Progress in Climate Change Downscaling Simulations in Southeast Asia

Tangang, Fredolin; Chung, Jing Xiang; Supari,; Ngai, Sheau Tieh; Salimun, Ester; Cruz, Faye; Narisma, Gemma; Ngo‐Duc, Thanh; Santisirisomboon, Jerasorn; Juneng, Liew; Sopaheluwakan, Ardhasena; Akhir, Mohd Fadzil; Mohd, Mohd Syazwan Faisal

doi:10.1007/978-981-16-0902-2_2

Cited by 9 publications

(11 citation statements)

References 76 publications

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“…Our results are in accordance with regional climate model simulations (Ngo-Duc et al, 2016;Tangang et al, 2018;Ge et al, 2019) showing a very high sensitivity of precipitation extremes to warming over the Southeast Asia. Our results demonstrate that together with total wet season precipitation the number of days of extreme rainfall and other extreme precipitation indices are also increasing abruptly over the last decades in parts of the MSEA.…”

Section: Discussionsupporting

confidence: 91%

Drivers of rainfall trends in and around Mainland Southeast Asia

Skliris¹,

Marsh²,

Haigh³

et al. 2022

Front. Clim.

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Observational rain gauge/satellite and reanalysis datasets since the 1950s are evaluated for trends in mean and extreme rainfall in and around Mainland Southeast Asia (MSEA). Rain gauge data indicate strong increases exceeding 50% in both annual mean precipitation and various extreme precipitation indices over Vietnam and the northwestern part of the peninsula since 1979. The remote influence of ENSO may partially explain the recent precipitation trend toward a more intense regional hydrological cycle, in response to predominant La Niña states over recent decades. Increasing precipitation in MSEA is also associated with increased monsoon intensity in southeast Asia and a northward shift of the monsoon activity center toward MSEA over 1979–2018. Warming-driven evaporation increases were obtained over the adjacent seas typically feeding precipitation over MSEA associated with a shift toward predominantly positive phases of the two major natural climate variability modes of the tropical Indian Ocean, namely the Indian Ocean Dipole and the Indian Ocean Basin Mode. A moisture budget analysis using ERA5 re-analysis data showed increasing oceanic moisture transports along the typical winter and summer moisture pathways toward the MSEA. However, results show that during summer the major part of increased moisture from the oceanic moisture sources ends up as precipitation over the oceanic regions adjacent to MSEA with ERA5 not being able to produce the observed positive trends in summer continental precipitation. On the other hand, ERA5 reveals pronounced increases in winter precipitation over the MSEA, in accordance with rain-gauge data, associated with strongly increasing transport of moisture originated from the western tropical Pacific and the South China Sea.

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Section: Discussionsupporting

confidence: 91%

Drivers of rainfall trends in and around Mainland Southeast Asia

Skliris¹,

Marsh²,

Haigh³

et al. 2022

Front. Clim.

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“…Overall, even though there was few pollutants that were proven as highly associated to PM10 during haze and non-haze, other pollutants that have not been described in this subchapter still have their contribution, but with small percentage. Therefore, even with minor difference predictions, both strategies have proved that meteorological factors were the significant response to haze occurrence (Tangang et al 2010;Payus et al 2013;Ramsey et al 2014).…”

Section: Ann Vs Mlr: the Establishment Of Relationship Between Pm10 A...mentioning

confidence: 98%

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Yusof

Azid

Sani³

et al. 2019

Mal. J. Fund. Appl. Sci.

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The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

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“…The RCM was developed to bridge the gap between the global but coarse estimates of the GCM, which typically had a spatial resolution of 100-300 km [32], and studies of regional phenomena with finer spatial resolutions [33], which are currently approximately 50 km to 12 km [34]. Driven by lateral boundary conditions and reanalysis data, RCMs can account for local-scale forcings and processes that are governed by complex topography, coastlines, inland bodies of water, and land cover distribution [35].…”

Section: Regional Climate Model (Rcm)mentioning

confidence: 99%

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

Kimmany,

Visessri,

Pech

et al. 2024

Water

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This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

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Progress in Climate Change Downscaling Simulations in Southeast Asia

Cited by 9 publications

References 76 publications

Drivers of rainfall trends in and around Mainland Southeast Asia

Drivers of rainfall trends in and around Mainland Southeast Asia

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

The Impact of Climate Change on Hydro-Meteorological Droughts in the Chao Phraya River Basin, Thailand

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