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CCD REDUCTION AND ANALYSIS [image] Background In this experiment you will analyse data taken at a telescope at Siding Spring Observatory, near Coonabarabran in NSW. You will measure the brightness and colours of the stars in an open cluster, and hence produce your own Hertzsprung-Russell (HR diagram). Using this information, you can work out the size, luminosity and temperature of the stars in this cluster. In the process, you will learn the techniques of CCD data reduction in professional optical astronomy. You will know more about what’s involved in observational astronomy than many professors of theoretical astronomy. CCDs Astronomers don’t look through telescopes any more. In fact, it is anatomically impossible to look through most large telescopes! Starting about 1880, photographic plates replaced the human eye as the most common astronomical instrument. The reasons for this are the same as for using any camera: a permanent record is created and long exposures mean that objects too faint to see with the eye can be recorded in detail. Starting about 1975, electronic detectors began replacing the photographic plate. The reason for the change is efficiency. A high quality astronomical photographic plate will record about 2% of the photons falling on it. The latest research-grade electronic detectors record more than 90% of the photons. They have other advantages too; since they generate digital data you can subtract off background light (a big advantage in a site like central Melbourne), and use all sorts of techniques to sharpen up your image. Most important of all, electronic detectors allow you to quantitatively measure the radiation coming from sources because they are linear devices— the number you get out of a CCD is directly proportional to the number of incident photons.[1] At their most basic, CCDs are simply photon counting devices. If you know how many photons an object has emitted, then you know how much energy it has emitted and you are on your way to understanding what it is/how it works. CCDs are basically silicon chips, with a very special circuit pattern etched on their surfaces. When photons hit the surface they generate electron-hole pairs. The electrons are attracted to a grid of electrodes etched into the chip. Each of these electrodes forms a potential well, and it traps and stores all the electrons produced in its part of the chip. So as time goes on, a pattern of charge builds up near these electrodes. The more light falls on a given part of the chip, the more electrons will accumulate near the electrodes there. The pattern of electrons building up on the chip is thus an image of whatever the CCD is looking at. When the observations are finished, the pattern of electrons on the grid of electrodes has to be measured and stored on computer. To do this, one end of the chip is connected to a set of wires. Under computer control, the voltages on the electrodes are set oscillating, so that a set of ripples of potential move across the chip. All the electrons are swept along by the ripples, moving to the edge of the chip where they are ’counted’ (the potential they generate is measured) and the result is sent to the computer. CCDs are now the dominant technology used in astronomy. The same technology is now widespread in devices such as video recorders and digital cameras. They are relatively cheap and reliable. It won’t hurt them if you expose them to light accidentally. And they can be very efficient; most astronomy grade CCDs can collect and store an incredible 99% of the photons that fall on them. With a CCD attached, a little telescope on the roof of the School of Physics is as powerful as the biggest telescope in the world would be using naked eye observations. The Hertzsprung-Russell Diagram Stars, like people, come in many shapes and sizes. Massive blue stars burn furiously and die young (like pop singers). Boring stars like our sun chug along for billions of years emitting yellow light before they run out of fuel, swell up into cool bloated supergiants and finally splutter to an end as piddling little white dwarfs. Most stars are quite well understood (almost anything looks easy to understand when it is as distant as a typical star!), but people keep finding bizarre stars that break the rules. In this exercise you will look at a cluster of stars, and measure their properties. You will use the available observations to figure out how bright they are, how hot, how large and more. In particular, you will plot your own Hertzsprung-Russell diagram, the famous plot of colour against brightness that allows you to classify stars into their myriad different types. A Brief Summary of What You Should Do 1. Familiarise yourself with the observational data provided on the linux computer and process it using the IRAF (Image Reduction and Analysis Facility) package. 2. Measure the brightness of all the stars in your processed data at several different wavelengths. 3. Plot the HR diagram and work out the properties of your stars. The Telescope This data was taken by Dr Alicia Oshlack (a former Ph.D. student in the astro group) using the 40 inch telescope owned by Mount Stromlo and Siding Spring Observatories. This telescope is able to be used by anyone, provided they submit a proposal detailing their proposed observations and giving a scientific justification for the use. This is essentially the same process used for all major astronomical telescopes across the world. This telescope has now been decommissioned, and is no longer used for astronomical research. In order to take observations, astronomers have to submit a proposal detailing their proposed observations and giving a scientific justification for the use. This is essentially the same process used for all major astronomical telescopes across the world. The Data If you look at a random bunch of stars, you get a very messy HR diagram. The reason is the stars are all at different distances. Faint red nearby stars may appear brighter than bright blue background stars, just because they are closer. For this reason, we will look at a cluster of stars. This ensures that nearly everything we see is at roughly the same distance (though we might accidentally get an occasional star in the foreground or background). All the stars in a cluster form at once, so everything we observe will have the same age. There is also a third advantage; because stars are crowded together in a cluster, we can get images of lots of stars at once. Clusters come in two types, open clusters (relatively nearby, and containing up to a few hundred stars) and globular clusters (more distant, and containing perhaps a million stars). Unfortunately globular clusters are so distant that with our telescope all the stars will seem to blur together. We will therefore observe open clusters. Table [tab:clusters] lists several bright open clusters that can be seen from southern Australia around autumn. However, we have multi-colour images of only one of these clusters – M93 – and so this will be the one you will analyse. [1] Hence, the raw CCD output is usually expressed in units of ’counts’. Typically, this is just an integer between 0 and 2¹⁶=65536 because the electronics on the chips utilise analog-to-digital converters with 16-bit resolution.

