Fit for model (Lost in Translation ep.4)
lost in translation is a funny take on the challenges oF communication in science. FOLLOW ALONG AS JARGON AND JUMBLE NAVIGATE THE CONFUSING WORLD OF LINGUISTIC MISCOMMUNICATIONS AND TRY NOT TO GET TOO LOST IN TRANSLATION. Read the past numbers here.
This comic is a collaboration between valeria (drawings & concept) and kenia (text & concept)
why do we use models and what are they?
Scientists are like detectives – they observe little clues in the real world and try to put together a picture of what’s really going on. But the real world can be super complicated! That’s where models come in. A model is like a mini version of the real thing that lets scientists experiment and test ideas without having to drag the whole real world into their lab. Models let scientists simplify complex systems down to the key players and interactions so they can get a better sense of what’s driving what.
Scientific models come in all shapes and sizes. Some are written out as formulas or algorithms full of symbols and numbers. Others are digital worlds created by computers that let scientists virtually recreate real environments and watch how things play out. No matter the style, models distill messy reality into an organized puzzle scientists can take apart, put together, and change to see how the pieces fit. Models aren’t meant to perfectly mimic nature in all its richness – that would defeat their purpose of bringing clarity. But grounded in empirical evidence and theory, they do help illuminate the underlying mechanisms at work behind the complex scenes all around us.
For example, climate scientists use computer models of the Earth’s atmosphere and oceans to better understand how our climate system works. These digital models take into account things like air temperature, wind currents, ocean temperatures, solar radiation, greenhouse gas levels, and more. By programming in the known physical properties and interactions between these different components, climate models act as a virtual Earth.
Scientists can then run simulations altering certain variables, like increasing carbon dioxide, to see how the whole modeled system responds over time. This allows them to test hypotheses about climate change without having to wait decades for real-world experiments. The models have predicted things like global temperature rise and sea level rise with surprising accuracy. Neat, huh?