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Artificial Intelligence for the Earth Systems Bulletin of the American Meteorological Society Earth Interactions Journal of Applied Meteorology and Climatology Journal of Atmospheric and Oceanic Technology Journal of Climate Journal of Hydrometeorology Journal of Physical Oceanography Journal of the Atmospheric Sciences Monthly Weather Review Weather and Forecasting Weather, Climate, and Society Meteorological Monographs

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JOURNALS

Artificial Intelligence for the Earth Systems Bulletin of the American Meteorological Society Earth Interactions Journal of Applied Meteorology and Climatology Journal of Atmospheric and Oceanic Technology Journal of Climate Journal of Hydrometeorology Journal of Physical Oceanography Journal of the Atmospheric Sciences Monthly Weather Review Weather and Forecasting Weather, Climate, and Society Meteorological Monographs

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C4MIP

Description:

The special collection on "Climate—Carbon Interactions in the CMIP5 Earth System Models" (C4MIP) includes a series of papers which analyze results from Earth System Model (ESM) simulations performed for the fifth Coupled Modeling Intercomparison Project (CMIP5; Taylor et al., 2011), with a specific focus on the global carbon cycle. These simulations are also contributing to the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). State-of-the-art ESMs represent global land and ocean carbon cycle-related biogeochemical processes and their interactions with the physical climate system.

The CMIP5 protocol includes historical and future simulations driven with representative concentration pathways (RCPs) of greenhouse gases. For each of the greenhouse gas RCPs, the corresponding emissions are also available. The CMIP5 protocol includes ESM simulations that are driven with either prescribed CO2 concentration or CO2 emissions. It also includes the idealized specified concentration scenario, in which the atmospheric CO2 increases at a rate of 1% per year.

The papers in this special collection include multi-model analyses for historical and future RCP scenarios, focusing on carbon cycle response to climate change, compatible emissions, feedback analysis, process-oriented model evaluation, more specific analysis such as the response of permafrost to future climate change, and the impact of land use change on terrestrial carbon cycle processes.

Collection organizers:
Pierre Friedlingstein, University of Exeter
Chris Jones, UK Met Office
Vivek Arora, Canadian Centre for Climate Modeling and Analysis

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Page:12

Sources of Uncertainty in Future Projections of the Carbon Cycle

Alan J. Hewitt
,
Ben B. B. Booth
,
Chris D. Jones
,
Eddy S. Robertson
,
Andy J. Wiltshire
,
Philip G. Sansom
,
David B. Stephenson
, and
Stan Yip

Abstract

The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

Journal: Journal of Climate Volume/Issue: Volume 29: Issue 20

DOI: https://doi.org/10.1175/JCLI-D-16-0161.1 Published Online: 15 Oct 2016

Abstract

Abstract

The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

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Scale-Dependent Performance of CMIP5 Earth System Models in Simulating Terrestrial Vegetation Carbon

Lifen Jiang
,
Yaner Yan
,
Oleksandra Hararuk
,
Nathaniel Mikle
,
Jianyang Xia
,
Zheng Shi
,
Jerry Tjiputra
,
Tongwen Wu
, and
Yiqi Luo

Abstract

Model intercomparisons and evaluations against observations are essential for better understanding of models’ performance and for identifying the sources of uncertainty in their output. The terrestrial vegetation carbon simulated by 11 Earth system models (ESMs) involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) was evaluated in this study. The simulated vegetation carbon was compared at three distinct spatial scales (grid, biome, and global) among models and against the observations (an updated database from Olson et al.’s “Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database”). Moreover, the underlying causes of the differences in the models’ predictions were explored. Model–data fit at the grid scale was poor but greatly improved at the biome scale. Large intermodel variability was pronounced in the tropical and boreal regions, where total vegetation carbon stocks were high. While 8 out of 11 ESMs reproduced the global vegetation carbon to within 20% uncertainty of the observational estimate (560 ± 112 Pg C), the simulated global totals varied nearly threefold between the models. The goodness of fit of ESMs in simulating vegetation carbon depended strongly on the spatial scales. Sixty-three percent of the variability in contemporary global vegetation carbon stocks across ESMs could be explained by differences in vegetation carbon residence time across ESMs (P < 0.01). The analysis indicated that ESMs’ performance of vegetation carbon predictions can be substantially improved through better representation of plant longevity (i.e., carbon residence time) and its respective spatial distributions.

