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 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 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.

Global vegetation and terrestrial carbon cycle changes after the last ice age

2011 ◽  
Vol 189 (4) ◽  
pp. 988-998 ◽  
Author(s):  
I. C. Prentice ◽  
S. P. Harrison ◽  
P. J. Bartlein
Keyword(s):  
Carbon Cycle ◽  
Ice Age ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Last Ice Age ◽  
Global Vegetation

scholarly journals Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches

2018 ◽  
Vol 373 (1760) ◽  
pp. 20170304 ◽  
Author(s):  
Ana Bastos ◽  
Pierre Friedlingstein ◽  
Stephen Sitch ◽  
Chi Chen ◽  
Arnaud Mialon ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
El Niño ◽  
El Nino ◽  
Carbon Sink ◽  
Terrestrial Carbon ◽  
Fire Emissions ◽  
Terrestrial Carbon Cycle ◽  
Depth Analysis ◽  
The Tropics ◽  
The Impact

Evaluating the response of the land carbon sink to the anomalies in temperature and drought imposed by El Niño events provides insights into the present-day carbon cycle and its climate-driven variability. It is also a necessary step to build confidence in terrestrial ecosystems models' response to the warming and drying stresses expected in the future over many continents, and particularly in the tropics. Here we present an in-depth analysis of the response of the terrestrial carbon cycle to the 2015/2016 El Niño that imposed extreme warming and dry conditions in the tropics and other sensitive regions. First, we provide a synthesis of the spatio-temporal evolution of anomalies in net land–atmosphere CO 2 fluxes estimated by two in situ measurements based on atmospheric inversions and 16 land-surface models (LSMs) from TRENDYv6. Simulated changes in ecosystem productivity, decomposition rates and fire emissions are also investigated. Inversions and LSMs generally agree on the decrease and subsequent recovery of the land sink in response to the onset, peak and demise of El Niño conditions and point to the decreased strength of the land carbon sink: by 0.4–0.7 PgC yr −1 (inversions) and by 1.0 PgC yr −1 (LSMs) during 2015/2016. LSM simulations indicate that a decrease in productivity, rather than increase in respiration, dominated the net biome productivity anomalies in response to ENSO throughout the tropics, mainly associated with prolonged drought conditions. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

scholarly journals Hysteresis of terrestrial carbon cycle to CO2 ramp-up and -down forcing

2021 ◽  
Author(s):  
Sowon Park ◽  
Jong-Seong Kug
Keyword(s):  
Carbon Cycle ◽  
Boreal Forests ◽  
Current Knowledge ◽  
Arctic Region ◽  
Terrestrial Carbon ◽  
Initial State ◽  
Terrestrial Biosphere ◽  
Terrestrial Carbon Cycle ◽  
Negative Emissions ◽  
Long Time

Abstract To prevent excessive global warming, we have faced a situation to reduce net carbon dioxide (CO2) emissions. However, the behavior of Earth’s terrestrial biosphere under negative emissions is highly uncertain. Herein, we show strong hysteresis in the terrestrial carbon cycle in response to CO2 ramp-up and -down forcing. Owing to the strong hysteresis lag, the terrestrial biosphere stores more carbon at the end of simulations than at its initial state, lessening the burden on net-negative emissions. This hysteresis is latitudinally dependent, showing a longer timescale of reversibility in high latitudes. Particularly, carbon in boreal forests can be stored for a long time. However, the hysteresis of the carbon cycle in the pan-Arctic region depends on the presence of permafrost processes. That is, unexpected irreversible carbon emissions may occur in permafrost even after achieving net-zero emissions, indicating the importance of permafrost processes, which is highly uncertain based on our current knowledge.

scholarly journals A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

2021 ◽  
Author(s):  
Hamze Dokoohaki ◽  
Bailey D. Morrison ◽  
Ann Raiho ◽  
Shawn P. Serbin ◽  
Michael Dietze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Soil Carbon ◽  
Model Data ◽  
Terrestrial Carbon ◽  
State Data ◽  
Flux Tower ◽  
Terrestrial Carbon Cycle ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.

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

2017 ◽  
Vol 14 (14) ◽  
pp. 3401-3429 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Ecosystem Dynamics ◽  
Full Description ◽  
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 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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