1Physical Geography and Ecosystem Analysis, Lund University, Lund, Sweden
2Research Centre Karlsruhe, Institute for Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany
3Laboratoire des Sciences du Climat et de l'Environnement – LSCE-IPSL, CEA-CNRS-UVSQ, UMR 8212, Gif-sur-Yvette, France
4NCAR, Boulder, Colorado, USA
5Department of Animal and Plant Science, University of Sheffield, Sheffield S10 2TN, UK
6Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
Abstract. Due to its effects on the atmospheric lifetime of methane, the burdens of tropospheric ozone and growth of secondary organic aerosol, isoprene is central among the biogenic compounds that need to be taken into account for assessment of anthropogenic air pollution. There is a great interest in better understanding the geographic distribution of isoprene emission, and the interaction of the drivers that underlie its seasonal, interannual and long-term variation. Lack of process-understanding on the scale of the leaf as well as of suitable observations to constrain and evaluate regional or even global simulation results add large uncertainties to past, present and future estimates of quantity and variability of isoprene emissions. Model intercomparison experiments, which for isoprene have not been performed before, can help to identify areas of largest uncertainty as well as important commonalities. Focusing on present-day climate conditions, we compare three global isoprene models that differ in their representation of vegetation and isoprene emission algorithm, with the aim to investigate the degree of between- vs. within model variation that is introduced by varying some of the models' main features, and to determine which spatial and/or temporal features are robust between models and different experimental set-ups. In their individual standard configurations, the models broadly agree with respect to the chief isoprene sources, emission seasonality, and interannual variability. However, the models are all quite sensitive to changes in one or more of their main model components and drivers (e.g., underlying vegetation fields, climate input) which can yield a strong increase or decrease in total annual emissions and seasonal patterns to a degree that cannot be reconciled with today's understanding of isoprene atmospheric chemistry. A careful adaptation of individual isoprene model components is therefore required when simulations are to be performed using non-standard model-configurations.