A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics

Motivation

Increasing heat stress due to climate change poses significant risks to human health and can lead to widespread social and economic consequences. Evaluating these impacts requires reliable datasets of heat stress projections. 

Data Record

CMIP6

We present a global dataset projecting future dry-bulb, wet-bulb, and wet-bulb globe temperatures under 1-4°C global warming scenarios (at 0.5°C intervals) relative to the preindustrial era, using outputs from 16 CMIP6 global climate models (GCMs) (Table 1). All variables were retrieved from the historical and SSP585 scenarios which were selected to maximize the warming signal.

Wet-bulb and wet-bulb globe temperature are calculated using the Davies-Jones[1] and Liljegren[2]  approach respectively.

The dataset was bias-corrected against ERA5 reanalysis by incorporating the GCM-simulated climate change signal onto the ERA5 baseline (1950-1976) at a 3-hourly frequency. It therefore includes a 27-year sample for each GCM under each warming target.

The data is provided at a fine spatial resolution of 0.25° x 0.25° and a temporal resolution of 3 hours, and is stored in a self-describing NetCDF format. Filenames follow the pattern "VAR_bias_corrected_3hr_GCM_XC_yyyy.nc", where:

"VAR" represents the variable (Ta, Tw, WBGT for dry-bulb, wet-bulb, and wet-bulb globe temperature, respectively),

"GCM" denotes the CMIP6 GCM name,

"X" indicates the warming target compared to the preindustrial period,

"yyyy" represents the year index (0001-0027) of the 27-year sample

Table 1 CMIP6 GCMs used for generating the dataset for Ta, Tw and WBGT.

GCM

Realization

GCM grid spacing

Ta

Tw

WBGT

ACCESS-CM2

r1i1p1f1

1.25ox1.875o

✓

✓

✓

BCC-CSM2-MR

r1i1p1f1

1.1ox1.125o

✓

✓

✓

CanESM5

r1i1p2f1

2.8ox2.8o

✓

✓

✓

CMCC-CM2-SR5

r1i1p1f1

0.94ox1.25o

✓

✓

✓

CMCC-ESM2

r1i1p1f1

0.94ox1.25o

✓

✓

✓

CNRM-CM6-1

r1i1p1f2

1.4ox1.4o

✓

✓

EC-Earth3

r1i1p1f1

0.7ox0.7o

✓

✓

✓

GFDL-ESM4

r1i1p1f1

1.0ox1.25o

✓

✓

✓

HadGEM3-GC31-LL

r1i1p1f3

1.25ox1.875o

✓

✓

✓

HadGEM3-GC31-MM

r1i1p1f3

0.55ox0.83o

✓

✓

✓

KACE-1-0-G

r1i1p1f1

1.25ox1.875o

✓

✓

✓

KIOST-ESM

r1i1p1f1

1.9ox1.9o

✓

✓

✓

MIROC-ES2L

r1i1p1f2

2.8ox2.8o

✓

✓

✓

MIROC6

r1i1p1f1

1.4ox1.4o

✓

✓

✓

MPI-ESM1-2-HR

r1i1p1f1

0.93ox0.93o

✓

✓

✓

MPI-ESM1-2-LR

r1i1p1f1

1.85ox1.875o

✓

✓

✓

ERA5

We also provide hourly Tw and WBGT derived from ERA5 reanalysis during 1950-2023 to enable analyses of heat stress changes from historical period to a warmer climate.

Data Access

An inventory of the dataset is available in this repository. The complete dataset, approximately 57 TB in size, is freely accessible via Purdue Fortress' long-term archive through Globus. The bias-corrected CMIP6 dataset is available at Globus Link1, and the ERA5 dataset is available at Globus Link2. After clicking the link, users may be prompted to log in with a Purdue institutional Globus account. You can switch to your institutional account, or log in via a personal Globus ID, Gmail, GitHub handle, or ORCID ID. Alternatively, the dataset can be accessed by searching for the universally unique identifier (UUID)—"6538f53a-1ea7-4c13-a0cf-10478190b901" for CMIP6, and “63242aea-d3e0-4aa4-9372-0e19dd0c6539” for ERA5 dataset—in Globus.

Dataset Validation

We validate the bias-correction method and show that it significantly enhances the GCMs' accuracy in reproducing both the annual average and the full range of quantiles for all metrics within an ERA5 reference climate state. This dataset is expected to support future research on projected changes in mean and extreme heat stress and the assessment of related health and socio-economic impacts.

For a detailed introduction to the dataset and its validation, please refer to our data descriptor currently under review at Scientific Data. We will update this information upon publication.