NEE estimates 2006–2019 over Europe from a pre-operational ensemble-inversion system
- 1Max-Planck Institute for Biogeochemistry, Jena, Germany
- 2Meteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst, Hohenpeissenberg, Germany
- 3Institute of Geoscience, Friedrich Schiller University, Jena, Germany
- 4AGH University of Science and Technology, Kraków, Poland
- 1Max-Planck Institute for Biogeochemistry, Jena, Germany
- 2Meteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst, Hohenpeissenberg, Germany
- 3Institute of Geoscience, Friedrich Schiller University, Jena, Germany
- 4AGH University of Science and Technology, Kraków, Poland
Abstract. 3-hourly Net Ecosystem Exchange (NEE) is estimated at spatial scales of 0.25 degrees over the European continent, based on the pre-operational inverse modelling framework CarboScope Regional
(CSR) for the years 2006 to 2019. To assess the uncertainty originating from the choice of a-priori flux models and observational data, ensembles of inversions were produced using three terrestrial ecosystem flux models, two ocean flux models, and three sets of atmospheric stations. We find that the station set ensemble accounts for 61 % of the total spread of the annually aggregated fluxes over the full domain when varying all these elements, while the biosphere and ocean ensembles resulted in much smaller contributions to the spread of 28 % and 11 %, respectively. These percentages differ over the specific regions of Europe, based on the availability of atmospheric data. For example, the spread of the biosphere ensemble is prone to be larger in regions that are less constrained by CO2 measurements. We further investigate the unprecedented increase of temperature and simultaneous reduction of Soil Water Content (SWC) observed in 2018 and 2019. We find that NEE estimates during these two years suggest an impact of drought occurrences represented by the reduction of Net Primary Productivity (NPP), which in turn lead to less CO2 uptake across Europe in 2018 and 2019, resulting in anomalies up to 0.13 and 0.07 PgC yr-1 above the climatological mean, respectively. Annual temperature anomalies also exceeded the climatological mean by 0.46 °C in 2018 and by 0.56 °C in 2019, while standardized-precipitation-evaporation-index (SPEI) anomalies declined to −0.20 and −0.05 SPEI units below the climatological mean in both 2018 and 2019, respectively. Therefore, the biogenic fluxes showed a weaker sink of CO2 in both 2018 and 2019 (−0.22±0.05 and −0.28±0.06 PgC yr-1, respectively) in comparison with the mean −0.36±0.07 PgC yr-1 calculated over the full analysed period (i.e., fourteen years). These translate into a continental-wide reduction of the annual sink by 39 % and 22 %, respectively, larger than the typical year-to-year standard deviation of 19 % observed over the full period.
Saqr Munassar et al.
Status: closed
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RC1: 'reviewer comment on acp-2021-873', Anonymous Referee #1, 03 Dec 2021
General comments
Authors present an analysis of the European terrestrial carbon cycle variability in 2006-2019 made with the pre-operational inverse modeling framework “CarboScope Regional''. The CO2 flux estimates are shown to be largely independent from the prior fluxes in the area of dense observations. The results confirm dominance of the observational constraint on fluxes and the importance of climate controls on the interannual flux variability. Authors find the inverse model predicts statistically significant positive CO2 flux anomalies in 2018-2019 related to hot and dry climate in those anomalous years. The paper is well written and can be considered for publication after minor revisions.
Detailed comments
L75-80 Although some of the information can be found in references, to improve readability it is useful to give few more details about the CSR such as optimization scheme and temporal resolution of flux corrections.
L104 Need to give detail – where station types come from.
L248-L300 The correlation of posterior fluxes with climate indices has been reported in detail. To enhance the validation of interannual flux variability, can authors add comparison with interannually varying regional flux estimates by independent process-based models, and possibly, top-down?
L407 Need a reference here on systematic bias in transport models.
Technical correctionsL18 Phrase ‘We further investigate the unprecedented increase of temperature …’ is somewhat incomplete, better write that one investigates the impact of ‘unprecedented increase ..’ on the carbon cycle.
L103 ‘South-eastern Europe (light red).’ Line out of place.
L265 ‘fluxes of both’ can be replaced with ‘fluxes estimated with both’
L405 ‘widespread scale’ can be reduced to ‘wide scale’
L424-425 The phrase ‘spatial correlation length of prior error’ can be reformulated, it would be more accurate to avoid using ‘prior’ as this spatial correlation is applied to posterior flux corrections.
L460 Paper number in Chevallier 2012b is missing (Global Biogeochem. Cycles, 26, GB1021, doi:10.1029/2010GB003974)
- AC1: 'ACs reply on RC1', Saqr Munassar, 19 Feb 2022
-
RC2: 'Reviewer comment on acp-2021-873', Anonymous Referee #2, 10 Jan 2022
- AC2: 'Reply on RC2', Saqr Munassar, 19 Feb 2022
Status: closed
-
RC1: 'reviewer comment on acp-2021-873', Anonymous Referee #1, 03 Dec 2021
General comments
Authors present an analysis of the European terrestrial carbon cycle variability in 2006-2019 made with the pre-operational inverse modeling framework “CarboScope Regional''. The CO2 flux estimates are shown to be largely independent from the prior fluxes in the area of dense observations. The results confirm dominance of the observational constraint on fluxes and the importance of climate controls on the interannual flux variability. Authors find the inverse model predicts statistically significant positive CO2 flux anomalies in 2018-2019 related to hot and dry climate in those anomalous years. The paper is well written and can be considered for publication after minor revisions.
Detailed comments
L75-80 Although some of the information can be found in references, to improve readability it is useful to give few more details about the CSR such as optimization scheme and temporal resolution of flux corrections.
L104 Need to give detail – where station types come from.
L248-L300 The correlation of posterior fluxes with climate indices has been reported in detail. To enhance the validation of interannual flux variability, can authors add comparison with interannually varying regional flux estimates by independent process-based models, and possibly, top-down?
L407 Need a reference here on systematic bias in transport models.
Technical correctionsL18 Phrase ‘We further investigate the unprecedented increase of temperature …’ is somewhat incomplete, better write that one investigates the impact of ‘unprecedented increase ..’ on the carbon cycle.
L103 ‘South-eastern Europe (light red).’ Line out of place.
L265 ‘fluxes of both’ can be replaced with ‘fluxes estimated with both’
L405 ‘widespread scale’ can be reduced to ‘wide scale’
L424-425 The phrase ‘spatial correlation length of prior error’ can be reformulated, it would be more accurate to avoid using ‘prior’ as this spatial correlation is applied to posterior flux corrections.
L460 Paper number in Chevallier 2012b is missing (Global Biogeochem. Cycles, 26, GB1021, doi:10.1029/2010GB003974)
- AC1: 'ACs reply on RC1', Saqr Munassar, 19 Feb 2022
-
RC2: 'Reviewer comment on acp-2021-873', Anonymous Referee #2, 10 Jan 2022
- AC2: 'Reply on RC2', Saqr Munassar, 19 Feb 2022
Saqr Munassar et al.
Saqr Munassar et al.
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