Earth System Science Data最新文献

{"title":"Airborne measurements of turbulence and cloud microphysics during PaCE 2022 using the Advanced Max Planck CloudKite Instrument (MPCK\u0000 +\u0000 )","authors":"Oliver Schlenczek,Freja Nordsiek,Claudia E. Brunner,Venecia Chávez-Medina,Birte Thiede,Eberhard Bodenschatz,Gholamhossein Bagheri","doi":"10.5194/essd-18-7557-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7557-2026","url":null,"abstract":"This paper presents the data set collected with the Advanced Max Planck CloudKite instrument (MPCK + ) during the Pallas Cloud Experiment (PaCE), conducted at Pallas, Finland, between 19 and 26 September 2022. The data set includes measurements of turbulence, wind shear, and cloud microphysics in the cloudy planetary boundary layer between 0 and 1200 m above ground with flight durations between 1.5 and 3 h. This data set is intended for researchers studying microphysics of boundary layer clouds in the sub-arctic at temperatures above freezing level, providing high-resolution airborne measurements of meteorological variables together with cloud droplet concentration and size distribution data. In particular, the high spatial resolution of less than 10 m between two cloud droplet size distribution samples, the low altitude and the long flight time allow unprecedented insights into stratocumulus and cumulus clouds. The aim of this paper is to describe the data collection process, the structure of the data set, and guidelines for potential users. The data set is available at https://doi.org/10.5281/zenodo.15700506 (Bagheri et al., 2025a) in NetCDF format.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"38 1","pages":"7557-7574"},"PeriodicalIF":11.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A standardized permafrost ground temperature collection for Canada 2025","authors":"Olivia Meier-Legault,Nicholas Brown,Larry Adjun,Michel Allard,Alejandro Alvarez,Maude Auclair,Alex Bevington,Samuel Bilodeau,William Cable,Olivia Carpino,Ariane Castagner,Lin Chen,Alexandre Chiasson,Ryan Connon,Stephanie Coulombe,Jeffrey Crompton,Derek Cronmiller,Gautier Davesne,Mason Dominico,Marc-André Ducharme,Timothy Ensom,Louise Farquharson,Vanessa Foord,Daniel Fortier,Philippe Fortier,Duane Froese,Samuel Gagnon,Francis Gauthier,Marten Geertsema,Etienne Godin,Galina Jonat,Steven V. Kokelj,Michelle Landry,Antoni Lewkowicz,Panya Lipovsky,Emmanuel L'Hérault,Hannah Macdonell,Lancelot Massé,Dmitry Nicolsky,Moya Painter,Leesee Papatsie,Victor Pozsgay,William Quinton,Vladimir Romanovsky,Ashley C. A. Rudy,Denis Sarrazin,Emilie Stewart-Jones,Donald Walker,Thomas Wright,Joseph Young,Stephan Gruber","doi":"10.5194/essd-18-7515-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7515-2026","url":null,"abstract":"Ground temperature (GT) is an essential variable for monitoring the thermal state of permafrost, developing models, and conducting fundamental research. However, the usability of GT data in Canada is often hampered by inconsistent file formats, a lack of metadata standardization between organizations, and varied temporal resolutions. This fragmentation has prevented the development of a comprehensive benchmark dataset, leaving researchers reliant on small, ad-hoc data subsets for model evaluation. To address this gap, we present a new, standardized collection of GT and ground surface temperature (GST) data from across Canada, designed to serve as a benchmark for synthesis and model evaluation. The collection integrates data from 29 published datasets and 13 unpublished contributions, encompassing 427 GT and 491 GST sites across British Columbia, Labrador, Northwest Territories, Nunavut, Québec, and Yukon. The dataset includes records from 1960 through 2025 and observations range in depth from 0 to 300 m. The longest individual time series comprises 34 consecutive years of daily data with no more than 10 % missing data per year. On average for all sites and depths, this value is 6 years. All included time series feature at least one year of measurements at daily resolution. We performed quality control and standardization using tempcf – a newly-developed python software tool – to document and remove anomalies, outliers, and drilling disturbances. Then we aggregated sub-daily data to daily means. The dataset is standardized into NetCDF files adhering to CF, ACDD, and IOOS metadata standards as well as a compatible permafrost-specific metadata profile. Data are provided as both individual NetCDF files and text files. They are fully accessible at https://doi.org/10.5281/zenodo.18022924 (version 0.9.1) (Meier-Legault et al., 2025) and via an ERDDAP server (https://data.permafrostnet.ca/erddap, last access: 21 July 2026), following FAIR principles. Several limitations remain, including time zone uncertainty in some datasets, and the lack of a standardized methodology for harmonizing site description metadata. Nevertheless, this collection improves the accessibility and usability of Canadian ground temperature data for national-scale permafrost research.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"64 1","pages":"7515-7534"},"PeriodicalIF":11.