This figure compares the observed and modeled FSS in South America fire regions. The black solid lines are observations for past years, and the black dashed horizontal lines represent the all year mean observed values. The orange lines are FSS derived from the empirical model. The orange shades indicate the range of predicted FSS in 2018 for each region. The numbers in the parentheses represent the coefficient of determination (r²) between observed and modeled FSS, and indicate model performance. Information on sea surface temperatures through May of 2018 were evaluated for these predictions. Click the boxes above the figure to see previous predictions using earlier data.

Here we define FSS as the sum of active fire counts (FC) during the fire season. Active fires are the thermal radiation anomalies created by fires that are detected by satellites, and the sum of active fire counts is related to the amount of biomass consumed. Our empirical model was based on active fire observations from Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite.

• Maps of monthly active fires as observed by MODIS in South America. Click "<" or ">" for active fire data in previous or next year/month. Click "Enable/Disable smoothing" to see the location of each observed fire.

• A map of FC density (in numbers per million hectares per year) averaged over 2001-2014. The location of 10 geographical regions considered in this study is marked. Click on the map for a larger version.

• Fraction of active fire counts in each region (among the 10 regions considered in this study). Move to pointer over each wedge to see the mean annual active fire counts in each region.

• Time series of total FC (in numbers per month) in the 10 regions of South America since 2000.

To predict FSS in South America, we used two climate indices that represent the sea surface temperature anomalies in Pacific and Atlantic: ONI (Ocean Niño Index) and AMO (Atlantic Multidecadel Oscillation index). The following figure shows time series of ONI and AMO since 2000.

• Linear regression analyses show that the annual FSS is most strongly correlated with ONI and AMO between October (previous year) and April. Here we show the monthly ONI and AMO values during this period eath year. Colors from dark blue to dark red represent the order of lowest to highest fire years.

• The following figure compares interannual variations of sea surface temperatures and fire season severity in South America. The upper panel shows the time series of mean ONI and AMO anomalies averaged over the period from October (previous year) and April (current year). The anomalies are relative to the 2001-2015 mean. The bottom panel shows the time series of annual FSS.

The method of annual FSS prediction is based on Chen et al. (2011) with some modifications. We developed our empirical model of FSS using fire counts detected by MODIS onboard NASA's Terra satellite along with Oceanic Niño Index (ONI) and Atlantic Multidecadal Oscillation index (AMO) SST anomaly time series. Sea surface temperatures prior to the onset of the fire season have the strongest relationship with the number of satellite observed fires during the fire season in many areas of South America. The lead times enable us to make a prediction for the upcoming fire season.

• Active fire counts

We used MODIS collection 5 global monthly fire location product (MCD14ML). We sampled the geographic coordinates of individual fire pixels (at a 1×1 km spatial resolution) that had a confidence level greater than 30%, and calculated the monthly FC within each 0.5° pixel after applying a cloud fraction correction. Persistent hot spots from MODIS observations and gas flare pixels in NOAA Global Gas Flare Estimates were excluded because the burning in these pixels is primarily associated with petroleum production rather than landscape fires. We then calculated the monthly FC for each region (6 states in Brazil (Acre, Amazonas, Maranhao, Mato Grosso, Para, Rondonia), 3 departments in Bolivia (El Beni, Pando, Santa Cruz), and one country (Peru)). The sum of FC during the fire season (defined as the 9-month period centered at the peak fire month) was recorded as the annual FSS for each region.

• ONI

The Oceanic Niño Index (ONI) is a 3-month mean SST anomaly in the Niño 3.4 region (5°N-5°S, 120°-170°W) of the Pacific. We obtained the ONI time series from the NOAA National Weather Service Climate Prediction Center.

• AMO

The Atlantic Multidecadal Oscillation index (AMO) represents a similar 3-month mean for the North Atlantic (0°-70°N). We obtained the AMO index time series from the NOAA Earth System Research Laboratory website.

We defined our empirical predictive model as a linear combination of the two climate indices sampled during the months of maximum correlation:

FSS_predicted(x,t,τ_c)=a(x,τ_c)×ONI[t,m(x)-τ_ONI(x,τ_c)]+b(x,τ_c)×AMO[t,m(x)-τ_AMO(x,τ_c)]+c(x,τ_c).

FSS_predicted is the predicted FSS in region x and year t. The parameter τ_c indicates the lead time (number of months before the peak fire month) when the prediction was made. a and b are spatial varying coefficients that represent the sensitivities of FSS in each region to ONI and AMO, individually, and c is a constant. ONI and AMO were sampled each year during months with lead times τ_ONI and τ_AMO relative to the peak fire month (m) in each region. Given a target τ_c, the optimal τ_ONI and τ_AMO values were derived from a series of linear regressions using ONI and AMO values at different months (with a cutoff(minimum) lead time of τ_c).

