Amazon rainforest increases photosynthesis in reponse to atmospheric dryness

2020 ◽  
Author(s):  
Julia K. Green ◽  
Pierre Gentine ◽  
Yao Zhang ◽  
Joe Berry ◽  
Philippe Ciais
Keyword(s):  
Vapor Pressure ◽  
Vapor Pressure Deficit ◽  
Carbon Sink ◽  
Gross Primary Production ◽  
Remote Sensing Data ◽  
Amazon Rainforest ◽  
Machine Learning Techniques ◽  
Earth System ◽  
System Models ◽  
Earth System Models

<p>Earth system models predict that atmospheric dryness reduces photosynthesis due to its reductive effect on stomatal conductance. However, while this representation may be appropriate in many environments, in the wet Amazonian tropical rainforest, this is not the case. Using remote sensing data combined with machine learning techniques (k-means clustering and artificial neural networks), we show that in the wettest parts of the Amazon rainforest, gross primary production and evapotranspiration continue to increase alongside atmospheric dryness, i.e. vapor pressure deficit, despite reductions in ecosystem conductance. On the other hand, Earth system models have the opposite photosynthetic response to vapor pressure deficit in the wettest part of the Amazon, overestimating its reductive effect on tropical vegetation photosynthesis and evapotranspiration, leading to an exaggerated carbon source to the atmosphere. As vapor pressure deficit is expected to increase with climate change, our study highlights the importance of reframing how we understand and represent the response of ecosystem photosynthesis to atmospheric dryness in the wettest ecosystems, to accurately quantify the future land carbon sink and atmospheric CO2 growth rate.</p>

scholarly journals Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

2015 ◽  
Vol 8 (4) ◽  
pp. 3235-3292 ◽  
Author(s):  
A. L. Atchley ◽  
S. L. Painter ◽  
D. R. Harp ◽  
E. T. Coon ◽  
C. J. Wilson ◽  
...  
Keyword(s):  
Carbon Sink ◽  
Thermal Response ◽  
Field Measurements ◽  
Early Summer ◽  
Active Layer Thickness ◽  
Earth System ◽  
Model Refinement ◽  
System Models ◽  
Developed Surface ◽  
Earth System Models

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

scholarly journals Resolving ecological feedbacks on the ocean carbon sink in Earth system models

2020 ◽  
Author(s):  
David I. Armstrong McKay ◽  
Sarah E. Cornell ◽  
Katherine Richardson ◽  
Johan Rockström
Keyword(s):  
Climate Change ◽  
Carbon Sink ◽  
Biological Pump ◽  
Earth System ◽  
Temperature Dependent ◽  
System Models ◽  
Plankton Ecology ◽  
Ocean Carbon ◽  
The Impact ◽  
Earth System Models

Abstract. The Earth’s oceans are one of the largest sinks in the Earth system for anthropogenic CO2 emissions, acting as a negative feedback on climate change. Earth system models predict, though, that climate change will lead to a weakening ocean carbon uptake rate as warm water holds less dissolved CO2 and biological productivity declines. However, most Earth system models do not incorporate the impact of warming on bacterial remineralisation and rely on simplified representations of plankton ecology that do not resolve the potential impact of climate change on ecosystem structure or elemental stoichiometry. Here we use a recently-developed extension of the cGEnIE Earth system model (ecoGEnIE) featuring a trait-based scheme for plankton ecology (ECOGEM), and also incorporate cGEnIE's temperature-dependent remineralisation (TDR) scheme. This enables evaluation of the impact of both ecological dynamics and temperature-dependent remineralisation on the soft-tissue biological pump in response to climate change. We find that including TDR strengthens the biological pump relative to default runs due to increased nutrient recycling, while ECOGEM weakens the biological pump by enabling a shift to smaller plankton classes. However, interactions with concurrent ocean acidification cause opposite sign responses for the carbon sink in both cases: TDR leads to a smaller sink relative to default runs whereas ECOGEM leads to a larger sink. Combining TDR and ECOGEM results in a net strengthening of the biological pump and a small net reduction in carbon sink relative to default. These results clearly illustrate the substantial degree to which ecological dynamics and biodiversity modulate the strength of climate-biosphere feedbacks, and demonstrate that Earth system models need to incorporate more ecological complexity in order to resolve carbon sink weakening.

