Forest structure and vegetation dynamics as a driver of global carbon uptake

2020 ◽  
Author(s):  
Ben Poulter ◽  
Leo Calle ◽  
Thomas Pugh ◽  
Nathan McDowell ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover Change ◽  
Forest Structure ◽  
Land Surface ◽  
Vegetation Dynamics ◽  
Carbon Uptake ◽  
Forest Age ◽  
Land Surface Models ◽  
Surface Models ◽  
Global Carbon

<p>The drivers for terrestrial carbon uptake remain unclear despite a clear signal that the land removes the equivalent of up to 25-30% of fossil fuel CO2 emissions each year. Recent work has confirmed sustained carbon uptake by the land that is proportional to anthropogenic emissions, meaning that the land 'sink' has strengthened over the past five decades, and with interannual variability driven by climate. Drivers responsible for sustained uptake include hypotheses related to lengthening growing season length, increasing nitrogen deposition, changes in the ratio of diffuse to direct radiation, and land-use and land cover change. More recently, land-use and land-cover change has been investigated as a driver of land carbon uptake owing to an emergence of global-scale datasets related to canopy disturbance, land use, and forest age. At the same time, land-surface models have increased their realism in terms of moving beyond 'big-leaf' model representation of ecosystems to including vertical structure and horizontal heteorogeneity via size-and-age structured approaches. This presentation will address recent work identified forest structure and vegetation dynamics as a driver for global carbon uptake and provide examples of how remote sensing observations have led to new datasets for initialization land-surface models. Compared to inventory-based approaches, land-surface models initialized with forest age show a lessor role in explaining net terrestrial carbon uptake at global scales, but at regional scales, vegetation structure is a key determinant of carbon exchange. New satellite missions improving forest structure observations are expected to reduce uncertainties and contribute substantially to ongoing land-surface model development.</p>

scholarly journals Inter-comparison of two land-surface models applied at different scales and their feedbacks while coupled with a regional climate model

2012 ◽  
Vol 16 (3) ◽  
pp. 1017-1031 ◽  
Author(s):  
F. Zabel ◽  
W. Mauser ◽  
T. Marke ◽  
A. Pfeiffer ◽  
G. Zängl ◽  
...  
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Regional Climate ◽  
Moderate Increase ◽  
Net Radiation ◽  
Land Surface Models ◽  
Near Surface ◽  
Temporal Behaviour ◽  
Surface Models ◽  
The Alps

Abstract. Downstream models are often used in order to study regional impacts of climate and climate change on the land surface. For this purpose, they are usually driven offline (i.e., 1-way) with results from regional climate models (RCMs). However, the offline approach does not allow for feedbacks between these models. Thereby, the land surface of the downstream model is usually completely different to the land surface which is used within the RCM. Thus, this study aims at investigating the inconsistencies that arise when driving a downstream model offline instead of interactively coupled with the RCM, due to different feedbacks from the use of different land surface models (LSM). Therefore, two physically based LSMs which developed from different disciplinary backgrounds are compared in our study: while the NOAH-LSM was developed for the use within RCMs, PROMET was originally developed to answer hydrological questions on the local to regional scale. Thereby, the models use different physical formulations on different spatial scales and different parameterizations of the same land surface processes that lead to inconsistencies when driving PROMET offline with RCM output. Processes that contribute to these inconsistencies are, as described in this study, net radiation due to land use related albedo and emissivity differences, the redistribution of this net radiation over sensible and latent heat, for example, due to different assumptions about land use impermeability or soil hydraulic reasons caused by different plant and soil parameterizations. As a result, simulated evapotranspiration, e.g., shows considerable differences of max. 280 mm yr−1. For a full interactive coupling (i.e., 2-way) between PROMET and the atmospheric part of the RCM, PROMET returns the land surface energy fluxes to the RCM and, thus, provides the lower boundary conditions for the RCM subsequently. Accordingly, the RCM responses to the replacement of the LSM with overall increased annual mean near surface air temperature (+1 K) and less annual precipitation (−56 mm) with different spatial and temporal behaviour. Finally, feedbacks can set up positive and negative effects on simulated evapotranspiration, resulting in a decrease of evapotranspiration South of the Alps a moderate increase North of the Alps. The inconsistencies are quantified and account for up to 30% from July to Semptember when focused to an area around Milan, Italy.

