Geophysical Research Letters最新文献

Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi-arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical-Infrared (IR) and microwave remote sensing observations to quantify plant-to-stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine-learning was employed to re-construct global satellite microwave vegetation optical depth (VOD) retrievals to 500-m resolution. The re-constructed results were able to delineate diverse vegetation conditions undetectable from the original 25-km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro-climate variability over diverse sub-regions. The re-constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil-vegetation-atmosphere continuum in heterogeneous drylands.

Global average upper atmosphere temperature changes linked with the Hunga volcanic eruption (January 2022) are analyzed based on satellite measurements and compared with chemistry-climate model simulations. Results show stratospheric cooling of −0.5 to −1.0 K in the middle and upper stratosphere during 2022 through middle 2023, followed by stronger cooling (−1.0 to −2.0 K) in the mesosphere after middle 2023. The cooling patterns follow the upward propagating water vapor (H₂O) anomalies from Hunga, and similar behavior is found between observations and model simulations. While the stratospheric cooling is mainly due to radiative cooling from enhanced H₂O, the mesospheric temperature changes result from ozone losses in the mesosphere, which are in-turn driven by HO_x radicals from Hunga H₂O. Comparisons with the multi-decade climate record show that Hunga impacts on stratospheric temperatures have similar magnitude, but opposite sign, to temperature effects from the large El Chichón (1982) and Pinatubo (1991) volcanic eruptions.

The summer monsoon onset over the Bay of Bengal and South China Sea signals the beginning of the Asian summer monsoon, critical for local fisheries, agriculture and livelihoods, so communities are concerned about its potential changes under global warming. Previous projections have suggested a delay, but the extent of this delay remains uncertain, undermining the reliability of the projections. Here, we show a significant correlation between the projected shift in Bay of Bengal/South China Sea monsoon onset and present-day sea surface temperature (SST) simulation over the western Pacific (WP). This emergent relationship arises from the spread of the precipitation response over the western-central Pacific to WP SST, as more precipitation induces stronger tropical upper-tropospheric warming, increasing westerly vertical shear near South Asia, and facilitating the onset delay. The rectified projections indicate that the delayed shift is almost halved compared to raw projections, and the intermodel uncertainty is reduced by 30%.

Flash droughts are rapidly developing extreme weather events with sudden onset and quick intensification. Global prediction of flash droughts at sub-seasonal time scales remains a great challenge. Current state-of-the-art dynamic models subject to large errors and demonstrate low skills in global flash drought prediction. Here, we develop a machine learning-based framework that uses meteorological forecasts as inputs to predict global root-zone soil moisture and flash droughts from 1 day to 2 week lead times. The results indicate that 33% and 24% global flash drought onset and termination events can be correctly predicted by machine learning at 7 day lead time, versus 19% and 11% fractions by state-of-the-art dynamic model. The developed machine learning model demonstrates substantial improvements over dynamic model in global soil moisture prediction, and thus enhances global flash drought forecasting skills in space and time. The presented framework may benefit global flash drought prediction and early warning at sub-seasonal scales.

The inhibition of foliar respiration by light is a crucial yet often overlooked component in estimating ecosystem respiration. However, current estimations of the light inhibition of ecosystem respiration are biased by ignoring the effects of moisture factors. We developed a novel physics-constrained machine learning method to quantify the extent of light inhibition (Re_li) driven by multiple factors in global ecosystems. Our findings revealed significant seasonal variations in light inhibition rate aligned with vegetation growth. Temperature predominantly influenced variations in Re_li, and the temperature-Re_li relationship was regulated by vapor pressure deficit rather than soil water content. A reassessment of global ecosystem respiration revealed that current Earth system models (ESMs) overestimate ecosystem respiration in mid-to-high latitude dryland regions, with a global average light inhibition strength of 0.51 (±0.16). Knowledge from this study provides an accurate understanding of light inhibition driven by temperature and moisture coupling in simulating carbon cycle.

Rice quality, which is intricately connected to market value and human nutrition, is sensitive to weather conditions. However, the relative importance of the different climatic factors is poorly understood, and the impact of climate change on rice quality has been little studied on a large scale. Here, using more than 35 years of rice quality data, we present the first effort to determine the key climate variables driving rice quality in China and Japan. Results show a significant decline in high quality rice rate (HRR, an indicator of rice quality), mainly driven by warm nighttime temperatures when they exceed a critical threshold estimated at 18°C and 12°C in China and Japan, respectively. Climate projections suggest a continuing decreasing trend in HRR under moderate and high emission scenarios by 2100. These findings emphasize the importance of breeding new heat tolerant cultivars to maintain stable rice quality in the future.

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

The Ganzi-Yushu fault (GYF) is one of the most seismically active fault systems in eastern Tibet, having experienced five M > 7.0 earthquakes over the past 300 years. Here, we use Sentinel-1 InSAR data spanning from 2014 to 2023 to derive the interseismic velocity fields along the GYF. We calculate the strain rate fields for the entire fault system, which reveal localized strain accumulation along the GYF as well as along two secondary faults within the Bayan Har block. The inversion results obtained from the elastic block model indicate left-lateral strike slip rates of 4.0–6.5 mm/yr along the GYF and five locked segments distributed along strike. Furthermore, we identify two shallow creeping segments on the InSAR velocity maps. Based on the locations of the creeping sections and their temporal decay characteristics, we infer that the shallow creep along the GYF is afterslip of the 2010 Yushu earthquake.

High-time-resolved mapping results of two rare reactivation processes following a negative return stroke are discovered and analyzed. At first, the discharges prior to the reactivation process were dominated by positive discharges lasting for tens of milliseconds in a limited space in the vicinity of a decayed negative leader channel. The positive discharges produced no detectable electric field change. Then, negative discharges started and propagated along the decayed downward negative leader channels at a speed exceeding 10⁶ m/s for a few microseconds and produced negative electric field changes. The analysis reveals that the processes are physically distinct from recoil leaders and side discharges in terms of propagation behaviors, electromagnetic characteristics, and time scale. The possible mechanisms of the processes are discussed. The observation suggests that the reactivation processes of the negative leaders may lead to subsequent return strokes.

We evaluate three identifiers of continental mantle earthquakes (CMEs) motivated by surface-wave normal-mode theory: the amplitude ratio of Sn to Lg, and the frequency content of Sn and of Lg, after wave propagation through continental crustal thinning. These flexible and easily applicable methods allow for potential new discoveries of CMEs. They rely on guided waves whose propagation is dependent on the uniformity of their waveguides. For a range of Moho models, we perform 2.5D axisymmetric simulations that reach the conventional distance and frequency ranges of observational studies; we compare results from four different source depths straddling the Moho. Our synthetics, and six south-Tibet earthquakes recorded by an array in Bangladesh, show our Sn/Lg identifier is robust in the presence of crustal thinning, but the identifying frequency contents of Sn and Lg are easily obscured. These results strengthen the utility of Sn/Lg methods for global studies of CMEs.