Example: Stardard vs Advanced? We should change the structure of this lab.

Key words α Nucleation, β TITANIUM ALLOY, Strengthening, Alloying elements

ABSTRACT Weather is a key driver of catastrophic wildfire seasons. On a global scale, different phases of the El Niño-Southern Oscillation, “El Niño” or “La Niña”, modulate weather and therefore wildfire activity. Ecuador is one the most biodiverse countries in the world and wildfires produce severe impacts to its ecosystems. This investigation explores for the first time the relationship between wildfire weather and El Niño-Southern Oscillation in the tropical Andes. Wildfire weather variability has been quantified using seasonal metrics of the McArthur Forest Fire Danger Index. The calculation of this index requires weather station data that were only available since 1997. Therefore, the availability of data was extended using the Twentieth Century Reanalysis Project to cover the period 1963-2010. Bushfire weather in the seasons April-May-June and July-August-September show significant correlations with the NINO3.4 index (r=0.37,p=0.003, n=47 and r=0.4, p=0.001, n=47 respectively). Additionally, a Chi-squared test showed that every extreme wildfire season in this region was linked to an ‘El Niño’ event. Our results demonstrate that ‘El Niño” events bring hot and dry conditions conducive to wildfires in the antecedent—April-May-June—and simultaneous—July-August-September—wildfire seasons in Ecuador. INTRODUCTION Wildfires are a global phenomenon that produces severe impacts . Catastrophic wildfire events claim lives , degrade the environment and destroy building infrastructure . Wildfires also contribute to biodiversity loss and affect the climate . They are part of the natural evolution of vegetation ecosystems . However, human actions amplify the impact over people, nature and infrastructure . In fact, wildfires have a strong anthropogenic ignition pattern. In addition, population growth in rural areas increase human vulnerability to this hazard. The threat of wildfires have encouraged extensive research on the behavior of its precursors. Weather is one of the key components that contributes to wildfire activity . 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 . 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 . 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) . ENSO produces negative rainfall anomalies in some wildfire-prone regions . 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) . ENSO also has an influence over ignition patterns. In some regions it fosters the occurrence of lightning strikes . 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 . Yet, there has not been enough research on its influence over other natural hazards. Wildfires occur all over South America . 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 . 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. This article comprises six sections. In the first section we provide background and justify the importance of this study. Section two describes the Ecuadorian Andean region as our study area. Section three presents details about the datasets employed in this research. Section four explains the different methods applied. Section five shows the results of our investigation. Finally, section six presents a discussion of the results and concluding remarks. Study area Geography Ecuador is located in the north-west side of South-America (see Figure 1). The country has a diverse geography. It comprises a continental territory between the latitudes 01o 28’ N and 05o 02’ S and longitudes 75o 11’ W and 81o 04’ W . The Galapagos Islands are also part of the country. These islands are approximately 1000 Km from the continent. The national territory has a total area of 256.370 Km² . The Andes mountains divide the continental Ecuador into three areas: the Coast, Andean and Amazon regions. Each of these regions has diverse geographical features, climate, and ecosystems. The highlands are the most wildfire-prone region because of its specific natural characteristics. The Andes mountains are the most important geographical feature of Ecuador—and South-America— . This mountain range crosses Ecuador from North to South. The Ecuadorian Andes comprises two flanks of mountains: the eastern and western ’Cordilleras’. Between these two flanks—that include active volcanoes—there are several inter-Andean valleys. Many of these Andean plateaus are over 3000 m above sea level. The steep elevations have an important