Evaluation of MEGAN-CLM parameter sensitivity to predictions of isoprene emissions from an Amazonian rainforest
- 1Lawrence Berkeley National Laboratory, Berkeley, California, 94720, USA
- 2Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
- 3Department of Civil and Environmental Engineering, Washington State University, Pullman Washington, 99163, USA
- 4Department of Geography, University of California, Berkeley, Berkeley, California, 94720, USA
- 5National Institute for Amazon Research (INPA), Ave. Andre Araujo 2936, Campus II, Building LBA, Manaus, AM 69.080-97, Brazil
Abstract. Tropical trees are known to be large emitters of biogenic volatile organic compounds (BVOC), accounting for up to 75% of the global isoprene budget. Once in the atmosphere, these compounds influence multiple processes associated with air quality and climate. However, uncertainty in biogenic emissions is two-fold, (1) the environmental controls over isoprene emissions from tropical forests remain highly uncertain; and (2) our ability to accurately represent these environmental controls within models is lacking. This study evaluated the biophysical parameters that drive the global Model of Emissions of Gases and Aerosols from Nature (MEGAN) embedded in a biogeochemistry land surface model, the Community Land Model (CLM), with a focus on isoprene emissions from an Amazonian forest. Upon evaluating the sensitivity of 19 parameters in CLM that currently influence isoprene emissions by using a Monte Carlo analysis, up to 61% of the uncertainty in mean isoprene emissions was caused by the uncertainty in the parameters related to leaf temperature. The eight parameters associated with photosynthetic active radiation (PAR) contributed in total to only 15% of the uncertainty in mean isoprene emissions. Leaf temperature was strongly correlated with isoprene emission activity (R2 = 0.89). However, when compared to field measurements in the Central Amazon, CLM failed to capture the upper 10–14 °C of leaf temperatures throughout the year (i.e., failed to represent ~32 to 46 °C), and the spread observed in field measurements was not representative in CLM. This is an important parameter to accurately simulate due to the non-linear response of emissions to temperature. MEGAN-CLM 4.0 overestimated isoprene emissions by 60% for a Central Amazon forest (5.7 mg m−2 h−1 vs. 3.6 mg m−2 h−1), but due to reductions in leaf area index (LAI) by 28% in MEGAN-CLM 4.5 isoprene emissions were within 7% of observed data (3.8 mg m−2 h−1). When a slight adjustment to leaf temperature was made to match observations, isoprene emissions increased 24%, up to 4.8 mg m−2 h−1. Air temperatures are very likely to increase in tropical regions as a result of human induced climate change. Reducing the uncertainty of leaf temperature in BVOC algorithms, as well as improving the accuracy of replicating leaf temperature output in land surface models is warranted in order to improve estimations of tropical BVOC emissions.
J. A. Holm et al.
J. A. Holm et al.
J. A. Holm et al.
5 citations as recorded by crossref.
- Novel tropical forests: response to global change J. Holm et al. 10.1111/nph.14407
- Thermal imaging in plant and ecosystem ecology: applications and challenges C. Still et al. 10.1002/ecs2.2768
- Quantifying regional, seasonal and interannual contributions of environmental factors on isoprene and monoterpene emissions estimates over eastern Texas L. Huang et al. 10.1016/j.atmosenv.2015.01.072
- Seasonal changes in the tropospheric carbon monoxide profile over the remote Southern Hemisphere evaluated using multi-model simulations and aircraft observations J. Fisher et al. 10.5194/acp-15-3217-2015
- Improving organic aerosol treatments in CESM / CAM 5: Development, application, and evaluation T. Glotfelty et al. 10.1002/2016MS000874