Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.16714

Forecasting carbon-risk hotspots ahead of the extreme 2026 El Niño

Xiaomeng Du·Runda Jiang·Philippe Ciais·Pierre Friedlingstein·Stephen Sitch·Mike O'Sullivan·Jefferson Goncalves de Souza·Tim Stripp·Piyu Ke·Yi Xi·Minxuan Sun·Xiaoran Wu·Mengjie Han·Shanshan Song·Qiang Zhang·Wei Li
View original source ↗Read source document ↗Export metadata ↓

Abstract

A potentially super El Niño is forecasted in 2026, raising the need to anticipate land carbon-cycle risks before its impacts unfold. We develop a prospective early-warning framework that uses dynamic global vegetation models driven by an ensemble of ECMWF seasonal climate forecasts and machine-learning burned area predictions. Forecasts indicate a $0.23 \pm 0.21$ PgC reduction in global net biome productivity in the second half of 2026, with hotspots of sink weakening in eastern South America and elevated fire risk in Southeast Asia.

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiaomeng Du, Runda Jiang, Philippe Ciais, Pierre Friedlingstein, Stephen Sitch, Mike O'Sullivan, Jefferson Goncalves de Souza, Tim Stripp, Piyu Ke, Yi Xi, Minxuan Sun, Xiaoran Wu, Mengjie Han, Shanshan Song, Qiang Zhang, Wei Li. 2026-10-12. Forecasting carbon-risk hotspots ahead of the extreme 2026 El Niño. https://arxiv.org/abs/2610.16714

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

KEEP EXPLORING

Related papers

arXiv2026

The hypervariational method: a general, automated technique based on innovations to tune the parameters of the Hybrid covariance matrix

The Hybrid forecast covariance model is an operationally successful way to build a background error covariance matrix. It is constructed as a weighted sum, with scalar weights, of the climatological covariance matrix and a localized ensemble covariance matrix, with a localization matrix typically depending on a localization length. For best performance, the Hybrid needs to be optimized by tuning those weights and, possibly, other parameters, such as the localization length. Here, a systematic and automated method for this tuning operation is introduced. This technique is referred to as hypervariational method, and it is formulated in terms of a cost function, built using a time series of innovations as an essential ingredient. This cost function, which depends on the parameters, is minimized when they have optimal values. Equations for its minimization, based on Newton's method, are derived. Besides the aforementioned, commonly used Hybrid, the hypervariational method is applicable to any known Hybrid (and to any set of parameters on which it depends analytically), including recently developed Hybrids, not necessarily constructed as weighted sums. Here, the hypervariational method is applied to tune the ensemble covariance matrix weight in the seminal-formulation Hybrid (which does not feature localization). This is done in a synthetic experimental setting where a theoretically-derived expression for the optimal value for that weight is known. Moreover, in that experimental setting, a further method, based on a linear fit procedure, to determine that optimal value is possible. Both the hypervariational method and that linear fit procedure are shown to produce an optimal weight value matching (up to a tolerance of $0.1$) the optimal value known from the theory. Furthermore, Newton's minimization in the hypervariational method is shown to converge immediately (at the first recursive step).

physics.ao-ph↗
arXiv2026

Extreme events and statistics in an AI emulator of a global storm-resolving model

We present HiRO-ACEv2 (High Resolution Output Ai2 Climate Emulator), a two-stage framework for AI emulation of the 3~km resolution X-SHiELD global storm-resolving model (GSRM), and highlight its ability to generate large ensembles of data for studying the characteristics of tropical cyclones and extreme events in high-resolution simulations at an inference computational cost over 300 times lower than that of traditional numerical GSRMs. HiRO-ACEv2 expands upon the precipitation-only HiRO-ACEv1 model to generate coherent precipitation, 10~m winds, and sea level pressure outputs, enabling a broader range of events to be simulated at high-resolution. Fine-resolution predictions are generated by running an autoregressive emulator of coarsened X-SHiELD at 100 km, whose multivariate outputs are regionally downscaled to 3~km resolution via a separately trained diffusion model. Particular importance is placed on capturing the statistics of extreme events during the diffusion downscaling step. Our ability to match the tails of distributions and generate realistic-looking extreme events in both the `perfect prediction' case as well as in free-running emulator rollouts is improved via the use of multiple denoisers at inference time and several updates to the data sampling, noise sampling, and loss weighting during training. HiRO-ACEv2 closely matches the high-resolution reference distributions of precipitation and near-surface winds out to their 99.9999th percentile values, and broadly tracks the reference distribution of tropical cyclone intensities.

physics.ao-ph↗