scholarly journals Resolving ecological feedbacks on the ocean carbon sink in Earth system models

2021 ◽  
Vol 12 (3) ◽  
pp. 797-818
Author(s):  
David I. Armstrong McKay ◽  
Sarah E. Cornell ◽  
Katherine Richardson ◽  
Johan Rockström
Keyword(s):  
Climate Change ◽  
Carbon Sink ◽  
Earth System ◽  
Temperature Dependent ◽  
System Models ◽  
Plankton Ecology ◽  
Poc Export ◽  
Ocean Carbon ◽  
The Impact ◽  
Earth System Models

Abstract. The Earth's oceans are one of the largest sinks in the Earth system for anthropogenic CO2 emissions, acting as a negative feedback on climate change. Earth system models project that climate change will lead to a weakening ocean carbon uptake rate as warm water holds less dissolved CO2 and as biological productivity declines. However, most Earth system models do not incorporate the impact of warming on bacterial remineralisation and rely on simplified representations of plankton ecology that do not resolve the potential impact of climate change on ecosystem structure or elemental stoichiometry. Here, we use a recently developed extension of the cGEnIE (carbon-centric Grid Enabled Integrated Earth system model), ecoGEnIE, featuring a trait-based scheme for plankton ecology (ECOGEM), and also incorporate cGEnIE's temperature-dependent remineralisation (TDR) scheme. This enables evaluation of the impact of both ecological dynamics and temperature-dependent remineralisation on particulate organic carbon (POC) export in response to climate change. We find that including TDR increases cumulative POC export relative to default runs due to increased nutrient recycling (+∼1.3 %), whereas ECOGEM decreases cumulative POC export by enabling a shift to smaller plankton classes (-∼0.9 %). However, interactions with carbonate chemistry cause opposite sign responses for the carbon sink in both cases: TDR leads to a smaller sink relative to default runs (-∼1.0 %), whereas ECOGEM leads to a larger sink (+∼0.2 %). Combining TDR and ECOGEM results in a net strengthening of POC export (+∼0.1 %) and a net reduction in carbon sink (-∼0.7 %) relative to default. These results illustrate the degree to which ecological dynamics and biodiversity modulate the strength of the biological pump, and demonstrate that Earth system models need to incorporate ecological complexity in order to resolve non-linear climate–biosphere feedbacks.

scholarly journals Amazon rainforest photosynthesis increases in response to atmospheric dryness

2020 ◽  
Vol 6 (47) ◽  
pp. eabb7232
Author(s):  
J. K. Green ◽  
J. Berry ◽  
P. Ciais ◽  
Y. Zhang ◽  
P. Gentine
Keyword(s):  
Carbon Sink ◽  
Amazon Rainforest ◽  
Machine Learning Techniques ◽  
Canopy Conductance ◽  
Learning Techniques ◽  
In Situ Observations ◽  
Soil Dryness ◽  
Earth System Models ◽  
Global Carbon

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in reality, this is not necessarily the case: In many of the wettest parts of this region, photosynthesis and biomass tend to increase with increased atmospheric dryness, despite the associated reductions in canopy conductance to CO2. These results can be largely explained by changes in canopy properties, specifically, new leaves flushed during the dry season have higher photosynthetic capacity than the leaves they replace, compensating for the negative stomatal response to increased dryness. As atmospheric dryness will increase with climate change, our study highlights the importance of reframing how we represent the response of ecosystem photosynthesis to atmospheric dryness in very wet regions, to accurately quantify the land carbon sink.

scholarly journals Plant functional type mapping for earth system models

2011 ◽  
Vol 4 (4) ◽  
pp. 993-1010 ◽  
Author(s):  
B. Poulter ◽  
P. Ciais ◽  
E. Hodson ◽  
H. Lischke ◽  
F. Maignan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Climate Variability ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Earth System ◽  
State Variables ◽  
System Models ◽  
Satellite Sensors ◽  
Earth System Models