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking recent extremes and interannual variability of global fire emissions using a near-real-time extension to the Global Fire Emissions Database","authors":"Yang Chen,Guido R. van der Werf,Mingquan Mu,Dave van Wees,Joanne V. Hall,Louis Giglio,Roland Vernooij,Douglas C. Morton,Li Xu,Tianjia Liu,Rebecca C. Scholten,Shane R. Coffield,Melanie B. Follette-Cook,Lesley Ott,James T. Randerson","doi":"10.5194/essd-18-7535-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7535-2026","url":null,"abstract":"The Global Fire Emissions Database (GFED) is widely used to quantify spatiotemporal variability and long-term trends in burned area and fire emissions, supporting assessments of fire impacts on ecosystems and atmospheric composition. GFED has historically relied on observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), but orbital drift and planned sensor decommissioning pose challenges for maintaining record continuity and near-real-time (NRT) monitoring. Here we present the GFED5 near-real-time extension (GFED5NRT), a global fire emissions dataset that enables daily NRT analyses using active fire observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). GFED5NRT uses biome- and region-specific lookup tables of effective fire area and fuel consumption, derived from VIIRS observations and standard GFED5 datasets, to estimate burned area and emissions from VIIRS active fire counts in a manner consistent with the GFED5 time series. Comparisons with GFED5 and independent datasets show strong agreement in spatial patterns, seasonal cycles, and interannual variability of fire activity. GFED5NRT captures recent major fire extremes and provides daily global NRT estimates of burned area and emissions for multiple trace gases and aerosols. Together, GFED5 and GFED5NRT provide a coherent framework for long-term analyses and NRT monitoring of evolving fire regimes in a changing climate. The GFED5NRT dataset is publicly available at https://doi.org/10.5281/zenodo.18702700 (Chen et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"27 1","pages":"7535-7555"},"PeriodicalIF":11.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coastal Atmosphere & Sea Time Series (CoASTS) and Bio-Optical mapping of Marine optical Properties (BiOMaP): the hyperspectral absorption coefficients by optically significant constituents","authors":"Jean-François Berthon,Giuseppe Zibordi","doi":"10.5194/essd-18-7499-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7499-2026","url":null,"abstract":"The Coastal Atmosphere & Sea Time Series (CoASTS) and the Bio-Optical mapping of Marine optical Properties (BiOMaP) programs were conceived and implemented to assist ocean color applications with field measurements of apparent and inherent optical properties, as well as concentration of optically significant water constituents. The CoASTS program led to the creation of time-series of bio-optical measurements at the “Acqua Alta” Oceanographic Tower (AAOT) site in the northern Adriatic Sea continued from 1995 up to 2016. The BiOMaP program supported the collection of equivalent bio-optical measurements across European Seas from 2000 up to 2022. This work focusses on CoASTS and BiOMaP hyperspectral absorption coefficients of optically significant constituents as determined applying standardized instruments, community measurement methods, extended quality control schemes and consolidated processing. The work, which complements a previous one by Zibordi and Berthon (2024) centred on multi-spectral data, presents the CoASTS and BiOMaP hyperspectral absorption coefficients of pigmented and non-pigmented particles in the 400–750 nm interval, and colored dissolved organic matter in the 350–680 nm one, both with a 3 nm spectral resolution at 2 nm increment. The data are available at https://doi.org/10.1594/PANGAEA.993510 (Berthon and Zibordi 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"4 1","pages":"7499-7514"},"PeriodicalIF":11.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-quality remote sensing reflectance products over China and US coast","authors":"Shuhui Zhao,Youlv Wu,Jingning Lv,Dan Zhao,Yan Zheng,Lian Feng","doi":"10.5194/essd-18-7475-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7475-2026","url":null,"abstract":"Remote sensing reflectance ( R rs ) is fundamental for deriving bio-optical properties of global surface waters. However, accurate atmospheric correction (AC) to derive R rs in coastal waters remains challenging due to strongly absorbing aerosols and complex water optics. To address this, we developed an improved processing framework that integrates flexible use of global gridded aerosol models better suited for coastal environments and incorporates tailored masking strategies. Based on this framework, we generated a high-quality R rs dataset from Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua observations, spanning 2003–2022 for the coastal waters of China and the United States (US) – two regions where complex water optics and frequent anthropogenic aerosols have long impeded retrieval accuracy and valid data yield. Compared to the NASA standard MODIS Aqua R rs products, the improved framework achieved a significant reduction in systematic bias. Multiple validation metrics support this, with the restoration of the mean regression slope to unity (1.00 ± 0.08 vs. 0.90 ± 0.06) across eight bands being the most prominent. Performance gains are particularly pronounced in the 488–555 nm range, which is most relevant to water color applications. The new dataset also yields significantly more valid retrievals, with regional mean increases of 56 % in the Chinese coastal waters and 18 % in the US coastal waters at 443 nm. Furthermore, it recovers fine-scale features in turbid waters and captures long-term dynamics, demonstrating its robustness for coastal monitoring. As a complementary resource optimized for estuaries, bays, and turbid coastal regions, the new dataset fills a critical gap in the ocean color data portfolio where standard products are less reliable. The dataset is available at https://doi.org/10.5281/zenodo.16413443 (Zhao et al., 2025).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"160 1","pages":"7475-7498"},"PeriodicalIF":11.