Figure 1a–c indicate the observed June–November 2015 mean anomalies in surface air temperature (ΔT), vertical pressure velocity at the 500 hPa level (Δω₅₀₀) and precipitation (ΔP) relative to the 1979–2016 averages. ERA-Interim reanalysis (ERA-I) data (Dee et al., 2011) are used for ΔT and Δω₅₀₀. Global Precipitation Climatology Project (GPCP) data (Adler et al., 2003) are analysed for ΔP. The largely positive ΔT over the eastern tropical Pacific Ocean (i.e. El Niño) is related to substantial downward motion anomalies (weakening of Walker circulation) and negative precipitation anomalies over the EA region (the area shown in Fig. 1g). The negative precipitation anomalies in June–November 2015 were the third largest since 1979 (the first and second largest anomalies are the 1997 and 1982 El Niño events).

Figure 1The observed climate conditions and fires. The June–November 2015 averaged anomalies of (a) surface air temperature (^∘C) and (b) vertical pressure velocity at the 500 hPa level (Pa s⁻¹, downward motions are positive) from ERA Interim reanalysis data (Dee et al., 2011) relative to the 1979–2016 mean. (c) The June–November 2015 averaged anomalies of precipitation from GPCP (Adler et al., 2003) (mm d⁻¹). The right panels indicate (d) fire fraction (%), (e) fire CO₂ emissions (g m⁻² month⁻¹) and (f) fire PM_2.5 emissions from GFED4s (van der Werf et al., 2017) between June and November 2015. (g) The red area indicates the EA region of the GFED4s. We use this definition of the EA area. Shading shows the land area ratio (no unit) used for weighting in the computation of EA averages.

In the EA region, the negative precipitation anomalies are associated with the enhanced fire fraction, fire CO₂ emissions and fire PM_2.5 emissions estimated from the Global Fire Emissions Database (GFED4s) (van der Werf et al., 2017) (Fig. 1d–f). By combining satellite information on fire activity and vegetation productivity, GFED4s provides monthly burned area, fire CO₂ and dry matter (DM) emissions data. We can also compute aerosol emissions by multiplying DM by the provided factors. The CO₂ and PM_2.5 emissions increase linearly as the burned areas expand (Supplement Fig. S1). Previous studies found that fire activity and related emissions have non-linear relationships with precipitation anomalies and accumulated water deficits (Lestari et al., 2014; Spessa, et al., 2015; Yin et al., 2016; Field et al., 2016). Figure 2 shows the empirical relationships between the EA-averaged precipitation anomalies (GPCP) and the EA cumulative burned area and fire CO₂ and PM_2.5 emissions (GFED4s) between 1997 and 2016. Here, we remove the 1979–2016 average from precipitation and divide the anomalies by their standard deviation value. As precipitation decreases, the burned area, fire CO₂ and PM_2.5 emissions increase exponentially. We estimate the fitting curves (solid curves in Fig. 2) by using the following equation:

where y is the burned area, CO₂ emissions or PM_2.5 emissions, and a and b are the intercept and regression coefficients, respectively. The coefficients of determination (R²) are higher than 0.7. We also estimate the 10 %–90 % confidence intervals of the fitting curves by applying a 1000-time random sampling of the observed data: we randomly resample 20-year samples from the original 20-year (1997–2016) data and compute a and b; we repeat the random resampling process 1000 times; we consider that the 10th percentile and 90th percentile values of the 1000 regression lines indicate the 10 %–90 % confidence intervals. These non-linear relationships are consistent with previous studies (Lestari et al., 2014; Spessa, et al., 2015; Yin et al., 2016; Field et al., 2016). We use the relationships in Fig. 2a–c as empirical functions to estimate burned area and fire emissions from the AGCM simulations of precipitation in Sect. 4.

Figure 2Empirical relationships between observed precipitation anomalies, burned area and fire emissions in the EA area between 1997 and 2016. The horizontal axes are the normalised June–November mean precipitation anomalies (no unit) of the GPCP. The vertical axes denote (a) burned area (km²), (b) CO₂ emissions (TgCO₂) and (c) PM_2.5 emissions (t) of GFED4s. The year 2015 values are indicated by red squares. Solid and dashed lines indicate the best estimates and the 10 %–90 % confidence intervals of the fitting curves from Eq. (1), respectively.

