Extreme Fire Season in California: A Glimpse Into the Future?

Abstract

The fire season in northern California during 2014 was the second largest in terms of burned areas since
1996. An increase in fire risk in California is attributable to human-induced climate change.

Full text

S1
OCTOBER 2015AMERICAN METEOROLOGICAL SOCIETY |
2. EXTREME FIRE SEASON IN CALIFORNIA: A GLIMPSE
INTO THE FUTURE?
Jin-Ho Yoon, S.-Y. Simon Wang, RobeRt R. gillieS, laWRence HippS,
ben KRavitz, and pHilip J. RaScH
Introduction. California has been under drought
conditions since 2012, and the drought worsened
considerably in the winter of 2013/14 (e.g., Wang et
al. 2014), which fueled an extreme fire season in 2014
(Hart et al. 2015). The early onset of the 2014 dry sea-
son (Supplemental Fig. S2.1) fueled an extraordinary
jump in wildfires. Between 1 January and 20 Septem-
ber, the California Department of Forestry and Fire
Protection reported thousands more fires than the
five-year average (www.fire.ca.gov). In early August,
a state of emergency was declared for a single wildfire
that had burned 32 000 acres (http://gov.ca.gov/news.
php?id=18645). This unusual fire season is expected
to continue well through 2015.
The connection between a warming climate and
lengthened fire seasons may seem intuitive, given
the general tendency toward a hot-and-dry climate
scenario and an earlier snowmelt (Westerling et al.
2006). However, what is not yet fully understood is
the extent to which the projected wetter climate in
California towards the latter part of the 21st century
(Neelin et al. 2013) could affect wildfire risk in the
future; this historica l drought and unusual fire season
also calls attention to possible impacts from human-
induced climate change.
Satellite merged data of burned area from the
fourth generation of the Global Fire Emissions Data-
base (GFED4; Giglio et al. 2013) was analyzed (online
supplemental material). Because the GFED4 product
may underestimate wildfire extent due to its limit in
the minimum detectable burned area and obscura-
tion by cloud cover, the Keetch–Byram Drought index
(KBDI; Janis et al. 2002; Keetch and Byram 1968),
routinely used by the United States Forest Service
for monitoring fire risk, was included as well. The
KBDI is computed with both the observational and
simulated daily precipitation and maximum surface
temperature. Obser vational dataset is from the North
American Land Data Assimilation phase 2 (NLDAS2;
Xia et al. 2012).
Fire extent of 2014. Figure 2.1 shows the annual
mean KBDI, the fractional area under extreme fire
risk (online supplemental material), and the burned
area averaged for entire California (Fig. 2.1a) and
northern California—north of 39°N (Fig. 2.1b). Both
the KBDI and the extreme fire risk exhibit a steady
increase over California since 1979 despite the rather
large interannual fluctuation. In terms of area burned
in GFED4, 2014 ranks the sixth largest in the entire
state and second in northern California; but in terms
of the KBDI and the extreme fire risk, 2014 ranks
first in both the entire state and northern California.
Also noteworthy is that the two largest burned areas
in northern California, over the 18-year record of
GFED4, occurred in 2012 and 2014. Spatially, the
area of higher fire risk in 2014, that is, a KBDI value
higher than 400, extends further north compared to
that of 2012 (Figs. 2.1e,f), consistent with the burned
area (Figs. 2.1c,d).
Attribution and projection. Wildfire simulations and
projections are general ly performed using stand-alone
vegetation models (e.g., Brown et al. 2004; Cook et
al. 2012; Luo et al. 2013; Scholze et al. 2006; Yue et al.
2013) driven by global climate model output. While
the advantage of using a stand-alone vegetation
model lies in its application to high spatial resolution
through downscaling, disadvantages include added
uncertainty produced from downscaling (e.g., Shukla
and Lettenmaier 2013; Yoon et al. 2012). In this study,
The fire season in northern California during 2014 was the second largest in terms of burned areas since
1996. An increase in fire risk in California is attributable to human-induced climate change.
AFFILIATIONS: Yoon, KR avitz, an d RaScH —Atmospheric
Sciences and Global Change Division, Pacific Northwest National
Laboratory, Richland, Washington; Wang, gil lieS, a nd HippS —
Utah Climate Center/Dept. Plants, Soils and Climate, Utah State
University, Logan, Utah
D O I: 10 . 117 5 / BA M S - D -1 5 - 0 0 114 . 1
A supplement to this article is available online (10.1175
/BAM S-D -15 -00114.1)

