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Luis Rodriguez Alcalde is a 3-D food printing specialist, electrical engineer and maker. In he founded 3DigitalCooks, a studio that exists to create innovative food experiences through 3-D food printing. Rodriguez Alcalde develops recipes, printers, extrusion mechanisms and design tools, seeking to engage more people in the process of 3-D printing. Through her work as a food technologist, experience designer, multimedia artist and educator, Emilie Baltz inspires meaningful conversations about how, and why, we consume.

She is an award-winning author and public speaker and one of the founding members of NEW INC, an arts and technology incubator hosted at the New Museum. Samantha Cole is a freelance science journalist. Grant Goldner is a New York-based industrial designer working at the intersection of design and the natural world. Part experiment, part sensory education, Harbor Food is a filtration tank that sits in the Hudson River and explores the possibility of growing edible oysters in polluted waters. The tank passively pumps water using rising tides and gravity through the filtration layers. The first layer contains oysters that remove toxins for layer two — providing potentially clean growing conditions for a second set of potentially edible oysters.

Jana Grcevich is a data scientist and astronomer whose academic work focuses on dwarf galaxies and interstellar gas. She holds a doctorate in astronomy from Columbia University and completed her postdoctoral research at the American Museum of Natural History. Justin Brice Guariglia is a contemporary visual artist who collaborates with philosophers and scientists to unearth the complexities of our rapidly changing world.

He uses varying materials, processes and scale to forge new ways of seeing and experiencing the world around us and personalizing the impact of the Anthropocene epoch. Justin has created the After Ice app in conjunction with development studios Strange Flavour and second verse. Using New York City as a test case, the app explains the imminent threat coastal cities face as sea levels rise over the course of the coming decades.

As part of Science Sandbox, Justin hopes to bring this app to version 2. He uses various mediums, including interactive media, to create socially conscious, data-driven artwork. Charles Philipp is a producer and the co-founder of Micro, a fleet of 6-foot-tall museums that introduce people to fundamental scientific principles in locations where they least expect to encounter them.

Founded in by Philipp and computational ecologist Amanda Schochet also a Science Sandbox New Lab fellow , Micro brings together designers, scientists, storytellers and artists to squeeze the best parts of museums down into boxes that can go anywhere.

Meet the Science Sandbox @ New Lab Fellows

Amanda Schochet is a computational ecologist and the co-founder of Micro, a fleet of 6-foot-tall museums that introduce people to fundamental scientific principles in locations where they least expect to encounter them. Founded in by Schochet and Charles Philipp also a Science Sandbox New Lab fellow , Micro brings together designers, scientists, storytellers and artists to squeeze the best parts of museums down into boxes that can go anywhere.

The museum uses 15 exhibits, five screens, eight sculptures, three optical illusions, a giant hologram and a liter of slime to tell a story about natural selection and environmental systems spanning millions of years. Campbell holds a Ph. Science Sandbox. Tags Science Sandbox.

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News Cosyne About Scientific Mission. What follows is the working documentation for this feature, and should help answer some of the questions you may have. See also the evolving FAQ. The energy balance of a planet determines the temperature at the surface. What is a planetary energy balance?

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The amount of energy coming in from the local sun is balanced by the outgoing energy emitted in the infrared. The hotter an object the more it emits in the infrared. One can set up an energy balance equation and solve it for the surface temperature. In the simplest version it also depends on the albedo or reflectivity : the amount of incoming energy from the star that is reflected back instead of absorbed.

You can also specify the fraction of outgoing infrared energy that gets absorbed and re-emitted towards the surface by the atmosphere on the way out. We know that the greenhouse effect keeps the Earth a bit over 30 degrees warmer than what it would be without an atmosphere at all, which is a lot in terms of global average temperature. Even a one or two degree change in global average temperature can involve major changes to the climate.

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You can change the albedo, or see what happens when you move the planet closer to or farther from the nearby star s. For Earth and soon Mars we do something a bit more complicated. The equator would be too warm. The poles would be too cold and have much larger temperature swings with the seasons than actually occur. This would be way too much number-crunching to do on your computer in real-time [1].

Luckily we can use a simpler model that was popular in the s before computers were quite so powerful, and which is still used to explore basic concepts about the climate. This meridional energy balance model gives us not just a global average surface temperature, but different temperatures at each of a fixed number of latitudes [2]. This model still balances the incoming and outgoing energy globally, but also transfers energy between equator and pole, represented mathematically as a one-dimensional diffusion.

We use a version that gives us two related equations to solve: one for temperature over land, and one for the ocean. Then we do some fancy number-crunching to determine all the temperatures at all the latitudes simultaneously.

Without doing a simulation as complicated as this one we could never capture the seasons! You can also understand one of the self-reinforcing cycles that amplifies changes in the climate. More reflection means less energy coming into the Earth system, which means it gets even colder and more snow and ice forms, and so on. The opposite can happen as well.

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You have to move it even closer to the Sun to get enough energy to melt the ice and start the warming version of the albedo feedback going. There are other self-reinforcing cycles in the climate, which we call feedbacks think of the piercing noise of a microphone too close to an amplifier — that kind of feedback , as well as other feedbacks that tend to diminish the effects of an initial change and put things back to equilibrium.

These processes play a big role in the overall sensitivity of the climate to a disturbance. More on that below. There are three ways to affect the climate of the Earth in the simulation in Universe Sandbox. First you can change the solar input.

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Change the luminosity of the Sun but be careful because you might also end up changing its mass, which will affect the orbits of the planets. Or add an additional star if you really want to heat things up. Or delete the Sun and watch the planet freeze.

Second you can change the orbit of the Earth or its tilt. Changing the tilt will affect how extreme the seasons are. If you give the Earth a less-circular orbit or move it closer to or farther from the Sun, you can see how sensitive we are to being in just the right spot in the solar system. Third you can change the parameters of the model itself. What if the heat capacity of the ocean were larger or smaller?

What if the heat were transferred less efficiently between land and ocean, or more efficiently between equator and pole? What values are within a realistic range? How sensitive is the simulation to the choice of parameters? Experiment and see. When you change the mass of the atmosphere, that new mass will affect the heat capacities.

It turns out, this is not so different from what scientists do sometimes to understand the climate system in more detail. Such sensitivity studies are done nonetheless to verify models. Does it capture some detail of the climate reasonably? How sensitive is the model is to changes in one input or another? Using computer models to do numerical experiments is not limited to climate science. The climate is but one case where scientists or social scientists study complex systems by prescribing a set of rules for the behavior of the parts and allowing the numerical simulation to reveal what emerges from how they interrelate [3].

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The equations that govern our climate models, be they simplified versions or ones with all the complexity our super-computers can handle, come from basic physics. How they interrelate, the sensitivity of the climate to changes — well, you have to run the model to see. The direct radiative effect of the additional greenhouse gases humans have been adding to the atmosphere since the start of the industrial age, is only part of the change in energy balance that warms the planet.

The feedbacks in the climate system, including albedo feedback, water vapor feedback, and various interactions within clouds among others can amplify or dampen the magnitude of the changes in temperature. This is the more difficult part to understand and simulate, and is responsible for much of the range of predictions of different models.

These essential elements of physical climate predictions are inherently the result of interactions between simulated parts, and can best be understood through model experiments.