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Weather is one of the key components that contributes to wildfire activity \cite{Powell1983, Williams1999, Mills2005, Haines, Mccaw2007, Dowdy2012, Engel2013}. Wildfire weather refers to the meteorological influence over wildfires over a period of time —usually within days—. Common variables associated with wildfire weather are temperature, relative humidity, wind speed and precipitation \cite{BoM2009}. Each variable exerts influence in the liberation of water content in vegetation. This process—Evapotranspiration—controls the availability of fuels to be burnt. Most of the research conducted on wildfires and climate focuses on this daily approach \cite{Roads2005}. Yet, the study of wildfire predictability on a seasonal scale is also important. A key factor to explore this perspective is El Niño Southern-Oscillation (ENSO) \cite{Swetnam1983, Simard1985a, Kitzberger2002, Beckage2003, Wooster2012}.   ENSO produces negative rainfall anomalies in some  wildfire-prone regions \cite{Swetnam1983, Simard1985a, Kitzberger2002, Beckage2003, Wooster2012}. The intensity of these anomalies depends on the regional influence of ENSO over climate. These rainfall deficits occur over the months before—and during–the wildfire season. ENSO's influence may even increase by the superposition of the Inter-decadal Pacific Oscillation (IPO) \cite{A2004}. ENSO also has an influence over ignition patterns. In some regions it fosters the occurrence of lightning strikes \cite{Beckage2003}. These facts encourage the investigation of new studies of ENSO and wildfire weather around the world. The west coast of South America is one of the regions most affected by ENSO. The coastal areas of Peru and Ecuador experience massive rainfall episodes during strong El Niño events \cite{Rodbell1999}. Yet, there has not been enough research on its influence over other natural hazards. Wildfires occur all over South America \cite{Manta2008}. However, studies about wildfire weather in the northern Andean region are scarce. This study aims to describe the seasonal variability of wildfire weather in Ecuador. Also, we investigate if there is a link between this type of weather and ENSO in this region. This is important because wildfires occur every year in this country producing severe impacts \cite{MinisteriodelAmbiente2013, SecretariadeAmbiente2013}. A better understanding of wildfire weather variability and its precursors can provide useful information. These insights could be of special interest to emergency response services and planning authorities.  
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Wildfires mostly occur in three geographical regions of Ecuador. The Andean region has large extensions of introduced Eucalyptus forests \cite{Anchaluisa2013}. Thus, the availability of fuels makes it the most wildfire-prone region. The fire season in the Ecuadorian Andes spans from July to November \cite{SecretariadeAmbiente2013}. Yet, the most critical months are July, August and September \cite{Estacio2012,SecretariadeAmbiente2013}. The coast and the Galapagos Islands experience wildfire episodes from January to May \cite{SecretariadeAmbiente2013}.   Wildfires produce severe impacts in Ecuador. Yet, information about these impacts is scare scarce  and disperse. Wildfires produced 21.570 Ha of area burnt in Ecuador in 2012 \cite{MinisteriodelAmbiente2013}. The Metropolitan District of Quito (MDQ) is particularly vulnerable to wildfires. The vulnerability is high because 15.4 percent of the national population lives here. Additionally, vegetation covers 60.46 percent of its territory \cite{SecretariadeAmbiente2013}. Wildfires destroyed 2700 Ha of the MDQ vegetation in 2009 \cite{Estacio2012}. In 2012, the area burnt by wildfires increased to 4.882,16 Ha \cite{SecretariadeAmbiente2013}. This wildfire season cost the MDQ more than 50 million dollars \cite{SecretariadeAmbiente2013}. Unfortunately, wildfires in Ecuador are anthropogenic in nature \cite{MinisteriodelAmbiente2013}. In fact, the MDQ reported that humans cause 95 percent of wildfires in the city \cite{SecretariadeAmbiente2013}. Burning forests to convert them to agricultural use is the main cause of wildfires in the country \cite{MinisteriodelAmbiente2013, SecretariadeAmbiente2013,Rodas2015}. Forcing land use changes for urbanization purposes is another reason people start wildfires \cite{SecretariadeAmbiente2013}. Rural communities burn agricultural waste, which is a traditional practice that fosters wildfire occurrences \cite{Estacio2012}. Rural people also burn regular waste because of the lack of waste management services. \cite{SecretariadeAmbiente2013}. Finally, wildfires occur because of the unconscious damaging  acts of arsonists \cite{MinisteriodelAmbiente2013,SecretariadeAmbiente2013}. \section{Data and methods} 
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\subsubsection{Weather station}  This study required weather station (WS) data from the Andean region in Ecuador. A limitation with this dataset was that only six stations had enough records for this study. The selected period of analysis was 1997-2012. This period had almost complete daily information.Unfortunately, this data was not homogenized.  The variables for this research are air temperature (Tmax), relative humidity (RH), wind speed (W) and precipitation (P). Each of the stations provided data on an eight-hourly basis. The hour selected for the computations was 1 p.m. local time (GMT 18:00). Table 1 describes each of the stations. \subsubsection{Reanalysis}  This research also used reanalysis data from the Twentieth Century Reanalysis Project (20CR) \cite{Compo2011}. The 20CR relies on a data assimilation algorithm of surface pressure values. Sea-surface temperature and sea ice distributions are boundary conditions in this model. The 20CR yields an ensemble mean of realizations. This produces the best possible representation of the state of the atmosphere. This dataset yields six-hourly data on 24 vertical levels. The 1000 hPa-level data is the first vertical level. Thus, it is the most likely to represent surface values. The hour selected for calculations with this data was 4 p.m. local time (GMT 06:00). This is the time closest to the hour adopted for most wildfire potential computations (3 p.m.) with the Australian Forest Fire Danger Index (FFDI)—to be explained in the methods section—. next—.  The 20CR has data with a resolution of 2\textsuperscript{o} x 2\textsuperscript{o}. Thus, the extracted data is a grid in the Ecuadorian Andean region. This dataset presents limitations common to all reanalysis products. There is greater uncertainty in regions were there are less observations. This is a particular problem in Africa, some regions in Asia and South America. There is less reliability in the first half of the century due to lack of observations. Although there have been studies that reflect this data represents well climate variability during this period \cite{Ashcroft2014}. Yet, studies for South America are not available. Additionally, some variables are better represented than others.  