scholarly journals Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

2018 ◽  
Author(s):  
Gregory Duveiller ◽  
Giovanni Forzieri ◽  
Eddy Robertson ◽  
Wei Li ◽  
Goran Georgievski ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Cover ◽  
Land Surface ◽  
Heat Fluxes ◽  
Satellite Observations ◽  
Evaluation Framework ◽  
Land Surface Models ◽  
Mitigation And Adaptation ◽  
Surface Models

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as to inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset is available at: https://doi.org/10.5281/zenodo.1182145.

scholarly journals Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations

2018 ◽  
Vol 10 (3) ◽  
pp. 1265-1279 ◽  
Author(s):  
Gregory Duveiller ◽  
Giovanni Forzieri ◽  
Eddy Robertson ◽  
Wei Li ◽  
Goran Georgievski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Observations ◽  
Evaluation Framework ◽  
Land Surface Models ◽  
Mitigation And Adaptation ◽  
Surface Models

Abstract. Land use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land–climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies. The dataset consisting of both harmonized model simulation and remote sensing estimations is available at https://doi.org/10.5281/zenodo.1182145.

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

2007 ◽  
Vol 164 (8-9) ◽  
pp. 1789-1809 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Dev Niyogi ◽  
Margaret A. LeMone ◽  
Fei Chen ◽  
Souleymane Fall
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Data Sets ◽  
Land Use Land Cover ◽  
Land Surface Models ◽  
Land Cover Data ◽  
Surface Models

scholarly journals Advances in Land Surface Modelling

2021 ◽  
Author(s):  
Eleanor M. Blyth ◽  
Vivek K. Arora ◽  
Douglas B. Clark ◽  
Simon J. Dadson ◽  
Martin G. De Kauwe ◽  
...  
Keyword(s):  
Land Use ◽  
Water Use ◽  
Land Surface ◽  
Climate Prediction ◽  
Data Availability ◽  
Land Surface Models ◽  
Surface Modelling ◽  
Surface Models ◽  
Weather And Climate ◽  
Land Surface Modelling

AbstractLand surface models have an increasing scope. Initially designed to capture the feedbacks between the land and the atmosphere as part of weather and climate prediction, they are now used as a critical tool in the urgent need to inform policy about land-use and water-use management in a world that is changing physically and economically. This paper outlines the way that models have evolved through this change of purpose and what might the future hold. It highlights the importance of distinguishing between advances in the science within the modelling components, with the advances of how to represent their interaction. This latter aspect of modelling is often overlooked but will increasingly manifest as an issue as the complexity of the system, the time and space scales of the system being modelled increase. These increases are due to technology, data availability and the urgency and range of the problems being studied.

scholarly journals Inferring past land-use induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

2012 ◽  
Vol 9 (9) ◽  
pp. 12505-12542
Author(s):  
J. P. Boisier ◽  
N. de Noblet-Ducoudré ◽  
P. Ciais
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Type ◽  
Satellite Observations ◽  
Land Surface Models ◽  
Historical Changes ◽  
Surface Models ◽  
Past Land Use

Abstract. Cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land-use induced land cover changes (LCC) on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a) uncertainties in the magnitude of historical LCC and, (b) differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We have derived monthly albedo climatologies for croplands and four other land-cover types from MODIS satellite observations. We have then estimated the changes in surface albedo since preindustrial times by combining these climatologies with the land-cover maps of 1870 and 1992 used by modelers in the context of the LUCID intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute) in winter and 2% in summer between 1870 and 1992 over areas that have experienced intense deforestation in the northern temperate regions. The MODIS-based reconstructions of historical changes in surface albedo were then compared to those simulated by the various models participating to LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the reconstructions, that is larger increases in winter than in summer driven by the presence of snow. However, individual models show significant differences with the satellite-based reconstructions, despite the fact that land-cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how land-surface models parameterize albedo. Another reason, of secondary importance, results from differences in the simulated snowpack. Our methodology is a useful tool not only to infer observations-based historical changes in land surface variables impacted by LCC, but also to point to major deficiencies within the models; we therefore suggest that it could be more widely developed and used in conjunction with other tools in order to evaluate global land-surface models.

