Global Observations Research Articles

The Challenges of Patients with Spinal CSF Leaks in Canada: A Cross-Sectional Online Survey.

Spinal CSF leak can cause disabling headaches and neurological symptoms. Lack of awareness, diagnostic delay and treatment inconsistencies affect the quality of CSF leak care globally. This is the first study aiming to identify and assess these challenges in Canada. A cross-sectional online survey of Canadian patients with spinal CSF leak was designed in collaboration with Spinal CSF Leak Canada, including questions on demographics, headache condition, investigations, treatments, quality of life, financial consequences and out-of-country care. The survey captured 103 respondents with confirmed spinal CSF leak diagnosis, of whom 56% were still suffering. The majority were female (80%), most being highly educated, with a mean age of 41.8 (SD: 10.37) years at the time of diagnosis. Inconsistencies in care resulted in variable durations for obtaining diagnosis and treatment. The majority of respondents (88%) had seen multiple physicians, and only 50% had seen a CSF leak specialist. Invasive imaging was not performed in 43%. CSF leak relapse after initial successful treatment occurred frequently (43%). The incidence of rebound intracranial hypertension was high (52.5%), and the treatment was difficult to access (77%). Out-of-country care was common (28%), and the impact on financial health was omnipresent (81.5%). The survey demonstrates significant gaps in spinal CSF leak care in Canada, similar to global observations. Lack of awareness and access, delayed care, and inconsistencies in investigations and management are common. Spinal CSF leak significantly impacts patients' physical, mental and financial well-being. Increased awareness, referral pathways and standardized treatment algorithms are key factors in optimizing patient care in Canada.

Spectra of solar shallow-water waves from bright point observations

Rossby waves, large-scale meandering patterns drifting in longitude, detected in the Sun, were recently shown to a play a crucial role in understanding “seasons” of space weather. Unlike Earth's purely classical atmospheric Rossby waves, the solar counterparts are strongly magnetized and most likely originate in the tachocline. Because of their deeper origin, detecting these magnetized Rossby waves is a challenging task that relies on careful observations of long-lived longitudinally drifting magnetic patterns at the surface and above. Here, we have utilized 3 years of global, synchronous observations of coronal bright point densities to obtain empirical signatures of dispersion relations that can be attributed to the simulated waves in the tachocline. By tracking the bright point densities at selected latitudes, we computed their wave-numbertimes frequency spectra. Wave-numbertimes frequency spectra were computed utilizing the Wheeler-Kiladis method. This method has been extensively used in the identification of equatorial waves in Earth's atmosphere by highlighting spectral peaks in the wave-numbertimes frequency space. Our results are compatible with the predictions of magneto-Rossby waves with typical periods of several months and inertio-gravity waves with typical periods of a few weeks, depending on the background magnetic field's strength and stratification at the convection zone base. Our analysis suggests that magnetized Rossby waves originate from the tachocline toroidal field of lesssim 15 kG. Global observations of bright points over extended periods will allow us to better constrain the stratification and magnetic field strength in the tachocline.

MIMO and PDM-based intersatellite optical link for high-speed data transfer and remote sensing application.

Inter-Satellite Optical Wireless Communication (Is-OWC) is a pivotal technology for advancing global connectivity and the effectiveness of space-based operations. It serves as the linchpin for various applications such as global internet coverage, Earth observation, and remote sensing, bolstering our capacity to monitor the planet and deliver essential services. This paper presents the design and performance evaluation of a Polarization Division Multiplexing (PDM) and Multiple Input Multiple Output (MIMO)-based Is-OWC system operating at a data rate of 60 Gbps. The system was tested under varying transmission distances, atmospheric turbulence, and pointing error conditions. At a transmission distance of 10,000 km, the system achieved a Bit Error Rate (BER) of 6.76 × 10⁻3 for Channel 1 (X polarization) and 7.1 × 10⁻3 for Channel 4 (Y polarization), both within Forward Error Correction (FEC) limits. The introduction of a 4 × 4 MIMO configuration extended the transmission range to 14,000 km, with a corresponding BER of 9.1 × 10⁻3 for Channel 1 and 8.7 × 10⁻⁵ for Channel 4. Eye diagrams confirm successful signal reception, demonstrating that the system can maintain high data rates and low error rates over long distances. The proposed PDM-MIMO system showcases high-capacity, robust performance under challenging conditions, such as turbulence and pointing errors, validating its suitability for space-based communication applications. These findings highlight the potential of the system for future deployments in satellite networks, offering reliable, high-throughput, and low-latency data transmission over extended distances.

