Impacts of Amazonia biomass burning aerosols assessed from short-range weather forecasts

Abstract

Abstract. The direct radiative impacts of biomass burning aerosols (BBA) on meteorology are investigated using shortrange forecasts from the Met Office Unified Model (MetUM) over South America during the South American Biomass Burning Analysis (SAMBBA). The impacts are evaluated using a set of three simulations: (i) no aerosols, (ii) with monthly mean aerosol climatologies and (iii) with prognostic aerosols modelled using the Coupled Large-scale Aerosol Simulator for Studies In Climate (CLASSIC) scheme. Comparison with observations show that the prognostic CLAS-SIC scheme provides the best representation of BBA. The impacts of BBA are quantified over central and southern Amazonia from the first and second day of 2-day forecasts during 14 September-3 October 2012. On average, during the first day of the forecast, including prognostic BBA reduces the clear-sky net radiation at the surface by 15 ± 1 W m −2 and reduces net top-of-atmosphere (TOA) radiation by 8 ± 1 W m −2 , with a direct atmospheric warming of 7 ± 1 W m −2 . BBA-induced reductions in all-sky radiation are smaller in magnitude: 9.0 ± 1 W m −2 at the surface and 4.0 ± 1 W m −2 at TOA. In this modelling study the BBA therefore exert an overall cooling influence on the Earthatmosphere system, although some levels of the atmosphere are directly warmed by the absorption of solar radiation. Due to the reduction of net radiative flux at the surface, the mean 2 m air temperature is reduced by around 0.1 ± 0.02 • C. The BBA also cools the boundary layer (BL) but warms air above by around 0.2 • C due to the absorption of shortwave radiation. The overall impact is to reduce the BL depth by around 19 ± 8 m. These differences in heating lead to a more anticyclonic circulation at 700 hPa, with winds changing by around 0.6 m s −1 . Inclusion of climatological or prognostic BBA in the MetUM makes a small but significant improvement in forecasts of temperature and relative humidity, but improvements were small compare with model error and the relative increase in forecast skill from the prognostic aerosol simulation over the aerosol climatology was also small. Locally, on a 150 km scale, changes in precipitation reach around 4 mm day −1 due to changes in the location of convection. Over Amazonia, including BBA in the simulation led to fewer rain events that were more intense. This change may be linked to the BBA changing the vertical profile of stability in the lower atmosphere. The localised changes in rainfall tend to average out to give a 5 % (0.06 mm day −1 ) decrease in total precipitation over the Amazonian region (except on day 2 with prognostic BBA). The change in water budget from BBA is, however, dominated by decreased evapotranspiration from the reduced net surface fluxes (0.2 to 0.3 mm day −1 ), since this term is larger than the corresponding changes in precipitation and water vapour convergence.

On the vertical distribution of smoke in the Amazonian atmosphere during the dry season

“…An evaluation of the CLASSIC scheme with detailed observations is important, as it highlights whether the simulated spatial patterns can be considered realistic when run at high resolution. Some aspects of the LAM aerosol simulations during SAMBBA were also evaluated by Kolusu et al (2015), and showed that the regional distribution and magnitude of AOD agrees well with observations. The current study adds the evaluation of the vertical profile to this assessment, and moreover gives an indication that the emission scaling factors used (1.7 for the MetUM and 3.4 for ECMWF-MACC) is reasonable.…”

“…The MetUM limited area model was set up for the SAMBBA campaign over the Amazonia domain (latitude 25 • S-18 • N, longitude 85-32 • W), and has a resolution of 12 km, with 70 levels in the vertical (Kolusu et al, 2015). Lateral boundary conditions for the meteorological fields were driven provided by the operational global configuration of the MetUM (Global Atmosphere 3.1, Walters et al, 2011).…”

Two global data sets of daily fire emission injection heights since 2003

“…These results are applicable only to GFAS biomass burning emissions used in the C-IFS model: other models also use a scaling factor for biomass burning emissions, but their values are different: 1.7 for the Met Office Unified Model limited-area model configuration over South America that was used for the SAMBBA campaign, which also made use of GFAS emissions (Kolusu et al, 2015;Marenco et al, 2016); GEOS-5 uses scaling factors for GFED emissions of 1.8 for savanna and grassland, 2.5 for tropical forest and 4.5 for extratropical forest (Colarco, 2011).…”

Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign

“…These scaling factors were not calculated precisely, but were found to give good overall correspondence between modelled and observed peak AODs (from AERONET and MODIS) over continental BB source regions in the tropics, and a consistent AOD contribution from BB emissions in CLASSIC and GLOMAP-mode over the BB source regions. Other modelling studies have also found it necessary to apply global scaling factors to increase aerosol emissions from BB sources to gain realistic AOD and/or particulate mass concentrations (Kaiser et al, 2012;Marlier et al, 2013;Petrenko et al, 2012;Tosca et al, 2013;Archer-Nicholls et al, 2016;Kolusu et al, 2015;Reddington et al, 2016). Note that observed AODs are also used to derive biome specific or spatially varying scaling factors in some top-down emission estimation methods such as the Quick Fire Emission Dataset (QFED; Darmenov and da Silva, 2015) and the Fire Energetics and Emissions Research (FEER) (Ichoku and Ellison, 2014), and these lead to global total particulate matter emissions approximately 2-3 times greater than GFED3.1 (Ichoku and Ellison, 2014).…”

“…These aerosols have a wide range of impacts (Voulgarakis and Field, 2015), including short-term influences on local and regional weather (e.g. Kolusu et al, 2015) and significant impacts on regional air quality and human health (Johnston et al, 2012;Reddington et al, 2015). They also have a significant role in climate change as they affect the global energy budget in a number of ways (e.g.…”