Quantifying the role of fire in the Earth system – Part 2: Impact on the net carbon balance of global terrestrial ecosystems for the 20th century

Fire is the primary form of terrestrial ecosystem disturbance on a global scale. It affects the net carbon balance of terrestrial ecosystems by emitting carbon directly and immediately into the atmosphere from biomass burning (the fire direct effect), and by changing net ecosystem productivity and land-use carbon loss in post-fire regions due to biomass burning and fire-induced vegetation mortality (the fire indirect effect). Here, we provide the first quantitative assessment of the impact of fire on the net carbon balance of global terrestrial ecosystems during the 20th century, and investigate the roles of fire's direct and indirect effects. This is done by quantifying the difference between the 20th century fire-on and fire-off simulations with the NCAR Community Land Model CLM4.5 (prescribed vegetation cover and uncoupled from the atmospheric model) as a model platform. Results show that fire decreases the net carbon gain of global terrestrial ecosystems by 1.0 Pg C yr⁻¹ averaged across the 20th century, as a result of the fire direct effect (1.9 Pg C yr^−1) partly offset by the indirect effect (−0.9 Pg C yr^−1). Post-fire regions generally experience decreased carbon gains, which is significant over tropical savannas and some North American and East Asian forests. This decrease is due to the direct effect usually exceeding the indirect effect, while they have similar spatial patterns and opposite sign. The effect of fire on the net carbon balance significantly declines until ∼1970 with a trend of 8 Tg C yr⁻¹ due to an increasing indirect effect, and increases subsequently with a trend of 18 Tg C yr⁻¹ due to an increasing direct effect. These results help constrain the global-scale dynamics of fire and the terrestrial carbon cycle

HESFIRE: a global fire model to explore the role of anthropogenic and weather drivers

Vegetation fires are a major driver of ecosystem dynamics and greenhouse gas emissions. Anticipating potential changes in fire activity and their impacts relies first on a realistic model of fire activity (e.g., fire incidence and interannual variability) and second on a model accounting for fire impacts (e.g., mortality and emissions). In this paper, we focus on our understanding of fire activity and describe a new fire model, HESFIRE (Human–Earth System FIRE), which integrates the influence of weather, vegetation characteristics, and human activities on fires in a stand-alone framework. It was developed with a particular emphasis on allowing fires to spread over consecutive days given their major contribution to burned areas in many ecosystems. A subset of the model parameters was calibrated through an optimization procedure using observation data to enhance our knowledge of regional drivers of fire activity and improve the performance of the model on a global scale. Modeled fire activity showed reasonable agreement with observations of burned area, fire seasonality, and interannual variability in many regions, including for spatial and temporal domains not included in the optimization procedure. Significant discrepancies are investigated, most notably regarding fires in boreal regions and in xeric ecosystems and also fire size distribution. The sensitivity of fire activity to model parameters is analyzed to explore the dominance of specific drivers across regions and ecosystems. The characteristics of HESFIRE and the outcome of its evaluation provide insights into the influence of anthropogenic activities and weather, and their interactions, on fire activityinfo:eu-repo/semantics/publishedVersio

The influence of soil communities on the temperature sensitivity of soil respiration

Soil respiration represents a major carbon flux between terrestrial ecosystems and the atmosphere, and is expected to accelerate under climate warming. Despite its importance in climate change forecasts, however, our understanding of the effects of temperature on soil respiration (RS) is incomplete. Using a metabolic ecology approach we link soil biota metabolism, community composition and heterotrophic activity, to predict RS rates across five biomes. We find that accounting for the ecological mechanisms underpinning decomposition processes predicts climatological RS variations observed in an independent dataset (n = 312). The importance of community composition is evident because without it RS is substantially underestimated. With increasing temperature, we predict a latitudinal increase in RS temperature sensitivity, with Q10 values ranging between 2.33 ±0.01 in tropical forests to 2.72 ±0.03 in tundra. This global trend has been widely observed, but has not previously been linked to soil communities

Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale