Reply on RC2

<p>Evaluating atmospheric transport simulations against observations helps refining bottom-up estimates of greenhouse gas fluxes and identifying gaps in our understanding of regional and category-specific contributions to atmospheric mole fractions. This insight is critical in the efforts to mitigate anthropogenic environmental impact. Beside total mole fractions, stable isotope ratios provide further constraints on source-sink processes [1-3].</p><p>Here, we present two receptor-oriented model simulations for carbon dioxide (CO<sub>2</sub>) mole fraction and δ<sup>13</sup>C-CO<sub>2</sub> stable isotope ratios for a nine year period (2009-2017) at the High Altitude Research Station Jungfraujoch (Switzerland, 3580 m asl). The model simulations of CO<sub>2</sub> were performed on a 3-hourly time-resolution with two backward Lagrangian particle dispersion models driven by two different numerical weather forecast fields: FLEXPART-COSMO and STILT-ECMWF. Anthropogenic CO<sub>2</sub> fluxes were based on the EDGAR v4.3 emissions inventory aggregated into 14 source categories representing fossil and biogenic fuel uses as well as emissions from cement production. Biospheric CO<sub>2</sub> fluxes representing the photosynthetic uptake and respiration of 8 plant functional types were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulated CO<sub>2</sub> emissions per source and sink category were weighted with category-specific δ<sup>13</sup>C-CO<sub>2</sub> signatures from published experimental studies. Background CO<sub>2</sub> values at the boundaries of both model domains were taken from global model simulations and the corresponding δ<sup>13</sup>C-CO<sub>2</sub> values were constructed as suggested in Ref. [3]. We compare the simulations to a unique data set of continuous in-situ observations of CO<sub>2</sub> mole fractions and δ<sup>13</sup>C-CO<sub>2</sub> stable isotope ratios by quantum cascade laser absorption spectroscopy as described in previous work [1, 4-5], available for the whole nine year period at the site.</p><p>The simulated atmospheric CO<sub>2</sub> and δ<sup>13</sup>C-CO<sub>2</sub> time-series are in good agreement with the observations and capture the observed variability at the models' 3-hourly time-resolution. This allows for an in-depth evaluation of the contribution of different CO<sub>2</sub> emission sources and for an allocation of source regions when Jungfraujoch is influenced by air masses from the planetary boundary layer. In brief, the receptor-oriented model simulations suggest that anthropogenic CO<sub>2</sub> contributions are primarily of fossil origin (90%). Anthropogenic emissions contribute between 60% in February, and 20% in July/August, to the CO<sub>2</sub> enhancements observed at Jungfraujoch. The remaining fraction is due to biosphere respiration, which thus largely dominates emissions during the summer season. However, intense photosynthetic CO<sub>2</sub> uptake during June, July and August roughly outweighs CO<sub>2</sub> contributions from anthropogenic activities and biosphere respiration at JFJ.</p><p> </p><p> </p><p>REFERENCES</p><p>[1] Tuzson et al., 2011. ACP, 11, 1685</p><p>[2] Röckmann et al., 2016. ACP, 16, 10469</p><p>[3] Vardag et al., 2016. Biogeosciences, 13, 4237</p><p>[4] Tuzson et al., 2008. Appl. Phys. B, 92, 451</p><p>[5] Sturm et al., 2013. AMT 6, 1659</p>