Journal: Journal of Climate Volume/Issue: Volume 28: Issue 13

DOI: https://doi.org/10.1175/JCLI-D-14-00270.1 Published Online: 01 Jul 2015

Abstract

Abstract

Model intercomparisons and evaluations against observations are essential for better understanding of models’ performance and for identifying the sources of uncertainty in their output. The terrestrial vegetation carbon simulated by 11 Earth system models (ESMs) involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) was evaluated in this study. The simulated vegetation carbon was compared at three distinct spatial scales (grid, biome, and global) among models and against the observations (an updated database from Olson et al.’s “Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database”). Moreover, the underlying causes of the differences in the models’ predictions were explored. Model–data fit at the grid scale was poor but greatly improved at the biome scale. Large intermodel variability was pronounced in the tropical and boreal regions, where total vegetation carbon stocks were high. While 8 out of 11 ESMs reproduced the global vegetation carbon to within 20% uncertainty of the observational estimate (560 ± 112 Pg C), the simulated global totals varied nearly threefold between the models. The goodness of fit of ESMs in simulating vegetation carbon depended strongly on the spatial scales. Sixty-three percent of the variability in contemporary global vegetation carbon stocks across ESMs could be explained by differences in vegetation carbon residence time across ESMs (P < 0.01). The analysis indicated that ESMs’ performance of vegetation carbon predictions can be substantially improved through better representation of plant longevity (i.e., carbon residence time) and its respective spatial distributions.

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Nonlinearity of Ocean Carbon Cycle Feedbacks in CMIP5 Earth System Models

Jörg Schwinger
,
Jerry F. Tjiputra
,
Christoph Heinze
,
Laurent Bopp
,
James R. Christian
,
Marion Gehlen
,
Tatiana Ilyina
,
Chris D. Jones
,
David Salas-Mélia
,
Joachim Segschneider
,
Roland Séférian
, and
Ian Totterdell

Abstract

Carbon cycle feedbacks are usually categorized into carbon–concentration and carbon–climate feedbacks, which arise owing to increasing atmospheric CO2 concentration and changing physical climate. Both feedbacks are often assumed to operate independently: that is, the total feedback can be expressed as the sum of two independent carbon fluxes that are functions of atmospheric CO2 and climate change, respectively. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), radiatively and biogeochemically coupled simulations have been undertaken to better understand carbon cycle feedback processes. Results show that the sum of total ocean carbon uptake in the radiatively and biogeochemically coupled experiments is consistently larger by 19–58 petagrams of carbon (Pg C) than the uptake found in the fully coupled model runs. This nonlinearity is small compared to the total ocean carbon uptake (533–676 Pg C), but it is of the same order as the carbon–climate feedback. The weakening of ocean circulation and mixing with climate change makes the largest contribution to the nonlinear carbon cycle response since carbon transport to depth is suppressed in the fully relative to the biogeochemically coupled simulations, while the radiatively coupled experiment mainly measures the loss of near-surface carbon owing to warming of the ocean. Sea ice retreat and seawater carbon chemistry contribute less to the simulated nonlinearity. The authors’ results indicate that estimates of the ocean carbon–climate feedback derived from “warming only” (radiatively coupled) simulations may underestimate the reduction of ocean carbon uptake in a warm climate high CO2 world.

Journal: Journal of Climate Volume/Issue: Volume 27: Issue 11

DOI: https://doi.org/10.1175/JCLI-D-13-00452.1 Published Online: 01 Jun 2014

Abstract

Abstract

Carbon cycle feedbacks are usually categorized into carbon–concentration and carbon–climate feedbacks, which arise owing to increasing atmospheric CO2 concentration and changing physical climate. Both feedbacks are often assumed to operate independently: that is, the total feedback can be expressed as the sum of two independent carbon fluxes that are functions of atmospheric CO2 and climate change, respectively. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), radiatively and biogeochemically coupled simulations have been undertaken to better understand carbon cycle feedback processes. Results show that the sum of total ocean carbon uptake in the radiatively and biogeochemically coupled experiments is consistently larger by 19–58 petagrams of carbon (Pg C) than the uptake found in the fully coupled model runs. This nonlinearity is small compared to the total ocean carbon uptake (533–676 Pg C), but it is of the same order as the carbon–climate feedback. The weakening of ocean circulation and mixing with climate change makes the largest contribution to the nonlinear carbon cycle response since carbon transport to depth is suppressed in the fully relative to the biogeochemically coupled simulations, while the radiatively coupled experiment mainly measures the loss of near-surface carbon owing to warming of the ocean. Sea ice retreat and seawater carbon chemistry contribute less to the simulated nonlinearity. The authors’ results indicate that estimates of the ocean carbon–climate feedback derived from “warming only” (radiatively coupled) simulations may underestimate the reduction of ocean carbon uptake in a warm climate high CO2 world.

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Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks

Pierre Friedlingstein
,
Malte Meinshausen
,
Vivek K. Arora
,
Chris D. Jones
,
Alessandro Anav
,
…(内容过长已截断)

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