Carl Christensen

Designing a runtime system for volunteer computing

Volunteer computing is a form of distributed computing in which the general public volunteers processing and storage to scientific research projects. BOINC, a middleware system for volunteer computing, is currently used by about 20 projects, to which 300,000 volunteers and 450,000 computers supply 350 TeraFLOPS of processing power. A BOINC client program runs on the volunteered hosts and manages the execution of applications. Together with a library linked to applications, it implements a runtime system providing process management, graphics control, checkpointing, file access, and other functions. This runtime system must handle widely varying applications, must provide features and properties desired by volunteers, and must work on many platforms. This paper describes the problems in designing a runtime system having these properties, and how these problems are solved in BOINC

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.

Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes

A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully. The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation. The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.

A novel strong-motion seismic network for community participation in earthquake monitoring

The Quake-Catcher Network (QCN) is breaking new ground in seismology by combining new micro-electro-mechanical systems (MEMS) technology with volunteer seismic station distributed computing. Rather than distributing just computations, the QCN allows volunteers to participate in scientific data collection and computation. Using these innovative tools, QCN will increase the number of strong-motion observations for improved earthquake detection and analysis in California, and throughout the world. QCNs increased density of seismic measurements will revolutionize seismology. The QCN, in concert with current seismic networks, may soon provide advanced alerts when earthquakes occur, estimate the response of a building to earthquakes even before they happen, and generate a greater understanding of earthquakes for scientists and the general public alike. Details of how one can join the QCN are outlined. In addition, we have activities on our website that can be used in K-16 classrooms to teach students basic seismology and physics concepts.

Regional probabilistic climate forecasts from a multithousand, multimodel ensemble of simulations

[1] A methodology for constraining climate forecasts, developed for application to the multithousand member perturbed physics ensemble of simulations completed by the distributed computing project ClimatePrediction.net, is here presented in detail. The methodology is extended to produce constrained forecasts of mean surface temperature and precipitation within 21 land-based regions and is validated with climate simulations from other models available from the IPCC (AR4) data set. The mean forecasted values of temperature and precipitation largely confirm prior results for the same regions. In particular, precipitation in the Mediterranean basin is shown to decrease and temperature over northern Europe is shown to increase with comparatively little uncertainty in the forecast (i.e., with tight constraints). However, in some cases the forecasts show large uncertainty, and there are a few cases where the forecasts cannot be constrained at all. These results illustrate the effectiveness of the methodology and its applicability to regional climate variables.

Climateprediction.net: A Global Community for Research in Climate Physics

The climateprediction.net project is undertaking climate research by harnessing spare computer cycles volunteered by businesses and members of the public. It is running a massive ensemble of climate simulations using a state-of-the-art climate model with the aim of quantifying the uncertainties in predictions of future climate change. Such a probabilistic forecast will support and improve decision-making in government and industry. The computational design builds and expands on the concepts of distributed computing, as demonstrated by projects such as SETI@home, but along with higher computational demands comes a much greater requirement and desire to involve participants in the details of the research. Issues of collaborative learning, educational tools, and participant involvement therefore form some of the core activities.