effect on local climate. The lapse rate create the conditions for different Bio-climatic zones to exit. This enhances biodiversity. Yet, the original vegetation of the Andean valleys has almost disappeared. Most of the endemic vegetation was replaced by the Australian specie _Eucalyptus globulus_ around 1860 . Other introduced species are the _Pinus radiata_ (from California) and the _Pinus patula_ (from Mexico). This occurred for commercial reasons with great ecological impact. Further, these species are prone to wildfires in the Andean dry season. Climate The tropical Andes climate has several drivers. The Amazon forest, oceanic currents, and the topography are among the main drivers . The Amazon forest—through Evapo-transpiration—produces a great amount of water vapor. This water precipitates along the western Cordillera because of an orographic precipitation process. This precipitation is also produced in the eastern Cordillera. Air masses advected from the Pacific Ocean produce this effect. The influence of the Pacific Ocean in the climate variability is important. The Pacific Decadal Oscillation (PDO) is the main driver of decadal climate variability in this region . On the other hand, the inter-annual variability depends on the El Niño Southern Oscillation (ENSO) . These influences create several climate regions within the Andes. Most of the Ecuadorian Andes—between 1500 to 3000 m—have a “Semi-Humid Mesothermal” climate . Mean temperatures in this region range from 8 oc to 20 oc. Maximum temperatures span from 22 oC and 30 oC. Minimum temperatures range from -4 oc to 5 oC. Air masses from the Pacific Ocean and the Amazon region create three seasons . Two wet seasons during the periods February-May and October-November. A dry season spans from June to September. Total annual precipitation ranges from 800 mm to 1500 mm along the Andes. Relative humidity varies from 65 to 85 percent. Fire Wildfires mostly occur in three geographical regions of Ecuador. The Andean region has large extensions of introduced Eucalyptus forests . Thus, the availability of fuels makes it the most wildfire-prone region. The fire season in the Ecuadorian Andes spans from July to November . Yet, the most critical months are July, August and September . The coast and the Galapagos Islands experience wildfire episodes from January to May . Wildfires produce severe impacts in Ecuador. Yet, information about these impacts is scarce and disperse. Wildfires produced 21.570 Ha of area burnt in Ecuador in 2012 . 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 . Wildfires destroyed 2700 Ha of the MDQ vegetation in 2009 . In 2012, the area burnt by wildfires increased to 4.882,16 Ha . This wildfire season cost the MDQ more than 50 million dollars . Unfortunately, wildfires in Ecuador are anthropogenic in nature . In fact, the MDQ reported that humans cause 95 percent of wildfires in the city . Burning forests to convert them to agricultural use is the main cause of wildfires in the country . Forcing land use changes for urbanization purposes is another reason people start wildfires . Rural communities burn agricultural waste, which is a traditional practice that fosters wildfire occurrences . Rural people also burn regular waste because of the lack of waste management services. . Finally, wildfires occur because of the damaging acts of arsonists . DATA AND METHODS Data 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. 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. Reanalysis This research also used reanalysis data from the Twentieth Century Reanalysis Project (20CR) . 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 next—. The 20CR has data with a resolution of 2o x 2o. 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 . Yet, studies for South America are not available. Additionally, some variables are better represented than others. ENSO Indices A common ENSO metric is the Southern Oscillation Index (SOI). The SOI represents the difference in normalized surface pressure between Taihiti and Darwin, Australia. We adopted the SOI’s calculation method proposed by the National Oceanic and Atmospheric Administration . The index reflects the changes in air-mass fluxes in the tropical Pacific Ocean. These fluxes, are a manifestation of changes in sea surface temperature (SST). The SOI has an open ended scale with positive and negative values. A negative SOI value represents above-average warm SST conditions in the eastern tropical Pacific Ocean. This