Vegetation Photosynthesis And Respiration Model Research Articles

Effect of atmospheric conditions and VPRM parameters on high-resolution regional CO2 simulations over East Asia

Atmospheric CO2 concentrations are largely affected by the surface CO2 flux and atmospheric wind. To estimate atmospheric CO2 concentrations over East Asia, the effects of atmospheric conditions and the parameters of Vegetation Photosynthesis and Respiration Model (VPRM) that simulates biogenic CO2 concentrations were evaluated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The VPRM in WRF-Chem requires parameter optimization for the experimental period and region. Total six experiments with two atmospheric fields (final analysis; FNL and fifth generation of European Centre for Medium-range Weather Forecasts atmospheric reanalysis; ERA5) and three VPRM parameter tables (US, Li, and Dayalu) were conducted to investigate the appropriate atmospheric field and VPRM parameter table for East Asia. For validation, two types of wind observations (SYNOP and SONDE) and two types of CO2 observations (surface CO2 observations and OCO-2 XCO2 observations) were used. The experiments using FNL showed a lower RMSE for surface winds, whereas those using ERA5 showed a lower RMSE for upper-air winds. On average, the surface wind RMSE in the experiments using FNL was lower than that using ERA5. With respect to surface CO2 observations, the experiments using the Li table showed relatively lower RMSEs compared to those using other tables. With respect to OCO-2 XCO2 observations, the Li table with FNL showed lower RMSEs than other combinations. Overall, the combination of the Li table and FNL was the most appropriate for simulating CO2 concentrations in East Asia using WRF-Chem with VPRM.

Improving the Estimation of Gross Primary Productivity across Global Biomes by Modeling Light Use Efficiency through Machine Learning

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.

Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia

In this study, the effects of meteorological data assimilation (DA) on high-resolution CO2 concentration simulations in East Asia were investigated using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and three-dimensional variational data assimilation (3DVAR). Experiments with and without meteorological DA were performed for June and December 2018. To determine an appropriate DA cycling interval, two experiments were performed at cycling intervals of 6 and 12 h. Compared to the meteorological fields simulated from experiment without DA, those simulated from experiments with DA were more similar to atmospheric observations. The CO2 concentrations simulated from the experiment with a 6 h DA cycling interval were highly similar to the observed CO2 concentrations. Compared to the root mean square error (RMSE) of the experiment without DA, the RMSE of simulated CO2 concentrations in the experiment with a 6 h DA interval decreased by 8.19% in June and 1.43% in December with respect to surface CO2 concentration observations, and decreased by 2.52% in June and 0.47% in December against column-averaged CO2 concentration (XCO2) observations of the Orbiting Carbon Observatory 2 (OCO-2). The improved 2 m temperature forecasts by DA enhanced biogenic CO2 simulations using the Vegetation Photosynthesis and Respiration Model (VPRM) in WRF-Chem. The improved wind speed and direction forecasts by DA led to improved simulations of the horizontal transport of CO2 concentrations and planetary boundary layer height. Overall, enhanced meteorological forecasts by meteorological DA using 3DVAR improved the accuracy of high-resolution CO2 concentration simulations using WRF-Chem in East Asia.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

Analysis of regional CO2 contributions at the high Alpine observatory Jungfraujoch by means of atmospheric transport simulations and δ13C

