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Abstract. Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25°) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2  ×  1015 grams of carbon per year (Pg C yr−1) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1) and minimum in 2013 (1.8 Pg C yr−1). These estimates were 11 % higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr−1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from http://www.globalfiredata.org.

Rapid and accurate assessment of global forest cover change is needed to focusconservation efforts and to better understand how deforestation is contributing to thebuildup of atmospheric CO2. Here we examined different ways to use land surfacetemperature (LST) to detect changes in tropical forest cover. In our analysis we usedmonthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS)observations of LST and Program for the Estimation of Deforestation in the BrazilianAmazon (PRODES) estimates of forest cover change. We also compared MODIS LSTobservations with an independent estimate of forest cover loss derived from MODISand Landsat observations. Our study domain of approximately 10° × 10° included theBrazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropicalforest cover in our study area, we found that using data sampled during the end of the dryseason (∼1–2 months after minimum monthly precipitation) had the greatest predictiveskill. During this part of the year, precipitation was low, surface humidity was at aminimum, and the difference between day and night LST was the largest. We used thisinformation to develop a simple temporal sampling algorithm appropriate for use inpantropical deforestation classifiers. Combined with the normalized difference vegetationindex, a logistic regression model using day‐night LST did moderately well at predictingforest cover change. Annual changes in day‐night LST decreased during 2006–2009relative to 2001–2005 in many regions within the Amazon, providing independentconfirmation of lower deforestation levels during the latter part of this decade as reported by PRODES.