Reconciling the carbon balance of northern Scandinavia through the integration of observations and modelling

<p>The boreal biome is an important component of the global carbon (C) cycle. However, current estimates of its sink-source strength at regional scales and its responses to climate change rely primarily on models and thus remain uncertain. We investigated the C balance over a north Scandinavian boreal region by integrating observations of land-atmosphere fluxes and atmospheric CO<sub>2</sub> concentrations at landscape to regional scales. We also placed a special focus to understand the impact of 2018 drought on the region. Flux estimates can be obtained through various techniques such as in-situ flux measurements, eddy covariance (EC) observations, vegetation modelling and inverse modelling of CO<sub>2</sub> observations. These techniques are however typically relevant at very different spatial scales ranging from plot scale to country-scale, which makes it difficult to compare them. The -Svartberget site (SVB), an established ICOS (Integrated Carbon Observation System) station in Northern Sweden offers a unique range of observations, from in-situ flux measurements to EC fluxes and tall-tower concentration measurements. Here we used several vegetation models and an atmospheric transport model to connect the different scales for the period 2016-2018. The land-atmosphere carbon fluxes are from four different vegetation models (VPRM, LPJ-GUESS, ORCHIDEE and SiBCASA) and are used in the LUMIA/FLEXPART atmospheric transport model (Lund University Modular Inversion Algorithm) to generate estimates of atmospheric CO<sub>2</sub> concentration. We found that the northern Sweden region remained as a C sink for the study period with models differed in sink strength. It was also noticed that the site SVB can be taken as a representative for the northern Sweden region. All models indicate similar but small reductions in the net CO<sub>2</sub> uptake for the drought year 2018 in northern Sweden except LPJ-GUESS that reveal limitations which call for further model improvement. Our work highlights the interest of using combined ecosystem,-atmosphere ICOS sites such as SVB in the Scandinavian region and shows that it is a promising way forward to monitor CO<sub>2</sub> fluxes at the regional scale.</p>

Real-time measurement of radionuclide concentrations and its impact on inverse modeling of ¹⁰⁶Ru release in the fall of 2017

Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard 1-week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA (Autonomous Monitor of Atmospheric Radioactive Aerosol) and CEGAM (carousel gamma spectrometry) real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters and allow for determining the moment when 106Ru arrived at the monitoring site within approx. 1 h and detecting activity concentrations as low as several mBq m−3 in 4 h intervals. These high-resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric transport model driven with meteorological data from the Global Forecast System (GFS), and we construct a source–receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. With Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared, and a better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237±107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA-reported (International Atomic Energy Agency) dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. In addition, we validated our findings also using the FLEXPART (FLEXible PARTicle dispersion) model coupled with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF).

Real-time measurement of radionuclide concentrations and its impact on inverse modeling of ¹⁰⁶Ru release in the fall of 2017

Abstract Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed; however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard one week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA and CEGAM real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters. These high resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hysplit atmospheric transport model driven with meteorological data from the global forecast system (GFS) and we construct a source-receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least-squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. On Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared and better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237 ± 107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA reported dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated.

A 3D-model inversion of methyl chloroform to constrain the atmospheric oxidative capacity

Variations in the atmospheric oxidative capacity, largely determined by variations in the hydroxyl radical (OH), form a key uncertainty in many greenhouse and other pollutant budgets, such as that of methane (CH4). Methyl chloroform (MCF) is an often-adopted tracer to indirectly put observational constraints on variations in OH. We investigated the budget of MCF in a 4DVAR inversion using the atmospheric transport model TM5, for the period 1998–2018, with the objective to derive information on interannual variations in OH and in its spatial distribution. We derived interannual variations in the global oxidation of MCF that bring simulated mole fractions of MCF within 1–2 % of the assimilated observations from the NOAA-GMD surface network at most sites. Additionally, the posterior simulations better reproduce aircraft observations used for independent validation. The derived OH variations showed robustness with respect to the prior MCF emissions and the prior OH distribution. The interannual variations were typically small (

Better constraining the CO2 plant uptake at global scale: joint assimilation of COS and CO2 atmospheric measurements into a transport model.

<p>Inverse systems that assimilate atmospheric carbon dioxide measurements (CO2) into a global atmospheric transport model, are commonly used together with anthropogenic emission inventories to infer net biospheric surface fluxes. However, when assimilating CO2 measurements only, the respiration fluxes cannot be disentangled from the gross primary production (GPP) fluxes, leaving few possibilities to interpret the inferred fluxes from a mechanistic point of view. Measurements of carbonyl sulfide (COS) may help to fill this gap: COS has similar diffusion pathway inside leaves as CO2 but is not re-emitted into the atmosphere by the plant respiration. We explore here the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to constrain GPP and respiration fluxes separately. To this end, we develop an analytic inverse system based on the 14 Plant functional Type (PFTs) as defined in the ORCHIDEE land surface model. The vegetation uptake of COS is parameterized as a linear function of GPP and of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A new parameterization of the atmosphere soil exchanges is also included. We use the system to optimize GPP and respiration fluxes separately at the seasonal scale over the globe. The results lead to a balanced COS global budget and a seasonality of the COS fluxes in better agreement with observations. We find a large sensitivity of the partition between the ocean emissions and the COS plant uptake to the LRU parameterizations.</p>

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.