The effect of short-term temperature exposure on vital physiological processes of mixoplankton and protozooplankton

Sudden environmental changes like marine heatwaves will become more intense and frequent in the future. Understanding the physiological responses of mixoplankton and protozooplankton, key members of marine food webs, to temperature is crucial. Here, we studied two dinoflagellates (one protozoo- and one mixoplanktonic), two ciliates (one protozoo- and one mixoplanktonic), and two cryptophytes. We report the acute (24 h) responses on growth and grazing to a range of temperatures (5–34 °C). We also determined respiration and photosynthetic rates for the four grazers within 6 °C of warming. The thermal performance curves showed that, in general, ciliates have higher optimal temperatures than dinoflagellates and that protozooplankton is better adapted to warming than mixoplankton. Our results confirmed that warmer temperatures decrease the cellular volumes of all species. Q10 coefficients suggest that grazing is the rate that increases the most in response to temperature in protozooplankton. Yet, in mixoplankton, grazing decreased in warmer temperatures, whereas photosynthesis increased. Therefore, we suggest that the Metabolic Theory of Ecology should reassess mixoplankton's position for the correct parameterisation of future climate change models. Future studies should also address the multigenerational response to temperature changes, to confirm whether mixoplankton become more phototrophic than phagotrophic in a warming scenario after adaptation

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)