The decadal state of the terrestrial carbon cycle Bloom, A. Anthony; Exbrayat, Jean-François; van der Velde, Ivar R. ...
Proceedings of the National Academy of Sciences - PNAS,
02/2016, Volume:
113, Issue:
5
Journal Article
Peer reviewed
Open access
The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence ...times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85–88%) in contrast to higher latitudes (73–82%),where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42–0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64–0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types.
Tree–grass savannas are a widespread biome and are highly valued for their ecosystem services. There is a need to understand the long‐term dynamics and meteorological drivers of both tree and grass ...productivity separately in order to successfully manage savannas in the future. This study investigated the interannual variability (IAV) of tree and grass gross primary productivity (GPP) by combining a long‐term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. On a seasonal basis, the primary drivers of tree and grass GPP were solar radiation in the wet season and soil moisture in the dry season. On an interannual basis, soil water availability had a positive effect on tree GPP and a negative effect on grass GPP. No linear trend in the tree–grass GPP ratio was observed over the 15‐year study period. However, the tree–grass GPP ratio was correlated with the modes of climate variability, namely the Southern Oscillation Index. This study has provided insight into the long‐term contributions of trees and grasses to savanna productivity, along with their respective meteorological determinants of IAV.
Tree–grass savannas are a widespread biome and are highly valued for their ecosystem services. This study investigated the interannual variability in tree and grass gross primary productivity (GPP) by combining a long‐term (15 year) eddy covariance flux record and model estimates of tree and grass GPP inferred from satellite remote sensing. Seasonal and interannual variability in tree and grass GPP was significantly correlated with water availability. Climate anomaly years were also responsible for interannual shifts in tree and grass GPP.
Recent large‐scale carbon (C) emissions from deforestation have been estimated by combining remotely‐sensed land use change information with satellite‐based aboveground biomass (AGB) data. However, ...these estimates are constrained to the satellite era while regions such as the Amazon basin have been heavily impacted by deforestation before this period. Assessing the net contribution of past tropical deforestation to the growth in atmospheric CO2 is therefore challenging. We address this lack of data by constructing two maps of potential AGB with a machine learning algorithm trained on the relationship between AGB and climate and topography in intact forest landscapes of the Amazon basin. Reconstructions converge to a current deficit of 11.5–12% in AGB or a net loss of ~7–8 Pg C of AGB in the Amazon basin compared to current estimates. This represents a net contribution of ~1.8 ppm of atmospheric CO2 or 1.5% of the historical growth.
Key Points
Reconstructions converge to an AGB deficit of 7.3–8.0 Pg C in the Amazon basin
Net contribution of ~1.8 ppm of Amazonian deforestation to atmospheric CO2
Net contribution is less than annual global fossil fuel emissions in 2010
Fire is a major component of the terrestrial carbon cycle that has been implemented in most current global terrestrial ecosystem models. Here we use terrestrial carbon cycle observations to ...characterize the importance of fire regime gradients in the spatial distribution of ecosystem functional properties such as carbon allocation, fluxes, and turnover times in the tropics. A Bayesian model‐data fusion approach is applied to an ecosystem carbon model to derive the posterior distribution of corresponding parameters for the tropics from 2000 to 2015. We perform the model‐data fusion procedure twice, that is, with and without imposing fire. Gradient of differences in model parameters and ecosystem properties in response to fire emerge between these experiments. For example, mean annual burned fraction correlates with an increase in carbon use efficiency and reductions in carbon turnover times. Further, our analyses reveal an increased allocation to more fire‐resistant tissues in the most frequently burned regions. As fire modules are increasingly implemented in global terrestrial ecosystem models, we recommend that model development includes a representation of the impact of fire on ecosystem properties as they may lead to large differences under climate change projections.
Key Points
The impact of fire on pantropical ecosystems is determined using an inverse modeling approach
Mean annual burned fraction correlates with increased productivity, more carbon allocated to structural pools, and shorter transit times
Large‐scale patterns of shifts in allocation are in agreement with field observations which points to the need to refine parameterizations
Our understanding of the terrestrial carbon cycle has been greatly enhanced since satellite observations of the land surface started. The advantage of remote sensing is that it provides wall-to-wall ...observations including in regions where in situ monitoring is challenging. This paper reviews how satellite observations of the biosphere have helped improve our understanding of the terrestrial carbon cycle. First, it details how remotely sensed information of the land surface has provided new means to monitor vegetation dynamics and estimate carbon fluxes and stocks. Second, we present examples of studies which have used satellite products to evaluate and improve simulations from global vegetation models. Third, we focus on model data integration approaches ranging from bottom-up extrapolation of single variables to carbon cycle data assimilation system able to ingest multiple types of observations. Finally, we present an overview of upcoming satellite missions which are likely to further improve our understanding of the terrestrial carbon cycle and its response to climate change and extremes.