Abstract. The sensitivity of global carbon and water cycling to climate variability is coupled directly to land cover and the distribution of vegetation. To investigate biogeochemistry-climate interactions, earth system models require a representation of vegetation distributions that are either prescribed from remote sensing data or simulated via biogeography models. However, the abstraction of earth system state variables in models means that data products derived from remote sensing need to be post-processed for model-data assimilation. Dynamic global vegetation models (DGVM) rely on the concept of plant functional types (PFT) to group shared traits of thousands of plant species into usually only 10–20 classes. Available databases of observed PFT distributions must be relevant to existing satellite sensors and their derived products, and to the present day distribution of managed lands. Here, we develop four PFT datasets based on land-cover information from three satellite sensors (EOS-MODIS 1 km and 0.5 km, SPOT4-VEGETATION 1 km, and ENVISAT-MERIS 0.3 km spatial resolution) that are merged with spatially-consistent Köppen-Geiger climate zones. Using a beta (ß) diversity metric to assess reclassification similarity, we find that the greatest uncertainty in PFT classifications occur most frequently between cropland and grassland categories, and in dryland systems between shrubland, grassland and forest categories because of differences in the minimum threshold required for forest cover. The biogeography-biogeochemistry DGVM, LPJmL, is used in diagnostic mode with the four PFT datasets prescribed to quantify the effect of land-cover uncertainty on climatic sensitivity of gross primary productivity (GPP) and transpiration fluxes. Our results show that land-cover uncertainty has large effects in arid regions, contributing up to 30% (20%) uncertainty in the sensitivity of GPP (transpiration) to precipitation. The availability of PFT datasets that are consistent with current satellite products and adapted for earth system models is an important component for reducing the uncertainty of terrestrial biogeochemistry to climate variability.

scholarly journals Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

2015 ◽  
Vol 8 (9) ◽  
pp. 2701-2722 ◽  
Author(s):  
A. L. Atchley ◽  
S. L. Painter ◽  
D. R. Harp ◽  
E. T. Coon ◽  
C. J. Wilson ◽  
...  
Keyword(s):  
Carbon Sink ◽  
Thermal Response ◽  
Field Measurements ◽  
Early Summer ◽  
Active Layer Thickness ◽  
Earth System ◽  
Model Refinement ◽  
System Models ◽  
Developed Surface ◽  
Earth System Models

Abstract. Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth system models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth system models challenge validation and parameterization of hydrothermal models. A recently developed surface–subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to achieve the goals of constructing a process-rich model based on plausible parameters and to identify fine-scale controls of ALT in ice-wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze–thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g., troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

scholarly journals Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sinks

2021 ◽  
Author(s):  
Bin He ◽  
Chen Chen ◽  
Shangrong Lin ◽  
Wenping Yuan ◽  
Hans W Chen ◽  
...  
Keyword(s):  
Vapor Pressure ◽  
Interannual Variability ◽  
Environmental Variables ◽  
Vapor Pressure Deficit ◽  
Carbon Sink ◽  
Gross Primary Production ◽  
Terrestrial Ecosystem ◽  
Carbon Sinks ◽  
Terrestrial Carbon ◽  
Climate Conditions

Abstract Interannual variability of the terrestrial ecosystem carbon sink is substantially regulated by various environmental variables and highly dominates the interannual variation of atmospheric carbon dioxide (CO2) concentrations. Thus, it is necessary to determine dominating factors affecting the interannual variability of the carbon sink to improve our capability of predicting future terrestrial carbon sinks. Using global datasets derived from machine learning methods and process-based ecosystem models, this study reveals that the interannual variability of the atmospheric vapor pressure deficit (VPD) was significantly negatively correlated with net ecosystem production (NEP) and substantially impacted the interannual variability of the atmospheric CO2 growth rate (CGR). Further analyses found widespread constraints of VPD interannual variability on terrestrial gross primary production (GPP), causing VPD to impact NEP and CGR. Partial correlation analysis confirms the persistent and widespread impacts of VPD on terrestrial carbon sinks compared to other environmental variables. Current Earth system models underestimate the interannual variability in VPD and its impacts on GPP and NEP. Our results highlight the importance of VPD for terrestrial carbon sinks in assessing ecosystems’ responses to future climate conditions.

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