Speakers and Topics
Francesca Di Giuseppe (ECMWF)
Fire and weather: How well can we predict fire from weather? how much is weather modified by fires?
The European Centre for Medium-Range Weather Forecasts is a leading institution in numerical weather prediction. In the last years, thanks to its crucial role in the management of some of the European Copernicus programs, ECMWF has been particularly active in demonstrating the capability of its weather forecasts to support sectoral applications. This effort has invested all time scales from the medium range (up to 10 days forecast) to the seasonal scale (up to 7 months ), including the the subseasonal to seasonal (S2S) range as well. As of today the ECMWF provides several datasets from three different fire danger rating systems; an historical reanalysis dataset, a daily medium range forecast and an extended range forecast. Following the Copernicus general data policy, all data are freely available to any user both public and commercial. The predictability of fire danger from ECMWF forecasts will be revised for few large fires which occurred in the last years
Given the impact that fire emissions from large fires have in modifying the surface radiative budget there is also an interest in including these phenomena into weather forecast. The longer range forecasts is the most likely time scale being affected by fire emissions and the subsequent smoke aerosols transport. Sub-seasonal to seasonal simulations performed prescribing observed fire emissions have already highlighted how the inclusion of this missing component can improve forecast scores up to 4 weeks.In its current setup, ECMWF model does not forecast emissions from fires while allowing these to be prescribed. However the challenge remains to design and implement a fully dynamical fire model which could allow to ignite and extinguish fires as required by long range simulations. In this short presentation I will also present some results from the ultimate challenge of including interactive fires into ECMWF numerical weather prediction system
Keren Mezuman (NASA GISS)
PyrE, an interactive fire module within the NASA-GISS Earth System Model
Fires directly affect the composition of the atmosphere and Earth’s radiation balance by emitting a suite of reactive gases and particles. Having an interactive fire module in an Earth System Model allows us to study the natural and anthropogenic drivers, feedbacks, and interactions of biomass burning in different time periods. To do so we have developed PyrE, the NASA-GISS interactive fire emissions model. PyrE uses the flammability, ignition, and suppression parameterization proposed by Pechony and Shindell (2009), and is coupled to a burned area and surface recovery parameterization. The burned area calculation follows CLM’s approach (Li et al., 2012), paired with an offline recovery scheme based on Ent’s Terrestrial Biosphere Model (Ent TBM) carbon pool turnover time. PyrE is driven by environmental variables calculated by climate simulations, population density data, MODIS fire counts and LAI retrievals, as well as GFED4s emissions. Since the model development required extensive use of reference datasets, in addition to comparing it to GFED4s BA, we evaluate it by studying the effect of fires on atmospheric composition and climate. Our results show good agreement globally, with some regional differences. Finally, we quantify the present day fire radiative forcing. The development of PyrE allowed us for the first time to interactively simulate climate and fire activity with GISS-ModelE3.
Sam Rabin (Karlsruhe Institute of Technology)
The Fire Model Intercomparison Project (FireMIP)
After a brief introduction to concepts in global fire modeling, this presentation introduces the goals and structure of the Fire Modeling Intercomparison Project (FireMIP). Preliminary results from the first phase (20th century) of the intercomparison will be presented.
Etienne Tourigny (Barcelona Supercomputing Center)
An observational study of the extreme wildfire events of California in 2017: quantifying the relative importance of climate and weather
The recent extreme wildfire events that occurred during the fall of 2017 in Northern and Southern California made world headlines due to their environmental and economic impacts as well as dramatic and catastrophic images. According to the National Centers for Environmental Information (NCEI), the 2017 fall wildfires in California and the Western U.S. generated financial losses estimated at $18 billion, making the 2017 fire season the most destructive in U.S. history. The factors thought to create such dramatic wildfires at the Wildland-Urban Interface (WUI) in California are numerous: a wetter than average winter of 2016 allowed for vegetation to grow abundantly, followed by the warmest summer in recorded history, which dried the excessive fuel, culminating to hot, dry and windy events known as Santa Ana winds in the South and Diablo winds in the North, which allowed for rapid and uncontrolled fire spread.
We will present an observational study of the extreme wildfire events of 2017 in California. Our goal is to better understand the relative importance of climate and weather in creating the conditions which lead to extreme wildfire events such as those of 2017. The study relies on the well known Canadian Fire Weather Index (FWI). This index has the advantage of being easy to compute and rely on easily obtainable data sources (daily values of temperature, precipitation, relative humidity and wind data), as well as accounting for the influence of wind magnitude and near-surface relative humidity, which are so important for wildfire activity during the peak fire season of California. This fire danger index is better suited than simpler drought indices such as the Keetch-Byram Drought Index (KBDI), which rely solely on daily temperature and precipitation.
As daily data sources we use the ERA-Interim and NARR re-analyses, gridded products covering an extensive period, and burned area is obtained through the MCD64 global burned area product. This allows the study of the temporal and spatial evolution of fire danger, compared to observed burned area, focusing on extreme events such as those of 2017. The study of the variability of FWI and its input data allow to separate the different physical controls on fire occurrence and understand the relative importance of seasonal climate and weather events.
We will also present a framework for seasonal prediction of fire risk based on FWI computed from operational seasonal products.