Earth system models simulate climate change

The climate is changing and we need to know what changes to expect and how long to expect them. Models of the Earth system, which simulate all relevant components of the Earth system, are the primary means of anticipating future changes in our climate.

Over the past century, our knowledge of the Earth has grown rapidly. Most research has focused on a single “sphere”, such as the atmosphere, hydrosphere, lithosphere or biosphere. But all of these spheres, and others, are connected, with strong interdependencies and feedbacks. For a comprehensive understanding, we must take a holistic view: the climatology of our planet can only be understood if we view the Earth as a cohesive and integrated entity.

Peter Lynch is Professor Emeritus at UCD School of Mathematics & Statistics – He blogs at

The science of the Earth system brings together researchers from all disciplines who study the Earth. It combines meteorology, oceanography, geology, biology, ecology and geography and also encompasses aspects of economics and sociology. Earth system science is crucial to understanding and solving the problem of climate change. It provides a rational basis for studying the different states of our planet in the past and allows us to anticipate imminent and more distant changes.

Biological processes are known to have had a powerful influence on the Earth system. The emergence of life has greatly altered the chemical balances in the ocean and produced an atmosphere favorable to the development of intelligent life. But human influence on the atmosphere and the oceans is now known to be significant and we must find ways to minimize or avoid any further damage.


The urgency to understand the influence of the evolution of the carbon cycle on the climate triggered the development of models of the Earth system, which revolutionized climate science. These models have evolved in a hierarchy, ranging from simple one-dimensional energy balance models, through atmospheric-only models and coupled atmosphere-ocean models – global climate models or GCMs – to Earth system models or ESMs.

These models include representation of the global carbon cycle, dynamic vegetation, atmospheric chemistry, ocean biogeochemistry, sea ice and continental ice caps. These models are able to represent human influence on the climate due to deforestation and greenhouse gas emissions. MES can thus help us to assess the effects of human policies, decisions and actions on the climate.

Climate forecasts continue to be marred by substantial uncertainties. Cloud treatment is a particular source of problems

By emitting greenhouse gases, we are engaged in an uncontrolled experiment on Earth. An ESM provides a virtual planet on which we can perform controlled experiments – not possible on real Earth. For example, we can double the atmospheric carbon content and generate the resulting climate. ESMs serve as laboratories, allowing us to study scenarios and estimate regional and global climate impacts under a wide variety of conditions.

Climate forecasts continue to be marred by substantial uncertainties. The treatment of clouds is a particular source of problems. Quantities are evaluated on a discrete spatial grid, and scales smaller than the grid cannot be correctly represented, although they can be essential for accurate predictions. The representation of biological and ecological processes is often crude, as the equations that govern them are generally unknown.

Traces of storm

Weather forecasting models have improved considerably over the past decades. Climate models have undergone parallel improvements, with better simulation of El Niño and storm tracks. But climate projections still carry unacceptable uncertainty. For example, for the critical question of how much extra carbon will produce one degree warming, different models give very different answers. This makes planning very difficult.

It is hoped that models of the Earth system can be drastically improved by combining them with machine learning schemes. With new artificial intelligence techniques, models can learn and improve using high-resolution, targeted observations and simulations of specific phenomena.

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