AGU Advances最新文献

Wildfires in the western US increasingly threaten infrastructure, air quality, and public health. Prescribed (“Rx”) fire is often proposed to mitigate future wildfires, but treatments remain limited, and few studies quantify their effectiveness on recent major wildfires. We investigate the effects of Rx fire treatments on subsequent burn severity across western US ecoregions and particulate matter (PM_2.5) emissions in California. Using high-resolution (30-m) satellite imagery, land management records, and fire emissions data, we employ a quasi-experimental design to compare Rx fire-treated areas with adjacent untreated areas to estimate the impacts of recent Rx fires (Fall 2018–Spring 2020) on the extreme 2020 wildfire season. We find that within 2020 wildfire burn areas where Rx fires were used prior to 2020, burn severity changed by −16% (p < 0.001) and smoke PM_2.5 emissions changed by −101 kg per acre (p < 0.1). Rx fires in the wildland-urban interface (“WUI”) were less effective in reducing burn severity and smoke PM_2.5 emissions than those outside the WUI. Overall, Rx fires led to a net reduction of −14% in PM_2.5 emissions, including those from the Rx fires themselves. The proposed policy of treating one million acres annually in California could reduce smoke emissions by 655,000 tons over the next 5 years, equivalent to 52% of the emissions from 2020 wildfires. Our analysis provides comprehensive estimates of the net benefits of Rx fire on subsequent burn severity and smoke PM_2.5 emissions in the western US, an empirical basis for evaluating proposed Rx fire expansions, and valuable constraints for future modeling.

Past research has shown that multiple climate subsystems might undergo abrupt shifts, such as the Arctic Winter sea ice or the Amazon rainforest, but there are large uncertainties regarding their timing and spatial extent. In this study we investigated when and where abrupt shifts occur in the latest generation of earth system models (CMIP6) under a scenario of 1% annual increase in CO₂. We considered 82 ocean, atmosphere, and land variables across 57 models. We used a Canny edge detection method to identify abrupt shifts occurring on yearly to decadal timescales, and performed a connected component analysis to quantify the spatial extent of these shifts. The systems analyzed include the North Atlantic subpolar gyre, Tibetan Plateau, land permafrost, Amazon rainforest, Antarctic sea ice, monsoon systems, Arctic summer sea ice, Arctic winter sea ice, and Barents sea ice. Except for the monsoon systems, we found abrupt shifts in all of these across multiple models. Despite large inter-model variations, higher levels of global warming consistently increase the risk of abrupt shifts in CMIP6 models. At a global warming of 1.5°C, six out of 10 studied climate subsystems already show large-scale abrupt shifts across multiple models.

The greenhouse gas “endangerment finding” of the U.S. Environmental Protection Agency (EPA), established in 2009 after a 2006 U.S. Supreme Court case (Massachusetts vs. EPA) in which we participated as amicus curiae (friends of the court), has become the basis for U.S. regulation of greenhouse gases in the years since. The current Administration of President Donald Trump is now seeking its repeal. Here, we review the role climate science played in that 2006 case, and how the scientific evidence that undergirds the endangerment finding has gotten stronger in the 16 years since. Finally, we consider what will be the fate of the endangerment finding—and indeed that of role of science in contributing to policy—in light of the current challenging environment for science in the U.S.

Accurate flood early warnings are critical to minimize damage and loss of life. Current large-scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI-based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four- to six-fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state-of-the-art global AI model. ECN-based forecasts offer superior economic value (up to four-fold) for decision-making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national-scale ensemble forecasts in minutes.

In upland environments, roots commonly extend deep below soil into partially saturated bedrock. This Bedrock Vadose Zone (BVZ) has been shown to store and circulate water, host organic carbon respiration and serve as a critical source of rock-derived nutrients. However, the extent to which deep roots influence chemical weathering rates remains poorly understood. Here, we report 4 years of depth-resolved major ion chemistry over a 16-m thick BVZ hosting a deep rhizosphere in a catchment subject to a Mediterranean climate. These data allow development and validation of a reactive transport model (RTM), revealing that the timescales of water storage and drainage in the BVZ are sufficient to facilitate substantial chemical weathering of the shale bedrock. However, observed solute concentrations are only reproduced by the RTM when we explicitly include measured rates of $��{\text{CO}}_{�2��\left(�g�\right)��}�$ production meters below soil driven by the deeply rooted forest. By combining direct observations and a process-based RTM we conclude that the carbon respiration promoted by deep roots significantly enhances chemical weathering rates in the BVZ, constituting 43% $��\pm �$ 3% of total solute flux from the base of the BVZ to the water table.

