Substantial Increases in Eastern Amazon and Cerrado Biomass Burning‐Sourced Tropospheric Ozone

The decline in Amazonian deforestation rates and biomass burning activity (2001–2012) has been shown to reduce air pollutant emissions (e.g., aerosols) and improve regional air quality. However, in the Cerrado region (savannah grasslands in northeastern Brazil), satellite observations reveal increases in fire activity and tropospheric column nitrogen dioxide (an ozone precursor) during the burning season (August‐October, 2005–2016), which have partially offset these air quality benefits. Simulations from a 3‐D global chemistry transport model (CTM) capture this increase in NO2 with a surface increase of ~1 ppbv per decade. As there are limited long‐term observational tropospheric ozone records, we utilize the well‐evaluated CTM to investigate changes in ozone. Here, the CTM suggests that Cerrado region surface ozone is increasing by ~10 ppbv per decade. If left unmitigated, these positive fire‐sourced ozone trends will substantially increase the regional health risks and impacts from expected future enhancements in South American biomass burning activity under climate change

Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O3 experiment

This is the final version. Available on open access from the European Geosciences Union via the DOI in this record.Code availability. This study uses JULES version 5.0 releases. The code and configuration for the SoyFACE runs can be downloaded via the Met Office Science Repository Service (MOSRS) at https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk (JULES Collaboration, 2018) (registration required) and are freely available subject to accepting the terms of the software licence. The Leaf Simulator can be downloaded from https://code.metoffice.gov.uk/trac/utils (Williams et al., 2018) (login required).Data availability. Unless otherwise noted, all site observations discussed in this paper were obtained from the site information pages of the AmeriFlux website hosted by the Oak Ridge National Laboratory (http://fluxnet.fluxdata.org/, AmeriFlux collaboration, 2018) or by personal communication with the Mead site research technologist. The longwave radiation, diffuse radiation, and air pressure from Bondville, Illinois, site can be obtained by the SURFRAD (surface radiation) network from ftp://aftp.cmdl.noaa.gov/data/radiation/surfrad/Bondville_IL/ (NOAA, 2018). The SoyFACE data used for the run are available on MOSRS at https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/driving_data (Ainsoworth, 2017a), https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/bin/SoyFACE_gas_exchange_data_2009.csv (Ainsoworth, 2017b), and https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/ancil_data (Ainsoworth, 2017c).Tropospheric ozone (O3) is the third most important anthropogenic greenhouse gas. O3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop) but does not have crop-specific O3 damage parameters and applies default C3 grass O3 parameters for soybean that underestimate O3 damage. Physiological parameters for O3 damage in soybean in JULES-crop were calibrated against leaf gas-exchange measurements from the Soybean Free Air Concentration Enrichment (SoyFACE) with O3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height and leaf carbon and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production.Natural Environment Research Council (NERC)European Commissio

Estimating PM 2.5 concentrations in Xi'an City using a generalized additive model with multi-source monitoring data

© 2015 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi'an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5