represents an ’El Niño’ phase of the phenomenon. El Niño brings wet conditions to the coasts of southern Ecuador and northern Peru. The opposite pattern occurs with a positive SOI value, representing a ’La Niña’ phase. Another ENSO index is the Oceanic Niño Index (ONI) . The ONI measures monthly SST values in the tropical Pacific Ocean. The measurements take place in the region from 5 oN to 5 oS latitude and 120 oW to 170 oW longitude. This is the ’Niño 3.4’ region. The index requires the calculation of 3-month running means and its climatology. The ONI is the difference between these two values. An ’El Niño’ condition exits if there is at least an anomaly of +0.5 oC in this computation. An anomaly of -0.5 oC indicates a ’La Niña’ phase. Methods Data Pre-processing The computation of wildfire potential usually requires continuous daily data. Thus, only six stations in the Ecuadorian Andean region were suitable for this study. The selected stations are representative of the ecosystems that are prone to wildfire. The available records in these stations spanned the period 1997-2012. Yet, the period of analysis is short. Thus, reanalysis data was bias-corrected to extend the availability of records. The bias-correction was necessary for several reasons. First, 1000 hPa reanalysis data does not represent surface level values. Second, there was a 3-hour difference between the time of the two dataset’s values. Third, reanalysis data in the highlands may differ in quality to sea-level outputs. Thus, we plotted probability density functions (PDFs) of the reanalysis and weather station datasets. This allowed to determine the differences between their data distributions. Applying linear scaling techniques was a suitable approach to bias-correct the required variables. The aim of the study was to estimate wildfire potential for the entire Ecuadorian Andean region. Thus, a calculation of daily averages for each wildfire weather variable yielded regional values. With the reanalysis dataset the procedure is straightforward. These data is spatially and temporally continuous. However, the weather stations data had some missing days. Therefore, calculating averages over daily anomalies made the results less sensitive to missing data. Forest Fire Danger Index (FFDI) calculation Ecuador currently does not have any scientifically designed wildfire potential index. Therefore, an alternative approach was to use an index suitable for this region. The FFDI is an Australian wildfire potential index . This index uses daily values of temperature, relative humidity and wind speed. The index also incorporates a drought factor. This factor represents the availability of fuels. This is measured indirectly using soil moisture indexes such us the Keetch–Byram drought index . The FFDI equation is expressed as follows: FFDI = 2e^{-0.45+0.987*Ln(DF)-0.0345RH+0.0338T+0.0234v} 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 . 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 90th percentile on the seasonal FFDI time series. This determined which were extreme wildfire seasons during the period 1963-2012. Investigation of ENSO influence over wildfire seasons We used several approaches to investigate ENSO’s link over wildfire seasons. Firstly, it was important to know the influence of ENSO in the months leading to the wildfire season JAS. Thus, we applied linear correlations of ENSO’s indexes (seasonal averages) with FFDI values. These averages comprised three-months seasons starting from October-November-December (OND) of the previous year. The correlation also included the SOI and Niño 3.4 values for the concurrent JAS season. Secondly, we explored the relationship of each wildfire variable with ENSO. This comprised exploring the link of concurrent FFDI variables (JAS) with antecedent (AMJ) and concurrent ENSO indexes values—SOI and Niño 3.4—. The computation of linear correlations allowed to establish these relationships. A third approach compared the extreme wildfire seasons with ENSO’s categories of intensity. We used the categories proposed by the Australian Bureau of Meteorology. Finally, we expanded this analysis using a χ² test. In this test, we used categories of seasonal FFDI and ENSO strength. The categories for seasonal FFDI were extreme (FFDI >90th percentile), medium (50th percentile<FFDI<=90th percentile), and low (FFD<=50 percentile). The numbers of ENSO years were added within three categories: very strong El Niño, Weak-Moderate-Strong El Niño, and La Niña. The strength of ENSO years corresponds to the Australian Bureau of Meteorology classification available at http://www.bom.gov.au/climate/enso/enlist/. RESULTS Figure 2 shows PDFs for each wildfire weather variable. These results represent the deviations between original WS and 20CR data. All 20CR PDFs have deviations from the WS data. The daily deviations from the mean including all seasons are 8.9 oC (Tmax), 11 (RH), 1.6 m/s (W) and 4.5 mm (P). Figure 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 for 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-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. 