The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files

The CMCC Global Ocean Physical Reanalysis System (C-GLORS) is used to simulate the state of the ocean in the last decades. It consists of a variational data assimilation system (OceanVar), capable of assimilating all in-situ observations along with altimetry data, and a forecast step performed by the ocean model NEMO coupled with the LIM2 sea-ice model. KEY STRENGTHS: - Data are available for a large number of ocean parameters - An extensive validation has been conducted and is freely available - The reanalysis is performed at high resolution (1/4 degree) and spans the last 30 years KEY LIMITATIONS: - Quality may be discontinuos and depend on observation coverage - Uncertainty estimates are simply derived through verification skill scores

The Global Plankton Database: an inventory and data from the Former Soviet Union expeditions

At present time, there is a lack of knowledge on the interannual climate-related variability of zooplankton communities of the tropical Atlantic, central Mediterranean Sea, Caspian Sea, and Aral Sea, due to the absence of appropriate databases. In the mid latitudes, the North Atlantic Oscillation (NAO) is the dominant mode of atmospheric fluctuations over eastern North America, the northern Atlantic Ocean and Europe. Therefore, one of the issues that need to be addressed through data synthesis is the evaluation of interannual patterns in species abundance and species diversity over these regions in regard to the NAO. The database has been used to investigate the ecological role of the NAO in interannual variations of mesozooplankton abundance and biomass along the zonal array of the NAO influence. Basic approach to the proposed research involved: (1) development of co-operation between experts and data holders in Ukraine, Russia, Kazakhstan, Azerbaijan, UK, and USA to rescue and compile the oceanographic data sets and release them on CD-ROM, (2) organization and compilation of a database based on FSU cruises to the above regions, (3) analysis of the basin-scale interannual variability of the zooplankton species abundance, biomass, and species diversity.

Debris flow magnitudes: a global catalog

The episodic occurrence of debris flow events in response to stochastic precipitation and wildfire events makes hazard prediction challenging. Previous work has shown that frequency-magnitude distributions of non-fire-related debris flows follow a power law, but less is known about the distribution of post-fire debris flows. As a first step in parameterizing hazard models, we use frequency-magnitude distributions and cumulative distribution functions to compare volumes of post-fire debris flows to non-fire-related debris flows. Due to the large number of events required to parameterize frequency-magnitude distributions, and the relatively small number of post-fire event magnitudes recorded in the literature, we collected data on 73 recent post-fire events in the field. The resulting catalog of 988 debris flow events is presented as an appendix to this article. We found that the empirical cumulative distribution function of post-fire debris flow volumes is composed of smaller events than that of non-fire-related debris flows. In addition, the slope of the frequency-magnitude distribution of post-fire debris flows is steeper than that of non-fire-related debris flows, evidence that differences in the post-fire environment tend to produce a higher proportion of small events. We propose two possible explanations: 1) post-fire events occur on shorter return intervals than debris flows in similar basins that do not experience fire, causing their distribution to shift toward smaller events due to limitations in sediment supply, or 2) fire causes changes in resisting and driving forces on a package of sediment, such that a smaller perturbation of the system is required in order for a debris flow to occur, resulting in smaller event volumes.

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.