Estimating gross primary productivity (GPP) is important for simulating the subsequent carbon cycle elements and assessing the capacity of terrestrial ecosystems to support the sustainable development of human society. Light use efficiency (LUE) models were widely used to estimate GPP due to their concise model structures. However, quantifying LUEmax (maximum light use efficiency) and representing the responses of photosynthesis to environmental factors are still subject to large uncertainties, which lead to substantial errors in GPP simulations. In this study, we developed a hybrid model based on machine learning and a LUE model for GPP estimates. This hybrid model was built by targeting LUE with a machine learning approach, namely multi-layer perceptron (MLP), and then, estimating GPP within a LUE model framework with the MLP-based LUE and other required inputs. We trained the hybrid LUE (H-LUE) model and then, compared it against two conventional LUE models, the vegetation photosynthesis model (VPM) and vegetation photosynthesis and respiration model (VPRM), regarding GPP estimation, using tower-based daily-scale observations from 180 flux sites that cover nine different plant function types (PFTs). The results revealed better performance (R2 = 0.86 and RMSE = 1.79 gC m−2 d−1 on the test dataset) of the H-LUE model compared to the VPM and VPRM. Evaluations of the three models under four different extreme conditions consistently revealed better performance of the H-LUE model, indicating greater adaptability of the model to varied environments in the context of climate change. Furthermore, we also found that the H-LUE model can reasonably represent the responses of the LUE to meteorological variables. Our study revealed the reliable and robust performance of the developed hybrid LUE when simulating GPP across global biomes, providing references for developing better hybrid GPP models.

Estimating biogenic CO 2 fluxes is essential to quantify urban anthropogenic emissions, yet urban vegetation heterogeneity presents a significant challenge to making accurate estimations. We have developed an hourly temporal, 10‐m spatial resolution biogenic CO 2 flux estimation framework based on the Vegetation Photosynthesis and Respiration Model (VPRM) and its variants (UrbanVPRM and VPRM‐modified). Unlike lower‐resolution models, our approach captures finer‐scale variability, particularly in fragmented urban green spaces like street trees and lawns. Results show that vegetation in Munich offsets 2.0%–2.8% of annual anthropogenic CO 2 emissions in the study domain, with tree‐covered areas as primary sinks and grasslands as net sources. During summer, daytime CO 2 uptake can match or exceed anthropogenic emissions. Evaluations employing city park field measurements and eddy covariance towers confirm strong performance of our models, while highlighting VPRM‐modified's advantage in grasslands and croplands, and UrbanVPRM's improvements in urban areas via impervious surface correction. These findings highlight the value of high‐resolution modeling in improving urban carbon flux assessments.

Abstract. Urban areas are significant contributors to global CO2 emissions, requiring detailed monitoring to support climate neutrality goals. This study presents a high-resolution modeling framework using GRAMM/GRAL, adapted for simulating atmospheric CO2 concentrations from anthropogenic and biospheric sources and sinks in Zurich, Switzerland. The framework resolves atmospheric concentrations at the building scale, and it employs a detailed inventory of anthropogenic emissions as well as biospheric fluxes, which were calculated using the Vegetation Photosynthesis and Respiration Model (VPRM). Instead of simulating the full dynamics of meteorology and atmospheric transport, the dispersion of CO2 is precomputed for more than 1000 static weather situations, from which the best match is selected for any point in time based on the simulated and measured meteorology in and around the city. In this way, time series over multiple years can be produced with minimal computational cost. Measurements from a dense network of mid-cost CO2 sensors are used to validate the model, demonstrating its capability to capture spatial and temporal CO2 variability. Applications to other cities are discussed, emphasizing the need for high-quality input data and tailored solutions for diverse urban environments. The work contributes to advancing urban CO2 monitoring strategies and their integration with policy frameworks.

India needs a high-resolution estimation of carbon sources and sinks to implement the country’s climate change action plans and mitigation strategy effectively. Current carbon estimates over the Indian region based on the “Bottom-up” approach suffer from significant uncertainty, which calls for more process-based models and atmospheric inverse modelling to obtain a more accurate budget. Inverse models constrain the carbon fluxes based on atmospheric observation of CO2 mole fractions. The unavailability of amble observations over the Indian domain critically impacts estimation accuracy. Fortunately, there are increasing efforts to improve the availability of CO2 observation over the domain. Along with the observations, the availability of a suitable transport model to simulate the CO2 distribution is essential to the accurate inverse estimation of carbon fluxes. The inability of coarse-resolution global models to simulate the fine-scale variability in CO2 distribution warrants developing a regional high-resolution modelling system. Here we evaluate the performance of a regional high-resolution modelling system which utilises meteorological fields from the Weather Research and Forecasting (WRF) model to simulate the CO2 transport over the Indian domain using a lagrangian particle dispersion model, Stochastic Time-Inverted Lagrangian Transport Model (STILT). Using lagrangian models enables us to study the CO2 distribution at very high resolution (even at sub-grid scale) with reduced cost. We use the vegetation photosynthesis and Respiration Model (VPRM), coupled with the modelling system, to simulate the biospheric fluxes. The anthropogenic and biomass burning fluxes are obtained from different available inventories. We use CO2 in-situ observations from different parts of the Indian domain, which utilises flask measurements and PICARRO CRDS instruments, to evaluate the modelling system. Our high-resolution modelling framework shows good skill in simulating the CO2 variability over the region. The results of the evaluation will be discussed in detail during the presentation.

