Earth and Space Science最新文献

The change in clouds during the day is critical to the Earth's energy balance and climatic evolution. However, there have been relatively few studies on cloud variations at daily timescales, owing to limitations of ground- and satellite-observations, especially for cirrus clouds. In this study, we examined the daytime cirrus variation (DCV) at the global scales and its associated effects on radiation budgets based on the International Satellite Cloud Climatology Project H data set. The changes in continental cirrus cover are more significant than that over the ocean, reaching a maximum of 17.3% in the afternoon. Over the tropical deep convection regions, cirrus cloud cover and optical depth exhibit large amplitudes during the daytime, closely linked to average properties of cirrus clouds. Using a process-based radiative transfer model, we calculated the variations in daytime cirrus cloud radiative forcing (CRF). After noon, cirrus clouds over both land and ocean generate the strongest shortwave (SW) cooling and longwave (LW) warming effects at the top of the atmosphere (TOA). At the global scale, daytime cirrus clouds cause an average net CRF of 3.6 W/m² at the TOA. If the DCV is neglected in the model, the SW cooling and LW warming effects are overestimated by 2.5 and 1.8 W/m² at the TOA, leading to a net radiation bias of 0.7 W/m². The findings provide key information for targeting specific aspects of the cirrus parameterization scheme in climate models.

Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.

The abnormal deformation of urban surfaces threatens the human living environment, and extreme regional weather can affect the response law of surface deformation. To explore the changes in surface time series response after extreme weather and the causes of deformation in Zhengzhou, the MTInSAR was used to obtain the surface deformation from 2020 to 2022, and the time series changes of groundwater equivalent water height were retrieved by GRACE. The results show that: (a) There are three large subsidence bowls in Zhengzhou, and the maximum subsidence rate is −40.2 mm/yr. (b) The extreme rainstorm in Zhengzhou alleviated the surface deformation quickly, lasting approximately 6 months. However, surface subsidence still occurred after the extreme rainstorm. The water storage coefficient of the elastic skeleton in the Zhengzhou area showed an increasing trend. (c) Precipitation can lead to surface uplift by influencing the change of groundwater level. There is a delay time of 0.75–1 month between groundwater level change and surface uplift response in the characteristic region. These results provide scientific data support and causal analysis for disaster prevention and reduction of abnormal deformation in Zhengzhou.

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite-sample performance of the proposed methodology. A series of Monte-Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non-Gaussian and heavy-tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non-central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods.

This study provided a comprehensive understanding of the source process of the 1819 M 7.7 Kachchh Indian earthquake using physics-based dynamic rupture modeling and strong ground motion simulations. We successfully simulated the spontaneous dynamic rupture along a curved non-planar fault using the 3-D curved-grid finite-difference method (CGFDM). The estimated earthquake magnitude is around 7.6, consistent with previous estimations. Our simulations accurately replicated macroscopic rupture patterns and surface deformation, showing agreement with observed data along the Allah Bund fault (ABF) with a maximum displacement ∼5.5 m at the Earth's surface. The maximum modeled coseismic slip on the fault was approximately 7.5 m. Notably, the ABF exhibited characteristics of a weak barrier (leaky barrier) at the bending part, allowing the rupture to propagate further. Despite limitations in surface deformation calculations, the modeled values aligned with the trend of surface fault slip, with a slight deviation in the epicenter toward the east compared to earlier studies. We observed a homogeneous principal stress oriented N25°E, consistent with the present day Indian plate motion. The estimated horizontal peak ground velocities (PGVh) and the maximum value of Intensity X⁺ aligns well with observations. Furthermore, conducting thorough case studies on significant earthquakes and potential seismic scenarios in stable continental regions is crucial. Such studies play a vital role in validating and improving dynamic rupture models. When combined with statistical methods, this research holds great promise for advancing seismic hazard assessments, earthquake engineering, and strategies for disaster management.

The Terminal Tracking Camera (TTCam) imaging system on the NASA Lucy Discovery mission consists of a pair of cameras that are being used mainly as a navigation and target acquisition system for the mission's asteroid encounters. However, a secondary science-focused function of the TTCam system is to provide wide-angle broadband images over a large range of phase angles around close approach during each asteroid flyby. The scientific data acquired by TTCam can be used for shape modeling and topographic and geologic analyses. This paper describes the pre-flight and initial in-flight calibration and characterization of the TTCams, including the development of a radiometric calibration pipeline to convert raw TTCam images into radiance and radiance factor (I/F) images, along with their uncertainties. Details are also provided here on the specific calibration algorithms, the origin and archived location of the required ancillary calibration files, and the archived sources of the raw calibration and flight data used in this analysis.

