Ed Blockley, Ann Keen
Preliminary results from the SIMIP mass budget inter-comparison being coordinated by the Met Office Hadley Centre through APPLICATE WP1
- Full Article: https://www.zenodo.org/record/3516674
Arduini, Gabriele; Balsamo, Gianpaolo; Dutra, Emanuel; Day, Jonathan J.; Boussetta, Souhail; Sandu, Irina
In this work a new multi-layer snow scheme for the ECMWF Integrated Forecasting System (IFS) is evaluated and the impact on global short- and medium-range weather forecasts analyzed.
The work described in the abstract has been presented at the European Geophysical Union (EGU) General Assembly 2019.
- Full Article: https://www.zenodo.org/record/3514731
Gabriele Arduini; Gianpaolo Balsamo; Emanuel Dutra; Jonathan J. Day; Irina Sandu; Souhail Boussetta; Thomas Haiden
Dataset used for the evaluation of a new multi-layer snow scheme developed for the ECMWF Integrated Forecasting System (IFS). The dataset includes offline experiments to be compared with in situ observations and time-series and statistics of different quantities (snow depth, 2-metre temperature, cloud cover etc.) used to evaluate the impact on coupled land-atmosphere weather forecasts, as reported in the manuscript "Impact of a multi-layer snow scheme on near-surface weather forecasts" by Arduini et al. (2019).
- Full Article: https://www.zenodo.org/record/3368192
Maisonnave E.; Voldoire A.;
The goal of this study is to evaluate the impact of a new flux calculation location, performed on each ocean/atmosphere grids intersection instead of on the coarse atmosphere grid. This document describes the implementation and validation of an intermediate complexity coupled model including NEMO and various instances of the SURFEX surface module of ARPEGE, where flux calculations are performed. Major impacts affect non solar heat flux, particularly in the marginal zone, during ice production phase, which is increased.
- Full Article: https://www.zenodo.org/record/3480076
Zampieri Lorenzo; Goessling Helge F; Jung Thomas
Coupled subseasonal forecast systems with dynamical sea ice have the potential of providing important predictive information in polar regions. Here, we evaluate the ability of operational ensemble prediction systems to predict the location of the sea ice edge in Antarctica. Compared to the Arctic, Antarctica shows on average a 30% lower skill, with only one system remaining more skillful than a climatological benchmark up to ∼30 days ahead. Skill tends to be highest in the west Antarctic sector during the early freezing season. Most of the systems tend to overestimate the sea ice edge extent and fail to capture the onset of the melting season. All the forecast systems exhibit large initial errors. We conclude that subseasonal sea ice predictions could provide marginal support for decision‐making only in selected seasons and regions of the Southern Ocean. However, major progress is possible through investments in model development, forecast initialization and calibration.
- Full Article: https://www.zenodo.org/record/3479436
Nikolay Koldunov; Sergey Danilov; Dmitry Sidorenko; Nils Hutter; Martin Losch; Helge Goessling; Natalja Rakowsky; Patrick Scholz; Dmitry Sein; Qiang Wang; Thomas Jung
Sea ice dynamics determine the drift and deformation of sea ice. Nonlinear physics, usually expressed in a viscous-plastic rheology, makes the sea ice momentum equations notoriously difficult to solve. At increasing sea ice model resolution the nonlinearities become stronger as linear kinematic features (leads) appear in the solutions. Even the standard elastic-viscous-plastic (EVP) solver for sea ice dynamics, which was introduced for computational efficiency, becomes computationally very expensive, when accurate solutions are required, because the numerical stability requires very short, and hence more, subcycling time steps at high resolution. Simple modifications to the EVP solver have been shown to remove the influence of the number of subcycles on the numerical stability. At low resolution appropriate solutions can be obtained with only partial convergence based on a significantly reduced number of subcycles as long as the numerical procedure is kept stable. This previous result is extended to high resolution where linear kinematic features start to appear. The computational cost can be strongly reduced in Arctic Ocean simulations with a grid spacing of 4.5 km by using modified and adaptive EVP versions because fewer subcycles are required to simulate sea ice fields with the same characteristics as with the standard EVP.
- Full Article: https://www.zenodo.org/record/3479287
Dmitry Sidorenko; Nikolay Koldunov; Q. Wang; Sergey Danilov; H. F. Goessling; O. Gurses; P. Scholz; D. V. Sein; E. Volodin; C. Wekerle; Thomas Jung;
Sea ice formation is accompanied by the rejection of salt which in nature tends to be mixed vertically by the formation of convective plumes. Here we analyze the influence of a salt plume parameterization in an atmosphere‐sea ice‐ocean model. Two 330‐yearlong simulations have been conducted with the AWI Climate Model. In the reference simulation, the rejected salt in the Arctic Ocean is added to the uppermost ocean layer. This approach is commonly used in climate modeling. In another experiment, employing salt plume parameterization, the rejected salt is vertically redistributed within the mixed layer based on a power law profile that mimics the penetration of salt plumes. We discuss the effects of this redistribution on the simulated mean state and on atmosphere–ocean linkages associated with the intensity of deepwater formation. We find that the salt plume parameterization leads to simultaneous increase of sea ice (volume and concentration) and decrease of sea surface salinity in the Arctic. The salt plume parameterization considerably alters the interplay between the atmosphere and the ocean in the Nordic Seas. The parameterization modifies the ocean ventilation; however, resulting changes in temperature and salinity largely compensate each other in terms of density so that the overturning circulation is not significantly affected.
- Full Article: https://www.zenodo.org/record/3479267
Nikolay Koldunov; Luisa Cristini
Large part of the project manager's work can be described in terms of retrieving, processing, analysing and synthesizing various types of data from different sources. The types of information become more and more diverse (including participants, task and financial details, and dates) and data volumes continue to increase, especially for large international collaborations. In this paper we explore the possibility of using the python programming language as a tool for retrieving and processing data for some project management tasks. python is a general-purpose programming language with a very rich set of libraries. In recent years python experienced explosive growth leading to development of several libraries that help to efficiently solve many data related tasks without very deep knowledge of programming in general and python in particular. In this paper we present some of the core python libraries that can be used to solve some typical project management tasks and demonstrate several real-world applications using a HORIZON 2020 type European project and as example.
- Full Article: https://www.zenodo.org/record/3479261
Välisuo, Ilona; Batté, Lauriane; Salas y Mélia, David; Ardilouze, Constantin; Chevallier, Matthieu;
Presentation given at the European Meteorological Society's annual meeting, organized from 9 to 13 September 2019 at the Technical University of Denmark in Lyngby, Copenhagen, Denmark. The presentation was part of the session UP2.4: The cryosphere and cold region processes in the global climate system.
- Full Article: https://www.zenodo.org/record/3461584