A global re-analysis of regionally resolved emissions and atmospheric mole fractions of SF6 for the period 2005–2021

Abstract. We determine the global emission distribution of the potent greenhouse gas sulfur hexafluoride (SF6) for the period 2005–2021 using inverse modelling. The inversion is based on 50 d backward simulations with the Lagrangian particle dispersion model (LPDM) FLEXPART and on a comprehensive observation data set of SF6 mole fractions in which we combine continuous with flask measurements sampled at fixed surface locations and observations from aircraft and ship campaigns. We use a global-distribution-based (GDB) approach to determine baseline mole fractions directly from global SF6 mole fraction fields at the termination points of the backward trajectories. We compute these fields by performing an atmospheric SF6 re-analysis, assimilating global SF6 observations into modelled global three-dimensional mole fraction fields. Our inversion results are in excellent agreement with several regional inversion studies in the USA, Europe, and China. We find that (1) annual US SF6 emissions strongly decreased from 1.25 Gg in 2005 to 0.48 Gg in 2021; however, they were on average twice as high as the reported emissions to the United Nations. (2) SF6 emissions from EU countries show an average decreasing trend of −0.006 Gg yr−1 during the period 2005 to 2021, including a substantial drop in 2018. This drop is likely a direct result of the EU's F-gas regulation 517/2014, which bans the use of SF6 for recycling magnesium die-casting alloys as of 2018 and requires leak detection systems for electrical switch gear. (3) Chinese annual emissions grew from 1.28 Gg in 2005 to 5.16 Gg in 2021, with a trend of 0.21 Gg yr−1, which is even higher than the average global total emission trend of 0.20 Gg yr−1. (4) National reports for the USA, Europe, and China all underestimated their SF6 emissions. (5) Our results indicate increasing emissions in poorly monitored areas (e.g. India, Africa, and South America); however, these results are uncertain due to weak observational constraints, highlighting the need for enhanced monitoring in these areas. (6) Global total SF6 emissions are comparable to estimates in previous studies but are sensitive to a priori estimates due to the low network sensitivity in poorly monitored regions. (7) Monthly inversions indicate that SF6 emissions in the Northern Hemisphere were on average higher in summer than in winter throughout the study period.

Crowd-sourced trait data can be used to delimit global biomes

Abstract. Terrestrial biomes and their biogeographic patterns have been derived from a large variety of variables including species distributions and bioclimate or remote sensing products. However, classifying the biosphere into biomes from a functional perspective using biophysical traits has rarely been tested. Such a trait-based biome classification has been limited by data availability. Here, we aimed to exploit crowd-sourced plant observations and trait databases to systematically assess which traits are most suitable for biome classification. We derived global patterns of 33 biophysical traits covering around 50 % of the land surface by combining crowd-sourced species distribution data from the Global Biodiversity Information Facility (GBIF) and trait observations from the TRY database. Using these trait maps as predictors for supervised cluster analyses, we tested to what extent we can reconstruct 31 published biome maps. A sensitivity analysis with randomly sampled combinations of traits was performed to identify the traits that are most appropriate for biome classification. Performance was quantified by comparing modeled biome maps and the respective observation-based biome maps. Finally, spatial gaps in the resulting biome maps were filled using species distribution models to obtain continuous global biome maps. We showed that traits can be used for biome classification and that the most appropriate traits are conduit density; rooting depth; height; and different leaf traits, including specific leaf area and leaf nitrogen content. The best performance of the biome classification was obtained for biome maps based on biogeographic zonation and species distributions, in contrast to biome maps derived from optical reflectance. The availability of crowd-sourced plant observations is heterogeneous, and, despite its exponential growth, large data gaps are prevalent. Nonetheless, it was possible to derive biome classification schemes from these data to predict global biome patterns with good agreement. Therefore, our analysis is a valuable approach towards understanding biome patterns based on biophysical traits and associated ecological strategies.

