scholarly journals Supplementary material to "Vegetation distribution and terrestrial carbon cycle in a carbon-cycle configuration of JULES4.6 with new plant functional types"

2018 ◽  
Author(s):  
Anna B. Harper ◽  
Andrew J. Wiltshire ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
Chris D. Jones ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Plant Functional Types ◽  
Vegetation Distribution ◽  
Terrestrial Carbon ◽  
Functional Types ◽  
Terrestrial Carbon Cycle ◽  
Supplementary Material

scholarly journals Vegetation distribution and terrestrial carbon cycle in a carbon-cycle configuration of JULES4.6 with new plant functional types

2018 ◽  
Author(s):  
Anna B. Harper ◽  
Andrew J. Wiltshire ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
Chris D. Jones ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Soil Carbon ◽  
Boreal Forests ◽  
Carbon Sink ◽  
Vegetation Distribution ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Vegetation Carbon ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES were expanded from 5 to 9 to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, that allows for any number of PFTs to compete based solely on their height, removing the previous hardwired dominance hierarchy where dominant types are assumed to outcompete subdominant types. With the new set of 9 PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former 5 PFT version. Improvements include the coverage of trees within tropical and boreal forests, and a reduction in shrubs, which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982–2011 was 133 PgC yr−1, compared to observation-based estimates between 123±8 (over the same time period) and 150–175 PgC yr−1. NPP from 2000–2013 was 72 PgC yr−1, compared to satellite-derived NPP of 55 PgC yr−1 over the same period and independent estimates of 56.2±14.3 PgC yr−1. The simulated carbon stored in vegetation is 542 PgC, compared to an observation-based range of 400–600 PgC. Soil carbon is much lower (1422 PgC) than estimates from measurements (>2400 PgC), with large underestimations of soil carbon in the tropical and boreal forests. We also examined some aspects of the historical terrestrial carbon sink as simulated by JULES. Between the 1900s and 2000s, increased atmospheric carbon dioxide levels enhanced vegetation productivity and litter inputs into the soils, while land-use change removed vegetation and reduced soil carbon. The result was a simulated increase in soil carbon of 57 PgC but a decrease in vegetation carbon by of PgC. JULES simulated a loss of soil and vegetation carbon of 14 and 124 PgC, respectively, due to land-use change from 1900–2009. The simulated land carbon sink was 2.0±1.0 PgC yr−1 from 2000–2009, in close agreement to estimates from the IPCC and Global Carbon Project.

scholarly journals Supplementary material to "Optimal model complexity for terrestrial carbon cycle prediction"

2020 ◽  
Author(s):  
Caroline A. Famiglietti ◽  
T. Luke Smallman ◽  
Paul A. Levine ◽  
Sophie Flack-Prain ◽  
Gregory R. Quetin ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Model Complexity ◽  
Optimal Model ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Supplementary Material

scholarly journals Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

2018 ◽  
Vol 11 (7) ◽  
pp. 2857-2873 ◽  
Author(s):  
Anna B. Harper ◽  
Andrew J. Wiltshire ◽  
Peter M. Cox ◽  
Pierre Friedlingstein ◽  
Chris D. Jones ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Soil Carbon ◽  
Boreal Forests ◽  
Carbon Sink ◽  
Vegetation Distribution ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Vegetation Carbon ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES was expanded from five to nine to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, which allows for any number of PFTs to compete based solely on their height; therefore, the previous hardwired dominance hierarchy is removed. With the new set of nine PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former five PFT version. Improvements include the coverage of trees within tropical and boreal forests and a reduction in shrubs, the latter of which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon (C) cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982 to 2011 is 133 Pg C yr−1, compared to observation-based estimates (over the same time period) between 123 ± 8 and 150–175 Pg C yr−1. NPP from 2000 to 2013 is 72 Pg C yr−1, compared to satellite-derived NPP of 55 Pg C yr−1 over the same period and independent estimates of 56.2 ± 14.3 Pg C yr−1. The simulated carbon stored in vegetation is 542 Pg C, compared to an observation-based range of 400–600 Pg C. Soil carbon is much lower (1422 Pg C) than estimates from measurements (> 2400 Pg C), with large underestimations of soil carbon in the tropical and boreal forests. We also examined some aspects of the historical terrestrial carbon sink as simulated by JULES. Between the 1900s and 2000s, increased atmospheric carbon dioxide levels enhanced vegetation productivity and litter inputs into the soils, while land use change removed vegetation and reduced soil carbon. The result is a simulated increase in soil carbon of 57 Pg C but a decrease in vegetation carbon of 98 Pg C. The total simulated loss of soil and vegetation carbon due to land use change is 138 Pg C from 1900 to 2009, compared to a recent observationally constrained estimate of 155 ± 50 Pg C from 1901 to 2012. The simulated land carbon sink is 2.0 ± 1.0 Pg C yr−1 from 2000 to 2009, in close agreement with estimates from the IPCC and Global Carbon Project.

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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