Earth system models and climate models are a complex integration of environmental variables used for understanding our planet. Earth system models simulate how chemistry, biology, and physical forces work together. These models are similar to but much more comprehensive than global climate models.
To understand Earth system models, it helps to first understand global climate models. Climate is the long-term pattern of weather variables. It includes temperature, rain and snowfall, humidity, sunlight, and wind and how they occur over many years. Climate models explain how these variables can change using mathematical analysis based on the physics of how energy, gases, and fluids move, combined with measurements taken from experiments, laboratories, and other observations in the real world.
Climate models include:
- The atmosphere including clouds, aerosols, and gases.
- The land surface and how it is covered by vegetation, snow and ice, lakes and rivers, and soil.
- Sea ice and the oceans.
- How all these components store and move the heat and carbon that warm the Earth’s atmosphere.
Global climate models treat the Earth as a giant grid. The size of each cell in the grid is determined by the power of the computer running the model. Just like a video game, higher resolution requires a much more powerful computer.
Earth system models include all the factors in climate models. But as complex as climate is, it is only one part of an even more complex Earth system. The goal of Earth system models is to understand how the Earth functions as a system of interdependent parts. These parts include the physical, chemical, and biological processes that all interact to shape our planet and the organisms on it. Earth system science is multidisciplinary, drawing on atmospheric science, oceanography, ecosystem ecology, soil microbiology, multi-sector analysis, and the core science disciplines of mathematics, chemistry, and physics.
Earth system models can help understand and provide critical information on water availability, drought, climate and temperature extremes, ice sheets and sea levels, and land-use change. They help scientists understand how plants, people, animals, and microbes all contribute to and are affected by the Earth’s climate. For example, different plants absorb carbon dioxide at different rates. Different landscapes—ice, oceans, natural vegetation, farmland, or cities—can change how the land absorbs or reflects sunlight. As temperatures and rainfall change, plants respond, changing the balance of carbon and atmospheric radiation. In the ocean, circulation patterns change the amount of plankton and seaweed.
These factors work on many time scales. The Sahara appears to have shifted back and forth from wet to dry over thousands to tens of thousands of years. Plants in a wet Sahara absorbs sunlight and store carbon, while a dry Sahara reflects sunlight and stores little carbon. These factors also work at very short time scales, such as the rapid expansion of cities in the 20th century into land formerly covered by plants, changing how the land reflects and stores heat and carbon. Chemical processes from the slow erosion of rock can release dust into the atmosphere, trapping more heat in the air. Short chemical processes such as pollution from industry and soot from forest fires can have similar effects.
Because Earth system models can include the effect of human decisions, they are useful tools for planning things like infrastructure, energy production and use, and landscape use. For example, an Earth system model could help a coastal city plan where to build a new highway to ensure that the new highway isn’t flooded if hurricanes become more severe in response to changes in the global climate.
Modeling the entire Earth or the Earth’s climate with sufficient accuracy is challenging for scientists. One solution is to create more powerful computers that can produce high resolution models with sophisticated ways of representing real-world variables. Another is reduced complexity models. These reduced complexity models provide lower resolution climate information but are easier and faster to run. This makes them perfect for research questions that do not require the detailed data provided by Earth system models. Researchers also use simplified models to quickly test narrow hypotheses about the planet. Researchers can also use focused multisector dynamic models to explore the interactions and interdependencies among specific human and natural systems.
- DOE began studying atmospheric, land, ocean, and environmental systems in the 1950s to understand the effects of fallout from nuclear explosions.
- Portions of the West Antarctic Ice Sheet that rest over water contain enough ice, if melted, to raise the global sea level by 3 meters (nearly 10 feet).
- Cotton-ball clouds (called shallow cumulus clouds) play an important role in cooling the Earth’s surface temperature.
