scholarly journals Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM

2019 ◽  
Author(s):  
Julia E. M. S. Nabel ◽  
Kim Naudts ◽  
Julia Pongratz
Keyword(s):  
Forest Management ◽  
Land Surface ◽  
Gross Primary Production ◽  
Surface Model ◽  
Forest Age ◽  
Age Classes ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
Global Vegetation ◽  
Age Structures

Abstract. Natural and anthropogenic disturbances, in particular forest management, affect forest age-structures all around the globe. Forest age-structures in turn influence biophysical and biogeochemical interactions of the vegetation with the atmosphere. Yet, many dynamic global vegetation models (DGVMs), including those used as land surface models (LSMs) in Earth system models (ESMs), do not account for subgrid forest age structures, despite being used to investigate land-use effects on the global carbon budget or simulating land–atmosphere interactions. In this paper we present a new scheme to introduce forest age-classes in hierarchical tile-based DGVMs combining benefits of recently applied approaches. Our scheme combines a computationally efficient age-dependent simulation of all relevant processes, such as photosynthesis and respiration, without loosing the information about the exact forest age, which is a prerequisite for the implementation of age-based forest management. This combination is achieved by using the hierarchy to track the area fraction for each age on an aggregated plant functional type level, whilst simulating the relevant processes for a set of age-classes. We describe how we implemented this scheme in JSBACH4, the LSM of the ICON-ESM. Subsequently, we compare simulation output against global observation-based products for gross primary production, leaf area index and above-ground biomass to assess the ability of simulations with and without age-classes to reproduce the annual cycle and large-scale spatial patterns of these variables. The comparisons show differences exponentially decreasing with the number of distinguished age-classes and linearly increasing computation costs. The results demonstrate the benefit of the introduction of age-classes, with the optimal number of age-classes being a compromise between computation costs and accuracy.

scholarly journals Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM

2020 ◽  
Vol 13 (1) ◽  
pp. 185-200 ◽  
Author(s):  
Julia E. M. S. Nabel ◽  
Kim Naudts ◽  
Julia Pongratz
Keyword(s):  
Forest Management ◽  
Land Surface ◽  
Surface Model ◽  
Earth System ◽  
Forest Age ◽  
Age Classes ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
Global Vegetation ◽  
Age Structures

Abstract. Natural and anthropogenic disturbances, in particular forest management, affect forest age structures all around the globe. Forest age structures in turn influence key land surface processes, such as photosynthesis and thus the carbon cycle. Yet, many dynamic global vegetation models (DGVMs), including those used as land surface models (LSMs) in Earth system models (ESMs), do not account for subgrid forest age structures, despite being used to investigate land-use effects on the global carbon budget or simulating biogeochemical responses to climate change. In this paper we present a new scheme to introduce forest age classes in hierarchical tile-based DGVMs combining benefits of recently applied approaches the first being a computationally efficient age-dependent simulation of all relevant processes, such as photosynthesis and respiration, using a restricted number of age classes and the second being the tracking of the exact forest age, which is a prerequisite for any implementation of age-based forest management. This combination is achieved by using the tile hierarchy to track the area fraction for each age on an aggregated plant functional type level, whilst simulating the relevant processes for a set of age classes. We describe how we implemented this scheme in JSBACH4, the LSM of the ICOsahedral Non-hydrostatic Earth system model (ICON-ESM). Subsequently, we compare simulation output to global observation-based products for gross primary production, leaf area index, and above-ground biomass to assess the ability of simulations with and without age classes to reproduce the annual cycle and large-scale spatial patterns of these variables. The comparisons show decreasing differences and increasing computation costs with an increasing number of distinguished age classes. The results demonstrate the benefit of the introduction of age classes, with the optimal number of age classes being a compromise between computation costs and error reduction.

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 A process-based <i>Sphagnum</i> plant-functional-type model for implementation in the TRIFFID Dynamic Global Vegetation Model

2019 ◽  
Author(s):  
Richard Coppell ◽  
Emanuel Gloor ◽  
Joseph Holden
Keyword(s):  
Water Table ◽  
Primary Productivity ◽  
Photosynthetically Active Radiation ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Type Model ◽  
Surface Model ◽  
Dynamic Global Vegetation Model ◽  
Active Radiation ◽  
Global Vegetation

