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Atmospheric monitoring of high northern latitudes (above 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s, but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes relative to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. We found that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results underscore the importance of climate-vegetation-carbon cycle feedbacks at high latitudes; moreover, they indicate that in recent decades, photosynthetic carbon uptake has reacted much more strongly to warming than have carbon release processes., (Copyright © 2016, American Association for the Advancement of Science.)

Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here, we review the literature on carbon cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cover type modulated by management. Although processes and sensitivities differ among biomes, based on expert opinion, we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to regain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks., (© 2015 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.

Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (mean =2.7) ×106 km 2 (37 %–51 % of the Amazon basin, mean =45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly <-0.5). In 2010, the affected area was about 16 % larger, ranging from 3.0 up to 4.4 (mean =3.6) ×106 km 2 (51 %–74 %, mean =61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean =3.2×106 km 2 ; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106 km 2 (40 %–69 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60 %), followed by the choice of the precipitation dataset (20 %) and the evapotranspiration dataset (20 %). Using a fixed, constant evapotranspiration rate instead of variable evapotranspiration can lead to an overestimation of drought stress in the parts of Amazon basin that have a more pronounced dry season (for example in 2010). We highlight that even for well-known drought events the spatial extent and intensity can strongly depend upon the drought indicator and the data sources it is calculated with. Using only one data source and drought indicator has the potential danger of under- or overestimating drought stress in regions with high measurement uncertainty, such as the Amazon basin. [ABSTRACT FROM AUTHOR]

The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth. [ABSTRACT FROM AUTHOR]

The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through https://gitlab.pik-potsdam.de/lpjml/LPJmL. We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere. [ABSTRACT FROM AUTHOR]

Grassland management affects the carbon fluxes of one-third of the global land area and is thus an important factor for the global carbon budget. Nonetheless, this aspect has been largely neglected or underrepresented in global carbon cycle models. We investigate four harvesting schemes for the managed grassland implementation of the dynamic global vegetation model (DGVM) Lund--Potsdam--Jena managed Land (LPJmL) that facilitate a better representation of actual management systems globally. We describe the model implementation and analyze simulation results with respect to harvest, net primary productivity and soil carbon content and by evaluating them against reported grass yields in Europe.We demonstrate the importance of accounting for differences in grassland management by assessing potential livestock grazing densities as well as the impacts of grazing, grazing intensities and mowing systems on soil carbon stocks. Grazing leads to soil carbon losses in polar or arid regions even at moderate livestock densities ( <0.4 livestock units per hectare -- LSUha enables assessments of the global grassland production and its impact on the terrestrial biogeochemical cycles but requires a global data set on current grassland management. [ABSTRACT FROM AUTHOR]

The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., submitted). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures, give hints of the strengths and shortcomings of the model to identify the need of further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in-situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied Open Source through a Gitlab repository. We hope that this will lead to new model developments and applications that improve model performance and possibly build up a new understanding of the terrestrial biosphere. [ABSTRACT FROM AUTHOR]

Global change includes multiple stressors to natural ecosystems ranging from direct climate and land-use impacts to indirect degradation processes resulting from fire. Humid tropical forests are vulnerable to projected climate change and possible synergistic interactions with deforestation and fire, which may initiate a positive feedback to rising atmospheric CO2. Here, we present results from a multifactorial impact analysis that combined an ensemble of climate change models with feedbacks from deforestation and accidental fires to quantify changes in Amazon Basin carbon cycling. Using the LPJmL Dynamic Global Vegetation Model, we modelled spatio-temporal changes in net biome production (NBP); the difference between carbon fluxes from fire, deforestation, soil respiration and net primary production. By 2050, deforestation and fire (with no CO2 increase or climate change) resulted in carbon losses of 7.4–20.3 Pg C with the range of uncertainty depending on socio-economic storyline. During the same time period, interactions between climate and land use either compensated for carbon losses due to wetter climate and CO2 fertilization or exacerbated carbon losses from drought-induced forest mortality (−20.1 to +4.3 Pg C). By the end of the 21st century, depending on climate projection and the rate of deforestation (including its interaction with fire), carbon stocks either increased (+12.6 Pg C) or decreased (−40.6 Pg C). The synergistic effect of deforestation and fire with climate change contributed up to 26–36 Pg C of the overall decrease in carbon stocks. Agreement between climate projections ( n=9), not accounting for deforestation and fire, in 2050 and 2098 was relatively low for the directional change in basin-wide NBP (19–37%) and aboveground live biomass (13–24%). The largest uncertainty resulted from climate projections, followed by implementation of ecosystem dynamics and deforestation. Our analysis partitions the drivers of tropical ecosystem change and is relevant for guiding mitigation and adaptation policy related to global change. [ABSTRACT FROM AUTHOR]

The response of land ecosystems to future climate change is among the largest uncertainties in the climate/carbon cycle feedback. Dynamic global vegetation models (DGVMs) are a key tool to assess the impacts of climate change on vegetation distribution, biomass, and land carbon exchange. Carbon cycle uncertainties in DGVMs depend partly on the simulated distribution and temporal changes in vegetation cover and its interactions with photosynthesis, biomass allocation and turnover. The present-day availability of multiple satellite observations can potentially help to constrain DGVMs. Here we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced florescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain phenology, productivity, and vegetation dynamic parameters in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle. However, the optimized model better reproduces observed spatial patterns of biomass and tree cover. The optimized model shows regionally-dependent increases and decreases in vegetation and ecosystem carbon turnover times. In summary, optimizations of dynamic global vegetation models against multiple satellite observation improve regional simulations of vegetation and carbon cycle dynamics. [ABSTRACT FROM AUTHOR]