S2 OCTOBER 2015
|
we analyzed both the KBDI and wildfire probabilities
computed directly within the Community Earth
System Model version 1 (CESM1), which are primar-
ily driven by the dryness of the surface soil and the
availability of fuel load, that is, vegetation (Thonicke
et al. 2001). Although CESM1’s spatial resolution of
1-degree is relatively coarse, the model does simulate
well the climate drivers of fire, such as precipitation
and surface air temperature of California (Wang et al.
2014). Further, the CESM1 has produced 30 members
(online supplemental material) spanning historical
(1920–2005) and future periods (2006–80; based on
RCP8.5 scenario), together with a pre-industrial sim-
ulation of 1800 years. These model outputs provide
a unique opportunity for a detection and attribution
study conducted here to assess wildfire probabilities
under climate change.
Projections for California did show a steady in-
crease of the fire risk based solely upon the KBDI
(Fig. 2.2a) and are consistent with recent studies
(Dennison et al. 2014; Lin et al. 2014; Luo et al. 2013;
van Mantgem et al. 2013) that indicate increased oc-
currence of area burned and wildfire intensity and
duration over the western United States. The CESM1
projects only a slight increase in annual precipitation
accompanied with increasing surface warming after
1990 through 2070 (Supplemental Figs. S2.2b,c), con-
sistent with those produced by the Coupled Model
Intercomparison Project Phase 5 (CMIP5) ensembles
(Neelin et al. 2013). At face value, these simulations
of a slightly wetter climate from 1990 onward could
explain the cessation of the simulated fire probability
increase at the end of 20th century (Supplemental Fig.
S2.2c). However, the KBDI and the extreme fire risk
measures, computed here in terms of the fractional
area and the extreme fire danger days (Figs. 2.2b,c),
do show a steady and rapid increase from early 1990s
and 2000s.
To what extent can the change of the extreme fire
risk over California be attributed to global warming?
First, we need to understand how much fluctuation
is caused by natural climate variability alone (e.g.,
Kitzberger et al. 2007). Analyzing the 1800-year
pre-industrial simulation of the CESM1 by treating
the simulation as 18 member ensembles of 100-year
simulation, the pre-industrial simulations envelops
entirely both the KBDI and the extreme fire risk mea-
sures f luctuation for the period spanning 1920–80
Fig. 2.1. Annual mean of the KBDI in black, fraction of the area that are under the extreme fire risk in red, which
is defined as KBDI > 600, and burned area from GFED4 in orange averaged for (a) California and (b) Northern
California. Spatial distribution of burned area in hectare (ha) from GFED4 averaged for (c) 2014 and (d) 2012,
and corresponding the annual mean KBDI in (e) and (f).

S3
OCTOBER 2015AMERICAN METEOROLOGICAL SOCIETY |
(Fig. 2.2). Beginning in the 1990s—the later part of
the historical simulation—a clear separation emerges
between the extreme fire risks driven by the anthro-
pogenic climate forcing and that of natural climate
variability. However, 2014 occurred in a period of
rapidly increasing extreme fire risk. The pace of
increasing extreme fire risk according to simulation
has accelerated since the early 21st century and is
expected to surpass the range of natural climate vari-
ability. Observations show much faster
increases of the KBDI and extreme fire
risk measures (gray lines in Fig. 2.2).
The accelerated increase in the
KBDI and the extreme fire risk in
relation to the projected wetter cli-
mate in California is intriguing. To
increase extreme fire risk, two basic
situations need to be present: one is
abundant fuel load (i.e., surface veg-
etation coverage enhanced through
precipitation), and the other is the
occurrence of a hot-and-dry climate
regime or drought to dry the vegeta-
tion. A process called CO2 fertilization
(Donohue et al. 2013) tends to increase
vegetation activity simply through the
uptake of an increasing atmospheric
CO2. Under such a scenario along with
a wetter climate, vegetation growth
would increase and subsequently
supply sufficient fuel load. Though
population growth and associated
urban area change are accounted for
in the model, the CESM1 produced
fire probability does not account for
incidence of human-caused fire igni-
tion, which correlate with population
growth.
The extent to which man-made
global warming has increased the
risk or strength of the recent drought
in California has been an active area
of research. The severity of the 2014
fire season in California was ana lyzed
and its potential link to anthropogenic
warming was suggested (Diffenbaugh
et al. 2015; Wang et al. 2015, 2014)
despite presence of natural climate
variability (Wang and Schubert 2014).
However, it is important to point out
from this study that, the increase in
extreme fire risk is expected within
the coming decade to exceed that of natural variabil-
ity and this serves as a n indication that anthropogenic
climate warming will likely play a significant role in
influencing California’s fire season.
Conclusions. The 2014 fire season saw the second larg-
est burned area in northern California since 1997,
next only to 2012, and ranks the highest since 1979
in the case of extreme fire risk over the entire state.
Fig. 2.2. (a) Annual mean of the KBDI from the large ensemble
simulation of the CESM1, (b) fractional area (%) under the extreme
fire risk, and (c) the extreme fire danger (days year−1) over
California. Red (blue) indicates the historical and RCP8.5 (pre-
industrial) runs. Gray lines indicate 50% of the 2nd order trend of
the KBDI and the extreme fire risk measures based on the NLDAS2.
To remove the climatological bias, starting points are adjusted to
be the same as the modeled ensemble mean of year 1979.

