Recent posts

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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How to Choose When and Where to Acquire Data to Calibrate Climate Models

Climate models depend on dynamics across a huge range of spatial and temporal scales. Resolving all scales that matter for climate–from the scales of cloud droplets to planetary circulations–will be impossible for the foreseeable future. Therefore, it remains critical to link what is unresolvable to variables resolved on the model grid scale. Parameterization schemes are a tool to bridge such scales; they provide simplified representations of the smallest scales by introducing new empirical parameters. An important source of uncertainty in climate projections comes from uncertainty about these parameters, in addition to uncertainties about the structure of the parameterization schemes themselves.…
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A library of large-eddy simulations for calibrating cloud parameterizations

Low clouds play an important role in Earth’s energy budget, but they are poorly represented in global climate models (GCMs). The resolution of GCMs, which is on the order of 100 km in the horizontal, is too coarse to resolve the boundary layer turbulence and convection controlling the clouds. As a result, GCMs rely on parameterizations to represent these processes, and inadequacies in the parameterizations lead to biases in GCM-simulated clouds. To improve parameterizations by calibration with data, we want to harness data from large-eddy simulations (LES). LES  directly resolve cloud dynamics and provide high-fidelity simulations in limited areas. However,…
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Oceananigans and CliMA-Ocean at Ocean Sciences 2022

The first-ever Oceananigans town hall at Ocean Sciences Every other February, oceanographers from around the world congregate to share new scientific insights, and tales of adventure on the high seas at AGU’s Ocean Sciences Meeting. But this February was a little different than past even-yeared Februaries — not only because oceanographers gathered virtually, but also because in 2022 the Climate Modeling Alliance unveiled their experimental ocean model to the world: Oceananigans. Oceananigans is Fast: Oceananigans is GPU-accelerated and compiled, and leverages multiple dispatch to run “minimal code” for any user experiment. Friendly: Oceananigans uses an intuitive, flexible, and extensible user…
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Toward a new Earth System Model: CliMA at AGU

Each fall, the American Geophysical Union unites scientists and policy makers from over 100 countries to discuss their scientific discoveries and their implications for societies at large. This year, members of the Climate Modeling Alliance presented research featuring our climate model development. Large and small-scale processes in climate With the intent to build a new Earth System Model (ESM) that is grounded in physics and designed for automated calibration using machine learning, Anna Jaruga presented On Coupling (and Separating) Subgrid-cale Turbulence and Cloud Microphysics Processes in Julia. In order to fully understand and resolve small-scale uncertainties such as those we…
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Large Eddy Simulations with ClimateMachine.jl

Climate models rely on parameterizations of physical processes whose direct numerical simulation (DNS) is infeasible because of its enormous computational cost. The accurate representation of unresolved processes below the grid scale of global climate models (GCMs), such as atmospheric turbulence and convection, is important for our ability to predict and understand climate. Large-eddy simulation (LES) frameworks are designed to allow studies of processes that are subgrid-scale in GCMs by examining smaller sections of the globe in greater detail. Processes whose relevant length-scales are smaller than the grid-scales in GCMs (typically tens to hundreds of kilometers) can be examined in greater…
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Quantifying Parameter and Structural Uncertainty in Climate Modeling

Robustly predicting Earth’s climate is one of the most complex challenges facing the scientific community today. By leveraging recent advances in the computational and data sciences, researchers at CliMA are developing new methods for calibrating climate models and quantifying their uncertainties.  In today’s climate models, the primary source of uncertainty are approximations of processes that cannot be explicitly resolved, such as turbulence in atmosphere and oceans and the convection sustaining clouds. These approximations, known as parameterization schemes, are a set of physical equations that, given some environmental conditions (such as wind and temperature) supplied by the climate model, predict the…
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Resolving Small-Scale Uncertainties in Climate Models

The dynamics of clouds and turbulence play a profoundly important role in the climate system, yet their scales are often too small to be resolved faithfully in global climate models. Therefore, climate models rely on subgrid-scale parameterizations for representing clouds and turbulence, which remain the most significant aspect of physical uncertainty in climate predictions. The wide range of cloud and turbulence regimes that occur in nature are often challenging to represent in a single parameterization. Research Scientist Yair Cohen, Postdoctoral Scholar Jia He, Graduate Student Ignacio Lopez-Gomez and their colleagues have presented a parameterization that does capture this wide range…
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Bridging the gap between disciplines at CliMA

Legendary basketball coach for the Chicago Bulls, Phil Jackson, once said, “The strength of the team is each individual member. The strength of each member is the team.” At CliMA, our team is made up of individuals from diverse academic backgrounds who have come together to work on one of the defining global problems of our time. The complexity of predicting climate change and ascertaining the associated uncertainties demands expertise from diverse, traditionally disparate, academic and professional backgrounds. At CliMA, we have assembled an A-list team that is taking on the challenge to provide the accurate and actionable climate predictions…
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CliMA 0.1: A first milestone in the next generation of climate models

At CliMA, we are building an Earth System Model (ESM) that harnesses more data than ever before to produce accurate and precise climate predictions. Today, the release of CliMA 0.1 brings us one step closer toward achieving our goals. CliMA v0.1.0 consists of code for large eddy simulations (LES) of turbulent flows in atmosphere and oceans and for dynamical cores of general circulation models (GCM) for the ocean and atmosphere. All CliMA code is written in Julia, a dynamic, high-level programming language for high-performance scientific computing. Caltech Postdoctoral Scholars Akshay Sridhar and Zhaoyi Shen, together with others, are in the…
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