Susan M. O'Neill

and 39 more

Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.

Osku Kemppinen

and 3 more

The Goddard Earth Observing System (GEOS) global atmospheric model tracks and transports several individual aerosol species. A key contribution by these aerosols is to interact with solar radiation. When calculating the aerosol-radiation interactions, pre-computed look-up tables of aerosol optical properties are used.We have recently finished an effort to update the aerosol optical properties used in the GEOS model. This has been accomplished with a full rewrite of the optical property simulation code into a Python-based version. In addition to structural changes to the code (outlined below), there are some concrete changes to the aerosol particle definitions. First, truncated lognormal size distributions have been replaced with non-truncated ones, and sub-bin size resolutions of all simulations have been enhanced. Additionally, non-spherical dust accuracy has been improved due to switching to a different spheroidal kernel database. Finally, dust size distribution has been changed from a continuous power law distribution to a bin-specific lognormal distributions.There are also significant changes in the convenience and flexibility of adding or modifying aerosol types for future work and other investigations. Particle properties are now defined exclusively in resource files rather than in the code. This allows for easy addition or modification of aerosol particles, with no need to touch the code. Relatedly, the code can be run either with a provided command-line interface, or via a custom script or notebook. That is, no Python knowledge is needed to generate aerosol particle optics, making the tool easy to use. Additionally, particle types are fully decoupled from specific size distributions, hydration schemes, and other type-specific properties. This also means changing, e.g., a particle size distribution type for a given particle is simple. Performance is significantly improved due to novel Mie simulation optimizations. This enables the aforementioned high size resolutions to be used without concerns for processing time.