Surveys in Geophysics最新文献

Intradecadal changes in GPS displacements have garnered significant attention within the research community; however, the existence of relatively stable intradecadal signals, as well as their characteristics and excitation sources, remains to be further confirmed. This study aims to comprehensively investigate this topic by reviewing relevant existing studies and analyzing over 50 diverse datasets. We first reanalyze two different GPS datasets, and based on those reanalyzed results, we unequivocally validate the existence of at least two intradecadal signals in GPS displacements, a significant ~ 5.9 yr periodic signal (with 4.2 ± 0.95 mm excitation amplitude and a Y_2,2 spatial pattern) as some previous studies suggested and a relatively weak ~ 4.8–5.4 yr signal, and we explain why some previous studies cannot detect the ~ 5.9 yr signal or find its actual spatial pattern. Reevaluating the data from the surface air pressure records (and related records), loading displacements, hydrological records, global mean sea level (GMSL), global mean surface temperature (GMST), and various climate indices demonstrate that there are indeed similar 5–7 yr oscillations as previously suggested, but they have clear differences with the ~ 5.9 yr GPS signal. Additionally, the presence of a ~ 4.7–5.3 yr signal in the in situ hydrological records, as well as a ~ 4.5–5.7 yr signal in surface air pressure, contributes to the ~ 4.8–5.4 yr signal observed in the GPS data, thereby influencing the identification of the 5.9 yr signal. The contrasting outcomes derived from hydrological models and in situ hydrological records indicate that the low-frequency components of the hydrological models lack reliability. As for the precise physical mechanism underlying the ~ 5.9 yr GPS signal, although we have eliminated climate changes as potential sources, it is still difficult to deduce a physical mechanism that could reasonably explain it.

The Earth Observation sciences are highly multidisciplinary with long value chains from the development, characterisation and deployment of sensors, through data processing and modelling, to the information services provided to decision makers in, for example, governments, companies and non-governmental organisations. A prerequisite to any multidisciplinary collaboration is effective communication and many communities involved in the value chains have developed vocabularies or terminologies to define terms from a particular viewpoint or legacy. However, these vocabularies are often inconsistent, with circular definitions, contradictions and using technical terms that are not defined. Here, three case studies from Earth Observation disciplines are considered involving challenges in the definition and use of the terms ‘observation’, ‘in-situ’ and ‘interoperable’. An approach is suggested for an initiative, starting in Earth Observation, to build a consistent thesaurus taking inspiration from the ISO 25964:2011 standard.

Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a data-driven method for discovering a wave equation. Although it managed to utilize the data to discover the two-dimensional (x, z) acoustic constant-density wave equation (u_{tt}=v^2(u_{xx}+u_{zz})) (subscripts of the wavefield, u, are second derivatives in time and space) in a homogeneous medium, it did not provide the complete equation form, where the velocity term is represented by a coefficient rather than directly given by (v^2). In this work, we redesign the framework, encoding both velocity information and candidate functional terms simultaneously. Thus, we use GA to simultaneously evolve the candidate functional and coefficient terms in the library. Also, we consider here the physics rationality and interpretability in the randomly generated potential wave equations, by ensuring that both-hand sides of the equation maintain balance in their physical units. We demonstrate this redesigned framework using the acoustic wave equation as an example, showing its ability to produce physically reasonable expressions of wave equations from noisy and sparsely observed data in both homogeneous and inhomogeneous media. Also, we demonstrate that our method can effectively discover wave equations from a more realistic observation scenario.

In this study, we investigate the representation of the global mean energy balance components in 10 atmospheric reanalyses, and compare their magnitudes with recent reference estimates as well as the ones simulated by the latest generation of climate models from the 6th phase of the coupled model intercomparison project (CMIP6). Despite the assimilation of comprehensive observational data in reanalyses, the spread amongst the magnitudes of their global energy balance components generally remains substantial, up to more than 20 Wm⁻² in some quantities, and their consistency is typically not higher than amongst the much less observationally constrained CMIP6 models. Relative spreads are particularly large in the reanalysis global mean latent heat fluxes (exceeding 20%) and associated intensity of the global water cycle, as well as in the energy imbalances at the top-of-atmosphere and surface. A comparison of reanalysis runs in full assimilation mode with corresponding runs constrained only by sea surface temperatures reveals marginal differences in their global mean energy balance components. This indicates that discrepancies in the global energy balance components caused by the different model formulations amongst the reanalyses are hardly alleviated by the imposed observational constraints from the assimilation process. Similar to climate models, reanalyses overestimate the global mean surface downward shortwave radiation and underestimate the surface downward longwave radiation by 3–7 Wm⁻². While reanalyses are of tremendous value as references for many atmospheric parameters, they currently may not be suited to serve as references for the magnitudes of the global mean energy balance components.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where ~ 90% of heat uptake manifests as an increase in ocean heat content (OHC). Various international groups provide OHC datasets derived from in situ and satellite observations, as well as from reanalyses ingesting many available observations. The WCRP formed the GEWEX-EEI Assessment Working Group to better understand discrepancies, uncertainties and reconcile current knowledge of EEI magnitude, variability and trends. Here, 21 OHC datasets and ocean heat uptake (OHU) rates are intercompared, providing OHU estimates ranging between 0.40 ± 0.12 and 0.96 ± 0.08 W m⁻² (2005–2019), a spread that is slightly reduced when unequal ocean sampling is accounted for, and that is largely attributable to differing source data, mapping methods and quality control procedures. The rate of increase in OHU varies substantially between − 0.03 ± 0.13 (reanalysis product) and 1.1 ± 0.6 W m⁻² dec⁻¹ (satellite product). Products that either more regularly observe (satellites) or fill in situ data-sparse regions based on additional physical knowledge (some reanalysis and hybrid products) tend to track radiometric EEI variability better than purely in situ-based OHC products. This paper also examines zonal trends in TOA radiative fluxes and the impact of data gaps on trend estimates. The GEWEX-EEI community aims to refine their assessment studies, to forge a path toward best practices, e.g., in uncertainty quantification, and to formulate recommendations for future activities.