How and to what extent does precipitation on multi-temporal scales and soil moisture at different depths determine carbon flux responses in a water-limited grassland ecosystem?

Controls of carbon flux in a semi-arid grassland ecosystem experiencing wetland loss: Vegetation patterns and environmental variables

[1] Estimating the current sources and sinks of carbon and projecting future levels of CO2 and climate require biospheric carbon models that cover the landscape. Such models inevitably suffer from deficiencies and uncertainties. This paper addresses how to quantify errors in modeled carbon fluxes and then trace them to specific input variables. To date, few studies have examined uncertainties in biospheric models in a quantitative fashion that are relevant to landscape-scale simulations. In this paper, we introduce a general framework to quantify errors in biospheric carbon models that “unmix” the contributions to the total uncertainty in simulated carbon fluxes and attribute the error to different variables. To illustrate this framework we apply and use a simple biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM), in boreal forests of central Canada, using eddy covariance flux measurement data from two main sites of the Canadian Carbon Program (CCP). We explicitly distinguish between systematic errors (“biases”) and random errors and focus on the impact of errors present in biospheric parameters as well as driver data sets (satellite indices, temperature, solar radiation, and land cover). Biases in downward shortwave radiation accumulated to the most significant amount out of the driver data sets and accounted for a significant percentage of the annually summed carbon uptake. However, the largest cumulative errors were shown to stem from biospheric parameters controlling the light-use efficiency and respiration-temperature relationships. This work represents a step toward a carbon model-data fusion system because in such systems the outcome is determined as much by uncertainties as by the measurements themselves.

Abstract. Optimized biogenic carbon fluxes for Europe were estimated from high-resolution regional-scale inversions, utilizing atmospheric CO2 measurements at 16 stations for the year 2007. Additional sensitivity tests with different data-driven error structures were performed. As the atmospheric network is rather sparse and consequently contains large spatial gaps, we use a priori biospheric fluxes to further constrain the inversions. The biospheric fluxes were simulated by the Vegetation Photosynthesis and Respiration Model (VPRM) at a resolution of 0.1° and optimized against eddy covariance data. Overall we estimate an a priori uncertainty of 0.54 GtC yr−1 related to the poor spatial representation between the biospheric model and the ecosystem sites. The sink estimated from the atmospheric inversions for the area of Europe (as represented in the model domain) ranges between 0.23 and 0.38 GtC yr−1 (0.39 and 0.71 GtC yr−1 up-scaled to geographical Europe). This is within the range of posterior flux uncertainty estimates of previous studies using ground-based observations.

Abstract. We develop PALM-CO2, a high-resolution urban carbon dioxide transport model with anthropogenic and biogenic carbon emissions. The model is based on an open-source urban flow large eddy simulation (LES) model, PALM, where we implemented a biogenic carbon emission module (Vegetation Photosynthesis and Respiration Model, VPRM) and customised output modules for carbon fluxes. PALM-CO2 is validated through a case study in London, comprising an 8 by 8 km2 domain covering the borough of Camden at a resolution of 10 m. Simulations are driven by reanalysis meteorological forcing and background CO2 concentrations, while the hourly anthropogenic emissions at 10-m resolution are explicitly derived in this study. Validation against eddy-covariance flux measurements inside the study region confirms that the model captures the diurnal variation of the turbulent transport and anthropogenic emissions. Additional validation against monthly biogenic flux diurnal profiles at a deciduous forest site in Czech Republic further confirms the biogenic flux module. The simulations reveal strong spatial heterogeneity in near-surface CO2 concentrations driven by building-induced turbulence, diurnal boundary layer evolution, and emission patterns. PALM-CO2 provides a high-resolution framework for investigating CO2 transport processes in complex urban and vegetated environments, providing improved quantification of urban emission sources.

