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>

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>

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

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.

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 Evaluation of Land Surface Models in Reproducing Satellite Derived Leaf Area Index over the High-Latitude Northern Hemisphere. Part II: Earth System Models

2013 ◽  
Vol 5 (8) ◽  
pp. 3637-3661 ◽  
Author(s):  
Alessandro Anav ◽  
Guillermo Murray-Tortarolo ◽  
Pierre Friedlingstein ◽  
Stephen Sitch ◽  
Shilong Piao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Northern Hemisphere ◽  
High Latitude ◽  
Land Surface ◽  
Earth System ◽  
Land Surface Models ◽  
Area Index ◽  
Surface Models ◽  
Earth System Models

scholarly journals Improving evapotranspiration in land surface models by using biophysical parameters derived from MSG/SEVIRI satellite

2011 ◽  
Vol 8 (5) ◽  
pp. 9113-9171 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Land Surface Models ◽  
Area Index ◽  
Continental Scale ◽  
Surface Models ◽  
Vegetation Parameters ◽  
The Impact

Abstract. Vegetation parameters derived from the geostationary satellite MSG/SEVIRI have been distributed at a daily frequency since 2007 over Europe, Africa and part of South America, through the LSA-SAF facility. We propose here a method to handle two new remote sensing products from LSA-SAF, leaf area index and Fractional Vegetation Cover, noted LAI and FVC respectively, for land surface models at MSG/SEVIRI scale. The developed method relies on an ordinary least-square technique and a land cover map to estimate LAI for each model plant functional types of the model spatial unit. The method is conceived to be applicable for near-real time applications at continental scale. Compared to monthly vegetation parameters from a vegetation database commonly used in numerical weather predictions (ECOCLIMAP-I), the new remote sensing products allows a better monitoring of the spatial and temporal variability of the vegetation, including inter-annual signals, and a decreased uncertainty on LAI to be input into land surface models. We assess the impact of using LSA-SAF vegetation parameters compared to ECOCLIMAP-I in the land surface model H-TESSEL at MSG/SEVIRI scale. Comparison with in-situ observations in Europe and Africa shows that the results on evapotranspiration are mostly improved, and especially in semi-arid climates. At last, the use of LSA-SAF and ECOCLIMAP-I is compared with simulations over a North-South Transect in Western Africa using LSA-SAF radiation forcing derived from remote sensing, and differences are highlighted.

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

2014 ◽  
Vol 15 (1) ◽  
pp. 489-503 ◽  
Author(s):  
Jatin Kala ◽  
Mark Decker ◽  
Jean-François Exbrayat ◽  
Andy J. Pitman ◽  
Claire Carouge ◽  
...  
Keyword(s):  
Primary Production ◽  
Leaf Area Index ◽  
Interannual Variability ◽  
Leaf Area ◽  
Land Surface ◽  
Carbon Fluxes ◽  
Land Surface Models ◽  
Area Index ◽  
Surface Models ◽  
Modis Lai

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.

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