European Centre for Space Applications and Telecommunications

Abstract. Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.

Abstract Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

One of the most important physical characteristics driving lifecycle events in lakes is stratification. Already subtle variations in the timing of stratification onset and break-up (phenology) are known to have major ecological effects, mainly by determining the availability of light, nutrients, carbon and oxygen to organisms. Despite its ecological importance, historic and future global changes in stratification phenology are unknown. Here, we used a lake-climate model ensemble and long-term observational data, to investigate changes in lake stratification phenology across the Northern Hemisphere from 1901 to 2099. Under the high-greenhouse-gas-emission scenario, stratification will begin 22.0 ± 7.0 days earlier and end 11.3 ± 4.7 days later by the end of this century. It is very likely that this 33.3 ± 11.7 day prolongation in stratification will accelerate lake deoxygenation with subsequent effects on nutrient mineralization and phosphorus release from lake sediments. Further misalignment of lifecycle events, with possible irreversible changes for lake ecosystems, is also likely.

Abstract. The Global Ozone Monitoring Experiment-2 (GOME-2) flies on the Metop series of satellites, the space component of the EUMETSAT Polar System. In this paper we will provide an overview of the instrument design, the on-ground calibration and characterization activities, in-flight calibration, and level 0 to 1 data processing. The current status of the level 1 data is presented and points of specific relevance to users are highlighted. Long-term level 1 data consistency is also discussed and plans for future work are outlined. The information contained in this paper summarizes a large number of technical reports and related documents containing information that is not currently available in the published literature. These reports and documents are however made available on the EUMETSAT web pages and readers requiring more details than can be provided in this overview paper will find appropriate references at relevant points in the text.

Spectrally-resolved water-leaving radiances (ocean colour) and inferred chlorophyll concentration are key to studying phytoplankton dynamics at seasonal and inter-annual scales, for a better understanding of the role of phytoplankton in marine biogeochemistry; the global carbon cycle; and the response of marine ecosystems to climate variability, change and feedback processes. Ocean colour data also have a critical role in operational observation systems monitoring coastal eutrophication, harmful algal blooms, and sediment plumes. The contiguous ocean-colour record reached 21 years in 2018; however, it is comprised of a number of one-off missions such that creating a consistent time-series of ocean-colour data requires merging of the individual sensors (including MERIS, Aqua-MODIS, SeaWiFS, VIIRS and OLCI) with differing sensor characteristics, without introducing artefacts. By contrast, the next decade will see consistent observations from operational ocean colour series with sensors of similar design and with a replacement strategy. Also, by 2029 the record will start to be of sufficient duration to discriminate climate change impacts from natural variability, at least in some regions. This paper describes current status and future prospects in the field of ocean colour focussing on large to medium resolution observations of oceans and coastal seas. It reviews the user requirements in terms of products and uncertainty characteristics and then describes features of current and future satellite ocean-colour sensors, both operational and innovative. The key role of in situ validation and calibration is highlighted as are ground segments that process the data received from the ocean-colour sensors and deliver analysis-ready products to end-users. Example applications of the ocean-colour data are presented, focussing on the climate data record and operational applications including water quality and assimilation into numerical models. Current capacity building and training activities pertinent to ocean are described and finally a summary of future perspectives is provided.

Abstract. A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Abstract. This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001–2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels. This product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA files: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25∘. Each set of products includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and atmospheric emission models. Average annual burned area from this product was 3.81 Mkm2, with maximum burning in 2011 (4.1 Mkm2) and minimum in 2013 (3.24 Mkm2). The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The validation indicates an overall accuracy of 0.9972, with much higher errors for the burned than the unburned category (global omission error of BA was estimated as 0.7090 and global commission as 0.5123). These error values are similar to other global BA products, but slightly higher than the NASA BA product (named MCD64A1, which is produced at 500 m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias −0.4033), as most existing global BA products. To understand the value of this product in detecting small fire patches (<100 ha), an additional validation sample of 52 Sentinel-2 scenes was generated specifically over Africa. Analysis of these results indicates a better detection accuracy of this product for small fire patches (<100 ha) than the equivalent 500 m MCD64A1 product, although both have high errors for these small fires. Examples of potential applications of this dataset to fire modelling based on burned patches analysis are included in this paper. The datasets are freely downloadable from the Fire_cci website (https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).