S4 OCTOBER 2015
|
Although both fire measures are based upon observa-
tions, these derived variables do exhibit uncertainty
(Giglio et al. 2013; Xia et al. 2012). The recent extreme
fire seasons have occurred in a time of drought. Some
measures of extreme fire risk are also expected to in-
crease in the future despite the overall lack of change
in the mean f ire probability and annual precipitation
simulated by climate models for the next 50 years. Our
result, based on the CESM1 outputs, indicates that
man-made global warming is likely one of the causes
that will exacerbate the areal extent and frequency
of extreme fire risk, though the inf luence of internal
climate variability on the 2014 and the future fire
season is difficult to ascertain. .
ACKNOWLEDGEMENTS. Research by Yoon,
Kravitz, and Rasch was supported by the Earth
System Modeling program in the Office of Science/
DOE and Wang, and Gillies by the WaterSMART
grant from the Bureau of Reclamation. Computation
was done at the National Energy Research Scientific
Computing Center and the Environmental Molecular
Sciences Laboratory at PNNL. CESM1 is supported by
the NSF and DOE. PNNL is operated for the Depart-
ment of Energy by Battelle Memorial Institute under
Contract DEAC05-76RLO1830.
Brown, T., B. Hall, and A. Westerling, 2004: e impact
of twenty-rst century climate change on wildland
re danger in the western United States: An applica-
tions perspective. Climatic Change, 62, 365–388.
Cook, B., N. Zeng, and J.-H. Yoon, 2012: Will Amazo-
nia dry out? Magnitude and causes of change from
IPCC climate model projections. Earth Interact., 16,
1-27, doi: 10.1175/2011EI39 8.1.
Dennison, P. E., S. C. Brewer, J. D. Arnold, and M. A.
Moritz, 2014: Large wildre trends in the western
United States, 1984–2011. Geophy. Res. Lett., 41,
2928–2933, doi:10.1002/2014GL059576.
Dienbaugh, N. S., D. L. Swain, and D. Touma, 2015:
Anthropogenic warming has increased drought risk
in California. Proc. Natl. Acad. Sci. USA, 11 2, 3931–
3936, doi:10.1073/pnas.1422385112.
Donohue, R. J., M. L. Roderick, T. R. McVicar, and G.
D. Farquhar, 2013: Impact of CO2 fertilization on
maximum foliage cover across the globe’s warm,
arid environments. Geophys. Res. Lett., 40, 3031–
3035, doi:10.1002/grl.50563.
Giglio, L., J. T. Randerson, and G. R. van der Werf,
2013: Analysis of daily, monthly, and annual burned
area using the fourth-generation global re emis-
sions database (GFED4). J. Geophys. Res. Biogeosci.,
118, 317–328, doi:10.1002/jgrg.20042.
Hart, S. J., T. Schoennagel, T. T. Veblen, and T. B. Chap-
man, 2015: Area burned in the western United States
is unaected by recent mountain pine beetle out-
breaks. Proc. Natl. Acad. Sci. USA, 112 , 4375–4380,
doi:10.1073/pnas.1424037112.
Janis, M. J., M. B. Johnson, and G. Fort hun, 2002: Near-
real time mapping of Keetch-Byram drought index
in the south-eastern United States. Int. J. Wildland
Fire, 11, 281–289.
Keetch, J. J., and G. M. Byram, 1968: A drought index
for forest re control. U.S.D.A. Forest Service Res.
Paper SE-38, 32 pp. [Available online at www.srs.
fs.usda.gov/pubs/rp/rp_se038.pdf.]
Kitzberger, T., P. M. Brown, E. K. Heyerdahl, T. W.
Swetnam, and T. T. Veblen, 2007: Contingent Pacif-
ic–Atlantic Ocean inuence on multicentury wild-
re synchrony over western North America. Proc.
Natl. Acad. Sci. USA, 104 , 543–548.
Lin, H.-W., J. L. McCarty, D. Wang, B. M. Rogers, D.
C. Morton, G. J. Collatz, Y. Jin, and J. T. Randerson,
2014: Management and climate contributions to
satellite-derived active re trends in the contiguous
United States. J. Geophys. Res. Biogeosci., 119, 645–
660, doi:10.1002/2013JG002382.
Luo, L., Y. Tang, S. Zhong, X. Bian, and W. E. Heilman,
2013: Will future climate favor more erratic wildres
in the western United States? J. Appl. Meteor. Clima-
tol., 52, 2410 –2417, doi :10.1175/JAMC-D-12- 0317.1.
Mo, K. C., and R. W. Higgins, 1998a: Tropical convec-
tion and precipitation regimes in the western United
States. J. Climate, 11, 2404–2423.
—
and
—
, 1998b: Tropical inuences on California
precipitation. J. Climate, 11, 412–430.
Neelin, J. D., B. Langenbrunner, J. E. Meyerson, A.
Hall, and N. Berg, 2013: California winter precipita-
tion change under global warming in the Coupled
Model Intercomparison Project Phase 5 Ensem-
ble. J. Climate, 26, 6238– 6256, doi:10.1175/JCLI-
D-12-00514.1.
Scholze, M., W. Knorr, N. W. Arnell, and I. C. Pren-
tice, 2006: A climate-change risk analysis for world
ecosystems. Proc. Natl. Acad. Sci. USA, 103, 13116–
1312 0.
REFERENCES

