Figures and related data of a review of the "Global and Russian energy outlook up to 2040"

The Global and Russian Energy Outlook up to 2040, prepared by the Energy Research Institute of the Russian Academy of Sciences and the Analytical Center for the Government of the Russian Federation, analyses the long-term changes in the main energy markets and thereby identifies the threats to the Russian economy and energy sector. Research has shown that shifts in the global energy sector, especially in hydrocarbon markets (primarily the development of technologies for shale oil and gas extraction), will result in a slowdown of Russia's economy by one percentage point each year on average due to a decrease in energy exports comparison with the official projections. Owing to the lack of development of an institutional framework, an outdated tax system, low competition and low investment efficiency, Russia will be the most sensitive to fluctuations in global hydrocarbon markets among all major energy market players within the forecast period.

Global Ocean Particulate Organic Carbon flux merged with satellite parameters

The efficiency of the biological pump of carbon to the deep ocean depends largely on the biologically mediated export of carbon from the surface ocean and its remineralization with depth. Global satellite studies have primarily focused on chlorophyll concentration and net primary production (NPP) to understand the role of phytoplankton in these processes. Recent satellite retrievals of phytoplankton composition now allow for the size of phytoplankton cells to be considered. Here, we improve understanding of phytoplankton size structure impacts on particle export, remineralization and transfer. Particulate organic carbon (POC) flux observations from sediment traps and 234Th are compiled across the global ocean. Annual climatologies of NPP, percent microplankton, and POC flux at four time series locations and within biogeochemical provinces are constructed, and sinking velocities are calculated to align surface variables with POC flux at depth. Parameters that characterize POC flux vs. depth (export flux ratio, labile fraction, remineralization length scale) are then fit to the aligned dataset. Times of the year dominated by different size compositions are identified and fit separately in regions of the ocean where phytoplankton cell size showed enough dynamic range over the annual cycle. Considering all data together, our findings support the paradigm of high export flux but low transfer efficiency in more productive regions and vice versa for oligotrophic regions. However, when parsing by dominant size class, we find periods dominated by small cells to have both greater export flux and lower transfer efficiency than periods when large cells comprise a greater proportion of the phytoplankton community.

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.