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The impact of missing data on the efficiency of homogenisation with ACMANTv3 is examined with simulated monthly surface air temperature test datasets. The homogeneous database is derived from an earlier benchmarking of daily temperature data in the USA, and then outliers and inhomogeneities (IHs) are randomly inserted into the time series. Three inhomogeneous datasets are generated and used, one with relatively few and small IHs, another one with IHs of medium frequency and size, and a third one with large and frequent IHs. All of the inserted IHs are changes to the means. Most of the IHs are single sudden shifts or pair of shifts resulting in platform-shaped biases. Each test dataset consists of 158 time series of 100 years length, and their mean spatial correlation is 0.68–0.88. For examining the impacts of missing data, seven experiments are performed, in which 18 series are left complete, while variable quantities (10–70%) of the data of the other 140 series are removed.The results show that data gaps have a greater impact on the monthly root mean squared error (RMSE) than the annual RMSE and trend bias. When data with a large ratio of gaps is homogenised, the reduction of the upper 5% of the monthly RMSE is the least successful, but even there, the efficiency remains positive. In terms of reducing the annual RMSE and trend bias, the efficiency is 54–91%. The inclusion of short and incomplete series with sufficient spatial correlation in all cases improves the efficiency of homogenisation with ACMANTv3.

Changes in climatic observations (such as station relocations and changes of instrumentation) often affect the spatial and temporal comparability of the data; therefore, an important part of improving the accuracy of observed climate variability is the time series homogenisation of the source data. In undertaking homogenisation, an essential step is the spatial comparison of the data within the same geographical region. To optimise the efficiency of homogenisation, we should know when and to what extent two series are of the same geographical origin from a climatic perspective, and how many partner series should be used. This study presents a number of novel experiments for obtaining objective answers to these questions. Monthly temperature test datasets were homogenised with ACMANT (Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series) by varying the number of partner series and their spatial correlations with the candidate series. First, a homogeneous benchmark is constructed from 2 regional subsets of a simulated surface air temperature dataset from earlier work. Various kinds of inhomogeneities are then inserted into the time series, producing 5 basic types of test datasets for each geographical region. Further variation is introduced by adding additional noise to some datasets, providing more diverse spatial correlations. The results indicate that for the identification and correction of long-lasting biases in the data, the optimal number of partner series is about 30. The optimum is largely independent from the frequency and intensity of inhomogeneities and from the spatial correlation between the candidate series and its partner series. This latter finding is unexpected; hence, its possible causes and the consequences are discussed and explored more fully here.

Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

If climate services are to lead to effective use of climate information in decision-making to enable the transition to a climate-smart, climate-ready world, then the question of trust in the products and services is of paramount importance. The Copernicus Climate Change Service (C3S) has been actively grappling with how to build such trust: provision of demonstrably independent assessments of the quality of products, which was deemed an important element in such trust-building processes. C3S provides access to essential climate variables (ECVs) from multiple sources to a broad set of users ranging from scientists to private companies and decision-makers. Here we outline the approach undertaken to coherently assess the quality of a suite of observation- and reanalysis-based ECV products covering the atmosphere, ocean, land, and cryosphere. The assessment is based on four pillars: basic data checks, maturity of the datasets, fitness for purpose (scientific use cases and climate studies), and guidance to users. It is undertaken independently by scientific experts and presented alongside the datasets in a fully traceable, replicable, and transparent manner. The methodology deployed is detailed, and example assessments are given. These independent scientific quality assessments are intended to guide users to ensure they use tools and datasets that are fit for purpose to answer their specific needs rather than simply use the first product they alight on. This is the first such effort to develop and apply an assessment framework consistently to all ECVs. Lessons learned and future perspectives are outlined to potentially improve future assessment activities and thus climate services.

Active blanket bogs are ombrotrophic peatland systems of the boreo-temperate zones, although blanket peat tends to form only under the warmest and wettest of those conditions. In Europe, this is common only in Scotland and Ireland, coincident with the oceanic climate, and constitutes a significant global component of this ecosystem. Associated with this Atlantic distribution, Ireland has 50% of the remaining blanket bogs of conservation importance within the Atlantic Biogeographic Region of Europe. It is anticipated that future climate change will place additional pressure on these systems. Active blanket bog distributions in Ireland were modelled using 7 bioclimatic envelope modelling techniques implemented in the BIOMOD modelling framework. The 1961 to 1990 baseline models achieved a very good agreement with the observed distribution, and suggest a strong dependency on climate. The discrimination ability of the fitted models was assessed using the area under the curve (range 0.915 to 0.976) of a receiver operating characteristic plot. An ensemble prediction from all the models was computed in BIOMOD and used to project changes based on outputs from a dynamically downscaled climate change scenario for 2031 to 2060. The consistent predictions between the individual models for the baseline change substantially for the climate change projections, with losses of ~−82% to gains of ~+15% projected depending on the individual model type. However, small gains in climate space in the Midlands, east and northeast of the country projected by the consensus model are unlikely to be realised as it will not be possible for new habitat to form.