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. Figure 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 65 units during the period 1997-2012. These data—WS and 20CR—have 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 2 shows linear correlation’s ’R’ coefficients of seasonal ENSO metrics with FFDI. The table shows how the evolution of ENSO in previous—and the concurrent 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). These 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 relationship further back in time. This is valid using the SOI and Niño 3.4 indices. These results encouraged further analysis into the concurrent (JAS) and antecedent (AMJ) seasons. Table 3 presents the results of linear correlations applied to every wildfire variable in the concurrent season, with ENSO indices in the antecedent and concurrent season. The relationships among FFDI variables are physically consistent. The R coefficients for these variables with ENSO indices are moderate are statistically significant at the 5 percent level. This holds valid for all variables with the exception of precipitation. 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 90th percentile for the period 1963-212. Table 4 shows that the most extreme seasons occurred during the years 1982, 1991, 1997, 2009 and 2012. Each season was compared with historical categorizations of strength of ENSO events. With the exception of the year 2012, extreme wildfire weather occurred during El Niño events. Table 4 expands on the categorical analysis of seasonal wildfire potential and ENSO strength with a χ² test. The table shows that most very strong El Niño events—two out of four years—have a connection with extreme wildfire seasons. Within the range of Weak-Moderate-Strong El Niño events, most years—13 out 22—occur during low wildfire potential—seasonal FFDI<=50th percentile—. However, two years belong to the extreme wildfire weather category. Finally, most La Niña years occur during medium and low seasonal FFDI—23 out 24 years—. DISCUSSION This study has investigated the variability of seasonal wildfire potential in Ecuador. The analysis included the examination of the relationship of wildfire weather and ENSO phases. Using a seasonal metric of wildfire potential—cumulative FFDI during the season July-August-September—, this study detected that five extreme seasons occurred during the period 1963-2012. An extreme season was defined as any year with a seasonal FFDI greater than the 90th percentile value. Most of these seasons—four out of five—occurred during diverse categories of El Niño strength. The El Niño events in the Tropical Andes produce a below-average precipitation pattern . Yet, this response is not uniform in the Ecuadorian Andes . No relationship exists between ENSO and precipitation during the wet season (October-May) . Yet, there is a consistent trend with below-average precipitation during the dry season (June-August) . This may occur because precipitation has some Atlantic influence as well . Additionally, ENSO can largely explain the inter-annual variability of temperature . El Niño events are associated with higher temperatures in most of the Ecuadorian Andes . 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 coarse resolution of the reanalysis data brings uncertainty to the results. 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 _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) has been used in several countries with positive results . 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. CONCLUDING REMARKS 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 . 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 . In fact, a predictive index has already been proposed for the Norhtern Patagonia region in Argentina . 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 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.

