Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Jatin Kala Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Mark Decker Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Jean-François Exbrayat Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Andy J. Pitman Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Claire Carouge Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Jason P. Evans Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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Gab Abramowitz Australian Research Council Centre of Excellence for Climate Systems Science, and Climate Change Research Centre, University of New South Wales, Sydney, New South Wales, Australia

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David Mocko SAIC at NASA Goddard Space Flight Centre, Greenbelt, Maryland

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Abstract

Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980–2008) are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from −90% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production.

Corresponding author address: Jatin Kala, Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre, Gate 11 Botany St., Level 4, Mathews Building, University of New South Wales, Sydney, NSW 2052 Australia. E-mail: j.kala@unsw.edu.au; jatin.kala.jk@gmail.com

Abstract

Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980–2008) are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from −90% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production.

Corresponding author address: Jatin Kala, Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre, Gate 11 Botany St., Level 4, Mathews Building, University of New South Wales, Sydney, NSW 2052 Australia. E-mail: j.kala@unsw.edu.au; jatin.kala.jk@gmail.com
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