Michael Kliphuis

Sahel rainfall variability and response to greenhouse warming

Received 19 April 2005; revised 21 July 2005; accepted 1 August 2005; published 10 September 2005. [1] The NCEP/NCAR re-analyses as well as ensemble integrations with an atmospheric GCM indicate that interannual variations in Sahel rainfall are related to variations in the mean sea level pressure (MSLP) over the Sahara. In turn the MSLP variations are related to the global distribution of surface air temperature (SAT). An increase in SAT over the Sahara, relative to the surrounding oceans, decreases the MSLP over the Sahara, thereby increasing the Sahel rainfall. We hypothesize that through this mechanism greenhouse warming will cause an increase in Sahel rainfall, because the warming is expected to be more prominent over the summer continents than over the oceans. This has been confirmed using an ensemble of 62 coupled model runs forced with a business as usual scenario. The ensemble mean increase in Sahel rainfall between 1980 and 2080 is about 1–2 mm day � 1 (25–50%) during July–September, thereby strongly reducing the probability of prolonged droughts. Citation: Haarsma, R. J., F. M. Selten, S. L. Weber, and M. Kliphuis (2005), Sahel rainfall variability and response to greenhouse warming, Geophys. Res. Lett., 32, L17702,

The Detection and Attribution of Climate Change Using an Ensemble of Opportunity

Abstract The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM...

High-Performance Distributed Multi-Model / Multi-Kernel Simulations: A Case-Study in Jungle Computing

High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.

On the complexities of utilizing large-scale lightpath-connected distributed cyberinfrastructure

In Autumn 2013, we—an international team of climate scientists, computer scientists, eScience researchers, and e‐Infrastructure specialists—participated in the enlighten your research global competition, organized to showcase advanced lightpath technologies in support of state‐of‐the‐art research questions. As one of the winning entries, our enlighten your research global team embarked on a very ambitious project to run an extremely high resolution climate model on a collection of supercomputers distributed over two continents and connected using an advanced 10 G lightpath networking infrastructure. Although good progress was made, we were not able to perform all desired experiments due to a varying combination of technical problems, configuration issues, policy limitations and lack of (budget for) human resources to solve these issues. In this paper, we describe our goals, the technical and non‐technical barriers, we encountered and provide recommendations on how these barriers can be removed so future project of this kind may succeed. Copyright