Abstract. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009–2017 with stable carbon isotope (δ13C–CO2) information. We applied two Lagrangian particle dispersion models driven by output from two different numerical weather prediction systems (FLEXPART–COSMO and STILT-ECMWF) in order to simulate CO2 concentration at JFJ based on regional CO2 fluxes, to estimate atmospheric δ13C–CO2, and to obtain model-based estimates of the mixed source signatures (δ13Cm). Anthropogenic fluxes were taken from a fuel-type-specific version of the EDGAR v4.3 inventory, while ecosystem fluxes were based on the Vegetation Photosynthesis and Respiration Model (VPRM). The simulations of CO2, δ13C–CO2, and δ13Cm were then compared to observations performed by quantum cascade laser absorption spectroscopy. The models captured around 40 % of the regional CO2 variability above or below the large-scale background and up to 35 % of the regional variability in δ13C–CO2. This is according to expectations considering the complex Alpine topography, the low intensity of regional signals at JFJ, and the challenging measurements. Best agreement between simulations and observations in terms of short-term variability and intensity of the signals for CO2 and δ13C–CO2 was found between late autumn and early spring. The agreement was inferior in the early autumn periods and during summer. This may be associated with the atmospheric transport representation in the models. In addition, the net ecosystem exchange fluxes are a possible source of error, either through inaccuracies in their representation in VPRM for the (Alpine) vegetation or through a day (uptake) vs. night (respiration) transport discrimination to JFJ. Furthermore, the simulations suggest that JFJ is subject to relatively small regional anthropogenic contributions due to its remote location (elevated and far from major anthropogenic sources) and the limited planetary boundary layer influence during winter. Instead, the station is primarily exposed to summertime ecosystem CO2 contributions, which are dominated by rather nearby sources (within 100 km). Even during winter, simulated gross ecosystem respiration accounted for approximately 50 % of all contributions to the CO2 concentrations above the large-scale background. The model-based monthly mean δ13Cm ranged from − 22 ‰ in winter to − 28 ‰ in summer and reached the most depleted values of − 35 ‰ at higher fractions of natural gas combustion, as well as the most enriched values of − 17 ‰ to − 12 ‰ when impacted by cement production emissions. Observation-based δ13Cm values were derived independently from the simulations by a moving Keeling-plot approach. While model-based estimates spread in a narrow range, observation-based δ13Cm values exhibited a larger scatter and were limited to a smaller number of data points due to the stringent analysis prerequisites.

A Modified Vegetation Photosynthesis and Respiration Model (VPRM) for the Eastern USA and Canada, Evaluated With Comparison to Atmospheric Observations and Other Biospheric Models

AbstractAtmospheric CO2 measurements from a dense surface network can help to evaluate terrestrial biosphere model (TBM) simulations of Net Ecosystem Exchange (NEE) with two key benefits. First, gridded CO2 flux estimates can be evaluated over regional scales, not possible using flux tower observations at discrete locations for model evaluation. Second, TBM ability to explain atmospheric CO2 fluctuations due to the biosphere can be directly tested, an important objective for anthropogenic emissions monitoring using atmospheric observations. Here, we customize the Vegetation Photosynthesis and Respiration Model (VPRM) for an eastern North American domain with strong biological activity upwind of urban areas. Parameters are optimized using flux tower observations from a historical database with sites in (and near) the domain. In addition, the respiration model (originally a linear function of temperature) is modified to account for impacts of changing foliage, non‐linear temperature, and water stress. Flux estimates from VPRM, the Carnegie‐Ames‐Stanford Approach (CASA) model and the Simple Biosphere Model v4 (SiB4), are convolved with footprints from atmospheric transport models for evaluation with CO2 observations at 21 towers in the domain, with roughly half of the towers used here for the first time. Results show that the new respiration model in VPRM helps to correct a growing season sink bias in the atmosphere associated with underestimated summertime respiration using the original model with annual parameters. The new VPRM also better explains fine‐scale atmospheric CO2 variability compared to other TBMs, due to higher resolution diagnostic phenology, the new respiration model, domain‐specific parameters, and high‐quality input data sets.

Implementation of Improved Parameterization of Terrestrial Flux in WRF‐VPRM Improves the Simulation of Nighttime CO2 Peaks and a Daytime CO2 Band Ahead of a Cold Front

AbstractEnhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)‐America mission and their formation mechanism is undetermined. Improved understanding and correct simulation of these CO2 bands are needed for unbiased inverse CO2 flux estimation. Such CO2 bands are hypothesized to be related to nighttime CO2 respiration and investigated in this study using WRF‐VPRM, a weather‐biosphere‐online‐coupled model, in which the biogenic fluxes are handled by the Vegetation Photosynthesis and Respiration Model (VPRM). While the default VPRM satisfactorily parameterizes gross ecosystem exchange, its treatment of terrestrial respiration as a linear function of temperature was inadequate as respiration is a nonlinear function of temperature and also depends on the amount of biomass and soil wetness. An improved ecosystem respiration parameterization including enhanced vegetation index, a water stress factor, and a quadratic temperature dependence is incorporated into WRF‐VPRM and evaluated in a year‐long simulation before applied to the investigation of the frontal CO2 band on August 4, 2016. The evaluation shows that the modified WRF‐VPRM increases ecosystem respiration during the growing season, and improves model skill in reproducing nighttime near‐surface CO2 peaks. A nested‐domain WRF‐VPRM simulation is able to capture the main characteristics of the August 4 CO2 band and informs its formation mechanism. Nighttime terrestrial respiration leads to accumulation of near‐surface CO2 in the region. As the cold front carrying low‐CO2 air moves southeastward, and strong photosynthesis depletes CO2 further southeast of the front, a CO2 band develops immediately ahead of the front.

