Evaluation of key parameters controlling phenology-induced variability of surface fluxes in land surface models

2021 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
Alirio Arboleda ◽  
...  
Keyword(s):  
Land Surface ◽  
Gross Primary Production ◽  
Global Scale ◽  
Plant Phenology ◽  
Surface Fluxes ◽  
Terrestrial Vegetation ◽  
Land Surface Models ◽  
Area Index ◽  
Wide Range ◽  
Surface Models

<p>Over the past decades, land surface models have evolved into advanced tools which comprise detailed process descriptions and interactions at a broad range of scales. One of the challenges in these models is the accurate simulation of plant phenology. It is a key element at the nexus of the simulated hydrological and carbon cycle, where the leaf area index (LAI) plays a major role in flux partitioning, water balance and gross primary production.<br>In this study, three well-established models are used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation. ORCHIDEE, ISBA-CC and the LSA-SAF algorithm each have a different approach to represent plant phenology. Whereas ISBA-CC has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and LSA-SAF is driven by remote-sensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at field scale. The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations.<br>These models are tools to extrapolate leaf-level processes to global scale climate predictions. The origin of the parameters controlling phenology-induced variability in these models ranges from plant-scale lab experiments to global-scale calibration. The aim of this study is to investigate the key parameters controlling phenology-induced variability in these models.</p>

Plant phenology evaluation of CRESCENDO land surface models

2020 ◽  
Author(s):  
Deborah Hemming ◽  
Daniele Peano ◽  
Stefano Materia ◽  
Taejin Park ◽  
David Warlind ◽  
...  
Keyword(s):  
Land Surface ◽  
Global Scale ◽  
Climate Impacts ◽  
Vegetation Phenology ◽  
Land Surface Models ◽  
The South ◽  
Area Index ◽  
Annual Variations ◽  
Surface Models ◽  
New Generation

<p>A new generation of land surface models (LSMs) have been developed in the framework of the EU-funded CRESCENDO project aiming to improve understanding of the Earth system as part of the community CMIP6 effort. <br>These new LSMs explicitly represent key processes in the carbon and nitrogen cycles, enabling more realistic vegetation-climate interactions to be simulated. For instance, vegetation phenology, the seasonality of vegetation, is explicitly represented in all these new LSMs. Intra- and inter-annual variations in vegetation phenology can substantially influence land-atmosphere exchanges of energy, moisture and carbon. Changes in phenological events also provide clear indicators of climate impacts on ecosystems. <br>Results are presented on the evaluation of phenological variability from offline runs of this new generation of LSMs. In particular, the timing of growing season onset and offset at global scale, and the Leaf Area Index (LAI) peak timing are investigated using monthly mean outputs. Three satellite-derived LAI datasets are used as benchmark observations for this evaluation.<br>In general, LSMs exhibit high skill in reproducing the observed phenology cycle in the North hemisphere mid- and high-latitudes, while lower skill is obtained in the South hemisphere. All LSMs simulate an offset in the timing of the active vegetative season characterized by later onset and LAI peak. Offset timings are slightly better captured by the LSMs. For these reasons, further development of the representation of phenology is required in LSMs, especially in the South hemisphere, where more complex vegetation and reduced in-situ observations are available.</p>

scholarly journals Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season

2021 ◽  
Vol 18 (7) ◽  
pp. 2405-2428
Author(s):  
Daniele Peano ◽  
Deborah Hemming ◽  
Stefano Materia ◽  
Christine Delire ◽  
Yuanchao Fan ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Comparison ◽  
Environmental Changes ◽  
Growing Season ◽  
Plant Phenology ◽  
Land Surface Models ◽  
Area Index ◽  
Carbon Cycles ◽  
Vegetation Modelling ◽  
Surface Models

Abstract. Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

scholarly journals Plant phenology evaluation of CRESCENDO land surface models – Part I: start and end of growing season

2020 ◽  
Author(s):  
Daniele Peano ◽  
Deborah Hemming ◽  
Stefano Materia ◽  
Christine Delire ◽  
Yuanchao Fan ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Comparison ◽  
Environmental Changes ◽  
Growing Season ◽  
Plant Phenology ◽  
Land Surface Models ◽  
Area Index ◽  
Vegetation Modelling ◽  
Surface Models ◽  
Broad Leaf

Abstract. Plant phenology plays a fundamental role in land-atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of beginning and end of the growing season, simulated by seven state-of-the-art European land surface models, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently-developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. Difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broad-leaf deciduous trees, while high variability is noted in regions dominated by broad-leaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

scholarly journals Ecosystem photosynthesis in land-surface models: a first-principles approach

2021 ◽  
Author(s):  
Giulia Mengoli ◽  
Anna Agusti-Panareda ◽  
Souhail Boussetta ◽  
Sandy Patricia Harrison ◽  
Carlo Trotta ◽  
...  
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Land Surface ◽  
Gross Primary Production ◽  
Vapour Pressure Deficit ◽  
Growth Conditions ◽  
Land Surface Models ◽  
Diurnal Cycles ◽  
Surface Models ◽  
Leaf Level

