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Machine Learning Brings Accuracy to Climate Forecasts


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.

Primitive climate models dating back to the 1970s were 'impressively accurate', rebutting sceptics

Daily Mail - Science & tech

Primitive climate models dating back to as early as the 1970s have turned out to be largely accurate, rebutting the long-running doubts of sceptics, a study has found. Experts assessed 17 old models -- including one that brought the issue of climate change to public light -- to see how accurate their temperature predictions were. Time must pass before model predictions can be compared with the actual average global temperatures, as short-term variations can obscure the real trend. The team found that most of the discrepancies between the studies and real-word figures came not from errors in the models but from unexpected emissions levels. The findings, the researchers conclude, provide reassurance that models being used today are likely to be reliable as well.

Advice Provision for Energy Saving in Automobile Climate-Control System

AI Magazine

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.

AI speeds up climate computations


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.

Analysis: How well have climate models projected global warming? Carbon Brief


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.