Challenges for mesoscale climatology execution on experimental grid computing systems

This paper discusses the challenges of executing a long-term application on a computational grid, which generates the climatology of the atmospheric numerical model BRAMS (Brazilian development on Regional Atmospheric Modeling System) using ensemble members. We have developed a workflow that submits climatology to the computational grid composed by three different grid middlewares (OurGrid, OAR/CiGri and Globus) and three clusters (situated in Porto Alegre, São José dos Campos and Cachoeira Paulista—Brazil). The application characteristics demand a processing grid, rather than a data grid, due to intensive computation and data transfer between the geographically distributed grid nodes. We achieved the goal of generating the climatology using a computational grid. However, we observed problems on application performance due data transfer and non-availability of the computational grid. Questions related to data storage/transfer and grid failures must be better treated to ensure application performance.

The climateprediction.net BBC climate change experiment: design of the coupled model ensemble

Perturbed physics experiments are among the most comprehensive ways to address uncertainty in climate change forecasts. In these experiments, parameters and parametrizations in atmosphere-ocean general circulation models are perturbed across ranges of uncertainty, and results are compared with observations. In this paper, we describe the largest perturbed physics climate experiment conducted to date, the British Broadcasting Corporation (BBC) climate change experiment, in which the physics of the atmosphere and ocean are changed, and run in conjunction with a forcing ensemble designed to represent uncertainty in past and future forcings, under the A1B Special Report on Emissions Scenarios (SRES) climate change scenario.

Uncertainty in predictions of the climate response to rising levels of greenhouse gases

The range of possibilities for future climate evolution needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty. Statistical estimates of model response uncertainty, based on observations of recent climate change, admit climate sensitivities--defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide--substantially greater than 5 K. But such strong responses are not used in ranges for future climate change because they have not been seen in general circulation models. Here we present results from the 'climateprediction.net' experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.

Constraints on future changes in climate and the hydrologic cycle

What can we say about changes in the hydrologic cycle on 50-year timescales when we cannot predict rainfall next week? Eventually, perhaps, a great deal: the overall climate response to increasing atmospheric concentrations of greenhouse gases may prove much simpler and more predictable than the chaos of short-term weather. Quantifying the diversity of possible responses is essential for any objective, probability-based climate forecast, and this task will require a new generation of climate modelling experiments, systematically exploring the range of model behaviour that is consistent with observations. It will be substantially harder to quantify the range of possible changes in the hydrologic cycle than in global-mean temperature, both because the observations are less complete and because the physical constraints are weaker.