Southern Annular Mode drives multicentury wildfire activity in southern South America

Abstract

The Southern Annular Mode (SAM) is the main driver of climate variability at mid to high latitudes in the Southern Hemisphere, affecting wildfire activity, which in turn pollutes the air and contributes to human health problems and mortality, and potentially provides strong feedback to the climate system through emissions and land cover changes. Here we report the largest Southern Hemisphere network of annually resolved tree ring fire histories, consisting of 1,767 fire-scarred trees from 97 sites (from 22 °S to 54 °S) in southern South America (SAS), to quantify the coupling of SAM and regional wildfire variability using recently created multicentury proxy indices of SAM for the years 1531-2010 AD. We show that at interannual time scales, as well as at multidecadal time scales across 37-54 °S, latitudinal gradient elevated wildfire activity is synchronous with positive phases of the SAM over the years 1665-1995. Positive phases of the SAM are associated primarily with warm conditions in these biomass-rich forests, in which widespread fire activity depends on fuel desiccation. Climate modeling studies indicate that greenhouse gases will force SAM into its positive phase even if stratospheric ozone returns to normal levels, so that climate conditions conducive to widespread fire activity in SAS will continue throughout the 21st century.

Keywords: AAO; climate modes; fire scars; synchrony; warming.

Fig. 1.

Modern trends and teleconnections of climate and the SAM in SSA. (A and B) Spatial correlation between observed precipitation and temperature (gridded July–February data; University of Delaware) with a linear time series (i.e., 1, 2, 3…) for the 1949–2008 period (A) and observed (July–February) SAM for the 1957–2008 period (B). (C) Mean spring–summer (October–March) values of the observed SAM for 1957–2012 (9, 12); www.nerc-bas.ac.uk/icd/gjma/sam.html. Significant correlation (P <0.05 and <0.01) are indicated with arrows in A and B. In C, a linear fit and low-pass Gaussian filter (bold line) highlight the low-frequency trend (11-y window), and values for Kendall’s τ statistic and two-sided P values are shown. The series was also tested for lag-1 correlation, which was found to be nonsignificant (P > 0.05).

Fig. 2.

Tree ring-based fire history and study regions (97 sites and 1,767 fire scars) in SSA. Study region locations (boxes) and primary sampled species (in parentheses) are indicated. Histograms show the fire-scarred trees (%; vertical bars on the left y axis) and sample depth (gray area on the right y axis; starting when ≥3 trees per region had been scarred at least once); note that the x- and y-axes are not scaled equally in each region (A). Light-blue arrows indicate the geographical domain of the dominant extratropical forcing of regional climate variability, as represented by the SAM. The number of sites sampled within each region is indicated by the size of the black circles on the map. Graphs in the far right column show reconstruction of the SAM index departures (December–February; Marshall, black line) (26) (B), temperature departures (mean annual reconstruction for the Southern Hemisphere; red line) (28) (C), precipitation departures (total December–February SSA reconstruction; blue line) (27) (D), SCFS departures (gray bars; 15-y moving averages) (E), and SCFA (black bars) and total number of search sites (i.e., sample depth; gray fill) (F). All climate series and the SCFS were standardized (z-scores, in SD units), detrended, and prewhitened. All annually resolved climate series (light-gray lines) were smoothed with a 15-y spline. Records from northwestern Argentina and central Chile were not used in building the subcontinental fire indices owing to their short sample depth.

Fig. 3.

Wavelet coherence between climate and fire at the subcontinental scale (SCFA, Left, and SCFS, Right) and SAM (A and D) temperature (B and E), and precipitation (C and F) index reconstructions. Red regions in the plots indicate the frequencies and times for which pairs of series were coherent. The cone of influence (white dashed line) and the significant coherent time-frequency regions (P < 0.01; 1,000 Monte Carlo simulations; black solid line) are indicated. All figures were computed using standardized (i.e., z-scores) series that were detrended and prewhitened (Methods). Data sources: SAM (December–February), (9); temperature index (mean annual reconstruction for the Southern Hemisphere), (28); precipitation index (total December–February SAS reconstruction), (27).