Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modeling or more advanced machine learning techniques, data-driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state-of-the-art Earth system models. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data-driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (i) enjoys the flexibility of statistical machine learning models and can learn from observations, and (ii) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform energy balance models, while its robust physical foundation safeguards against the pitfalls of purely data-driven models. We also illustrate how FaIRGP can be used to obtain estimates of top-of-atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data-driven methods in climate emulation.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation data to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.

Deep-learning software may help scientists predict extreme weather patterns more accurately than relying on today's weather prediction models alone. Simulations involving complex differential equations are run on supercomputers to predict the weather. The accuracy of forecasts using this approach have improved over time, though it's still tricky to pinpoint extreme events like cold spells or heat waves. "It may be that we need faster supercomputers to solve the governing equations of the numerical weather prediction models at higher resolutions," Pedram Hassanzadeh, an assistant professor at the United States' Rice University's Department of Mechanical Engineering, said on Tuesday. "But because we don't fully understand the physics and precursor conditions of extreme-causing weather patterns, it's also possible that the equations aren't fully accurate, and they won't produce better forecasts, no matter how much computing power we put in." Here's where AI may come in handy.

Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance.

Over 700 Earth observation satellites are orbiting our planet, transmitting hundreds of terabytes of data to downlink stations every day. Processing and extracting useful information is a huge data challenge, with volumes rising quasi-exponentially. And, it's not just a problem of the data deluge: our climate system, and environmental processes more widely, work in complex and non-linear ways. Artificial intelligence and, in particular, machine learning is helping to meet these challenges, as the need for accurate knowledge about global climate change becomes more urgent. ESA's Climate Change Initiative provides the systematic information needed by the UN Framework Convention on Climate Change.

A book from 1984 bears testimony to Dr Carsten Brockmann's long interest in artificial intelligence (AI). Today he is applying this knowledge at an ever-increasing pace to his other interest, climate change. "What was theoretical back then is now becoming best practice," says Brockmann, who believes AI has the power to address pressing challenges facing climate researchers. Orbiting our planet with sensors pointing Earthwards are over 700 Earth observation satellites, transmitting hundreds of terabytes each day to downlink stations. Processing and extracting useful information is a huge data challenge, with volumes rising quasi-exponentially.

The El Niño/La Niña pattern in the Pacific Ocean is notorious for its long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as "teleconnections," and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Chicago, University of Wisconsin-Madison and the University of California-Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate. Researchers will apply data science methods such as machine learning, network analysis and predictive modeling to the growing flood of climate data. "There are fundamental challenges pervasive in data science that are epitomized in the climate science setting, making this collaboration a nice opportunity for advances on a number of fronts," said Rebecca Willett, professor of computer science and statistics at UChicago.

Hurricane Harvey, shown in 2017. A new data project hopes to sniff out weather patterns. The El Nino and La Nina patterns in the Pacific Ocean are notorious for their long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as teleconnections, and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Wisconsin–Madison, the University of Chicago, and the University of California, Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate.

David Mitchell pulls into the parking lot of the Desert Research Institute, an environmental science outpost of the University of Nevada, perched in the dry red hills above Reno. On this morning, wispy cirrus clouds draw long lines above the range. Mitchell, a lanky, soft-spoken atmospheric physicist, believes these frigid clouds in the upper troposphere may offer one of our best fallback plans for combating climate change. But Mitchell, an associate research professor at the institute, thinks there might be a way to counteract the effects of these clouds. It would work like this: Fleets of large drones would crisscross the upper latitudes of the globe during winter months, sprinkling the skies with tons of extremely fine dust-like materials every year. If Mitchell is right, this would produce larger ice crystals than normal, creating thinner cirrus clouds that dissipate faster.