The global atmospheric tracer model TM2

TM2 is a three-dimensional atmospheric transport model which solves the continuity equation for an arbitrary number of atmospheric tracers on an Eulerian grid spanning the entire globe. It is driven by stored meteorological fields from analyses of a weather forecast model or from output of an atmospheric general circulation model. Tracer advection is calculated using the “slopes scheme” of Russell and Lerner [1981]. Vertical transport due to convective clouds is computed using a simplified version of the cloud mass flux scheme of Tiedke [1989]. Turbulent vertical transport is calculated by stability dependent vertical diffusion according to the scheme by Louis [1979]

Impact of adversarial examples on deep learning models for biomedical image segmentation

Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully crafted samples that force machine learning models to make mistakes during testing time. These malicious samples have been shown to be highly effective in misguiding classification tasks. However, research on the influence of adversarial examples on segmentation is significantly lacking. Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models. Specifically, we expose the vulnerability of these models to adversarial examples by proposing the Adaptive Segmentation Mask Attack (ASMA). This novel algorithm makes it possible to craft targeted adversarial examples that come with (1) high intersection-over-union rates between the target adversarial mask and the prediction and (2) with perturbation that is, for the most part, invisible to the bare eye. We lay out experimental and visual evidence by showing results obtained for the ISIC skin lesion segmentation challenge and the problem of glaucoma optic disc segmentation. An implementation of this algorithm and additional examples can be found at https://github.com/utkuozbulak/adaptive-segmentation-mask-attack

How does the terrestrial carbon exchange respond to inter-annual climatic variations? : A quantification based on atmospheric CO2 data

The response of the terrestrial net ecosystem exchange (NEE) of CO2 to climate variations and trends may crucially determine the future climate trajectory. Here we directly quantify this response on inter-annual timescales by building a linear regression of inter-annual NEE anomalies against observed air temperature anomalies into an atmospheric inverse calculation based on long-term atmospheric CO2 observations. This allows us to estimate the sensitivity of NEE to inter-annual variations in temperature (seen as a climate proxy) resolved in space and with season. As this sensitivity comprises both direct temperature effects and the effects of other climate variables co-varying with temperature, we interpret it as "inter-annual climate sensitivity". We find distinct seasonal patterns of this sensitivity in the northern extratropics that are consistent with the expected seasonal responses of photosynthesis, respiration, and fire. Within uncertainties, these sensitivity patterns are consistent with independent inferences from eddy covariance data. On large spatial scales, northern extratropical and tropical interannual NEE variations inferred from the NEE-T regression are very similar to the estimates of an atmospheric inversion with explicit inter-annual degrees of freedom. The results of this study offer a way to benchmark ecosystem process models in more detail than existing effective global climate sensitivities. The results can also be used to gap-fill or extrapolate observational records or to separate inter-annual variations from longer-term trends.Peer reviewe

Assessing the role of deep rooted vegetation in the climate system with model simulations: mechanism, comparison to observations and implications for Amazonian deforestation

Deep rooted vegetation (of up to 68 m) has been found in many parts of the tropics. However, models of the general atmospheric circulation (GCMs) typically use rooting depths of less than 2 m in their land surface parametrizations. How does the incorporation of deep roots into such a model affect the simulated climate? We assess this question by using a GCM and find that deeper roots lead to a pronounced seasonal response. During the dry season, evapotranspiration and the associated latent heat flux are considerably increased over large regions leading to a cooling of up to 8 K. The enhanced atmospheric moisture is transported towards the main convection areas in the inner tropical convergence zone where it supplies more energy to convection thus intensifying the tropical circulation patterns. Comparison to different kinds of data reveals that the simulation with deeper roots is much closer to observations. The inclusion of deep roots also leads to a general increased climatic sensitivity to rooting depth change. We investigate this aspect in the context of the climatic effects of large-scale deforestation in Amazonia. Most of the regional and remote changes can be attributed to the removal of deep roots. We conclude that deep rooted vegetation is an important part of the tropical climate system. Without the consideration of deep roots, the present-day surface climate cannot adequately be simulated

