as in the paper.

* HistN2D_XY_pi_Pr00_landcoast: frequency of the instability/dryness joint distribution for non-raining conditions

* HistN2D_XY_pi_Pr6_landcoast: frequency of the instability/dryness joint distribution for raining conditions

* HistN2D_XY_pi_landcoast: frequency of the instability/dryness joint distribution without conditioning on precipitation rate

* MaxWBT3_XY_pi_land:coast Binned maximum WBT, wide bin, to plot the orange contours in panels c--d

* HistN2D3_XY_pi_landcoast: frequency of the instability/dryness joint distribution without conditioning on precipitation rate, wide bin, to mask the edges of the WBT contours in panels c--d that have sample counts <= 10

* Xbins_landcoast: Midpoints of bins for the boundary-layer instability measure; bins are generated by linearly spacing the range of samples (+-0.5) into 100 portions

* Ybins_landcoast: Midpoints of bins for the LFT dryness measure; bins are generated by linearly spacing the range of samples (+-0.5) into 100 portions

* Xbins3_landcoast: Midpoints of wide bins for the boundary-layer instability measure; wide bins are generated by linearly spacing the range of samples (+-0.5) into 30 portions

* Ybins3_landcoast: Midpoints of wide bins for the LFT dryness measure; wide bins are generated by linearly spacing the range of samples (+-0.5) into 30 portions

* fMSEdev_pi_Pr6_landcoast_Mean: The boundary-layer instability averaged over raining conditions, to plot the cyan star

* SubSatPlus_pi_Pr6_landcoast_Mean: The LFT dryness measure averaged over raining conditions, to plot the cyan star

The variables above are in common in HistN2D_ERA5_MSEmeasures_150dayLat30.nc, HistN2D_Entr0_MSEmeasures_150dayLat30.nc, HistN2D_Entr1_MSEmeasures_150dayLat30.nc. The variables below are only in HistN2D_ERA5_MSEmeasures_150dayLat30.nc.

* fMSEdev_pi_top1ST_landcoast_Mean: BL instability conditioned on the top 1% WBT, to plot the dark red star

* SubSatPlus_pi_top1ST_landcoast_Mean: LFT dryness measure conditioned on the top 1% WBT, to plot the dark red star

* WBT_pi_top1ST_landcoast_Mean: The mean top 1% WBT, to annotate

* fMSEdev_pi_top1ST_landcoast: Flattened array of BL instability conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells

* SubSatPlus_pi_top1ST_landcoast: Flattened array of LFT dryness conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells

* WBT_pi_top1ST_landcoast: Flattened array of WBT conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells

Figure 3:

Panels a--b: HistN2D_ERA5_MSEmeasures_150dayLat30.nc

Panels c--f: MappedTop1ST_ERA5_MSEmeasures_150dayLat30.nc

List of variables used for Panels a--b (switch "landcoast", i.e. land+coast, for one of ["land", "ocean", "coast", "landcoast"]):

* MeanW500_XY_pi_Pr00_landcoast: Binned mean omega at 500 hPa, non-raining, in plotting Fig. 3a-b, bin counts < 3 are masked (use HistN2D_XY_pi_Pr00_landcoast for masking)

* MeanW500_XY_pi_Pr6_landcoast: Binned mean omega at 500 hPa, raining

* MaxWBT3_XY_pi_land:coast Binned maximum WBT, wide bin, to plot the dark red contours

* HistN2D3_XY_pi_landcoast: frequency of the instability/dryness joint distribution without conditioning on precipitation rate, wide bin, to mask the edges of the WBT contours that have samples counts <= 10

List of variables used for Panels c--f (switch "land", for one of [land, ocean, coast]):

* savePrMapped_pi_top1ST_land: Local conditional mean Pr for the mapped top 1% WBT; in units of mm/day

* saveW500Mapped_pi_top1ST_land: Local conditional mean omega 500 for the mapped top 1% WBT; in units of Pa/s

* saveFracPr00Mapped_pi_top1ST_land: Local fraction of Pr < 0.5 mm/day for the mapped top 1% WBT

* saveFracPr6Mapped_pi_top1ST_land: Local fraction of Pr >= 6 mm/day for the mapped top 1% WBT

* saveFracST1UpMapped_pi_top1ST_land: Local fraction of upward motion (omega500 < 0) for the mapped top 1% WBT

* saveFracST1DownMapped_pi_top1ST_land: Local fraction of downward motion (omega500 > 0) for the mapped top 1% WBT

Figure 4:

- HistN2D_CESM2_MSEmeasures_150dayLat30.nc

A list of variables similar to Figure 2a--b; "pi" in variable names represents the base climate, and "W" represents the 4xCO2 climate state.

