THE INTERNET IS THINKING
Climate Science
This science thread follows climate science through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗31 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Recent sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones ↗
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost. Yet, whether this success extends to extreme events remains contested. Earlier evaluations have exposed important shortcomings, but have largely focused on deterministic models and metrics, leaving the capabilities of the latest probabilistic models less understood. We conduct an extensive benchmark of tropical cyclone forecasting, comparing GenCast, FourCastNet 3, and FGN with ECMWF's operational physics-…
- arXiv · Published · First archived
Optical Coherent Combining in Multi-Aperture Free-Space Receivers through Atmospheric Turbulence via Optically Computed Alignment Gradients ↗
Multi-aperture reception converts spatial diversity into a coherent-combining gain, but only if the relative phase and polarization of the collected field replicas are aligned before they are summed. In a satellite-to-ground downlink the atmosphere perturbs amplitude, phase and polarization, so the alignment must track a fluctuating channel rather than a fixed offset. This paper proposes a new optical-domain combining scheme in which the alignment information is computed by light itself: the ascent directions that maximize the combined output power are formed by optical interference and read o…
- arXiv · Published · First archived
Energy Flow Shapes the Decomposition Dynamics of a Reactive Atmospheric Intermediate ↗
The unimolecular dynamics of Criegee intermediates depend strongly on their internal energy, which can differ substantially between nascent atmospheric species and laboratory-prepared reactants. Here, the decomposition dynamics of {\it anti-}CH$_3$CHOO are investigated using molecular dynamics simulations on a neural network-based potential energy surface under thermal and mode-selective vibrational excitation. Simulations were performed at internal energies representative of those released during ozonolysis (chemical/thermal preparation, TP) or following vibrational excitation (VibEx) along t…
- arXiv · Published · First archived
Variable Masking for Climate Emulation in ACE ↗
In the recent AI Model Intercomparison Project (AIMIP) benchmark, the ACE2.1-ERA5 emulator was shown to underestimate the true near-surface warming trend both during the training period and the out-of-sample extrapolation period. In this work, we present M-ACE2S (Masked-ACE2S), a modified version of the stochastic ACE2S model, which uses variable masking during training and reduces the reliance of M-ACE2S on any single input channel. We show that M-ACE2S improves long-term near-surface air temperature prediction. We also present a novel data normalization strategy called "global mean removal",…
- arXiv · Published · First archived
Forecast lead time and the timing of crew deployment for weather-driven power outages using AI weather models ↗
Utilities request mutual aid crews to restore power after severe storms, and requesting early secures more crews but relies on less accurate forecasts. We examined how forecast lead time affects the value of outage predictions for crew deployment, using operational forecasts from two AI weather models, GraphCast and FourCastNet, to predict outage occurrence for counties across the contiguous United States 24 to 120 hours ahead. Forecasts were evaluated by the share of outage burden captured by the counties where crews would be staged. Captured burden declined with lead time: the loss between t…
- arXiv · Published · First archived
Improved Precipitation Variability in the Community Atmosphere Model through Implementation of a Neural-Network Deep Convection Scheme ↗
Conventional deep convection schemes remain a primary source of uncertainty in global climate models, yet replacing them with data-driven alternatives while maintaining physical consistency and numerical stability is an open challenge. We present a hybrid configuration of the Community Atmosphere Model version 6 (CAM6), in which a fully connected deep neural network replaces the Zhang-McFarlane deep convection scheme (ZM), while radiation, turbulence, and other cloud processes are left unchanged. The network is trained on output from a storm-resolving global model - the global System for Atmos…
- arXiv · Published · First archived
PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data ↗
High-resolution precipitation datasets are essential for weather forecasting, hydrological modeling, and climate risk assessment, yet observation-based gridded products often lack the spatial detail required for regional applications. Here we present PrecipCorrDiff, a diffusion-based downscaling framework built by modifying the original corrector diffusion based archetecture that incorporates a customized loss function and a multi-scale residual generation strategy to improve the precipitation especially the representation of extremes. PrecipCorrDiff generates 0.0625° resolution India Meteorol…
- arXiv · Published · First archived
Generative modelling for multivariate spatio-temporal downscaling of monthly climate fields ↗
Inferring climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. The downscaling of modelled climate fields faces a trade-off between data storage constraints, simulation efficiency, and resolution requirements. While many methods for spatial downscaling have been proposed, joint spatio-temporal downscaling has received comparable little attention. When deriving, for example, daily fields from climate datasets that are available only as monthly averages at coarser spatial resolution, it is highly chal…
- arXiv · Published · First archived
Unmasking Compensating Errors in Numerical Weather Models: A Process-Additive Neural Network Approach ↗
This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PANN branch represents the tendency error associated with a separate physical process, and the summed branch outputs approximate the total error relative to a reference tendency. Once trained successfully, the branch outputs are expected to provide process-level error attributions and, when averaged, estimates of process-level bias. Initial…
- arXiv · Published · First archived
Physical Processes Driving Year-to-Year Surface Temperature Variability Changes in a Warming Climate ↗
Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase in the tropics and subtropics. Here, we examine year-to-year temperature variability in the CESM Large Ensemble in concert with an idealized stochastic moist energy balance model (SMEBM) with effective surface heat capacity and local feedback parameter specified based on CESM. We find that the zonal-mean CESM variability change pattern largely persists after filtering out the impact of El Niño. The SMEBM largely captures this change patt…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Climate Science
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Related threads
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.