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.

Abundance and biomass of mesozooplankton in the Levantine Basin during the Bilim 2 cruise in April 2008

The Sesame dataset contains mesozooplankton data collected during April 2008 in the Levantine Basin (between 33.20 and 36.50 N latitude and between 30.99 and 31.008 E longitude). Mesozooplankton samples were collected by using a WP-2 closing net with 200 µm mesh size during day hours (07:00-18:00). Samples were taken from 0-50, 50-100, 100-200 m layers at 5 stations in Levantine Basin The dataset includes samples analyzed for mesozooplankton species composition, abundance and total mesozooplankton biomass. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling biomass was measured by weighing filters and then determined by sampling volume. The samples were sieved sequentially through meshes of 500 and 200 micron to separate the mesozooplankton into size fractions. The entire sample (1/2) or an aliquot of the taxon-specific mesozooplankton abundance and the total abundance of the mesozooplankton were was analyzed under the binocular microscope. Minimum 500 individuals of mesozooplankton were identified and numerated at higher taxonomic level. Taxonomic identification was done at the METU- Institute of Marine Sciences by Alexandra Gubanova,Tuba Terbiyik using the relevant taxonomic literatures. Mesozooplankton abundance and biomass were estimated by Zahit Uysal and Yesim Ak.

Wet tundra polygon centers and distance between polygon centroids on Samoylov Island, Lena Delta, Siberia, Russia, with links to shape files

Subgrid processes occur in various ecosystems and landscapes but, because of their small scale, they are not represented or poorly parameterized in climate models. These local heterogeneities are often important or even fundamental for energy and carbon balances. This is especially true for northern peatlands and in particular for the polygonal tundra, where methane emissions are strongly influenced by spatial soil heterogeneities. We present a stochastic model for the surface topography of polygonal tundra using Poisson-Voronoi diagrams and we compare the results with available recent field studies. We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system to the main small-scale processes within the single polygons.