Uncertainty in model projections of future climate change arises due to internal variability, multiple possible emission scenarios, and different model responses to anthropogenic forcing. To robustly quantify uncertainty in multi-model ensembles, inter-dependencies between models as well as a models ability to reproduce observations should be considered. Here, a model weighting approach, which accounts for both independence and performance, is applied to European temperature and precipitation projections from the CMIP5 archive. Two future periods representing mid-and endof-century conditions driven by the high-emission scenario RCP8.5 are investigated. To inform the weighting, six diagnostics based on three observational estimates are used to also account for uncertainty in the observational record. Our findings show that weighting the ensemble can reduce the interquartile spread by more than 20% in some regions, increasing the reliability of projected changes. The mean temperature change is most notably impacted by the weighting in the Mediterranean, where it is found to be 0.35°C higher than the unweighted mean in the end-of-century period. For precipitation the largest differences are found for Northern Europe, with a relative decrease in precipitation of 2.4% and 3.4% for the two future periods compared to the unweighted case. Based on a perfect model test, it is found that weighting the ensemble leads to an increase in the investigated skill score for temperature and precipitation while minimizing the probability of overfitting.
Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to "small problems", well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge.The biggest potential for big-data elements, we argue, lies in socioeconomic climate research.Big data affects increasingly many aspects of our lives. The large volumes of data gathered and stored are the basis of the recommendations we receive when shopping online and the way in which we connect to people all over the world via social media 1 . Naturally, this has led to debates about how increasing volumes of data and new analytic tools might impact scientific research. An emerging view is that largely theory-free data-driven models will supplant models
Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.
The genetic diversity, quality and suitability of seeds and planting stock is crucial for the short and long-term resilience of restored forest landscapes. However, these genetic aspects are widely neglected during both planning and implementation of restoration. Decisions on seed sourcing during implementation of forest landscape restoration (FLR) initiatives often prioritize short-term cost savings over long-term benefits. Such considerations result in strategies that favor rapid and cheap mass production of homogeneous plants and, thus, quantity over quality, with no regard for genetic diversity. This paper explores in detail the economic cost of improved integration of genetic diversity into restoration projects and tests the assumption that the benefits accruing from better integration of diversity exceed the costs. Using a bottom-up cost model, based on peer reviewed scientific literature, we analyse different FLR cost drivers, integrating genetic quality, in relation to the total costs of a range of tree-based restoration interventions, with a focus on seed sourcing, and tree species selection. The results indicate that the integration of genetic diversity into the management and planning of landscape restoration projects increased the costs incurred at the beginning of FLR interventions, specifically during seed sourcing, and species selection. These additional costs were largely due to the increased effort for the collection of genetically diverse and suitably adapted seed lots. However, despite this initial increase in costs the overall costs of restoration decreased substantially, due to cost savings relating to replacement costs of replanting. Even without these savings, the inclusion of genetic diversity is advisable since the costs involved in the integration of diversity are negligible compared to other restoration costs, such as labor costs related to controlling vegetative competition. We conclude that the expected long-term benefits associated with high genetic diversity far outweigh the costs. It also highlights that investing in genetic diversity as part of FLR is the smart thing to do to ensure cost effective and resilient landscape restoration. Restoration policies need to incentivise consideration of genetic diversity.
In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change
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