Climatology and annual cycle of global ocean dissolved oxygen

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

Earth observation imaging technologies, particularly multispectral sensors, produce extensive high-dimensional data over time, thus offering a wealth of information on global dynamics. These data encapsulate crucial information in essential climate variables, such as varying levels of soil moisture and temperature. However, current cutting-edge machine learning models, including deep learning ones, often overlook the treasure trove of multidimensional data, thus analyzing each variable in isolation and losing critical interconnected information. In our study, we enhance conventional convolutional neural network models, specifically those based on the embedded temporal convolutional network framework, thus transforming them into models that inherently understand and interpret multidimensional correlations and dependencies. This transformation involves recasting the existing problem as a generalized case of N-dimensional observation analysis, which is followed by deriving essential forward and backward pass equations through tensor decompositions and compounded convolutions. Consequently, we adapt integral components of established embedded temporal convolutional network models, like encoder and decoder networks, thus enabling them to process 4D spatial time series data that encompass all essential climate variables concurrently. Through the rigorous exploration of diverse model architectures and an extensive evaluation of their forecasting prowess against top-tier methods, we utilize two new, long-term essential climate variables datasets with monthly intervals extending over four decades. Our empirical scrutiny, particularly focusing on soil temperature data, unveils that the innovative high-dimensional embedded temporal convolutional network model-centric approaches markedly excel in forecasting, thus surpassing their low-dimensional counterparts, even under the most challenging conditions characterized by a notable paucity of training data.

<p>Glaciers are shrinking due to global warming, resulting in a diminishing contribution of ice- and snowmelt to headwaters with consequences on freshwater ecosystems. The stable isotopic compositions in natural waters (δ<sup>18</sup>O and δ<sup>2</sup>H) respond to environmental variation very sensitively and can indicate the change of geographic environment or mark the recharge of runoff (Boral 2019, Zuecco 2019). Thus, stable isotopes have been used as natural tracers to constrain the contributions of different water sources to streamflow, including snowmelt, icemelt and groundwater baseflow (Boral 2019). Within this context, we tested if water stable isotopes are spatio-temporal tracers of: i) water in periglacial habitats, being the isotopic signature of surface water inherited from the snow/icemelt, groundwater, and rainfall; ii) regional (year-specific) meteorological conditions, being the isotopic signature of precipitations affected by air temperature, humidity and aqueous vapour origin, ascribing stable isotopes in the list of the “essential climate variables″ (ECV). In this light, we investigated the ionic and isotopic composition (δ<sup>18</sup>O and δ<sup>2</sup>H) of six high altitude streams and one pond in the Italian Alps (Noce and Sarca basins) during the ablation season in 2018. Differences between habitat types (pond, kryal, rhithral, krenal) were detected. More negative values of δ<sup>18</sup>O and δ<sup>2</sup>H were recorded in the kryal and glacio-rhithral sites dominated by ice and snowmelt, in early summer. Less negative values were recorded in these sites in late summer and in krenal sites, dominated by groundwater and rainfall inputs. The isotopic results also showed that the complex alpine orography influences the air masses and moist, ultimately resulting in isotopic differences in precipitations of neighbouring, but distinct catchments (Sarca and Noce basins). As average, less negative values were recorded in the Sarca basin, characterized by a higher contribution of precipitation of Mediterranean origin. Finally, isotopic composition of the entire water population appeared to be strongly influenced by the regional climatic anomaly of the year 2018, which was anomalously warm in respect to the historical series 1961- 1990. This study will provide additional clues for the climate-change debate, proposing water isotopes as “essential climate variables″ indicators for assessing change in a warmer future.</p><p> </p><p><strong>Keywords: </strong>stable isotopes, glaciers, essential climatic variables</p><p><strong> </strong></p><p><strong>

