Observation of the Earth gravity field from space: from the beginnings till future missions based on quantum physics

The Global Geodetic Observing System (GGOS) or the International Association of Geodesy (IAG) is currently focusing on the definition of Essential Geodetic Variables (EGVs). Essential Variables (EVs) serve as basic metrics that encapsulate critical aspects of geodetic observations, products and results, ensuring a structured framework for observing, understanding and modelling the Earth system, and for providing the fundamental layer (i.e. geodetic reference frames) for National Administrations and sustainable development. Today, essential variables in geodesy are able to offer unprecedented opportunities to improve reliability, consistency and accuracy of geodetic measurements and products. These variables enable the scientific and policy-making communities to address pressing challenges such as monitoring sea level rise and climate change effects, understanding Earth dynamics, supporting disaster risk reduction, and facilitating reference infrastructure for sustainable development. By focusing on variables deemed 'essential', resources can be strategically allocated to maximise their impact on achieving specific objectives and ensure efficient data collection and use.In addition, EGVs promote interdisciplinary cooperation and international standardisation, providing a common language and reference for geodetic research and applications. The definition and adoption of EGVs will facilitate that geodetic data remain robust, traceable and relevant to advance science, inform policy and support societal needs. Establishing a comprehensive and widely accepted catalogue of EGVs, accompanied by well-defined requirements and stewardship, is critical to realising these benefits and meeting the growing demands on geodetic science in a rapidly changing world.The Global Climate Observing System (GCOS) was the first community to introduce the concept of EVs, the Essential Climate Variables (ECVs), which have been widely adopted by the scientific and policy communities. Subsequently, the Global Ocean Observing System (GOOS) defined a complementary set of Essential Ocean Variables (EOVs) with standards aligned with the ECVs. Similarly, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), a partner of the Biodiversity Observing Network of the Group on Earth Observations (GEO BON), has initiated the definition of Essential Biodiversity Variables (EBVs). There are currently other ongoing initiatives to introduce additional sets of EVs, not only to describe the Earth System, but also the socio-economic system, including for example urban development, energy and minerals, health, agriculture, etc. In this international and interdisciplinary context, this contribution presents the progress made by GGOS in defining a catalogue of essential geodetic variables that is fully consistent with the concept and the existing essential climate and ocean variables ECVs, EOVs.

At the GEO-GNOME Status and Scoping Workshop held in Bern in May 20181 , key objectives and tasks listed on its work plan 2017-2019 were revised (see also Adler et al, 2018). The importance of climate as one key driver of environmental change in mountains, with relevant consequences for social-ecological systems, was reiterated. Given already existing initiatives on essential climate variables from observations and modelling, an opportunity was identified to focus attention on mountain-specific needs of key variables allowing from understanding and tracking changes in mountains and their consequences. A transect network of in-situ climate data over elevation gradients (Unified High Elevation Observing Platform, UHOP3 ), together with consistent time series of EO (satellite) data, was suggested as a means to address key observational data gaps and to improve our understanding of processes of elevation-dependent warming (EDW) and elevation-dependent climate change (EDCC) in mountains, and support a systematic strategy for identifying and collecting new observation data. Essential Climate Variables (ECVs) relevant for mountains were identified as a first starting point. They include “pure climate” variables like temperature, precipitation, snow, radiation, wind, etc., as well as other important variables/drivers such as land-cover. With the support from the European Space Agency (ESA), Future Earth (FE), and the Mountain Research Initiative (MRI), the 2019 GEO-GNOME workshop aimed at identifying and selecting ECVs required in high elevation contexts for the monitoring and better understanding of ‘mountain climate change’, including considerations for integration between in-situ measurements, EO satellite data and modelling. The WMO/GCOS existing catalogue of ECVs was used as initial basis for compiling our list of essential mountain variables. The possibility to use ESA’s existing datasets on ECVs within its Climate Change Initiative (CCI) program was also considered. The key output and outcome of this workshop is a list of ECVs required to observe the processes of elevation-dependent climate change in mountains. In addition, a data collection protocol with identified existing data-sources and criteria for required data quality (particularly the spatial and temporal resolution required) for selected key processes was produced. There are huge observational and information gaps in mountain environments and agreeing on joint protocols and data resolution needed for each relevant process would allow filling these gaps. Collecting data on other environmental processes in addition and in relation to strictly climate-related processes will strengthen GEO-GNOME’s ability to identify relevant data and information that meet the needs of management, policy and scientific research. Making this data discoverable and accessible via suitable data portals, such as the GEO-GNOME Global Earth Observation System of Systems (GEO-GNOME GEOSS, under development), will further facilitate data use and research collaborations.