Abstract. Peatlands are important carbon stores and Sphagnum moss represents a critical peatland genus contributing to carbon exchange and storage. However, gas fluxes in Sphagnum-dominated systems are poorly represented in Dynamic Global Vegetation Models (DGVMs) which simulate, via incorporation of Plant Functional Types (PFTs), biogeochemical and energy fluxes between vegetation, the land surface and the atmosphere. Mechanisms characterised by PFTs within DGVMs include photosynthesis, respiration and competition and, in more recent DGVMs, sub-daily gas-exchange processes regulated by leaf 10 stomata. However, Sphagnum, like all mosses, are non-vascular plants and do not exhibit stomatal regulation. In order to achieve a level of process detail consistent with existing vascular vegetation PFTs within DGVMs, this paper describes a new process-based non-vascular-PFT model that is implemented within the TRIFFID DGVM used by the JULES land surface model. The new PFT model was tested against extant published field and laboratory studies of peat assemblage-net primary productivity, assemblage-gross primary productivity, assemblage respiration, water-table position, incoming 15 photosynthetically active radiation, temperature, and canopy dark respiration. The PFT model’s parameters were roughly tuned and the PFT model easily produced curves of the correct shape for peat assemblage-net primary productivity against water-table position, incoming photosynthetically active radiation and temperature, suggesting that it replicates the internal productivity mechanism of Sphagnum for the first time. Minor modifications should also allow it to be used across a range of other bryophytes enabling this non-vascular PFT model to have enhanced functionality.

scholarly journals Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

2019 ◽  
Vol 11 (6) ◽  
pp. 735 ◽  
Author(s):  
Moustapha Tall ◽  
Clément Albergel ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Françoise Guichard ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Burkina Faso ◽  
Land Surface ◽  
Gross Primary Production ◽  
Added Value ◽  
Surface Model ◽  
Strong Impact ◽  
Area Index ◽  
Soil Layers ◽  
Global Land

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

scholarly journals Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental US

2021 ◽  
Author(s):  
Anthony Mucia ◽  
Bertrand Bonan ◽  
Clément Albergel ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Land Surface ◽  
Positive Impact ◽  
Gross Primary Production ◽  
Soil Surface ◽  
Passive Microwave ◽  
Surface Model ◽  
Full Potential ◽  
Area Index

Abstract. The land data assimilation system, LDAS-Monde, developed by the Research Department of the French Meteorological service (Centre National de Recherches Météorologiques – CNRM) is capable of well representing Land Surface Variables (LSVs) from regional to global scales. It jointly assimilates satellite-derived observations of leaf area index (LAI) and surface soil moisture (SSM) into the Interactions between Soil Biosphere and Atmosphere (ISBA) land surface model (LSM), increasing the accuracy of the model simulations and forecasts of the LSVs. The assimilation of vegetation variables directly impacts RZSM through seven control variables consisting in soil moisture of seven soil layers from the soil surface to 1 m depth. This capability is particularly useful in dry conditions, where SSM and RZSM are decoupled to a large extent. However, this positive impact does not reach its full potential due to the low temporal availability of optical-based LAI observations, at best, every ten days, and can suffer from months of no data over regions and seasons with heavy cloud cover such as winter or monsoon conditions. In that context, this study investigates the assimilation of low frequency passive microwave vegetation optical depth (VOD), available in almost all weather conditions, as a proxy of LAI. The Vegetation Optical Depth Climate Archive (VODCA) dataset provides near-daily observations of vegetation conditions, far more frequently than optical based product such as LAI. This study's goal is to convert the more frequent X-band VOD observations into proxy-LAI observations through linear re-scaling and to assimilate them in place of direct LAI observations. Seven assimilation experiments were run from 2003 to 2018 over the contiguous United States (CONUS), with 1) no assimilation, the assimilation of 2) SSM, 3) LAI, 4) re-scaled VODX, 5) re-scaled VODX only when LAI observations available, 6) LAI + SSM, and 7) re-scaled VODX + SSM. This study analyzes these assimilation experiments by comparing to satellite derived observations and in situ measurements and is focused on the variables of LAI, SSM, gross primary production (GPP), and evapotranspiration (ET). Each experiment is driven by atmospheric forcing reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5. Results showed improved representation of GPP and ET by assimilating re-scaled VOD in place of LAI. Additionally, the joint assimilation of vegetation related variables (i.e. LAI or re-scaled VOD) and SSM demonstrates a small improvement in the representation of soil moisture over the assimilation of any dataset by itself.

scholarly journals Regional-Scale Data Assimilation with The Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM) Over Siberia

2020 ◽  
Author(s):  
Hazuki Arakida ◽  
Shunji Kotsuki ◽  
Shigenori Otsuka ◽  
Yohei Sawada ◽  
Takemasa Miyoshi
Keyword(s):  
Spatial Distribution ◽  
Data Assimilation ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Spatially Explicit ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
Global Vegetation