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The MIROC5 AGCM (Watanabe et al., 2010) has a 160 km horizontal resolution. We perform 10-member long-term (1979–2016) historical simulations (Hist-long) of the MIROC5 AGCM forced by the observed sea surface temperature (SST) (HadISST, Rayner et al., 2003) and anthropogenic and natural external forcing factors (Shiogama et al., 2013, 2014). Here, the observed ΔP and Δω₅₀₀ are divided by their standard deviation values. The ΔP and Δω₅₀₀ of each ensemble member are also divided by their own standard deviation values. The correlations of the 1979–2016 time series of ΔP and Δω₅₀₀ between the observations and the ensemble averages of the MIROC5 simulations are 0.90 and 0.87, respectively (Fig. 3a–b). When we apply the above normalisation process as a simple bias correction technique, it is found that the MIROC5 model has good hindcast skill regarding interannual variability in the EA-averaged ΔP and Δω₅₀₀. The precipitation and vertical motion anomalies are closely related to the Niño 3.4 SST (an index of El Niño–Southern Oscillation) in the observations (correlations are −0.89 and 0.76, respectively) (Fig. 3c–d). There is also a high correlation value between ΔP and Δω₅₀₀ (−0.87) (Fig. 3e). We show that El Niño (La Niña) accompanies descending wind (ascending wind) in the EA area (Fig. 3d), leading to negative (positive) ΔP (Fig. 3e and c). The MIROC5 model represents well these relationships between Niño 3.4, ΔP and Δω₅₀₀ in the observations (Fig. 3c–e); i.e. the regression lines of MIROC5 in Fig. 3c–e are close to those in the observations.

Figure 3Evaluations of the MIROC5 simulations of the EA-averaged precipitation and vertical air motions. Panels (a) and (b) show the normalised June–November mean time series of (a) ΔP (no unit) and (b) Δω₅₀₀ (no unit). Red lines are the observations. Light blue lines are the 10 ensemble members of Hist-long, and blue lines are the ensemble mean. The other panels are scatter plots of (c) ΔP and the Niño 3.4 index (^∘C), (d) Δω₅₀₀ and the Niño 3.4 index, and (e) ΔP and Δω₅₀₀. Red diamonds are the observed values. Small light-blue crosses are the 10 ensemble members of Hist-long, and large blue diamonds indicate the ensemble mean values. The red and blue lines indicate the regression lines of the observations and the ensemble averages of Hist-long, respectively.

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To investigate whether historical anthropogenic climate change affected the precipitation anomalies during the 2015 El Niño event, we analyse the outputs of two large ensembles, one with factual historical forcing (Hist) and one with counterfactual natural forcing (Nat) of MIROC5 for June–November 2015 (Shiogama et al., 2013, 2014). These simulations are called probabilistic event attribution experiments, and they contribute to the international Climate and Ocean: Variability, Predictability and Change (CLIVAR) C20C+ Detection and Attribution project (Stone et al., 2019). The Hist ensemble is forced by historical anthropogenic and natural external forcing factors and also observational data of SST and sea ice (HadISST, Rayner et al., 2003). The Nat ensemble is forced by historical natural forcing factors and hypothetical “natural” SST and sea ice patterns where long-term anthropogenic signals were removed. Anthropogenic SST changes were estimated by taking the ensemble mean differences between the all-forcing historical runs and the natural-forcing historical runs of the CMIP5 AOGCMs. The multimodel averaged anthropogenic signal was subtracted from the HadISST data, and the Nat sea ice was estimated by using an empirical function that computes observed sea ice concentrations from surface temperature (Stone et al., 2019). Please note that both the Hist and Nat ensembles have 2015 El Niño components in the spatial patterns of SST, but the prescribed long-term warming anomalies in SST are different from each other. We performed 100-member runs of the 2006–2016 period for both Hist and Nat. Please see Shiogama et al. (2013, 2014) and Stone et al. (2019) for details regarding the experimental design.

