Likelihood of unprecedented drought and fire weather during Australia’s 2019 megafires

Between June 2019 and March 2020, thousands of wildfires spread devastation across Australia at the tragic cost of many lives, vast areas of burnt forest, and estimated economic losses upward of AU$100 billion. Exceptionally hot and dry weather conditions, and preceding years of severe drought across Australia, contributed to the severity of the wildfires. Here we present analysis of a very large ensemble of initialized climate simulations to assess the likelihood of the concurrent drought and fire-weather conditions experienced at that time. We focus on a large region in southeast Australia where these fires were most widespread and define two indices to quantify the susceptibility to fire from drought and fire weather. Both indices were unprecedented in the observed record in 2019. We find that the likelihood of experiencing such extreme susceptibility to fire in the current climate was 0.5%, equivalent to a 200 year return period. The conditional probability is many times higher than this when we account for the states of key climate modes that impact Australian weather and climate. Drought and fire-weather conditions more extreme than those experienced in 2019 are also possible in the current climate.

Development of Google Earth Engine Fire Weather Index Calculator for Indonesian Fire Danger Rating System

Tropical peatlands make up almost ten percent of the land surface in Indonesia, making peat fires detrimental not only for global atmospheric carbon levels, but also to public health and socioeconomic activities in the region. Indonesian Fire Danger Rating System (FDRS) was developed based on the Canadian Forest Fire Weather Index System (CFFWIS), using three different fuel codes and three indices representing fire behaviour. Daily Fire Weather Index (FWI) calculation is done by the Meteorological Climatological and Geophysical Agency (BMKG) with data from its synoptic weather stations network. Distribution of such weather stations are sparse, therefore this paper reports on the development of Fire Weather Index calculator on Google Earth Engine, using high resolution weather data, provided by weather model and remote-sensing open datasets. The resulting application is capable of generating daily maps of FWI components to be used by the Indonesian Fire Danger Rating System.

A Global View of Observed Changes in Fire Weather Extremes: Uncertainties and Attribution To Climate Change.

In many parts of the world, wildfires have become more frequent and intense in recent decades, raising concerns about the extent to which climate change contributes to the nature of extreme fire weather occurrences. However, studies seeking to attribute fire weather extremes to climate change are hitherto relatively rare and show large disparities depending on the employed methodology. Here, an empirical-statistical method is implemented as part of a global probabilistic framework to attribute recent changes in the likelihood and magnitude of extreme fire weather. The results show that the likelihood of climate-related fire risk has increased by at least a factor of four in approximately 40% of the world’s fire-prone regions as a result of rising global temperature. In addition, a set of recent fire weather events, occurring during a recent 5-year period (2014-2018) and identified as exceptional due to the extent to which they exceed previous maxima, are, in most cases, associated with an increase likelihood resulting from rising global temperature. The study’s conclusions highlight important uncertainties and sensitivities associated with the selection of indices and metrics to represent extreme fire weather and their implications for the findings of attribution studies. Among the recommendations made for future efforts to attribute fire weather extremes is the consideration of multiple fire weather indicators and communication of their sensitivities.

Assessing the role of short-term weather forecasts in fire manager tactical decision-making: a choice experiment

Background Weather plays an integral role in fire management due to the direct and indirect effects it has on fire behavior. However, fire managers may not use all information available to them during the decision-making process, instead utilizing mental shortcuts that can bias decision-making. Thus, it is important to evaluate if (and how) fire managers use information like weather forecasts when making tactical decisions. We explore USDA Forest Service fire manager confidence in relative humidity, precipitation, and wind models. We then use a choice experiment where key weather attributes were varied to explore how sensitive fire managers were to changes in specific weather variables when choosing to directly or indirectly attack a fire that is transitioning to extended attack. Results Respondents were less confident in the accuracy of wind and precipitation forecasts than relative humidity or weather forecasts more generally. The influence of weather information on the decision depended on the framing used in the choice experiment; specifically, whether respondents were told the initial strategy had been to directly or indirectly attack the fire. Across conditions, fire managers generally preferred to indirectly attack the fire. Decisions about the tactics to apply going forward were more sensitive to time in season when the fire was occurring and wind and precipitation forecasts than to other attributes. Conclusions The results have implications for the design of decision support tools developed to support fire management. Results suggest how fire managers’ use of fire weather information to evaluate forecast conditions and adjust future management decisions may vary depending on the management decision already in place. If fire weather-based decision support tools are to support the use of the best available information to make fire management decisions, careful attention may be needed to debias any effect of prior decisions. For example, decision support tools may encourage users to “consider the opposite,” i.e., consider if they would react differently if different initial decision with similar conditions were in place. The results also highlight the potential importance of either improving wind and precipitation forecast models or improving confidence in existing models.