Three Years of Δ14CO2 Observations from Maize Leaves in the Netherlands and Western Europe

Atmospheric Δ14CO2 measurements are useful to investigate the regional signals of anthropogenic CO2 emissions, despite the currently scarce observational network for Δ14CO2. Plant samples are an easily attainable alternative, which have been shown to work well as a qualitative measure of the atmospheric Δ14CO2 signals integrated over the time a plant has grown. Here, we present the 14C analysis results for 89 individual maize (Zea mays) plant samples from 51 different locations that were gathered in the Netherlands in the years 2010 to 2012, and from western Germany and France in 2012. We describe our sampling strategy and results, and include a comparison to a model simulation of the Δ14CO2 that would be accumulated in each plant over a growing season. Our model simulates the Δ14CO2 signatures in good agreement with observed plant samples, resulting in a root-mean-square deviation (RMSD) of 3.30‰. This value is comparable to the measurement uncertainty, but still relatively large (20–50%) compared to the total signal. It is also comparable to the spread in Δ14CO2 values found across multiple plants from a single site, and to the spread found when averaging across larger regions. We nevertheless find that both measurements and model capture the large-scale (>100 km) regional Δ14CO2 gradients, with significant observation-model correlations in all three countries in which we collected samples. The modeled plant results suggest that the largest gradients found in the Netherlands and Germany are associated with emissions from energy production and road traffic, while in France, the 14CO2 enrichment from nuclear sources dominates in many samples. Overall, the required model-based interpretation of plant samples adds additional uncertainty to the already relatively large measurement uncertainty in Δ14CO2, and we suggest that future fossil fuel monitoring efforts should prioritize other strategies such as direct atmospheric sampling of CO2 and Δ14CO2

The importance of crop growth modeling to interpret the Δ14CO2 signature of annual plants

[1] The 14C/C abundance in CO2(¿14CO2) promises to provide useful constraints on regional fossil fuel emissions and atmospheric transport through the large gradients introduced by anthropogenic activity. The currently sparse atmospheric ¿14CO2 monitoring network can potentially be augmented by using plant biomass as an integrated sample of the atmospheric ¿14CO2. But the interpretation of such an integrated sample requires knowledge about the day¿to¿day CO2 uptake of the sampled plants. We investigate here the required detail in daily plant growth variations needed to accurately interpret regional fossil fuel emissions from annual plant samples. We use a crop growth model driven by daily meteorology to reproduce daily fixation of ¿14CO2 in maize and wheat plants in the Netherlands in 2008. When comparing the integrated ¿14CO2 simulated with this detailed model to the values obtained when using simpler proxies for daily plant growth (such as radiation and temperature), we find differences that can exceed the reported measurement precision of ¿14CO2(~2‰). Furthermore, we show that even in the absence of any spatial differences in fossil fuel emissions, differences in regional weather can induce plant growth variations that result in spatial gradients of up to 3.5‰ in plant samples. These gradients are even larger when interpreting separate plant organs (leaves, stems, roots, or fruits), as they each develop during different time periods. Not accounting for these growth¿induced differences in ¿14CO2 in plant samples would introduce a substantial bias (1.5–2¿ppm) when estimating the fraction of atmospheric CO2 variations resulting from nearby fossil fuel emission

A survey for variable young stars with small telescopes: First results from HOYS-CAPS

Variability in Young Stellar Objects (YSOs) is one of their primary characteristics. Long-term, multi-filter, high-cadence monitoring of large YSO samples is the key to understand the partly unusual light-curves that many of these objects show. Here we introduce and present the first results of the HOYS-CAPS citizen science project which aims to perform such monitoring for nearby (d<kpc) and young (age<10Myr) clusters and star forming regions, visible from the northern hemisphere, with small telescopes. We have identified and characterised 466 variable (413 confirmed young) stars in 8 young, nearby clusters. All sources vary by at least 0.2mag in V, have been observed at least 15 times in V, R and I in the same night over a period of about 2yrs and have a Stetson index of larger than 1. This is one of the largest samples of variable YSOs observed over such a time-span and cadence in multiple filters. About two thirds of our sample are classical T-Tauri stars, while the rest are objects with depleted or transition disks. Objects characterised as bursters show by far the highest variability. Dippers and objects whose variability is dominated by occultations from normal interstellar dust or dust with larger grains (or opaque material) have smaller amplitudes. We have established a hierarchical clustering algorithm based on the light-curve properties which allows the identification of the YSOs with the most unusual behaviour, and to group sources with similar properties. We discuss in detail the light-curves of the unusual objects V2492Cyg, V350Cep and 2MASSJ21383981+5708470

The 2014-2017 outburst of the young star ASASSN-13db: A time-resolved picture of a very low-mass star between EXors and FUors

