V. Palonen

Spatial and Temporal Patterns in Black Carbon Deposition to Dated Fennoscandian Arctic Lake Sediments from 1830 to 2010

Black carbon (BC) is fine particulate matter produced by the incomplete combustion of biomass and fossil fuels. It has a strong climate warming effect that is amplified in the Arctic. Long-term trends of BC play an important role in assessing the climatic effects of BC and in model validation. However, few historical BC records exist from high latitudes. We present five lake-sediment soot-BC (SBC) records from the Fennoscandian Arctic and compare them with records of spheroidal carbonaceous fly-ash particles (SCPs), another BC component, for ca. the last 120 years. The records show spatial and temporal variation in SBC fluxes. Two northernmost lakes indicate declining values from 1960 to the present, which is consistent with modeled BC deposition and atmospheric measurements in the area. However, two lakes located closer to the Kola Peninsula (Russia) have recorded increasing SBC fluxes from 1970 to the present, which is likely caused by regional industrial emissions. The increasing trend is in agreement with a Svalbard ice-core-BC record. The results suggest that BC deposition in parts of the European Arctic may have increased over the last few decades, and further studies are needed to clarify the spatial extent of the increasing BC values and to ascertain the climatic implications.

Pushing the limits of AMS radiocarbon dating with improved Bayesian data analysis

We present an improved version of the continuous autoregressive (CAR) model, a Bayesian data analysis model for accelerator mass spectrometry (AMS). Measurement error is taken to be Poisson-distributed, improving the analysis for samples with only a few counts. This, in turn, enables pushing the limit of radiocarbon measurements to lower concentrations. On the computational side, machine drift is described with a vector of parameters, and hence the user can examine the probable shape of the trend. The model is compared to the conventional mean-based (MB) method, with simulated measurements representing a typical run of a modern AMS machine and a run with very old samples. In both comparisons, CAR has better precision, gives much more stable uncertainties, and is slightly more accurate. Finally, some results are given from Helsinki AMS measurements of background sample materials, with natural diamonds among them.

Compatibility of Atmospheric 14CO2 Measurements: Comparing the Heidelberg Low-Level Counting Facility to International Accelerator Mass Spectrometry (AMS) Laboratories

Combining atmospheric Δ14CO2 data sets from different networks or laboratories requires secure knowledge on their compatibility. In the present study, we compare Δ14CO2 results from the Heidelberg low-level counting (LLC) laboratory to 12 international accelerator mass spectrometry (AMS) laboratories using distributed aliquots of five pure CO2 samples. The averaged result of the LLC laboratory has a measurement bias of –0.3 ±0.5‰ with respect to the consensus value of the AMS laboratories for the investigated atmospheric Δ14C range of 9.6 to 40.4‰. Thus, the LLC measurements on average are not significantly different from the AMS laboratories, and the most likely measurement bias is smaller than the World Meteorological Organization (WMO) interlaboratory compatibility goal for Δ14CO2 of 0.5‰. The number of intercomparison samples was, however, too small to determine whether the measurement biases of the individual AMS laboratories fulfilled the WMO goal.

RADIOCARBON DATING OF IRON: A NORTHERN CONTRIBUTION

The iron dating project Aikarauta has been launched in Finland. This paper presents the results of the preliminary investigations. The ability for radiocarbon measurement by accelerator mass spectrometry (AMS) of iron in Finland has been demonstrated by using coal-produced iron as reference material. An elemental analyzer has been harnessed to measure the carbon content of small iron samples. In addition, we have hypothesized that a fingerprint of the limestone usage in the smelting process is the high Ca content of iron and slag. This has been examined by performing an iron smelting experiment with limestone as flux, by making elemental analyses of ingredients and the resulting slag and iron, and by a 14C analysis of the produced iron. It is possible that limestone dilutes the 14C contents of the produced iron, making its age determination challenging.

Using car4ams, the Bayesian AMS Data Analysis Code

The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the GPL license. Included in the package is an R program that analyzes the car4ams output and summarizes the results in graphical and spreadsheet formats. We describe the main properties of the CAR analysis and the use of the 2 parts of the program package for radiocarbon AMS data analysis.