S5
OCTOBER 2015AMERICAN METEOROLOGICAL SOCIETY |
Shukla, S., and D. P. Lettenmaier, 2013: Multi-RCM
ensemble downscaling of NCEP CFS winter season
forecasts: Implications for seasonal hydrologic fore-
cast skill. J. Geophys. Res. Atmos., 118 , 10770–1079 0,
doi:10.1002/jgrd. 50628.
onicke, K., S. Venevsky, S. Sitch, and W. Cramer,
2001: e role of re disturbance for global vegeta-
tion dynamics: Coupling re into a Dynamic Global
Vegetation Model. Global Ecol. Biogeogr., 10, 661–
67 7.
van Mantgem, P. J., J. C. B. Nesmith, M. Keifer, E. E.
Knapp, A. Flint, and L. Flint, 2013: Climatic stress
increases forest re severity across the western
United States. Ecol. Lett., 16, 1151–1156, doi:10.1111/
ele.12151.
Wang, H., and S. Schubert, 2014: Causes of the extreme
dry conditions over California during early 2013 [in
“Explaining Extreme Events of 2013 from a Climate
Perspective”]. Bull. Amer. Meteor. Soc., 95 (9), S7–
S11.
Wang, S.-Y. S., W.-R. Huang, and J.-H. Yoon, 2015: e
North American winter ‘dipole’ and extremesactiv-
ity: A CMIP5 assessment. Atmos. Sci. Lett., 16, 338 –
345, doi:10.1002/asl2.565.
—
, L. Hipps, R . R. Gillies, and J.-H. Yoon, 2014: Prob-
able causes of the abnormal ridge accompanying the
2013-2014 California drought: ENSO precursor and
anthropogenic warming footprint. Geophys. Res.
Lett., 41, 3220–3226, doi:10.1002/2014GL059748.
Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T.
W. Swetnam, 2006: Warming and earlier spring in-
crease western US forest wildre activity. Science,
313, 940–943.
Xia, Y. L., and Coauthors, 2012: Continental-scale wa-
ter and energy ux analysis and validation for the
North American Land Data Assimilation System
project phase 2 (NLDAS-2): 1. Intercomparison and
application of model products. J. Geophys. Res., 117,
D03109, doi:10.1029/2011JD0160 48 .
Yoon, J. H., K. Mo, and E. F. Wood, 2012: Dynamic-
model-based seasonal prediction of meteorological
drought over the contiguous United States. J. Hydro-
mete or., 13, 463– 482, doi:10.1175/JH M-D-11-038.1.
Yue, X., L. J. Mickley, J. A. Logan, and J. O. Kaplan,
2013: Ensemble projections of wildre activ-
ity and carbonaceous aerosol concentrations over
the western United States in the mid-21st century.
Atmos. Environ., 77, 767–780, doi:10.1016/j.at-
mosenv.2013.06.003.