Figure 5: involve data from multiple models. [*modelname*] = ['CanESM5','CESM2','GFDL-CM4','IPSL-CM6A-LR','MIROC6','MPI-ESM1-2-HR']

Panel a--b: HistN2D_ComBins_[

*modelname*]_MSEmeasures_150dayLat30.ncMeanStd_[

*modelname*]_ScalingMeasures_150dayLat30.nc

List of variables: similar to those for Figure 4. Common bins are used for all models. For BL instability, we divide (-50 kJ/kg, 50 kJ/kg) into 150 portions, and for LFT dryness, we divide (-200 kJ/kg, 20 kJ/kg) into 150 portions.

**Extended Data:**

Figure ED1:

- HistN2D_ERA5_MSEmeasures_150dayLat30.nc

List of variables: similar to Figure 2a--b

Figure ED2:

- HistN2D_ERA5_MSEmeasures_150dayLat30.nc

List of variables:

* histN2D_XZ_pi_Pr00_land: Frequency of the joint distribution between wet-bulb temperature and boundary-layer instability for non-raining conditions, divided by the variable "Ncount_X_pi_Pr00_land" (defined below in Figure ED8) to get the fractional frequency.

* histN2D_XZ_pi_Pr6_lan: Frequency of the joint distribution between wet-bulb temperature and boundary-layer instability for raining conditions, divided by the variable "Ncount_X_pi_Pr6_land" (defined below in Figure ED8) to get the fractional frequency.

* WBT_pi_Pr6_land_Mean: WBT averaged over rainy conditions, to plot the cyan star

- fMSEdev_pi_Pr6_land_Mean: The boundary-layer instability averaged over rainy conditions, to plot the cyan star
- fMSEdev_pi_top1ST_land_Mean: BL instability conditioned on the top 1% WBT, to plot the dark red star
- WBT_pi_top1ST_land_Mean: The mean top 1% WBT, to plot the dark red star
- fMSEdev_pi_top1ST_land: Flattened array of BL instability conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells
- SubSatPlus_pi_top1ST_land: Flattened array of LFT dryness conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells
- WBT_pi_top1ST_land: Flattened array of WBT conditioned on the top 1% WBT; the top 1% is sampled from days*gridcells

Figure ED3:

- HistN2D_ComBins_[
*modelname*]_MSEmeasures_150dayLat30.nc\ [*modelname*] = ['CanESM5','CESM2','GFDL-CM4','IPSL-CM6A-LR','MIROC6','MPI-ESM1-2-HR']

List of variables: similar to those for Figure 5.

Figure ED4:

- HistN2D_ComBins_[
*modelname*]_MSEmeasures_150dayLat30.nc\ [*modelname*] = ['CanESM5','CESM2','GFDL-CM4','IPSL-CM6A-LR','MIROC6','MPI-ESM1-2-HR']

List of variables: similar to those for Figure 5.

Figure ED5:

- HistN2D_OnlyLat20_ERA5_MSEmeasures_150dayLat20.nc

List of variables: similar to those for Figure 2a--b.

Figure ED6:

- HistN2D_NoWTG_ERA5_MSEmeasures_150dayLat30.nc

List of variables: similar to those for Figure 2a--b.

Figure ED7: WBT calculated with two methods. (Note: to compare the two, minus 273.15 from (1) to match the units.)

- (1) DailyDataERA5_WBT_150dayLat30_[
*year*].nc; [*year*] = ['1980','1981',...,'1989']; Files contain WBT values calculated from the MetPy package, units are in Kelvin. - (2) DailyDataERA5_WBT-DJ_Conv1e-3_150dayLat30_1980-1989.nc; this file contains WBT values calculated from the Davies-Jones Method, units are in Celsius.

Figure ED8:

- HistN2D_ERA5_MSEmeasures_150dayLat30.nc

List of variables: (switch "land" for one of ["land", "ocean", "coast"]):

* Ncount_X_pi_Pr00_land: Count, non-raining, P < 0.5 mm/day

* Ncount_X_pi_Pr01_land: Count, non-raining, Count, moderate-raining, 0.5 <= P < 6 mm/day

* Ncount_X_pi_Pr6_land: Count, raining, P >= 6 mm/day

* Ncount_X_pi_land: Count, total

Figure ED9:

- HistN2D_ERA5_MSEmeasures_150dayLat30.nc

List of variables: similar to those for Figure 2a-b

Figure ED10:

- HistN2D_CESM2_MSEmeasures_150dayLat30.nc

List of variables: similar to those for Figure ED8. Variable names containing "pi" are for the base climate, and those containing "W" are for the 4xCO2 climate.

### Sharing/Access information

Other raw data used to calculate the variable values contained in the data files:

ERA5 hourly reanalysis data can be downloaded from the ECMWF Climate Data Store (https://cds.climate.copernicus.eu).

CMIP6 model outputs can be downloaded from the CMIP6 data archive (https://esgf-node.llnl.gov/search/cmip6/)

## Funding

United States Department of Energy, Award: DE-SC0023244

National Science Foundation, Award: AGS-1936810

United States Department of Energy, Award: AGS-2225956

National Oceanic and Atmospheric Administration, Award: NA21OAR4310354