Analysis of CO<sub>2</sub> spatio-temporal variations in China using a weather–biosphere online coupled model

Abstract. The dynamics of atmospheric CO2 has received considerable attention in the literature, yet significant uncertainties remain within the estimates of contribution from the terrestrial flux and the influence of atmospheric mixing. In this study we apply the WRF-Chem model configured with the Vegetation Photosynthesis and Respiration Model (VPRM) option for biomass fluxes in China to characterize the dynamics of CO2 in the atmosphere. The online coupled WRF-Chem model is able to simulate biosphere processes (photosynthetic uptake and ecosystem respiration) and meteorology in one coordinate system. We apply WRF-Chem for a multi-year simulation (2016–2018) with integrated data from a satellite product, flask samplings, and tower measurements to diagnose the spatio-temporal variations of CO2 fluxes and concentrations in China. We find that the spatial distribution of CO2 was dominated by anthropogenic emissions, while its seasonality (with maxima in April 15 ppmv higher than minima in August) was dominated by the terrestrial flux and background CO2. Observations and simulations revealed a consistent increasing trend in column-averaged CO2 (XCO2) of 2.46 ppmv (0.6 % yr−1) resulting from anthropogenic emission growth and biosphere uptake. WRF-Chem successfully reproduced ground-based measurements of surface CO2 concentration with a mean bias of −0.79 ppmv and satellite-derived XCO2 with a mean bias of 0.76 ppmv. The model-simulated seasonality was also consistent with observations, with correlation coefficients of 0.90 and 0.89 for ground-based measurements and satellite data, respectively. Tower observations from a background site at Lin'an (30.30∘ N, 119.75∘ E) revealed a strong correlation (−0.98) between vertical CO2 and temperature gradients, suggesting a significant influence of boundary layer thermal structure on the accumulation and depletion of atmospheric CO2.

Dynamical Downscaling of CO2 in 2016 Over the Contiguous United States Using WRF‐VPRM, a Weather‐Biosphere‐Online‐Coupled Model

AbstractEcosystem function (particularly CO2 fluxes and the subsequent atmospheric transport), synoptic‐scale weather (e.g., midlatitude cyclones), and interactions between ecosystems and the atmosphere can be investigated using a weather‐biosphere‐online‐coupled model. The Vegetation Photosynthesis and Respiration Model (VPRM) was coupled with the Weather Research and Forecasting (WRF) model in 2008 to simulate “weather‐aware” biospheric CO2 fluxes and subsequent transport and dispersion. The ability of the coupled WRF‐VPRM modeling system to simulate the CO2 structures within midlatitude cyclones, however, has not been evaluated due to the lack of data within these weather systems. In this study, VPRM parameters previously calibrated off‐line using eddy covariance tower data over North America are implemented into WRF‐VPRM. The updated WRF‐VPRM is then used to simulate spatiotemporal variations of CO2 over the contiguous United States at a horizontal grid spacing of 12 km for 2016 using an optimized downscaling configuration. The downscaled fields are evaluated using remotely sensed data from the Orbiting Carbon Observatory‐2, Total Carbon Column Observing Network, and in situ aircraft measurements from Atmospheric Carbon and Transport‐America missions. Evaluations show that WRF‐VPRM captures the monthly variation of column‐averaged CO2 concentrations (XCO2) and episodic variations associated with frontal passages. The downscaling also successfully captures the horizontal CO2 gradients across fronts and vertical CO2 contrast between the boundary layer and the free troposphere. WRF‐VPRM modeling results indicate that from May to September, biogenic fluxes dominate variability in XCO2 over most of the contiguous United States, except over a few metropolitan areas such as Los Angeles.

