scholarly journals Leaf phenology as one important driver of seasonal changes in isoprene emissions in central Amazonia

2018 ◽  
Vol 15 (13) ◽  
pp. 4019-4032 ◽  
Author(s):  
Eliane G. Alves ◽  
Julio Tóta ◽  
Andrew Turnipseed ◽  
Alex B. Guenther ◽  
José Oscar W. Vega Bustillos ◽  
...  
Keyword(s):  
Leaf Age ◽  
Leaf Phenology ◽  
Evergreen Forest ◽  
Forest Site ◽  
Area Index ◽  
Isoprene Emission ◽  
Evergreen Forests ◽  
Central Amazonia ◽  
Meteorological Observations ◽  
Emission Capacity

Abstract. Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of the seasonal patterns of isoprene fluxes and the associated mechanistic controls is still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest site with meteorological observations and with tower-mounted camera leaf phenology to improve our understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature cannot totally explain isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf age class (e.g., leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R2=0.59, p<0.05). Attempting to better represent leaf phenology in the Model of Emissions of Gases and Aerosols from Nature (MEGAN 2.1), we improved the leaf age algorithm by utilizing results from the camera-derived leaf phenology that provided LAI categorized into three different leaf ages. The model results show that the observations of age-dependent isoprene emission capacity, in conjunction with camera-derived leaf age demography, significantly improved simulations in terms of seasonal variations in isoprene fluxes (R2=0.52, p<0.05). This study highlights the importance of accounting for differences in isoprene emission capacity across canopy leaf age classes and identifying forest adaptive mechanisms that underlie seasonal variation in isoprene emissions in Amazonia.

scholarly journals Leaf phenology as one important driver of seasonal changes in isoprene emission in central Amazonia

2018 ◽  
Author(s):  
Eliane G. Alves ◽  
Julio Tóta ◽  
Andrew Turnipseed ◽  
Alex B. Guenther ◽  
José Oscar W. Vega Bustillos ◽  
...  
Keyword(s):  
Seasonal Changes ◽  
Leaf Age ◽  
Leaf Phenology ◽  
Evergreen Forest ◽  
Seasonal Patterns ◽  
Biological Factors ◽  
Area Index ◽  
Evergreen Forests ◽  
Central Amazonia ◽  
Meteorological Observations

Abstract. Isoprene fluxes vary seasonally with changes in environmental factors (e.g., solar radiation and temperature) and biological factors (e.g., leaf phenology). However, our understanding of seasonal patterns of isoprene fluxes and associated mechanistic controls are still limited, especially in Amazonian evergreen forests. In this paper, we aim to connect intensive, field-based measurements of canopy isoprene flux over a central Amazonian evergreen forest with meteorological observations and with tower-camera leaf phenology to improve understanding of patterns and causes of isoprene flux seasonality. Our results demonstrate that the highest isoprene emissions are observed during the dry and dry-to-wet transition seasons, whereas the lowest emissions were found during the wet-to-dry transition season. Our results also indicate that light and temperature can not totally explain the isoprene flux seasonality. Instead, the camera-derived leaf area index (LAI) of recently mature leaf-age class (e.g. leaf ages of 3–5 months) exhibits the highest correlation with observed isoprene flux seasonality (R2 = 0.59, p 

Isoprene emission in central Amazonia - from measurements to model estimates

2020 ◽  
Author(s):  
Eliane Gomes-Alves ◽  
Tyeen Taylor ◽  
Pedro Assis ◽  
Giordane Martins ◽  
Rodrigo Souza ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Plant Traits ◽  
Leaf Age ◽  
Environmental Drivers ◽  
Good Representation ◽  
Leaf Demography ◽  
Isoprene Emission ◽  
Environmental Controls ◽  
Central Amazonia ◽  
Emission Capacity