Welcome! This is Cobi's open notebook for tutoring work within the Melbourne School of Population and Global Health. You can email me on cobi.smith@unimelb.edu.au or check the course guide for more information.

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We develop a quantitative method for determining Stratosphere to Troposphere Transport events (STTs) and a minimum bound for this transported ozone quantity using ozonesondes over Melbourne, Macquarie Island, and Davis.

TODO

Hi, I am Aliza and I _will_ tell you a LITTLE bit about my friend _AUTHOREA_! BEFORE WE BEGIN CAN I ALIZAASK YOU A FEW QUESTIONS? - Are you writing a thesis? - Are you a frequent LaTeX user? It’s OKAY |we SUPPORT markdown, bibtex etc... (let me just show you) - Would you like to learn Late_x? Her_e is link that will help you out! - Are you using any programming LANGUAGE to handle all your quantitative data i.e. javascript or Ipython notebook? Here is a cool link you will love!! WHY SHOULD YOU CHOOSE AUTHOREA Authorea is a collaborative research tool. It WILL save you from doing mundane tasks. Authorea provides a platform for: 1. Authorea provides a platform for collaborative writing and review of your manuscript 2. It has an easy automated citation mechanism 3. It is a one-stop repository for all your figures’ DATA, code, and _editing_, and even lets you get pre-publication feedback from your peers. 4. version control helps you keep track of what changes you have made. Have you hear of Git ALL THE THINGS YOU CAN DO WITH AUTHOREA 1. Produce neat readable work 2. Invite _CO-AUTHORS_ and work on the document at the same time 3. Attach interactive graphs to your article 4. Work offline using git (need to know a little about git and github) 5. Add and manage citations 6. Write mathematical equations 7. Add comments! 8. Export files in PDF and other formats. 9. follow and unfollow to know who made changes via email... If you like you need help go here But that all the boring stuff... lets do something cool

The Pacific-South American (PSA) pattern is an important mode of climate variability in the mid-to-high southern latitudes. It is widely recognized as the primary mechanism by which the El Niño-Southern Oscillation (ENSO) influences the south-east Pacific and south-west Atlantic, and in recent years has also been suggested as a mechanism by which longer-term tropical sea surface temperature trends can influence the Antarctic climate. This study presents a novel methodology for objectively identifying the PSA pattern. By rotating the global coordinate system such that the equator (a great circle) traces the approximate path of the pattern, the identification algorithm utilizes Fourier analysis as opposed to a traditional Empirical Orthogonal Function approach. The climatology arising from the application of this method to ERA-Interim reanalysis data reveals that the PSA pattern has a strong influence on temperature and precipitation variability over West Antarctica and the Antarctic Peninsula, and on sea ice variability in the adjacent Amundsen, Bellingshausen and Weddell Seas. Identified seasonal trends towards the negative phase of the PSA pattern are consistent with warming observed over the Antarctic Peninsula during autumn, but are inconsistent with observed winter warming over West Antarctica. Only a weak relationship is identified between the PSA pattern and ENSO, which suggests that the pattern might be better conceptualized as preferred regional atmospheric response to various external (and internal) forcings.

When we look to the individuals of the same variety or sub-variety of our older cultivated plants and animals, one of the first points which strikes us, is, that THEY GENERALLY DIFFER MUCH MORE FROM EACH OTHER, than do the individuals of any one species or variety in a state of nature.

_Oh, an empty article!_ You can get started by DOUBLE CLICKING this text block and begin editing. You can also click the INSERT button below to add new block elements. Or you can DRAG AND DROP AN IMAGE right onto this text. Happy writing! Oh hey, there’s in-line formatting code. I wasn’t expecting that. INTRODUCTION _In the beginning_, there was a big bang...and then bacteria... (well, technically they were probably closer to Archaea) and a lot later birds and humans... Some authors disagree

Hey, welcome. Double click anywhere on the text to start writing. In addition to simple text you can also add text formatted in BOLDFACE, _italic_, and yes, math too: E = mc²! Add images by drag’n’drop or click on the “Insert Figure” button. OK I’ve just unlocked a block that Matthew’s editing. What now ?

SECTION ONE - CRAZY STUFF _Oh, an empty article!_ You can get started by DOUBLE CLICKING this text block and begin editing. You can also click the INSERT button below to add new block elements. Or you can DRAG AND DROP AN IMAGE right onto this text. Happy writing! Figure [fig1] shows awesome researchers at work. writes an article. TODO Does this thing do many LaTeX commands?

THIS IS TRAINING EXERCISE! This was a training article for RezBaz delegates to familiarize themselves with the _Authorea_ and _LaTeX_ interface. Article continuity is very lacking, and it should not be relied upon for content .