Data from the CYGNSS mission, originally conceived to monitor tropical cyclones, are being investigated here for land applications as well. In this paper, a methodology for soil moisture (SM) retrieval from CYGNSS data is presented. The approach derives Level 3 gridded daily SM estimations, over the latitudinal band covered by CYGNSS, at a resolution of 36 km × 36 km, using the CYGNSS reflectivity over land, coupled with ancillary vegetation and roughness information from the SMAP mission. The results are compared globally with SM measurements from SMAP, which are assumed to be ground truth, showing a good agreement, and a global root-mean-square difference of 0.07 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . A more extensive comparison is performed over two test regions-Texas in the United States and New South Wales in Australia-where reference data from SMAP are complemented with measurements from the SMOS mission. The results over both regions are generally consistent with the global results, and a good agreement is observed between CYGNSS and reference SM measurements from SMAP and SMOS. The study demonstrates that SM can be successfully retrieved from the CYGNSS mission on a global scale and using ancillary information about the overlying vegetation and the characteristics of the soil. The results open up further future perspectives for global, high-resolution SM products from spaceborne Global Navigation Satellite System-Reflectometry data.

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).

Abstract. In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).

Abstract Lake surfaces are warming worldwide, raising concerns about lake organism responses to thermal habitat changes. Species may cope with temperature increases by shifting their seasonality or their depth to track suitable thermal habitats, but these responses may be constrained by ecological interactions, life histories or limiting resources. Here we use 32 million temperature measurements from 139 lakes to quantify thermal habitat change (percentage of non-overlap) and assess how this change is exacerbated by potential habitat constraints. Long-term temperature change resulted in an average 6.2% non-overlap between thermal habitats in baseline (1978–1995) and recent (1996–2013) time periods, with non-overlap increasing to 19.4% on average when habitats were restricted by season and depth. Tropical lakes exhibited substantially higher thermal non-overlap compared with lakes at other latitudes. Lakes with high thermal habitat change coincided with those having numerous endemic species, suggesting that conservation actions should consider thermal habitat change to preserve lake biodiversity.

Abstract Long‐term lake ice phenological records from around the Northern Hemisphere provide unique sensitive indicators of climatic variations, even prior to the existence of physical meteorological measurement stations. Here, we updated ice phenology records for 60 lakes with time‐series ranging from 107–204 years to provide the first re‐assessment of Northern Hemispheric ice trends since 2004 by adding 15 additional years of ice phenology records and 40 lakes to our study. We found that, on average, ice‐on was 11.0 days later, ice‐off was 6.8 days earlier, and ice duration was 17.0 days shorter per century over the entire record for each lake. Trends in ice‐on and ice duration were six times faster in the last 25‐year period (1992–2016) than previous quarter centuries. More extreme events in recent decades, including late ice‐on, early ice‐off, shorter periods of ice cover, or no ice cover at all, contribute to the increasing rate of lake ice loss. Reductions in greenhouse gas emissions could limit increases in air temperature and abate losses in lake ice cover that would subsequently limit ecological, cultural, and socioeconomic consequences, such as increased evaporation rates, warmer water temperatures, degraded water quality, and the formation of toxic algal blooms.

Abstract. The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA), in cooperation with the Japan Aerospace Exploration Agency (JAXA), to measure global profiles of aerosols, clouds and precipitation properties together with radiative fluxes and derived heating rates. The simultaneous measurements of the vertical structure and horizontal distribution of cloud and aerosol fields, together with outgoing radiation, will be used in particular to evaluate their representation in weather forecasting and climate models and to improve our understanding of cloud and aerosol radiative impact and feedback mechanisms. To achieve the objective, the goal is that a retrieved scene with footprint size of 10 km × 10 km is measured with sufficiently high resolution that the atmospheric vertical profile of short-wave (solar) and long-wave (thermal) flux can be reconstructed with an accuracy of 10 W m−2 at the top of the atmosphere. To optimise the performance of the two active instruments, the platform will fly at a relatively low altitude of 393 km, with an equatorial revisit time of 25 d. The scientific payload consists of four instruments: an atmospheric lidar, a cloud-profiling radar with Doppler capability, a multi-spectral imager and a broadband radiometer. Co-located measurements from these instruments are processed in the ground segment, which produces and distributes a wide range of science data products. As well as the Level 1 (L1) product of each instrument, a large number of multiple-instrument L2 products have been developed, in both Europe and Japan, benefiting from the data synergy. An end-to-end simulator and several test scenes have been developed that simulate EarthCARE observations and provide a development and test environment for L1 and L2 processors. Within this paper the EarthCARE observational requirements are addressed. An overview is given of the space segment with a detailed description of the four science instruments, demonstrating how the observational requirements will be met. Furthermore, the elements of the space segment and ground segment that are relevant for science data users are described and the data products are introduced.