Ocean-scale patterns of environment and climate changes driving global marine phytoplankton biomass dynamics.

Effects of marine environment and climate changes on phytoplankton dynamics in global oceans have received increasing attention but remain a mystery. This study used a comprehensive approach combining correlation and information flow to explore relationships among phytoplankton biomass, marine environment, and climate forcing based on global observations over the past multi-decadal period. Correlation and causality between phytoplankton biomass and environmental factors exhibit spatial asymmetry-regions where environmental factors directly drive biomass variations were concentrated in oceanic currents and subtropical circulations. Temperature, light, and mixed layer depth show pronounced influences on global phytoplankton interdecadal variations. Climate forcing over interdecadal timescales directly affects phytoplankton biomass in the equatorial Pacific, South Pacific, and Indian Oceans, with more uncertain biomass variability in the equatorial Pacific due to multiple climate events. Our findings revealed that environment and climate changes directly affect phytoplankton interdecadal variability only in specific regions at the oceanic scale.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

A Novel Sea State Classification Scheme of the Global CFOSAT Wind and Wave Observations

AbstractOcean waves are essential elements across the air‐sea interface, regulating momentum and energy transfer. The mixture of wind sea and ocean swell coupled with surface winds results in diverse sea state conditions that modify the local air‐sea interaction. Previous classifications of wind waves and swells are mostly binary that are insufficient to represent the complexity of sea states. In this study, we utilize wind and wave measurements from the China‐France Oceanography Satellite (CFOSAT) to construct an observational wind‐wave ensemble. Four key parameters: wind speed, significant wave height, inverse wave age, and spectral width are selected out of six variables based on their correlations. Employing the unsupervised learning of k‐means clustering, global sea states are categorized into six distinct classes. These classes, characterized by unique centroids and separated in the feature space, represent specific wind regimes and degrees of wave development. Global occurrence highlights that each sea state is region‐specific, bridging the spatial gap of swell and wind sea dominated areas, respectively. This new grouping scheme complements the traditional wind sea and/or swell classification by resolving the diversity of wave regimes. The six‐class classification enables us to identify transitional states and hybrid conditions that may have been overlooked in the binary classification scheme, which shall help investigate the impact of ocean waves on the air‐sea interaction under varying sea states.

Consistency evaluation of tropospheric ozone from ozonesonde and IAGOS (In-service Aircraft for a Global Observing System) observations: vertical distribution, ozonesonde types, and station–airport distance

Abstract. The vertical distribution of tropospheric O3 from ozonesondes is compared with that from In-service Aircraft for a Global Observing System (IAGOS) measurements collected at 23 pairs of sites between about 30° S and 55° N from 1995 to 2021. Profiles of tropospheric O3 from IAGOS are generally in good agreement with ozonesonde observations from electrochemical concentration cells (ECCs), Brewer–Mast sondes, and carbon–iodine sensors, with average biases of 2.58, −0.28, and 0.67 ppb and correlation coefficients (R) of 0.72, 0.82, and 0.66, respectively. Agreement between aircraft and Indian-sonde observations is poor, with an average bias of 15.32 ppb and an R value of 0.44. The O3 concentration observed by ECC sondes is, on average, 5 %–10 % higher than that observed by IAGOS, and the relative bias increases modestly with altitude. For other sonde types, there are some seasonal and altitudinal variations in the relative bias with respect to the IAGOS measurements, but these appear to be caused by local differences. The distance between the station and airport, when within 4° (latitude and longitude), has little effect on the comparison results. For the ECC ozonesondes, the overall bias with respect to the IAGOS measurements varies from 5.7 to 9.8 ppb when the station pairs are grouped by station–airport distances of <1° (latitude and longitude), 1–2°, and 2–4°. Correlations for these groups correspond to R=0.8, 0.9, and 0.7. These comparison results provide important information for merging ozonesonde and IAGOS measurement datasets. They can also be used to evaluate the relative biases of different sonde types in the troposphere, using the aircraft as a transfer standard.