The Earth system is enduring multiple, interacting stressors causing immense and irreversible change to its biosphere. The unprecedented magnitude of human influence on the planet is a cause for much concern, but also an opportunity to mitigate undesirable impact by instituting socio-ecological management strategies and monitoring their effectiveness. To do so, local and global policy developers and decision makers must have access to clear and globally consistent information streams that serve as planetary diagnostics. This need is being addressed by several international, multi-agency networks which have developed sets of "Essential Variables" (EVs) to improve consistency in the observation of key phenomena within their domains of operation. For example, the implementation of the Essential Climate Variables (ECVs) led by the Global Climate Observing System (GCOS) has set an example being followed in the marine and biodiversity domains by, respectively, the Global Ocean Observing System's (GOOS) Essential Ocean Variables (EOVs) and the Group on Earth Observation Biodiversity Observation Network (GEO BON)'s Essential Biodiversity Variables (EBVs). Each of these EV collections has a subset dedicated to biological and ecological phenomena. This is greatly encouraging for biodiversity science at large, but also runs the risk of parallel and conflicting developments occuring in the EV communities addressing thecross-cutting theme of biodiversity. As a consequence, there is an urgent need for these systems, and the communities behind them, to pursue tight conceptual and technical interoperability in aid of a coherent treatment of biodiversity in planetary-scale monitoring. In this contribution, we will describe our current research and implementation of a semantic interoperability layer for the EVs, with a focus on biology and ecology. Applying the best practices and core infrastructure of the Open Biological and Biomedical Ontology (OBO) Foundry and Library, we have leveraged both mature and emerging reference ontologies targeting the domains of ecosystems and environment, population and communities, and high-level ecological phenomena. This interoperable collection of ontologies constitutes a foundation for highly expressive EV knowledge representation and management through robust, FAIR-compliant technologies. Further, we are coordinating these efforts with semantic technology adopted by UN agencies, representing global directives and indicators of the UN Sustainable Development Agenda for 2030. With open editorial models behind each of these ontologies, we aim to offer a scalable and inclusive system to bridge data and information products across the EVs through machine-accessible knowledge representation.

Essential climate variables (ECVs) are physical, chemical, or biological parameters supporting the characterization of the climate system. The oceanic ECVs temperature and salinity have been defined for the sea surface (upper 1 mm) and bulk water (upper 2 m). Direct observation and sampling of the sea surface from ships and buoys are limited due to the potential loss of integrity of the sea surface and contamination by the research platform. The design, development, and operation of a research vehicle with autonomous capabilities based on commercially available components are described in this study. The autonomous operation and fully automated sampling enable high-resolution mapping of the sea surface microlayer (SML), i.e., the upper < 1-mm layer and the near-surface layer (<1.2 m). Sampling the SML is based on a rotating glass disk sampler. The autonomous surface vehicle HALOBATES has been equipped with multiple conductivity–temperature–depth (CTD) sensors in a flow-through system to measure both sea surface and bulk temperature and salinity. The performance of the autopilot under various scenarios is demonstrated. Typical anomalies of ECVs within the SML and near-surface layer are evident and forced by both atmospheric and oceanic processes. Data analyses reveal HALOBATES’ ability to resolve thermohaline structure on different spatial and temporal scales, for example, small-scale freshwater lenses and thermohaline changes across oceanic fronts. The integration of an acoustic Doppler current profiler supports the interpretation of thermohaline structures. HALOBATES is a platform with cutting-edge technology for understanding climate-related processes between the ocean and atmosphere and providing high-resolution sea surface data for validating satellite products of ECVs. Significance Statement An autonomous surface vehicle was developed to address the challenges in the in situ measurement of essential climate variables at the sea surface (upper 1 mm) and near-surface layer (upper 1 m) of the ocean. Autonomous operation for up to 12 h and fully automated sampling enable high-resolution mapping of the sea surface and near-surface layers. The ability of the vehicle to resolve thermohaline structures at various spatial and temporal scales is demonstrated, providing valuable data on freshwater fluxes (evaporation minus precipitation) between the ocean and the atmosphere. This cutting-edge technology enhances the understanding of climate-related processes, offering crucial “sea surface data” for validating satellite products of essential climate variables.

Burned area and surface albedo products: Assessment of change consistency at global scale

The Essential Climate Variable (ECV) soil moisture (SM) datasets, originated from the European Space Agency, have revealed great potential for application in hydrology and agriculture. Hence, it is essential to continuously enhance the data quality and spatial completeness to satisfy the increasing scientific research requirements. In this study, we explore the potential possibility of Soil Moisture Active Passive (SMAP) datasets in filling the gaps of ECV SM. The comprehensive assessment results show that: (1) The data missing percent of gap-filled ECV decreases 20% on average, which can be one step closer to generate a seamlessly covered global land surface SM product with favorable quality. (2) Compared to the original ECV, the gap-filled ECV products express similar good response to the in-situ measurements, suggesting that the SMAP SM products could be taken to efficiently fill the gaps and consistently maintain favorable accuracy at the same time. (3) Compared to the in-situ measurements, the original ECV SM products demonstrate extremely high probability density peak percentages. Fortunately, this eminent high value could be effectively rectified through gap-filling progress using SMAP. Overall, this study conducts objective and detailed evaluation on the performance of applying SMAP to fill the gaps of ECV, and is expected to act as a valuable reference in ECV SM gap-filling method.