In the past twenty years, gravimetry missions have demonstrated a unique capability to monitor not only major climate-related changes of the Earth directly from space - quantifying the melt of large glaciers and ice sheets, global sea level rise, continental draught, major flooding events, and also effects of large earthquakes and tsunamis. Adding to fundamental knowledge of the Earth, a quantum gravimetry mission will provide essential climate variables (ECV) of unprecedented quality for ground water, mass balance of ice sheets and glaciers, heat and mass transport,.. as demonstrated – within limits of past technology – by successful missions like GOCE and GRACE (FO). In order to respond to the increasing demand of the user community for sustained mass change observations at higher spatial and temporal resolution, ESA and NASA are at the moment coordinating their activities and are harmonizing their cooperation scenarios in an implementation framework, called MAGIC (MAss change and Geosciences International Constellation). In future post -MAGIC mission, a combination of classical sensors with CAI, or at a later stage a full quantum sensor will bring up the Quantum Missions for Climate to sensitivity that will open to many applications and user needs with respect to water management and hazard prevention among others [1] [2]. Special note must be taken also on the adoption of Quantum Technology (QT) for Earth Observation by the European Commission (COM), notably in the Horizon Europe programme, under the thrust of Commissioner T. Breton, and of the inclusion of QT in ESA Agenda 2025.COM and ESA are setting up a process that would realize a Pathfinder Mission to demonstrate the scientific and technical maturity of quantum gravimetry in space with a view to implement a ground-breaking Quantum Mission for Climate and other applications in the next decade.Several studies related to these new sensor concepts were initiated at ESA, mainly focusing on technology development for different instrument configurations (gravity gradiometers and satellite-to-satellite ranging systems) and including validation activities, e.g. two successful airborne surveys with a CAI gravimeter. A new study has been initiated in 2022, the Quantum Space Gravimetry for Earth Mass Transport (QSG4EMT) with the focus on QSG mission architectures that monitor Earth's mass transport processes and development of QSG user requirements.A technology roadmap will also be outlined for potential implementation of a Quantum Space Gravimetry Pathfinder mission before the end of this decade, aimed at improving state of the art accelerometers in the low frequency band and pave the way to developing a Quantum Mission for Climate in continuity and enhancement of MAGIC. [1] ESA-EC User Requirements workshop for Space Gravimetry Mission, Nov 2021.[2] Towards a sustained observing system for mass transport to understand global change and to benefit society, NASA/ESA Interagency Gravity Science Working Group (IGSWG), TUD-IGSWG-2016-01.

The European Space Agency (ESA) leads on observing the Earth's changing climate from space. Its flagship programme, the Climate Change Initiative (CCI), draws together over 40 years of data from ESA's own satellite missions and those from other space agencies - from past as well as currently active in -orbit ins trumentation. The CCI science teams focus on R&D activities to generate long -term, global climate data records that describe the evolution of key components of the Earth's climate system, as defined by the Global Climate Observing System (GCOS) (see https://public.wmo.int/en/programmes/global-climate-observing-system) in support of the United Nations Framework Convention on Climate Change (see https://unfccc.int/). Currently more than 20 Essential Climate Variables (ECV) of the 54 GCOS defined ECVs have been addressed by science teams involved in the CCI. All ECV datasets are fully validated and have high levels of traceability and consistency, including quantitative estimates of uncertainty required by both climate science and modelling communities. In its contribution to climate and Earth system science, this programme has published over 700 peer-reviewed articles, and supported the Intergovernmental Panel on Climate Change's (IPCC) headline statements on climate in both its fifth Assessment Report and subsequent reports, such as the ‘Special Report on Oceans and Cryosphere in a Changing Climate’, with ongoing involvement in the IPCC's sixth assessment cycle. Besides providing an overview on CCI, this presentation will make the link between CCI and ESA's Soil Moisture and Ocean Salinity (SMOS) mission, demonstrating the contribution that SMOS data can make in the creation of climate data records (CDR). Several CDRs are already including SMOS data on a regular basis, such as the sea surface salinity and soil moisture long-term data sets. There is also potential for using SMOS data for sea ice, biomass and vegetation climate data records.