Numerical simulation of atmospheric CO2 concentration and flux over the Korean Peninsula using WRF-VPRM model during Korus-AQ 2016 campaign.

We conducted regional scale CO2 simulations using the Weather Research and Forecasting model (WRF) coupled with the Vegetation Photosynthesis and Respiration Model (VPRM). We contrasted simulated concentrations with column, ground and aircraft observations during the Korea-United States Air Quality (KORUS-AQ) 2016 field campaign. Overall, WRF-VPRM slightly underestimates CO2 concentrations at ground and column monitoring sites, but it significantly underestimates at an inland tower measurement site, especially within the stable (nocturnal) boundary layer in nighttime. The model successfully captures the airborne vertical profiles but showed a large offset within the planetary boundary layer (PBL) in the areas surrounding Seoul and around the Taeahn point source emissions in the west coastal area of the Korean Peninsula. A case study flight intended to capture Chinese influence observed no clear signals of long-range transport of CO2, due mainly to the much larger magnitude of background CO2 concentrations. The calculated Net Ecosystem Exchange (NEE) with flux measurements at a tower site in the South Korean Peninsula has also been evaluated comparing with CO2 flux measurements at a flux tower site, resulting in the underestimation by less than a factor of 1.

Listening to the Forest: An Artificial Neural Network‐Based Model of Carbon Uptake at Harvard Forest

AbstractThe terrestrial biosphere strongly modulates atmospheric CO2 mixing ratios, whose inexorable rise propels anthropogenic climate change. Modeling and mechanistically understanding C uptake by the terrestrial biosphere are thus of broad societal concerns. Yet despite considerable progress, scaling up point observations to landscape and larger scales continues to frustrate analyses of the anthropogenically perturbed global C cycle. While that up‐scaling is our overarching motivation, here we focus on one of its elements, modeling C uptake at a given site. We devise a novel artificial neural network (ANN)‐based model of C uptake at Harvard Forest that combines locally observed and remotely sensed variables. Most of our model predictors are those used by an established ecosystem C uptake model, the Vegetation Photosynthesis and Respiration Model (VPRM), easing comparisons. To those, we add observed cumulative antecedent precipitation and soil temperature. We find that model errors are much larger in winter, indicating that better understanding and modeling of respiration will likely discernibly improve model performance. Comparing the ANN and VPRM results reveals errors attributed to unrealistic treatment of temperature in the VPRM formulation, indicating that better representation of temperature dependencies is also likely to enhance model skill. By judiciously comparing VPRM and ANN errors we thus overcome ANNs' notoriety for concealing the mechanisms underlying their predictive skills. We demonstrate their ability to identify outstanding ecosystem science knowledge gaps and particularly fruitful corresponding model development directions, improving site specific and up‐scaling flux modeling and understanding of the climate impacts of the northern forest.

Estimation of Carbon Sequestration in Tropical Peat Swamp Forest in Central Kalimantan Using Satellite Based on Primary Productivity

One of approach that can be used to estimate the carbon sequestration by vegetation is to calculate the Gross Primary Productivity (GPP) in Central Kalimantan. GPP is total carbon that can be absorbed by vegetation to be used in the process of photosynthesis. The purpose of this study is to estimate the value of GPP using Vegetation Photosynthesis and Respiration Model (VPRM) and analyze the data comparison between GPP value data derived from flux tower and GPP value data from MODIS data. The field data from flux tower was taken by Hirano et al. (2007) from January 1, 2004 to December 31, 2005. The MODIS data is used MODIS Surface Reflectance Level 3 data year 2004 to 2005. According to the result of this study, the maximum GPP value year 2004 and 2005 showed 302.365 gC m-2 per month (February 2004) and 366.841 gC m-2 per month (June 2005). The minimum GPP value year 2004 and 2005 was 166.003 gC m-2 per month (November 2004) and 187.663 gC m-2 per month (March 2005). The total value of GPP in year 2004 was 1,134.231 gC m-2 yr-1 and in year 2005 the value was 1,109.001 gC m-2 yr-1. The correlation coefficient between GPP value from flux tower and GPP value from MODIS – VPRM showed in dry season, r = 0.766 and in rainy season, r = 0,839.

