Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

Schürmann, Gregor; Kaminski, Thomas W.; Köstler, Christoph; Voßbeck, Michael; Kattge, Jens; Giering, Ralf; Rödenbeck, Christian; Heimann, Martin; Zaehle, Sönke

doi:10.5194/gmd-9-2999-2016

Cited by 38 publications

(53 citation statements)

References 76 publications

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“…Since then several global terrestrial ecosystem models have been included in CCDAS employing a variational approach (e.g. Schürmann et al, 2016;Peylin et al, 2016).…”

Section: Examples Of Terrestrial Carbon Cycle Data Assimilationmentioning

confidence: 99%

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

et al. 2017

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Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well errorcharacterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO 2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005), emphasising the rapid advance in relevant space-based observations.

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“…Since then several global terrestrial ecosystem models have been included in CCDAS employing a variational approach (e.g. Schürmann et al, 2016;Peylin et al, 2016).…”

Section: Examples Of Terrestrial Carbon Cycle Data Assimilationmentioning

confidence: 99%

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

et al. 2017

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“…Sippel et al (2016) use a deviation of the 2012 spring and late summer FAPAR from the respective long-term means to analyse the effect of a drought event on vegetation activity over North America and explain the response mechanism of the carbon balance as inferred from other data streams (Wolf et al, 2016). A consistency check against other data streams and a process model is provided by simultaneous assimilation of the FAPAR product with further data streams, in particular the atmospheric carbon dioxide record (Kaminski et al, 2013;Schürmann et al, 2016). Consistency to further data streams is also implicitly checked in diagnostic model set-ups, for example when the FAPAR product is used as a forcing field for simulation of photosynthesis .…”

Section: Validationmentioning

confidence: 99%

“…Such complementarity has been demonstrated for Earth observa-tion (EO) products Pinty et al, 2011b) of the fraction of absorbed photosynthetically active radiation (FAPAR), which provide information on the vegetation phenology and colour, for example. The effect on carbon and water fluxes of assimilating FAPAR in addition to atmospheric carbon dioxide samples is, for example, quantified by Kaminski et al (2012) and Schürmann et al (2016).…”

Section: Introductionmentioning

confidence: 99%

“…The strength of this constraint is quantified by significant reductions in uncertainty in simulated terrestrial carbon fluxes diagnosed over (Kaminski et al, 2002;Rayner et al, 2005) or predicted after (Scholze et al, 2007;Rayner et al, 2011) the assimilation window. In recent multi-data stream assimilation studies at global scale (Scholze et al, 2016;Schürmann et al, 2016), the constraint through the flask sampling network has proven essential to achieving realistic magnitudes of the terrestrial carbon sink. The flask sampling network alone does, however, only constrain a subspace of the space of unknown process parameters.…”

Section: Introductionmentioning

confidence: 99%

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Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Kaminski¹,

Pinty

Voßbeck³

et al. 2017

Biogeosciences

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Abstract. Earth observation (EO) land surface products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation-soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a 1-D representation of the radiative transfer within the canopy-soil system, such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but they also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index, which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to the validation of JRC-TIP products, including the comparison against in situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. Since many aspects of JRC-TIP are generic, the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.

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“…In ORCHIDEE as reported by Lathière et al (2006), the respective PFT-dependent LAI max values are 4.5 for both boreal BDT and NET, and 2.5 for a C 3 grass (same LAI max also used for BDS). In JSBACH according to Schürmann et al (2016), the PFT-dependent LAI max value was 5 for extratropical deciduous trees (BDTs), 1.7 for coniferous evergreen trees (NETs), and 3 for C 3 grass (same LAI max also used for BDS).…”

Section: Comparison Of Lai Max Mapsmentioning

confidence: 99%

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

et al. 2018

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Abstract. Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface-atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAI max ), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAI max ) of managed forests in Fennoscandia, we compared our LAI max map with reference LAI max maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAI max values used in three leading land models. Comparison of the LAI max maps showed that our product provides a spatially more realistic description of LAI max in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

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Constraining a land-surface model with multiple observations by application of the MPI-Carbon Cycle Data Assimilation System V1.0

Cited by 38 publications

References 76 publications

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data

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