Aeolus Winds Improve Arctic Weather Prediction

AbstractIt has been proven that assimilating winds from the Aeolus global Doppler Wind Lidar (DWL) would enhance the predictive skill of weather forecast models. In this study, we use a series of observing system experiments to examine how currently assimilated “operational” wind products and Aeolus winds impact environment and climate change Canada's global forecast system over the data‐sparse Arctic region. Aeolus winds improve the tropospheric wind and temperature forecasts by about 0.7%–0.9% of error reduction (a 15%–20% effect compared to the impact of operational wind products) while having little impact on specific humidity. In particular, Aeolus winds have an impact on forecasts of strong wind days on the wind and temperature fields that is double the impact for weaker wind days. On the other hand, the operational wind products do not show as large a discrepancy in impact between forecasts of disturbed and normal days. These findings suggest significant potential for global DWL observations to enhance severe weather prediction in polar regions.

EarthCODE – ESA’s Earth Science Collaborative Open Development Environment

Abstract. Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

Enriching Earth observation datasets through semantics for climate change applications: The EIFFEL ontology.

Earth Observation (EO) datasets have become vital for decision support applications, particularly from open satellite portals that provide extensive historical datasets. These datasets can be integrated with in-situ data to power artificial intelligence mechanisms for accurate forecasting and trend analysis. However, researchers and data scientists face challenges in finding appropriate EO datasets due to inconsistent metadata structures and varied keyword descriptions. This misalignment hinders the discoverability and usability of EO data. To address this challenge, the EIFFEL ontology (EIFF-O) is proposed. EIFF-O introduces taxonomies and ontologies to provide (i) global classification of EO data and (ii) linkage between different datasets through common concepts. The taxonomies specified by the European Association of Remote Sensing Companies (EARSC) have been formalized and implemented in EIFF-O. Additionally, EIFF-O incorporates:1.An Essential Climate Variable (ECV) ontology, defined by the Global Climate Observing System (GCOS), is embedded and tailored for Climate Change (CC) applications.2.The Sustainable Development Goals (SDG) ontology is included to facilitate linking datasets to specific targets.3.The ontology extends schema.org vocabularies and promotes the use of JavaScript Object Notation for Linked Data (JSON-LD) formats for semantic web integration. EIFF-O provides a unified framework that enhances the discoverability, usability, and application of EO datasets. The implementation of EIFF-O allows data providers and users to bridge the gap between varied metadata descriptions and structured classification, thereby facilitating better linkage and integration of EO datasets. The EIFFEL ontology represents a significant advancement in the organization and application of EO datasets. By embedding ECV and SDG ontologies and leveraging semantic web technologies, EIFF-O not only streamlines the data discovery process but also supports diverse applications, particularly in Climate Change monitoring and Sustainable Development Goals achievement. The open-source nature of the ontology and its associated tools promotes rapid adoption among developers.

Generating Long-term GRACE-like Total Water Storage Change Products using Conditional Generative Adversarial Networks

Abstract. Since 2002, the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GRACE-FO, hereafter GRACE) missions have offered global observations of total water storage (TWS). However, the relatively short record of GRACE data poses a significant challenge for researchers to investigate the full range and long-term variability in TWS. In this study, we present RecGAN, a novel Conditional Generative Adversarial Network (CGAN) comprising a RecNet generator and pixel discriminator. Our approach aims to generate long-term GRACE-like TWS observations by calibrating the WaterGAP Global Hydrology Model (WGHM). The generator is trained to produce observations conforming to the distribution of GRACE data, while the discriminator is trained to assess whether each generated pixel resembles GRACE data. Our results show that RecGAN effectively enhances the consistency between GRACE observations and WGHM-derived TWS changes, achieving improved correlation coefficients, Nash-Sutcliffe Efficiency, and Normalized Root-Mean-Square Error. In addition, RecGAN is robust to different GRACE mascon data, crop sizes used during the training period, and hydrological models targeted for calibration. This study illustrates a promising application of employing CGANs to fine-tune the WGHM output to match GRACE observations. This approach enables the generation of longterm TWS change datasets, which are invaluable for evaluating long-term water storage fluctuations, allocating water resources, and forecasting future hydrological extremes.