Coastal zones are highly dynamical systems affected by a variety of natural and anthropogenic forcing factors that include sea level rise, extreme events, local oceanic and atmospheric processes, ground subsidence, etc. However, so far, they remain poorly monitored on a global scale. To better understand changes affecting world coastal zones and to provide crucial information to decision-makers involved in adaptation to and mitigation of environmental risks, coastal observations of various types need to be collected and analyzed. In this white paper, we first discuss the main forcing agents acting on coastal regions (e.g., sea level, winds, waves and currents, river runoff, sediment supply and transport, vertical land motions, land use) and the induced coastal response (e.g., shoreline position, estuaries morphology, land topography at

Tissint (Morocco) is the fifth martian meteorite collected after it was witnessed falling to Earth. Our integrated mineralogical, petrological, and geochemical study shows that it is a depleted picritic shergottite similar to EETA79001A. Highly magnesian olivine and abundant glass containing martian atmosphere are present in Tissint. Refractory trace element, sulfur, and fluorine data for the matrix and glass veins in the meteorite indicate the presence of a martian surface component. Thus, the influence of in situ martian weathering can be unambiguously distinguished from terrestrial contamination in this meteorite. Martian weathering features in Tissint are compatible with the results of spacecraft observations of Mars. Tissint has a cosmic-ray exposure age of 0.7 ± 0.3 million years, consistent with those of many other shergottites, notably EETA79001, suggesting that they were ejected from Mars during the same event.

