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

Scholze, Marko LU ; Buchwitz, Michael; Dorigo, Wouter A.; Guanter, Luis and Quegan, Shaun (2017) In Biogeosciences 14(14). p.3401-3429
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... (More)

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 error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 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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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Biogeosciences
volume
14
issue
14
pages
29 pages
publisher
Copernicus Publications
external identifiers
  • scopus:85025133727
  • wos:000405866100002
ISSN
1726-4170
DOI
10.5194/bg-14-3401-2017
language
English
LU publication?
yes
id
063fb77c-41cd-4c6e-9f65-1398cfbfd6f5
date added to LUP
2017-07-31 09:57:56
date last changed
2017-09-18 11:40:22
@article{063fb77c-41cd-4c6e-9f65-1398cfbfd6f5,
  abstract     = {<p>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 error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 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).<br/><br/>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.</p>},
  author       = {Scholze, Marko and Buchwitz, Michael and Dorigo, Wouter A. and Guanter, Luis and Quegan, Shaun},
  issn         = {1726-4170},
  language     = {eng},
  month        = {07},
  number       = {14},
  pages        = {3401--3429},
  publisher    = {Copernicus Publications},
  series       = {Biogeosciences},
  title        = {Reviews and syntheses : Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems},
  url          = {http://dx.doi.org/10.5194/bg-14-3401-2017},
  volume       = {14},
  year         = {2017},
}