ASASSN-13db is a M5-type star with a protoplanetary disk, the lowest mass star known to experience accretion outbursts. Since its discovery in 2013, it has experienced two outbursts, the second of which started in November 2014 and lasted until February 2017. We use high- and low-resolution spectroscopy and time-resolved photometry from the ASAS-SN survey, the LCOGT and the Beacon Observatory to study the lightcurve and the dynamical and physical properties of the accretion flow. The 2014-2017 outburst lasted for nearly 800 days. A 4.15d period in the lightcurve likely corresponds to rotational modulation of a star with hot spot(s). The spectra show multiple emission lines with variable inverse P-Cygni profiles and a highly variable blueshifted absorption below the continuum. Line ratios from metallic emission lines (Fe I/Fe II, Ti I/Ti II) suggest temperatures of $\sim$5800-6000 K in the accretion flow. Photometrically and spectroscopically, the 2014-2017 event displays an intermediate behavior between EXors and FUors. The accretion rate (\.{M}=1-3$\times$10$^{-7}$M$_\odot$/yr), about 2 orders of magnitude higher than the accretion rate in quiescence, is not significantly different from the accretion rate observed in 2013. The absorption features in the spectra suggest that the system is viewed at a high angle and drives a powerful, non-axisymmetric wind, maybe related to magnetic reconnection. The properties of ASASSN-13db suggest that temperatures lower than those for solar-type stars are needed for modeling accretion in very low-mass systems. Finally, the rotational modulation during the outburst reveals that accretion-related structures settled after the begining of the outburst and can be relatively stable and long-lived. Our work also demonstrates the power of time-resolved photometry and spectroscopy to explore the properties of variable and outbursting stars. (Abridged)

Using annual plants as atmospheric 14CO2 samplers for regional fossil fuel emissions estimates: crop modeling and intensive sampling approaches

Using radiocarbon (14C) as a tracer for fossil fuel emissions is promising, even as sampling atmospheric 14CO2 for long periods of time is demanding and expensive. An alternative is to use plants to record the atmospheric carbon isotopic abundances, as plants naturally integrate carbon during their growing period by photosynthesis. A main uncertainty in this approach, however, is the unknown time period in which the uptake of CO2 has taken place. How plants “sample” the atmospheric carbon and transport it to their different parts depends strongly on their growth and developmental pattern. We use the Weather Research and Forecast model (WRF) together with a mechanistic crop growth model to quantify the representativeness of plant sampled atmospheric 14C mixing ratios on a regional scale. We compare our modeled results to measured 14C in maize and wheat samples from a region in the north of the Netherlands, affected by urban CO2 plumes as well as a local power plant. We find based on the modeled results that even in the absence of spatial fossil fuel gradients in the atmosphere, differences in plant growth rates can introduce Δ14C gradients of up to 3.5‰ over plants in the Netherlands. We furthermore use the simulated plant growth rates to narrow the period for which a plant sample can be used as a proxy, which will help to lower the uncertainty on estimated fossil fuel emissions. Our work provides first steps towards quantitatively using plant 14C sampling for verification of regional fossil fuel emissions. Map of Δ14C signature (in ‰) of spring wheat at flowering day, with grid resolution of 4x4 km. Plant growth is simulated by mechanistic crop growth model (SUCROS 2) with weather data over the growing season provided by WRF model. The temporal evolution of the 14C signature of the atmosphere is spatially uniform over the domain. The figure shows that differences in daily growth can introduce Δ14C gradients of up to 3.5‰ even in the absence of spatial fossil fuel gradients

A new region-aware bias-correction method for simulated precipitation in areas of complex orography

Regional climate modelling is used to simulate the hydrological cycle, which is fundamental for climate impact investigations. However, the output of these models is affected by biases that hamper its direct use in impact modelling. Here, we present two high-resolution (2 km) climate simulations of precipitation in the Alpine region, evaluate their performance over Switzerland and develop a new bias-correction technique for precipitation suitable for complex topography. The latter is based on quantile mapping, which is applied separately across a number of non-overlapping regions defined through cluster analysis. This technique allows removing prominent biases while it aims at minimising the disturbances to the physical consistency inherent in all statistical corrections of simulated data. The simulations span the period 1979–2005 and are carried out with the Weather Research and Forecasting model (WRF), driven by the ERA-Interim reanalysis (hereafter WRF-ERA), and the Community Earth System Model (hereafter WRF-CESM). The simulated precipitation is in both cases validated against observations in Switzerland. In a first step, the area is classified into regions of similar temporal variability of precipitation. Similar spatial patterns emerge in all datasets, with a clear northwest–southeast separation following the main orographic features of this region. The daily evolution and the annual cycle of precipitation in WRF-ERA closely reproduces the observations. Conversely, WRF-CESM shows a different seasonality with peak precipitation in winter and not in summer as in the observations or in WRF-ERA. The application of the new bias-correction technique minimises systematic biases in the WRF-CESM simulation and substantially improves the seasonality, while the temporal and physical consistency of simulated precipitation is greatly preserved