Abstract. Atmospheric inverse modelling has the potential to provide observation-based estimates of greenhouse gas emissions at the country scale, thereby allowing for an independent validation of national emission inventories. Here, we present a regional-scale inverse modelling study to quantify the emissions of methane (CH4) from Switzerland, making use of the newly established CarboCount-CH measurement network and a high-resolution Lagrangian transport model. In our reference inversion, prior emissions were taken from the "bottom-up" Swiss Greenhouse Gas Inventory (SGHGI) as published by the Swiss Federal Office for the Environment in 2014 for the year 2012. Overall we estimate national CH4 emissions to be 196 ± 18 Gg yr−1 for the year 2013 (1σ uncertainty). This result is in close agreement with the recently revised SGHGI estimate of 206 ± 33 Gg yr−1 as reported in 2015 for the year 2012. Results from sensitivity inversions using alternative prior emissions, uncertainty covariance settings, large-scale background mole fractions, two different inverse algorithms (Bayesian and extended Kalman filter), and two different transport models confirm the robustness and independent character of our estimate. According to the latest SGHGI estimate the main CH4 source categories in Switzerland are agriculture (78 %), waste handling (15 %) and natural gas distribution and combustion (6 %). The spatial distribution and seasonal variability of our posterior emissions suggest an overestimation of agricultural CH4 emissions by 10 to 20 % in the most recent SGHGI, which is likely due to an overestimation of emissions from manure handling. Urban areas do not appear as emission hotspots in our posterior results, suggesting that leakages from natural gas distribution are only a minor source of CH4 in Switzerland. This is consistent with rather low emissions of 8.4 Gg yr−1 reported by the SGHGI but inconsistent with the much higher value of 32 Gg yr−1 implied by the EDGARv4.2 inventory for this sector. Increased CH4 emissions (up to 30 % compared to the prior) were deduced for the north-eastern parts of Switzerland. This feature was common to most sensitivity inversions, which is a strong indicator that it is a real feature and not an artefact of the transport model and the inversion system. However, it was not possible to assign an unambiguous source process to the region. The observations of the CarboCount-CH network provided invaluable and independent information for the validation of the national bottom-up inventory. Similar systems need to be sustained to provide independent monitoring of future climate agreements.

Species distribution modeling (SDM) is an important tool to assess the impact of global environmental change. Many species exhibit ecologically relevant intraspecific variation, and few studies have analyzed its relevance for SDM. Here, we compared three SDM techniques for the highly variable species Pinus contorta. First, applying a conventional SDM approach, we used MaxEnt to model the subject as a single species (species model), based on presence–absence observations. Second, we used MaxEnt to model each of the three most prevalent subspecies independently and combined their projected distributions (subspecies model). Finally, we used a universal growth transfer function (UTF), an approach to incorporate intraspecific variation utilizing provenance trial tree growth data. Different model approaches performed similarly when predicting current distributions. MaxEnt model discrimination was greater (AUC – species model: 0.94, subspecies model: 0.95, UTF: 0.89), but the UTF was better calibrated (slope and bias – species model: 1.31 and −0.58, subspecies model: 1.44 and −0.43, UTF: 1.01 and 0.04, respectively). Contrastingly, for future climatic conditions, projections of lodgepole pine habitat suitability diverged. In particular, when the species' intraspecific variability was acknowledged, the species was projected to better tolerate climatic change as related to suitable habitat without migration (subspecies model: 26% habitat loss or UTF: 24% habitat loss vs. species model: 60% habitat loss), and given unlimited migration may increase amount of suitable habitat (subspecies model: 8% habitat gain or UTF: 12% habitat gain vs. species model: 51% habitat loss) in the climatic period 2070–2100 (SRES A2 scenario, HADCM3). We conclude that models derived from within-species data produce different and better projections, and coincide with ecological theory. Furthermore, we conclude that intraspecific variation may buffer against adverse effects of climate change. A key future research challenge lies in assessing the extent to which species can utilize intraspecific variation under rapid environmental change.

