HCPD-CA: high-resolution climate projection dataset in central Asia
<p>Central Asia (referred to as CA) is one of the climate change hot spots due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerabil...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-05-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/14/2195/2022/essd-14-2195-2022.pdf |
| _version_ | 1818243783933820928 |
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| author | Y. Qiu J. Feng Z. Yan J. Wang |
| author_facet | Y. Qiu J. Feng Z. Yan J. Wang |
| author_sort | Y. Qiu |
| collection | DOAJ |
| description | <p>Central Asia (referred to as CA) is one of the climate change hot spots due
to the fragile ecosystems, frequent natural hazards, strained water
resources, and accelerated glacier melting, which underscores the need of
high-resolution climate projection datasets for application to
vulnerability, impacts, and adaption assessments in this region. In this
study, a high-resolution (9 km) climate projection dataset over CA (the
HCPD-CA dataset) is derived from dynamically downscaled results based on
multiple bias-corrected global climate models and contains four geostatic
variables and 10 meteorological elements that are widely used to drive
ecological and hydrological models. The reference and future periods are
1986–2005 and 2031–2050, respectively. The carbon emission scenario is
Representative Concentration Pathway (RCP) 4.5. The evaluation shows that
the data product has good quality in describing the climatology of all the
elements in CA despite some systematic biases, which ensures the suitability
of the dataset for future research. Main features of projected climate
changes over CA in the near-term future are strong warming (annual mean
temperature increasing by 1.62–2.02 <span class="inline-formula"><sup>∘</sup></span>C) and a significant increase in
downward shortwave and longwave flux at the surface, with minor changes in other
elements (e.g., precipitation, relative humidity at 2 m, and wind speed at
10 m). The HCPD-CA dataset presented here serves as a scientific basis for
assessing the potential impacts of projected climate changes over CA on many
sectors, especially on ecological and hydrological systems. It has the DOI
<a href="https://doi.org/10.11888/Meteoro.tpdc.271759">https://doi.org/10.11888/Meteoro.tpdc.271759</a>
(Qiu, 2021).</p> |
| first_indexed | 2024-12-12T14:06:37Z |
| format | Article |
| id | doaj.art-fcf60c13731449cf8c1d1c9a93632050 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-12T14:06:37Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-fcf60c13731449cf8c1d1c9a936320502022-12-22T00:22:13ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162022-05-01142195220810.5194/essd-14-2195-2022HCPD-CA: high-resolution climate projection dataset in central AsiaY. QiuJ. FengZ. YanJ. Wang<p>Central Asia (referred to as CA) is one of the climate change hot spots due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in this region. In this study, a high-resolution (9 km) climate projection dataset over CA (the HCPD-CA dataset) is derived from dynamically downscaled results based on multiple bias-corrected global climate models and contains four geostatic variables and 10 meteorological elements that are widely used to drive ecological and hydrological models. The reference and future periods are 1986–2005 and 2031–2050, respectively. The carbon emission scenario is Representative Concentration Pathway (RCP) 4.5. The evaluation shows that the data product has good quality in describing the climatology of all the elements in CA despite some systematic biases, which ensures the suitability of the dataset for future research. Main features of projected climate changes over CA in the near-term future are strong warming (annual mean temperature increasing by 1.62–2.02 <span class="inline-formula"><sup>∘</sup></span>C) and a significant increase in downward shortwave and longwave flux at the surface, with minor changes in other elements (e.g., precipitation, relative humidity at 2 m, and wind speed at 10 m). The HCPD-CA dataset presented here serves as a scientific basis for assessing the potential impacts of projected climate changes over CA on many sectors, especially on ecological and hydrological systems. It has the DOI <a href="https://doi.org/10.11888/Meteoro.tpdc.271759">https://doi.org/10.11888/Meteoro.tpdc.271759</a> (Qiu, 2021).</p>https://essd.copernicus.org/articles/14/2195/2022/essd-14-2195-2022.pdf |
| spellingShingle | Y. Qiu J. Feng Z. Yan J. Wang HCPD-CA: high-resolution climate projection dataset in central Asia Earth System Science Data |
| title | HCPD-CA: high-resolution climate projection dataset in central Asia |
| title_full | HCPD-CA: high-resolution climate projection dataset in central Asia |
| title_fullStr | HCPD-CA: high-resolution climate projection dataset in central Asia |
| title_full_unstemmed | HCPD-CA: high-resolution climate projection dataset in central Asia |
| title_short | HCPD-CA: high-resolution climate projection dataset in central Asia |
| title_sort | hcpd ca high resolution climate projection dataset in central asia |
| url | https://essd.copernicus.org/articles/14/2195/2022/essd-14-2195-2022.pdf |
| work_keys_str_mv | AT yqiu hcpdcahighresolutionclimateprojectiondatasetincentralasia AT jfeng hcpdcahighresolutionclimateprojectiondatasetincentralasia AT zyan hcpdcahighresolutionclimateprojectiondatasetincentralasia AT jwang hcpdcahighresolutionclimateprojectiondatasetincentralasia |