Fire is a key interaction between the atmosphere and the land surface (Bowman et al., 2009). Its impacts are wide-ranging: it influences forest succession (Bond and Keeley, 2005), is a tool for deforestation (van der Werf et al., 2009) and an important natural carbon source (Bowman et al., 2013), while it also provides a major natural hazard to humans through property and infrastructure destruction and air quality degradation (Johnston et al., 2012; Marlier et al., 2013). Biomass burning emissions are not only substantial in magnitude (Lamarque et al., 2010), but also drive the variability of atmospheric composition (Spracklen et al., 2007; Voulgarakis et al., 2010, 2015) and impact short-term climate forcing (Tosca et al., 2013).

There are feedbacks between fire and climate: low-humidity conditions cause droughts, which enhance fire activity (Field et al., 2009), which, in turn, emits aerosols and trace gases (Akagi et al., 2011), influencing the abundances of radiatively active atmospheric constituents, cloud formation and lifetime, and in turn precipitation, and surface albedo (Voulgarakis and Field, 2015). Bistinas et al. (2014) showed global fire frequency is correlated with land-use, vegetation type and meteorological factors (dry days, soil moisture and maximum temperature) and that human presence tends to noticeably reduce fire activity (land-management, landscape fragmentation and urbanisation). Examining and quantifying such impacts and feedbacks is paramount to Earth system models (ESMs), yet to integrate vegetation fires presents many challenges as it intricately links multiple disciplines from ecology to atmospheric chemistry, physics and climate science.

Integration of fires into dynamic global vegetation models (DGVMs) was the first step towards fire within ESMs (e.g. Arora and Boer, 2005; Fosberg et al., 1999; Li et al., 2012; Pfeiffer et al., 2013; Sitch et al., 2003; Thonicke et al., 2001, 2010; Venevsky et al., 2002; Yue et al., 2014). Vegetation fires have been implemented into only a few ESMs, e.g. ECHAM (Lasslop et al., 2014) and the Community ESM (Li et al., 2013, 2014, p. 2).

Here, we present and evaluate the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO) and its implementation. INFERNO is a necessarily simple parameterization that focuses on the large-scale occurrence of fires and is suitable for ESM application. The model uses a few key driving variables while retaining a broadly accurate parameterization for fire emissions. INFERNO's performance against observations and well-established and operationally relevant fire indices is presented.

Monthly lightning data were obtained from LIS-OTD (Lightning Imaging Sensor – Optical Transient Detector) observations for 2013 (Christian et al., 2003) and was recycled for every year in the simulation. These detections were converted to cloud-to-ground strikes using the relationship presented in Prentice and Mackerras (1977). Land use and population density were obtained from the HYDE data set (Hurtt et al., 2011) and then linearly interpolated to create inter-annually varying data. Finally, annual CO2 concentrations, which affect vegetation dynamics, were prescribed as a global average following the data set prepared for the global carbon budget (Le Quéré et al., 2015).

To test the sensitivity to the meteorological input, JULES simulations were driven by meteorology from both CRU-NCEP (Climate Research Unit and National Center for Environmental Prediction) v5 (https://crudata.uea.ac.uk/cru/data/ncep/), and WFDEI (Weedon et al., 2014) with precipitation from the GPCC (Schneider et al., 2013). Both data sets were used on a 6-hourly basis.

Outside of these driving variables, JULES was configured according to the TRENDY project (Sitch et al., 2015; Peng et al., 2015); 100-year spin-up was performed repeating the 1990–2000 conditions 10-fold. Four configurations were used to create simulations covering 1990–2013, although to validate INFERNO only the 1997–2010 period was analysed. The first three use CRU-NCEP meteorology with each of our three ignitions modes (see Sect. 2.1.1); constant ignitions (mode 1), prescribed lightning and constant anthropogenic ignitions (mode 2), and both natural and anthropogenic ignitions varying with prescribed lightning and population density (mode 3). The fourth simulation assumes mode 1 (constant ignitions), while meteorology is prescribed from WFDEI and precipitation from GPCC.

