Quantifying the Amazon Isoprene Budget: Reconciling Top-down versus Bottom-up Emission Estimates Project
The term climate change is now a household phrase and we are used to hearing about rising greenhouse gas levels and global warming. One of the first events that increased the public's awareness of environmental issues was the discovery of the Antarctic ozone hole in the 1980s. Ozone is a gas that comprises only a tiny fraction of all the gases that make up the atmosphere but it is very important in climate. At high altitudes (about 15 to 30 km), there is lots of ozone which is good thing for the planet, as it shields the Sun's harmful UV radiation. However, ozone is a toxic substance and if it builds up within the troposphere (the lowermost part of the atmosphere) and at the surface then this is not good. Tropospheric ozone is bad for us because it is (a) a greenhouse gas, and (b) and air pollutant that affects the human respiratory system and agricultural crop yields. Ozone is produced near the surface when substances known as volatile organic compounds (VOCs) are emitted from the surface and subsequently react within the atmosphere. VOCs can be emitted from human activities, but they are predominantly emitted by vegetation that grows on land. Of all the biogenic VOCs emitted into the atmosphere, none is more important than isoprene owing to its ability to quickly react with other compounds (to produce tropospheric ozone) and because it is emitted in large amounts. Isoprene is also important, as it is a source of very small particles called secondary organic aerosol (SOA) that scatter light, which influences how the Earth warms, and which also have adverse health effects. We need to know (a) when, (b) where and (c) how much isoprene is emitted into the atmosphere in order to better understand tropospheric ozone and SOA. Currently we use generic computer models that are based on observations to simulate the amount of isoprene emitted from different types of vegetation, such as trees or grasses. Isoprene emissions from the Amazon Basin, which contains the world's largest rain forest and is thought to be one of the biggest isoprene sources, are poorly quantified since it is very difficult to measure the emissions in this largely inaccessible and remote region. Satellite observations of a gas called formaldehyde (HCHO), contain information on isoprene emissions, and can be used to determine the amount of isoprene emitted from terrestrial vegetation. The overall goal of my proposal is to use satellite observations of HCHO to accurately quantify isoprene emissions from the Amazon Basin. To achieve this goal I will develop a new unique high resolution model for the Amazon, which will be able to simulate isoprene emissions and atmospheric chemistry at finer spatial scales than have been able previously. I will then compare the isoprene emissions from this 'bottom-up' model with the 'top-down' isoprene emissions inferred from the satellite observations of HCHO, to identify regions or time periods where there is significant disagreement between the model and the observations, which highlights where we have poor understanding of the isoprene emissions. I will then develop an improved isoprene emission model by fine tuning the 'bottom-up' emission model to the inferred 'top-down' emissions, taking into account individual scenes (utilizing the high spatial resolution of the nested-grid) and different seasons. By reconciling the differences between the 'bottom-up' model and the 'top-down' emissions we will gain a more accurate estimate of how much isoprene is emitted from the Amazon, and more importantly gain a better understanding of the factors that influence when it is emitted. This research is important because the Amazon Basin is also one of the regions identified as being most susceptible to climate change, and it is crucial we determine the key factors that influence its isoprene emissions in order to improve confidence in our ability to predict future climate.
1) Develop a high resolution, nested-grid chemistry-transport model, centred over the Amazon, which will be driven using two bottom-up isoprene emission models that are based on fundamentally different approaches to simulate isoprene fluxes
2) Compare the simulated isoprene emissions and oxidation products, from the two bottom-up inventories, against each other and in situ observations to assess which is more accurate
3) Optimally estimate Amazon isoprene emissions using a Bayesian approach constrained by satellite observations of formaldehyde
4) Develop emission model parameterizations that will reconcile the spatial and temporal differences between the top-down and bottom-up estimates
5) Quantify the difference of the bottom-up and top-down isoprene emissions on the Amazonian atmospheric chemistry
This project was funded by NERC under grant NE/G013810/1.