In 2015, El Niño contributed to severe droughts in equatorial
Asia (EA). The severe droughts enhanced fire activity in the dry season
(June–November), leading to massive fire emissions of CO2 and aerosols.
Based on large event attribution ensembles of the MIROC5 atmospheric global
climate model, we suggest that historical anthropogenic warming increased
the chances of meteorological droughts exceeding the 2015 observations in
the EA area. We also investigate changes in drought in future climate
simulations, in which prescribed sea surface temperature data have the same
spatial patterns as the 2015 El Niño with long-term warming trends.
Large probability increases of stronger droughts than the 2015 event are
projected when events like the 2015 El Niño occur in the 1.5 and 2.0 ∘C warmed climate ensembles according to the Paris
Agreement goals. Further drying is projected in the 3.0 ∘C
ensemble according to the current mitigation policies of nations.
We use observation-based empirical functions to estimate burned area, fire
CO2 emissions and fine (<2.5µm) particulate matter
(PM2.5) emissions from these simulations of precipitation. There are no
significant increases in the chances of burned area and CO2 and
PM2.5 emissions exceeding the 2015 observations due to past
anthropogenic climate change. In contrast, even if the 1.5 and
2.0 ∘C goals are achieved, there are significant increases in the
burned area and CO2 and PM2.5 emissions. If global warming reaches
3.0 ∘C, as is expected from the current mitigation policies of
nations, the chances of burned areas and CO2 and PM2.5 emissions
exceeding the 2015 observed values become approximately 100 %, at least in
the single model ensembles.
We also compare changes in fire CO2 emissions due to climate change
and the land-use CO2 emission scenarios of five shared socioeconomic
pathways, where the effects of climate change on fire are not considered.
There are two main implications. First, in a national policy context, future
EA climate policy will need to consider these climate change effects
regarding both mitigation and adaptation aspects. Second is the consideration
of fire increases changing global CO2 emissions and mitigation
strategies, which suggests that future climate change mitigation studies
should consider these factors.
Introduction
El Niño events, often characterised by their positive sea surface
temperature (SST) anomalies in the central and eastern tropical Pacific
Ocean, accompany a weakening of the Walker circulation in the equatorial
Pacific region. In the equatorial Asia region (EA, the area denoted in Fig. 1g), the weakening of the Walker circulation due to major El Niño events
corresponds to downward motion anomalies and less convection (negative
precipitation anomalies) (Santoso et al., 2017). The 2015/2016 major El
Niño event (the strongest since 1997/1998) induced negative precipitation
anomalies and enhanced the severe drought in the EA region during the dry season
(June–November) of 2015 (Field et al., 2016; Liu et al., 2017; Santoso et
al., 2017). Parts of the EA region are tropical peatlands that contain
tremendous amounts of soil organic carbon (Page et al., 2011) and huge
biomass (Baccini et al., 2012, 2017; Saatchi et al., 2011). Coupled with
anthropogenic land-use change (e.g. expansion of oil palm plantations on
peatlands), the severe drought increased fire activity in forests and
peatlands, leading to large economic losses (at least USD 16.1 billion for
Indonesia) and significant impacts on ecology and human health (Taufik et
al., 2017; World Bank, 2016; Hartmann et al., 2018). The fires enhanced the
emissions of CO2 and aerosols (Yin et al., 2016; Field et al., 2016;
Koplitz et al., 2016; Stockwell et al., 2016; Liu et al., 2017). The fire
carbon emissions of 2015 were the largest since the 1997 El Niño event
(Yin et al., 2016). The estimated 2015 CO2-equivalent biomass burning
emissions for all Indonesia (1.5 billion metric tons of CO2) were between
the 2013 annual fossil fuel CO2 emissions of Japan and India (Field et
al., 2016). The massive emissions of ozone precursors and aerosols,
including fine (<2.5µm) particulate matter (PM2.5),
caused severe haze across much of EA (Field et al., 2016), resulting in the
excess deaths of approximately 100 300 people (Koplitz et al., 2016).
In a previous study (Lestari et al., 2014), we suggested that recent fire
events in Sumatra were exacerbated by human-induced drying trends based on
analyses of two sets of historical simulations of the MIROC5 atmospheric
global climate model (AGCM) (Watanabe et al., 2010) with and without
anthropogenic warming. Lestari et al. (2014) and Yin et al. (2016) projected
future increases in the frequency of droughts and fires based on analyses
of the coupled atmosphere–ocean global climate model (AOGCM) ensembles of
the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al.,
2012).
Although Lestari et al. (2014) showed the anthropogenic effects on the
historical trends in droughts, it is not clear how historical climate change affected the particular drought event of 2015. Because
extreme events can occur by natural variability alone, it is difficult in
principle to attribute a particular event to anthropogenic climate change.
