Editing IPCC:AR6/WGI/Chapter-Atlas (section)

=== Atlas.5.4 South East Asia ===

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==== [[#Atlas.5.4.1|Atlas.5.4.1]] Key Features of the Regional Climate and Findings from Previous IPCC Assessments ====

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===== Atlas.5.4.1.1 Key Features of the Regional Climate =====

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The South East Asia region is composed of countries that are part of Indochina (or mainland South East Asia) and countries that are very archipelagic in nature and have strong land-ocean-atmosphere interactions, including those that are part of the Maritime Continent and the Philippines. Its climate is mainly tropical (i.e., hot and humid with abundant rainfall). Rainfall seasonal variability in the region is mainly affected by the synoptic-scale monsoon systems, the north–south migration of the Inter-tropical Convergence Zone (ITCZ) and tropical cyclones (mainly for the Philippines and Indochina), while intra-seasonal variability can be influenced by the MJO (Annex IV). Temperature and especially rainfall are also interannually affected by ENSO and Indian Ocean basin and Dipole (IOB/IOD) modes ( [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] and Table Atlas.1).

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===== Atlas.5.4.1.2 Findings From Previous IPCC Assessments =====

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The AR5 WGI showed that the mean annual temperature of South East Asia has been increasing at a rate of 0.14°C–0.20°C per decade since the 1960s, along with an increasing number of warm days and nights, and a decreasing number of cold days and nights ( [[#Christensen--2013|Christensen et al., 2013]] ). The AR5 also reported the lack of sufficient observational records to allow for a full understanding of past precipitation trends in most of the Asian region, including South East Asia, and that precipitation trends that were available differed considerably across the region and between seasons ( [[#Christensen--2013|Christensen et al., 2013]] ).

On projected changes, findings from AR5 showed that warming is ''very likely'' to continue with substantial sub-regional variations over South East Asia ( [[#Christensen--2013|Christensen et al., 2013]] ). The median increase in temperature over land projected by the CMIP5 ensemble mean ranges from 0.8°C in RCP2.6 to 3.2°C in RCP8.5 by the end of the 21st century. Moderate future increases in precipitation are ''very likely'' , with projected ensemble mean increases of 1% in RCP2.6 to 8% in RCP8.5 by 2100. In SR1.5, there is a projected increase in flooding and runoff over South East Asia for a 1.5°C to 2°C global warming, and these will increase even more for a greater than 2°C level of warming ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ).

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==== [[#Atlas.5.4.2|Atlas.5.4.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Within the last decade, there has been an increasing number of studies on climatic trends over South East Asia, carried out on a regional basis ( [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ) or focused on specific countries ( [[#Cinco--2014|Cinco et al., 2014]] ; [[#Villafuerte--2014|Villafuerte et al., 2014]] ; [[#Mayowa--2015|Mayowa et al., 2015]] ; [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ; [[#Guo--2017a|Guo et al., 2017a]] ; [[#Supari--2017|Supari et al., 2017]] ; [[#Sa’adi--2019|Sa’adi et al., 2019]] ; [[#Tan--2021|Tan et al., 2021]] ). They document ''virtually certain'' significant increases in mean as well as extreme temperature. The minimum temperature extremes ''very likely'' warmed faster compared to the maximum temperature. Temperatures, including extremes, are strongly influenced by ENSO in the region ( [[#Cinco--2014|Cinco et al., 2014]] ; [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Cheong--2018|Cheong et al., 2018]] ). Over much of the region, extreme high temperatures occurred mostly in April and almost all April extreme temperatures occur in El Niño years ( [[#Thirumalai--2017|Thirumalai et al., 2017]] ). In most of South East Asia (except for the north-eastern areas), there was ''likely'' an increase in the number of warm nights with El Niño episodes within the period 1972–2010 ( [[#Cheong--2018|Cheong et al., 2018]] ).

Changes in mean precipitation are less spatially coherent over South East Asia. Over Thailand, the average number of rain days has decreased by 1.3 to 5.9 days per decade while average daily rainfall intensity has increased by 0.24–0.73 mm day <sup>–1</sup> per decade ( [[#Limsakul--2016|Limsakul and Singhruck, 2016]] ). Precipitation is also affected by ENSO events ( [[#Tangang--2017|Tangang et al., 2017]] ; Supari et al., 2018). Over South East Asia, there has been a significant increase in the amount of precipitation and its extremes with La Niña episodes in the past decades, especially during the winter monsoon period ( ''high confidence'' ) ( [[#Villafuerte--2015|Villafuerte and Matsumoto, 2015]] ; [[#Limsakul--2016|Limsakul and Singhruck, 2016]] ; [[#Cheong--2018|Cheong et al., 2018]] ).