Evaluation was performed against the published data for GFEDv3, FINNv1, Global Fire Assimilation System (GFAS) and GFEDv4. We also used the data from GFEDv4s (http://globalfiredata.org, manuscript in preparation) and GFEDv4 (Giglio et al., 2013) to calculate grid-specific emissions and burnt area. The GFED passes satellite observation of burnt area through the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model in order to obtain emissions from open burning. GFEDv4 (Giglio et al., 2013) innovates on GFEDv3 (Giglio et al., 2010) mainly through an updated algorithm to retrieve burnt area from MODIS satellite products and an increased spatial and temporal resolution, to 0.25∘ and daily resolution (this resolution was assessed in Mangeon et al., 2015). Meanwhile GFEDv4s also includes the contribution from small fires (Randerson et al., 2012). The Fire Inventory from NCAR version 1.0 (FINNv1; Wiedinmyer et al., 2011) provides high-resolution (both temporal and spatial) global emissions of trace gas and particle emissions from open burning of biomass. It focuses on rapid availability and assimilation in real-time forecast and follows a similar process to GFED to estimate emission, but its burnt area is obtained directly from fire pixel using land cover (Wiedinmyer et al., 2011). The GFAS (Kaiser et al., 2012), unlike the aforementioned products, directly assess emissions from satellite-observed fire radiative power more apt at detecting small fires and avoiding the uncertainty of biogeochemical models.

Maps of the burnt area and emitted carbon are displayed in Fig. 2, their resolution is 192 longitude by 145 latitude grid cells (1.875∘ × 1.24∘). The results from INFERNO used a configuration with CRU-NCEP meteorology and the third ignition mode: interactive lighting and anthropogenic ignitions. We compare our results with downscaled means from GFED. INFERNO accurately diagnoses total fire occurrence and emissions over the 1997–2010 period: we found a spatial correlation of R=0.66 for burnt area and R=0.59 for emitted carbon, both passing the t test with 95 % significance. In addition, regional mean yearly budgets are compared with GFED in Table B1. Compared to GFEDv4, we notice INFERNO estimates higher burnt area in all regions apart from Australia and New Zealand, and southern hemisphere Africa. Meanwhile emitted carbon is underestimated in boreal regions and equatorial Asia, but overestimated in most other regions (significantly in Southern Hemisphere America). Over the studied period, C4 grass were the main contributors to burnt area in INFERNO (a mean 2.34 million km2 year-1), meanwhile broadleaf evergreen trees (tropical) led to the most emitted carbon (a mean 1.48 Pg year-1). GFEDv4 projects the grid-box with maximum burnt area within the Central African Republic (87 % of grid fraction burnt per year), while INFERNO finds a maximum burnt area of 57 %, slightly to the north (south-east of Lake Tchad). The discrepancy is much larger for emissions, with a maximum emitted carbon of 1.47 kg m-2 year-1 in Indonesia predicted by GFEDv4s, against 0.4 kg m-2 year-1 for INFERNO, in Angola. These results could be expected, as INFERNO focuses on capturing global biomass burning, it will not represent such extremes of burning; furthermore, the immense emitted carbon observed in Indonesia follows from undiagnosed peat fires. INFERNO's approach to burnt area only considers trees, grass and shrub cover and was determined heuristically; meanwhile, Hantson et al. (2014) found global fire size was mostly influenced by precipitation, aridity and human activity (population density and croplands). Further parameterizations for fire size exist (e.g. Hantson et al., 2015, 2016), which could improve INFERNO burnt area estimates while maintaining simplicity and traceability.

Figure 3 shows the modelled global annual average biomass burning emissions and burnt area from 1997 to 2010. The three ignition methods are evaluated: fully interactive ignitions (red) predict the highest carbon emissions while interactive lightning with constant human ignitions (blue) the lowest. WFDEI was observed to lead to more biomass burning emissions in tropical forest areas (and in particular the borders of rainforests), while CRU-NCEP favoured burning in near-desert areas (the Sahel, India and south American grasslands). We expect this result to be significantly influenced by differences in precipitation (GPCC for WFDEI runs and CRU for CRU-NCEP; Schneider et al., 2013).

