Terrestrial Carbon–Cycle Feedback to Climate Warming

Luo, Yiqi

doi:10.1146/annurev.ecolsys.38.091206.095808

Cited by 468 publications

(438 citation statements)

References 153 publications

(172 reference statements)

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“…While temperature influences almost all ecosystem processes , it is still not well understood whether or not climate warming would stimulate net ecosystem carbon (C) release, accelerate buildup of atmospheric CO 2 concentration, and then amplify climate warming (Luo 2007). Global models that couple climate change with C cycles all predicted a positive feedback that climate warming accerelates CO 2 buildup in the amtosphere by 20 -200 ppmv and amplifies climate warming by 0.1 to 1.5 °C (Friedlingstein et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Terrestrial carbon-cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest

Luo

Sherry

Zhou

et al. 2008

Global Change Biology

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110

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Warming-stimulated plant biomass production, due to enhanced C 4 dominance, extended growing seasons, and increased nitrogen uptake and use efficiency, offset increased soil respiration, leading to no change in soil C storage at our site. However, biofuel feedstock harvest by biomass removal resulted in significant soil C loss in the clipping and control plots but was carbon negative in the clipping and warming plots largely because of positive interactions of warming and clipping in stimulating root growth. Our results demonstrate that plant production processes play a critical role in regulation of ecosystem carbon-cycle feedback to climate change in both the current ambient and future warmed world.

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Section: Introductionmentioning

confidence: 99%

Terrestrial carbon-cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest

Luo

Sherry

Zhou

et al. 2008

Global Change Biology

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110

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“…Another study, based on natural forest stands in New Zealand, found an AWP increase of between 5 and 20 • C −1 for forests, assuming no change in forest structure and species composition (Coomes et al, 2014). The analysed plots were spread throughout New Zealand, and warmer temperatures coincided with higher radiation (Mackintosh, 2016). Hence, the analysed temperature effect also includes the influence of radiation.…”

Section: The Study Designmentioning

confidence: 94%

“…In the case of higher mean annual temperature, we observe an elongation of the vegetation period. This leads to higher forest productivity (if other resources are not limiting (Luo, 2007) and explains why SI MAT is often positive. However, warmer summer temperatures can also lead to a F. J.…”

Section: The Study Designmentioning

confidence: 99%

“…In the case of deciduous trees, the length of the vegetation period (leaf onset to fall) additionally affects the annual photoproduction (e.g. Haxeltine and Prentice, 1996;Luo, 2007;Horn and Schulz, 2011;Gutiérrez and Huth, 2012;Sato et al, 2007). If the photosynthesis rate is much larger than the respiration rate (high AWP; for instance, low ratio of maintenance respiration to photosynthesis under full light in Fig.…”

Section: B4 Si Values Of Single Treesmentioning

confidence: 99%

“…Climate change alters wood production by modifying the rates of photosynthesis and respiration rates of trees (Barber et al, 2000;Luo, 2007;Peñuelas and Filella, 2009;Reyer et al, 2014). Changes in forest productivity have been observed in past decades all over the world (Nemani et al, 2003;Boisvenue and Running, 2006;Seddon et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests

Bohn

Huth

2018

Biogeosciences

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Abstract. Rising temperatures due to climate change influence the wood production of forests. Observations show that some temperate forests increase their productivity, whereas others reduce their productivity. This study focuses on how species composition and forest structure properties influence the temperature sensitivity of aboveground wood production (AWP). It further investigates which forests will increase their productivity the most with rising temperatures. We described forest structure by leaf area index, forest height and tree height heterogeneity. Species composition was described by a functional diversity index (Rao's Q) and a species distribution index ( AWP ). AWP quantified how well species are distributed over the different forest layers with regard to AWP. We analysed 370 170 forest stands generated with a forest gap model. These forest stands covered a wide range of possible forest types. For each stand, we estimated annual aboveground wood production and performed a climate sensitivity analysis based on 320 different climate time series (of 1-year length). The scenarios differed in mean annual temperature and annual temperature amplitude. Temperature sensitivity of wood production was quantified as the relative change in productivity resulting from a 1 • C rise in mean annual temperature or annual temperature amplitude. Increasing AWP positively influenced both temperature sensitivity indices of forest, whereas forest height showed a bell-shaped relationship with both indices. Further, we found forests in each successional stage that are positively affected by temperature rise. For such forests, large AWP values were important. In the case of young forests, low functional diversity and small tree height heterogeneity were associated with a positive effect of temperature on wood production. During later successional stages, higher species diversity and larger tree height heterogeneity were an advantage. To achieve such a development, one could plant below the closed canopy of even-aged, pioneer trees a climax-species-rich understorey that will build the canopy of the mature forest. This study highlights that forest structure and species composition are both relevant for understanding the temperature sensitivity of wood production.

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Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method

2014

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Long-term land carbon (C) cycle feedback to climate change is largely determined by dynamics of soil organic carbon (SOC). However, most evaluation studies conducted so far indicate that global land models predict SOC poorly. This study was conducted to evaluate predictions of SOC by the Community Land Model with Carnegie-Ames-Stanford Approach biogeochemistry submodel (CLM-CASA'), investigate underlying causes of mismatches between model predictions and observations, and calibrate model parameters to improve the prediction of SOC. We compared modeled SOC to the SOC pools from a globally gridded observation data product and found that CLM-CASA' on average underestimated SOC pools by 65% (r 2 = 0.28). We applied data assimilation to CLM-CASA' to estimate SOC residence times, C partitioning coefficients among the pools, and temperature sensitivity of C decomposition. The model with calibrated parameters explained 41% of the global variability in the observed SOC, which was substantial improvement from the initial 27%. The SOC and litter C feedback to changing climate differed between models with original and calibrated parameters: after 95 years of climate change (2006-2100), soils released 48 Pg C less in the calibrated than in the noncalibrated model and litter released 6.5 Pg C less in the calibrated than the noncalibrated model. Thus, assimilating estimated soil carbon data into the model improved model performance and reduced the amount of C released under changing climate. To further reduce the uncertainty in the soil carbon prediction, we need to explore alternative model structures, improve representation of ecosystems, and develop additional global datasets for constraining model parameters.

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Terrestrial Carbon–Cycle Feedback to Climate Warming

Cited by 468 publications

References 153 publications

Terrestrial carbon-cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest

Terrestrial carbon-cycle feedback to climate warming: experimental evidence on plant regulation and impacts of biofuel feedstock harvest

Species composition and forest structure explain the temperature sensitivity patterns of productivity in temperate forests

Evaluation and improvement of a global land model against soil carbon data using a Bayesian Markov chain Monte Carlo method

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