Analysis of total column CO₂ and CH₄ measurements in Berlin with WRF-GHG

Though they cover less than 3 % of the global land area, urban areas are responsible for over 70 % of the global greenhouse gas (GHG) emissions and contain 55 % of the global population. A quantitative tracking of GHG emissions in urban areas is therefore of great importance, with the aim of accurately assessing the amount of emissions and identifying the emission sources. The Weather Research and Forecasting model (WRF) coupled with GHG modules (WRF-GHG) developed for mesoscale atmospheric GHG transport can predict column-averaged abundances of CO2 and CH4 (XCO2 and XCH4). In this study, we use WRF-GHG to model the Berlin area at a high spatial resolution of 1 km. The simulated wind and concentration fields were compared with the measurements from a campaign performed around Berlin in 2014 (Hase et al., 2015). The measured and simulated wind fields mostly demonstrate good agreement. The simulated XCO2 shows quite similar trends with the measurement but with approximately 1 ppm bias, while a bias in the simulated XCH4 of around 2.7 % is found. The bias could potentially be the result of relatively high background concentrations, the errors at the tropopause height, etc. We find that an analysis using differential column methodology (DCM) works well for the XCH4 comparison, as corresponding background biases are then canceled out. From the tracer analysis, we find that the enhancement of XCH4 is highly dependent on human activities. The XCO2 enhancement in the vicinity of Berlin is dominated by anthropogenic behavior rather than biogenic activities. We conclude that DCM is an effective method for comparing models to observations independently of biases caused, e.g., by initial conditions. It allows us to use our high-resolution WRF-GHG model to detect and understand major sources of GHG emissions in urban areas

Why do inverse models disagree? A case study with two European CO2 inversions

We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope-Regional (CSR) and Lund University Modular Inversion Algorithm (LUMIA)) over Europe in 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT (Stochastic Time-Inverted Lagrangian Transport) and FLEXPART (FLEXible PARTicle) were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by using two different estimates from the global inversion systems CarboScope (TM3) and TM5-4DVAR. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems, show a relatively large spread for the annual fluxes, ranging between −0.72 and 0.20 PgC yr−1, which is larger than the a priori uncertainty of 0.47 PgC yr−1. The discrepancies in annual budget are primarily caused by differences in the mesoscale transport model (0.51 PgC yr−1), in comparison with 0.23 and 0.10 PgC yr−1 that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions led to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the transport model schemes. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical prior fluxes and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.</p

Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models

An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time-Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two Weather Research and Forecasting (WRF)–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach and (2) an alternative “volume source influence (VSI)” approach where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from 2-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during the night (RMSD =6.3 ppm) compared to the afternoon (RMSD =3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD =3.4 ppm), which is similar to its performance during afternoon (RMSD =3.7 ppm). Representing nearby point source emissions with the VSI approach could thus be a first step towards exploiting nocturnal observations in STILT. The ability to use nighttime observations in atmospheric inversions would dramatically increase the observational data and allow for the investigation of different source mixtures or diurnal cycles. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of 1 million tonnes of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm during situations with low mixing heights smaller than 500 m

Reconciling the Carbon Balance of Northern Sweden Through Integration of Observations and Modelling

The boreal biome plays an important role in the global carbon cycle. However, current estimates of its sink-source strength and responses to changes in climate are primarily derived from models and thus remain uncertain. A major challenge is the validation of these models at a regional scale since empirical flux estimates are typically confined to ecosystem or continental scales. The Integrated Carbon Observation System (ICOS)-Svartberget atmospheric station (SVB) provides observations including tall tower eddy covariance (EC) and atmospheric concentration measurements that can contribute to such validation in Northern Sweden. Thus, the overall aim of this study was to quantify the carbon balance in Northern Sweden region by integrating land-atmosphere fluxes and atmospheric carbon dioxide (CO2) concentrations. There were three specific objectives. First, to compare flux estimates from four models (VPRM, LPJ-GUESS, ORCHIDEE, and SiBCASA) to tall tower EC measurements at SVB during the years 2016-2018. Second to assess the fluxes' impact on atmospheric CO2 concentrations using a regional transport model. Third, to assess the impact of the drought in 2018. The comparison of estimated concentrations with ICOS observations helped the evaluation of the models' regional scale performance. Both the simulations and observations indicate there were similar reductions in the net CO2 uptake during drought. All the models (except for SiBCASA) and observations indicated the region was a net carbon sink during the 3-year study period. Our study highlights a need to improve vegetation models through comparisons with empirical data and demonstrate the ICOS network's potential utility for constraining CO2 fluxes in the region

