Biotic and abiotic parameters and cynobacteria abundance during an experimental set-up with varying pCO2 conditions

Heterocystous cyanobacteria of the genus Nodularia form extensive blooms in the Baltic Sea and contribute substantially to the total annual primary production. Moreover, they dispense a large fraction of new nitrogen to the ecosystem when inorganic nitrogen concentration in summer is low. Thus, it is of ecological importance to know how Nodularia will react to future environmental changes, in particular to increasing carbon dioxide (CO2) concentrations and what consequences there might arise for cycling of organic matter in the Baltic Sea. Here, we determined carbon (C) and dinitrogen (N2) fixation rates, growth, elemental stoichiometry of particulate organic matter and nitrogen turnover in batch cultures of the heterocystous cyanobacterium Nodularia spumigena under low (median 315 µatm), mid (median 353 µatm), and high (median 548 µatm) CO2 concentrations. Our results demonstrate an overall stimulating effect of rising pCO2 on C and N2 fixation, as well as on cell growth. An increase in pCO2 during incubation days 0 to 9 resulted in an elevation in growth rate by 84 ± 38% (low vs. high pCO2) and 40 ± 25% (mid vs. high pCO2), as well as in N2 fixation by 93 ± 35% and 38 ± 1%, respectively. C uptake rates showed high standard deviations within treatments and in between sampling days. Nevertheless, C fixation in the high pCO2 treatment was elevated compared to the other two treatments by 97% (high vs. low) and 44% (high vs. mid) at day 0 and day 3, but this effect diminished afterwards. Additionally, elevation in carbon to nitrogen and nitrogen to phosphorus ratios of the particulate biomass formed (POC : POP and PON : POP) was observed at high pCO2. Our findings suggest that rising pCO2 stimulates the growth of heterocystous diazotrophic cyanobacteria, in a similar way as reported for the non-heterocystous diazotroph Trichodesmium. Implications for biogeochemical cycling and food web dynamics, as well as ecological and socio-economical aspects in the Baltic Sea are discussed.

Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment, links to sourcecode of model and grides

The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3?km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70?% parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3?km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.