Reply to the interactive comment from P. Rayner on ``A biogenic CO2 flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO2 analyses and forecasts'' by Agust\\'{\\i}-Panareda et al.

2016 ◽  
Author(s):  
Anna Agusti-Panareda
Keyword(s):  
Large Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Adjustment Scheme ◽  
Flux Adjustment

scholarly journals A biogenic CO<sub>2</sub> flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO<sub>2</sub> analyses and forecasts

2016 ◽  
Author(s):  
A. Agustí-Panareda ◽  
S. Massart ◽  
F. Chevallier ◽  
G. Balsamo ◽  
S. Boussetta ◽  
...  
Keyword(s):  
Real Time ◽  
Regional Scale ◽  
Global Scale ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Model Errors ◽  
Adaptive Calibration ◽  
Adjustment Scheme ◽  
Flux Adjustment ◽  
Biogenic Fluxes

Abstract. Forecasting atmospheric CO2 daily at the global scale with a good accuracy like it is done for the weather is a challenging task. However, it is also one of the key areas of development to bridge the gaps between weather, air quality and climate models. The challenge stems from the fact that atmospheric CO2 is largely controlled by the CO2 fluxes at the surface, which are difficult to constrain with observations. In particular, the biogenic fluxes simulated by land surface models show skill in detecting synoptic and regional-scale disturbances up to sub-seasonal time-scales, but they are subject to large seasonal and annual budget errors at global scale, usually requiring a posteriori calibration. This paper presents a scheme to diagnose and mitigate model errors associated with biogenic fluxes within an atmospheric CO2 forecasting system. The scheme is an adaptive calibration referred to as Biogenic Flux Adjustment Scheme (BFAS) and it can be applied automatically in real time throughout the forecast. The BFAS method improves the continental budget of CO2 fluxes in the model by combining information from three sources: (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, (3) simulated fluxes from the model. The method is shown to produce enhanced skill in the daily CO2 10-day forecasts without requiring continuous manual intervention. Therefore, it is particularly suitable for near-real-time CO2 analysis and forecasting systems.

scholarly journals A biogenic CO<sub>2</sub> flux adjustment scheme for the mitigation of large-scale biases in global atmospheric CO<sub>2</sub> analyses and forecasts

2016 ◽  
Vol 16 (16) ◽  
pp. 10399-10418 ◽  
Author(s):  
Anna Agustí-Panareda ◽  
Sébastien Massart ◽  
Frédéric Chevallier ◽  
Gianpaolo Balsamo ◽  
Souhail Boussetta ◽  
...  
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Regional Scale ◽  
Global Scale ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Model Errors ◽  
Adjustment Scheme ◽  
Flux Adjustment ◽  
Biogenic Fluxes

Abstract. Forecasting atmospheric CO2 daily at the global scale with a good accuracy like it is done for the weather is a challenging task. However, it is also one of the key areas of development to bridge the gaps between weather, air quality and climate models. The challenge stems from the fact that atmospheric CO2 is largely controlled by the CO2 fluxes at the surface, which are difficult to constrain with observations. In particular, the biogenic fluxes simulated by land surface models show skill in detecting synoptic and regional-scale disturbances up to sub-seasonal time-scales, but they are subject to large seasonal and annual budget errors at global scale, usually requiring a posteriori adjustment. This paper presents a scheme to diagnose and mitigate model errors associated with biogenic fluxes within an atmospheric CO2 forecasting system. The scheme is an adaptive scaling procedure referred to as a biogenic flux adjustment scheme (BFAS), and it can be applied automatically in real time throughout the forecast. The BFAS method generally improves the continental budget of CO2 fluxes in the model by combining information from three sources: (1) retrospective fluxes estimated by a global flux inversion system, (2) land-use information, (3) simulated fluxes from the model. The method is shown to produce enhanced skill in the daily CO2 10-day forecasts without requiring continuous manual intervention. Therefore, it is particularly suitable for near-real-time CO2 analysis and forecasting systems.