scholarly journals The contribution of land-use change versus climate variability to the 1940s CO<sub>2</sub> plateau: Former Soviet Union as a test case

2017 ◽  
Author(s):  
Ana Bastos ◽  
Anna Peregon ◽  
Érico A. Gani ◽  
Sergey Khudyaev ◽  
Chao Yue ◽  
...  
Keyword(s):  
Land Use ◽  
Soviet Union ◽  
Land Use Change ◽  
Land Surface ◽  
Former Soviet Union ◽  
Terrestrial Ecosystems ◽  
High Latitudes ◽  
Land Surface Models ◽  
Vegetation Growth ◽  
Surface Models

Abstract. According to the ice-core record, atmospheric CO2 growth rate (plateau) stalled during the 1940s, in spite of maintained anthropogenic emissions from fossil fuel burning and land-use change. Bastos et al. (2016) have shown that the state-of-the-art reconstructions of CO2 sources and sinks do not allow closing the global CO2 budget during this period. Their study indicates that even considering an enhancement of the ocean sink, still a gap sink of 0.4–1.5 PgC.yr−1 in terrestrial ecosystems is needed to explain the CO2 stabilization. They hypothesised that (i) the major socioeconomic and demographic disruptions during World War II (WWII) may have led to massive land-abandonment, resulting in an additional sink from regrowing natural vegetation which is not accounted for in most reconstructions and/or (ii) the warming registered at the same time, especially in the high-latitudes, might have led to increased vegetation growth and an enhancement of the natural sink. Here, we test the different contributions of these two factors in the Former Soviet Union (FSU), motivated by several reasons. On the one hand, the territory of the FSU encompasses 15 % of the terrestrial surface, 20 % of the global soil organic carbon pool and is responsible for a considerable fraction of the present-day terrestrial CO2 sink. On the other hand, heavy economic and demographic losses have been registered in FSU during WWII, together with likely decrease in farmland due to occupation, destruction of infrastructure and shortages of manpower. Here we present a newly compiled dataset of annual agricultural area in FSU, which better matches other socioeconomic indicators and reports a decrease in cropland of ca. 62 Mha between 1940–1943. We use an updated version of the land-surface model ORCHIDEE, ORCHIDEE-MICT, which is specifically developed to better represent high-latitude processes to simulate the carbon fluxes in terrestrial ecosystems over the 20th century. Using our new cropland dataset, we test the different contributions of the land-use change and the decadal warming reported in the 1940s to explain the plateau. As reference, we compare our results with the gap sink estimated by the group of land-surface models in Bastos et al. (2016): 0.7 PgC/yr. We find that the massive cropland decrease between 1940–1943, even if short-termed, could result in an additional decadal sink of 0.04–0.07 PgC/yr, i.e. 6–10 % of the gap sink required to explain the plateau. The ORCHIDEE-MICT simulations also indicate a very strong enhancement of the terrestrial sink by 0.4 PgC/yr, explaining about 60 % of the gap sink from the TRENDYv4 models. This enhancement is mainly explained by tree-growth in high-latitudes coincident with strongest warming sustained over the 1940–1949 decade, which is not captured by any of the other land-surface models. Even if land-abandonment during WWII might contribute to a relatively small fraction of the sink required to explain the plateau, it is still non-negligible, especially since such events have likely been registered in other regions. The vegetation growth in high-latitudes simulated by ORCHIDEE-MICT and absent in other models appears to be supported by tree-ring records, highlighting the relevance of improving the representation of high-latitude hydrological and soil processes in order to better capture decadal variability in the terrestrial CO2 sink.

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