This study investigated the atmospheric concentrations of carbon dioxide (CO2) in the Metropolitan Area of So Paulo (MASP) and its surroundings, using the Weather Research and Forecasting model coupled with the greenhouse gas module (WRF-GHG).The primary objective was to quantify anthropogenic emissions and biogenic fluxes, as well as to understand the spatial and temporal dynamics of CO2 in one of the largest and most complex urban areas in Latin America.Biogenic fluxes were simulated using the Vegetation Photosynthesis and Respiration Model (VPRM), with optimized parameters to represent tropical ecosystems (Atlantic Forest, Cerrado, and sugarcane) based on local flux tower data.Parameter optimization significantly improved the representation of CO2 fluxes for the Cerrado and sugarcane ecosystems, capturing seasonal dynamics such as higher CO2 uptake during the rainy season.For the Cerrado, the correlation coefficient remained the same at R= 0.34 for default and optimized parameters, while the mean bias improved significantly, decreasing from 0.59 to -0.05 mol m-2 s-1.For sugarcane, the correlation improved from 0.78 to 0.81, and the mean bias was reduced from 1.86 to 0.01 mol m-2 s-1 after optimization.These results highlight the effectiveness of parameter tuning, particularly for the sugarcane ecosystem.However, the model faced challenges in representing the Atlantic Forest.The simulated net fluxes for the Atlantic Forest showed that this biome may be a source of CO2, rather than a sink.In some periods, this overestimation overshadowed the productivity of this ecosystem, highlighting the need for advanced methods to improve flux estimations.CO2 simulations using WRF-GHG, integrating biogenic fluxes and anthropogenic emissions, captured distinct seasonal and diurnal patterns.At the urban site (IAG), the model underestimated CO2 concentrations, showing a negative bias due to the complexity of representing dense vehicular traffic emissions.In contrast, at the vegetated and elevated site (PDJ), the simulations showed excellent agreement with observational data, with very small positive biases.These results highlight the model's ability to reproduce CO2 dynamics in less urbanized areas influenced more by atmospheric mixing and regional transport processes.Seasonality analyses revealed higher CO2 concentrations during winter (approximately 430 ppm), primarily associated with increased atmospheric stability and reduced vegetation uptake, particularly in August.Conversely, during summer (around 422 ppm), lower atmospheric stability, higher wind speeds, and elevated humidity levels contributed to lower CO2 concentrations.Additionally, increased agricultural productivity during this period enhanced CO2 absorption from the atmosphere.Furthermore, the comparison between biogenic and anthropogenic scenarios demonstrated the contributions of both fluxes to CO2 dynamics in MASP, emphasizing the importance of incorporating combined emissions into atmospheric models.As the first evaluation of CO2 dynamics in So Paulo, this study provides an essential foundation to understand the complex interactions between biogenic fluxes, anthropogenic emissions, topography, and meteorology in the largest urban area in South America.