We also analyse the 100-member ensembles of 11-year simulations with 1.5 and 2.0 ^∘C warming relative to pre-industrial levels. We performed those experiments as a contribution to the HAPPI project (Mitchell et al., 2016, 2017, 2018; Shiogama et al., 2019). Since the ensemble-averaged global warming of the CMIP5 Representative Concentration Pathway 2.6 (RCP2.6) experiments is 1.55 ^∘C, for the 1.5 ^∘C runs, we used the RCP2.6 anthropogenic forcing agents (e.g. greenhouse gases) in 2095 and the ensemble mean 2091–2100 averaged SST anomalies of the RCP2.6 runs of the CMIP5 AOGCMs. The SST anomalies (Supplement Fig. S2, top panel) are changes in the CMIP5 multimodel mean SST for each month, between the decadal average of 2091–2100 RCP2.6 and the decadal average of 2006–2015 RCP8.5. We added those SST anomalies to the 2006–2016 observed SST data of HadISST. To estimate the sea ice concentration, we applied a linear sea ice–SST relationship estimated from observations (Supplement Figs. S3–S4) (Mitchell et al., 2017). For the 2.0 ^∘C runs, we used the weighted sum of RCP2.6 and RCP4.5 ($\mathrm{0.41}\times \mathrm{RCP}\mathrm{2.6}+\mathrm{0.59}\times \mathrm{RCP}\mathrm{4.5}$) of the well-mixed greenhouse gas concentrations in 2095 and the ensemble mean 2091–2100 averaged SST anomalies of the CMIP5 AOGCM ensembles (Supplement Fig. S2, middle panel) because the weighted sum of the global mean temperature change values of the ensemble-averaged CMIP5 RCP2.6 and RCP4.5 runs is 2.0 ^∘C. Please see Mitchell et al. (2017) for details regarding the experimental design. Notably, these future simulations have the same components as the 2015 El Niño event in terms of the spatial patterns of SST, but the prescribed long-term warming anomalies in SST have been added. Therefore, we can investigate drought events when events like the 2015 El Niño occur in 1.5 and 2.0 ^∘C warmed climates relative to pre-industrial levels.

Furthermore, we run the 100-member 3.0 ^∘C ensemble (10-year simulations based on the 2006–2015 HadISST data) as an extension of the HAPPI project. Following the original HAPPI methodology, we add SST and sea ice concentration anomalies that represent additional warming in a 3 ^∘C warmer world compared to pre-industrial values. The SST anomalies (Supplement Fig. S2, bottom panel) are changes in the CMIP5 multimodel mean SST for the decadal average of 2006–2015 in RCP8.5 and the decadal average of 2091–2100 in a combined scenario of RCP4.5 and RCP8.5, i.e. $\mathrm{0.686}\times \mathrm{RCP}\mathrm{4.5}+\mathrm{0.314}\times \mathrm{RCP}\mathrm{8.5}$ (Lo et al., 2019). The CMIP5 multimodel global mean temperature in 2091–2100 is approximately 3 ^∘C warmer than the 1861–1880 mean in this combined scenario; hence, this scenario describes 3 ^∘C global warming above pre-industrial levels. For the sea ice concentration anomalies, we find the coefficients of this linear relationship from pre-existing 1.5 and 2 ^∘C SST and sea ice anomalies. We apply this relationship to the 3 ^∘C SST anomalies to estimate the sea ice concentration anomalies, which are then added to the observed 2006–2015 data (see Mitchell et al., 2017). Supplement Figs. S3–S4 show the sea ice concentrations in both hemispheres in the 1.5, 2 and 3 ^∘C experiments. The same weightings for RCP4.5 and RCP8.5 in the combined scenario equivalent to 3 ^∘C warming are also applied to greenhouse gas concentrations. This study is the first to report results from the HAPPI extension (i.e. the 3 ^∘C runs) using MIROC5.

To compute the normalised values of EA-averaged ΔP and Δω₅₀₀ of the Hist, Nat, 1.5, 2.0 and 3.0 ^∘C runs, we subtract a long-term mean value of a given single member of Hist-long and divide anomalies by the standard deviation value of that Hist-long member. This normalisation process enables us to produce $\mathrm{100}\times \mathrm{10}=\mathrm{1000}$ samples of normalised ΔP and Δω₅₀₀ data for each of the Hist, Nat, 1.5, 2.0 and 3.0 ^∘C ensembles.

The difference patterns of surface air temperature (≈ prescribed SST difference patterns over the ocean) in Hist–Nat and 1.5 ^∘C–Nat, 2.0 ^∘C–Nat and 3.0 ^∘C–Nat have greater warming in the Niño 3.4 region than the tropical (30^∘ S–30^∘ N) ocean average values (Fig. 4). The relatively higher warming in the Niño 3.4 region accompanies downward motion anomalies in the EA region (Fig. 5a), enhancing negative precipitation anomalies when an El Niño occurs (Fig. 5b). Notably, the prescribed SST difference between the Niño 3.4 region and the tropical ocean mean is larger in the 1.5 ^∘C runs than in the 2.0 ^∘C runs. As a result, the amplitude of negative precipitation in the 1.5 ^∘C runs is slightly greater than that in the 2.0 ^∘C runs, as mentioned below, at least in these ensembles. It is not clear why the ensemble average of the CMIP5 RCP2.6 runs (i.e. the prescribed SST anomalies of the 1.5 ^∘C runs) has a larger SST difference between the Niño 3.4 region and the tropical ocean mean than that of the weighted sum of RCP2.6 and RCP4.5 (the 2.0 ^∘C runs).

