Historical and future anthropogenic warming effects on droughts, fires and fire emissions of CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in equatorial Asia when 2015-like El Niño events occur

Shiogama, Hideo; Hirata, Ryuichi; Hasegawa, Tomoko; Fujimori, Shinichiro; Ishizaki, Noriko N.; Chatani, Satoru; Mitchell, Dann; Lo, Y. T. Eunice

doi:10.5194/esd-11-435-2020

Cited by 16 publications

(7 citation statements)

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“…Our estimate is smaller than that of GFED, but still comparable to the latest Japanese inventory (338 TgC yr −1 for 2018 (GIO and MOE, 2020)). Using an atmospheric climate model, Shiogama et al (2020) projected that Equatorial Asia would experience stronger droughts than that in 2015 in a future warmer climate condition. To reduce fire-induced carbon emissions from such drought events, chances of ignition must be reduced.…”

Section: Discussionmentioning

confidence: 99%

Estimation of fire-induced carbon emission from Equatorial Asia in 2015 by using in situ aircraft and ship observations

Niwa¹,

Sawa²,

Nara³

et al. 2020

Preprint

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Abstract. The inverse analysis was used to estimate the fire carbon emission in Equatorial Asia induced by the big El Niño in 2015. This inverse analysis is unique because it extensively used high-precision atmospheric mole fraction data of carbon dioxide (CO2) from the commercial aircraft observation project. By comparisons with independent shipboard observations, especially carbon monoxide (CO) data, the validity of the estimated fire-induced carbon emission was elucidated. The best estimate, which used both aircraft and shipboard CO2 observations, indicated 273 Tg C for fire emission during September–October 2015. This two-month-long emission accounts for 75 % of the annual total fire emission and 45 % of the annual total net carbon flux within the region, indicating that fire emission is a dominant driving force of interannual variations of carbon fluxes in Equatorial Asia. Several sensitivity experiments demonstrated that aircraft observations could measure fire signals, though they showed a certain degree of sensitivity to prior fire-emission data. The inversions coherently estimated smaller fire emissions than the priors, partially because of the small contribution of peatland fires, indicated by enhancement ratios of CO and CO2 observed by the ship. In the future warmer climate condition, Equatorial Asia would experience more severe droughts and have risks for releasing a large amount of carbon into the atmosphere. Therefore, the continuation of aircraft and shipboard observations is fruitful for reliable monitoring of carbon fluxes in Equatorial Asia.

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Section: Discussionmentioning

confidence: 99%

Estimation of fire-induced carbon emission from Equatorial Asia in 2015 by using in situ aircraft and ship observations

Niwa¹,

Sawa²,

Nara³

et al. 2020

Preprint

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“…They have been used to create an observational large ensemble (McKinnon et al, 2017;McKinnon and Deser, 2018) and test dynamical adjustment techniques (Deser et al, 2016;Lehner et al, 2017). They have also been used to develop new methodologies such as utilising the ensemble dimension for analysis (Herein et al, 2017;Maher et al, 2018Maher et al, , 2019Haszpra et al, 2020a, b) and to develop and test statistical methods for isolating the forced response (Sippel et al, 2020;Wills et al, 2020). Such ensembles have additionally provided important information for policy makers, such as whether emission reductions are likely to be detectable in the coming years, or whether they could be masked by internal variability (Lehner et al, 2016;Tebaldi and Wehner, 2018;Marotzke, 2019;Spring and Ilyina, 2020).…”

Section: An Introduction To Smilesmentioning

confidence: 99%

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

2021

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Abstract. Single model initial-condition large ensembles (SMILEs) are valuable tools that can be used to investigate the climate system. SMILEs allow scientists to quantify and separate the internal variability of the climate system and its response to external forcing, with different types of SMILEs appropriate to answer different scientific questions. In this editorial we first provide an introduction to SMILEs and an overview of the studies in the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. These studies analyse a range of different types of SMILEs including global climate models (GCMs), regionally downscaled climate models (RCMs), a hydrological model with input from a RCM SMILE, a SMILE with prescribed sea surface temperature (SST) built for event attribution, a SMILE that assimilates observed data, and an initialised regional model. These studies provide novel methods, that can be used with SMILEs. The methods published in this issue include a snapshot empirical orthogonal function analysis used to investigate El Niño–Southern Oscillation teleconnections; the partitioning of future uncertainty into model differences, internal variability, and scenario choices; a weighting scheme for multi-model ensembles that can incorporate SMILEs; and a method to identify the required ensemble size for any given problem. Studies in this special issue also focus on RCM SMILEs, with projections of the North Atlantic Oscillation and its regional impacts assessed over Europe, and an RCM SMILE intercomparison. Finally a subset of studies investigate projected impacts of global warming, with increased water flows projected for future hydrometeorological events in southern Ontario; precipitation projections over central Europe are investigated and found to be inconsistent across models in the Alps, with a continuation of past tendencies in Mid-Europe; and equatorial Asia is found to have an increase in the probability of large fire and drought events under higher levels of warming. These studies demonstrate the utility of different types of SMILEs. In the second part of this editorial we provide a perspective on how three types of SMILEs could be combined to exploit the advantages of each. To do so we use a GCM SMILE and an RCM SMILE with all forcings, as well as a naturally forced GCM SMILE (nat-GCM) over the European domain. We utilise one of the key advantages of SMILEs, precisely separating the forced response and internal variability within an individual model to investigate a variety of simple questions. Broadly we show that the GCM can be used to investigate broad-scale patterns and can be directly compared to the nat-GCM to attribute forced changes to either anthropogenic emissions or volcanoes. The RCM provides high-resolution spatial information of both the forced change and the internal variability around this change at different warming levels. By combining all three ensembles we can gain information that would not be available using a single type of SMILE alone, providing a perspective on future research that could be undertaken using these tools.

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“…The 50-member simulations of MIROC6-LE are sufficient to analyze differences in regional T and T x changes between ssp126 and ssp119 (scenarios relevant to the 2 • C and 1.5 • C goals of the Paris Agreement) but not sufficient to obtain significant differences in P and P x changes over more than half of the world. Atmospheric global climate models (AGCMs) are more cost-effective tools to produce large ensembles (e.g., 100 members) for future climate change projections at given warming levels and for event attribution studies than CGCMs, while AGCM simulations can only inform projections and attribution statements conditionally with respect to prescribed SST patterns (Shiogama et al, 2016(Shiogama et al, , 2020Mizuta et al, 2017;Mitchell et al, 2017;Imada et al, 2017;Stone et al, 2019;Fujita et al, 2020;Nosaka et al, 2021). Combined analyses of AGCM LEs (e.g., Mitchell et al, 2017;Shiogama et al, 2019b) and CGCM LEs could be useful for discussions of differences in precipitation changes between the 2 • C and 1.5 • C warmer climates, but it should be noted that climate change projections can be significantly different between AGCM and CGCM simulations (Uhe et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

Shiogama,

Tatebe,

Hayashi

et al. 2023

Earth Syst. Dynam.

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Abstract. Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.

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Historical and future anthropogenic warming effects on droughts, fires and fire emissions of CO<sub>2</sub> and PM<sub>2.5</sub> in equatorial Asia when 2015-like El Niño events occur

Cited by 16 publications

References 40 publications

Estimation of fire-induced carbon emission from Equatorial Asia in 2015 by using in situ aircraft and ship observations

Estimation of fire-induced carbon emission from Equatorial Asia in 2015 by using in situ aircraft and ship observations

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses

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