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change. Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change. In September 2020, a tag on the back of a Eurasian blackbird (Turdus merula) tagged in Belarus, that had migrated to its wintering grounds in Albania, switched on its transmitter as the International Space Station (ISS) passed 410 km above. The tag sent global positioning system (GPS) location data on the bird´s recent whereabouts as well as onboard sensor data, which the International Cooperation for Animal Research Using Space (ICARUS) receiver aboard the Russian Zvezda Module of the ISS picked up and returned to scientists back on Earth [1.Belyaev M. et al.Development of technology for monitoring animal migration on Earth using scientific equipment on the ISS RS.in: Proceedings of the 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), St. Petersburg, Russia. 2020Crossref Scopus (6) Google Scholar] (Figure 1). While only 223 bytes in size, this transmission rang in a new epoch for space-based Earth observations and biological sensing. The new system, based on digital Internet of Things (IoT) technology, will allow the relay of position and behavior from myriad low-cost, miniaturized tracking tags (now 4g, soon 3g, optionally solar powered) at almost global scale and in near-real time. A connected global system of thousands of mobile ‘animal sensors’ has the potential to provide a quantum leap for the biological understanding and monitoring of our planet. The environmental associations of animals that drive their movements, finely tuned by evolution, offer an unrivalled biological lens into these habitats themselves. This concept flips the traditional satellite-based Earth observation paradigm: rather than globe-orbiting sensors capturing images of the planet’s surface for subsequent interpretation, animals, through countless individual movement decisions, seek out their preferred conditions, sensing the quality and health of ecosystems in real time (Figure 2). Realizing this capability, however, requires engagement from agencies and scientists worldwide to support decentralized coordinated data collection and, to catalyze this engagement, a global demonstration campaign. The blackbird’s data transmission was a long-anticipated milestone (https://www.icarus.mpg.de) [1.Belyaev M. et al.Development of technology for monitoring animal migration on Earth using scientific equipment on the ISS RS.in: Proceedings of the 27th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), St. Petersburg, Russia. 2020Crossref Scopus (6) Google Scholar]. With a new transmission scheme, two-way communication, and mass-produced hardware, ICARUS has not only reduced the size and cost of tracking tags but also increased the number that can be monitored concurrently. Through the ability to simultaneously return data from millions of ‘wearables for wildlife’, ICARUS complements existing satellite (Argos, Iridium) and ground-based (e.g., GSM, IoT) networks to dramatically expand the number and diversity of animals that can be tracked. The initial drive for animal tracking has come from animal behavior and migration research. Earlier generations of GPS tags revealed previously unknown migration paths and seasonal gatherings, identified vital corridors and refugia in conservation, and documented important epidemiological links [2.Kays R. et al.Terrestrial animal tracking as an eye on life and planet.Science. 2015; 348: eaaa2478Crossref Scopus (721) Google Scholar,3.Hussey N.E. et al.Aquatic animal telemetry: a panoramic window into the underwater world.Science. 2015; 348: 1255642Crossref PubMed Scopus (715) Google Scholar,10.Tian H. et al.Avian influenza H5N1 viral and bird migration networks in Asia.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: 172-177Crossref PubMed Scopus (123) Google Scholar]. Data growth and collaboration have enabled some of the first comparative studies discovering behavioral adjustments to human land use [4.Tucker M.A. et al.Moving in the Anthropocene: global reductions in terrestrial mammalian movements.Science. 2018; 359: 466-469Crossref PubMed Scopus (487) Google Scholar] and changes of movements across the Arctic due to climate change [5.Davidson S.C. et al.Ecological insights from three decades of animal movement tracking across a changing Arctic.Science. 2020; 370: 712-715Crossref PubMed Scopus (35) Google Scholar]. In addition, they have stimulated excitement about the emergence of an entirely new type of animal sentinel-based evidence supporting biodiversity conservation in a rapidly changing world [6.Rutz C. et al.COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife.Nat. Ecol. Evol. 2020; 4: 1156-1159Crossref PubMed Scopus (219) Google Scholar,7.Jetz W. et al.Essential biodiversity variables for mapping and monitoring species populations.Nat. Ecol. Evol. 2019; 3: 539-551Crossref PubMed Scopus (150) Google Scholar,11.Turner W. Sensing biodiversity.Science. 2014; 346: 301-302Crossref PubMed Scopus (148) Google Scholar]. Unlike the caged canary in the coal mine, free-ranging animals pick their own paths and are thus naturally intelligent sensors, fine-tuned by evolution. They actively seek out, or avoid, a set of environmental conditions and show distinct reactions to unusual weather, storms, and some natural disasters [8.Wikelski M. Tertitski G. Living sentinels for climate change effects.Science. 2016; 352: 775-776Crossref PubMed Scopus (23) Google Scholar]. When linked to concurrently remotely sensed data from satellites, and through sensors’ onboard tags, their movement tracks record individually encountered environmental conditions. This enables an unprecedented quantification of the habitat use, environmental niches and ecological boundaries of animals and, with baseline data in place, real-time monitoring of change. Thereby, tracked animals can add essential biological meaning to the vast, ongoing remote-sensing data collection and act as canaries in the coal mine set free: signalers and sentinels of environmental conditions through their selection, avoidance, or death. The satellite–animal interlink could extend to active digital handholding: satellites could be tasked with following particular individuals for extra information or, in real time, tune into those showing abnormal behavior or sudden avoidance of places expected to be suitable. Agencies or conservation groups could receive alerts if typically used habitats or conservation areas are suddenly avoided or cause death (e.g., due to illegal encroachment or hunting). Such a system would substantially enhance ecological-change detection from remotely sensed signals, complementing existing data and approaches, for example, for remotely sensed deforestation alerts or spatially fixed conservation technology, such as camera traps. Imagine a representative set of 100 000 animals from 500 species equipped with space-based GPS tracking tags that deliver half-hourly data. At a 3g tag size, such a system is able to address around 40% of birds and over 50% of mammals (i.e., a total of ca 7000 potential species) and hundreds of species of crocodiles, turtles, and large lizards (for a 5% weight limit). This expanded hyper-speciose taxonomic (and geographic) scope opens