However, comparisons of observations and large ensemble simulations can help
us evaluate the degree to which human influence has affected the probability
of a particular event (Allen, 2003). Such an approach is called probabilistic
event attribution (PEA) (Pall et al., 2011; Shiogama et al., 2013). In the PEA
approach, two sets of large ensembles (e.g. 100 members) are generally
performed. The first is historical simulations of an AGCM driven by the
historical values of anthropogenic (e.g. greenhouse gases) and natural
forcing (solar and volcanic activities) agents and by the observed SST and
sea ice concentration (SIC). The second is counterfactual natural runs
driven by pre-industrial anthropogenic and historical natural forcing agents
and by the observed values of SST and SIC cooled according to estimates of
anthropogenic warming (Stone et al., 2019) (see Sect. 3 for more details).
Note that the components of interannual variations in the SST data are not
modified in the natural forcing ensemble. Therefore, for example, we can
assess how anthropogenic warming affected the probabilities of drought
events exceeding the observed value in the 2015 major El Niño event by
comparing the distributions of members in historical and natural forcing
ensembles. In this study, based on the PEA approach, we examine whether
historical climate change increased not only the probabilities of drought
but also those of fire and fire emissions of CO2 and PM2.5 during
the June–November dry season of 2015. The lower computing costs of AGCM compared to
AOGCM enable us to perform large ensembles, which are necessary for PEA. We
use the 100-member PEA ensembles of MIROC5 (Shiogama et al., 2014) that have
been used for many attribution studies on single extreme events (e.g.
Shiogama et al., 2014; Kim et al., 2018; Hirota et al., 2018).
Although Lestari et al. (2014) and Yin et al. (2016) showed increases in
droughts and fires in the future transient projection ensembles of AOGCMs, it is not
clear how future anthropogenic warming affects droughts and fire when events
like the 2015 El Niño occur in a future warmer climate. It is also
important to investigate changes in extreme events at 1.5
and 2.0 ∘C warming levels to inform stakeholders, since
the Paris Agreement set the 1.5 ∘C and 2 ∘C long-term climate stabilisation
goals (United
Nations Framework Convention on Climate Change, 2015). In this study, we
examine how the probabilities of drought, fire and fire emissions of
CO2 and PM2.5 would change when major El Niño events like 2015
occur in 1.5 and 2.0 ∘C warmed
climates. We analyse large (100-member) ensembles of the MIROC5 AGCM under
the Half a degree Additional warming, Prognosis and Projected Impacts
(HAPPI) project, which was initiated in response to the Paris Agreement
(Mitchell et al., 2016, 2017, 2018; Shiogama et al., 2019). These MIROC5
HAPPI ensembles have been used, for example, to study the changes in extremely
hot days (Wehner et al., 2018), extreme heat-related mortality (Mitchell et
al., 2018), tropical rainy season length (Saeed et al., 2018) and global
drought (Liu et al., 2018) at 1.5 and
2.0 ∘C global warming. There is a significant “emissions
gap”, which is the gap between where we are likely to be and where we need
to be (United Nations Environment Programme, 2018). The current mitigation
policies of nations would lead to global warming of approximately
3.2 ∘C (with a range of 2.9–3.4 ∘C) by 2100 (United
Nations Environment Programme, 2018). Therefore, it is worthwhile to compare
changes in extreme events and impacts in cases where the
1.5 and 2.0 ∘C goals are achieved and where they are not.
Therefore, we perform and analyse a large ensemble of a 3.0 ∘C warmed climate.
By using the above ensembles, we answer the following questions:
Has historical climate change significantly affected the probabilities of
drought, fire and fire emissions of CO2 and PM2.5?
How do the probabilities of drought, fire and fire emissions in 2015-like
major El Niño years change if we can limit global warming to
1.5 and 2.0 ∘C? Adaptation investments
are necessary to reduce the associated impacts.
If we overshoot the 1.5 and 2.0 ∘C goals
to the current trajectory of 3.0 ∘C, how will drought, fire
and fire emissions be altered? A comparison of the results for
1.5/2.0 and 3.0 ∘C
indicates the potential benefits of mitigation efforts to achieve the goals
of the Paris Agreement.
Although conversions of forest and peatlands to agriculture and plantations
of oil palm are also important factors for fire activity (Marlier et al.,
2013, 2015; Kim et al., 2015), we do not examine the effects of land-use
change in this study. In Sects. 2 and 3, we describe the empirical
functions and model simulations used in this study, respectively. In Sect. 4, we examine changes in precipitation, fire and fire emissions. Finally,
Sect. 5 contains the conclusions.
Empirical functions
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).