Heathland habitats in Ireland occur primarily in an oceanic setting which is strongly influenced by changes in the climate. Because of the oceanic environment, Ireland has a high proportion of the northern Atlantic wet heaths and alpine and boreal heaths of high conservation value within Europe. Future climate change is widely expected to place additional pressure on these systems. Seven bioclimatic envelope modelling techniques implemented in the BIOMOD modelling framework were used to model wet heath and alpine and boreal heath distributions in Ireland. The 1961−1990 baseline models closely matched the observed distribution and emphasise the strong dependency on climate. Mean winter precipitation, mean winter temperature and elevation were found to be important model components. The fitted model's discrimination ability was assessed using the area under the curve of a receiver operating characteristic plot; the true skill statistic; and Cohen's kappa. A BIOMOD ensemble prediction from all the models was used to project changes based on a climate change scenario for 2031−2060 dynamically downscaled from the Hadley Centre HadCM3-Q16 global climate model. The climate change projections for the individual models change markedly from the consistent baseline predictions. Although the consensus models project gains in climate space for both habitats in other parts of the country, new habitat formation in these areas is unlikely, as current (and hence near-future) land use and other conditions are not likely to favour expansion.

The roughness of the seas is rarely mentioned as a major factor in the economic or social welfare of a region. In this study, the relationship between the ocean wave climate and the economy of the Western Isles of Scotland is examined. This sparsely populated region has a high dependency on marine activities, and ferry services provide vital links between communities. The seas in the region are among the roughest in the world during autumn and winter, however, making maintenance of a reliable ferry service both difficult and expensive. A deterioration in wave and wind climate either in response to natural variability or as a regional response to anthropogenic climate change is possible. Satellite altimetry and gale-frequency data are used to analyze the contemporary response of wave and wind climate to the North Atlantic Oscillation (NAO). The sensitivity of wave climate to the NAO extends to ferry routes that are only partially sheltered and are exposed to ocean waves; thus, the reliability of ferry services is sensitive to NAO. Any deterioration of the wave climate will result in a disproportionately large increase in ferry-service disruption. The impacts associated with an unusually large storm event that affected the region in January 2005 are briefly explored to provide an insight into vulnerability to future storm events.

With recent warming trends projected to amplify over the coming century, there are concerns surrounding the impacts on mountain regions. Despite these concerns, global (GCMs) and regional climate models (RCMs) fail to capture local scale-dependent controls on upland climates. A modelling framework combining climate model outputs and station data is presented and used to explore possible future changes to temperature with altitude in the Scottish Highlands. The approach was extended by modelling shifts in seasonal isotherm values associated with existing vegetation zones. To achieve this, temperature lapse rate models (LRMs) were applied to 1961–1990 baseline (BL) observed station data for selected stations in the eastern Highlands using seasonally representative lapse rate values (LRVs) derived from paired station values. Tests against 3 upland station records ranging from 663 to 1245 m indicated a credible model performance for the mean seasonal maximum (Tmax) and minimum (Tmin) BL values evaluated. Following derivation of seasonal isotherm values for the present upper limit of vegetation zones via the LRMs, selected scenario data outputs from the corresponding Hadley Centre RCM (HadRM3H) 50 × 50 km grid cells were used to project future changes to BL values via the LRMs. The findings suggest substantial shifts in the isotherm associated with each zone for the scenarios selected, with shifts in Tminmore marked than those for Tmax, although substantial uncertainties remain. Following an exploration of the results for the region, we suggest that a refinement to the approach linked to a wider modelling effort incorporating other important controlling variables for upland species could inform future management initiatives for mountain areas more generally.

This book focuses on developing and updating prospective and practicing chemistry teachers' pedagogical content knowledge. The 11 chapters of the book discuss the most essential theories from general and science education, and in the second part of each of the chapters apply the theory to examples from the chemistry classroom.

The global climate is warming and there is consensus that recent warming trends will amplify, as the present century progresses, in response to a continued build up of atmospheric greenhouse gas (GHG) concentrations. However, there are limitations associated with Global Climate Model (GCM) and Regional Climate Model (RCM) outputs for topographically diverse regions. Strategic management decisions relating to maritime upland communities require locally resolved projections of change across a range of elevations, which are not supplied by the present generation of models. Here, some of these challenges are addressed via a series of distinctive analyses. Quality controlled baseline station data are used to assess performance outputs for seasonal mean values of temperature and precipitation from an RCM at representative locations across the region. In the case of temperature these inter-comparisons indicate a warm bias in the RCM-simulated seasonal minima for the transition seasons of spring and autumn, whereas for summer maxima there is a cold bias in RCM-simulated values. RCM-generated outputs of future changes to temperature and precipitation are then variably combined with station data to model altitudinal changes at western and eastern upland locations. These analyses indicate a substantial upward migration in key seasonal temperature isotherms associated with present vegetation zones for the climate change scenarios used. This approach is then extended by applying selected outputs to conduct Climate Change Impact Assessments (CCIAs) for the scenarios used in a series of upland case studies. The analyses flag a number of remaining research challenges. Principally, these are that scale-dependent controls on local topo-climates are not adequately captured in the GCM driven RCM projection. While the approach delivers more refined local-scale projections of possible change across a range of elevations than has hitherto been available, residual uncertainties associated with the use of GCM/RCM outputs remain.