Vegetation regulates land-atmosphere water and energy exchanges and is an essential component of land-surface models (LSMs). However, LSMs have been handicapped by assumptions that equate acclimated photosynthetic responses to environment with fast responses observable in the laboratory. These time scales can be distinguished by including specific representations of acclimation, but at the cost of further increasing parameter requirements. Here we develop an alternative approach based on optimality principles that predict the acclimation of carboxylation and electron-transport capacities, and a variable controlling the response of leaf-level carbon dioxide drawdown to vapour pressure deficit (VPD), to variations in growth conditions on a weekly to monthly time scale. In the 'P model', an optimality-based light-use efficiency model for gross primary production (GPP) on this time scale, these acclimated responses are implicit. Here they are made explicit, allowing fast and slow response time-scales to be separated and GPP to be simulated at sub-daily timesteps. The resulting model mimics diurnal cycles of GPP recorded by eddy-covariance flux towers in a temperate grassland and boreal, temperate and tropical forests, with no parameter changes between biomes. Best performance is achieved when biochemical capacities are adjusted to match recent midday conditions. This model suggests a simple and parameter-sparse method to include both instantaneous and acclimated responses within an LSM framework, with many potential applications in weather, climate and carbon-cycle modelling.

scholarly journals Evaluation of leaf-level optical properties employed in land surface models – example with CLM 5.0

2019 ◽  
Author(s):  
Titta Majasalmi ◽  
Ryan M. Bright
Keyword(s):  
Optical Properties ◽  
Optical Property ◽  
Land Surface ◽  
Surface Fluxes ◽  
Estimation Methods ◽  
Leaf Angle ◽  
Land Surface Models ◽  
Community Land Model ◽  
Canopy Volume ◽  
Surface Models

Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although Plant Functional Type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the time-invariant optical properties table of the Simple Biosphere (SiB) model (later referred as SiB-table) presented 30-years ago by Dorman and Sellers (1989) which has since become adopted by many LSMs. This revisit was needed as much of the data underlying the SiB-table was not formally reviewed or published or was based on older papers or personal communications (i.e. the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g. Community Land Model (CLM), Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB-table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e. tropical, boreal, temperate) and phenology (i.e. deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops and grasses and by photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to be appropriate. However, in the near-infrared and shortwave infrared bands (NIR+SWIR; e.g. 701–2500 nm, referred as NIR) notable differences between CLM default and measured estimates were observed, thus suggesting that NIR optical properties need updating in the model. For example, for conifer PFTs, the measured mean needle albedo estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat-leaves, the measured mean leaf albedo values in NIR were 20 %, 14 % and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle definitions were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and introduce the concept and application of photon recollision probability (p). The p may be used to upscale needle spectra into shoot spectra to meet the common assumption that foliage is located randomly within the canopy volume (behind canopy radiative transfer calculation) to account for multiple scattering effects caused by needles clustered into shoots.

Phenology-induced energy and carbon fluxes in land surface models

2020 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Fabienne Maignan ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Terrestrial Ecosystems ◽  
Carbon Fluxes ◽  
Land Surface Models ◽  
Energy Fluxes ◽  
Wide Range ◽  
Surface Models ◽  
Modelling Strategies ◽  
Phenological Cycle

<p>The annual phenological cycle is of key importance for the carbon and energy fluxes in terrestrial ecosystems. Although the processes controlling budburst and leaf senescence are fairly well known, the connection between plant phenology and the carbon fluxes remains a challenging aspect in land surface modelling (LSM). In this study, the modelling strategies of three well stablished LSM are compared. The LSM considered in this study were: ORCHIDEE, ISBA-A-gs and the model driving the LSA-SAF evapotranspiration product (https://landsaf.ipma.pt). The latter model does not simulate the carbon fluxes but focuses on the computation of evapotranspiration and energy fluxes.<br>The phenological cycle is simulated explicitly in the ORCHIDEE model, using empirical relations based on temperature sum, water availability, and other variables. In the ISBA-A-gs model, phenology and LAI development is fully photosynthesis-driven. The phenology in the LSA-SAF model is driven by remote sensing forcing variables, such as LAI observations. Alternatively, the assimilation of remote sensing LAI products is a convenient method to improve the simulated phenological cycle in land surface models. A dedicated module for this operation is available in ISBA-A-gs.<br>Simulations were performed over a wide range of climatological conditions and plant functional types. The results were then validated with in-situ measurements conducted at Fluxnet stations. In addition to the comparison between measured and modelled carbon fluxes, the validation in this study included the intra-annual variation in the simulated phenological cycle.</p>

Ecosystem sensitivity to land-surface models and leaf area index

1996 ◽  
Vol 13 (1-4) ◽  
pp. 89-98 ◽  
Author(s):  
W.J. Parton ◽  
A. Haxeltine ◽  
P. Thornton ◽  
R. Anne ◽  
Melannie Hartman
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Models ◽  
Area Index ◽  
Ecosystem Sensitivity ◽  
Surface Models

scholarly journals Climate Impacts of Making Evapotranspiration in the Community Land Model (CLM3) Consistent with the Simple Biosphere Model (SiB)