Fit-for-Purpose Water in the Baltic Sea Region

Climate change is visible everywhere, including in Baltic Sea Region (BSR). One of the symptoms is water scarcity, occurring relatively locally but persisting over longer and longer periods. The European and Global Drought Observatories (https://drought.emergency.copernicus.eu) present increasing BSR areas where soil moisture is alarmingly low. Water shortages cause technical and organizational problems in water utilities, agriculture, energy, industry, etc. Hence, competition for water between industry, agriculture, and municipal services has emerged. Sustainable water resource management requires solutions that rationalize and optimize the use of available resources with particular care for the environment. One of the methods of remedying local problems is closing local water and nutrient cycles by recovering water from urban wastewater and using rainwater

An Empirical Parameterization of the Subgrid‐Scale Distribution of Water Vapor in the UTLS for Atmospheric General Circulation Models

AbstractTemperature and water vapor are known to fluctuate on multiple scales. In this study 27 years of airborne measurements of temperature and relative humidity from In‐service Aircraft for a Global Observing System (IAGOS) are used to parameterize the distribution of water vapor in the upper troposphere and lower stratosphere. The parameterization is designed to simulate water vapor fluctuations within gridboxes of atmospheric general circulation models (AGCMs) with typical size of a few tens to a few hundred kilometers. The distributions currently used in such models are often not supported by observations at high altitude. More sophisticated distributions are key to represent ice supersaturation, a physical phenomenon that plays a major role in the formation of natural cirrus and contrail cirrus. Here the observed distributions are fitted with a beta law whose parameters are adjusted from the gridbox mean variables. More specifically the standard deviation and skewness of the distributions are expressed as empirical functions of the average temperature and specific humidity, two typical prognostic variables of AGCMs. Thus, the distribution of water vapor is fully parameterized for a use in these models. The new parameterization reproduces the observed distributions with a determination coefficient always greater than 0.917 and with a mean value of 0.997. The parameterization is robust to a selection of various geographical subsets of data and to gridbox sizes varying between 25 and 300 km.

Interoperable and scalable echosounder data processing with Echopype

Abstract Echosounders are high-frequency sonar systems used to sense fish and zooplankton underwater. Their deployment on a variety of ocean observing platforms is generating vast amounts of data at an unprecedented speed from the oceans. Efficient and integrative analysis of these data, whether across different echosounder instruments or in combination with other oceanographic datasets, is crucial for understanding marine ecosystem response to the rapidly changing climate. Here we present Echopype, an open-source Python software library designed to address this need. By standardizing data as labeled, multi-dimensional arrays encoded in the widely embraced netCDF data model following a community convention, Echopype enhances the interoperability of echosounder data, making it easier to explore and use. By leveraging scientific Python libraries optimized for distributed computing, Echopype achieves computational scalability, enabling efficient processing in both local and cloud computing environments. Echopype’s modularized package structure further provides a unified framework for expanding support for additional instrument raw data formats and incorporating new analysis functionalities. We plan to continue developing Echopype by supporting and collaborating with the echosounder user community, and envision that the growth of this package will catalyze the integration of echosounder data into broader regional and global ocean observation strategies.