Among the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS), groundwater is one of the terrestrial ECVs in the field of hydrology. As the world’s largest distributed freshwater storage, groundwater is a key resource for mankind, industrial, and agricultural demands, and for ecosystems. Very recently, in its Implementation Plan of 2022, GCOS defined terrestrial water storage (TWS) as a new hydrological ECV. The state variable TWS quantifies the net effect of climatic, hydrological and anthropogenic change on the continental water cycle and is essential for closing the terrestrial water balance. In spite of their importance, there is no data service or product yet on the ECVs groundwater and TWS in Copernicus, the European Union’s Earth observation program. The EU-funded project G3P (Global Gravity-based Groundwater Product) recently developed a satellite-based global-scale data set of groundwater storage anomalies (GWSA) for the period 2002-2020, with monthly resolution and on a 0.5-degree global grid. We present this data service developed as a prototype for later implementation into the EU Copernicus Climate Change Service. G3P is a global data set of groundwater storage variations as a cross-cutting extension of the existing Copernicus portfolio. G3P capitalizes from the unique capability of the satellite gravimetry mission GRACE (Gravity Recovery and Climate Experiment, 2002-2017) and its successor mission GRACE-FO (GRACE-Follow-On, since 2018) being the only remote sensing techniques to monitor subsurface mass variations, and from other satellite-based water storage products to provide a data set of groundwater storage change for large areas with global coverage. G3P is obtained by using a mass balance approach, i.e., by subtracting satellite-based water storage compartments such as snow water equivalent, root-zone soil moisture, glacier mass, and surface water storage from GRACE/GRACE-FO monthly TWS anomalies. The resulting TWS and groundwater data sets are currently made available via the GravIS portal and within GGMN, the Global Groundwater Monitoring Network of IGRAC, the International Groundwater Resources Assessment Centre. The GravIS (‘Gravity Information Service’, gravis.gfz-potsdam.de) portal is operated by the German Research Centre for Geosciences (GFZ), together with the Technische Universität Dresden and the Alfred-Wegener-Institute (AWI). It facilitates the dissemination of user-friendly products of mass variations in the Earth system, based on GRACE/GRACE-FO. In addition to TWS and GWSA data, GravIS provides ocean bottom pressure (OBP) variations from which global mean barystatic sea-level rise can be estimated, as well as mass changes of the Greenland and Antarctic ice sheets. All these data sets can be interactively displayed at the portal and are freely available for download, either provided as gridded products or as regional averages. This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.

Future measurements for climate monitoring

Gravity Field Constraints on the Upper Mantle of Northwestern Europe

The Southern Ocean (SO), delinated to the north by the Antarctic convergence, is a unique environment that experiences rapid change in some areas while remaining relatively untouched by human activities. At the same time, these ecosystems are under severe threat from climate change and other stressors. While our understanding of SO biological processes (e.g., species distributions, feeding ecology, reproduction) has greatly improved in recent years, biological data for the region remains patchy, sparse, and unstandardised depending on the taxonomic group (Griffiths et al. 2014). Due to the scarcity of standardised observations and data, it is difficult to model and predict SO ecosystem responses to climate change, which is often accompanied by other anthropogenic pressures, such as fishing and tourism. Understanding the dynamics and change in the SO necessitates a comprehensive system of observations, data management, scientific analysis, and ensuing policy recommendations. It should be built as much as feasible from current platforms and standards, and it should be visible, verifiable and shared in accordance with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Van de Putte and Griffiths 2021). For this we need to identify the stakeholders' needs, sources of data, the algorithms for analysing the data and the infrastructure on which to run the algorithms (Benson and Brooks 2018). Existing synergistic methods for identifying selected variables for (life) monitoring include Essential Biodiversity Variables (EBVs; Pereira and Ferrier 2013), Essential Ocean Variables (EOVs; Miloslavich and Bax 2018), Essential Climate Variables (ECVs; Bojinski and Verstraete 2014), and ecosystem Essential Ocean Variables (eEOVs; Constable and Costa 2016). (For an overview see Muller-Karger and Miloslavich 2018.) These variables, can be integrated into the Southern Ocean Observation System (SOOS) and SOOSmap but also national or global systems (e.g., Group on Earth Observations-Biodiversty Observation Network (GEO-BON)). The resulting data products can in turn be used to inform policy makers. The use of Essential Variables (EVs) marks a significant step forward in the monitoring and assessment of SO ecosystems. However, these EVs will necessitate prioritising certain variables and data collection. Here we present the outcomes of a workshop organised in August 2023 that aimed to outline the set Essential Variables and workflows required for a distributed system that can translate biodiversity data (and environmental data) into policy-relevant data products. The goals of the workshop were: Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs. Create an inventory of EVs relevant for the Southern Ocean based on existing efforts by the GEO-BON and the Marine Biodiversity Observation Network (MBON). Identify data requirements and data gaps for calculating such EVs and prioritise EVs to work on. Identify existing workflows and tools. Develop a framework for developing the workflows required to turn public biodiversity data into relevant EVs.