Expendable Bathythermographs (XBTs) are oceanographic instruments that fall through the ocean's water column and measure ocean temperature with depth. In many instances, however, XBTs continue to record temperature after they impact the seabed. Here we show evidence that XBTs produce unique temperature responses when they impact the seabed that depend directly on seabed physical properties. Specifically, standard-use XBTs (e.g., T-4s and T-5s), when deployed above a mud-rich seabed, require significant time (tens of minutes) to equilibrate to steady-state seafloor temperatures after seabed impact. In contrast, XBTs deployed above sand-rich sediments equilibrate to seabed temperatures rapidly (<5 min) after seafloor impact. One explanation for this difference in temperature response is that XBTs deployed above mud-rich sediment penetrate into low permeability marine muds that jacket the XBT, where diffusive heat flow dominates. Both observations and numerical modeling results support the hypothesis that XBTs impacting muddy seafloors exhibit slow, diffusion-dominated heat flow, while XBTs impacting harder, sand-rich seabed sites exhibit rapid seafloor temperature equilibration, consistent with advection-driven heat flow and little if any XBT seabed penetration. Given that >644k XBT measurements exist publicly (via the National Oceanographic and Atmospheric Administration website), and >74,000 XBTs record temperatures post seabed impact, we suggest that XBT data represents a large, low-cost, and currently untapped data set for characterizing seabed physical properties globally.

Current understanding of the ammonia distribution in Jupiter's atmosphere is provided by observations from major ground-based facilities and spacecraft, and analyzed with sophisticated retrieval models that recover high fidelity information, but are limited in spatial and temporal coverage. Here we show that the ammonia abundance in Jupiter's upper troposphere, which tracks the overturning atmospheric circulation, can be simply, but reliably determined from continuum-divided ammonia and methane absorption-band images made with a moderate-sized Schmidt-Cassegrain telescope (SCT). In 2020–2021, Jupiter was imaged in the 647-nm ammonia absorption band and adjacent continuum bands with a 0.28-m SCT, demonstrating that the spatially resolved ammonia optical depth could be determined with such a telescope. In 2022–2023, a 619 nm methane-band filter was added to provide a constant reference against which to correct the ammonia abundances (column-averaged mole fraction) for cloud opacity variations. These 0.28-m SCT results are compared with observations from: (a) the MUSE instrument on ESO's Very Large Telescope (b) the TEXES mid-infrared spectrometer used on NASA's InfraRed Telescope Facility; and (c) the Gemini telescopes, and are shown to provide reliable maps of ammonia abundance. Meridional and longitudinal features are examined, including the Equatorial Zone (EZ) ammonia enhancement, the North Equatorial Belt depletion, depletion above the Great Red Spot, and longitudinal enhancements in the northern EZ. This work demonstrates meaningful ammonia monitoring can be achieved with small telescopes that can complement spacecraft and major ground-based facility observations.

Accurate topographic data acquired at appropriate spatio-temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion-Multi View Stereo (SfM-MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi-permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3-month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ-4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long-profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real-time processing.

The discrepancy in the observed global mean sea level budget increased significantly since 2016, but the budget discrepancy over basin-scales is unclear. In this contribution, we investigate the sea level budget discrepancies in major basins with observations from satellite altimetry, satellite gravimetry, and Argo floats. During 2016–2020, we find substantial discrepancy of 5.72 ± 0.98 mm/yr over the North Atlantic Ocean, and the basin scale discrepancies are smaller elsewhere. Our analysis suggests that three factors, including the wet tropospheric correction (WTC) effect, deep ocean warming signal, and the contemporary ocean bottom deformation (OBD), together reduce the discrepancy by only 1 mm/yr for the North Atlantic Ocean. We decompose sea level observations into the spherical harmonic domain and observe increased discrepancy in low-degree variations of C₁₀ and C₂₁ since 2016. These two coefficients result in a contrasting signal between the North and South Atlantic Ocean and contribute to the large discrepancy over the North Atlantic Ocean. We further demonstrate that the C₁₀ and C₂₁ discrepancies are independent of the three factors. However, we find regional salinity biases in the Argo data that reduce the discrepancy for the North Atlantic Ocean. Our findings add to the debate about recent sea level budget and imply that further analysis of the Argo North Atlantic data set may be useful.