A 10-km CMIP6 downscaled dataset of temperature and precipitation for historical and future Vietnam climate

Autor: Quan Tran-Anh, Thanh Ngo-Duc, Etienne Espagne, Long Trinh-Tuan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Data, Vol 10, Iss 1, Pp 1-12 (2023)
Druh dokumentu: article
ISSN: 2052-4463
DOI: 10.1038/s41597-023-02159-2
Popis: Abstract High-resolution climate projections are mandatory for many applications and impact assessments in environmental and management studies. In response to the needs in Vietnam, this study constructs a new precipitation and temperature daily dataset for Vietnam, at a high spatial resolution of 0.1° × 0.1°, based on the outputs of 35 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Bias Correction and Spatial Disaggregation (BCSD) method is adopted to bias-correct monthly GCM simulations using observation data, then subsequently temporally disaggregate them into daily data. The new dataset is called CMIP6-VN, covering the present-time period 1980–2014 and future projections for 2015–2099 from both CMIP6 tier-1 (Shared Socioeconomic Pathways (SSPs) 1–1.26, 2–4.5, 3–7.0, and 5–8.5) and tier-2 (SSPs 1–1.9, 4–3.4, 4–6.0) experiments. Results indicated the good performance of CMIP6-VN for the historical period, suggesting that the dataset could be used for studies on climate change assessment and impacts in Vietnam.
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