<p>In the frame of the CubeGrav project, funded by the German Research Foundation, Cube-satellite networks for geodetic Earth observation are investigated on the example of the monitoring of Earth’s gravity field. Satellite gravity missions are an important element of Earth observation from space, because geodynamic processes are frequently related to mass variations and mass transport in the Earth system. As changes in gravity are directly related to mass variability, satellite missions observing the Earth’s time-varying gravity field are a unique tool for observing mass redistribution among the Earth’s system components, including global changes in the water cycle, the cryosphere, and the oceans. The basis for next generation gravity missions (NGGMs) is based on the success of the single satellite missions CHAMP and GOCE as well as the dual-satellite missions GRACE and GRACE-FO launched so far, which are all conventional satellites.    <br>In particular, feasibility as well as economic efficiency play a significant role for future missions, with a focus on increasing spatio-temporal resolution while reducing error effects. The latter include the aliasing of the time-varying gravity fields due to the under-sampling of the geophysical signals and the uncertainties in geophysical background models. The most promising concept for a future gravity field mission from the studies investigated is a dual-pair mission consisting of a polar satellite pair and an inclined (approx. 70°) satellite pair. Since the costs for a realization of the Bender constellation are very high, this contribution presents results of the CubeGrav project and focuses on alternative concepts in the form of different constellations and formations of small satellites. The latter includes both satellite pairs and chains consisting of trailing satellites. The aim is to provide a cost-effective alternative to the previous gravity field satellites while simultaneously increasing the spatiotemporal resolution and minimizing the above-mentioned error effects.</p><p>In numerical closed-loop simulations, the impact of different satellite formations and constellations will be investigated for the retrieval of monthly gravity fields. The configurations differ in the orbital setup including the number of orbital planes and key orbit parameters like altitude and inclination. The ground track coverage of the selected orbits will be analysed since an improved spatial sampling with specific sub-cycles is beneficial for estimating short-temporal gravity fields which will be co-parametrized in the overall solution approach. Due to the large number of observations, it is possible to retrieve sub-daily gravity fields down to quarter-day resolution, which exceeds the capabilities of the existing gravity mission like GRACE or GRACE-FO by far. These (sub-)daily gravity field solutions can also improve the overall monthly gravity product, which will be proven for several satellite constellations and formations. All in all, the opportunities and limits of multiple satellites pairs and chains of trailing satellites for achieving the highest possible spatial and temporal resolution shall be analysed in detail.</p>

<div> <p>Satellite gravimetry missions like GRACE and now GRACE-FO measure the global gravity field and its variations in time. Gravity field solutions are typically estimated monthly, but a higher accuracy and a better temporal resolution is required for various applications in the geosciences. With the addition of the laser ranging interferometer (LRI) to GRACE-FO, a significant improvement over GRACE concerning inter-satellite ranging was achieved. The determination of the non-gravitational forces acting on the satellites, however, remained conceptually unchanged. In ground-based applications, e. g., gravimetry and inertial navigation, the progress in the development of cold atom interferometry (CAI) leads to drift-free, accurate, smaller, more robust and reliable quantum sensors. Experiments on sounding rockets and aeroplanes demonstrate the potential of this technique and open up possibilities for applications on satellite missions.</p> <p>We investigate potential next-generation gravity missions (NGGM) following the GRACE design, employing an LRI with GRACE-FO characteristics and the utilisation of CAI in combination with classical accelerometers. A CAI accelerometer also offers the possibility to better determine degree 2 gravity field coefficients, due to its long-term stability. A closed-loop simulator has been developed to test different scenarios of orbit configurations and system/instrument parameters. Regarding the orbit configurations, parameters like inter-satellite distance, orbit altitude and repeat cycle are varied. The results will be evaluated based on recovered gravity fields.</p> <p>As further benefit, the concept of a CAI based drag-free control system is investigated and its impact on possible satellite orbits for NGGMs and the resulting gravity fields is discussed. As the control system is of critical importance for the success of the mission, key parameters are analysed. Furthermore, the requirement for the drag compensation depends on the knowledge of the accelerometer’s scale factor. Related to this aspect, requirements on the drag compensation are derived for different scenarios. We will present first results of the simulation studies.</p> <p>H.W. acknowledges support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2123 “QuantumFrontiers, Project-ID 390837967“. M.S. acknowledges initial funding for the DLR Institute by the Ministry of Science and Culture of the German State of Lower Saxony from “Niedersächsisches Vorab”.</p> </div>