The 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 PM2.5 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 PM2.5
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
PM2.5 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 PM2.5 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 PM2.5
emissions increase exponentially. We estimate the fitting curves (solid
curves in Fig. 2) by using the following equation:
ln(y)=a+bΔP,
where y is the burned area, CO2 emissions or PM2.5 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.
Empirical 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) PM2.5 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.
Model simulations
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.
Evaluations 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.
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
(0.41×RCP2.6+0.59×RCP4.5) 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. 0.686×RCP4.5+0.314×RCP8.5 (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 100×10=1000 samples of normalised ΔP and Δω500
data for each of the Hist, Nat, 1.5, 2.0 and 3.0 ∘C ensembles.
Changes in precipitation, burned area and fire emissions of CO2 and
PM2.5
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).
Surface 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.
Relationships 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).
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 100×10=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 PM2.5 (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 100×10ΔP 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 PM2.5 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 PM2.5 emissions (93 % (70 %–98 %),
81 % (54 %–94 %), and 98 % (85 %–100 %), respectively).
Changes 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) PM2.5 emissions (t).
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.
The 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.
Conclusions
By applying the probabilistic event attribution approach based on the MIROC5
AGCM ensembles, we suggested that historical anthropogenic warming
significantly increased the chances of severe meteorological drought
exceeding the 2015 observations in the EA area during the 2015 major El
Niño event (from 2 % (1 %–4 %) in Nat to 9 % (6 %–14 %) in Hist). By
performing and analysing the HAPPI (1.5 and 2.0 ∘C
warming) and HAPPI extension (3.0 ∘C warming) runs, we showed that
the probabilities of drought exceeding the 2015 observations will largely
increase: 82 % (76 %–87 %), 67 % (60 %–74 %), and 93 % (89 %–96 %),
respectively.
Drying trends tend to exacerbate fire activity. By combining these
experiments and the empirical functions, we also implied that historical
anthropogenic drying had tended to increase the chances of the burned area,
CO2 emissions and PM2.5 emissions exceeding the 2015 observations,
but those changes were not statistically significant. In contrast, if the
2.0 ∘C goal is achieved, the chances of exceeding the observed
values will substantially increase for the burned area from 23 %
(3 %–52 %) in Hist to 81 % (50 %–95 %) for 2.0 ∘C, CO2
emissions from 23 % (3 %–47 %) to 81 % (55 %–93 %), and PM2.5
emissions from 24 % (3 %–49 %) to 81 % (54 %–94 %). These results
agree well with Lestari et al. (2014) and Yin et al. (2016), who showed that
the AOGCM ensemble of CMIP5 projected future long-term trends of drying and
enhanced fire CO2 emissions. We further suggest that the risks of drought
and fire significantly increase when events like the 2015 El Niño occur
in future warmer climates even if the 1.5 and 2.0 ∘C
goals are achieved. The impacts of these changes on droughts, burned areas
and fire emissions should be reduced by adaptation investments.
If we cannot limit global warming to 2.0 ∘C and it reaches
3.0 ∘C as expected from the current emissions gap (United
Nations Environment Programme, 2018), the chances of exceeding the observed
values further increase for the burned area, CO2 emissions and
PM2.5 emissions. Although the differences between 2.0 and
3.0 ∘C are not statistically significant for the burned area and
the CO2 and PM2.5 emissions, the 50th percentile values of
probabilities exceeding the 2015 observations first reach approximately
100 % in the 3.0 ∘C runs. These additional changes relative to
2.0 ∘C indicate the effects of the failures of mitigation
policies. Conversely, these changes indicate the potential benefits of
limiting the current trajectory of 3 ∘C global warming to the
Paris Agreement goals.
Forest-based climate mitigation has a key role in meeting the goals of the
Paris Agreement (Grassi et al., 2017). We also suggested that changes in
fire CO2 emissions due to future warming can increase the need for
modifying fire CO2 emission scenarios for future climate projections.
Although we focused on the influences of climate change on burned area and
fire emissions, land-use and land-cover changes are also important factors.
To avoid fire intensification due to drying climates, effective land
management policies for protecting forests and peatlands are necessary
(Marlier et al., 2015; Kim et al., 2015; Koplitz et al., 2016; World Bank,
2016).
This study is based on the single model ensembles using particular SST
anomaly patterns. A future work to compare multimodel simulations using
multiple estimates of warming patterns in SST would be useful.
Data availability
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.
Author contributions
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.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
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.
Acknowledgements
We thank the reviewers and the editor for their useful comments. The MIROC5
simulations were performed using the Earth Simulator at JAMSTEC and the NEC
SX at NIES.
Financial support
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.
Review statement
This paper was edited by Nicola Maher and reviewed by three anonymous referees.
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