arXiv subjects

Daniele Visioni

Publications and source records attributed to Daniele Visioni.

4 recordsLinked to original sources

arXiv2026

Impacts of social and impact heterogeneity on social-climate outcomes

Regional heterogeneity in social characteristics, temperature change, and vulnerability to climate impacts is likely to influence the magnitude of anthropogenic climate change, but has not been considered in coupled social-climate models, which seek to represent interactions between social and climate dynamics. Here, we examine how the projected mean global temperature anomaly and population support for mitigation respond to heterogeneity in these factors across five regions of the world, using a coupled social-climate model. We find that heterogeneity in climate impacts increases the temperature anomaly by 0.2$^\circ$C, while social heterogeneity increases it by an additional 0.1$^\circ$C. The projected temperature anomaly also increases with higher variability in climate impacts across regions, even for the same average global climate impact. Finally, we identify a social-climate tipping point, where low vulnerability to impacts under existing social conditions in one region can tip the system into an alternative slow-mitigation, high-temperature state. Our results show that heterogeneity in climate impacts leads to higher global mean temperatures and efforts to reduce global disparities could improve both social and climate outcomes.

physics.soc-ph↗

arXiv2026

Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior

High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small-scale processes. Existing machine learning downscaling methods often require paired low- and high-resolution data for supervised learning, are tied to fixed regions or scale factors during inference, and can be computationally expensive to train and run in physical space. Here we introduce Longwang, a zero-shot latent generative framework for global spatiotemporal precipitation downscaling. Longwang learns a context-conditioned latent generative prior and combines it with a physically informed observation operator through posterior sampling, enabling daily O(10 km) precipitation fields to be generated from monthly O(100 km) inputs. On ERA5 reanalysis, Longwang outperforms standard posterior sampling with an unconditional generative prior in reconstructing fine-scale spatial patterns, preserving temporal coherence, and recovering extreme precipitation intensities. The framework further generalizes to historical climate simulations and future climate projections under substantial distribution shift.

physics.ao-ph↗

arXiv2025

Spatiotemporal Pyramid Flow Matching for Climate Emulation

Generative models have the potential to transform the way we emulate Earth's changing climate. Previous generative approaches rely on weather-scale autoregression for climate emulation, but this is inherently slow for long climate horizons and has yet to demonstrate stable rollouts under nonstationary forcings. Here, we introduce Spatiotemporal Pyramid Flows (SPF), a new class of flow matching approaches that model data hierarchically across spatial and temporal scales. Inspired by cascaded video models, SPF partitions the generative trajectory into a spatiotemporal pyramid, progressively increasing spatial resolution to reduce computation and coupling each stage with an associated timescale to enable direct sampling at any temporal level in the pyramid. This design, together with conditioning each stage on prescribed physical forcings (e.g., greenhouse gases or aerosols), enables efficient, parallel climate emulation at multiple timescales. On ClimateBench, SPF outperforms strong flow matching baselines and pre-trained models at yearly and monthly timescales while offering fast sampling, especially at coarser temporal levels. To scale SPF, we curate ClimateSuite, the largest collection of Earth system simulations to date, comprising over 33,000 simulation-years across ten climate models and the first dataset to include simulations of climate interventions. We find that the scaled SPF model demonstrates good generalization to held-out scenarios across climate models. Together, SPF and ClimateSuite provide a foundation for accurate, efficient, probabilistic climate emulation across temporal scales and realistic future scenarios. Data and code is publicly available at https://github.com/stanfordmlgroup/spf .

cs.CV↗

arXiv2024

Kicking the Can Down the Road: Understanding the Effects of Delaying the Deployment of Stratospheric Aerosol Injection

Climate change is a prevalent threat, and it is unlikely that current mitigation efforts will be enough to avoid unwanted impacts. One potential option to reduce climate change impacts is the use of stratospheric aerosol injection (SAI). Even if SAI is ultimately deployed, it might be initiated only after some temperature target is exceeded. The consequences of such a delay are assessed herein. This study compares two cases, with the same target global mean temperature of 1.5C above preindustrial, but start dates of 2035 or a delayed start in 2045. We make use of simulations in the Community Earth System Model version 2 with the Whole Atmosphere Coupled Chemistry Model version 6 (CESM2-WACCM6), using SAI under the SSP2-4.5 emissions pathway. We find that delaying the start of deployment (relative to the target temperature) necessitates lower net radiative forcing (-30%) and thus larger sulfur dioxide injection rates (+20%), even after surface temperatures converge, to compensate for the extra energy absorbed by the Earth system. However, many of the surface climate differences between the 2035 and 2045 start simulations appear to be small during the 10-25 years following the delayed SAI start, although longer simulations would be needed to assess any longer-term impacts in this model. In addition, irreversibilities and tipping points that might be triggered during the period of increased warming may not be adequately represented in the model but could change this conclusion in the real world.

physics.ao-ph↗