Abstract. A framework has been developed that allows validating CO2 column averaged volume mixing ratios (VMRs) retrieved from ground-based solar absorption measurements using Fourier transform infrared spectrometry (FTS) against measurements made in-situ (such as from aircrafts and tall towers). Since in-situ measurements are done frequently and at high accuracy on the global calibration scale, linking this scale with FTS total column retrievals ultimately provides a calibration scale for remote sensing. FTS, tower and aircraft data were analyzed from measurements during the CarboEurope Regional Experiment Strategy (CERES) from May to June 2005 in Biscarrosse, France. Carbon dioxide VMRs from the MetAir Dimona aircraft, the TM3 global transport model and Observations of the Middle Stratosphere (OMS) balloon based experiments were combined and integrated to compare with the FTS measurements. The comparison allows for calibrating the retrieved carbon dioxide VMRs from the FTS. The Stochastic Time Inverted Lagrangian Transport (STILT) model was then utilized to identify differences in surface influence regions or footprints between the FTS and the aircraft CO2 concentrations. Additionally, the STILT model was used to compare carbon dioxide concentrations from a tall tower situated in close proximity to the FTS station. The STILT model was then modified to produce column concentrations of CO2 to facilitate comparison with the FTS data. These comparisons were additionally verified by using the Weather Research and Forecasting – Vegetation Photosynthesis and Respiration Model (WRF-VPRM). The differences between the model-tower and the model-FTS were then used to calculate an effective bias of approximately −2.5 ppm between the FTS and the tower. This bias is attributed to the scaling factor used in the FTS CO2 data, which was to a large extent derived from the aircraft measurements made within a 50 km distance from the FTS station: spatial heterogeneity of carbon dioxide in the coastal area caused a low bias in the FTS calibration. Using STILT for comparing remotely sensed CO2 data with tower measurements of carbon dioxide and quantifying this comparison by means of an effective bias, provided a framework or a "transfer standard" that allowed validating the FTS retrievals versus measurements made in-situ.

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

Diffuse radiation can promote the vegetation photosynthesis. Radiation use efficiency (RUE = GPP/PAR) simulated by Terrestrial Ecosystem Carbon Flux GPP Model (TEC), Vegetation Photosynthesis Model (VPM), Eddy Covariance Light Use Efficiency Model (EC‐LUE), and Diffuse Fraction‐Based Two‐Leaf Terrestrial Ecosystem Carbon Flux Model (DTEC) models were against the measured radiation‐use efficiency (RUE) from FLUXNET under different bins of diffuse photosynthetically active radiation fraction (FDIFFPAR) in this study. Our results showed that the observed RUE increased linearly with FDIFFPAR. The differences of RUE between the first bin and other bin (RUEbin) from two big‐leaf models were underestimated when the FDIFFPAR is higher than 0.5 except in evergreen broadleaf forests and RUEbin from the DTEC model was overestimated when the FDIFFPAR is higher than 0.7. The role of temperature and vapor pressure deficit in the diffuse radiation effects on vegetation photosynthesis was underestimated in EC‐LUE, TEC, VPM. Although many studies applied LUE models to assess the effects of diffuse radiation on vegetation photosynthesis, our results suggested that these models were needed to be improved.

Precision, accuracy, and temporal resolution are key to make full use of atmospheric trace gas records in ice cores. These aspects will become especially crucial for ice cores that aim to extend the ice core record to the last 1.5 Myr, i.e., across the Mid Pleistocene Transition (as currently drilled within the European project Beyond EPICA – Oldest Ice Core (BE-OIC)). The ice from this period is expected to be close to bedrock and, due to glacier flow, extremely thinned with 15,000 years of climate history contained in only one meter of ice. Accordingly, for a century-scale resolution, the sample vertical extent must be reduced to a few cm containing only about 1−2 mL air STP. We present a novel combined system for the extraction and the simultaneous measurement of CO2, CH4, and N2O concentrations, as well as δ13CO2, which achieves a vertical resolution of 1−2 cm with precisions of 0.4 ppm, 3 ppb, 1 ppb and 0.04 ‰, respectively. This is accomplished by employing a directional and continuous laser induced sublimation followed by analysis of the sample gas by quantum cascade laser absorption spectroscopy (QCLAS). Besides the low sample volume requirements and the vertical resolution capabilities, the described method holds additional advantages over previous methods, including the immunity of the highly specific QCLAS analysis to drilling fluid contamination as well as the non-destructive nature of the spectroscopic gas analysis. The combined extraction and analysis system was extensively tested by sublimating gas-free ice with introduction of a standard gas to determine the accuracy and characterize potential artefacts. Moreover, Antarctic ice samples were measured to confirm the measurement performance, covering the range of variability expected in Pleistocene ice and to highlight the vertical resolution capabilities critical for its application within BE-OIC.