&lt;p&gt;Isoprene regulates large-scale biogeochemical cycles by influencing atmospheric chemical and physical processes, and its dominant sources to the global atmosphere are the tropical forests. Although global and regional model estimates of isoprene emission have been optimized in the last decades, modeled emissions from tropical vegetation still carry high uncertainty due to a poor understanding of the biological and environmental controls on emissions. It is already known that isoprene emission quantities may vary significantly with plant traits, such as leaf phenology, and with the environment; however, current models still lack of good representation for tropical plant species due to the very few observations available. In order to create a predictive framework for the isoprene emission capacity of tropical forests, it is necessary an improved mechanistic understanding on how the magnitude of emissions varies with plant traits and the environment in such ecosystems. In this light, we aimed to quantify the isoprene emission capacity of different tree species across leaf ages, and combine these leaf measurements with long-term canopy measurements of isoprene and its biological and environmental drivers; then, use these results to better parameterize isoprene emissions estimated by MEGAN. We measured at the Amazon Tall Tower Observatory (ATTO) site, central Amazonia: (1) isoprene emission capacity at different leaf ages of 21 trees species; (2) isoprene canopy mixing ratios during six campaigns from 2013 to 2015; (3) isoprene tower flux during the dry season of 2015 (El-Ni&amp;#241;o year); (3) environmental factors &amp;#8211; air temperature and photosynthetic active radiation (PAR) - from 2013 to 2018; and (4) biological factors &amp;#8211; leaf demography and phenology (tower based measurements) from 2013 to 2018. We then parameterized the leaf age algorithm of MEGAN with the measurements of isoprene emission capacity at different leaf ages and the tower-based measurements of leaf demography and phenology. Modeling estimates were later compared with measurements (canopy level) and five years of satellite-derived isoprene emission (OMI) from the ATTO domain (2013-2017). Leaf level of isoprene emission capacity showed lower values for old leaves (&gt; 6 months) and young leaves (&lt; 2 months), compared to mature leaves (2-6 months); and our model results suggested that this affects seasonal ecosystem isoprene emission capacity, since the demography of the different leaf age classes varied a long of the year. We will present more results on how changes in leaf demography and phenology and in temperature and PAR affect seasonal ecosystem isoprene emission, and how modeling can be improved with the optimization of the leaf age algorithm. In addition, we will present a comparison of ecosystem isoprene emission of normal years (2013, 2014, 2017 years) and anomalous years (2015 - El-Ni&amp;#241;o; and 2016 - post El-Ni&amp;#241;o), and discuss how a strong El-Ni&amp;#241;o year can influence plant functional strategies that can be carried over to the consecutive year and potentially affect isoprene emission.&lt;/p&gt;

scholarly journals Inferring Amazon leaf demography from satellite observations of leaf area index

2011 ◽  
Vol 8 (5) ◽  
pp. 10389-10421 ◽  
Author(s):  
S. Caldararu ◽  
P. I. Palmer ◽  
D. W. Purves
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Amazon Basin ◽  
Leaf Age ◽  
Leaf Phenology ◽  
Spatial And Temporal Variability ◽  
Demographic Model ◽  
Area Index ◽  
Starting Point

Abstract. Seasonal and year-to-year variations in leaf cover imprint significant spatial and temporal variability on biogeochemical cycles, and affect land-surface properties related to climate. We develop a demographic model of leaf phenology based on the hypothesis that trees seek an optimal Leaf Area Index (LAI) as a function of available light and soil water, and fitted it to spaceborne observations of LAI over the Amazon Basin, 2001–2005. We find the model reproduces the spatial and temporal LAI distribution whilst also predicting geographic variation in leaf age from the basin center (2.1 ± 0.2 yr), through to the lowest values over the deciduous Eastern Amazon (6 ± 2 months). The model explains the observed increase in LAI during the dry season as a net addition of leaves in response to increased solar radiation. We anticipate our work to be a starting point from which to develop better descriptions of leaf phenology to incorporate into more sophisticated earth system models.

scholarly journals Supplementary material to "Leaf phenology as one important driver of seasonal changes in isoprene emission in central Amazonia"

2018 ◽  
Author(s):  
Eliane G. Alves ◽  
Julio Tóta ◽  
Andrew Turnipseed ◽  
Alex B. Guenther ◽  
José Oscar W. Vega Bustillos ◽  
...  
Keyword(s):  
Seasonal Changes ◽  
Leaf Phenology ◽  
Isoprene Emission ◽  
Central Amazonia ◽  
Supplementary Material

scholarly journals Inferring Amazon leaf demography from satellite observations of leaf area index