Extreme Precipitation Over the Southern Slope of the Tibetan Plateau and the Associated Atmospheric Circulation Anomalies

AbstractThe southern slope of the Tibetan Plateau (SSTP) is one of the rainiest regions in the world where geological hazards caused by extreme precipitation often occur. This study investigates the characteristics and mechanisms of extreme precipitation over SSTP from June to September during 2001–2020 using Global Precipitation Measurement satellite observation. The extreme precipitation days are defined as the days with top 5% of regional‐mean daily precipitation over SSTP in this period, which has an average precipitation of 27.2 mm/d. Averaging over the extreme precipitation days, precipitation peaks at an altitude of about 300 m, coinciding with the climatological maximum precipitation, but with a much larger value of 37.2 mm/d than the climatology of 11.9 mm/d. Composite analysis of circulations on extreme days reveals significant circulation anomalies in both the lower and upper troposphere. Specifically, the lower‐tropospheric circulations are characterized by significant westerly anomalies over northern India, and the upper‐tropospheric circulations are characterized by northerly anomalies over the central Tibetan Plateau, which are statistically independent. The lower‐tropospheric westerly anomalies blowing toward SSTP are blocked by the topography, favoring extreme precipitation over SSTP. The upper‐tropospheric northerly anomalies, on the other hand, correspond to anomalous northeasterlies north of SSTP in the middle troposphere and southeasterlies to the south in the lower troposphere, and the convergence of these circulation anomalies favors extreme precipitation over SSTP. Lastly, the lower‐tropospheric westerly and upper‐tropospheric northerly anomalies, respectively, correspond to less precipitation over the South Asian monsoon region and the Tibetan Plateau.

A Quantitative Comparison and Validation of Finite‐Fault Models: The 2011 Tohoku‐Oki Earthquake

AbstractLarge earthquakes rupture faults over hundreds of kilometers within minutes. Finite‐fault models image these processes and provide observational constraints for understanding earthquake physics. However, finite‐fault inversions are subject to non‐uniqueness and uncertainties. The diverse range of published models for the well‐recorded 2011 9.0 Tohoku‐Oki earthquake illustrates this challenge, and its rupture process remains under debate. Here, we comprehensively compare 32 published finite‐fault models of the Tohoku‐Oki earthquake. We aim to identify the most coherent slip features of the Tohoku‐Oki earthquake from these slip models and develop a new method for quantitatively analyzing their variations. We find that the models correlate poorly at 1‐km subfault size, irrespective of the data type. In contrast, model agreement improves significantly with increasing subfault sizes, consistently showing that the largest slip occurs up‐dip of the hypocenter near the trench. We use the set of models to test the sensitivity of available teleseismic, regional seismic, and geodetic observations. For the large Tohoku‐Oki earthquake, we find that the analyzed finite‐fault models are less sensitive to slip features smaller than 64 km. When we use the models to compute synthetic seafloor deformation, we observe strong variations in the synthetics, suggesting their sensitivity to small‐scale slip features. Our newly developed approach offers a quantitative framework to identify common features in distinct finite‐fault slip models and to analyze their robustness using regional and global geophysical observations for megathrust earthquakes. Our results indicate that dense offshore instrumentation is critical for resolving the rupture complexities of megathrust earthquakes.

Potential of constructing all‐encompassing soil–plant‐atmosphere‐continuum stations and datasets from meteorological, flux, soil moisture station networks and plant‐relevant observations

AbstractThe establishment of SPAC (soil–plant‐atmosphere continuum) stations is essential for comprehensive monitoring of land‐atmosphere interactions and ecological and hydrological processes. This paper addresses the critical limitations of existing observation networks, which often rely on single‐aspect observations, resulting in insufficient data for a holistic understanding of SPAC dynamics. Specifically, SPAC stations provide critical multi‐variable observations that enhance process‐based model calibration and physical constraints and improve the empirical basis of data‐driven models. Advanced technologies such as machine learning and remote sensing are proposed to transform current weather and soil moisture stations into quasi‐SPAC sites capable of estimating carbon and water flux data. Additionally, the strategic placement of new SPAC sites in regions projected to be sensitive to future climate change and climate risks, as indicated by models such as CMIP6, is recommended. Furthermore, promoting comprehensive observational systems like Europe's Integrated Carbon Observation System (ICOS) in other regions, establishing a unified management framework and coordinating the upgrading of existing global observation networks are essential steps. Ultimately, the proposed enhancements will advance global ecological and hydrological studies, providing a more integrated and accurate understanding of the SPAC system and its responses to climate variability.

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

AbstractThe East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.