In this presentation, the mycotoxin levels-as analysed by the analytical centre for mycotoxin surveillance of the state food laboratory (LAVES Braunschweig)-for approximately 500 food samples are reported. The samples were collected in the year 2009 at retail in the German federal state of Lower Saxony. Aflatoxin and ochratoxin A were analysed in dried fruits, spices, cereals and tree nuts. Ochratoxin A was detected in all samples of dried vine fruits, at levels up to 8.1 μg/kg. Aflatoxins and ochratoxin A were also found in nutmeg and curry powder: the maximum regulatory levels for aflatoxins were exceeded in 25% of the nutmeg samples. Nearly all samples of basmati rice contained aflatoxins, although at levels below the maximum regulatory level in all but one sample. Aflatoxins were also detected in about 50% of hazelnut samples, in 20% of the samples the maximum levels was exceeded (maximum 23.2 μg/kg). In contrast, aflatoxin contents in pistachios were surprisingly low. Fusarium toxins were analysed in cereals and cereal products such as flour, bread, and pasta. Deoxynivalenol (DON) was the predominant toxin found in these samples: DON was found in about 40% of the samples, although the maximum levels were not exceeded (max. 418 μg/kg). Fumonisins (FBs) and zearalenone (ZEA) were specifically analysed in maize products (snacks, flour and oil). Most of these samples (80%) were positive, but at levels not exceeding the maximum levels. Maximum levels were 98 μg/kg (ZEA) and 577 μg/kg (sum of FB1 and FB2). Ergot alkaloids (six major alkaloids) were analysed in rye flour, and approximately 50% were positive. The highest concentration of ergot alkaloids was 1,063 μg/kg; the predominant alkaloids were ergotamine and ergocristine. In conclusion, the results indicate that continuous and efficient control measures for mycotoxins in a wide range of critical foods are necessary to ensure compliance with maximum levels. Although the mycotoxin levels in the vast majority of samples were below maximum levels, year-to-year variation and changes in the production of relevant commodities may result in a different picture in the future.

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<p>Here we present an overview of results emerging from a project to develop prototype decadal climate prediction services, funded by the EU Copernicus Climate Change Service (C3S). The field of interannual to decadal climate prediction has matured rapidly over the last ~15 years, becoming an established part of the Coupled Model Intercomparison Project (CMIP) process with multi-model decadal climate predictions made in CMIP5 and CMIP6 (DCPP MIP). It has further been highlighted by the recent creation of the WMO Lead Centre for Annual-to-Decadal Climate Prediction. Whilst these activities have led to rapid development in our understanding of decadal climate predictability and mechanisms driving global and regional annual to decadal climate variability, the creation of useful climate services on this timescale is still in its infancy.</p><p>This EU funded project was designed to start to address decadal climate services and brings together many of the key European institutions involved in decadal climate predictions from four different countries: Germany (DWD), Italy (CMCC), Spain (BSC) and the UK (Met Office). Each partner is working with a different sector: infrastructure, energy, agriculture and insurance where they have been developing a prototype decadal climate service in partnership with a user in that sector. Here we report on the progress made so far and highlight a number of key lessons learned along the way. These include the use of both large multi-model ensembles and more predictable large-scale circulation indicators in order to give skilful regional predictions of user relevant variables. We also describe the development of a common product format to present forecast information to users, this contains essential information about the current probabilistic forecast, retrospective forecast skill and reliability.</p>

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<p>DWD provides operational seasonal and decadal predictions of the German climate prediction system since 2016 and 2020, respectively. We plan to present these predictions together with post-processed ECMWF sub-seasonal forecast products on the DWD climate prediction website www.dwd.de/climatepredictions. In March 2020, this climate service was published with decadal predictions for the coming years; sub-seasonal and seasonal predictions for the coming weeks and months will follow.</p><p>The user-oriented evaluation and design of this climate service has been developed in close cooperation with users from various sectors at workshops of the German MiKlip project and will be consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with the prediction skill for 1-year and 5-year means/ sums of temperature and precipitation for different regions (World, Europe, Germany, German regions).</p><p>For Germany, the statistical downscaling EPISODES was applied to reach high spatial resolution required by several climate data users. Decadal predictions were statistically recalibrated in order to adjust bias, drift and standard deviation and optimize ensemble spread. We used the MSESS and RPSS to evaluate the skill of climate predictions in comparison to reference predictions, e.g. ‘observed climatology’ or ‘uninitialized climate projections’ (which are both applied by users until now as an alternative to climate predictions). The significance was tested via bootstraps.</p><p>Within the ‘basic climate predictions’ section, a user-oriented traffic light indicates whether regional-mean climate predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than reference predictions. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction itself (via the color of dots) and its skill (via the size of dots representing the skill categories of the traffic light). The co-development of this climate prediction application with users from different sectors strongly improves the comprehensibility and applicability by users in their daily work.</p><p>In addition to sub-seasonal and seasonal predictions, plans for future extensions of this climate service include multi-year seasonal predictions, e.g. 5-year summer or winter means, combined products for climate predictions and climate projections, further user-oriented, extreme or large-scale variables, e.g. ENSO, or high-resolution applications for German cities based on statistically downscaled predictions.</p>