Deep earthquakes at depths below 500 km are under prohibitive pressure and temperature conditions for brittle failure. Individual events show diverse rupture behaviors and a coherent mechanism to explain their rupture nucleation, propagation, and characteristics has yet to be established. We systematically resolve the rupture processes of 40 large $��M�>�7�$ deep earthquakes from 1990 to 2023 and compare the rupture details to their local metastable olivine wedge (MOW) structures informed from thermo-mechanical simulations in seven subduction zones. Our results suggest that these events likely initiate from metastable olivine transformations within the cold slab core and rupture beyond the MOW due to sustained weakening from molten rock at the rupture tip. Over half of the $��M�>�7�$ earthquakes likely rupture beyond the MOW boundary and are controlled by both mechanisms. Rupturing outside the MOW boundary leads to greater moment release, increased geometric complexity, and a reduction in rupture length, causing greater stress drops.

We divide the atmosphere into distinct spheres based on their physical, chemical, and dynamical traits. In deriving chemical budgets and climate trends, which differ across spheres, we need clearly defined boundaries. Our primary spheres are the troposphere and stratosphere (∼99.9% by mass), and the boundary between them is the tropopause. Every global climate-weather model has one or more methods to calculate the lapse rate tropopause, but these involve subjective choices and are known to fail near the sub-tropical jets and polar regions. Age-of-air tracers clock the effective time-distance from the tropopause, allowing unambiguous separation of stratosphere from troposphere in the chaotic jet regions. We apply a global model with synthetic tracer e90 (90-day e-folding), focusing on ozone and temperature structures about the tropopause using ozone sonde and satellite observations. We calibrate an observation-consistent tropopause for e90 using tropics-plus-midlatitudes and then apply it globally to calculate total tropospheric air-mass and tropopause ozone values. The tropopause mixing barrier for the current UCI CTM is identified by a transition in the vertical transport gradient to stratospheric values of 15 days km⁻¹, corresponding to an e90 tropopause at 81 ± 2 ppb with a global tropospheric air mass of 82.2 ± 0.3%. The best e90 tropopause based on sonde pressures is 70–80 ppb; but that for ozone is 80–90 ppb, implying that the CTM tropopause ozone values are too large. This approach of calibrating an age-of-air tropopause can be readily applied to other models and possibly used with observed age-of-air tracers like sulfur hexafluoride.

Global cooling during the Late Cenozoic led to periodic glaciations in many mountain regions. The repeated waxing and waning of glaciers and ice sheets resulted in continuously changing erosion regimes that modified the underlying topography. While some studies have argued that relief increased due to glaciation, others have argued that glaciations limit relief. The (pre-glacial or glacial?) origin of elevated low-relief surfaces (ELRS) in mountain belts is similarly controversial. ELRS have been used to reconstruct pre-glacial landscapes, trace patterns of glacial incision, and infer tectonic uplift; it is thus important to test to what extent, and under which circumstances, glaciations may produce ELRS. We use a glacial landscape-evolution model to quantify the integrated effect of Pliocene-Quaternary glaciations on the topography of a mountain range. Our simulations show that glaciations can produce ELRS by shielding bedrock under slow-moving, non-erosive ice at intermediate elevations, with erosion focused on ice-free summits and at lower elevations. We term this mechanism for ELRS formation the glacial shelter effect. The final extent of ELRS strongly depends on the climatic history and the efficacy of local erosion, which affect both ELRS formation and preservation. Specifically, efficient fluvial erosion during warm interglacials may dissect previously formed ELRS. Modeled ELRS are distributed across elevations because their formation and preservation depend on ice extent rather than on the average equilibrium-line altitude. Our model results provide a comprehensive framework for the impact of glaciations on topography that explains both the presence and absence of ELRS in glaciated areas.

Recent changes in US oceanographic assets are impacting scientists' ability to access seafloor and sub-seafloor materials and thus constraining progress on science critical for societal needs. Here we identify national infrastructure needs to address critical science questions. This commentary reports on community-driven discussions that took place during the 3-day FUTURE of US Seafloor Sampling Capabilities 2024 Workshop, which used an “all-hands-on-deck” approach to assess seafloor and sub-seafloor sampling requirements of a broad range of scientific objectives, focusing on capabilities that could be supported through the US Academic Research Fleet (US-ARF) now or in the near future. Cross-cutting issues identified included weight and size limitations in the over-boarding capabilities of the US-ARF, a need to access material at depths greater than ∼20 m below the seafloor, sampling capabilities at the full range of ocean depths, technologies required for precise navigation-guided sampling and drilling, resources to capitalize on the research potential of returned materials, and workforce development.