2009 ◽  
Vol 10 (2) ◽  
pp. 374-394 ◽  
Author(s):  
Peter J. Lawrence ◽  
Thomas N. Chase
Keyword(s):  
Climate Sensitivity ◽  
Land Surface ◽  
Climate Modeling ◽  
Land Surface Models ◽  
Surface Hydrology ◽  
Community Land Model ◽  
Wide Range ◽  
Surface Models ◽  
Biosphere Model ◽  
Surface Parameterizations

Abstract In recent climate sensitivity experiments with the Community Climate System Model, version 3 (CCSM3), a wide range of studies have found that the Community Land Model, version 3 (CLM3), simulates mean global evapotranspiration with low contributions from transpiration (15%), and high contributions from soil and canopy evaporation (47% and 38%, respectively). This evapotranspiration partitioning is inconsistent with the consensus of other land surface models used in GCMs. To understand the high soil and canopy evaporation and the low transpiration observed in the CLM3, select individual components of the land surface parameterizations that control transpiration, canopy and soil evaporation, and soil hydrology are compared against the equivalent parameterizations used in the Simple Biosphere Model, versions 2 and 3 (SiB2 and SiB3), and against more recent developments with CLM. The findings of these investigations are used to develop new parameterizations for CLM3 that would reproduce the functional dynamics of land surface processes found in SiB and other alternative land surface parameterizations. Global climate sensitivity experiments are performed with the new land surface parameterizations to assess how the new SiB, consistent CLM land surface parameterizations, influence the surface energy balance, hydrology, and atmospheric fluxes in CLM3, and through that the larger-scale climate modeled in CCSM3. It is found that the new parameterizations enable CLM to simulate evapotranspiration partitioning consistently with the multimodel average of other land surface models used in GCMs, as evaluated by Dirmeyer et al. (2005). The changes in surface fluxes also resulted in a number of improvements in the simulation of precipitation and near-surface air temperature in CCSM3. The new model is fully coupled in the CCSM3 framework, allowing a wide range of climate modeling investigations without the surface hydrology issues found in the current CLM3 model. This provides a substantially more robust framework for performing climate modeling experiments investigating the influence of land cover change and surface hydrology in CLM and CCSM than the existing CLM3 parameterizations. The study also shows that changes in land surface hydrology have global scale impacts on model climatology.

Closing the water budget at the global scale using observations, remote sensing, and reanalyses

2021 ◽  
Author(s):  
Fanny Lehmann ◽  
Brahma Dutt Vishwakarma ◽  
Jonathan Bamber
Keyword(s):  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Global Scale ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Cold Regions ◽  
Climate Zones ◽  
Surface Models ◽  
Terrestrial Water

<p>Despite the accuracy of GRACE terrestrial water storage estimates and the variety of global hydrological datasets providing precipitations, evapotranspiration, and runoff data, it remains challenging to find datasets satisfying the water budget equation at the global scale.</p><p>We select commonly used and widely-assessed datasets. We use several precipitations (CPC, CRU, GPCC, GPCP, GPM, MSWEP, TRMM, ERA5 Land, MERRA2), evapotranspiration (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GLEAM, MOD16, SSEBop, ERA5 Land, MERRA2), and runoff (land surface models CLSM, Noah, VIC from GLDAS 2.0, 2.1, and 2.2; GRUN, ERA5 Land, MERRA2) datasets to assess the water storage change over more than 150 hydrological basins. Both mascons and spherical harmonics coefficients are used as the reference terrestrial water storage from different centres processing GRACE data. The analysis covers a wide range of climate zones over the globe and is conducted over 2003-2014.</p><p>The water budget closure is evaluated with Root Mean Square Deviation (RMSD), Nash-Sutcliffe Efficiency (NSE), and seasonal decomposition. Each dataset is assessed individually across all basins and dataset combinations are also ranked according to their performances. We obtain a total of 1080 combinations, among which several are suitable to close the water budget. Although none of the combinations performs consistently well over all basins, GPCP precipitations provide generally good results, together with GPCC and GPM. A better water budget closure is generally obtained when using evapotranspiration from Catchment Land Surface Models (GLDAS CLSM), while reanalyses ERA5 Land and MERRA2 are especially suitable in cold regions. Concerning runoff, the machine learning GRUN dataset performs remarkably well across climate zones, followed by ERA5 Land and MERRA2 in cold regions. We also highlight highly unrealistic values in evapotranspiration computed with version 2.2 of GLDAS (using data assimilation from GRACE) in most of the cold basins. Our results are robust as changing the GRACE product from one centre to the other does not affect our conclusions.</p>

scholarly journals On the Nature of Soil Moisture in Land Surface Models

2009 ◽  
Vol 22 (16) ◽  
pp. 4322-4335 ◽  
Author(s):  
Randal D. Koster ◽  
Zhichang Guo ◽  
Rongqian Yang ◽  
Paul A. Dirmeyer ◽  
Kenneth Mitchell ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Land Surface Models ◽  
Common Information ◽  
Surface Models ◽  
Moisture State ◽  
Environmental Prediction

Abstract The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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