scholarly journals Deglacial carbon cycle changes observed in a compilation of 127 benthic <i>δ</i><sup>13</sup>C time series (20–6 ka)

2018 ◽  
Vol 14 (8) ◽  
pp. 1229-1252 ◽  
Author(s):  
Carlye D. Peterson ◽  
Lorraine E. Lisiecki
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Large Scale ◽  
Deep Ocean ◽  
Atmospheric Co2 ◽  
Last Deglaciation ◽  
Terrestrial Biosphere ◽  
Spatial Coverage ◽  
Ocean Carbon

Abstract. We present a compilation of 127 time series δ13C records from Cibicides wuellerstorfi spanning the last deglaciation (20–6 ka) which is well-suited for reconstructing large-scale carbon cycle changes, especially for comparison with isotope-enabled carbon cycle models. The age models for the δ13C records are derived from regional planktic radiocarbon compilations (Stern and Lisiecki, 2014). The δ13C records were stacked in nine different regions and then combined using volume-weighted averages to create intermediate, deep, and global δ13C stacks. These benthic δ13C stacks are used to reconstruct changes in the size of the terrestrial biosphere and deep ocean carbon storage. The timing of change in global mean δ13C is interpreted to indicate terrestrial biosphere expansion from 19–6 ka. The δ13C gradient between the intermediate and deep ocean, which we interpret as a proxy for deep ocean carbon storage, matches the pattern of atmospheric CO2 change observed in ice core records. The presence of signals associated with the terrestrial biosphere and atmospheric CO2 indicates that the compiled δ13C records have sufficient spatial coverage and time resolution to accurately reconstruct large-scale carbon cycle changes during the glacial termination.

scholarly journals Generic analysis of the response of calcifying microalgae to an elevation of pCO2 : qualitative vs quantitative analysis

2008 ◽  
Vol Volume 9, 2007 Conference in... ◽  
Author(s):  
Olivier Bernard ◽  
Antoine Sciandre
Keyword(s):  
Large Scale ◽  
Co2 Concentration ◽  
Emiliania Huxleyi ◽  
Atmospheric Co2 ◽  
Quantitative Prediction ◽  
General Level ◽  
Sont Encore ◽  
Chemostat Cultures ◽  
International Audience ◽  
Parameter Values

International audience Calcifying microalgae can play a key role in atmospheric CO2 trapping through large scale precipitation of calcium carbonate in the oceans. However, recent experiments revealed that the associated fluxes may be slow down by an increase in atmospheric CO2 concentration. In this paper we design models to account for the decrease in calcification and photosynthesis rates observed after an increase of pCO2 in Emiliania huxleyi chemostat cultures. Since the involved mechanisms are still not completely understood, we consider various models, each of them being based on a different hypothesis. These models are kept at a very general level, by maintaining the growth and calcification functions in a generic form, i.e. independent on the exact shape of these functions and on parameter values. The analysis is thus performed using these generic functions where the only hypothesis is an increase of these rates with respect to the regulating carbon species. As a result, each model responds differently to a pCO2 elevation. Surprisingly, the only models whose behaviour are in agreement with the experimental results correspond to carbonate as the regulating species for photosynthesis. Finally we show that the models whose qualitative behaviour are wrong could be considered as acceptable on the basis of a quantitative prediction error criterion. Les microalgues calcifiantes jouent un rôle clé dans le piégeage du CO2 atmosphérique d’origine anthropique en précipitant du carbonate de calcium qui sédimente au fond des océans. Toutefois, des expériences en laboratoire ont suggéré que cette activité biologique pourrait être diminuée par l’augmentation de la pression partielle de CO2 (pCO2) dans les océans qui a tendance à s’ équilibrer avec celle de l’atmosphère. Dans ce papier, nous concevons des modèles dynamiques pour essayer de simuler la diminution des taux de calcification et de photosynthèse observés chez Emiliania huxleyi après une hausse de la pCO2 reproduite en chémostat. Comme les mécanismes physiologiques impliqués sont encore loin d’ être complètement élucidés, nous considérons différents modèles, chacun d’eux étant basé sur une hypothèse biologique différente. Ces modèles, construits en utilisant des fonctions génériques pour caractériser les processus de croissance et de calcification, peuvent être analysés indépendamment de la forme exacte de ces fonctions et de la valeur des paramètres. L’ étude s’appuie donc sur ces fonctions génériques où la seule hypothèse est une régulation de ces taux par une des trois formes qui composent la totalité du carbone inorganique dissous : le CO2, les carbonates et les bicarbonates. Il s’en suit que chaque modèle réagit différemment à une élévation de la pCO2. Contrairement aux hypothèses classiquement admises, notre étude montre que les seuls modèles dont le comportement est en accord avec les résultats expérimentaux sont ceux pour lesquels une régulation de la photosynthèse par les carbonates a été supposée, ce qui corrobore les conclusions de travaux récents. Enfin, nous montrons que les modèles dont le comportement qualitatif est mauvais ne seraient pas rejetés sur la base d’un critère quantitatif d’erreur de prédiction.