Figure Atlas.11 shows trends in mean temperature and precipitation during 1961–2015 for two global datasets, indicating a significant overall warming over South East Asia ( ''high confidence'' ), with higher rates of warming in Malaysia, Indonesia, and the southern areas of mainland South East Asia ( ''low confidence'' ). Annual mean precipitation trends ( [[#Atlas.1.4.1|Atlas.1.4.1]] and the Interactive Atlas, which includes the regional dataset Aphrodite) over the region are mostly not significant except for increases over parts of Malaysia, Vietnam and the southern Philippines ( ''medium confidence'' ).

It is important to note that the availability, quality, and temporal and spatial density of observation data may lead to uncertainties and varying results in South East Asia ( [[#Juneng--2016|Juneng et al., 2016]] ). Some efforts have been made to produce better observationally-based gridded datasets for the region (e.g., [[#Nguyen-Xuan--2016|Nguyen-Xuan et al., 2016]] ; [[#van%20den%20Besselaar--2017|van den Besselaar et al., 2017]] ; [[#Yatagai--2020|Yatagai et al., 2020]] ).

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==== [[#Atlas.5.4.3|Atlas.5.4.3]] Assessment of Model Performance ====

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Performance in simulating rainfall over South East Asia varies among CMIP5 GCMs ( ''high confidence'' ). Only some are capable of reasonably simulating the rainfall seasonal cycle and spatial pattern ( [[#Siew--2013|Siew et al., 2013]] ; [[#Raghavan--2018|Raghavan et al., 2018]] ). Over mainland South East Asia, the performance of CMIP5 GCMs in simulating rainfall during the wet season was superior to that for annual and dry-season precipitation (J. [[#Li--2019|]] [[#Li--2019|Li et al., 2019]] ).

RCMs have been intensively used over the region in recent years in a series of single or multi-model experiments and there is ''medium confidence'' that they reproduce reasonably well seasonal climate patterns of temperature, precipitation and large-scale circulation over the different sub-regions of South East Asia with added values compared to their host GCMs ( [[#Kwan--2014|Kwan et al., 2014]] ; [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] , 2017; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Juneng--2016|Juneng et al., 2016]] ; [[#Katzfey--2016|Katzfey et al., 2016]] ; [[#Loh--2016|Loh et al., 2016]] ; [[#Raghavan--2016|Raghavan et al., 2016]] ; [[#Cruz--2017|Cruz et al., 2017]] ; [[#Ratna--2017|Ratna et al., 2017]] ; [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ; [[#Nguyen-Thuy--2021|Nguyen-Thuy et al., 2021]] ). RCM ensemble means tend to outperform the individual models in representing the climatological mean state ( [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ; [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ; [[#Nguyen-Thi--2021|Nguyen-Thi et al., 2021]] ). There is relatively high consistency among the simulations of historical climate over mainland South East Asia compared to those over the Maritime Continent for both seasonal and interannual variability ( [[#Ngo-Duc--2017|Ngo-Duc et al., 2017]] ). The consistency in rainfall simulations was lower than for temperature simulations.

Some RCMs showed a systematic cold bias ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ; [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ; [[#Loh--2016|Loh et al., 2016]] ; [[#Cruz--2017|Cruz and Sasaki, 2017]] ; [[#Cruz--2017|Cruz et al., 2017]] ) that was mainly due to model physics ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ) and/or the biases in the SST forcing ( [[#Ngo-Duc--2014|Ngo-Duc et al., 2014]] ). A few simulations revealed a warm bias over some areas such as in the Maritime Continent ( [[#Cruz--2017|Cruz et al., 2017]] ) or Vietnam ( [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ). The biases for rainfall in GCMs and RCMs over South East Asia were found to be less systematic with wet or dry biases depending on the sub-regions ( [[#Manomaiphiboon--2013|Manomaiphiboon et al., 2013]] ; [[#Kwan--2014|Kwan et al., 2014]] ; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Juneng--2016|Juneng et al., 2016]] ; Supari et al., 2020; [[#Tangang--2020|Tangang et al., 2020]] ; [[#Nguyen-Thi--2021|Nguyen-Thi et al., 2021]] ), although wet biases were more pronounced in RCMs ( [[#Kwan--2014|Kwan et al., 2014]] ; [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Kirono--2015|Kirono et al., 2015]] ; [[#Juneng--2016|Juneng et al., 2016]] ; Supari et al., 2020; [[#Tangang--2020|Tangang et al., 2020]] ). Some RCMs overestimated rainfall interannual variability ( [[#Juneng--2016|Juneng et al., 2016]] ) while some others underestimated it ( [[#Kirono--2015|Kirono et al., 2015]] ). Simulated rainfall amount is sensitive to the choice of convective scheme ( [[#Juneng--2016|Juneng et al., 2016]] ; [[#Ngo-Duc--2017|Ngo-Duc et al., 2017]] ) and the choice of land surface scheme ( [[#Chung--2018|Chung et al., 2018]] ). Rainfall biases in current climate simulations can be greatly reduced if a bias adjustment method such as quantile mapping is applied ( [[#Trinh-Tuan--2018|Trinh-Tuan et al., 2018]] ). The pattern of tropical cyclone numbers in the region were reasonable represented by RCM outputs ( [[#Van%20Khiem--2014|Van Khiem et al., 2014]] ; [[#Kieu-Thi--2016|Kieu-Thi et al., 2016]] ; [[#Herrmann--2020|Herrmann et al., 2020]] ).