2012 ◽  
Vol 9 (4) ◽  
pp. 1389-1404 ◽  
Author(s):  
S. Caldararu ◽  
P. I. Palmer ◽  
D. W. Purves
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Amazon Basin ◽  
Leaf Age ◽  
Leaf Phenology ◽  
Spatial And Temporal Variability ◽  
Demographic Model ◽  
Area Index ◽  
Starting Point

Abstract. Seasonal and year-to-year variations in leaf cover imprint significant spatial and temporal variability on biogeochemical cycles, and affect land-surface properties related to climate. We develop a demographic model of leaf phenology based on the hypothesis that trees seek an optimal leaf area index (LAI) as a function of available light and soil water, and fit it to spaceborne observations of LAI over the Amazon basin, 2001–2005. We find the model reproduces the spatial and temporal LAI distribution whilst also predicting geographic variation in leaf age from the basin centre (2.1 ± 0.2 years), through to the lowest values over the deciduous eastern and southern Amazon (6 ± 2 months). The model explains the observed increase in LAI during the dry season as a net addition of leaves in response to increased solar radiation. We anticipate our work to be a starting point from which to develop better descriptions of leaf phenology to incorporate into more sophisticated earth system models.

On the relationship of NDVI with leaf area index in a deciduous forest site

2005 ◽  
Vol 94 (2) ◽  
pp. 244-255 ◽  
Author(s):  
Quan Wang ◽  
Samuel Adiku ◽  
John Tenhunen ◽  
André Granier
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Deciduous Forest ◽  
Forest Site ◽  
Area Index ◽  
Relationship Of ◽  
The Relationship

scholarly journals Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)

2006 ◽  
Vol 6 (1) ◽  
pp. 107-173 ◽  
Author(s):  
A. Guenther ◽  
T. Karl ◽  
P. Harley ◽  
C. Wiedinmyer ◽  
P. I. Palmer ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Model Development ◽  
Terrestrial Ecosystems ◽  
Global Climate Models ◽  
Earth System ◽  
Area Index ◽  
Isoprene Emission ◽  
Global Emission ◽  
High Emission

Abstract. Reactive gases and aerosols are produced by terrestrial ecosystems, processed within plant canopies, and can then be emitted into the above-canopy atmosphere. Estimates of the above-canopy fluxes are needed for quantitative earth system studies and assessments of past, present and future air quality and climate. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) is described and used to quantify net terrestrial biosphere emission of isoprene into the atmosphere. MEGAN is designed for both global and regional emission modeling and has global coverage with ~1 km2 spatial resolution. Field and laboratory investigations of the processes controlling isoprene emission are described and data available for model development and evaluation are summarized. The factors controlling isoprene emissions include biological, physical and chemical driving variables. MEGAN driving variables are derived from models and satellite and ground observations. Broadleaf trees, mostly in the tropics, contribute about half of the estimated global annual isoprene emission due to their relatively high emission factors and because they are often exposed to conditions that are conducive for isoprene emission. The remaining flux is primarily from shrubs which are widespread and dominate at higher latitudes. MEGAN estimates global annual isoprene emissions of ~600 Tg isoprene but the results are very sensitive to the driving variables, including temperature, solar radiation, Leaf Area Index, and plant functional type. The annual global emission estimated with MEGAN ranges from about 500 to 750 Tg isoprene depending on the driving variables that are used. Differences in estimated emissions are more than a factor of 3 for specific times and locations. It is difficult to evaluate isoprene emission estimates using the concentration distributions simulated using chemistry and transport models due to the substantial uncertainties in other model components. However, comparison with isoprene emissions estimated from satellite formaldehyde observations indicates reasonable agreement. The sensitivity of isoprene emissions to earth system changes (e.g., climate and landcover) suggests potentially large changes in future emissions. Using temperature distributions simulated by global climate models for year 2100, MEGAN estimates that isoprene emissions increase by more than a factor of two. This is considerably greater than previous estimates and additional observations are needed to evaluate and improve the methods used to predict future isoprene emissions.

