Philip Bett, Karina Williams, Chantelle Burton, Adam Scaife, Andrew Wiltshire, Richard Gilham

Seasonal forecasts of global CO₂ concentrations rely on the well-documented relationship with the El Niño–Southern Oscillation (ENSO), combined with estimated anthropogenic emissions. Here, we investigate the skill of the GloSea5 seasonal forecasting system for two carbon cycle processes that underlie the global CO₂–ENSO relationship: the impact of meteorological conditions on CO₂ uptake by vegetation (characterised by net primary productivity, NPP), and on fire occurrences (characterised by fire risk indices). In the tropics, during El Niño events, CO₂ uptake by vegetation is reduced and fires occur more frequently, leading to higher global CO₂ levels.

We use the McArthur forest fire index, calculated from daily data from several meteorological variables. We also assess a simpler fire index, based solely on seasonal mean temperature and relative humidity, since seasonal forecasts based on simpler combinations of model output (fewer input variables, averaged over longer periods/larger regions) can be more skillful than more complicated metrics, which retain more noise.

For NPP, the skill is high in regions that respond strongly to ENSO, such as equatorial South America in boreal winter, and northeast Brazil in boreal summer. There is also skill in some regions without a strong ENSO response. For the fire risk indices, there is significant skill across large parts of the tropics, including in Indonesia, southern and eastern Africa, and parts of the Amazon Basin. We relate this skill to the underlying meteorological variables, finding that fire risk in particular follows similar patterns to relative humidity.

On the seasonal-mean timescale, the McArthur index offers no benefits over the simpler fire index: they show the same relationship to burnt area and response to ENSO, and the same levels of skill, in almost all cases. Our results highlight potentially useful prediction skill, as well as important limitations, for seasonal forecasts of land-surface impacts of climate variability.