Regional superparameterization in a global circulation model using Large Eddy Simulations

As a computationally attractive alternative for global large eddy simulations (LESs), we investigate the possibility of using comprehensive three‐dimensional LESs as a superparameterization that can replace all traditional parameterizations of atmospheric processes that are currently used in global models. We present the technical design for a replacement of the parameterization for clouds, convection, and turbulence of the global atmospheric model of the European Centre for Medium‐Range Weather Forecasts by the Dutch Atmospheric Large Eddy Simulation model. The model coupling consists of bidirectional data exchange between the global model and the high‐resolution LES models embedded within the columns of the global model. Our setup allows for selective superparameterization, that is, for applying superparameterization in local regions selected by the user, while keeping the standard parameterization of the global model intact outside this region. Computationally, this setup can result in major geographic load imbalance, because of the large difference in computational load between superparameterized and nonsuperparameterized model columns. To resolve this issue, we use a modular design where the local and global models are kept as distinct model codes and organize the model coupling such that all the local models run in parallel, separate from the global model. First simulation results, employing this design, demonstrate the potential of our approach

The Oceanographic Multipurpose Software Environment (OMUSE v1.0)

In this paper we present the Oceanographic Multipurpose Software Environment (OMUSE). OMUSE aims to provide a homogeneous environment for existing or newly developed numerical ocean simulation codes, simplifying their use and deployment. In this way, numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales can be easily designed. Rapid development of simulation models is made possible through the creation of simple high-level scripts. The low-level core of the abstraction in OMUSE is designed to deploy these simulations efficiently on heterogeneous high-performance computing resources. Cross-verification of simulation models with different codes and numerical methods is facilitated by the unified interface that OMUSE provides. Reproducibility in numerical experiments is fostered by allowing complex numerical experiments to be expressed in portable scripts that conform to a common OMUSE interface. Here, we present the design of OMUSE as well as the modules and model components currently included, which range from a simple conceptual quasi-geostrophic solver to the global circulation model POP (Parallel Ocean Program). The uniform access to the codes' simulation state and the extensive automation of data transfer and conversion operations aids the implementation of model couplings. We discuss the types of couplings that can be implemented using OMUSE. We also present example applications that demonstrate the straightforward model initialization and the concurrent use of data analysis tools on a running model. We give examples of multiscale and multiphysics simulations by embedding a regional ocean model into a global ocean model and by coupling a surface wave propagation model with a coastal circulation model

Performance optimization and load-balancing modeling for superparametrization by 3D LES

In order to eliminate climate uncertainty w.r.t. cloud and convection parametrizations, superpramaterization (SP) [1] has emerged as one of the possible ways forward. We have implemented (regional) superparametrization of the ECMWF weather model OpenIFS [2] by cloud-resolving, three-dimensional large-eddy simulations. This setup, described in [3], contains a two-way coupling between a global meteorological model that resolves large-scale dynamics, with many local instances of the Dutch Atmospheric Large Eddy Simulation (DALES) [4], resolving cloud and boundary layer physics. The model is currently prohibitively expensive to run over climate or even seasonal time scales, and a global SP requires the allocation of millions of cores. In this paper, we study the performance and scaling behavior of the LES models and the coupling code and present our implemented optimizations. We mimic the observed load imbalance with a simple performance model and present strategies to improve hardware utilization in order to assess the feasibility of a world-covering superparametrization. We conclude that (quasi-)dynamical load-balancing can significantly reduce the runtime for such large-scale systems with wide variability in LES time-stepping speeds

Representing cloud mesoscale variability in superparameterized climate models

In atmospheric modeling, superparameterization (SP) has gained popularity as a technique to improve cloud and convection representations in large-scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in SP lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large-scale model. We demonstrate this phenomenon in a regional SP experiment with the global model OpenIFS coupled to the local model Dutch Atmospheric Large Eddy Simulation, as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. As a starting point for mitigating the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model's total water content is enhanced in order to match the global model's cloud condensate amount. The proposed scheme enhances the cloud condensate amount in the test cases, however a large discrepancy remains, caused by rapid dissipation of the clouds added by the proposed scheme

Creating a reusable cross-disciplinary multi-scale and multi-physics framework: From AMUSE to OMUSE and beyond

Here, we describe our efforts to create a multi-scale and multi-physics framework that can be retargeted across different disciplines. Currently we have implemented our approach in the astrophysical domain, for which we developed AMUSE (github.com/amusecode/amuse ), and generalized this to the oceanographic and climate sciences, which led to the development of OMUSE (bitbucket.org/omuse ). The objective of this paper is to document the design choices that led to the successful implementation of these frameworks as well as the future challenges in applying this approach to other domains

A Python interface to the Dutch Atmospheric Large-Eddy Simulation

We present a Python interface for the Dutch Atmospheric Large Eddy Simulation (DALES), an existing Fortran code for high-resolution, turbulence-resolving simulation of atmospheric physics. The interface is based on an infrastructure for remote and parallel function calls and makes it possible to use and control the DALES weather simulations from a Python context. The interface is designed within the OMUSE framework, and allows the user to set up and control the simulation, apply perturbations and forcings, collect and analyse data in real time without exposing the user to the details of setting up and running the parallel Fortran DALES code. Another significant possibility is coupling the DALES simulation to other models, for example larger scale numerical weather prediction (NWP) models that can supply realistic lateral boundary conditions. Finally, the Python interface to DALES can serve as an educational tool for exploring weather dynamics, which we demonstrate with an example Jupyter notebook

Open weather and climate science in the digital era

The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weathe

ElsevierSoftwareX /SOFTX_2019_279

forked from omuse-geoscience/omuse: The Oceanographic Multi-purpose Software Environment: a package for multi-physics and multi-scale earth science simulations

Superparameterization of OpenIFS with DALES

sp-coupler is python code to couple different atmospheric models, namely the ECMWF OpenIFS global weather model and DALES, a small-scale atmospheric LES. In particular, sp-coupler aims at attaching an LES instances at a selected set of gridpoints of OpenIFS to establish a regional superparametrization of the global model. In such a setup, the two models run in a leap-frog cadence where the DALES models are forced by OpenIFS and produce turbulence, convection and cloud process tendencies for this GCM. The python scripts use the OMUSE framework to control DALES and OpenIFS and exchange the coupling data