climateprediction.net is a research project that uses Internet-connected computers to do research in climate science. You can participate by downloading and running a free program on your computer.
climateprediction.net is based at the University of Oxford.
Already joined? Log in.
New study going out to volunteer's machines
Indian Ocean experiment
This experiment aims to evaluate the importance of the Indian Ocean Dipole (IOD) event of the winter 2019/2020 on the strong and well predicted North Atlantic Oscillation (NAO) of the same winter. The NAO is of large importance to European winter weather as it governs the winds over the North Atlantic which are related to the mild and wet winters of northern Europe. It has been shown that the NAO is in some years influenced by anomalous tropical ocean conditions (like El Nino). We aim to show that the strong positive IOD of 2019/2020 had an influence on the NAO and resulting warm conditions of this winter. We use the large ensemble of the OpenIFS@Home to identify the connections between the tropical Indian Ocean and the North Atlantic region.
CPDN app-name: oifs_43r3
Run time: ~8 hrs/task on a modern CPU
Max memory: ~7Gb
Total number of files: 783 files
Model output: 8.5Mb per output step (uncompressed)
Total size of uploaded files: 2.6GB
Checkpoint filesize: ~800Mb (these are periodically created & deleted in the slot dir and not uploaded)
(The total size of the upload is one member zipped.)
20 Feb 2023, 16:14:10 UTC · Discuss
New study going out to volunteer's machines
STORMS: Investigating how low-pressure systems may change in the future
(actual project name - “Quantifying controls on the intensity, variability and impacts of extreme European STORMS”)
Victoria Sinclair, Clément Bouvier
Institute for Atmospheric and Earth System Research,
University of Helsinki, Finland
Throughout the year, low-pressure systems regularly move across Europe, usually from west to east, bringing cloud, rain and windy weather. Sometimes these weather systems can become very intense, and the winds and rain associated with them can cause damage to buildings and infrastructure, flooding, and can disrupt electricity supply and travel. Although the short-term weather forecasts of these storms are now quite accurate, it still remains uncertain how these storms, and their impacts, are likely to change in the future as our climate changes. Some of this uncertainty is because our understanding of what controls the strength and impacts of these storms is incomplete.
The aim of this project is to understand what controls the strength and structure of these low-pressure systems. We will quantify how the atmospheric state that the low-pressure systems develop in affects the strength and structure of these low-pressure systems. This atmospheric state can be described by various parameters, for example, the mean temperature, moisture content, and upper-level wind speeds (i.e. the strength and width of the jet stream). Since there are lots of different parameters we want to study (not just the ones described above), we want to do lots of experiments in a high controlled manner. Therefore, we will run a large ensemble of simulations of idealised low-pressure systems using the numerical weather prediction model OpenIFS. Although the simulations are idealised, the weather systems that develop look very like real weather systems that we observed in reality. Each ensemble member differs in its initial atmospheric state, and we choose these initial states to cover everything from the current climate to past pre-industrial climates to the most extreme future climate projections. This is exciting because although idealised simulations of low-pressure systems have been performed before, this is the first time that such an extensive exploration of the parameter space will be conducted.
Once we have the results from the large ensemble, we will calculate different measures of the strength of the storms and then use machine learning techniques to see how these relate to the initial states. Our results will hopefully increase in confidence in how these storms and their impacts will change in the future.
Please ensure 'Leave non-GPU tasks in memory while suspended' is set in Disk/Memory options of boincmgr
CPDN app-name: oifs_43r3_bl
Run time: ~6 hrs/task on a modern CPU
Max memory: ~7Gb
Total number of files: 354 files
Model output: 16Mb per output step (uncompressed)
Total size of uploaded files: 1.5GB
Checkpoint filesize: ~800Mb (these are periodically created & deleted in the slot dir and not uploaded)[/b]
13 Feb 2023, 11:32:41 UTC · Discuss
Request to volunteers to please enable: 'Leave non-GPU tasks in memory'
The OpenIFS model batches require the option 'Leave non-GPU in memory while suspended' to be enabled under boincmgr -> Disk&Memory. This will prevent the task from frequently restarting and reduce the risk of task failure.
5 Jan 2023, 16:54:57 UTC · Discuss
Request for volunteers running pre-7.10 BOINC clients to update, due to an issue with pre-7.10 client versions not being able to connect with the project
Could we please ask climateprediction.net project volunteers to update their BOINC client software to the latest version. There is currently an issue with older client versions (pre-7.10) not being able to communicate with the climateprediction.net project. Updating your BOINC client software to the latest version will alleviate this issue.
21 Feb 2022, 14:16:27 UTC · Discuss
Reminder for Unix/Linux Volunteers to ensure they have installed the 32-bit compatibility libraries
Just a reminder to all volunteers running climateprediction.net in Unix/Linux. Unix/Linux machines need to have the 32-bit compatibility libraries manually installed. If these libraries are not installed climateprediction.net workunits will crash when running. See the climateprediction.net forum for more information on this: https://www.cpdn.org/forum_thread.php?id=8008.
24 Oct 2019, 14:51:53 UTC · Discuss