Abstract. We describe a new rural network of four densely placed ( < 100 km apart), continuous atmospheric carbon (CO 2 , CH 4 , and CO) measurement sites in north-central Switzerland and analyze its suitability for regional-scale (∼ 100-500 km) carbon flux studies. We characterize each site for the period from March 2013 to February 2014 by analyzing surrounding land cover, observed local meteorology, and sensitivity to surface fluxes, as simulated with the Lagrangian particle dispersion model FLEXPART-COSMO (FLEXible PARTicle dispersion model-Consortium for Small-Scale Modeling).The Beromünster measurements are made on a tall tower (212 m) located on a gentle hill. At Beromünster, regional CO 2 signals (measurement minus background) vary diurnally from −4 to +4 ppmv, on average, and are simulated to come from nearly the entire Swiss Plateau, where 50 % of surface influence is simulated to be within 130-260 km distance. The Früebüel site measurements are made 4 m above ground on the flank of a gently sloping mountain. Nearby (< 50 km) pasture and forest fluxes exert the most simulated surface influence, except during convective summertime days when the site is mainly influenced by the eastern Swiss Plateau, which results in summertime regional CO 2 signals varying diurnally from −5 to +12 ppmv and elevated summer daytime CH 4 signals (+30 ppbv above other sites). The Gimmiz site measurements are made on a small tower (32 m) in flat terrain. Here, strong summertime regional signals (−5 to +60 ppmv CO 2 ) stem from large, nearby (< 50 km) crop and anthropogenic fluxes of the Seeland region, except during warm or windy days when simulated surface influence is of regional scale (< 250 km). The Lägern-Hochwacht measurements are made on a small tower (32 m) on top of the steep Lägern crest, where simulated surface influence is typically of regional scale (130-300 km) causing summertime regional signals to vary from −5 to +8 ppmv CO 2 . Here, considerable anthropogenic influence from the nearby industrialized region near Zurich causes the average wintertime regional CO 2 signals to be 5 ppmv above the regional signals simultaneously measured at the Früebüel site.We find that the suitability of the data sets from our current observation network for regional carbon budgeting studies largely depends on the ability of the high-resolution (2 km) atmospheric transport model to correctly capture the temporal dynamics of the stratification of the lower atmosphere at the different sites. The current version of the atmospheric transport model captures these dynamics well, but it clearly reaches its limits at the sites in steep topography and at the sites that generally remain in the surface layer. Trace gas transport and inverse modeling studies will be necessary to determine the impact of these limitations on our ability to dePublished by Copernicus Publications on behalf of the European Geosciences Union. 11148B. Oney et al.: Greenhouse gas observation network characterization rive reliable regional-scale carbon flux estimate...

Regional-scale inverse modeling of atmospheric carbon dioxide (CO 2 ) holds promise to determine the net CO 2 fluxes between the land biosphere and the atmosphere. This approach requires not only high fidelity of atmospheric transport and mixing, but also an accurate estimation of the contribution of the anthropogenic and background CO 2 signals to isolate the biospheric CO 2 signal from the atmospheric CO 2 variations. Thus, uncertainties in any of these three components directly impact the quality of the biospheric flux inversion. Here, we present and evaluate a carbon monoxide (CO)-based method to reduce these uncertainties solely on the basis of co-located observations. To this end, we use simultaneous observations of CO 2 and CO from a background observation site to determine the background mole fractions for both gases, and the regional anthropogenic component of CO together with an estimate of the anthropogenic CO/CO 2 mole fraction ratio to determine the anthropogenic CO 2 component. We apply this method to two sites of the CarboCount CH observation network on the Swiss Plateau, Beromünster and Lägern-Hochwacht, and use the high-altitude site Jungfraujoch as background for the year 2013. Since such a background site is not always available, we also explore the possibility to use observations from the sites themselves to derive the background. We contrast the method with the standard approach of isolating the biospheric CO 2 component by subtracting the anthropogenic and background components simulated by an atmospheric transport model. These tests reveal superior results from the observation-based method with retrieved wintertime biospheric signals being small and having little variance. Both observation-and model-based methods have difficulty to explain observations from late-winter and springtime pollution events in 2013, when anomalously cold temperatures and northeasterly winds tended to bring highly CO-enriched air masses to Switzerland. The uncertainty of anthropogenic CO/CO 2 emission ratios is currently the most important factor limiting the method. Further, our results highlight that care needs to be taken when the background component is determined from the site's observations. Nonetheless, we find that future atmospheric carbon monitoring efforts would profit greatly from at least measuring CO alongside CO 2 .