Assessing biotic contributions to CO<sub>2</sub> fluxes in northern China using the Vegetation, Photosynthesis and Respiration Model (VPRM-CHINA) and observations from 2005 to 2009

Abstract. Accurately quantifying the spatiotemporal distribution of the biological component of CO2 surface–atmosphere exchange is necessary to improve top-down constraints on China's anthropogenic CO2 emissions. We provide hourly fluxes of CO2 as net ecosystem exchange (NEE; µmol CO2 m−2 s−1) on a 0.25∘×0.25∘ grid by adapting the Vegetation, Photosynthesis, and Respiration Model (VPRM) to the eastern half of China for the time period from 2005 to 2009; the minimal empirical parameterization of the VPRM-CHINA makes it well suited for inverse modeling approaches. This study diverges from previous VPRM applications in that it is applied at a large scale to China's ecosystems for the first time, incorporating a novel processing framework not previously applied to existing VPRM versions. In addition, the VPRM-CHINA model prescribes methods for addressing dual-cropping regions that have two separate growing-season modes applied to the same model grid cell. We evaluate the VPRM-CHINA performance during the growing season and compare to other biospheric models. We calibrate the VPRM-CHINA with ChinaFlux and FluxNet data and scale up regionally using Weather Research and Forecasting (WRF) Model v3.6.1 meteorology and MODIS surface reflectances. When combined with an anthropogenic emissions model in a Lagrangian particle transport framework, we compare the ability of VPRM-CHINA relative to an ensemble mean of global hourly flux models (NASA CMS – Carbon Monitoring System) to reproduce observations made at a site in northern China. The measurements are heavily influenced by the northern China administrative region. Modeled hourly time series using vegetation fluxes prescribed by VPRM-CHINA exhibit low bias relative to measurements during the May–September growing season. Compared to NASA CMS subset over the study region, VPRM-CHINA agrees significantly better with measurements. NASA CMS consistently underestimates regional uptake in the growing season. We find that during the peak growing season, when the heavily cropped North China Plain significantly influences measurements, VPRM-CHINA models a CO2 uptake signal comparable in magnitude to the modeled anthropogenic signal. In addition to demonstrating efficacy as a low-bias prior for top-down CO2 inventory optimization studies using ground-based measurements, high spatiotemporal resolution models such as the VPRM are critical for interpreting retrievals from global CO2 remote-sensing platforms such as OCO-2 and OCO-3 (planned). Depending on the satellite time of day and season of crossover, efforts to interpret the relative contribution of the vegetation and anthropogenic components to the measured signal are critical in key emitting regions such as northern China – where the magnitude of the vegetation CO2 signal is shown to be equivalent to the anthropogenic signal.

Multi-species inversion and IAGOS airborne data for a better constraint of continental-scale fluxes

Abstract. Airborne measurements of CO2, CO, and CH4 proposed in the context of IAGOS (In-service Aircraft for a Global Observing System) will provide profiles from take-off and landing of airliners in the vicinity of major metropolitan areas useful for constraining sources and sinks. A proposed improvement of the top-down method to constrain sources and sinks is the use of a multispecies inversion. Different species such as CO2 and CO have partially overlapping emission patterns for given fuel-combustion-related sectors, and thus share part of the uncertainties related both to the a priori knowledge of emissions and to model–data mismatch error. We use a regional modelling framework consisting of the Lagrangian particle dispersion model STILT (Stochastic Time-Inverted Lagrangian Transport) combined with the high-resolution (10 km × 10 km) EDGARv4.3 (Emission Database for Global Atmospheric Research) emission inventory, differentiated by emission sector and fuel type for CO2, CO, and CH4, and combined with the VPRM (Vegetation Photosynthesis and Respiration Model) for biospheric fluxes of CO2. Applying the modelling framework to synthetic IAGOS profile observations, we evaluate the benefits of using correlations between different species' uncertainties on the performance of the atmospheric inversion. The available IAGOS CO observations are used to validate the modelling framework. Prior uncertainty values are conservatively assumed to be 20 %, for CO2 and 50 % for CO and CH4, while those for GEE (gross ecosystem exchange) and respiration are derived from existing literature. Uncertainty reduction for different species is evaluated in a domain encircling 50 % of the profile observations' surface influence over Europe. We found that our modelling framework reproduces the CO observations with an average correlation of 0.56, but simulates lower mixing ratios by a factor of 2.8, reflecting a low bias in the emission inventory. Mean uncertainty reduction achieved for CO2 fossil fuel emissions is roughly 38 %; for photosynthesis and respiration flux it is 41 and 44 % respectively. For CO and CH4 the uncertainty reduction is roughly 63 and 67 % respectively. Considering correlation between different species, posterior uncertainty can be reduced by up to 23 %; such a reduction depends on the assumed error structure of the prior and on the considered time frame. The study suggests a significant uncertainty constraint on regional emissions using multi-species inversions of IAGOS in situ observations.