Uncertainties of predictions of future atmospheric CO2 concentrations

Linear carbon cycle models, tuned to reproduce the CO2 increase observed at Mauna Loa, independently of their individual assumptions, predict almost identical CO2 concentration trends for fossil energy scenarios assuming a slightly increasing production in the next few decades. The basic information for such prognoses therefore is the airborne fraction observed over the last 20 years. Uncertainties in this quantity are due to possible errors in the estimate of fossil fuel consumption and the corresponding CO2 emission, possible natural fluctuations in the baseline CO2 level, and uncertainties regarding the biospheric CO2 input and uptake as a result of deforestation and reforestation and land management. Depending on different assumptions the effective airborne fraction, defined as the ratio of CO2 increase due to fossil fuel CO2 alone to the integrated CO2 production, might be as low as 0.38 or as high as 0.72, compared to the apparent airborne fraction of 0.55. The effective airborne fraction derived from carbon cycle models, considering only the CO2 uptake by the ocean, lies in the range 0.60–0.70. A value as low as 0.40 seems therefore highly improbable. A high biospheric anthropogenic CO2 input therefore must have been accompanied by a high CO2 fertilization effect. Model considerations, however, are not in contradiction with a high biospheric input with the maximum production before 1958, which also would imply low preindustrial CO2 concentrations in the range 270–280 ppm as reported recently

Measurements of greenhouse gases and related tracers at Bialystok tall tower station in Poland

Quasi-continuous, in-situ measurements of atmospheric CO2, O2/N2, CH4, CO, N2O, and SF6 have been performed since August 2005 at the tall tower station near Bialystok, in Eastern Poland, from five heights up to 300 m. Besides the in-situ measurements, flask samples are filled approximately weekly and measured at Max-Planck Institute for Biogeochemistry for the same species and, in addition, for H2, Ar/N2 and the stable isotopes 13C and 18O in CO2. The in-situ measurement system was built based on commercially available analysers: a LiCor 7000 for CO2, a Sable Systems "Oxzilla" FC-2 for O2, and an Agilent 6890 gas chromatograph for CH4, CO, N2O and SF6. The system was optimized to run continuously with very little maintenance and to fulfill the precision requirements of the CHIOTTO project. The O2/N2 measurements in particular required special attention in terms of technical setup and quality assurance. The evaluation of the performance after more than three years of operation gave overall satisfactory results, proving that this setup is suitable for long term remote operation with little maintenance. The precision achieved for all species is within or close to the project requirements. The comparison between the in-situ and flask sample results, used to verify the accuracy of the in-situ measurements, showed no significant difference for CO2, O2/N2, CH4 and N2O, and a very small difference for SF6. The same comparison however revealed a statistically significant difference for CO, of about 6.5 ppb, for which the cause could not be fully explained. From more than three years of data, the main features at Bialystok have been characterized in terms of variability, trends, and seasonal and diurnal variations. CO2 and O2/N2 show large short term variability, and large diurnal signals during the warm seasons, which attenuate with the increase of sampling height. The trends calculated from this dataset, over the period August 2005 to December 2008, are 2.02±0.46 ppm/year for CO2 and -23.2±2.5 per meg/year for O2/N2. CH4, CO and N2O show also higher variability at the lower sampling levels, which in the case of CO is strongly seasonal. Diurnal variations in CH4, CO and N2O mole fractions can be observed during the warm season, due to the periodicity of vertical mixing combined with the diurnal cycle of anthropogenic emissions. We calculated increase rates of 10.1±4.4 ppb/year for CH4, (-8.3)±5.3 ppb/year for CO and 0.67±0.08 ppb/year for N2O. SF6 shows only few events, and generally no vertical gradients, which suggests that there are no significant local sources. A weak SF6 seasonal cycle has been detected, which most probably is due to the seasonality of atmospheric circulation. SF6 increased during the time of our measurement at an average rate of 0.29±0.01 ppt/year