Calibration of TCCON column-averaged CO2: the first aircraft campaign over European TCCON sites

The Total Carbon Column Observing Network (TCCON) is a ground-based network of Fourier Transform Spectrometer (FTS) sites around the globe, where the column abundances of CO2, CH4, N2O, CO and O2 are measured. CO2 is constrained with a precision better than 0.25% (1-σ). To achieve a similarly high accuracy, calibration to World Meteorological Organization (WMO) standards is required. This paper introduces the first aircraft calibration campaign of five European TCCON sites and a mobile FTS instrument. A series of WMO standards in-situ profiles were obtained over European TCCON sites via aircraft and compared with retrievals of CO2 column amounts from the TCCON instruments. The results of the campaign show that the FTS measurements are consistently biased 1.1% ± 0.2% low with respect to WMO standards, in agreement with previous TCCON calibration campaigns. The standard a priori profile for the TCCON FTS retrievals is shown to not add a bias. The same calibration factor is generated using aircraft profiles as a priori and with the TCCON standard a priori. With a calibration to WMO standards, the highly precise TCCON CO2 measurements of total column concentrations provide a suitable database for the calibration and validation of nadir-viewing satellite

On the representation of IAGOS/MOZAIC vertical profiles in chemical transport models:contribution of different error sources in the example of carbon monoxide

Utilising a fleet of commercial airliners, MOZAIC/IAGOS provides atmospheric composition data on a regular basis that are widely used for modelling applications. Due to the specific operational context of the platforms, such observations are collected close to international airports and hence in an environment characterised by high anthropogenic emissions. This provides opportunities for assessing emission inventories of major metropolitan areas around the world, but also challenges in representing the observations in typical chemical transport models. We assess here the contribution of different sources of error to overall modeldata mismatch using the example of MOZAIC/IAGOS carbon monoxide (CO) profiles collected over the European regional domain in a time window of 5 yr (20062011). The different sources of error addressed in the present study are: 1) mismatch in modelled and observed mixed layer height; 2) bias in emission fluxes and 3) spatial representation error (related to unresolved spatial variations in emissions). The modelling framework combines a regional Lagrangian transport model (STILT) with EDGARv4.3 emission inventory and lateral boundary conditions from the MACC reanalysis. The representation error was derived by coupling STILT with emission fluxes aggregated to different spatial resolutions. We also use the MACC reanalysis to assess uncertainty related to uncertainty sources 2) and 3). We treat the random and the bias components of the uncertainty separately and found that 1) and 3) have a comparable impact on the random component for both models, while 2) is far less important. On the other hand, the bias component shows comparable impacts from each source of uncertainty, despite both models being affected by a low bias of a factor of 22.5 in the emission fluxes. In addition, we suggested methods to correct for biases in emission fluxes and in mixing heights. Lastly, the evaluation of the spatial representation error against modeldata mismatch between MOZAIC/IAGOS observations and the MACC reanalysis revealed that the representation error accounts for roughly 1520% of the modeldata mismatch uncertainty

Strong radiative effect induced by clouds and smoke on forest net ecosystem productivity in central Siberia

Aerosols produced by wildfires are a common phenomenon in boreal regions. For the Siberian taiga, it is still an open question if the effects of aerosols on atmospheric conditions increase net CO2 uptake or photosynthesis. We investigated the factors controlling forest net ecosystem productivity (NEP) and explored how clouds and smoke modulate radiation as a major factor controlling NEP during fire events in the years 2012 and 2013. To characterize the underlying mechanisms of the NEP response to environmental drivers, Artificial Neural Networks (ANNs) were trained by eddy covariance flux measurements nearby the Zotino Tall Tower Observatory (ZOTTO). Total photosynthetically active radiation, vapour pressure deficit, and diffuse fraction explain at about 54-58% of NEP variability. NEP shows a strong negative sensitivity to VPD, and a small positive to f(dlf). A strong diffuse radiation fertilization effect does not exist at ZOTTO forest due to the combined effects of low light intensity, sparse canopy and low leaf area index. Results suggests that light intensity and canopy structure are important factors of the overall diffuse radiation fertilization effect.Peer reviewe

Coupled weather research and forecasting–stochastic time-inverted lagrangian transport (WRF–STILT) model

This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top–down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF–STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.Engineering and Applied Science