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The bias-correction process comprised the application of linear scaling techniques for every wildfire weather variable. However, the bias-correction of all variables did not improve the seasonal FFDI calculation (not shown). Only the temperature bias-correction significantly improved the FFDI results. Thus, for this study we only show the bias-correction results for this variable (see Figure 4).   Figure 4 shows PDFs of original and bias-corrected Tmax data. The application of additive linear scaling successfully centers the mean of the 20CR. Additionally, Figure 5 presents original and bias-corrected results of the seasonal time series for Tmax. Afeter After  the bias-correction, 20CR maximum temperature data has a comparable magnitude with WS data. The linear scaling did not affect the correlation coefficients between the two types of data. Figure 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. 
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The El Niño events in the Tropical Andes produce a below-average precipitation pattern \cite{Garreaud2009}. Yet, this response is not uniform in the Ecuadorian Andes \cite{Villacis2003}. No relationship exists between ENSO and precipitation during the wet season (October-May) \cite{Villacis2003}. Yet, there is a consistent trend with below-average precipitation during the dry season (June-August) \cite{Villacis2003}. This may occur because precipitation has some Atlantic influence as well \cite{Vuille_2000}. Additionally, ENSO can largely explain the inter-annual variability of temperature \cite{Vuille_2000}. El Niño events are associated with higher temperatures in most of the Ecuadorian Andes \cite{Vuille_2000}. However, these studies relied on a relatively short period—approximately 30 years—of weather station data. Moreover, they include only some wildfire variables —temperature and precipitation—. In spite of this, they show a consistent agreement on the effect of El Niño over environmental conditions conducive to wildfires. This agrees with our overall results. Nevertheless, this should be carefully analyzed considering the use of reanalysis data in this study.  The general limitations of reanalysis data were discussed in the data and methods section. The results shown in Table 3 demonstrate that the precipitation produced by ENSO's teleconnections is not well represented by the 20CR data. This variable should be strongly influenced by ENSO. Yet, the linear correlations do not show any link. This is an unexpected result especially for the dry season. The rest of the wildfire variables do show a statistically significant relationship with ENSO. In spite of the acknowledged biases in the data, the relationship is physically consistent. Yet, the course coarse  resolution of the reanalysis data brings uncertainty to the results. Unfortunately, the The  quantification of these uncertainties is beyond the scope of this study. This could be accomplished examining each 20CR realization. An alternative approach could be the use of other reanalysis products. Although, the limitations may be similar. Another source of uncertainty is the selected wildfire index. The FFDI is an empirically designed index calibrated for southeast Australia. To our knowledge, this index has not being tested outside of Australia. The ecosystems of southeast Australia and the Ecuadorian Andes share the wildfire-prone \textit{Eucaliptus globulus} vegetation. Yet, the particular characteristics of the Tropical Andean ecosystems may produce imprecise wildfire potential results. The computation of alternative indexes could provide a source of validation. The Canadian Forest Fire Weather Index (FWI) \cite{VanWagner1987} has been used in several countries with positive results \cite{Dowdy2009}. Although computationally more complex to calculate, it could be interesting to test how well it represents fire weather in Ecuador compared to the FFDI. A future endeavor might be as well the design of an Ecuadorian Fire Weather Index System. Finally, another approach to strengthen this analysis would be the investigation of ENSO'influence over wildfire activity rather than fire weather.  
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Roboust long-term wildfire activity records do not exist in Ecuador. The MDQ has started to produce more detailed information about wildfire impacts based on satellite data starting from the year 2009 \cite{Estacio2012}. However, the rest of the country lacks this information that would be valuable to evaluate ENSO's influence over wildfires. For example, basic fire statistics such as the number of fire occurrences and area burnt per year would provide additional metrics to evaluate the impact of El Niño events over wildfires in Ecuador. Further investigation of ENSO's influence over wildfires in Ecuador could enhance our capacity to anticipate these events.  Several studies agree that there is potential to use ENSO to forecast wildfire seasons \cite{Simard1985a,Nicholls2007a,Wooster2012,Harris2013}. In fact, a predictive index has already been proposed for the Norhtern Patagonia region in Argentina \cite{Kitzberger2002}. However, as acknowledged by the author, the use of statistical-empirical methods to investigate seasonal wildfire predictability has limitations. For example, the assumption of stable ENSO phase's relationships over a long period of time. Another important assumption to consider is the fixed timing of ENSO arrival. In spite of this, simple indexes indices  based on the relationship of ENSO and wildfire weather—or activity—can provide valuable information for decision makers. This information will facilitate timely decisions that can minimize economic impacts, protect the environment, and potentially save lives.