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==== [[#Atlas.5.4.4|Atlas.5.4.4]] Assessment and Synthesis of Projections ====

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Mean temperature in South East Asia is projected to continue to rise through the 21st century ( ''virtually certain'' , ''very high confidence'' ). Projections by multi-model regional climate simulations of CORDEX-SEA showed a temperature increment over land under RCP8.5 to range from 3°C–5°C by the end of the 21st century relative to the pre-1986–2005 period ( [[#Tangang--2018|Tangang et al., 2018]] ). For the same periods, the average mean temperature increase over land projected by CMIP5 (CMIP6) varies, with 10th–90th percentile ranges, from 0.7°C to 1.3°C (0.7°C to 1.8°C) under RCP2.6 (SSP1-2.6) to 2.8°C to 4.4°C (2.6°C to 4.8°C) under RCP8.5 (SSP5-8.5) (Interactive Atlas). For all GWLs the land region is projected to warm by a slightly smaller amount than the global average, with 10th–90th percentile ranges for CMIP5 (CMIP6) of 1.2°C–1.6°C (1.2°C–1.5°C) for the 1.5°C GWL and of 3.3°C–4.0°C (3.3°C–3.9°C) for the 4°C GWL relative to the 1850–1900 baseline (calculated from RCP8.5 (SSP5-8.5) projections). Changes for other warming levels, periods and emissions pathways are shown in Figure Atlas.1 7 and can be explored in the Interactive Atlas.

Projections of future rainfall changes are highly variable among sub-regions of South East Asia and among the models ( ''high confidence'' ). The CMIP5 and CMIP6 ensembles showed an increase in annual mean precipitation over most land areas by the mid- and late 21st century, although only with a strong model agreement for higher warming levels (Figure Atlas.1 7 and the Interactive Atlas), while CORDEX produces a general decrease in projected precipitation (Figure Atlas.1 7). Based on CORDEX South East Asia multi-model simulations, significant and robust increases of mean rainfall over Indochina and the Philippines were projected while there is a drying tendency over the Maritime Continent during DJF for the early, mid and end of the 21st century periods under both RCP4.5 and RCP8.5 (Figure Atlas.1 9; [[#Tangang--2020|Tangang et al., 2020]] ). At the end of the 21st century during DJF and under RCP8.5, an increase of 20% in mean rainfall is projected over Myanmar, northern central Thailand and northern Laos, and of 5–10% over the eastern Philippines and northern Vietnam. During JJA, significantly drier conditions are projected over almost the entire South East Asia region except over Myanmar and northern Borneo. Over the Indonesian region, especially Java, Sumatra and Kalimantan, as much as a 20–30% decrease in mean rainfall is projected during JJA by the end of the 21st century. The projected drier condition over Indonesia from CORDEX is consistent with that of [[#Kusunoki--2017|Kusunoki (2017)]] , [[#Giorgi--2019|Giorgi et al. (2019)]] , [[#Kang--2019|Kang et al. (2019)]] and Supari et al. (2020) and is associated with enhanced subsidence over the region ( [[#Kang--2019|Kang et al., 2019]] ; [[#Tangang--2020|Tangang et al., 2020]] ).

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[[File:d8e07088a9b5c23845ff3b862e06780d IPCC_AR6_WGI_Atlas_Figure_19.png]]

'''Figure Atlas.19''' '''|''' '''The RCM-projected changes in mean precipitation between the early (2011–2040), mid- (2041–2070) and late (2071–2099) 21st century and the historical period 1976–2005.''' Data are obtained from the CORDEX-SEA downscaling simulations. Diagonal lines indicate areas with low model agreement (less than 80%). Figure adapted from [[#Tangang--2020|Tangang et al. (2020)]] .

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==== [[#Atlas.5.4.5|Atlas.5.4.5]] Summary ====

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It is ''virtually certain'' that annual mean temperature has been increasing in South East Asia in the past decades while changes in annual mean precipitation are less spatially coherent though with some increasing trends over parts of Malaysia, Vietnam and the southern Philippines ( ''medium confidence'' ).

Although various biases still exist, there is ''high confidence'' that the models can reproduce seasonal climate patterns well over the different sub-regions of South East Asia. There is ''medium confidence'' that the RCMs show added value compared to their host GCMs over the region.

Projections show continued warming over South East Asia, but ''likely'' by a slightly smaller amount than the global average. Projected changes in rainfall over South East Asia vary, depending on model, sub-region and season ( ''high confidence'' ), with consistent projections of increases in annual mean rainfall from CMIP5 and CMIP6 over most land areas ( ''medium confidence'' ) and decreases in summer rainfall from CORDEX projections over much of Indonesia ( ''medium confidence'' ).

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