deletions | additions       diff --git a/untitled.tex b/untitled.tex  index 9903176..7b4bc4f 100644  --- a/untitled.tex  +++ b/untitled.tex 
...
\end{equation}  The variables represented are drought factor (DF), relative humidity (RH), temperature (T) and wind speed (v). This empirical index is valid for the Southeastern forest ecosystem of Australia. In this wildfire-prone ecosystem, the eucalyptus is the dominant vegetation. On the other side of the world, Latin-America —including Ecuador— experienced a massive introduction of this specie \cite{Anchaluisa2013}.Thus, \cite{Anchaluisa2013}. Thus,  is reasonable to expect that this index may represent the Ecuadorian wildfire potential. The application of this index with weather station and reanalysis data yielded daily values. With weather station data the calculation comprised the period 1997-2012. The bias-corrected reanalysis data allowed to extend the method for the period 1963-2012. For each year, a sum of the daily FFDI values provided a seasonal metric of wildfire potential. This cumulative FFDI corresponds to the wildfire season July-August-September (JAS). Finally, we calculated the 85th percentile on the seasonal FFDI time series. This determined which were extreme wildfire seasons during the period 1963-2012.  
...
\section{Results}  Figure 1 2  shows PDFs for each wildfire weather variable. These results represent the deviations between original WS and 20CR reanalysis data. All 20CR PDFs have deviations from the WS data. The daily deviations from the mean including all seasons are 8.9 \textsuperscript{o}C (Tmax), 11 (RH), 1.6 m/s (W) and 4.5 mm (P). Figure 2 3  presents time series of the same variables. The values correspond to seasonal averages for Tmax, RH and W during JAS. P corresponds to a seasonal sum. The figure shows Pearson's linear correlation coefficients between 20CR and WS data forfor  16 seasons. The results were statistically significant only for W (r=0.52,p=0.04). The bias-correction process comprised the application of linear scaling techniques for every wildfire weather variable. However, the bias-corrected bias-correction  of all variables did not improve the seasonal FFDI calculation. Only thebias-correction of maximum  temperature bias-correction  significantly improved the FFDI results. Thus, for this study we only show the bias-correction results for this variable. Figure 3 4  shows PDFs of original and bias-corrected Tmax data. The application of additive linear scaling successfully centers the mean of the 20CR. Figure 4 5  presents original and bias-corrected results of the seasonal time series for Tmax. 20CR maximum temperature data now has a comparable magnitude with WS. The linear scaling did not affect the correlation coefficients between the two types of data. Figure 5 6  presents the results of the FFDI calculation with original and bias-corrected data. The results show PDFs of WS and bias-corrected 20CR data. The bias-correction increases the similarity between the distribution of data. Figure 6 7  presents seasonal (JAS) time series of FFDI. The figure displays three time series. FFDI calculated with WS data comprises the period 1997-2012. Two more time series show FFDI calculated with reanalysis data. The calculation used original (20CR) and bias-corrected (20CR(bc)) reanalysis data. These time series span the period 1963-2012. The FFDI time series calculated with original reanalysis data over-estimates seasonal wildfire potential. The overestimation is on average of is—on average—of  65 units during the period 1997-2012. These data —WS data—WS  and 20CR— has 20CR—has  a moderate linear correlation (r=0.53,p=0.04). FFDI calculated with bias-corrected reanalysis data under-estimates the FFDI computed with observed values. The magnitude of the underestimation is less than the initial over-estimation (47 units). The bias-correction does not significantly affect the linear correlation between WS and 20CR(bc) data (r=0.53,p=0.05). These results encouraged us to extend the FFDI calculation for the period 1963-2012. Having an extended FFDI dataset allowed us to investigate the influence of ENSO over seasonal wildfire variability in Ecuador. Table 1 2  shows linear correlation's 'r' coefficients of seasonal ENSO metrics with FFDI. The table shows how the evolution of ENSO in previous —and previous—and  the concurrent season— is season—is  associated with wildfire potential. Previous seasons start one year earlier on October-November-December (OND), and continue in three-months intervals including the wildfire season July-August-September (JAS). This correlations show that the influence of ENSO is associated to wildfire potential in the preceding (AMJ) and concurrent (JAS) season. ENSO does not seem to have a direct relationshipin  further back in time. This is valid using the SOI and Niño 3.4 indexes. This investigation also explored the relationship between ENSO and extreme wildfire seasons. Our definition of extreme wildfire seasons filtered years with cumulative FFDI above the 85th percentile for the period 1963-212. The most extreme seasons during this period were 1982, 1991, 1997, 2009 and 2012. Each season was compared with historical categorizations of strength of ENSO events.