Recent posts

ESM as a puzzle: making CliMA accessible piece by piece

by Alexandre A. Renchon, Katherine Deck, Renato Braghiere: In the realm of scientific advancement, enhancing Earth System Models (ESMs) stands out as a paramount objective. Presently, however, these models remain enigmatic enclaves for many researchers, akin to inscrutable black boxes. The labyrinthine nature of ESMs, coupled with their high computational demands, usage of esoteric programming languages, and the absence of lucid documentation and user interfaces, contribute to this opacity. To surmount these obstacles, CliMA is creating a new era of accessible ESM features for the global scientific community: Modernized Programming: Departing from convention, CliMA adopts a contemporary programming language, Julia.…
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Unsupervised Downscaling of Climate Simulations

by Tobias Bischoff and Katherine Deck Climate simulations play a crucial role in understanding and predicting climate change scenarios. However, the spatial resolution that simulations can be carried out with is often limited by computational resources to around ~50-250 km in the horizontal. This leads to a lack of high-resolution detail; moreover, since small-scale dynamical processes can influence behavior on larger scales, coarse resolution simulations can additionally be biased compared to a high-resolution “truth”. For example, simulations run at coarse resolutions fail to accurately capture important phenomena such as convective precipitation, tropical cyclone dynamics, and local effects from topography and…
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How do we estimate climate parameters? An introduction to ensemble Kalman inversion

By Eviatar Bach and Oliver Dunbar To understand this blog post, you will need some basic familiarity with probability (Bayes’ theorem, covariance) and multivariate calculus. In climate modeling, small-scale processes that cannot be resolved, such as convection and cloud physics, are represented using parameterizations (see two previous blog posts here and here). The parameterizations depend on uncertain parameters, which leads to uncertainty in simulations of future climates. At CliMA, we use observations of the current climate, as well as high-resolution simulations, to estimate these parameters. The learning problem is challenging, as the parameterized processes typically are not directly observable, and…
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CliMA Land: A next-generation land surface model that bridges vegetation processes and remote sensing

by Yujie Wang and Renato Braghiere: Climate model predictions of future land carbon sink strength show significant discrepancies. To enhance predictive accuracy and reduce inter-model disagreements, it is crucial to improve the representation of vegetation processes and calibrate the models using more observational data. However, the limitations of computational resources in the past have hindered the integration of new theories and advances into traditional climate models, which often rely on statistical models to parameterize vegetation processes instead of mechanistic and physiological models (such as stomatal control models). Additionally, the preference for faster models has limited the incorporation of complex features (e.g.,…
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CliMA Researchers Upgrade their Uncertainty Pipeline

CliMA provides state-of-the-art data assimilation and machine learning tools that enable users to calibrate their models using large amounts of data. CliMA’s research scientists and engineers continually upgrade these tools and made several breakthroughs in the past months. CliMA’s data assimilation and machine learning (DA/ML) team, led by Oliver Dunbar, improved the scalability of one of their machine learning emulators. Think of emulators as accelerated data-driven models, trained to replicate the evolution of a complex system (in our case, a climate model).  Once trained, emulators can be run millions of times using few computational resources. These large samples are essential…
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A Discussion with CliMA’s Lead Software Developer Simon Byrne on his Team’s Parallel Input/Output Work

CliMA’s software team, led by Simon Byrne, added a software interface to ClimaCore.jl for saving and loading data from distributed simulations. We caught up with Dr. Byrne near the Gong inside the CliMA conference room; an edited version of our interview is reproduced below. Leilani Rivera-Dotson: Why did your team implement this interface? Dr. Byrne: There are two main reasons we need to be able to save and load data: saving the quantities of interest, such as temperature and precipitation, for analysis and other post-processing tasks; capturing the state of the model so that we can reproduce or resume the…
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A New Numerical Scheme for Nonlinear Ocean Dynamics

Simone Silvestri and Greg Wagner have made substantial progress in improving the numerical accuracy of the ocean component of the CliMA model, Clima-Ocean. Typical finite-volume ocean models for climate prediction use “second-order” schemes to discretize terms in the ocean’s momentum balance. But second-order momentum advection schemes are noisy, and therefore must be paired with artificial viscous terms to suppress spurious oscillations and prevent numerical instability. Artificial viscosity has the unfortunate side effect of artificially suppressing ocean mesoscale turbulence, which plays a fundamental role in Earth’s climate system by transporting heat poleward and determining the ocean’s density stratification. Improving the representation…
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Learning about climate model parameterizations as an inverse problem

[latexpage] By Ignacio Lopez-Gomez Over the past few years, machine learning has become a fundamental component of some newly developed Earth system parameterizations. These parameterizations offer the potential to improve the representation of biological, chemical, and physical processes by learning better approximations derived from data. Parameters within data-driven representations are learned using some kind of algorithm, in a process referred to as model training or calibration. Gradient-based supervised learning is the dominant training method for such parameterizations, mainly due to the availability of efficient and easy-to-use open source implementations with extensive user bases (e.g., scikit-learn, TensorFlow, PyTorch). However, these learning…
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How often will natural disasters occur in a warming climate?

Weather disasters are extremely damaging to humans (e.g., severe storms, heat waves, and flooding), our livelihoods (e.g., drought and wildfire), and to the environment (e.g., coral bleaching via marine heatwaves). Although heavy storms, severe drought, and prolonged heat waves are rare, they account for the majority of the resulting negative impacts. For individuals, governments, and businesses to be able to best prepare for these events, their frequency and severity need to be quantified accurately. A major challenge, however, is that we need to form our estimates using a limited amount of historical data and simulations, where extreme events appear rarely.…
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GriddingMachine: A new database and software for sharing global datasets

Researchers are spending way too much time finding, reading, and processing public data. The ever increasing amount of data, various data formats, and different data layouts are increasing the time spent on handling data—before getting ready for scientific analysis. While the intention of sharing data is to facilitate their broad use and promote research, the increasing fragmentation makes it harder to find and access the data. Taking my personal experience as an example, I spent months to identify, download, and standardize the global datasets we use with the CliMA Land model, which came in a plethora of formats (e.g., NetCDF,…
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