Review of Global Evaluation of Gross Primary Productivity in the JULES Land Surface Model by D. Slevin et al.

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Land Surface Model ◽  
Gross Primary Productivity ◽  
Surface Model ◽  
Global Evaluation

scholarly journals Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

2017 ◽  
Vol 10 (7) ◽  
pp. 2651-2670 ◽  
Author(s):  
Darren Slevin ◽  
Simon F. B. Tett ◽  
Jean-François Exbrayat ◽  
A. Anthony Bloom ◽  
Mathew Williams
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Land Surface ◽  
Land Surface Model ◽  
Gross Primary Productivity ◽  
Surface Model ◽  
Global Evaluation ◽  
Annual Average ◽  
Model Simulations ◽  
The Tropics

Abstract. 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).

scholarly journals Global Evaluation of Gross Primary Productivity in the JULES Land Surface Model

2016 ◽  
Author(s):  
Darren Slevin ◽  
Simon F. B. Tett ◽  
Jean-François Exbrayat ◽  
A. Anthony Bloom ◽  
Mathew Williams
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Land Surface ◽  
Land Surface Model ◽  
Gross Primary Productivity ◽  
Surface Model ◽  
Global Evaluation ◽  
Annual Average ◽  
Model Simulations ◽  
The Tropics

Abstract. This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at 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, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that 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 %. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. 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 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).

Global evaluation of the nutrient enabled version of land surface model ORCHIDEE-CNP (v1.2)

2020 ◽  
Author(s):  
Yan Sun ◽  
Daniel S Goll ◽  
Jinfeng Chang ◽  
Philippe Ciais ◽  
Betrand Guenet ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Comprehensive Evaluation ◽  
Large Fraction ◽  
Land Surface Model ◽  
P Uptake ◽  
Surface Model ◽  
Global Evaluation ◽  
Total N ◽  
P Fluxes

<p>Future land carbon (C) uptake under climate changes and rising atmospheric CO<sub>2</sub> is influenced by nitrogen (N) and phosphorus (P) constraints. A few existing land surface models (LSMs) account for both N and P dynamics, but lack comprehensive evaluation. This will lead to large uncertainty in estimating the P effect on terrestrial C cycles. With the increasing number of measurements and data-driven products for N- and P- related variables, comprehensive model evaluations on large scale is becoming feasible.</p><p>In this study, we evaluated the performance of ORCHIDEE-CNP (v1.2) which explicitly simulates N and P cycles in plant and soil, in four aspects: 1) terrestrial C fluxes, 2) N and P fluxes and budget, 3) leaf and soil stoichiometry and 4) resource use efficiencies. We found that ORCHIDEE-CNP improves the simulation of the magnitude of gross primary productivity (GPP) due to more realistic strength of the CO<sub>2</sub> fertilization effect of GPP than the without-nutrient-version ORCHIDEE. However, ORCHIDEE-CNP cannot capture the positive and increasing C sink in North Hemisphere over past decades, which is mainly due to that a large fraction of N and P ‘locked’ in soil organic matter cannot be re-allocated into vegetation and leads to a strong N and P limitation on plant growth. ORCHIDEE-CNP generally simulates comparable global total N and P fluxes (e.g. N biofixation, P weathering, N and P uptake etc.) for both natural and agricultural biomes. Overall, ORCHIDEE-CNP doesn’t performance worse in C fluxes than ORCHIDEE, and gives reasonable N and P cycles, which is acceptable in simulating the coupling relationships between C, N and P cycles can be used to explore the nutrient limitations on land C sink from present to the future. </p>

Supplementary material to "Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)"

2020 ◽  
Author(s):  
Yan Sun ◽  
Daniel S. Goll ◽  
Jinfeng Chang ◽  
Philippe Ciais ◽  
Betrand Guenet ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Global Evaluation ◽  
Supplementary Material

scholarly journals Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)

2021 ◽  
Vol 14 (4) ◽  
pp. 1987-2010
Author(s):  
Yan Sun ◽  
Daniel S. Goll ◽  
Jinfeng Chang ◽  
Philippe Ciais ◽  
Betrand Guenet ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Production Efficiency ◽  
Carbon Sink ◽  
A Priori ◽  
Land Surface Model ◽  
Terrestrial Ecosystems ◽  
Surface Model ◽  
Global Evaluation ◽  
Biomass Turnover

Abstract. The availability of phosphorus (P) and nitrogen (N) constrains the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions in both space and time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2), which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the Northern Hemisphere (NH) during recent decades despite an a priori realistic gross primary productivity (GPP) response to rising CO2. This result suggests either that processes other than CO2 fertilization, which are omitted in ORCHIDEE-CNP such as changes in biomass turnover, are predominant drivers of the northern land sink and/or that the model parameterizations produce emerging nutrient limitations on biomass growth that are too strict in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry as well as plant P use efficiency, pointing towards P limitations that are too severe towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

scholarly journals Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)

2020 ◽  
Author(s):  
Yan Sun ◽  
Daniel S. Goll ◽  
Jinfeng Chang ◽  
Philippe Ciais ◽  
Betrand Guenet ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Production Efficiency ◽  
Carbon Sink ◽  
A Priori ◽  
Land Surface Model ◽  
Terrestrial Ecosystems ◽  
Surface Model ◽  
Global Evaluation ◽  
The North

Abstract. The availability of phosphorus (P) and nitrogen (N) constrain the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions both in space and in time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2) which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote-sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with data-driven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the North Hemisphere (NH) during the recent decades, despite an a priori realistic GPP response to rising CO2. This result suggests either that other processes than CO2 fertilization which are omitted in ORCHIDEE-CNP, such as changes in biomass turnover, are predominant drivers of the northern land sink, and/or that the model parameterizations produce too strict emerging nutrient limitations on biomass growth in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry and plant P use efficiency pointing towards a too severe P limitations towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption.

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