The study of climate change has become an important topic, because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GHGs) and climate change is an important factor to understand. To investigate this linkage, we used machine-learning models based on essential climate variables to investigate the relationship between the GHGs and the rhythm of climate variable change. The research investigates how machine learning techniques can be applied to climatic data to build an ML model that able to predict the state of climate variables for the short and long term. By selecting a candidate model, will help in climate adaptation and mitigation, also determine at what level GHGs should be kept and their corresponding concentrations in order to avoid climate events and crises. The used models are long short-term memory, autoencoders, and convolutional neural network. Alternatively, the data set has been selected from UK National Centre for Earth Observation (NCEO) and Copernicus Climate Change Services (C3Ss). We compared the performance of these techniques and the best candidate was the Head CNN; based on performance metrics such as Root-Mean-Squared-Error (RMSE): 5.378, 2.395, and 15.923, and R Coefficient: 0.607, 0.806, and 0.539 for the variables temperature, CO2, and CH4 respectively. We were able to link the greenhouse gas emission to essential climate variables with high accuracy based on the reading of this geographic area.

Validation of satellite-derived essential climate variable (ECV) datasets requires comparison against independent measurements. These independent measurements, which include ground-based, airborne, and other non-satellite-based measurements, are typically the product of a different measurement system and may include some contribution from models. These reference data therefore have their own characteristics, uncertainties, and limitations which must be accounted for in the validation process. In addition, they typically differ from the data to be validated in spatio-temporal resolution, sensitivity, and sampling. As such, comparisons to independent data do not necessarily yield clear feedback on the quality of satellite data and insufficient awareness of these issues can lead to erroneous interpretation. This is the cost of leaving the laboratory and studying the real world. In this review paper, we examine the challenges and limitations of evaluating satellite-derived datasets with independent measurements, using examples across different ECVs within the terrestrial, ocean, and atmospheric domains. Drawing from other studies, we discuss issues with the reference datasets themselves, issues specific to use of these data for validation, and issues resulting from the comparison methodology. We conclude with recommendations to the community based on this review. In this, we highlight the importance of continued efforts towards (1) advancing uncertainty modelling of reference datasets and quality control knowledge and procedures, (2) establishing and communicating limitations in reference data, (3) reference data (and metadata) timeliness and preservation, and (4) best practices for the validation methodologies that address the spatio-temporal differences of the measurements.

<strong class="journal-contentHeaderColor">Abstract.</strong> Earth observation from space provides a highly valuable basis for atmospheric and climate science, in particular also through climate benchmark data from suitable remote sensing techniques. Measurements by Global Navigation Satellite System (GNSS) radio occultation (RO) qualify to produce such benchmark data records as they globally provide accurate and long-term stable datasets for essential climate variables (ECVs) such as temperature. This requires a rigorous processing from the raw RO measurements to ECVs, with narrow uncertainties. In order to fully exploit this potential, Wegener Center&rsquo;s Reference Occultation Processing System (rOPS) Level 1a (L1a) processing subsystem includes uncertainty estimation in both precise orbit determination (POD) and excess phase profile derivation. Here we introduce the new rOPS L1a excess phase processing, the first step in the RO profiles retrieval down to atmospheric profiles, which extracts the atmospheric excess phase from raw SI-traceable RO measurements. This excess phase processing, for itself algorithmically concise, includes integrated quality control and uncertainty estimation, which requires a complex framework of various subsystems that we first introduce before describing the implementation of the core algorithms. The quality control and uncertainty estimation, computed per RO event, are supported by reliable forward-modeled excess phase profiles based on the POD orbit arcs and collocated short-range forecast profiles of the European Reanalysis ERA5. The quality control removes or alternatively flags excess phase profiles of insufficient or degraded quality. The uncertainty estimation accounts both for relevant random and systematic uncertainty components and the resulting (total) uncertainty profiles serve as starting point for the subsequent uncertainty propagation through the retrieval processing chain down to the atmospheric ECV profiles. We also evaluated the quality and reliability of the resulting excess phase profiles based on Metop-A/B/C RO datasets for three 3-month periods in 2008, 2013, and 2020 by way of a sensitivity analysis for three representative atmospheric layers (tropo-, strato-, mesosphere), investigating consistency with ERA5-derived profiles, influences of different orbit and clock inputs and consistency across the different Metop satellites. These consistencies range from centimeter to submillimeter levels, indicating that the new processing can provide highly accurate and robust excess phase profiles. Furthermore, cross-validation and inter-comparison with excess phase data from the established data providers EUMETSAT and UCAR revealed subtle discrepancies but overall very close agreement, with larger differences against UCAR in the boundary layer. The new rOPS L1a processing can hence be considered capable to produce reliable long-term data records including uncertainty estimation for the benefit of climate applications.

