Reviewing the role of precipitation and soil moisture in driving the terrestrial carbon cycle variability in Europe: recent advances and known unknowns

2020 ◽  
Author(s):  
Gabriele Messori ◽  
Guiomar Ruiz-Pérez ◽  
Stefano Manzoni ◽  
Giulia Vico
Keyword(s):  
Soil Moisture ◽  
Carbon Cycle ◽  
Climate Indices ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Wide Range ◽  
Holistic Understanding ◽  
Comprehensive Picture ◽  
Known Unknowns

<p>The terrestrial biosphere is a key component of the global carbon cycle and is heavily influenced by climate. This interaction spans a wide range of temporal (from sub-daily to paleoclimatic) and spatial (from local to continental and global) scales and a multitude of bio-physical processes. In part due to this complexity, a comprehensive picture of the physical links and correlations between climate drivers and carbon cycle metrics at different scales remains elusive, framing the scope of this contribution. Here, we specifically explore how precipitation, soil moisture and aggregated climate variability indices relate to the variability of the European terrestrial carbon cycle at sub-daily to interannual scales (i.e. excluding long-term trends). We first discuss broad areas of agreement and disagreement in the literature. For example, while most carbon cycle proxies tend to correlate positively with precipitation, responses to soil moisture and climate indices are more variable. In fact, soil moisture often correlates positively with productivity in water-limited environments, and negatively in light limited ones, or can exhibit nonlinear relations with the carbon cycle proxies. We then conclude by outlining some existing knowledge gaps and by proposing avenues for improving our holistic understanding of the role of climate drivers in modulating the terrestrial carbon cycle.</p>

Impacts of soil hydrologial modeling on long-term terrestrial carbon cycle inferred from CCDAS (Carbon Cycle Data Assimilation System)

2020 ◽  
Author(s):  
Mousong Wu ◽  
Marko Scholze ◽  
Fei Jiang ◽  
Hengmao Wang ◽  
Wenxin Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Carbon Cycle ◽  
Carbon Budget ◽  
Terrestrial Carbon ◽  
Global Carbon Budget ◽  
Terrestrial Carbon Cycle ◽  
Global Carbon ◽  
Data Assimilation System ◽  
Assimilation System

<p>The terrestrial carbon cycle is an important part of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in estimating carbon fluxes, which impacts global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. In this context, accurate soil moisture data will improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36 years (1980-2015) of surface soil moisture data as provided by the ESA CCI in combination with atmospheric CO<sub>2</sub> concentration observations at global scale. We will present the methods used for assimilating long-term remotely sensed soil moisture into the terrestrial biosphere model, and demonstrate the importance of soil moisture in modeling ecosystem carbon cycle processes. We will also investigate the impacts of soil moisture on the terrestrial carbon cycle during climate extremes at various scales.</p>

scholarly journals Role of Physical Exercise and Nutraceuticals in Modulating Molecular Pathways of Osteoarthritis

2021 ◽  
Vol 22 (11) ◽  
pp. 5722
Author(s):  
Alessandro de Sire ◽  
Nicola Marotta ◽  
Cinzia Marinaro ◽  
Claudio Curci ◽  
Marco Invernizzi ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Synergistic Effects ◽  
Molecular Pathways ◽  
Epigenetic Changes ◽  
Pathological Features ◽  
Oxidative Signaling ◽  
Cartilage Extracellular Matrix ◽  
Wide Range ◽  
Comprehensive Picture

Osteoarthritis (OA) is a painful and disabling disease that affects millions of patients. Its etiology is largely unknown, but it is most likely multifactorial. OA pathogenesis involves the catabolism of the cartilage extracellular matrix and is supported by inflammatory and oxidative signaling pathways and marked epigenetic changes. To delay OA progression, a wide range of exercise programs and naturally derived compounds have been suggested. This literature review aims to analyze the main signaling pathways and the evidence about the synergistic effects of these two interventions to counter OA. The converging nutrigenomic and physiogenomic intervention could slow down and reduce the complex pathological features of OA. This review provides a comprehensive picture of a possible signaling approach for targeting OA molecular pathways, initiation, and progression.

scholarly journals Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle

2018 ◽  
Vol 13 (6) ◽  
pp. 064023 ◽  
Author(s):  
Benjamin Quesada ◽  
Almut Arneth ◽  
Eddy Robertson ◽  
Nathalie de Noblet-Ducoudré
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Cycle ◽  
Land Cover Change ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Anthropogenic Land Use

scholarly journals Spatiotemporal patterns of terrestrial carbon cycle during the 20th century

2009 ◽  
Vol 23 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Shilong Piao ◽  
Philippe Ciais ◽  
Pierre Friedlingstein ◽  
Nathalie de Noblet-Ducoudré ◽  
Patricia Cadule ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
20Th Century ◽  
Spatiotemporal Patterns ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

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

2017 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Full Description ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrological, 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 5 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by model-data fusion or 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 10 well error-characterized 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 15 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 20 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.

scholarly journals Examining the sensitivity of the terrestrial carbon cycle to the expression of El Niño

2020 ◽  
Author(s):  
Lina Teckentrup ◽  
Martin G. De Kauwe ◽  
Andrew J. Pitman ◽  
Benjamin Smith
Keyword(s):  
Carbon Cycle ◽  
El Niño ◽  
El Nino ◽  
Terrestrial Carbon ◽  
Near Surface ◽  
Terrestrial Carbon Cycle ◽  
El Niño Events ◽  
Ep El Niño ◽  
The Impact ◽  
El Nino Events

Abstract. The El Niño‐Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central–Pacific (CP) and (ii) Eastern–Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ–GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.

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