Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models

Anav, Alessandro; Friedlingstein, Pierre; Kidston, M.; Bopp, Laurent; Ciais, Philippe; Cox, Peter M.; Jones, Chris D.; Jung, Martin; Myneni, Ranga B.; Zhu, Zaichun

doi:10.1175/jcli-d-12-00417.1

Cited by 452 publications

(556 citation statements)

References 120 publications

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“…Large inter-model disagreements were found for NPP and heterotrophic respiration (Rh). In agreement with Anav et al (2013), Shao et al (2013) also noted that the CMIP5 historical simulations tend to overestimate photosynthesis and LAI. Todd-Brown et al (2013) compared and evaluated 11 CMIP5 ESMs in terms of their variations in soil carbon.…”

Section: T Ziehn Et Al: Access-esm1 Historical Simulationssupporting

confidence: 77%

“…In contrast, for the ocean an underestimation of the net primary production (NPP) was noted for a number of models. Anav et al (2013) also found significant regional variations in model performance.…”

Section: T Ziehn Et Al: Access-esm1 Historical Simulationsmentioning

confidence: 84%

“…However, according to Anav et al (2013) none of the CMIP5 models had an MVI close to the threshold of 0.5. Also note that for the calculation of the MVI for precipitation we had to exclude 60 land points (mainly coastal points) due to inconsistencies in the regridding.…”

Section: 1)mentioning

confidence: 91%

“…The definition of a limit to decide if a model performs well or poor is rather arbitrary. However, Scherrer (2011) and Anav et al (2013) have used a threshold of MVI < 0.5. For a number of carbon-related variables, we calculate the inter-annual variability (IAV), defined as the standard deviation of detrended annual-mean values.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…According to Anav et al (2013) only a few CMIP5 models show an MVI of lower than 0.5 (although their calculation is based on present day, i.e. [1986][1987][1988][1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005].…”

Section: Land Temperature and Precipitationmentioning

confidence: 99%

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The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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Abstract. Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land-atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change).The integrated ocean-atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959-2005. The seasonal cycle of simulated atmospheric CO 2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.

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Section: T Ziehn Et Al: Access-esm1 Historical Simulationssupporting

confidence: 77%

Section: T Ziehn Et Al: Access-esm1 Historical Simulationsmentioning

confidence: 84%

Section: 1)mentioning

confidence: 91%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Land Temperature and Precipitationmentioning

confidence: 99%

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The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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Sensitivity of global terrestrial carbon cycle dynamics to variability in satellite‐observed burned area

Poulter

Cadule

Cheiney

et al. 2015

Global Biogeochemical Cycles

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Fire plays an important role in terrestrial ecosystems by regulating biogeochemistry, biogeography, and energy budgets, yet despite the importance of fire as an integral ecosystem process, significant advances remain to improve its prognostic representation in carbon cycle models. To recommend and to help prioritize model improvements, this study investigates the sensitivity of a coupled global biogeography and biogeochemistry model, LPJ, to observed burned area measured by three independent satellite-derived products, GFED v3.1, L3JRC, and GlobCarbon. Model variables are compared with benchmarks that include pantropical aboveground biomass, global tree cover, and CO 2 and CO trace gas concentrations. Depending on prescribed burned area product, global aboveground carbon stocks varied by 300 Pg C, and woody cover ranged from 50 to 73 Mkm 2 . Tree cover and biomass were both reduced linearly with increasing burned area, i.e., at regional scales, a 10% reduction in tree cover per 1000 km 2 , and 0.04-to-0.40 Mg C reduction per 1000 km 2 . In boreal regions, satellite burned area improved simulated tree cover and biomass distributions, but in savanna regions, model-data correlations decreased. Global net biome production was relatively insensitive to burned area, and the long-term land carbon sink was robust,~2.5 Pg C yr À1, suggesting that feedbacks from ecosystem respiration compensated for reductions in fuel consumption via fire. CO 2 transport provided further evidence that heterotrophic respiration compensated any emission reductions in the absence of fire, with minor differences in modeled CO 2 fluxes among burned area products. CO was a more sensitive indicator for evaluating fire emissions, with MODIS-GFED burned area producing CO concentrations largely in agreement with independent observations in high latitudes. This study illustrates how ensembles of burned area data sets can be used to diagnose model structures and parameters for further improvement and also highlights the importance in considering uncertainties and variability in observed burned area data products for model applications.

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Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models

Hoffman

Randerson

Arora³

et al. 2014

JGR Biogeosciences

144

147

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The strength of feedbacks between a changing climate and future CO 2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations-in which atmospheric CO 2 levels were computed prognostically-for historical and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006-2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO 2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO 2 . Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO 2 biases and future CO 2 levels for the multimodel ensemble. We used this relationship to create a contemporary CO 2 tuned model (CCTM) estimate of the atmospheric CO 2 trajectory for the 21st century. The CCTM yielded CO 2 estimates of 600 ± 14 ppm at 2060 and 947 ± 35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO 2 , CO 2 -induced radiative forcing, and CO 2 -induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO 2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO 2 from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.

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Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models

Cited by 452 publications

References 120 publications

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

Sensitivity of global terrestrial carbon cycle dynamics to variability in satellite‐observed burned area

Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models

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