CO2 Transport, Variability, and Budget over the Southern California Air Basin Using the High-Resolution WRF-VPRM Model during the CalNex 2010 Campaign

AbstractTo study regional-scale carbon dioxide (CO2) transport, temporal variability, and budget over the Southern California Air Basin (SoCAB) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign period, a model that couples the Weather Research and Forecasting (WRF) Model with the Vegetation Photosynthesis and Respiration Model (VPRM) has been used. Our numerical simulations use anthropogenic CO2 emissions of the Hestia Project 2010 fossil-fuel CO2 emissions data products along with optimized VPRM parameters at “FLUXNET” sites, for biospheric CO2 fluxes over SoCAB. The simulated meteorological conditions have been validated with ground and aircraft observations, as well as with background CO2 concentrations from the coastal Palos Verdes site. The model captures the temporal pattern of CO2 concentrations at the ground site at the California Institute of Technology in Pasadena, but it overestimates the magnitude in early daytime. Analysis of CO2 by wind directions reveals the overestimate is due to advection from the south and southwest, where downtown Los Angeles is located. The model also captures the vertical profile of CO2 concentrations along with the flight tracks. The optimized VPRM parameters have significantly improved simulated net ecosystem exchange at each vegetation-class site and thus the regional CO2 budget. The total biospheric contribution ranges approximately from −24% to −20% (daytime) of the total anthropogenic CO2 emissions during the study period.

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

Inner Mongolia, China, contains the world’s largest grassland ecosystem. This has many areas of wetland, which provide important ecological services, especially carbon sequestration in the semi-arid terrestrial ecosystems. However, the area of wetland has decreased sharply in the past two decades. This study examined ways to recognize and extract wetland from grassland to determine the difference among net ecosystem exchange (NEE), gross primary production (GPP) and ecosystem respiration (Reco) between wetland and grassland and to evaluate the influence of wetland loss on carbon sequestration in the grassland. The eddy covariance (EC) flux technique was coupled with the Vegetation Photosynthesis and Respiration Model (VPRM) to upscale the spatial patterns of carbon flux. The results showed that the region was a carbon source in 2016, probably caused by overharvesting and degradation of forests. The value of the NEE (average: 6.63 ± 3.50 g C m−2 d−1) in grassland was apparently higher than that in wetland (average: 0.86 ± 1.69 g C m−2 d−1), which suggested that the capability of carbon sequestration in wetland was still stronger than that in grassland even in carbon loss condition. The results showed a positive relationship between aboveground biomass (AGB) and ground-based daily GPP or Reco for both wetland and grassland and a negative relationship between AGB and NEE. The ground-based daily NEE was also significantly related to soil water content (SWC) but showed no relationship with daily precipitation (PRE), which suggested that SWC was a more important impact factor than precipitation on CO2 flux exchange in the study area. The change between wetland and grassland did not influence the positive relationship between AGB or SWC and CO2 flux. Our study provides a new way to determine the spatial CO2 flux exchange and its controlling factors (environmental variables and vegetation patterns) and to successfully analyze its differences in wetland and grassland.

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?

