scholarly journals Oil palm modelling in the global land-surface model ORCHIDEE-MICT

2020 ◽  
Author(s):  
Yidi Xu ◽  
Philippe Ciais ◽  
Le Yu ◽  
Wei Li ◽  
Xiuzhi Chen ◽  
...  
Keyword(s):  
Oil Palm ◽  
Land Surface ◽  
Carbon Allocation ◽  
Land Surface Model ◽  
Rapid Expansion ◽  
Surface Model ◽  
Ecological Processes ◽  
Area Index ◽  
Global Land ◽  
Improved Model

Abstract. Oil palm is the most productive oil crop that provides ~40 % of the global vegetable oil supply, with 7 % of the cultivated land devoted to oil plants. The rapid expansion of oil palm cultivation is seen as one of the major cause for deforestation emissions and threatens the conservation of rain forest and swamp areas and their associated ecosystem services in tropical areas. Given the importance of oil palm in oil production and its adverse environmental consequences, it is important to understand the physiological and phenological processes of oil palm and its impacts on the carbon, water and energy cycles. In most global vegetation models, oil palm is represented by generic plant functional types (PFT) without specific representation of its morphological, physical and physiological traits. This would cause biases in the subsequent simulations. In this study, we introduced a new specific PFT for oil palm in the global land surface model ORCHIDEE-MICT (v8.4.2). The specific morphology, phenology and harvest process of oil palm were implemented, and the plant carbon allocation scheme was modified to support the growth of branch, leaf and fruit component of each phytomer. A new age-specific parameterization scheme for photosynthesis, autotrophic respiration, and carbon allocation was also developed for the oil palm PFT, based on observed physiology, and was calibrated by observations. The improved model generally reproduces the leaf area index, biomass density and fruit yield during the life cycle at 14 observation sites. Photosynthesis, carbon allocation and biomass components for oil palm also agree well with observations. This explicit representation of oil palm in global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.

scholarly journals Oil palm modelling in the global land surface model ORCHIDEE-MICT

2021 ◽  
Vol 14 (7) ◽  
pp. 4573-4592
Author(s):  
Yidi Xu ◽  
Philippe Ciais ◽  
Le Yu ◽  
Wei Li ◽  
Xiuzhi Chen ◽  
...  
Keyword(s):  
Oil Palm ◽  
Land Surface ◽  
Carbon Allocation ◽  
Land Surface Model ◽  
Rapid Expansion ◽  
Surface Model ◽  
Ecological Processes ◽  
Area Index ◽  
Global Land ◽  
Improved Model

Abstract. Oil palm is the most productive oil crop that provides ∼ 40 % of the global vegetable oil supply, with 7 % of the cultivated land devoted to oil plants. The rapid expansion of oil palm cultivation is seen as one of the major causes for deforestation emissions and threatens the conservation of rain forest and swamp areas and their associated ecosystem services in tropical areas. Given the importance of oil palm in oil production and its adverse environmental consequences, it is important to understand the physiological and phenological processes of oil palm and its impacts on the carbon, water and energy cycles. In most global vegetation models, oil palm is represented by generic plant functional types (PFTs) without specific representation of its morphological, physical and physiological traits. This would cause biases in the subsequent simulations. In this study, we introduced a new specific PFT for oil palm in the global land surface model ORCHIDEE-MICT (v8.4.2, Organising Carbon and Hydrology in Dynamic Ecosystems–aMeliorated Interactions between Carbon and Temperature). The specific morphology, phenology and harvest process of oil palm were implemented, and the plant carbon allocation scheme was modified to support the growth of the branch and fruit component of each phytomer. A new age-specific parameterization scheme for photosynthesis, autotrophic respiration and carbon allocation was also developed for the oil palm PFT, based on observed physiology, and was calibrated by observations. The improved model generally reproduces the leaf area index, biomass density and fruit yield during the life cycle at 14 observation sites. Photosynthesis, carbon allocation and biomass components for oil palm also agree well with observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.

scholarly journals Supplementary material to "Oil palm modelling in the global land-surface model ORCHIDEE-MICT"

