Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

Abstract

Abstract. Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN and with atmospheric observations

“…This mixture of high and low emission factors for the MODIS LAI range 4-6 m 2 m −2 gives a reduced average emission factor causing the downturn. This is an illustration of the deficiencies in vegetation mapping adversely affecting BVOC emissions modelling, similar to the findings of Arneth et al (2011), Zhao et al (2016, Huang et al (2015), Otter et al (2003), Warneke et al (2010) and Langford et al (2010).…”

Modeling emissions for three-dimensional atmospheric chemistry transport models

“…MEGAN 2.1 components are shown in Figure 7. MEGAN can either run stand-alone for generating emission inventories or be incorporated as an online component of chemistry transport models (prepared for Comprehensive Air Quality Model with extensions [CAMx] and CMAQ, Weather Research and Forecasting with Chemistry model [WRF-Chem]; e.g., Pouliot and Pierce 2009;Zhao et al 2016). A 30-yr global BVOC emission data set based on MEGAN 2.1 is presented by Sindelarova et al (2014).…”

“…Another BVOC emission inventory for Europe aiming for an improved seasonality and land-cover component was developed by Oderbolz et al (2013). Further sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions (Zhao et al 2016).…”

Impact of topography on black carbon transport to the southern Tibetan Plateau during the pre-monsoon season and its climatic implication

“…The Tibetan Plateau (TP) is the highest plateau in the world with an average elevation over 4 km and an area of approximately 2.5 × 10 6 km 2 ; it is known as the world's third pole (Qiu, 2008), and its enormous dynamic and thermal effects have a huge impact on large-scale atmospheric circulation through the energy exchange with the atmosphere, especially the troposphere, such as Asian monsoons (e.g., Ye and Wu, 1998;Duan and Wu, 2005;Wu et al, 2007Wu et al, , 2012aBoos and Kuang, 2013;Chen and Bordoni, 2014;He et al, 2019;Zhao et al, 2019). In addition, the glacial melting water of the TP is one of the important sources of water resources of the Indus River, Ganges River, Yangtze River and Yellow River in Asia (e.g., Singh and Bengtsson, 2004;Barnett et al, 2005;Immerzeel et al, 2010;Lutz et al, 2014).…”