The increasing number of satellites and advancements in climate models has improved the weather forecasting over the last many years. Weather forecasting, is not a perfect science; it still needs a lot of improvement in terms of timing, location, and intensity of forecast weather. The same goes for analyzing climate change. And, the prime reason behind this is lack of data to make more accurate forecasts. Global warming researchers face lack of important data.
Azaria, Amos (Carnegie Mellon University) | Rosenfeld, Ariel (Bar-Ilan University) | Kraus, Sarit (Bar-Ilan University) | Goldman, Claudia V. (Advanced Technical Center, General Motors Israel) | Tsimhoni, Omer (General Motors Warren Technical Center)
Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system.
Recently, machine learning has been applied to the problem of predicting future climates, informed by the multi-model ensemble of physics-based climate models that inform the Intergovernmental Panel on Climate Change (IPCC). Past work (Monteleoni et al., 2011, McQuade and Monteleoni, 2012) demonstrated the promise of online learning algorithms applied to this problem. Here we propose a novel approach, using sparse matrix completion.
Realistic climate simulations require huge reserves of computational power. An LMU study now shows that new algorithms allow interactions in the atmosphere to be modeled more rapidly without loss of reliability. Forecasting global and local climates requires the construction and testing of mathematical climate models. Since such models must incorporate a plethora of physical processes and interactions, climate simulations require enormous amounts of computational power. And even the best models inevitably have limitations, since the phenomena involved can never be modeled in sufficient detail.
Scientists have been making projections of future global warming using climate models of increasing complexity for the past four decades. These models, driven by atmospheric physics and biogeochemistry, play an important role in our understanding of the Earth's climate and how it will likely change in the future. Carbon Brief has collected prominent climate model projections since 1973 to see how well they project both past and future global temperatures, as shown in the animation below. While some models projected less warming than we've experienced and some projected more, all showed surface temperature increases between 1970 and 2016 that were not too far off from what actually occurred, particularly when differences in assumed future emissions are taken into account. While climate model projections of the past benefit from knowledge of atmospheric greenhouse gas concentrations, volcanic eruptions and other radiative forcings affecting the Earth's climate, casting forward into the future is understandably more uncertain.