Dynamic power management: from portable devices to high performance computing

Electronic applications are nowadays converging under the umbrella of the cloud computing vision. The future ecosystem of information and communication technology is going to integrate clouds of portable clients and embedded devices exchanging information, through the internet layer, with processing clusters of servers, data-centers and high performance computing systems. Even thus the whole society is waiting to embrace this revolution, there is a backside of the story. Portable devices require battery to work far from the power plugs and their storage capacity does not scale as the increasing power requirement does. At the other end processing clusters, such as data-centers and server farms, are build upon the integration of thousands multiprocessors. For each of them during the last decade the technology scaling has produced a dramatic increase in power density with significant spatial and temporal variability. This leads to power and temperature hot-spots, which may cause non-uniform ageing and accelerated chip failure. Nonetheless all the heat removed from the silicon translates in high cooling costs. Moreover trend in ICT carbon footprint shows that run-time power consumption of the all spectrum of devices accounts for a significant slice of entire world carbon emissions. This thesis work embrace the full ICT ecosystem and dynamic power consumption concerns by describing a set of new and promising system levels resource management techniques to reduce the power consumption and related issues for two corner cases: Mobile Devices and High Performance Computing.

Observations of the northern seasonal polar cap on Mars III: CRISM/HiRISE observations of spring sublimation

We analyze a series of targeted CRISM and HiRISE observations of seven regions of interest at high latitudes in the Northern polar regions of Mars. These data allow us to investigate the temporal evolution of the composition of the seasonal ice cap during spring, with a special emphasis on peculiar phenomena occurring in the dune fields and in the vicinity of the scarps of the North Polar Layered Deposits (NPLDs). The strength of the spectral signature of CO2 ice continuously decreases during spring whereas the one of H2O ice first shows a strong increase until Ls = 50°. This evolution is consistent with a scenario previously established from analysis of OMEGA data, in which a thin layer of pure H2O ice progressively develops at the surface of the volatile layer. During early spring (Ls < 10°), widespread jet activity is observed by HiRISE while strong spectral signatures of CO2 ice are detected by CRISM. Later, around Ls = 20-40°, activity concentrates at the dune fields where CRISM also detects a spectral enrichment in CO2 ice, consistent with "Kieffer's model" (Kieffer, H.H. [2007]. J. Geophys. Res. 112, E08005. doi:10.1029/2006JE002816) for jet activity. Effects of wind are prominent across the dune fields and seem to strongly influence the sublimation of the volatile layer. Strong winds blowing down the scarps could also be responsible for the significant spatial and temporal variability of the surface ice composition observed close to the NPLD.

Observation errors in early historical upper-air observations

Upper-air observations are a fundamental data source for global atmospheric data products, but uncertainties, particularly in the early years, are not well known. Most of the early observations, which have now been digitized, are prone to a large variety of undocumented uncertainties (errors) that need to be quantified, e.g., for their assimilation in reanalysis projects. We apply a novel approach to estimate errors in upper-air temperature, geopotential height, and wind observations from the Comprehensive Historical Upper-Air Network for the time period from 1923 to 1966. We distinguish between random errors, biases, and a term that quantifies the representativity of the observations. The method is based on a comparison of neighboring observations and is hence independent of metadata, making it applicable to a wide scope of observational data sets. The estimated mean random errors for all observations within the study period are 1.5 K for air temperature, 1.3 hPa for pressure, 3.0 ms−1for wind speed, and 21.4° for wind direction. The estimates are compared to results of previous studies and analyzed with respect to their spatial and temporal variability.

Establishing the skill of climate field reconstruction techniques for precipitation with pseudoproxy experiments

This study aims at assessing the skill of several climate field reconstruction techniques (CFR) to reconstruct past precipitation over continental Europe and the Mediterranean at seasonal time scales over the last two millennia from proxy records. A number of pseudoproxy experiments are performed within the virtual reality ofa regional paleoclimate simulation at 45 km resolution to analyse different aspects of reconstruction skill. Canonical Correlation Analysis (CCA), two versions of an Analog Method (AM) and Bayesian hierarchical modeling (BHM) are applied to reconstruct precipitation from a synthetic network of pseudoproxies that are contaminated with various types of noise. The skill of the derived reconstructions is assessed through comparison with precipitation simulated by the regional climate model. Unlike BHM, CCA systematically underestimates the variance. The AM can be adjusted to overcome this shortcoming, presenting an intermediate behaviour between the two aforementioned techniques. However, a trade-off between reconstruction-target correlations and reconstructed variance is the drawback of all CFR techniques. CCA (BHM) presents the largest (lowest) skill in preserving the temporal evolution, whereas the AM can be tuned to reproduce better correlation at the expense of losing variance. While BHM has been shown to perform well for temperatures, it relies heavily on prescribed spatial correlation lengths. While this assumption is valid for temperature, it is hardly warranted for precipitation. In general, none of the methods outperforms the other. All experiments agree that a dense and regularly distributed proxy network is required to reconstruct precipitation accurately, reflecting its high spatial and temporal variability. This is especially true in summer, when a specifically short de-correlation distance from the proxy location is caused by localised summertime convective precipitation events.

Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads

The presented approach describes a model for a rule-based expert system calculating the temporal variability of the release of wet snow avalanches, using the assumption of avalanche triggering without the loading of new snow. The knowledge base of the model is created by using investigations on the system behaviour of wet snow avalanches in the Italian Ortles Alps, and is represented by a fuzzy logic rule-base. Input parameters of the expert system are numerical and linguistic variables, measurable meteorological and topographical factors and observable characteristics of the snow cover. Output of the inference method is the quantified release disposition for wet snow avalanches. Combining topographical parameters and the spatial interpolation of the calculated release disposition a hazard index map is dynamically generated. Furthermore, the spatial and temporal variability of damage potential on roads exposed to wet snow avalanches can be quantified, expressed by the number of persons at risk. The application of the rule base to the available data in the study area generated plausible results. The study demonstrates the potential for the application of expert systems and fuzzy logic in the field of natural hazard monitoring and risk management.

A 49 year hindcast of surface winds over the Iberian Peninsula

The use of hindcast climatic data is quite extended for multiple applications. However, this approach needs the support of a validation process to allow its drawbacks and, therefore, confidence levels to be assessed. In this work, the strategy relies on an hourly wind database resulting from a dynamical downscaling experiment, with a spatial resolution of 10 km, covering the Iberian Peninsula (IP), driven by the ERA40 reanalysis (1959–2001) extended by European Centre for Medium-Range Weather Forecast (ECMWF) analysis (2002–2007) and comprising two main steps. Initially, the skill of the simulation is evaluated comparing the quality-tested observational database (Lorente-Plazas et al., 2014) at local and regional scales. The results show that the model is able to portray the main features of the wind over the IP: annual cycles, wind roses, spatial and temporal variability, as well as the response to different circulation types. In addition, there is a significant added value of the simulation with respect to driving conditions, especially in regions with a complex orography. However, some problems are evident, the major drawback being the systematic overestimation of the wind speed, which is mainly attributed to a missrepresentation of frictional forces. The model skill is also lower along the Mediterranean coast and for the Pyrenees. In a second phase, the high spatio-temporal resolution of the pseudo-real wind database is used to explore the limitations of the observational database. It is shown that missing values do not affect the characterisation of the wind climate over the IP, while the length of the observational period (6 years) is sufficient for most regions, with only a few exceptions. The spatial distribution of the observational sampling schemes should be enhanced to improve the correct assessment of all IP wind regimes, particularly in some mountainous areas.

Part of the global DOC versus AOU (dissolved organic carbon/apparent oxygen utilization) data compilation, Pacific Ocean

Total organic carbon (TOC) was analyzed on four transects along 140°W in 1992 using a high temperature combustion/discrete injection (HTC/DI) analyzer. For two of the transects, the analyses were conducted on-board ship. Mixed-layer concentrations of organic carbon varied from about 80 µM C at either end of the transect (12°N and 12°S) to about 60 µM C at the equator. Total organic carbon concentrations decreased rapidly below the mixed-layer to about 38-40 µM C at 1000 m across the transect. Little variation was observed below this depth; deep water concentrations below 2000 m were virtually monotonic at about 36 µM C. Repeat measurements made on subsequent cruises consistently found the same concentrations at 1000 m or deeper, but substantial variations were observed in the mixed-layer and the upper water column above 400 m depth. Linear mixing models of total organic carbon versus sigmaT exhibited zones of organic carbon formation and consumption. TOC was found to be inversely correlated with apparent oxygen utilization (AOU) in the region between the mixed-layer and the oxygen minimum. In the mixed-layer, TOC concentrations varied seasonally. Part of the variations in TOC at the equator was driven by changes in the upwelling rate in response to variations in physical forcing related to an El Niño and to the passage of tropical instability waves. TOC export fluxes, calculated from simple box models, averaged 8±4 mmol C/m**2/day at the equator and also varied seasonally. These export fluxes account for 50-75% of the total carbon deficit and are consistent with other estimates and model predictions.

Numerical modeling of marine radiocarbon reservoir ages during the last deglaciation

A critical problem in radiocarbon dating is the spatial and temporal variability of marine reservoir ages (MRAs). We assessed the MRA evolution during the last deglaciation by numerical modeling, applying a self-consistent iteration scheme in which an existing radiocarbon chronology (derived by Hughen et al., Quat. Sci. Rev., 25, pp. 3216-3227, 2006) was readjusted by transient, 3-D simulations of marine and atmospheric Delta14C. To estimate the uncertainties regarding the ocean ventilation during the last deglaciation, we considered various ocean overturning scenarios which are based on different climatic background states (PD: modern climate, GS: LGM climate conditions). Minimum and maximum MRAs are included in file 'MRAminmax_21-14kaBP.nc'. Three further files include MRAs according to equilibrium simulations of the preindustrial ocean (file 'C14age_preindustrial.nc'; this is an update of our results published in 2005) and of the glacial ocean (files 'C14age_spinupLGM_GS.nc' and 'C14age_spinupLGM_PD.nc').