Abstract Climate
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Building user-driven climate adaptation products
Abstract Climate adaptation products have traditionally been developed using a supply-driven model reliant on available climate information, leading to usability gaps1,2,3,4. To better meet user needs, the climate services field has recognized a need to shift towards a demand-driven model emphasizing co-production, that is, user-driven, scientifically informed products created through shared knowledge practices1,2,3,4,5. However, co-production can be challenging, especially for researchers...
Multiscale Dynamics of Heatwave Persistence and Intensity Under Climate Change
Announce Type: new Abstract: Climate change is expected to increase heatwave risk, but exceedance frequency alone cannot explain why some regions show stronger amplification in event persistence. This study develops an integrated event-dynamical workflow to diagnose changes in warm-season heatwaves and link them to coherent, multiscale structures of temperature variability. Heatwaves are identified over southern Canada using a fixed historical 90th percentile threshold (2001-2010 reference,...
A mathematical framework for dynamic emergent constraints in climate science
Announce Type: new Abstract: Emergent constraints in climate science are empirical relations that link the response to a forcing of a physical observable to the properties of other observables, with the aim of reducing climate change projection uncertainties. Here we use recent results in linear response theory to develop a mathematical framework for dynamic emergent constraints, a class of emergent constraints linking the response of different observables to the same forcing. We show how...
Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling
arXiv:2606.07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators. A key challenge is determining how limited high-resolution simulations should be distributed across a changing climate trajectory to capture both forced climate response and internal variability. Using the CESM2 Large Ensemble...
Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
arXiv:2501.12500v3 Announce Type: replace Abstract: Understanding climate dynamics requires going beyond correlations in observational data to uncover the underlying causal process. Latent drivers such as atmospheric processes play a central role in temporal dynamics, while direct causal influences also exist among geographically proximate observed variables. Traditional Causal Representation Learning (CRL) typically focuses on latent factors but overlooks such observable-to-observable...
Disentangling the effects of sea surface temperature and CO$_2$ in global machine learned weather-climate emulators
arXiv:2606.07928v1 Announce Type: new Abstract: While previous versions of the Ai2 Climate Emulator (ACE) have been trained with CO$_2$ as a forcing, they are only accurate within a narrow range of scenarios, for example climate over the last 80 years forced by observed sea surface temperature (SST), sea ice, and CO$_2$ (AMIP), or equilibrium or near-equilibrium climates with CO$_2$ concentrations ranging from 1x to 4x that of the present day. Attempting to simulate climate forced by AMIP...
An Agent-Based Model for Migration Decision-Making Under Higher Frequency of Extreme Climate Events
Announce Type: new Abstract: This paper develops an agent-based model of climate-related human migration that links repeated environmental shocks to individual migration decision-making through the joint evolution of perceived risk, aspirations to migrate, and migration capability. Building on the aspirations-capabilities framework, the model represents migration as an emergent outcome of two opposing dynamics: shocks increase perceived risk and raise aspirations to move, while...
ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis
arXiv:2604.16922v3 Announce Type: replace Abstract: Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of analytical tools have created significant bottlenecks, constraining scientific discovery to fragmented and labor-intensive workflows. While the emergence Large Language Models (LLMs) offers a transformative paradigm to scale scientific expertise, existing explorations remain largely confined to...
U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts
arXiv:2606.04658v1 Announce Type: new Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulations are computationally expensive, planners typically evaluate fewer than ten manual designs. \gls{qd} algorithms offer a way to systematically illuminate the design space, but they require surrogate models to be practical.
Probabilistic storyline attribution using machine learning
arXiv:2606.02550v1 Announce Type: cross Abstract: A fundamental goal in climate attribution is to estimate how forced climate change contributes to observed extreme weather events. The storyline attribution method compares an observed weather event, conditional on its atmospheric dynamic state (i.e., atmospheric circulation), in the current, 'factual' climate to an event with very similar circulation conditions in a hypothetical, 'counterfactual' climate.