an entirely new phase of animal-based Earth observation. Deploying this many tags is certainly a challenge, but remember, the ISS-tracked blackbird was preceded by tens of thousands of blackbirds equipped with leg bands instead. Thanks to a vast international network of volunteers, ca 3.5 million individual birds have been captured and marked every year since 1960, globally [9.Kestenholz M. et al.Bird Ringing for Science and Conservation. EURING, 2011Google Scholar] (with <1% ever resighted or recovered to provide a second data point), and probably hundreds of thousands of mammals. While not all species will be straightforward or justifiable targets for GPS tags, the potential set is large enough to enable ecologically representative and global coverage. Past experience and initial ICARUS interest suggest that wildlife agencies, non-governmental organizations, scientists, and bird banders would carry the large majority of deployments, with coordination and targeted campaigns needed to ensure coverage. The International Bio-Logging Society (https://www.bio-logging.net) could play a role in supporting such a global coordination. With a receiver in place, tag hardware cost at scale decreasing to US$100 or less each, and a yearly redeployment of 50 000 new tags, this results in a US$10–15 million annual cost, tremendous value added to environmental satellite missions at a small fraction of their typical cost. We expect that, combined with other data on traits and behaviors, space–time–environment information from thousands of species will enable a more functional interpretation of the ecosystem consequences of biodiversity. Across scales of organismal organization, but also across space and time, these measurements will allow pinning down of the plasticity and adaptive potential around realized change in animal niches and space use. The detailed capture of individual lifetime tracks, when linked with environmental and individual phenotypic and genomic data, provides an unprecedented tool for evolutionary study and offers new life-history, geospatial, and environmental niche dimensions for specimens archived or exhibited in museums. For potential animal reservoirs of infectious diseases, Earth observation with animal sensors can help to identify potential hotspots of disease transmission and map and monitor the potential for long-distance and cross-border transmissions [10.Tian H. et al.Avian influenza H5N1 viral and bird migration networks in Asia.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: 172-177Crossref PubMed Scopus (123) Google Scholar]. Tracking of individuals with antibodies offers epidemiologists the potential to pinpoint the location of the true hosts of zoonotics such as Ebola and coronavirus disease 2019 (COVID-19). With so many animals tracked, many intriguing stories will emerge about individual animals that will have the potential to capture the imagination of people worldwide. The tracked animals provide the daily drama that can be part of digitally-rich media campaigns around tagged individuals that support education and discovery, and can engage citizen scientists to collect ancillary observations, enriching the data record even further. The potential to adopt and follow single individuals and their fates can connect people to biodiversity issues, both at their doorstep and far away, and support educational uses and conservation funding. Realizing these opportunities will require the engagement of and contributions from government agencies, the science community, and beyond. At agency level, a shift in traditional perceptions and approaches to Earth observation and monitoring will be required, together with interagency collaboration among and within nations. The ICARUS ground-to-space IoT is designed to be an open system for any organization to join and augment the global readout capacity or leverage for an improved system. The success of the presented vision will also rely on global collaboration and coordination of biodiversity monitoring among sovereign territories. With the GEO Biodiversity Observation Network (https://geobon.org) and its associated research community, international platforms and scientific principles for globally coordinated and integrated biodiversity monitoring are in place. Through model-based integration with other biodiversity data in platforms such as Map of Life (https://mol.org), the envisioned animal-based Earth observation can inform Essential Biodiversity Variables and indicators for the tracking of progress toward international goals on maintaining ecological integrity and connectivity or provide management-relevant short-term forecasting [7.Jetz W. et al.Essential biodiversity variables for mapping and monitoring species populations.Nat. Ecol. Evol. 2019; 3: 539-551Crossref PubMed Scopus (150) Google Scholar]. As tag deployments will rely on individual scientist’s participation, a willingness to follow agreed data standards and share data is vital. Effective Earth observation via animals will thus require development and openness around new data-sharing and -use models, including the near-immediate sharing of limited anonymized information that near-real time monitoring and model-based short-term forecasting depend on. Community engagement is needed to develop effective approaches for the citation of tracking data to support appropriate attribution and recognition. As one scales this vision to a truly global endeavor, challenges certainly remain, including sufficient capacity to support best scientific practice, benefit sharing, and the engagement of regional and local stakeholders. With the ICARUS system now online, a globally coordinated ‘100 000 animal sentinels’ campaign is possible and would establish an unrivalled bioenvironmental baseline record. With the larger community engaged, it would be the start of ongoing real-time sensing of living conditions on Earth by animals themselves. Akin to hyperspectral remote sensing systems [12.Schimel D. et al.Prospects and pitfalls for spectroscopic remote sensing of biodiversity at the global scale.in: Remote Sensing of Plant Biodiversity. Springer, 2020: 503-518Crossref Scopus (9) Google Scholar], it would realize hyper-speciose, and thus multifaceted, in situ biological Earth observation. No interests are declared. Biological Earth observation with animal sensors: (Trends in Ecology and Evolution , 293–298; 2022)Jetz et al.Trends in Ecology & EvolutionMay 21, 2022In BriefSix supporting authors were omitted from the article ‘ Biological Earth observation with animal sensors ´ when it was published. The corrected supporting author list appears below. We apologise for this oversight. Full-Text PDF Open Access

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

Abstract. Sea state data are of major importance for climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data, last access: 25 August 2020) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/ (last access: 25 August 2020). Three products are available: a multi-mission along-track L2P product (http://dx.doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (http://dx.doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (http://dx.doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c).