Surveys in Geophysics最新文献

As climate change continues, the likelihood of passing critical thresholds or tipping points increases. Hence, there is a need to advance the science for detecting such thresholds. In this paper, we assess the needs and opportunities for Earth Observation (EO, here understood to refer to satellite observations) to inform society in responding to the risks associated with ten potential large-scale ocean tipping elements: Atlantic Meridional Overturning Circulation; Atlantic Subpolar Gyre; Beaufort Gyre; Arctic halocline; Kuroshio Large Meander; deoxygenation; phytoplankton; zooplankton; higher level ecosystems (including fisheries); and marine biodiversity. We review current scientific understanding and identify specific EO and related modelling needs for each of these tipping elements. We draw out some generic points that apply across several of the elements. These common points include the importance of maintaining long-term, consistent time series; the need to combine EO data consistently with in situ data types (including subsurface), for example through data assimilation; and the need to reduce or work with current mismatches in resolution (in both directions) between climate models and EO datasets. Our analysis shows that developing EO, modelling and prediction systems together, with understanding of the strengths and limitations of each, provides many promising paths towards monitoring and early warning systems for tipping, and towards the development of the next generation of climate models.

As observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

This study uses an oceanic energy budget to estimate the ocean heat transport convergence in the North Atlantic during 2005–2018. The horizontal convergence of the ocean heat transport is estimated using ocean heat content tendency primarily derived from satellite altimetry combined with space gravimetry. The net surface energy fluxes are inferred from mass-corrected divergence of atmospheric energy transport and tendency of the ECMWF ERA5 reanalysis combined with top-of-the-atmosphere radiative fluxes from the clouds and the Earth’s radiant energy system project. The indirectly estimated horizontal convergence of the ocean heat transport is integrated between the rapid climate change-meridional overturning circulation and heatflux array (RAPID) section at 26.5°N (operating since 2004) and the overturning in the subpolar north atlantic program (OSNAP) section, situated at 53°–60°N (operating since 2014). This is to validate the ocean heat transport convergence estimate against an independent estimate derived from RAPID and OSNAP in-situ measurements. The mean ocean energy budget of the North Atlantic is closed to within ± 0.25 PW between RAPID and OSNAP sections. The mean oceanic heat transport convergence between these sections is 0.58 ± 0.25 PW, which agrees well with observed section transports. Interannual variability of the inferred oceanic heat transport convergence is also in reasonable agreement with the interannual variability observed at RAPID and OSNAP, with a correlation of 0.54 between annual time series. The correlation increases to 0.67 for biannual time series. Other estimates of the ocean energy budget based on ocean heat content tendency derived from various methods give similar results. Despite a large spread, the correlation is always significant meaning the results are robust against the method to estimate the ocean heat content tendency.

This study aims to revisit the classic “hot tower” hypothesis proposed by Riehl and Simpson (Malkus) in 1958 and revisited in 1979. Our investigation centers on the convective mass flux of hot towers within the tropical trough zone, using geostationary (GEO) satellite data and an innovative analysis technique, known as ML16, which integrates various data sources, including hot tower heights, ambient profiles, and a plume model, to determine convective mass flux. The GEO-based ML16 approach is evaluated against collocated ground-based radar wind profiler observations, showing broad agreement. Our GEO-based estimate of hot tower convective mass flux, 2.8 × 10¹¹–3.4 × 10¹¹ kg s⁻¹, is similar to the revisited estimate in Riehl and Simpson (1979), 2.6–3.0 × 10¹¹ kg s⁻¹. Additionally, our analysis gives a median count of around 550 hot towers with a median size of about 11 km, in contrast to the previous estimates of 1600–2400 hot towers, each characterized by a fixed size of 5 km. We discuss the causes of these discrepancies, emphasizing the fundamental differences between the two approaches in characterizing tropical hot towers. While both approaches have various uncertainties, the evidence suggests that greater credibility should be placed on results derived from direct satellite observations. Finally, we identify future opportunities in Earth Observations that will provide more accurate measurements, enabling further evaluation of the role played by tropical hot towers in mass transport.

We revisit the derivation of the linear relationships connecting the variations of the Earth’s length-of-day (more specifically its mass term ΔLOD_mass), polar oblateness (ΔJ₂), and total moment of inertia (ΔT) caused by geophysical mass transports. The three integral quantities are expressed as inner products of the perturbation, either in the form of density change in the Eulerian description or deformation in the Lagrangian description, with pertinent base functions arising from distinct physical principles. We discuss various cases of mass transport processes regarding whether or not T is conserved, or ΔT = 0. When and only when ΔT = 0, the ΔLOD_mass and ΔJ₂ become proportional to each other and hence mutually convertible. This latter practice has long been common, albeit often taken for granted, in the literature notably with respect to the mass transports in surface geophysical fluids and by the glacial isostatic adjustment (GIA) that awaits numerical assessments per physics-based GIA models. We point to subtleties and caveats that tend to be misrepresented, namely, the distinction of ΔLOD_mass from the observed ΔLOD, and the extent of the core’s participation in the angular momentum exchanges across the core-mantle boundary.

Integral formulas represent a methodological basis for the determination of gravitational fields generated by planetary bodies. In particular, spherical integral transformations are preferred for their symmetrical properties with the integration domain being the entire surface of the sphere. However, global coverage of boundary values is rarely guaranteed. In practical calculations, we therefore split the spherical surface into a near zone and a far zone, for convenience, by a spherical cap. While the gravitational effect in the near zone can be evaluated by numerical integration over available boundary values, the contribution of the far zone has to be precisely quantified by other means. Far-zone effects for the isotropic integral transformations and those depending on the direct azimuth have adequately been discussed. On the other hand, this subject has only marginally been addressed for the spherical integral formulas that are, except for other variables, also functions of the backward azimuth. In this article, we significantly advance the existing geodetic methodology by deriving the far-zone effects for the two classes of spherical integral transformations: (1) the analytical solutions of the horizontal, horizontal–horizontal, and horizontal–horizontal–horizontal BVPs including their generalisations with arbitrary-order vertical derivative of respective boundary conditions and (2) spatial (vertical, horizontal, or mixed) derivatives of these generalised analytical solutions up to the third order. The integral and spectral forms of the far-zone effects are implemented in MATLAB software package, and their consistency is tested in closed-loop simulations. The presented methodology can be employed in upward/downward continuation of potential field observables or for a quantification of error propagation through spherical integral transformations.

In the tropics, deep convection, which is often organized into convective systems, plays a crucial role in the water and energy cycles by significantly contributing to surface precipitation and forming upper-level ice clouds. The arrangement of these deep convective systems, as well as their individual properties, has recently been recognized as a key feature of the tropical climate. Using data from Africa and the tropical Atlantic Ocean as a case study, recent shifts in convective organization have been analyzed through a well-curated, unique record of METEOSAT observations spanning four decades. The findings indicate a significant shift in the occurrence of deep convective systems, characterized by a decrease in large, short-lived systems and an increase in smaller, longer-lived ones. This shift, combined with a nearly constant deep cloud fraction over the same period, highlights a notable change in convective organization. These new observational insights are valuable for refining emerging kilometer-scale climate models that accurately represent individual convective systems but struggle to realistically simulate their overall arrangement.

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.

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.