In water-limited ecosystems, hydrological processes significantly affect the carbon flux. The semi-arid grassland ecosystem is particularly sensitive to variations in precipitation (PRE) and soil moisture content (SMC), but to what extent is not fully understood. In this study, we estimated and analyzed how hydrological variables, especially PRE at multi-temporal scales (diurnal, monthly, phenological-related, and seasonal) and SMC at different soil depths (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm) affect the carbon flux. For these aims, eddy covariance data were combined with a Vegetation Photosynthesis and Respiration Model (VPRM) to simulate the regional gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem exchange of CO2 (NEE). Interestingly, carbon flux showed no relationship with diurnal PRE or phenological-related PRE (precipitation in the growing season and non-growing season). However, carbon flux was significantly related to monthly PRE and to seasonal PRE (spring + summer, autumn). The GPP, Reco, and NEE increased in spring and summer but decreased in autumn with increasing precipitation due to the combined effect of salinization in autumn. The GPP, Reco, and NEE were more responsive to SMC at 0–20 cm depth than at deeper depths due to the shorter roots of herbaceous vegetation. The NEE increased with increasing monthly PRE because soil microbes responded more quickly than plants. The NEE significantly decreased with increasing SMC in shallow surface due to a hysteresis effect on water transport. The results of our study highlight the complex processes that determine how and to what extent PRE at multi-temporal scale and SMC at different depths affect the carbon flux response in a water-limited grassland.

Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: quantification of the European terrestrial CO<sub>2</sub> fluxes

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.

Optimization and evaluation of vegetation photosynthesis and respiration model using the measurements collected from the forest site of subtropical coniferous-evergreen

Aims Determination of carbon cycling model parameters is critical to simulate the net ecosystem CO2 exchange (NEE). The objectives of this study were to determine the parameters of vegetation photosynthesis and respiration model (VPRM) and improve the calculation of NEE to benefit regional modeling of CO2. Methods Two schemes are examined in optimization of the parameters in VPRM. Two years CO2 flux and me- teorological observational data in 2010-2011 at the Qianyanzhou (QYZ) eddy tower site are used to determine the parameters in VPRM and another full year flux observational data in 2012 are used to evaluate the model per- formance. Several statistics metrics are calculated to evaluate the model performance on NEE simulations. Important findings The results indicate, traditional method with Michaelis-Menten equation is not suitable to determine the parameters of VPRM, whereas the method with parameters retrieved from the VPRM calculation equation provides much more reasonable results. The parameter of maximum light use efficiency (λ) is critical for the VPRM calculation of NEE. Our result is larger than the typical value of C3 plant (1/6), but consistent with the other studies. Using the optimized parameters, VPRM is able to capture NEE variations for different seasons. The statistics calculation with one-year NEE simulation shows that, the mean bias is -0.86 μmol·m -2 ·s -1 and correla- tion coefficient is 0.72. Overall, the VPRM performs much better in growing season than the non-growing season when the peak value of NEE is underestimated by 52%. The VPRM simulated NEE shows better agreement with

Comparing Lagrangian and Eulerian models for CO<sub>2</sub> transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM

Abstract. We present simulations of atmospheric CO2 concentrations provided by two modeling systems, run at high spatial resolution: the Eulerian-based Weather Research Forecasting (WRF) model and the Lagrangian-based Stochastic Time-Inverted Lagrangian Transport (STILT) model, both of which are coupled to a diagnostic biospheric model, the Vegetation Photosynthesis and Respiration Model (VPRM). The consistency of the simulations is assessed with special attention paid to the details of horizontal as well as vertical transport and mixing of CO2 concentrations in the atmosphere. The dependence of model mismatch (Eulerian vs. Lagrangian) on models' spatial resolution is further investigated. A case study using airborne measurements during which two models showed large deviations from each other is analyzed in detail as an extreme case. Using aircraft observations and pulse release simulations, we identified differences in the representation of details in the interaction between turbulent mixing and advection through wind shear as the main cause of discrepancies between WRF and STILT transport at a spatial resolution such as 2 and 6 km. Based on observations and inter-model comparisons of atmospheric CO2 concentrations, we show that a refinement of the parameterization of turbulent velocity variance and Lagrangian time-scale in STILT is needed to achieve a better match between the Eulerian and the Lagrangian transport at such a high spatial resolution (e.g. 2 and 6 km). Nevertheless, the inter-model differences in simulated CO2 time series for a tall tower observatory at Ochsenkopf in Germany are about a factor of two smaller than the model-data mismatch and about a factor of three smaller than the mismatch between the current global model simulations and the data.