Seasonal temperature response over the Indochina Peninsula to a worst-case high-emission forcing: a study with the regionally coupled model ROM

Abstract

Changes of surface air temperature (SAT) over the Indochina Peninsula (ICP) under the Representative Concentration Pathway (RCP) 8.5 scenario are projected for wet and dry seasons in the short-term (2020-2049) and long-term (2070-2099) future of the twenty-first century. A first analysis on projections of the SAT by the state-of-the-art regionally coupled atmosphere-ocean model ROM, including exchanges of momentum, heat, and water fluxes between the atmosphere (Regional Model) and ocean (Max Planck Institute Ocean Model) models, shows the following results: (i) In both seasons, the highest SAT occurs over the southern coastal area while the lowest over the northern mountains. The highest warming magnitudes are located in the northwestern part of the ICP. The regionally averaged SAT over the ICP increases by 2.61°C in the wet season from short-to long-term future, which is slightly faster than that of 2.50°C in the dry season. (ii) During the short-term future, largest SAT trends occur over the southeast and northwest ICP in wet and dry seasons, respectively. On regional average, the wet season is characterized by a significant warming rate of 0.22°C decade −1 , while it is non-significant with 0.11°C decade −1 for the dry season. For the long-term future, the rapid warming is strengthened significantly over whole ICP, with trends of 0.51°C decade −1 and 0.42°C decade −1 in wet and dry seasons, respectively. (iii) In the long-term future, more conspicuous warming is noted, especially in the wet season, due to the increased downward longwave radiation. Higher CO 2 concentrations enhancing the greenhouse effect can be attributed to the water vaporgreenhouse feedback, which, affecting atmospheric humidity and counter radiation, leads to the rising SAT.

Will population exposure to heat extremes intensify over Southeast Asia in a warmer world?

“…Climate conditions of the SEA mainland are significantly modulated by the Asian-Australian monsoon regime (Matsumoto 1997, Webster et al 1998, Wang et al 2001, Zhou et al 2011, Ge et al 2021b and the El Niño/Southern Oscillation (ENSO; Barnett et al 1999, Wang and Chan 2002, McPhaden et al 2006, Ge et al 2017, McGregor and Ebi 2018. In the past few decades, SEA has already experienced intensified extreme events, imposing widespread heat-related risks that have led to considerable social and economic losses (IPCC 2013, Zhu et al 2020b, Ge et al 2021a. In April 2016, record-breaking droughts that accompanied long durations of temperature extremes resulted in many deaths and massive socioeconomic impacts (Thirumalai et al 2017).…”

How Striking Is the Intergenerational Difference in Exposure to Compound Heatwaves Over Southeast Asia?

“…The relative root‐mean‐square error (RMSE′) is used to quantitatively estimate the empirical reliability and the robustness of the climate simulation capability (Sillmann et al., 2013; Zhu et al., 2020): $$${\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}^{\prime }=\frac{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}-{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}}}{{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}}}$$$ where RMSE Median being the ensemble median of RMSEs for the existing models. Generally, a negative (positive) RMSE′ denotes that the stimulation performance is superior (inferior) to the majority of the models.…”

“…A higher value indicates a higher correlation between the model and observations: $$$\mathrm{C}\mathrm{O}\mathrm{R}=\frac{\sum \nolimits_{i=1}^{n}\left({X}_{i}-\overline{X}\right)\left({Y}_{i}-\overline{Y}\right)}{\sqrt{\sum \nolimits_{i=1}^{n}{\left({X}_{i}-\overline{X}\right)}^{2}}\sqrt{\sum \nolimits_{i=1}^{n}{\left({Y}_{i}-\overline{Y}\right)}^{2}}}$$$ The ratio of the standard deviation (RSD) is used to recognize information about the differences in amplitude between the models and the observations: $$$\mathrm{R}\mathrm{S}\mathrm{D}=\frac{{\sigma }_{m}}{{\sigma }_{o}}$$$ where σ m and σ o are the spatial standard deviations of the model runs and the observational data, respectively. A value closer to unity represents a better match of variabilities between the model simulation and the observational dataset. The relative root‐mean‐square error (RMSE′) is used to quantitatively estimate the empirical reliability and the robustness of the climate simulation capability (Sillmann et al., 2013; Zhu et al., 2020): $$${\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}^{\prime }=\frac{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}-{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{M}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{a}\mathrm{n}}}{{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{M}\m...$$…”

“…3. The relative root-mean-square error (RMSE′) is used to quantitatively estimate the empirical reliability and the robustness of the climate simulation capability (Sillmann et al, 2013;Zhu et al, 2020):…”

Subseasonal forecasts of precipitation over maritime continent in boreal summer and the sources of predictability

“…In the context of global warming, extreme weather events such as floods and droughts are revealed to be increasingly frequent, which emerge serious threats to both economic society and human health (Zhang et al, 2015;Zhu et al, 2020a;2020b). Recently, there has been a surge of interest to develop the precise seamless forecasts, playing a crucial role in disaster reduction (WMO, 2015;Yuan et al, 2016;Rauser et al, 2017).…”