Variation in Fine Root Characteristics and Nutrient Dynamics Across Alaskan Ecosystems

Ecosystems - Carbon cycle perturbations in high-latitude ecosystems associated with rapid warming can have implications for the global climate. Belowground biomass is an important component of the...     

Carbon Accumulation Patterns During Post-Fire Succession in Cajander Larch (Larix cajanderi) Forests of Siberia

Increased fire activity within boreal forests could affect global terrestrial carbon (C) stocks by decreasing stand age or altering tree recruitment, leading to patterns of forest regrowth that differ from those of pre-fire stands. To improve our understanding of post-fire C accumulation patterns within boreal forests, we evaluated above- and belowground C pools within 17 Cajander larch (Larix cajanderi) stands of northeastern Siberia that varied in both years since fire and stand density. Early-successional stands (<20-year old) exhibited low larch recruitment, and consequently, low density, aboveground larch biomass, and aboveground net primary productivity (ANPP_tree). Mid-successional stands (21- to 70-year old) were even-aged with considerable variability in stand density. High-density mid-successional stands had 21 times faster rates of ANPP_tree than low-density stands (252 vs. 12?g?C?m^?2?y^?1) and 26 times more C in aboveground larch biomass (2,186 vs. 85?g?C?m^?2). Density had little effect on total soil C pools. During late-succession (>70-year old), aboveground larch biomass, ANPP_tree, and soil organic layer C pools increased with stand age. These stands were low density and multi-aged, containing both mature trees and new recruits. The rapid accumulation of aboveground larch biomass in high-density, mid-successional stands allowed them to obtain C stocks similar to those in much older low-density stands (~8,000?g?C?m^?2). If fire frequency increases without altering stand density, landscape-level C storage could decline, but if larch density also increases, large aboveground C pools within high-density stands could compensate for a shorter successional cycle.     

Vegetation controls on northern high latitude snow‐albedo feedback: observations and CMIP5 model simulations

The snow‐masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large‐scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow‐albedo feedback is controlled largely by the contrast between snow‐covered and snow‐free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow‐covered and snow‐free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow‐albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.     

Scaling an Instantaneous Model of Tundra NEE to the Arctic Landscape

We scale a model of net ecosystem CO₂ exchange (NEE) for tundra ecosystems and assess model performance using eddy covariance measurements at three tundra sites. The model, initially developed using instantaneous (seconds–minutes) chamber flux (~m²) observations, independently represents ecosystem respiration (ER) and gross primary production (GPP), and requires only temperature (T), photosynthetic photon flux density (I ₀), and leaf area index (L) as inputs. We used a synthetic data set to parameterize the model so that available in situ observations could be used to assess the model. The model was then scaled temporally to daily resolution and spatially to about 1 km² resolution, and predicted values of NEE, and associated input variables, were compared to observations obtained from eddy covariance measurements at three flux tower sites over several growing seasons. We compared observations to modeled NEE calculated using T and I ₀ measured at the towers, and L derived from MODIS data. Cumulative NEE estimates were within 17 and 11% of instrumentation period and growing season observations, respectively. Predictions improved when one site-year experiencing anomalously dry conditions was excluded, indicating the potential importance of stomatal control on GPP and/or soil moisture on ER. Notable differences in model performance resulted from ER model formulations and differences in how L was estimated. Additional work is needed to gain better predictive ability in terms of ER and L. However, our results demonstrate the potential of this model to permit landscape scale estimates of NEE using relatively few and simple driving variables that are easily obtained via satellite remote sensing.