<p><span id="divtagdefaultwrapper" dir="ltr"><span lang="en-US">Convective motions in the Earth’s liquid core are known to  generate temporal variations of the magnetic field and of the length of day. Mass redistribution associated with these motions and exchange of matter with the lower mantle at the core mantle boundary (CMB) may eventually also contribute to the temporal variations of the gravity field, possibly detectable in the data of the GRACE and GRACE Follow On missions. In a pioneering work, Mandea et al., 2012 detected compelling spatio-temporal correlations at interannual time scale between the gravity and magnetic fields measured respectively by the GRACE and CHAMP satellite missions. These correlations were later interpreted by these authors as the results of physico-chemical interactions between the core and the mantle at the CMB. While such mechanisms are plausible, their mere existence, order of magnitude and  time scales remain an open question. Here we present the </span><span lang="en-US"> GRACEFUL project, recently selected by the  "Synergy" programme of the </span><span lang="en-US">European Research Council</span><span lang="en-US">, which objective is to  explore in more detail the previously reported observations described above, in particular the interannual co-variations of the magnetic and gravity fields, as well as their link with deep Earth processes.  This presentation is focussed on the  gravity field component, in particular on the search for the deep Earth signal that we hope to be able to detect i</span><span lang="en-US">n the  GRACE/GRACE FO data,  </span><span lang="en-US">after removing all other contributions due to water mass redistributions  occuring in the surface fluid evelopes, as well as  unrelated solid Earth signals associated with the Glacial Isostatic Adjustment and large earthquakes.</span></span></p>

The Climate Data Record (CDR) is a time series of measurements of sufficient length, consistency and continuity to determine climate variability and change. The generation of ECVs (Essential Climate Variables)/CDRs needs to put strong emphasis on the generation of fully described, error-characterized and consistent satellite-based ECV products (Zeng et al. in Remote Sensing 11:1–28, 2019). For example, generation of many ECVs, such as in the ESA (European Space Agency) CCI (Climate Change Initiative) projects (Plummer et al. in Remote Sens Environ 203:2–8, 2017), requires ancillary information about the state of the atmosphere, e.g., cloud screening for SST (sea surface temperature) and atmospheric correction for space-borne altimeters. As such, the consistency between the various ECV products (e.g. cloud flagged in one ECV and non-flagged in another one) extends to ensuring consistency in the approaches of CDR generation. The in-situ datasets also need to be continuously characterized in terms of their long-term accuracy, stability and homogeneity. Reanalysis results, as an alternative source of ECV, requires similar endeavors to investigate its consistency (Zeng et al. in Int J Appl Earth Obs Geoinf 42:150–161, 2015).

Climate research, monitoring, prediction, and related services rely on accurate observations of the atmosphere, land, and ocean, adequately sampled globally and over sufficiently long time periods. The Global Climate Observing System, set up under the auspices of United Nations organizations and the International Council for Science to help ensure the availability of systematic observations of climate, developed the concept of essential climate variables (ECVs). ECV data records are intended to provide reliable, traceable, observation-based evidence for a range of applications, including monitoring, mitigating, adapting to, and attributing climate changes, as well as the empirical basis required to understand past, current, and possible future climate variability. The ECV concept has been broadly adopted worldwide as the guiding basis for observing climate, including by the United Nations Framework Convention on Climate Change (UNFCCC), WMO, and space agencies operating Earth observation satellites. This paper describes the rationale for these ECVs and their current selection, based on the principles of feasibility, relevance, and cost effectiveness. It also provides a view of how the ECV concept could evolve as a guide for rational and evidence-based monitoring of climate and environment. Selected examples are discussed to highlight the benefits, limitations, and future evolution of this approach. The article is intended to assist program managers to set priorities for climate observation, dataset generation and related research: for instance, within the emerging Global Framework for Climate Services (GFCS). It also helps the observation community and individual researchers to contribute to systematic climate observation, by promoting understanding of ECV choices and the opportunities to influence their evolution.

Regional gravity variations in Europe from superconducting gravimeters

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.