An improvement in our process-based understanding of carbon (C) exchange in the Arctic and its climate sensitivity is critically needed for understanding the response of tundra ecosystems to a ...changing climate. In this context, we analysed the net ecosystem exchange (NEE) of CO2 in West Greenland tundra (64∘ N) across eight snow-free periods in 8 consecutive years, and characterized the key processes of net ecosystem exchange and its two main modulating components: gross primary production (GPP) and ecosystem respiration (Reco). Overall, the ecosystem acted as a consistent sink of CO2, accumulating-30 gCm-2 on average (range of -17 to -41 gCm-2) during the years 2008–2015, except 2011 (source of 41 gCm-2), which was associated with a major pest outbreak. The results do not reveal a marked meteorological effect on the net CO2 uptake despite the high interannual variability in the timing of snowmelt and the start and duration of the growing season. The ranges in annual GPP (-182 to -316 gCm-2) and Reco (144 to 279 gCm-2) were>5 fold larger than the range in NEE. Gross fluxes were also more variable (coefficients of variation are 3.6 and 4.1 % respectively) than for NEE (0.7 %). GPP and Reco were sensitive to insolation and temperature, and there was a tendency towards larger GPP and Reco during warmer and wetter years. The relative lack of sensitivity of NEE to meteorology was a result of the correlated response of GPP and Reco. During the snow-free season of the anomalous year of 2011, a biological disturbance related to a larvae outbreak reduced GPP more strongly than Reco. With continued warming temperatures and longer growing seasons, tundra systems will increase rates of C cycling. However, shifts in sink strength will likely be triggered by factors such as biological disturbances, events that will challenge our forecasting of C states.
The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical ...countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya’s forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha−1 yr−1) than wooded grasslands (0.6 t C ha−1 yr−1), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks.
This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at ...various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).
Forest ecosystems play a crucial role in the global carbon cycle by sequestering a considerable fraction of anthropogenic CO2, thereby contributing to climate change mitigation. However, there is a ...gap in our understanding about the carbon dynamics of eucalypt (broadleaf evergreen) forests in temperate climates, which might differ from temperate evergreen coniferous or deciduous broadleaved forests given their fundamental differences in physiology, phenology and growth dynamics. To address this gap we undertook a 3-year study (2010–2012) of eddy covariance measurements in a dry temperate eucalypt forest in southeastern Australia. We determined the annual net carbon balance and investigated the temporal (seasonal and inter-annual) variability in and environmental controls of net ecosystem carbon exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (ER). The forest was a large and constant carbon sink throughout the study period, even in winter, with an overall mean NEE of −1234 ± 109 (SE) g C m−2 yr−1. Estimated annual ER was similar for 2010 and 2011 but decreased in 2012 ranging from 1603 to 1346 g C m−2 yr−1, whereas GPP showed no significant inter-annual variability, with a mean annual estimate of 2728 ± 39 g C m−2 yr−1. All ecosystem carbon fluxes had a pronounced seasonality, with GPP being greatest during spring and summer and ER being highest during summer, whereas peaks in NEE occurred in early spring and again in summer. High NEE in spring was likely caused by a delayed increase in ER due to low temperatures. A strong seasonal pattern in environmental controls of daytime and night-time NEE was revealed. Daytime NEE was equally explained by incoming solar radiation and air temperature, whereas air temperature was the main environmental driver of night-time NEE. The forest experienced unusual above-average annual rainfall during the first 2 years of this 3-year period so that soil water content remained relatively high and the forest was not water limited. Our results show the potential of temperate eucalypt forests to sequester large amounts of carbon when not water limited. However, further studies using bottom-up approaches are needed to validate measurements from the eddy covariance flux tower and to account for a possible underestimation in ER due to advection fluxes.
Multi-model averaging techniques provide opportunities to
extract
additional information from large ensembles of simulations. In
particular, present-day model skill can be used to evaluate their
...potential performance in future climate simulations. Multi-model
averaging methods have been used extensively in climate and
hydrological sciences, but they have not been used to constrain
projected plant productivity responses to climate change, which is
a major uncertainty in Earth system modelling. Here, we use three
global observationally orientated estimates of current net primary
productivity (NPP) to perform a reliability ensemble averaging (REA) method
using 30 global simulations of the 21st century change in NPP based
on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)
”business as usual” emissions scenario. We find that the three
REA methods support an increase in global NPP by the end of the 21st
century (2095–2099) compared to 2001–2005, which is 2–3 %
stronger than the ensemble ISIMIP mean value of
24.2 Pg C y−1. Using REA also leads to a 45–68 %
reduction in the global uncertainty of 21st century NPP projection,
which strengthens confidence in the resilience of the
CO2 fertilization effect to climate change. This reduction
in uncertainty is especially clear for boreal ecosystems although it
may be an artefact due to the lack of representation of nutrient
limitations on NPP in most models. Conversely, the large uncertainty
that remains on the sign of the response of NPP in semi-arid regions
points to the need for better observations and model development in
these regions.
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