Xingjie Lu

Biogeographic variation in evergreen conifer needle longevity and impacts on boreal forest carbon cycle projections

Significance How evergreen tree needle longevity varies from south to north in the boreal biome is poorly quantified and therefore ignored in vegetation and earth system models. This is problematic, because needle longevity translates directly into needle turnover rate and profoundly affects carbon cycling in both nature and computer models. Herein we present data for five widespread boreal conifers, including pines and spruces, from >125 sites along a 2,000-km gradient. For each species, individuals in colder, more northern environments had longer needle life span, highlighting its importance to evergreen ecological success. Incorporating biogeography of needle longevity into a global model improved predictions of forest productivity and carbon cycling and identified specific problems for models that ignore such variability. Leaf life span is an important plant trait associated with interspecific variation in leaf, organismal, and ecosystem processes. We hypothesized that intraspecific variation in gymnosperm needle traits with latitude reflects both selection and acclimation for traits adaptive to the associated temperature and moisture gradient. This hypothesis was supported, because across 127 sites along a 2,160-km gradient in North America individuals of Picea glauca, Picea mariana, Pinus banksiana, and Abies balsamea had longer needle life span and lower tissue nitrogen concentration with decreasing mean annual temperature. Similar patterns were noted for Pinus sylvestris across a north–south gradient in Europe. These differences highlight needle longevity as an adaptive feature important to ecological success of boreal conifers across broad climatic ranges. Additionally, differences in leaf life span directly affect annual foliage turnover rate, which along with needle physiology partially regulates carbon cycling through effects on gross primary production and net canopy carbon export. However, most, if not all, global land surface models parameterize needle longevity of boreal evergreen forests as if it were a constant. We incorporated temperature-dependent needle longevity and %nitrogen, and biomass allocation, into a land surface model, Community Atmosphere Biosphere Land Exchange, to assess their impacts on carbon cycling processes. Incorporating realistic parameterization of these variables improved predictions of canopy leaf area index and gross primary production compared with observations from flux sites. Finally, increasingly low foliage turnover and biomass fraction toward the cold far north indicate that a surprisingly small fraction of new biomass is allocated to foliage under such conditions.

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment

Abstract Multifactor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date, such models have only been tested against single‐factor experiments. We applied 10 TBMs to the multifactor Prairie Heating and CO2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor experiments can be used to constrain models and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above‐ground net primary productivity (range: 31–390 g C m−2 yr−1). Comparison with data highlighted model failures particularly with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single‐factors treatments was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the N cycle models, N availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO2‐induced water savings to extend the growing season length. Observed interactive (CO2 × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As the models did not correctly represent these processes under ambient and single‐factor conditions, little extra information was gained by comparing model predictions against interactive responses. We outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.

Gross primary production responses to warming, elevated CO2, and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland

Abstract Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO2 (eCO2) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007–2012) of flux‐derived GPP data from the Prairie Heating and CO2 Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (Amax) and light‐use efficiency (Q) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antecedent (past) timescales. The model fits the observed GPP well (R2 = 0.79), which was confirmed by other model performance checks that compared different variants of the model (e.g. with and without antecedent effects). Stimulation of cumulative 6‐year GPP by warming (29%, P = 0.02) and eCO2 (26%, P = 0.07) was primarily driven by enhanced C uptake during spring (129%, P = 0.001) and fall (124%, P = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tairant) and vapor pressure deficit (VPDant) effects on Amax (over the past 3–4 days and 1–3 days, respectively) were the most significant predictors of temporal variability in GPP among most treatments. The importance of VPDant suggests that atmospheric drought is important for predicting GPP under current and future climate; we highlight the need for experimental studies to identify the mechanisms underlying such antecedent effects. Finally, posterior estimates of cumulative GPP under control and eCO2 treatments were tested as a benchmark against 12 terrestrial biosphere models (TBMs). The narrow uncertainties of these data‐driven GPP estimates suggest that they could be useful semi‐independent data streams for validating TBMs. &NA; Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO2 (eCO2) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007–2012) of flux‐derived GPP data from the Prairie Heating and CO2 Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light‐response model whose basic formulation has been successfully used in a variety of ecosystems. Stimulation of cumulative 6‐year GPP by warming (29%, P = 0.02) and eCO2 (26%, P = 0.07) was primarily driven by enhanced C uptake during spring (129%, P = 0.001) and fall (124%, P = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tairant) and vapor pressure deficit (VPDant) effects were the most significant predictors of temporal variability in GPP among most treatments. Figure. No caption available.

Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models

Differences in the predicted carbon and water fluxes by different global land models have been quite large and have not decreased over the last two decades. Quantification and attribution of the uncertainties of global land surface models are important for improving the performance of global land surface models, and are the foci of this study. Here we quantified the model errors by comparing the simulated monthly global gross primary productivity (GPP) and latent heat flux (LE) by two global land surface models with the model-data products of global GPP and LE from 1982 to 2005. By analyzing model parameter sensitivities within their ranges, we identified about 2–11 most sensitive model parameters that have strong influences on the simulated GPP or LE by two global land models, and found that the sensitivities of the same parameters are different among the plant functional types (PFT). Using parameter ensemble simulations, we found that 15%–60% of the model errors were reduced by tuning only a few (<4) most sensitive parameters for most PFTs, and that the reduction in model errors varied spatially within a PFT or among different PFTs. Our study shows that future model improvement should optimize key model parameters, particularly those parameters relating to leaf area index, maximum carboxylation rate, and stomatal conductance.

Linear and nonlinear effects of dominant drivers on the trends in global and regional land carbon uptake: 1959 to 2013

Changes in atmospheric CO2 levels, surface temperature, or precipitation have been identified to have significantly contributed to the estimated increase in the terrestrial carbon uptake rate over the last few decades; however, those analyses did not consider the interactions. Using the Australian community land surface model (Community Atmosphere Biosphere Land Exchange), we performed factorial experiments to quantify the importance of external drivers (climate drivers and atmospheric CO2) and their interactions on annual terrestrial carbon uptake (FL), excluding land use change and fires, from 1959 to 2013. Our model simulations show a trend of 0.025 ± 0.015 Pg C yr−2 (or ~1.5% yr−1) in global FL for 1959–2013, which is largely attributed to the positive influences of the increased atmospheric CO2 (0.050 ± 0.001 Pg C yr−2) and negative influences of changes in climate (−0.026 ± 0.014 Pg C yr−2). Globally, the contribution of the nonlinear effects of dominant drivers to the simulated trend in FL is small ( 35%), particularly in the boreal forests and semiarid regions. The interactions between temperature and CO2 or temperature and precipitation can dominate the simulated trend in parts of Europe, southeastern North America, southern China, and some semiarid regions. This modeling result suggests that the effects of nonlinear interactions of drivers on the trend of land carbon uptake should be considered in future studies.

Transient Traceability Analysis of Land Carbon Storage Dynamics: Procedures and Its Application to Two Forest Ecosystems

Uptake of anthropogenically emitted carbon (C) dioxide by terrestrial ecosystem is critical for determining future climate. However, Earth system models project large uncertainties in future C storage. To help identify sources of uncertainties in model predictions, this study develops a transient traceability framework to trace components of C storage dynamics. Transient C storage (X) can be decomposed into two components, C storage capacity (Xc) and C storage potential (Xp). Xc is the maximum C amount that an ecosystem can potentially store and Xp represents the internal capacity of an ecosystem to equilibrate C input and output for a network of pools. Xc is co-determined by net primary production (NPP) and residence time (τN), with the latter being determined by allocation coefficients, transfer coefficients, environmental scalar, and exit rate. Xp is the product of redistribution matrix (τch) and net ecosystem exchange. We applied this framework to two contrasting ecosystems, Duke Forest and Harvard Forest with an ecosystem model. This framework helps identify the mechanisms underlying the responses of carbon cycling in the two forests to climate change. The temporal trajectories of X are similar between the two ecosystems. Using this framework, we found that different mechanisms leading to a similar trajectory between the two ecosystems. This framework has potential to reveal mechanisms behind transient C storage in response to various global change factors. It can also identify sources of uncertainties in predicted transient C storage across models and can therefore be useful for model intercomparison.