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Leveraging the signature of heterotrophic respiration on atmospheric CO<sub>2</sub> for model benchmarking

2020 ◽  
Vol 17 (5) ◽  
pp. 1293-1308 ◽  
Author(s):  
Samantha J. Basile ◽  
Xin Lin ◽  
William R. Wieder ◽  
Melannie D. Hartman ◽  
Gretchen Keppel-Aleks
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Heterotrophic Respiration ◽  
Atmospheric Co2 ◽  
Scale Model ◽  
Future Research ◽  
Tracer Transport ◽  
Test Bed ◽  
Model Simulations ◽  
Soil Heterotrophic Respiration

Abstract. Spatial and temporal variations in atmospheric carbon dioxide (CO2) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO2, in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

Challenges in Linking Atmospheric CO2 Concentrations to Fluxes at Local and Regional Scales

1992 ◽  
Vol 40 (5) ◽  
pp. 697 ◽  
Author(s):  
MR Raupach ◽  
OT Denmead ◽  
FX Dunin
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Wheat Crop ◽  
Turbulent Transfer ◽  
Near Surface ◽  
Scalar Dispersion ◽  
Source Sink

We describe relationships between atmospheric CO2 concentration variations and CO2 source-sink distributions, at two important scales between the single plant and the whole earth: the vegetation canopy and the atmospheric planetary boundary layer. For both these scales, it is shown how knowledge of turbulence and scalar dispersion can be applied to infer CO2 source-sink distributions or fluxes from concentration measurements. At the canopy scale, the turbulent transfer of CO2 and other scalars is non-diffusive close to any point source or sink in the canopy, but diffusive at greater distances. This distinction leads to a physically tenable description of turbulent transfer, and thence to an 'inverse method' for finding the vertical profiles of sources and sinks in the canopy from measured concentration profiles. The method is tested with data from a wheat crop. At the scale of the planetary boundary layer, we consider the daily CO2 concentration drawdown (the depression of the near-surface CO2 concentration below the free-atmosphere value) of typically 20-40 ppm. This is determined by both the regionally averaged CO2 uptake at the surface and the growth of the daytime convective boundary layer (CBL). It is shown that, for a column of air which fills the CBL and is moved across the landscape by the mean wind, the net cumulative surface CO2 flux (in mol m-2) is given to a good approximation by h(t)[Cm(t) - C+]/V, where h(t) is CBL depth, Cm(t) the CO2 concentration in the CBL column in mol mol-1, C+ the concentration above the CBL, V the molar volume and time t is measured from the time at which Cm = C+ in the morning, typically about 0800 hours local time. The resulting CO2 flux estimates are regionally averaged over the trajectory followed by the column. This 'CBL budget method' for inferring surface fluxes is compared with direct measurements of CO2 fluxes, with satisfactory results. The technique has application to scalars other than CO2.

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