Articles | Volume 9, issue 8
Model description paper
16 Aug 2016
Model description paper |  | 16 Aug 2016

INFERNO: a fire and emissions scheme for the UK Met Office's Unified Model

Stéphane Mangeon, Apostolos Voulgarakis, Richard Gilham, Anna Harper, Stephen Sitch, and Gerd Folberth

Abstract. Warm and dry climatological conditions favour the occurrence of forest fires. These fires then become a significant emission source to the atmosphere. Despite this global importance, fires are a local phenomenon and are difficult to represent in large-scale Earth system models (ESMs). To address this, the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO) was developed. INFERNO follows a reduced complexity approach and is intended for decadal- to centennial-scale climate simulations and assessment models for policy making. Fuel flammability is simulated using temperature, relative humidity (RH) and fuel load as well as precipitation and soil moisture. Combining flammability with ignitions and vegetation, the burnt area is diagnosed. Emissions of carbon and key species are estimated using the carbon scheme in the Joint UK Land Environment Simulator (JULES) land surface model. JULES also possesses fire index diagnostics, which we document and compare with our fire scheme. We found INFERNO captured global burnt area variability better than individual indices, and these performed best for their native regions. Two meteorology data sets and three ignition modes are used to validate the model. INFERNO is shown to effectively diagnose global fire occurrence (R = 0.66) and emissions (R = 0.59) through an approach appropriate to the complexity of an ESM, although regional biases remain.

Short summary
To understand the role of fires in the Earth system, global fire models are required. In this paper we describe the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO). It follows a reduced complexity approach using mainly temperature, humidity and precipitation. INFERNO was found to perform well on a global scale and to maintain regional patterns over the 1997–2011 period of study, despite regional biases particularly linked to fuel consumption.