An Information-Efficient Bayesian Model for AMS Data Analysis

A Bayesian model for accelerator mass spectrometry (AMS) data analysis is presented. Instrumental drift is modeled with a continuous autoregressive (CAR) process, and measurement uncertainties are taken to be Gaussian. All sam- ples have a parameter describing their true value. The model adapts itself to different instrumental parameters based on the data, and yields the most probable true values for the unknown samples. The model is able to use the information in the meas- urements more efficiently. First, all measurements tell something about the overall instrument performance and possible drift. The overall machine uncertainty can be used to obtain realistic uncertainties even when the number of measurements per sam- ple is small. Second, even the measurements of the unknown samples can be used to estimate the variations in the standard level, provided that the samples have been measured more than once. Third, the uncertainty of the standard level is known to be smaller nearer a standard. Fourth, even though individual measurements follow a Gaussian distribution, the end result may not. For simulated data, the new Bayesian method gives more accurate results and more realistic uncertainties than the conven- tional mean-based (MB) method. In some cases, the latter gives unrealistically small uncertainties. This can be due to the non- Gaussian nature of the final result, which results from combining few samples from a Gaussian distribution without knowing the underlying variance and from the normalization with an uncertain standard level. In addition, in some cases the standard error of the mean does not represent well the true error due to correlations within the measurements resulting from, for exam- ple, a changing trend. While the conventional method fails in these cases, the CAR model gives representative uncertainties.

Bayesian Periodic Signal Detection Applied to INTCAL98 Data

A Bayesian multiple-frequency model has been developed for spectral analysis of data with unknown correlated noise. A description of the model is given and the method is applied to decadal atmospheric INTCAL98 delta (super 14) C data. The noise of the INTCAL98 data is found to be red, and there seems to be no support for continuous harmonic frequencies in the data.

A portable methane sampling system for radiocarbon-based bioportion measurements and environmental CH4 sourcing studies

Radiocarbon measurements can be used to deduce the proportion of renewable to fossil carbon in materials. While these biofraction measurements are performed routinely on solid and liquid substances, measurements of gaseous samples, such as methane, are still scarce. As a pioneering effort, we have developed a field-capable sampling system for the selective capture of CH4 for radiocarbon-concentration measurements. The system allows for biofraction measurements of methane by accelerator mass spectrometry. In environmental research, radiocarbon measurements of methane can be used for fingerprinting different sources of methane emissions. In metrology and industry, biofraction measurements can be utilized to characterize biogas/natural gas mixtures within gas-line networks. In this work, the portable sampling system is described in detail and reference measurements of biofractions of gaseous fuel samples are presented. Low-concentration (1-ppm-CH4) sampling for environmental applications appears feasible but has not been fully tested at present. This development allows for multitude of future applications ranging from Arctic methane emissions to biogas insertion to gas networks.

Seasonal and Diurnal Variations in Atmospheric and Soil Air 14CO2 in a Boreal Scots Pine Forest

We present a radiocarbon ( 14 C) dataset of tropospheric air CO 2 , forest soil air CO 2 , and soil CO 2 emissions over the course of one growing season in a Scots pine forest in southern Finland. The CO 2 collection for 14 C accelerator mass spectrometry (AMS) analysis was done with a portable, suitcase-sized system, using molecular sieve cartridges to selectively trap CO 2 . The piloting measurements aimed to quantify the spatial, seasonal and diurnal changes in the 14 C content of CO 2 in a northern forest site. The atmospheric samples collected above the canopy showed a large seasonal variation and an 11‰ difference between day and nighttime profiles in August. The higher Δ 14 C values during night are partly explained by a higher contribution of 14 C-elevated soil CO 2 , accumulating in the nocturnal boundary layer when vertical mixing is weak. We observed significant seasonal trends in Δ 14 C-CO 2 at different soil depths that reflected changes in the shares of autotrophic and heterotrophic respiration. Also the observed diurnal variation in the Δ 14 C values in soil CO 2 highlighted the changes in the origin of CO 2 , with root activity decreasing more for the night than decomposition.