Carbon flux from decomposing wood and its dependency on temperature, wood N2 fixation rate, moisture and fungal composition in a Norway spruce forest

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Changing climate and outbreaks of forest pest insects in a cold northern country, Finland

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Changing climate—changing pathogens: Toxoplasma gondii in North-Western Europe

In this review, we describe the effects of global climate change for one specific pathogen: the parasite Toxoplasma gondii. It is postulated that an increase of T. gondii prevalence in humans can occur in some regions of North-Western Europe as a result of changing environmental conditions. Such a change can be predicted by using Global Climate Change models. We have elaborated such a prediction for one scenario (SRES A1) by using one specific model (CCSR/NRIES) as an example. Next to environmental factors, also anthropogenic factors may contribute to increased prevalence of T. gondii in this region. In order to counter the potential severe consequences of a potential increase resulting from the combination of climatic and anthropogenic factors, there is an urgent need for the development of a human vaccine. Until a vaccine that offers complete protection is developed, the emphasis should be on treatment optimization and prevention

Tests of Monte Carlo independent column approximation in the ECHAM5 atmospheric GCM

The Monte Carlo Independent Column Approximation ( McICA) method for computing domain- average radiative fluxes allows a flexible treatment of unresolved cloud structure, and it is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Here, tests of McICA in the ECHAM5 atmospheric GCM are reported. ECHAM5 provides an interesting test bed for McICA because it carries prognostic variables for the subgrid- scale probability distribution of total water content, which allows us to determine subgrid- scale cloud variability directly from the resolved- scale model variables. Three experiments with differing levels of radiative noise, each consisting of ten 6- yr runs, are performed to estimate the impact of McICA noise on simulated climate. In an experiment that attempted to deliberately maximize McICA noise, a systematic reduction in low cloud fraction occurred. For a more reasonable implementation of McICA, the impact of noise is very small, although statistically discernible. In terms of the impacts of noise, McICA appears to be a viable approach for use in ECHAM5. However, to improve the simulation of cloud radiative effects, realistic representation of both unresolved and resolved cloud structures is needed, which remains a challenging problem. Comparison of ECHAM5 data with a global cloud system - resolving model dataset and with International Satellite Cloud Climatology Project data suggested two problems related to unresolved cloud structures. First, ECHAM5 appears to underestimate subgrid- scale cloud variability. This problem seems partly related to the use of the beta distribution scheme for total water content in ECHAM5: in its current form, the scheme is unable to generate highly inhomogeneous clouds ( relative standard deviation of condensate amount > 1). Second, it appears that in ECHAM5, overcast cloud layers occur too frequently and partially cloudy layers too rarely. This problem is not unique to the beta distribution scheme; in fact, it is more pronounced when using an alternative, relative humidity - based cloud fraction scheme