Climate services that rely on the provision of climate forecasts at sub-seasonal, seasonal or decadal time scales (S2S2D) are widely exploited these days. To make them helpful for decision-making, state-of-the-art climate forecast model outputs are tailored to user needs. The conjunction of scientific knowledge and scientific exploration to fulfil users' needs determines the post-processing workflow, from selecting the datasets to visualising the product.&amp;#160;Consequently, scientists need to perform different combinations of possible workflows to evaluate the quality of the final products, ensuring the results are meaningful and that best practices (such as cross-validation strategies) are followed. For instance, essential climate variables (ECVs) can be calibrated before computing an indicator, or the indicator based on ECVs could be calibrated instead. Furthermore, several combinations of forecast systems and observation-based datasets can be explored to provide the most convenient product. On the other hand, the required computational resources can be a limitation depending on the total data size involved in the post-processing workflow.To efficiently and flexibly handle all the requirements when exploring and delivering climate services based on S2S2D predictions, the Earth Sciences department of the Barcelona Supercomputing Center has developed the SUbseasoNal to decadal climate forecast post-processIng and asSEssmenT suite, so-called SUNSET. This suite takes advantage of existing software packages and tools (such as startR, CSTools, s2dv, CSDownscale and Autosubmit) to facilitate the definition of the workflow and its parallelisation on HPC machines when available.To tailor climate products for each application and sector (e.g. agriculture, energy, water management, or health), the scientist can decide on the post-processing required steps, such as region selection, regridding method and resolution, anomaly calculation, and downscaling and bias-adjustment methods. SUNSET also allows the creation and visualisation of climate forecast products, such as maps for the most likely tercile. It performs the verification of the products using deterministic and probabilistic metrics, which can be visualised with maps and summarised with scorecards. The storage of final numerical outputs is also designed to consider the need to be ingested by third-party applications or impact models.SUNSET is available in a public repository licensed under the GPLv3 license, and it is under continuous development. The suite is being used in scientific projects such as CERISE, where the next generation of the Copernicus Climate Change Service seasonal climate forecasts are under development.

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitoring climate dynamics, with urban land cover and temperature being particularly relevant in historic urban contexts. This study analyses long-term trends and statistical associations between satellite-based ECVs and urbanisation indicators within the Historic Urban Landscape (HUL) of Sfax (Tunisia) from 1985 to 2021. Using the Digital Earth Africa (DEA) data cube, we derived six urban spectral indices (USIs), land surface temperature, air temperature at 2 m, wind characteristics, and precipitation from Landsat and ERA5 reanalysis data. An automated and reproducible Python-based workflow was implemented to assess USI behaviour, evaluate their performance against the Global Human Settlement Layer (GHSL), and explore spatio-temporal co-variations between urbanisation and climate variables. Results reveal a consistent increase in air and surface temperatures alongside a decreasing precipitation trend over the study period. The USIs demonstrate comparable accuracy levels (≈88–90%) in delineating urban areas, with indices based on SWIR and NIR bands (NDBI, BUI, NBI) showing the strongest statistical associations with temperature variables. Correlation and multivariate regression analyses indicate that temporal variations in USIs are more strongly associated with air temperature than with land surface temperature; however, these relationships reflect statistical co-variation rather than causality. By integrating satellite-based ECVs within a data cube framework, this study provides an operational methodology for long-term monitoring of urban-climate interactions in historic Mediterranean cities, supporting both climate adaptation strategies and the objectives of the UNESCO HUL approach.

Taking stock of nature: Essential biodiversity variables explained

A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks

An estimate of uncertainty is essential to understanding what information is conveyed by data and how it relates to the wider context of what one intended to measure. It can be difficult to know how to use uncertainty during the analysis of environmental data and the best way to present that information within a dataset. In many common uses, such as calculating statistical significance, it is easy to make mistakes due to incomplete or inappropriate use of the available uncertainty information. Uncertainty is itself uncertain, such that many practical or empirical solutions are available when a comprehensive uncertainty budget is impractical to produce. This manuscript collects actionable guidance on how uncertainty can be used, presented, and calculated when working with essential climate variables (ECVs). This includes qualitative discussions of the utility of uncertainties, explanations of common misconceptions, advice on presentation style, and plain descriptions of the essential equations. Selected worked examples are included on the propagation of uncertainties, particularly for data aggregation and merging. Uncertainty need not be off-putting as even incomplete uncertainty budgets add value to any observation. This paper aims to provide a starting point, or refresher, for researchers in the environmental sciences to make more complete use of uncertainty in their work.