Based on the data (ONI and AMO) availability and the peak fire month, we derived the τ_c for each region. We then applied the predictive model with corresponding coefficients (a, b, and c) and optimal lead times (τ_ONI and τ_AMO) to derive the FSS in the target fire year. The range of the prediction was calculated using the 1-sigma uncertainty estimates for the parameters of the predictive model. Therefore, we have a set of predictions derived from different months (though with different confidence).

• Chen, Y., J. T. Randerson, D. C. Morton, R. S. DeFries, G. J. Collatz, P. S. Kasibhatla, L. Giglio, Y. Jin, M. E. Marlier, Forecasting fire season severity in South America using sea surface temperature anomalies, Science, 334, 787-791, 2011. [link]
• Chen Y., I. Velicogna, J. S. Famiglietti, and J. T. Randerson, Satellite observations of terrestrial water storage provide early warning information about drought and fire season severity in the Amazon, J. Geophys. Res. - Biogeosciences, 118, 1-10, 2013. [link]
• Chen, Y., J. T. Randerson, D. C. Morton, Y. Jin, G. J. Collatz, P. S. Kasibhatla, G. R. van der Werf, R. S. DeFries, Long-term trends and interannual variability of forest, savanna and agricultural fires in South America, Carbon Management. 4(6), 617-638. [link]
• de Linage, C., J. S. Famiglietti, and J. T. Randerson (2014), Statistical prediction of terrestrial water storage changes in the Amazon Basin using tropical Pacific and North Atlantic sea surface temperature anomalies, Hydrol Earth Syst Sc, 18(6), 2089-2102.[link]
• Chen, Y., J. T. Randerson, D. C. Morton (2015), Tropical North Atlantic ocean-atmosphere interactions synchronize forest carbon losses from hurricanes and Amazon fires, Geophysical Research Letters, 42(15), 6462-6470.[link]
• Chen, Y., D. C. Morton, N. Andela, L. Giglio, and J. T. Randerson (2016), How much global burned area can be forecast on seasonal time scales using sea surface temperatures? Environmental Research Letters, 11(4), 045001. [link]

• FC : Active fire counts, defined as the number of fire/hotspots observed by satellite.
• AMO: Atlantic Multi-decadal Ossilation index, representing sea surface temperature anomaly in North Atlantic. We used 3-month mean of Kalplan SST anomalies in North Atlantic (0-70N). Data available at NOAA Earth System Research Laboratory website.
• Fire season: The fire season is defined here as the period from 4 months before the peak fire month to 4 months after the peak fire month.
• FSS: Fire season severity, defined as the sum of FC during the fire season (4 months before the peak fire month to 4 months after the peak fire month) for each year.
• FSSI: FSS index, a measure of FSS based on historical mean values and standard deviation in the same region. FSSI = 50*(1+ERF((FSS-FSS_oavg)/sqrt(2)/FSS_ostd), where FSS_oavg and FSS_ostd are mean and standard deviation of observed FSS during 2001-2010. ERF is the error function.
• MODIS: Moderate Resolution Imaging Spectroradiometer, an earth observation remote sensing instrument on board the Terra satellite and Aqua satellite.
• ONI: Ocean Niño Index, representing sea surface temperature anomaly in Eastern tropical Pacific. We used 3-month mean of ERSST.v3b SST anomalies in the Niño 3.4 region (5N-5S, 120-170W). Data available at NOAA Climate Prediction Center website.
• Terra: A NASA research satellite in a sun-synchronous orbit around the earth. It carries a payload of five remote sensors including MODIS.

This work is funded by the Gordon and Betty Moore Foundation through Grant GBMF3269 and the US Agency for International Development (USAID).

This work is the result of a collaboration between University of California, Irvine (Yang Chen and Jim Randerson), NASA Goddard Space Flight Center (Doug Morton Niels Andela, and James Collatz), Columbia Univeristy (Ruth DeFries and Miriam Marlier), University of Maryland (Louis Giglio), and Duke University (Prasad Kasibhatla).

NASA provided the satellite observations of fires and NOAA provided the sea surface temperature time series used in our analysis. The interactive figures and maps were generated using Google's Chart API , Maps API, and Fusion table API .

Doug Morton at NASA Goddard Space Flight Center and Pineda Llopart Serrano translated this forecast website to Portuguese and Spanish.

The model predictions contained on this website are highly experimental. They cannot be used to predict the occurrence of individual fires. Use of this information for planning purposes should also draw upon other independent and reliable climate information sources. The Regents of The University of California will not be liable for any consequences that may occur if you rely on this information.