DOE Office of Science: Contributions to Earth Systems and Climate Models
The Department of Energy (DOE) Office of Science Biological and Environmental Research (BER) program supports Earth systems and climate modeling through several related efforts. The Earth and Environmental Systems Modeling program (EESM) develops and applies models to increase scientific understanding of the factors in the integrated Earth system. It works on research as diverse as infrastructure planning and the development of advanced representations of the Earth. To build the computer codes needed to run complex Earth system and climate models on DOE’s fastest computers, DOE supports the Energy Exascale Earth System Model (E3SM) project through the BER Earth System Model Development (ESMD) program. The E3SM is a massive computer model of the planet designed to work on DOE’s Leadership Computing Facility supercomputers. E3SM will provide scientists and policymakers with predictions of the changing Earth system at the spatial resolutions necessary to make informed decisions. Finally, DOE’s Regional and Global Modeling Analysis (RGMA) program advances capabilities to design and analyze global and regional Earth system model simulations.
“Cotton-ball clouds (called shallow cumulus clouds) play an important role in cooling the Earth’s surface temperature.”
There is an old saying that a chain is only as strong as its weakest link. What the article fails to make clear is that clouds, in general, are second only to the impact that solar radiation has on the weather and climate. Clouds typically reflect about 30% of the total sunlight reaching Earth, redistribute heat from the surface, and slow cooling at night. However, we don’t have a computer fast enough to actually model those “Cotton-ball clouds” as distinct entities. Therefore, modelers have to do what is called “parameterize” the cloud-energy exchanges. That means, they can’t use data in real time, they can’t solve the essential fluid dynamics differential equations, and they can’t deal with clouds at their actual relative scale. Instead, they have to rely on averages, and subjective estimates on how all the “real-world variables” interact. Think about that. The goal of Climate Models (formerly called General Circulation Models) is to calculate the results from the behavior of all those “real-world variables.” That is why they are commonly referred to as “physics-based models.” However, a critical piece of that can’t be calculated, nor can actual measurements be included in the calculations. They have to rely on subjective assessments of what they want to calculate. That is not unlike having a sophisticated guidance system for an ICBM, but having to rely on someone sticking a wet finger in the air to estimate the direction and speed of the wind pushing on the rocket.
The Earth System Models are unlikely to produce correct results if the Climate Models produce unreliable outputs. We know that all of the models, except probably the Russian models, produce an ensemble of ‘projections’ that have been running warm compared to reality; the latest, CMIP-6, is running even warmer. At the regional level, different models sometimes predict conflicting precipitation — that is, floods instead of drought. Some variables have to be reined in, or clamped, when calculated intermediate-values reach physically impossible values. That is, what had been a variable becomes a constant. That is like putting a cast on a runner’s leg! A strong argument has been made that because of inherent uncertainty in initial measurements, and subsequent propagation of the uncertainties, that the envelope of uncertainty grows with each time step. That means that, while the nominal value of any or all intermediate variables may appear reasonable, the range of uncertainty grows so large as to make the nominal value(s) unreliable.
The models have utility in helping climatologists understand how the climate system works. However, it is a serious mistake to assume that their projections are reliable, and to base energy policies on them.
Chaos theory arose from the observation that weather forecasting accurately for more than a few days in advance is impossible, due to the number of variables and their non-linear interaction. In general, the theory posits that in a complex system, a tiny difference in initial state can cause a huge difference in end state. Weather forecasting is simpler than climate forecasting (of which it’s a subset) and climate modelling is a subset of earth system modelling. So let’s take a precautionary approach rather than relying on such models.
“So let’s take a precautionary approach rather than relying on such models.”
Just what are you suggesting? Are you suggesting that we should engage in ‘brain storming’ to conjecture all the bad things that COULD happen — without regard to the probability — and then attempt to mitigate them without regard to costs or social consequences? That we should act without evidence that a hypothetical problem will actually rise to the level of an existential threat? No country is building space craft to intercept and destroy asteroids, even though we know that they periodically impact Earth and have the potential to wipe out all life instantly! Why should climate ‘threats’ be treated differently than tangible threats that have happened before?
Initialised Earth system models incorporating big data from modern monitoring systems and machine learning in an environment of rapidly growing computer power makes all these old arguments from commenters above irrelevant.