Adaptation needs local temperature projections, yet their uncertainty remains large. Historical temperature differences between observations and the model-ensemble mean contain unforced variability, substantially limiting constraints on future local warming. Whether neighboring records strengthen these constraints, and at what spatial scale, remains a key unresolved question. We show that distance-weighted averaging of observation–model differences within a fixed radius improves skill over using only the target-grid record. This improvement arises because nearby locations share forced warming signals, allowing averaging to reduce noise while retaining information about future warming at the target. We therefore develop an adaptive observational constraint that selects the neighborhood radius and weights neighboring information according to its noise and relevance to target warming. It improves skill over the target-grid constraint in 33 of 34 SSP5-8.5 models at a 50-year lead and in both lower-forcing SSPs. Applied to HadCRUT5, it lowers projected late-century local warming under SSP5-8.5 and narrows spread.
We introduce a sink-oriented Rossby wave river (WR) framework that traces remote wave activity along continuous propagation pathways converging toward specific sinks, and use the Li-Yang wave ray flux to diagnose the global WRs converging into the East Asian summer monsoon (EASM) region. At 850 hPa, Southern Hemisphere and pan-tropical pathways dominate and converge near the Philippines; at 500 hPa, low-latitude easterly and midlatitude westerly WRs become comparable in strength; at 200 hPa, WR propagation becomes concentrated along the jet waveguide. Strikingly, the two major 500-hPa WRs undergo nearly head-on confluence, forming a climatological Rossby wave front that closely coincides with the Meiyu front. Vertically, the dominant pathway shifts from the southern monsoon channel below to the northwestern westerly waveguide aloft, with the transition occurring above the Meiyu–Changma–Baiu rainband. This sink-oriented perspective reframes EASM teleconnections as an interconnected global wave-propagation network linking remote Rossby wave sources to regional climate impacts.
Wildfires alter land surface albedo and temperature, modulating the radiative budget and climate. Previous studies have predominantly reported enhanced shortwave absorption induced by post-fire surface darkening over forests. Here, using two-decade (2001–2020) satellite observations of wildfires, we show that post-fire summer surface albedo and radiative balance shifts vary significantly across ecosystems. Broadleaf forests and grasslands exhibit a brightening effect, because the exposure of bright soils and rapid vegetation regrowth outweigh the deposition of dark carbonaceous residues. The post-fire surface brightening then amplifies the warming-driven radiative cooling. Conversely, in needleleaf forests, shrublands, and savannas, post-fire surface darkening increases shortwave absorption, yet warming-driven longwave radiative losses remain dominant, leading to net radiative cooling. Surface radiative fluxes exhibit high sensitivity to fire size in mid-to-high latitude regions driven by distinct fire-ecosystem feedbacks. Therefore, the ecosystem heterogeneity must be considered when assessing the effects of wildfires on the surface radiative budget and climate.
Large uncertainties in atmospheric organic aerosol (OA) hygroscopicity substantially limit the accurate evaluation of aerosol physicochemical evolution, aqueous-phase processing, and aerosol-cloud climatic effects. Traditional retrieval methods generally adopt simplified homogeneous mixing assumptions or oxidation-dependent parameterizations, which largely neglect ambient mixing-state heterogeneity and may cause systematic biases in secondary organic aerosol hygroscopicity (κSOA) estimation. Herein, based on wintertime field observations in Beijing, we develop a mixing state-resolved approach to retrieve size-resolved κSOA by accounting for the partitioning of aerosol chemical components between hydrophobic and hygroscopic modes. Approximately 93% of the retrieved κSOA values exceed 0.20, with a campaign average of 0.47 ± 0.20, whereas conventional schemes severely underestimate ambient κSOA by approximately 50%. Such biases are particularly pronounced for larger particles (>80 nm) with complex mixing transitions. Chemical and mechanistic validations confirm that elevated κSOA is dominated by highly hygroscopic low-molecular-weight organic acids formed via aqueous acid-catalyzed reactions. Benefiting from the refined κSOA constraint, our approach substantially improves aerosol liquid water closure (R2 = 0.96), reduces low-humidity water-uptake underestimation, and enhances cloud condensation nuclei number concentration prediction accuracy by 3–10%. This study highlights that explicitly resolving aerosol mixing state is critical for narrowing OA hygroscopicity uncertainties and reliably simulating aerosol environmental and climatic effects.
Upscale convective growth (UCG) is the key process forming mesoscale convective systems that dominate warm-season heavy rainfall. A recent decade of South China events shows coastal UCG is misaligned with convection initiation (CI): it peaks at night, when CI is weakest. Nocturnal UCG reflects higher organizational efficiency, not more frequent triggering, despite daytime peak instability and CI. Both contrasts arise from the same boundary-layer wind cycle. By day, opposing sea-breeze and offshore oceanic departure winds yield a weak, narrow coastal convergence band, and northward storm motion carries systems away from it. At night, onshore oceanic departure winds converge with the land breeze, forming a stronger, broader lifting corridor, while the same southerly enhancement reorients the low-level wind maximum, making storm motion zonal and band-aligned. Enhanced coastal lifting and along-band propagation favor nocturnal UCG. Climate-sensitive land-sea thermal contrast and phase-locked oceanic wind anomalies could reshape coastal heavy-rainfall hazards beyond South China.