Abstract This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of dormancy and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated tree LAI, gross primary production (GPP), and above ground biomass. Most remarkably, the spatial distribution of tree LAI was estimated separately from undergrowth LAI because the SEIB-DGVM simulated the vertical structure of forest explicitly, and because satellite-observed LAI provided information on the onset and the end of the leaf season of tree and undergrowth, respectively. The DA system also provided the spatial distribution of the model parameters for tree separately from those of undergrowth. DA experiments started dormancy of trees more than a month earlier than the default phenology model and resulted in a decrease of the GPP.

scholarly journals Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO<sub>2</sub> and H<sub>2</sub>O fluxes at five European maize (<i>Zea mays</i> L.) sites

2011 ◽  
Vol 8 (6) ◽  
pp. 1721-1736 ◽  
Author(s):  
L. Li ◽  
N. Vuichard ◽  
N. Viovy ◽  
P. Ciais ◽  
T. Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Management Practices ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Terrestrial Ecosystem ◽  
Surface Model ◽  
Model Errors ◽  
Area Index ◽  
European Maize

Abstract. This paper is a modelling study of crop management impacts on carbon and water fluxes at a range of European sites. The model is a crop growth model (STICS) coupled with a process-based land surface model (ORCHIDEE). The data are online eddy-covariance observations of CO2 and H2O fluxes at five European maize cultivation sites. The results show that the ORCHIDEE-STICS model explains up to 75 % of the observed daily net CO2 ecosystem exchange (NEE) variance, and up to 79 % of the latent heat flux (LE) variance at five sites. The model is better able to reproduce gross primary production (GPP) variations than terrestrial ecosystem respiration (TER) variations. We conclude that structural deficiencies in the model parameterizations of leaf area index (LAI) and TER are the main sources of error in simulating CO2 and H2O fluxes. A number of sensitivity tests, with variable crop variety, nitrogen fertilization, irrigation, and planting date, indicate that any of these management factors is able to change NEE by more than 15 %, but that the response of NEE to management parameters is highly site-dependent. Changes in management parameters are found to impact not only the daily values of NEE and LE, but also the cumulative yearly values. In addition, LE is shown to be less sensitive to management parameters than NEE. Multi-site model evaluations, coupled with sensitivity analysis to management parameters, thus provide important information about model errors, which helps to improve the simulation of CO2 and H2O fluxes across European croplands.

scholarly journals A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes

2015 ◽  
Vol 8 (7) ◽  
pp. 2035-2065 ◽  
Author(s):  
K. Naudts ◽  
J. Ryder ◽  
M. J. McGrath ◽  
J. Otto ◽  
Y. Chen ◽  
...  
Keyword(s):  
Forest Management ◽  
Land Surface ◽  
Large Scale ◽  
Gross Primary Production ◽  
Canopy Structure ◽  
Basal Area ◽  
Tree Height ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Validation Data

Abstract. Since 70 % of global forests are managed and forests impact the global carbon cycle and the energy exchange with the overlying atmosphere, forest management has the potential to mitigate climate change. Yet, none of the land-surface models used in Earth system models, and therefore none of today's predictions of future climate, accounts for the interactions between climate and forest management. We addressed this gap in modelling capability by developing and parametrising a version of the ORCHIDEE land-surface model to simulate the biogeochemical and biophysical effects of forest management. The most significant changes between the new branch called ORCHIDEE-CAN (SVN r2290) and the trunk version of ORCHIDEE (SVN r2243) are the allometric-based allocation of carbon to leaf, root, wood, fruit and reserve pools; the transmittance, absorbance and reflectance of radiation within the canopy; and the vertical discretisation of the energy budget calculations. In addition, conceptual changes were introduced towards a better process representation for the interaction of radiation with snow, the hydraulic architecture of plants, the representation of forest management and a numerical solution for the photosynthesis formalism of Farquhar, von Caemmerer and Berry. For consistency reasons, these changes were extensively linked throughout the code. Parametrisation was revisited after introducing 12 new parameter sets that represent specific tree species or genera rather than a group of often distantly related or even unrelated species, as is the case in widely used plant functional types. Performance of the new model was compared against the trunk and validated against independent spatially explicit data for basal area, tree height, canopy structure, gross primary production (GPP), albedo and evapotranspiration over Europe. For all tested variables, ORCHIDEE-CAN outperformed the trunk regarding its ability to reproduce large-scale spatial patterns as well as their inter-annual variability over Europe. Depending on the data stream, ORCHIDEE-CAN had a 67 to 92 % chance to reproduce the spatial and temporal variability of the validation data.

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