Search for: All records

Abstract Models project that climate change is increasing the frequency of severe storm events such as hurricanes. Hurricanes are an important driver of ecosystem structure and function in tropical coastal and island regions and thus impact tropical forest carbon (C) cycling. We used the DayCent model to explore the effects of increased hurricane frequency on humid tropical forest C stocks and fluxes at decadal and centennial timescales. The model was parameterized with empirical data from the Luquillo Experimental Forest (LEF), Puerto Rico. The DayCent model replicated the well-documented cyclical pattern of forest biomass fluctuations in hurricane-impacted forests such as the LEF. At the historical hurricane frequency (60 years), the dynamic steady state mean forest biomass was 80.9 ± 0.8 Mg C/ha during the 500-year study period. Increasing hurricane frequency to 30 and 10 years did not significantly affect net primary productivity but resulted in a significant decrease in mean forest biomass to 61.1 ± 0.6 and 33.2 ± 0.2 Mg C/ha, respectively (p < 0.001). Hurricane events at all intervals had a positive effect on soil C stocks, although the magnitude and rate of change of soil C varied with hurricane frequency. However, the gain in soil C stocks was insufficient to offset the larger losses from aboveground biomass C over the time period. Heterotrophic respiration increased with hurricane frequency by 1.6 to 4.8%. Overall, we found that an increasing frequency of tropical hurricanes led to a decrease in net ecosystem production by − 0.2 ± 0.08 Mg C/ha/y to − 0.4 ± 0.04 Mg C/ha/y for 30–10-year hurricane intervals, respectively, significantly increasing the C source strength of this forest. These results demonstrate how changes in hurricane frequency can have major implications for the tropical forest C cycle and limit the potential for this ecosystem to serve as a net C sink. 

Abstract Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C‐only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit versus implicit representation of soil microbial activity, as represented in the MIcrobial‐MIneral Carbon Stabilization (MIMICS) and Carnegie‐Ames‐Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity, and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS‐CN model, which also increases the inferred SOM turnover time. Over the historical period, however, warming‐induced acceleration of SOM decomposition over high latitude ecosystems increases rates of N mineralization in MIMICS‐CN. This reduces N limitation and results in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS‐CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios. 

Abstract Wildfire is an essential earth‐system process, impacting ecosystem processes and the carbon cycle. Forest fires are becoming more frequent and severe, yet gaps exist in the modeling of fire on vegetation and carbon dynamics. Strategies for reducing carbon dioxide (CO₂) emissions from wildfires include increasing tree harvest, largely based on the public assumption that fires burn live forests to the ground, despite observations indicating that less than 5% of mature tree biomass is actually consumed. This misconception is also reflected though excessive combustion of live trees in models. Here, we show that regional emissions estimates using widely implemented combustion coefficients are 59%–83% higher than emissions based on field observations. Using unique field datasets from before and after wildfires and an improved ecosystem model, we provide strong evidence that these large overestimates can be reduced by using realistic biomass combustion factors and by accurately quantifying biomass in standing dead trees that decompose over decades to centuries after fire (“snags”). Most model development focuses on area burned; our results reveal that accurately representing combustion is also essential for quantifying fire impacts on ecosystems. Using our improvements, we find that western US forest fires have emitted 851 ± 228 Tg CO₂(~half of alternative estimates) over the last 17 years, which is minor compared to 16,200 Tg CO₂from fossil fuels across the region.