Figure 4Surface air temperature warming patterns in 2015. (a) ΔT differences between 3.0 ^∘C and Nat (^∘C). The 30^∘ S–30^∘ N ocean averaged value is subtracted. The black box indicates the Niño 3.4 region. The other panels are the same as panel (a) but for (b) 2.0 ^∘C and Nat, (c) 1.5 ^∘C and Nat and (d) Hist and Nat.

Figure 5Relationships between Niño 3.4 warming and EA vertical motion and precipitation anomalies of the ensemble mean. The horizonal axes show differences in the 2015 T anomalies between the Niño 3.4 area and the 30^∘ S–30^∘ N ocean (^∘C). The vertical axes are (a) Δω₅₀₀ (no unit) and (b) ΔP (no unit) for the year 2015. Crosses denote the ensemble averages of Nat (purple), Hist (black), 1.5 ^∘C (light blue), 2.0 ^∘C (green) and 3.0 ^∘C (red).

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The 10-member ensembles of Lestari et al. (2014) were too small to estimate probabilities of droughts. Our large ensemble simulations enable us to estimate the probabilities of drought exceeding the observed value. Historical anthropogenic climate change has significantly increased the chance of ΔP being more negative than the observed value from 2 % (1 %–4 %) in Nat to 9 % (6 %–14 %) in Hist (Fig. 6a). Here, we use the cumulative histograms of $\mathrm{100}\times \mathrm{10}=\mathrm{1000}$ samples of ΔP to estimate the probabilities of ΔP. The values in parentheses indicate the 10 %–90 % confidence interval estimated by applying the 1000-time resampling: we randomly resample 100×10 data from the original 100×10 samples of ΔP and compute the probabilities of drought exceeding the 2015 observed value; we repeat the random resampling process 1000 times and consider the 10th percentile and 90th percentile values of the 1000 estimates of probability as the 10 %–90 % bounds. Even if the 1.5 and 2.0 ^∘C goals of the Paris Agreement are achieved (in the 1.5 and 2.0 ^∘C runs), the chance of exceeding the observed value significantly increases from 9 % (6 %–14 %) in Hist to 82 % (76 %–87 %) and 67 % (60 %–74 %), respectively. In the current trajectory of 3.0 ^∘C warming (in the 3.0 ^∘C runs), the chance of exceeding the observed value becomes 93 % (89 %–96 %).

By combining the ΔP of MIROC5 (Fig. 6a) and the empirical relationships in Fig. 2, we assess the historical and future changes in burned areas and fire emissions of CO₂ and PM_2.5 (Fig. 6b–d). We consider uncertainties by combining randomly resampled ΔP and resampled regression factors of Eq. (1): (i) we compute the regression factors of Eq. (1) using randomly resampled data (the same as the process used to estimate the uncertainty ranges of the regression lines); (ii) we randomly resample 100×10 data from the original 100×10 samples of ΔP; (iii) we use the regression factors of (i) and the 100×10 ΔP samples of (ii) to compute the 1000 estimates of fire or emissions and estimate the probability of exceeding the observed values; (iv) the processes of (i)–(iii) are repeated 1000 times; and (v) the 10th percentile and 90th percentile values of the 1000 estimates of the probabilities of exceeding the observed values are considered to be the 10 %–90 % bounds. Historical anthropogenic drying has increased the probability of exceeding the observed values of the burned area (from 5 % (0 %–18 %) to 23 % (3 %–52 %)), CO₂ emissions (from 5 % (0 %–15 %) to 23 % (3 %–47 %)), and PM_2.5 emissions (from 2 % (0 %–5 %) to 24 % (3 %–49 %)), but these changes are not statistically significant due to the large uncertainties. In the 1.5, 2.0 and 3.0 ^∘C runs, the chances of exceeding the observed values significantly increase for the burned area (93 % (66 %–99 %), 81 % (50 %–95 %), and 98 % (84 %–100 %), respectively); for CO₂ emissions (92 % (72 %–98 %), 81 % (55 %–93 %), and 98 % (86 %–100 %), respectively); and for PM_2.5 emissions (93 % (70 %–98 %), 81 % (54 %–94 %), and 98 % (85 %–100 %), respectively).

Figure 6Changes in the cumulative probability functions. (a) The vertical axis indicates the probability (%) of ΔP being lower than a given horizontal value (no unit). Solid lines denote the 50 % values of the 1000 random samples of the Nat (purple), Hist (black), 1.5 ^∘C (light blue), 2.0 ^∘C (green) and 3.0 ^∘C (red) ensembles. The vertical dotted line is the observed 2015 value. The other panels show the probabilities of exceeding the given horizontal values for (b) the burned area (km²), (c) CO₂ emissions (TgCO₂) and (d) PM_2.5 emissions (t).

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We contextualise the estimated fire CO₂ emissions within the future emissions scenarios. Although the above analyses focus on the year when the 2015-like El Niño events occurred, long-term mean fire CO₂ emissions are also important for mitigation policies. Here, we use the simulated June–November mean precipitation anomalies of 11 years (2006–2016), instead of using only the 2015 data, and the empirical function of Fig. 2b to estimate the cumulative probability function of fire CO₂ emissions in the EA area in the 2.0 ^∘C runs (Fig. 7). The fire CO₂ emissions of the 11-year period including both El Niño and non-El Niño years (Fig. 7) are much less than those in the year 2015 with the major El Niño (Fig. 6c) due to low fire CO₂ emissions in the non-El Niño years (Fig. 2). However, these fire CO₂ emissions can have substantial implications for mitigation policies. The vertical lines in Fig. 7 are land-use CO₂ emission scenarios for the year 2100 including fire emissions for the east and south-east Asia regions except China and Japan in the five shared socioeconomic pathway (SSP) scenarios from the Asia–Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) (Fujimori et al., 2012). AIM/CGE is one of the integrated assessment models (economic models) that produced the emissions data of SSP scenarios for the Coupled Model Intercomparison Project Phase 6 and the sixth assessment report of the Intergovernmental Panel on Climate Change (Riahi et al., 2017; Fujimori et al., 2017). Please note that land-use CO₂ emissions for the year 2100 are not linearly related to the SSP numbers because the SSP numbers did not indicate radiative forcing levels. The chances of exceeding the emissions of SSP1, 2, 3, 4 and 5 are 77 % (70 %–84 %), 34 % (28 %–39 %), 13 % (10 %–18 %), 37 % (31 %–41 %), and 77 % (70 %–84 %), respectively. Although these probability values highly depend on the SSP scenarios, the results are substantial in all the SSP scenarios. Because the CO₂ emissions in the AIM/CGE model include a wider area and emission sources other than the EA fire emissions of CO₂, this comparison is conservative. In the SSP simulations of AIM/CGE, fire CO₂ emissions are computed by using functions of land-cover changes, and climate change effects on fires are not considered. Therefore, it is suggested that implementing climate change effects on fire CO₂ emissions in integrated assessment models can significantly affect SSP land-use CO₂ emissions and studies on mitigation pathways, which in turn would be highly relevant to national and global climate policies. We suggest that additional fire CO₂ emissions due to climate change should be considered in possible CMIP7 activities.

Figure 7The red curves are the cumulative probability function of CO₂ emissions (TgCO₂ yr⁻¹) in June–November of 2006–2016 for the 2.0 ^∘C runs. Solid and dashed lines denote the 50 % values and the 10 %–90 % confidence intervals, respectively. The vertical lines indicate annual land-use CO₂ emission scenarios for the year 2100 (including fire emissions of CO₂) for the east and south-east Asia regions, except China and Japan, for the five SSP baseline scenarios of the AIM/CGE model.

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The data from the MIROC5 model, ERA-I, GPCP and GFED4s used in this article can be downloaded from https://portal.nersc.gov/cascade/data/downloader.php?get_dirs= (C20C+ Detection and Attribution Project, 2020), https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim (ECMWF, 2020), https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html (NOAA/ESRL/PSL, 2020), and https://www.globalfiredata.org/data.html (GFED, 2020), respectively. The data of AIM/CGE can be accessed by contacting the corresponding author.

The supplement related to this article is available online at: https://doi.org/10.5194/esd-11-435-2020-supplement.

HS, RH, TH, SF and SC designed the analysis. HS performed the analysis and wrote the first draft of the paper. HS, YTEL and DM proposed and performed the HAPPI extension runs. All authors contributed to the interpretation of the results and to the writing of the paper.

The authors declare that they have no conflict of interest.

This article is part of the special issue “Large Ensemble Climate Model Simulations: Exploring Natural Variability, Change Signals and Impacts”. It is not associated with a conference.

This research has been supported by ERTDF 2-1702 (Environmental Restoration and Conservation Agency, Japan), the Integrated Research Program for Advancing Climate Models (TOUGOU, grant no. JPMXD0717935457) and the Climate Change Adaptation research programmes of NIES. This research used the science gateway resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract no. DE-AC02-05CH11231.

This paper was edited by Nicola Maher and reviewed by three anonymous referees.