scholarly journals Sensitivity and predictive uncertainty of the ACASA model at a spruce forest site

2010 ◽  
Vol 7 (11) ◽  
pp. 3685-3705 ◽  
Author(s):  
K. Staudt ◽  
E. Falge ◽  
R. D. Pyles ◽  
K. T. Paw U ◽  
T. Foken
Keyword(s):  
Latent Heat ◽  
Goodness Of Fit ◽  
Net Ecosystem Exchange ◽  
Heat Fluxes ◽  
Transfer Model ◽  
Forest Site ◽  
Area Index ◽  
Predictive Uncertainty ◽  
Mountainous Regions ◽  
Generalized Likelihood Uncertainty Estimation

Abstract. The sensitivity and predictive uncertainty of the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA) was assessed by employing the Generalized Likelihood Uncertainty Estimation (GLUE) method. ACASA is a stand-scale, multi-layer soil-vegetation-atmosphere transfer model that incorporates a third order closure method to simulate the turbulent exchange of energy and matter within and above the canopy. Fluxes simulated by the model were compared to sensible and latent heat fluxes as well as the net ecosystem exchange measured by an eddy-covariance system above the spruce canopy at the FLUXNET-station Waldstein-Weidenbrunnen in the Fichtelgebirge Mountains in Germany. From each of the intensive observation periods carried out within the EGER project (ExchanGE processes in mountainous Regions) in autumn 2007 and summer 2008, five days of flux measurements were selected. A large number (20000) of model runs using randomly generated parameter sets were performed and goodness of fit measures for all fluxes for each of these runs were calculated. The 10% best model runs for each flux were used for further investigation of the sensitivity of the fluxes to parameter values and to calculate uncertainty bounds. A strong sensitivity of the individual fluxes to a few parameters was observed, such as the leaf area index. However, the sensitivity analysis also revealed the equifinality of many parameters in the ACASA model for the investigated periods. The analysis of two time periods, each representing different meteorological conditions, provided an insight into the seasonal variation of parameter sensitivity. The calculated uncertainty bounds demonstrated that all fluxes were well reproduced by the ACASA model. In general, uncertainty bounds encompass measured values better when these are conditioned on the respective individual flux only and not on all three fluxes concurrently. Structural weaknesses of the ACASA model concerning the soil respiration calculations and the simulation of the latent heat flux during dry conditions were detected, with improvements suggested for each.

scholarly journals Phenology as a strategy for carbon optimality: a global model

2014 ◽  
Vol 11 (3) ◽  
pp. 763-778 ◽  
Author(s):  
S. Caldararu ◽  
D. W. Purves ◽  
P. I. Palmer
Keyword(s):  
Leaf Growth ◽  
Mechanistic Model ◽  
Leaf Age ◽  
Light Availability ◽  
Carbon Assimilation ◽  
Carbon Gain ◽  
Area Index ◽  
Growing Season Length ◽  
Tropical Areas ◽  
Gains And Losses

Abstract. Phenology is essential to our understanding of biogeochemical cycles and the climate system. We develop a global mechanistic model of leaf phenology based on the hypothesis that phenology is a strategy for optimal carbon gain at the canopy level so that trees adjust leaf gains and losses in response to environmental factors such as light, temperature and soil moisture, to achieve maximum carbon assimilation. We fit this model to five years of satellite observations of leaf area index (LAI) using a Bayesian fitting algorithm. We show that our model is able to reproduce phenological patterns for all vegetation types and use it to explore variations in growing season length and the climate factors that limit leaf growth for different biomes. Phenology in wet tropical areas is limited by leaf age physiological constraints while at higher latitude leaf seasonality is limited by low temperature and light availability. Leaf growth in grassland regions is limited by water availability but often in combination with other factors. This model will advance the current understanding of phenology for ecosystem carbon models and our ability to predict future phenological behaviour.

Sign in / Sign up

Export Citation Format

Share Document