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Models used to investigate impacts of climatic changes on spatio-temporal vegetation dynamics need to balance required accuracy with computational feasibility. To enhance the computational efficiency of these models, upscaling methods are required that maintain key fine-scale processes influencing vegetation dynamics. In this paper, an adjustable method – the dynamic two-layer classification concept (D2C) – for the upscaling of time- and space-discrete models is presented. D2C aims to separate potentially repetitive calculations from those specific to single grid cells. The underlying idea is to extract processes that do not require information about a grid cell's neighbourhood to a reduced-size non-spatial layer, which is dynamically coupled to the original two-dimensional layer. The size of the non-spatial layer is thereby adaptive and depends on dynamic classifications according to pre-specified similarity criteria. I present how D2C can be used in a model implementation on the example of TreeMig-2L, a new, efficient version of the intermediate-complexity forest-landscape model TreeMig. To discuss the trade-off between computational expenses and accuracy, as well as the applicability of D2C, I compare different model stages of TreeMig-2L via simulations of two different application scenarios. This comparison of different model stages demonstrates that applying D2C can strongly reduce computational expenses of processes calculated on the new non-spatial layer. D2C is thus a valuable upscaling method for models and applications in which processes requiring information about the neighbourhood constitute the minor share of the overall computational expenses. [ABSTRACT FROM AUTHOR]

Particle number concentration and size distribution are important variables needed to constrain the role of atmospheric particles in the Earth radiation budget, both directly and indirectly through CCN activation. They are also linked to regulated variables such as particle mass (PM) and therefore of interest to air quality studies. However, data on their long-term variability are scarce, in particular at high altitudes. In this paper, we investigate the diurnal and seasonal variability of the aerosol total number concentration and size distribution at the puy de Dôme research station (France, 1465 m a.s.l.). We report a variability of aerosol particle total number concentration measured over a five-year (2003-2007) period for particles larger than 10 nm and aerosol size distributions between 10 and 500 nm over a two-year period (January 2006 to December 2007). Concentrations show a strong seasonality with maxima during summer and minima during winter. A diurnal variation is also observed with maxima between 12:00 and 18:00 UTC. At night (00:00-06:00 UTC), the median hourly total concentration varies from 600 to 800 cm-3 during winter and from 1700 to 2200 cm-3 during summer. During the day (08:00-18:00 UTC), the concentration is in the range of 700 to 1400 cm-3 during winter and of 2500 to 3500 cm-3 during summer. An averaged size distribution of particles (10- 500 nm) was calculated for each season. The total aerosol number concentrations are dominated by the Aitken mode integral concentrations, which drive most of the winter to summer total concentrations increase. The night to day increase in dominated by the nucleation mode integral number concentration. Because the site is located in the free troposphere only a fraction of the time, in particular at night and during the winter season, we have subsequently analyzed the variability for nighttime and free tropospheric (FT)/residual layer (RL) conditions only. We show that a seasonal variability is still observed for these FT/RL conditions. The FT/RL seasonal variation is due to both seasonal changes in the air mass origin from winter to summer and enhanced concentrations of particles in the residual layer/free troposphere in summer. The later observation can be explained by higher emissions intensity in the boundary layer, stronger exchanges between the boundary layer and the free troposphere as well as enhanced photochemical processes. Finally, aerosols mean size distributions are calculated for a given air mass type (marine/continental/regional) according to the season for the specific conditions of the residual layer/free troposphere. The seasonal variability in aerosol sources seems to be predominant over the continent compared to the seasonal variation of marine aerosol sources. These results are of regional relevance and can be used to constrain chemical-transport models over Western Europe. [ABSTRACT FROM AUTHOR]

It is a common task to partition the synergistic impacts of drivers in the environmental sciences. However, there is no mathematically precise solution to this partition task. Here I present a line-integral-based method, which addresses the sensitivity to the drivers throughout the drivers' evolutionary paths so as to ensure a precise partition. The method reveals that the partition depends on both the change magnitude and pathway (timing of the change) but not on the magnitude alone unless used for a linear system. To illustrate this method, I applied the Budyko framework to partition the effects of climatic and catchment conditions on the temporal change in the runoff for 19 catchments from Australia and China. The proposed method reduces to the decomposition method when assuming a path in which climate change occurs first, followed by an abrupt change in catchment properties. The proposed method re-defines the widely used sensitivity at a point as the path-averaged sensitivity. The total-differential and the complementary methods simply concern the sensitivity at the initial and/or the terminal state, so they cannot give precise results. Although the path-averaged sensitivities varied greatly among the catchments, they can be readily predicted within the Budyko framework. As a mathematically accurate solution, the proposed method provides a generic tool for conducting quantitative attribution analyses. [ABSTRACT FROM AUTHOR]