Evaluation of Land Surface Models in Reproducing  Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

Murray‐Tortarolo, Guillermo; Anav, Alessandro; Friedlingstein, Pierre; Sitch, Stephen; Piao, Shilong; Zhu, Zaichun; Poulter, Benjamin; Zaehle, Sönke; Ahlström, Anders; Lomas, M.; Levis, Samuel; Viovy, Nicolas; Zeng, Ning

doi:10.3390/rs5104819

Cited by 96 publications

(122 citation statements)

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“…However, the effect of CO 2 on soil moisture is unlikely to be a significant source of uncertainty in the context of this study as Leipprand and Gerten (2006) showed, using simulations from LPJ, that a doubling of CO 2 leads to only 1 % increase in soil moisture. Orchidee, LPJ and OCN are obtained from the TRENDY consortium (Sitch et al 2013, Murray-Tortarolo et al 2013. Simulations from their S3 set-up were considered, which are driven with carbon dioxide from ice core and NOAA, historical climate forcing from CRU and NCEP, and land use change from the HYDE database (Hurtt et al 2011).…”

Section: Observational Estimatesmentioning

confidence: 99%

Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data

Mueller

Zhang

2015

Climatic Change

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The amount of soil moisture affects water availability, the occurrence of droughts and floods, and the frequency and intensity of heat waves in many regions across the globe. Here, we evaluate historical trends in soil moisture estimated by land-surface models (LSMs) with observed atmospheric forcing and trends simulated by global climate models participating in the Coupled Models Inter-comparison Project Phase 5 (CMIP5). We classify northern hemispheric land into wet and dry regions and analyze soil moisture changes in these regions. We find a significant decrease in soil moisture from 1951 to 2005 in the northern hemispheric land areas, in particular in dry regions, both in LSM and CMIP5 model simulations. Soil moisture trends in wet regions are less consistent among simulations. The increase in the area affected by drought (defined as the area where soil moisture is below its 10th percentile) from 1951 to 2005 is estimated to be 20 % (LSMs) and 30 % (CMIP5 models). A comparison between soil moisture simulated by LSMs and CMIP5 model output under different external forcings suggests that anthropogenic forcing contributed significantly to the observed drying and could explain the increase in the area affected by drought. As increases in atmospheric greenhouse gas concentrations will continue in the near future, dry areas are projected to become drier and larger in extent, which could negatively impact future water supply and food security.

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Section: Observational Estimatesmentioning

confidence: 99%

Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data

Mueller

Zhang

2015

Climatic Change

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“…Consequently, phenology shifts may feedback on climate with unclear magnitude and sign (Anav et al, 2013;MurrayTortarolo et al, 2013;Peñuelas et al, 2009). As a result of the significant changes that are already affecting the structure and function of Arctic ecosystems, it is critical to understand and quantify the C dynamics of the terrestrial tundra and taiga and their responses to climate change (McGuire et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

López‐Blanco

Exbrayat

Lund

et al. 2018

Preprint

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Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. The Arctic accounts 15 for approximately 50% of the global soil organic C stock, emphasizing the important role of Arctic regions in the global C cycle. Recent studies have pointed to the poor understanding of C pools turnover, although remain unclear as to whether productivity or biomass dominate the biases. Here, we use an improved version of the CARDAMOM data-assimilation system, to produce pan-Arctic terrestrial C-related variables without using traditional plant functional type or steady-state assumptions.Our approach integrates a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely 20 state of the high latitude C cycle at a 1° x 1° resolution for the first 15 years of the 21 st century, but also to provide general guidance about the controlling biases in the turnover dynamics. As average, CARDAMOM estimates 513 (456, 579), 245(208, 290) and 204 (109, 427) g C m -2 yr -1 (90% confidence interval) from photosynthesis, autotrophic and heterotrophic respiration respectively, suggesting that the pan-Arctic region acted as a likely sink -55 (-152, 157) g C m -2 yr -1 , weaker in tundra and stronger in taiga, but our confidence intervals remain large (and so the region could be a source of C). In general, 25we find a good agreement between CARDAMOM and different sources of assimilated and independent data at both pan-Arctic and local scale. Using CARDAMOM as a benchmarking tool for global vegetation models (GVM), we also conclude that turnover time of vegetation C is weakly simulated in vegetation models and is a major component of error in their forecasts.Our findings highlight that GVM modellers need to focus on the vegetation C stocks dynamics, but also their respiratory losses, to improve our process-based understanding of internal C cycle dynamics in the Arctic. 30Earth Syst. Dynam. Discuss., https://doi

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“…However, terrestrial biosphere models tend to have large uncertainties, for example, in phenology (Richardson et al, 2012;Murray-Tortarolo et al, 2013) and in spatial distributions of plant species (Cheaib et al, 2012). Recently, data assimilation (DA) methods which incorporate observation data into models have been applied to terrestrial biosphere models to reduce the uncertainties in the state variables and model parameters Peng et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Arakida

Miyoshi

Ise

et al. 2017

Nonlin. Processes Geophys.

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Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

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Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

Abstract: Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades.

Cited by 96 publications

References 50 publications

Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data

Causes of drying trends in northern hemispheric land areas in reconstructed soil moisture data

Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

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