2020 ◽  
Author(s):  
Yidi Xu ◽  
Philippe Ciais ◽  
Le Yu ◽  
Wei Li ◽  
Xiuzhi Chen ◽  
...  
Keyword(s):  
Oil Palm ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Global Land ◽  
Supplementary Material

Developing a triple tree-ring constraint for tree growth and physiology in a global land surface model

2021 ◽  
Author(s):  
Jonathan Barichivich ◽  
Philippe Peylin ◽  
Valérie Daux ◽  
Camille Risi ◽  
Jina Jeong ◽  
...  
Keyword(s):  
Tree Ring ◽  
Land Surface ◽  
Forest Ecosystems ◽  
Ring Width ◽  
Land Surface Model ◽  
Surface Model ◽  
Growth Variability ◽  
Tree Ring Data ◽  
Surface Models ◽  
Global Land

<p>Gradual anthropogenic warming and parallel changes in the major global biogeochemical cycles are slowly pushing forest ecosystems into novel growing conditions, with uncertain consequences for ecosystem dynamics and climate. Short-term forest responses (i.e., years to a decade) to global change factors are relatively well understood and skilfully simulated by land surface models (LSMs). However, confidence on model projections weaken towards longer time scales and to the future, mainly because the long-term responses (i.e., decade to century) of these models remain unconstrained. This issue limits confidence on climate model projections. Annually-resolved tree-ring records, extending back to pre-industrial conditions, have the potential to constrain model responses at interannual to centennial time scales. Here, we constrain the representation of tree growth and physiology in the ORCHIDEE global land surface model using the simulated interannual variability of tree-ring width and carbon (Δ<sup>13</sup>C) and oxygen (δ<sup>18</sup>O) stable isotopes in six sites in boreal and temperate Europe.  The model simulates Δ<sup>13</sup>C (r = 0.31-0.80) and δ<sup>18</sup>O (r = 0.36-0.74) variability better than tree-ring width variability (r < 0.55), with an overall skill similar to that of other state-of-the-art models such as MAIDENiso and LPX-Bern. These results show that growth variability is not well represented, and that the parameterization of leaf-level physiological responses to drought stress in the temperate region can be improved with tree-ring data. The representation of carbon storage and remobilization dynamics is critical to improve the realism of simulated growth variability, temporal carrying over and recovery of forest ecosystems after climate extremes. The simulated physiological response to rising CO2 over the 20th century is consistent with tree-ring data in the temperate region, despite an overestimation of seasonal drought stress and stomatal control on photosynthesis. Photosynthesis correlates directly with isotopic variability, but correlations with δ<sup>18</sup>O combine physiological effects and climate variability impacts on source water signatures. The integration of tree-ring data (i.e. the triple constraint: width, Δ<sup>13</sup>C and δ<sup>18</sup>O) and land surface models as demonstrated here should contribute towards reducing current uncertainties in forest carbon and water cycling.</p>

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

scholarly journals Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

2017 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Delphine Jennifer Leroux ◽  
Hélène Dewaele ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Gross Primary Production ◽  
Mediterranean Area ◽  
Surface Model ◽  
Strong Impact ◽  
Control Variables ◽  
Area Index ◽  
Model Control ◽  
Global Land

Abstract. In this study, a global Land Data Assimilation system (LDAS-Monde) is tested over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface Soil Moisture (SM) and Leaf Area Index (LAI) observations to constrain the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. Surface SM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow-dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 cm to 100 cm depth). A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and surface SM have an impact on the different control variables. From the assimilation of surface SM, the LDAS is more effective in modifying soil-moisture from the top layers of soil as model sensitivity to surface SM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 cm to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Assimilation impact shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. The assimilation impact's evaluation is successfully carried out using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observations based estimates of up-scaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement.

scholarly journals The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2014 ◽  
Vol 7 (1) ◽  
pp. 361-386 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

scholarly journals Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products

2006 ◽  
Vol 111 (D18) ◽  
Author(s):  
Anne-Laure Gibelin ◽  
Jean-Christophe Calvet ◽  
Jean-Louis Roujean ◽  
Lionel Jarlan ◽  
Sietse O. Los
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Area Index
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