Comparisons to FINNv1, GFEDv4, GFASv1 and GFEDv3 were restricted to their budgets published in Kaiser et al. (2012), van der Werf et al. (2010), Wiedinmyer et al. (2011) and Giglio et al. (2013) respectively. Meanwhile we calculated global emissions from GFEDv4s (http://globalfiredata.org, revision of van der Werf et al., 2010, in preparation).

Biomass burning emissions and burnt area simulated by the model follow similar trends to GFEDv3, although with a smaller inter-annual variability in the model. Carbon emissions from all simulations fall within 1 standard deviation of GFEDv3, apart from 3 years: 1997, 1998 and 2001. Note that for these years, emissions in GFED were obtained from the lower-resolution AVHRR rather than MODIS. 1997 and 1998 were strong El Niño years during which droughts in equatorial Asia led to extreme emissions from land-clearing fires, a recurrent problem in the region (Field et al., 2009). Indeed in 1997, in the region contained between 20∘ S–20∘ N and 90–160∘ E (or equatorial Asia), GFEDv3 estimate emissions of 1.07 PgC, while INFERNO (with CRU-NCEP and fully interactive ignitions) estimates 0.15 PgC. Unfortunately, peat is not modelled in JULES and thus neither is peat present in our fire scheme. It was estimated tropical peat fires alone produced an average of 0.1 PgC per year from 1997 to 2009, and 0.7 PgC in 1997 in particular (van der Werf et al., 2010). Furthermore, 2002 and 2006 also saw important peat burning, with GFEDv3 estimating peat emissions of 0.16 and 0.21 PgC respectively. In both of these years, the trend in INFERNO differs from GFEDv3's (stagnation in 2002 and decrease in 2006). Peat-lands can be significant in equatorial Asia but also boreal regions where their combustion leads to the release of long-stored carbon (Turetsky et al., 2015). In 1998 and 2001, the difference in emissions could not be attributed to a particular location. While fire emissions from Equatorial Asia were underestimated, GFEDv3 observed lower emissions over Africa compared to INFERNO, which seems to be the key driver of our discrepancies.

Table 2 shows the budgets for four latitudinal bands across the various simulations performed. The second ignition mode (constant anthropogenic and interactive lightning ignitions at any time and place) appears to consistently predict lower emissions and burnt area (with the exception of low latitudes). Furthermore, the main impact of using an ignition model that varies with both natural and anthropogenic ignitions is a reduction of fires at low (tropical and sub-tropical) latitudes, and an increase in equatorial regions. Indeed, when compared to constant ignitions (mode 1), interactive ignitions (mode 3) predict more emissions in forest encroachment regions (noticeably surrounding the Congo and Amazon rainforests), and less in heavily populated areas (Nigeria, India). Meanwhile, we observed interactive lightning ignitions (mode 2) significantly reduced burning in grassland–savannah environments. We link this to the predominance of cloud-to-ground lightning strikes in a wet environment within the LIS-OTD data set (e.g. the Congo rainforest; Christian et al., 2003) and fewer strikes (and ignitions) in the more flammable grasslands and savannahs. These issues are visible in Fig. B1, which shows difference maps of the four model configurations, for 1997–2010 mean yearly totals. Equatorial and boreal regions include peat that leads to large fuel consumption, which is unaccounted for in JULES, suggesting that our model will inherently underestimate emissions from these regions.

Species-specific average emissions produced by the INFERNO scheme are shown in Table 3 in Tg per year for the 1997–2010 period. CO and CH4 appear to be produced in noticeably larger quantities than in observation-based emission estimates. This hints at an overrepresentation of smouldering-type combustion. In INFERNO this might be due to the emission factors used, or the type of vegetation burnt.

In order to examine whether our flammability can represent fire occurrence, three other fire indices were diagnosed, namely the McArthur, Nesterov and Canadian fire indices. These indices were obtained seamlessly during the model runs, therefore utilizing the same meteorological and hydrological driving variables, and the same vegetation conditions. Their predictions were regressed with GFEDv4 1997–2010 annual burnt area (Giglio et al., 2013). This analysis relies on the assumption that fire indices can be used as a proxy for the variability of fire occurrence and spread, and eventually of burnt area (not the magnitude). Only areas that had been observed to burn sometime between 1997 and 2010 were sampled; to avoid accounting for high fire indices in non-vegetated areas such as the Sahara.

