The analysis of changes in the timing of life cycle-events of organisms (phenology) has been able to contribute significantly to the assessment of potential impacts of climate change on ecology. These phenological responses of species to changes in climate are likely to have significant relevance for socio-economic issues such as agriculture, forestry and human health and have proven able to play a role in raising environmental awareness and education on climate change. This paper presents the European Phenology Network (EPN), which aims to increase the efficiency, added value and use of phenological monitoring and research, and to promote the practical use of phenological data in assessing the impact of global (climate) change and possible adaptation measures. The paper demonstrates that many disciplines have to deal with changes in the timing of life-cycle events in response to climate change and that many different user groups are involved. Furthermore, it shows how EPN addresses issues such as (1) raising public awareness and education, (2) the integration and co-operation of existing observing systems, (3) integration and access to phenological information and (4) communication.
We used six models, ranging from simple parameter-sparse models to complex process-based 7 models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial 8 degree of uncertainty about parameter values was expressed in a prior probability distribution. 9Inventory data for Scots pine on tree height and diameter, with estimates of measurement 10 uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and 11Finland. From each country, we used data from two sites of the National Forest Inventories (NFI), 12 and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested 13 against the PSP-data. Calibration was done both per country and for all countries simultaneously, 14 thus yielding country-specific and generic parameter distributions. We assessed model 15 performance by sampling from prior and posterior distributions and comparing the growth 16 predictions of these samples to the observations at the PSP"s. 17We found that BC reduced uncertainties strongly in all but the most complex model. 18 Surprisingly, country-specific BC did not lead to clearly better within-country predictions than 19 generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the 20 most plausible model before calibration, with 4C taking its place after calibration. In this BMC, 21 model plausibility was quantified as the relative probability of a model being correct given the 22 information in the PSP-data. We discuss how the method of model initialisation affects model 23 performance. Finally, we show how BMA affords a robust way of predicting forest growth that 24 accounts for both parametric and model structural uncertainty. 25 26 27
The parameter uncertainty of process-based models has received little attention in 26 climate change impact studies. This paper aims to integrate parameter uncertainty 27 into simulations of climate change impacts on forest net primary productivity (NPP). 28We used either prior (uncalibrated) or posterior (calibrated using Bayesian 29 calibration) parameter variations to express parameter uncertainty. We assessed the 30 effect of parameter uncertainty on projections of the process-based model 4C in Scots 31 pine (Pinus sylvestris) stands under climate change. We compared the uncertainty 32 induced by differences between climate models with the uncertainty induced by 33 parameter variability and climate models together. This paper shows that the 34 uncertainty of simulated changes in NPP induced by climate model and parameter 35 uncertainty is substantially higher than the uncertainty of NPP changes induced by 36 climate model uncertainty alone. It however also highlights that the direction of NPP 37 change is mostly consistent between the simulations using the standard parameter 38 setting of 4C and the majority of the simulations including parameter uncertainty.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.