Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1602.08462

Atmospheric ionization by high-fluence, hard spectrum solar proton events and their probable appearance in the ice core archive

Adrian L. Melott·Brian C. Thomas·Claude M. Laird·Ben Neuenswander·Dimitra Atri
View original source ↗Read source document ↗Export metadata ↓

Abstract

Solar energetic particles ionize the atmosphere, leading to production of nitrogen oxides. It has been suggested that some such events are visible as layers of nitrate in ice cores, yielding archives of energetic, high fluence solar proton events (SPEs). There has been controversy, due to slowness of transport for these species down from the upper stratosphere; past numerical simulations based on an analytic calculation have shown very little ionization below the mid stratosphere. These simulations suffer from deficiencies: they consider only soft SPEs and narrow energy ranges; spectral fits are poorly chosen; with few exceptions secondary particles in air showers are ignored. Using improved simulations that follow development of the proton-induced air shower, we find consistency with recent experiments showing substantial excess ionization down to 5 km. We compute nitrate available from the 23 February 1956 SPE, which had a high fluence, hard spectrum, and well-resolved associated nitrate peak in a Greenland ice core. For the first time, we find this event can account for ice core data with timely (~ 2 months) transport downward between 46 km and the surface, thus indicating an archive of high fluence, hard spectrum SPE covering the last several millennia. We discuss interpretations of this result, as well as the lack of a clearly-defined nitrate spike associated with the soft-spectrum 3-4 August 1972 SPE. We suggest that hard-spectrum SPEs, especially in the 6 months of polar winter, are detectable in ice cores, and that more work needs to be done to investigate this.

Explore related subjects

physics.ao-phastro-ph.EPastro-ph.IMastro-ph.SRphysics.geo-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Adrian L. Melott, Brian C. Thomas, Claude M. Laird, Ben Neuenswander, Dimitra Atri. 2016-02-26. Atmospheric ionization by high-fluence, hard spectrum solar proton events and their probable appearance in the ice core archive. https://doi.org/10.1002/2015jd024064

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Physical Limit of Neural Hypoxia Detection in the Black Sea from Satellite Observations

Coastal hypoxia (O_2 < 63 [mmol / m^3]) threatens ocean health worldwide. On continental shelves, summer stratification prevents bottom oxygen consumed by respiration from being renewed, making monitoring essential to protect vulnerable ecosystems and reduce biodiversity loss. Although satellite observations are increasingly available, their potential to infer subsurface oxygen remains largely unexplored. We frame this as a Bayesian inverse problem relating surface observations to the complete three-dimensional physical and biogeochemical states of the Black Sea. Here, we solve it using a deep generative neural network trained on numerical model outputs that provides a tractable and computationally efficient approximation of the true posterior distribution of sea states. We find that accurate state estimation is limited to the mixed layer, because its homogeneity makes surface conditions representative of subsurface states. During summer, we detect 38% of all hypoxic events shelf-wide with a precision of 47%. Improving the results will likely require longer assimilation windows or subsurface observations.

physics.ao-ph↗
arXiv2026

CNN-based forecasting of early winter NAO using sea surface temperature

The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic sector, influencing temperature and precipitation across Europe. While the NAO's impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST anomalies. However, this NAO response to SST anomalies is believed to be weak and nonlinear. Former studies highlight that during early winter (November-December), El Nino Southern Oscillation (ENSO) events modulate the NAO, with El Nino (La Nina) events being linked to positive (negative) NAO phases, and an opposite effect observed in late winter (January-February). Indian Ocean SSTs and the North Atlantic Horseshoe SST anomaly have also been suggested as contributors to early winter NAO variability. However, climate models often struggle to capture these SST-NAO teleconnections, particularly in early winter. To address this, a statistical framework based on convolutional neural networks (CNNs) is developed to predict the early winter NAO using observed SST fields one-, two-, and three-month before. A linear model serves as a benchmark, and both models are trained on ERA5 reanalysis data from 1940 to 2023. A sensitivity analysis is used to interpret the CNN's decision-making process, revealing that it focuses on regions such as the tropical Pacific and North Atlantic, confirming results from previous works. The CNN outperforms the linear model, highlighting the value of capturing nonlinear SST-NAO relationships. Prediction skill appears to be linked to ENSO, with strong ENSO events associated with greater skill in forecasting the NAO than neutral events. These findings underscore the potential of deep learning to build medium-range NAO prediction.

physics.ao-ph↗
arXiv2026

PepC-Global: A Basin-Tuned Probabilistic Tropical Cyclone Model with Enhanced Out-of-Sample Skill and Climate-Sensitive Over-Land Decay

We present PepC-Global, a global version of the Princeton environment-dependent probabilistic tropical cyclone (PepC) framework that uniquely implements basin-wise tuning within a single unified tropical cyclone climatology model. PepC-Global represents tropical cyclone climatology as a coupled stochastic process linking genesis, track, and intensity conditioned on large-scale environmental predictors. Each of the genesis, track, and intensity modules outperforms widely used linear models in out-of-sample tests. The intensity module also incorporates an environment-dependent over-land decay model, offering greater sensitivity to climate change signals than conventional fixed decay rate approaches. Systematic evaluation against observations demonstrates that PepC-Global closely reproduces genesis basin-wise frequency, seasonal cycles, interannual variability, and spatial distributions. The model also accurately captures basin-wise track patterns, along-coastline landfall frequency, and intensity statistics including lifetime maximum intensity and landfall intensity distributions. PepC-Global provides a versatile tool for probabilistic tropical cyclone hazard and risk assessment and a practical framework for investigating changes in tropical cyclone activity across future climate scenarios.

physics.ao-ph↗