Table 4 shows the result of our analysis. Ignitions followed mode 1: in this mode ignitions are constant; therefore, the only variability in burnt area (and performance) is due to INFERNO's flammability scheme. The McArthur index performs poorly at high latitudes (it was made for Australia), but outperforms the other indices in low latitude regions. The Canadian and Nesterov indices correlate best with observed burnt area in high latitude regions (for which they were developed). Altogether, INFERNO's burnt area appears to follow observed burnt area better than the sole usage of a fire index.

Through a minimalistic approach we propose a parameterization for fire occurrence of appropriate complexity for application at large spatial scales within an ESM context: the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO). It directly only varies according to precipitation (and resulting soil moisture), temperature and humidity, and indirectly it utilises vegetation. It is also capable of explicitly simulating ignitions using lightning and anthropogenic information. While our scheme manages to represent fire occurrence on large scales (both spatial and temporal), it performs best at low latitudes. INFERNO's burnt area scheme appears superior to the use of fire indices alone (Nesterov, McArthur and basic Canadian) for capturing annual burnt area variations, and thus one form of fire impact. However, due to the nature of our analysis (fire danger and burnt area remain different quantities) this does not imply INFERNO should supersede fire weather indices for operational purposes, neither has our algorithm been built for numerical weather prediction or seasonal fire danger forecasting.

Nonetheless, our current simulations suggest the variability in emissions is underestimated by INFERNO, in particular the impact of the 1997–1998 El Niño and the subsequent La Niña, which may be attributable to the lack of representation of peat in the model, critical to biomass burning in equatorial Asia and boreal areas. The use of different present-day meteorological data sets has an important impact on the magnitude and variability of our diagnostics. Using WFDEI-GPCC rather than CRU-NCEP led to more burnt area but lower fuel consumption and eventually less emitted carbon (this follows from grasslands burning rather than forests). Vegetation zone interfaces were key to this difference. Similarly, lightning appears to more frequently ignite fires in wet environments (rainforests) while flammable environments (savannah, grasslands) with rarer lightning are sensitive to the presence of an anthropogenic ignition source. Including a scheme to parameterise human impacts appears to significantly reduce fires in heavily populated areas, while favouring their encroachment of rainforests (the vicinity of which are an anthropogenic ignition “sweet spot” in our parameterization). Nevertheless, there is much uncertainty attributed to human induced emissions and effects on fire regime (Marlon et al., 2008; Thonicke et al., 2010). Accordingly, we include different modes to examine the impact of ignitions (human or natural) in INFERNO.

The implementation of INFERNO within the Met Office's Unified Model and its significance for present-day atmospheric composition and climate will be investigated in a separate paper. To close the vegetation–fire feedback, INFERNO will eventually need to remove carbon from vegetation and to include tree mortality. While a strength of the model is its minimalistic approach, the scheme holds potential for improvements. For instance, litter influences flammability but only live vegetation leads to emissions while in reality litter significantly contributes to observed fuel consumption (van Leeuwen et al., 2014). Similarly, we predict that the inclusion of peat within JULES would improve its fire diagnostics, especially for locations with large fuel consumptions (e.g. equatorial Asia and boreal climates; van der Werf et al., 2010). Given the predictability of emissions from peat fires in relation to precipitation (van der Werf et al., 2008), this would be a promising area of exploration. The value of this model being its simplicity and linearity, any improvements to INFERNO should follow this vision; complex parameterizations are better suited for process-based fire schemes (e.g. Lasslop et al., 2014; Li et al., 2013, p. 1).

Information on the JULES land surface model can be found at http://jules-lsm.github.io/. INFERNO is included in JULES v4.5 and is included in this documentation. The JULES source code can be accessed via the Met Office's science repository (requires registration): https://code.metoffice.gov.uk/trac/jules. In particular, the version of the code used to produce the outputs included in this study can be accessed at https://code.metoffice.gov.uk/trac/jules/browser/main/branches/dev/stephanemangeon/vn4.3.1_inferno.