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

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.

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Inferring Amazon leaf demography from satellite observations of leaf area index

Biogeosciences Discussions ◽

10.5194/bgd-8-10389-2011 ◽

2011 ◽

Vol 8 (5) ◽

pp. 10389-10421 ◽

Cited By ~ 2

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.

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Seasonal leaf dynamics for tropical evergreen forests in a process-based global ecosystem model

Geoscientific Model Development ◽

10.5194/gmd-5-1091-2012 ◽

2012 ◽

Vol 5 (5) ◽

pp. 1091-1108 ◽

Cited By ~ 42

Author(s):

M. De Weirdt ◽

H. Verbeeck ◽

F. Maignan ◽

P. Peylin ◽

B. Poulter ◽

...

Keyword(s):

Seasonal Variation ◽

French Guiana ◽

Photosynthetic Capacity ◽

Seasonal Pattern ◽

Leaf Age ◽

Ecosystem Model ◽

Seasonal Patterns ◽

Evergreen Forests ◽

Leaf Litterfall ◽

The Impact

Abstract. The influence of seasonal phenology on canopy photosynthesis in tropical evergreen forests remains poorly understood, and its representation in global ecosystem models is highly simplified, typically with no seasonal variation of canopy leaf properties taken into account. Including seasonal variation in leaf age and photosynthetic capacity could improve the correspondence of global vegetation model outputs with the wet–dry season CO2 patterns measured at flux tower sites in these forests. We introduced a leaf litterfall dynamics scheme in the global terrestrial ecosystem model ORCHIDEE based on seasonal variations in net primary production (NPP), resulting in higher leaf turnover in periods of high productivity. The modifications in the leaf litterfall scheme induce seasonal variation in leaf age distribution and photosynthetic capacity. We evaluated the results of the modification against seasonal patterns of three long-term in-situ leaf litterfall datasets of evergreen tropical forests in Panama, French Guiana and Brazil. In addition, we evaluated the impact of the model improvements on simulated latent heat (LE) and gross primary productivity (GPP) fluxes for the flux tower sites Guyaflux (French Guiana) and Tapajós (km 67, Brazil). The results show that the introduced seasonal leaf litterfall corresponds well with field inventory leaf litter data and times with its seasonality. Although the simulated litterfall improved substantially by the model modifications, the impact on the modelled fluxes remained limited. The seasonal pattern of GPP improved clearly for the Guyaflux site, but no significant improvement was obtained for the Tapajós site. The seasonal pattern of the modelled latent heat fluxes was hardly changed and remained consistent with the observed fluxes. We conclude that we introduced a realistic and generic litterfall dynamics scheme, but that other processes need to be improved in the model to achieve better simulations of GPP seasonal patterns for tropical evergreen forests.

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

10.5194/bg-2018-26-supplement ◽

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

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Inferring Amazon leaf demography from satellite observations of leaf area index

Biogeosciences ◽

10.5194/bg-9-1389-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 1389-1404 ◽

Cited By ~ 33

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.

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New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Nature Communications ◽

10.1038/s41467-021-20994-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Hirofumi Hashimoto ◽

Weile Wang ◽

Jennifer L. Dungan ◽

Shuang Li ◽

Andrew R. Michaelis ◽

...

Keyword(s):

Cloud Cover ◽

Seasonal Patterns ◽

Optical Remote Sensing ◽

Amazon Forest ◽

Evergreen Forests ◽

Geostationary Satellites ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

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