4. Conclusions
A dynamical downscaling method based on the MPI-ESM-1-2-HR of CMIP6 was
used to perform a comprehensive regional climate analysis. Based on the
hourly 2 m temperature data over three decades (the 2010s, 2040s, and
2090s) in three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), we
explored the summer thermal environment, hot extremes, and heatwaves at
present and in the future. Compared with previous studies on hot
extremes, our regional simulation data were driven by the newly released
global climate data. Furthermore, the future projections of land use
change in the mid-century were incorporated, which is a novel addition
to the research.
The results indicated that different scenarios share similar spatial
patterns of the daytime and nighttime 2 m temperature in the 2040s, but
the patterns significantly differ in the 2090s. SSP1-2.6, the
sustainability pathway, will correspond to slightly lower daytime and
nighttime temperatures in the 2090s than those in the 2040s. The
opposite trends will be observed for SSP2-4.5 and SSP5-8.5, with a
significant increase in the temperature and an expansion of hot areas in
the 2090s. The PDFs of the 2 m temperature were analyzed to clarify the
temperature distribution in the different scenarios over time. The PDFs
of the 2 m temperatures and temperature extremes will shift to higher
values in SSP2-4.5 and SSP5-8.5. In contrast, the PDF of the
temperatures in SSP1-2.6 in the 2090s will shift to lower values.
The spatial patterns of different heatwave metrics suggest that parts of
Guangdong, Foshan and Jiangmen will remain the hottest places in the
PRD. Several coastal areas may experience more frequent hot extremes in
the 2090s in SSP2-4.5, and some rural areas may suffer from heatwaves
where the average daily maximum temperature will exceed 35°C in the
2090s. The nighttime temperature is expected to increase faster than the
daytime temperature, posing a health risk to vulnerable populations
especially for females and individuals aged more than 70 years.
According to the heatwave analysis of the spatial mean over the PRD,
although the temperatures in SSP1-2.6 will decrease in the 2090s, the
extreme heat frequency and intensity will not decrease considerably, and
the heatwave intensity will remain the same as that in the 2040s. In the
other two scenarios, the extreme heat frequency and intensity will keep
increasing in the 2040s and 2090s.
Through this research, we hope to incentivize policymakers to implement
appropriate mitigation measures and urban planning strategies to ensure
people’s comfort. Considering different future climate outcomes,
different social infrastructure sectors may be equipped to better
prepare for the future. Moreover, the outcomes from the worst-case
scenario may serve as an alert for people to adopt more sustainable
lifestyles.
This research has certain limitations. Although the urban surface
characteristics were considered, the anthropogenic heat flux was not
considered. The definition of heatwaves used in our research allows for
the possibility of overlapping dates for warm nights and warm days. This
aspect of the definition may limit our ability to differentiate
different types of heatwaves (daytime heatwave, nighttime heatwave,
daytime-nighttime compound heatwave) and mask important signals
associated with the specific mechanisms driving heatwaves in the PRD
(Thomas et al. 2020; Wu and Luo 2020; Luo et al. 2022). Moreover,
several studies also highlighted that the daytime- and nighttime
compound heatwaves may impact human health more than solely daytime and
nighttime heatwaves (Wang et al. 2021).
Acknowledgments.
We appreciate the assistance of the Hong Kong Observatory (HKO), which
provided the meteorological data. The work described in this paper was
supported by a grant from the Research Grants Council of the Hong Kong
Special Administrative Region, China (Project Nos. AoE/E-603/18,
C7041-21GF, R4046-18, and T31-603/21-N), the Guangdong-Hongkong-Macau
Joint Laboratory Grant GDST20IP05, and National Natural Science
Foundation of China Grant 42007203.
Data Availability Statement.
The CMIP6 large-scale climate dataset is available athttps://esgf-data.dkrz.de/search/cmip6-dkrz/.
The ECMWF Reanalysis data version 5 (ERA5) used for bias correction is
available athttps://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.
The downscaled WRF output files, observation data, and other processed
data files are preserved at Zenodo Repositoryhttps://doi.org/10.5281/zenodo.7206739.
The workflow and scripts for plotting are available at Zenodo Repositoryhttps://doi.org/10.5281/zenodo.7207845.
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