Fire season severity outlook
Sea surface temperatures (SSTs) in the tropical Pacific Ocean during October-April were at record high levels relative to the 2001-2015 period of satellite fire observations because of the strong 2015-2016 El Niño. Concurrently, SSTs in the tropical Atlantic during January-April of 2016 were above average. Combined, the elevated SSTs in both oceans provide evidence for extremely high fire risk for the Amazon during the 2016 dry season. Pará, Mato Grosso and Amazonas have the highest risk according to our forecast.
This webpage presents a prediction of fire risk for the 2016 dry season in high biomass burning regions of South America. The following figure presents fire season severity indices (FSSI, ranging from 0-100) for 6 states in Brazil (Acre, Amazonas, Maranhão, Mato Grosso, Pará, and Rondônia), 3 departments in Bolivia (El Beni, Pando, and Santa Cruz), and one country (Peru) using sea surface temperature information through the end of May. Green indicates below average predictions of fire activity whereas orange and red indicate above average activity. A detailed description of the prediction method is given here.
Recent fire weather observations: Precipitation and Terrestrial Water Storage (TWS)
NASA's GRACE satellites observed below average TWS over most of Amazonia in March 2016, indicating less soil moisture recharge from wet season precipitation than in previous years. This is consistent with lower rainfall totals measured at gauge stations. Low accumulated water storage at the onset of the dry season provides additional support for high fire risk over Amazonia in 2016.
The following maps show cumulative precipitation (cPPT, Aug 2015-May 2016, upper panels) from the Global Precipitation Climatology Centre (GPCC) and terrestrial water storage (TWS, Mar 2016, lower panels) from NASA's Gravity Recovery and Climate Experiment (GRACE) satellites. Maps on the left indicate the absolute anomalies in cPPT and TWS. The right-hand panels show standardized anomalies, normalized using the standard deviation of historical data. Without access to sufficient soil water, even deep-rooted Amazon trees may experience drought effects, reducing evapotranspiration and lowering atmospheric moisture levels during the dry season. Reduced atmospheric moisture, in turn, dries surface fuels and increases fire risk.
Fire Season Severity (FSS) predictions compared to observations
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 2016 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 2016 were evaluated for these predictions. Click the boxes above the figure to see previous predictions using earlier data.
Satellite observation of active fires
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.
Ocean climate indices
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.
Relationship between ocean climate indices and 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.
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.
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.
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:
FSSpredicted is the predicted FSS in region x and year t. The parameter τc 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 τONI and τAMO relative to the peak fire month (m) in each region. Given a target τc, the optimal τONI and τAMO values were derived from a series of linear regressions using ONI and AMO values at different months (with a cutoff(minimum) lead time of τc).
Based on the data (ONI and AMO) availability and the peak fire month, we derived the τc for each region. We then applied the predictive model with corresponding coefficients (a, b, and c) and optimal lead times (τONI and τAMO) 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).
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.