Climate AI

TL;DR for operators Neptune is a global ocean and sea-ice emulator that produces daily states out to 60 days. On held-out 2019-2021 reanalysis data, its 1° model generally beats training-set climatology for most variables and preserves large-scale temperature, sea-surface-height, ocean-heat-content, ENSO, and Indian Ocean Dipole structure well into the forecast horizon. The qualification problem begins when the forecast is examined by variable, region, and spatial scale. Currents and mixed-layer depth deteriorate more rapidly, energetic current systems remain difficult, fine-scale velocity variability is smoothed, and some sea-ice regions become less reliable at long lead times. The 0.25° version produces higher-resolution output but does not uniformly improve forecast diagnostics. ...

TL;DR for operators Models are often sold as if accuracy were a passport: one clean number, stamped at the border, cleared for deployment. FLUXtrapolation is a useful reminder that the border is usually where the problem begins. The paper introduces a benchmark for predicting hourly ecosystem fluxes — carbon, water, and energy exchanges between ecosystems and the atmosphere — when direct measurements exist only at sparse flux-tower sites.1 The mechanism is simple and unpleasant: train models where towers exist, then test them in progressively less comfortable situations where the future, the geography, or the temperature regime has shifted. ...

TL;DR for operators Climate work is not short of data. It is short of usable pathways through data. EarthLink, the system studied in this paper, is best understood as an orchestration layer for climate science: it plans analyses, retrieves relevant data, generates code, runs diagnostics, checks results, produces reports, and stores validated query-code-result patterns for reuse.1 ...