Isoprene and monoterpene emission rate variability: Model evaluations and sensitivity analyses

TLDR

Isoprene and monoterpene emissions from plants are largely controlled by light and leaf temperature, and a simple exponential relationship between emission rate and temperature captures most of the variation. The emission temperature dependence is modeled by an enzyme activation equation with a β of 0.09 K⁻¹, accounting for denaturation at high temperatures. Algorithms that incorporate light and temperature responses predict isoprene emissions with ~90 % of diurnal variability and within 35 % error, outperforming earlier models, and show emissions rise with light up to 700–900 μmol m⁻² s⁻¹ and with temperature until ~40 °C before declining.

Abstract

The emission of isoprene and monoterpenes from plants is influenced by light and leaf temperature, which account for almost all short‐term variations (minutes to days) and a large part of spatial and long‐term variations. The temperature dependence of monoterpene emission varies among monoterpenes, plant species, and other factors, but a simple exponential relationship between emission rate ( E ) and leaf temperature ( T ), E = E s [exp (β( T − T s ))], provides a good approximation. A review of reported measurements suggests a best estimate of β = 0.09 K −1 for all plants and monoterpenes. Isoprene emissions increase with photosynthetically active radiation up to a saturation point at 700–900 μmol m −2 s −1 . An exponential increase in isoprene emission is observed at leaf temperatures of less than 30°C. Emissions continue to increase with higher temperatures until a maximum emission rate is reached at about 40°C, after which emissions rapidly decline. This temperature dependence can be described by an enzyme activation equation that includes denaturation at high temperature. Algorithms developed to simulate these light and temperature responses perform well for a variety of plant species under laboratory and field conditions. Evaluations with field measurements indicate that these algorithms perform significantly better than earlier models which have previously been used to simulate isoprene emission rate variation. These algorithms account for about 90% of observed diurnal variability and can predict diurnal variations in hourly averaged isoprene emissions to within 35%.