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The OpenSat4Weather dataset: Ku-band satellite link data for precipitation monitoring","authors":"Roberto Nebuloni,Maximilian Graf,Greta Cazzaniga,François Mercier,Maxime Turko","doi":"10.5194/essd-18-7455-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7455-2026","url":null,"abstract":"The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for detecting and quantifying precipitation. Consequently, SMLs have the potential to serve as low-cost rainfall sensors complementing conventional instruments. However, a major challenge for opportunistic sensors, including both SMLs and their terrestrial counterpart – Commercial Microwave Links (CMLs) – is related to data ownership and accessibility constraints, possibly hindering progress in the development of processing tools and validation studies. This limitation highlights the importance of openly accessible datasets. While CML open datasets are already available, there are no large-scale SML datasets in public repositories. To address this gap, we introduce the OpenSat4Weather dataset, a comprehensive and openly accessible collection of measurements from 215 SML sensors deployed in Southern France, over a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166 (Nebuloni et al., 2025b). OpenSat4Weather also includes concurrent conventional measurements, namely, 6 min rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data were derived from the gauge-adjusted weather radar product PANTHERE provided by Météo-France. Additionally, the dataset stores ERA5 reanalysis data of the 0 °C isotherm height for rain height estimation, which are essential for accurately converting the received signal level into rainfall intensity. In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional networks are sparse or unavailable.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"11 1","pages":"7455-7473"},"PeriodicalIF":11.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ground-motion dataset for shallow earthquakes in Colombia","authors":"Daniel Martinez-Jaramillo,Sreeram-Reddy Kotha,F. Ramón Zúñiga,Pierre Lacan","doi":"10.5194/essd-18-7403-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7403-2026","url":null,"abstract":"The tectonics of Colombia is characterised by the interaction of multiple oceanic plates subducting beneath the continent, generating active transpressional deformation and a wide range of subduction-related and crustal earthquakes. We present a flatfile containing ground-motion intensity measurements (IMs) from 667 shallow earthquakes. Magnitudes were homogenised using S-wave corner-frequency-based estimates where a reliable agency-reported moment magnitude was unavailable. The dataset includes 7550 three-component acceleration records that were uniformly processed. The computed IMs include peak ground velocity, peak ground acceleration, and 5 %-damped spectral accelerations for 31 periods ranging from 0.01 to 8 s. Furthermore, the Fourier Amplitude Spectra for each component is provided in the frequency range from 0.04 to 50.00 Hz. Epicentral and hypocentral distances are reported for all events, while finite-fault distance metrics are estimated for earthquakes with M > 5.5. Site conditions are characterised using V S30 and the horizontal-to-vertical spectral ratio (H/V). Hypocentre locations are constrained using the ISC-EHB catalogue and the Integrated Seismological Catalogue of the Colombian Geological Survey. For validation and consistency checks, the dataset was compared to a global and region-adapted Ground Motion Prediction Model. This flatfile constitutes a valuable resource for seismic hazard analysis, ground-motion modelling, risk assessment, earthquake engineering applications, seismic-site response and source physics characterisation in Colombia, surrounding regions, and other comparable tectonic environments. The dataset is available at https://doi.org/10.5281/zenodo.22031680 (Martinez-Jaramillo et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"297 1","pages":"7403-7415"},"PeriodicalIF":11.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A global base temperature dataset for residential building energy demand modelling","authors":"Xiujuan He,Jiyong Eom,Sha Yu,Shu Liu,Wenru Xu,Yuyu Zhou","doi":"10.5194/essd-18-7417-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7417-2026","url":null,"abstract":"Accurate building energy demand modelling is critical to decarbonizing regional energy systems. The cooling and heating degree-day models are widely used due to their simplicity and low data requirements; however, the lack of accurate base temperature data limits their performance. In particular, the scarcity of high temporal resolution building energy demand data constrains regional-scale base temperature estimation through conventional methods such as the energy signature method and the performance line method. To address this limitation, this study develops a global regional-scale base temperature dataset based on the BiLSTM neural network framework with an attention mechanism. The dataset includes both cooling base temperature (Tcool) and heating base temperature (Theat) for each region, defined at a spatial scale equivalent to a U.S. state or a Chinese province. The BiLSTM framework demonstrates strong performance, with RMSE values of 1.39 °C for training and 1.33 °C for testing, and Pearson correlation coefficients of 0.84 for Tcool and 0.70 for Theat. Predicted results show that global Tcool ranges from 19–25 °C and Theat from 14–18 °C, consistent with physical principles. External validations using 16 independent datasets demonstrate that the predicted base temperatures consistently improve the accuracy of building energy demand modelling, reducing RMSE by approximately 10 % for both cooling and heating, compared to official or empirical base temperatures. This dataset supplements sparse observational base temperature data and enhances the accuracy of building energy demand modelling, contributing to low-carbon energy system planning and broader climate impact assessment. The proposed global Tbase dataset can be acquired from https://doi.org/10.6084/m9.figshare.30646376.v2 (He et al., 2025b).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"53 1","pages":"7417-7454"},"PeriodicalIF":11.4,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WAPOSAL: a multi-regional wave dataset from satellite altimetry for significant wave height, period estimation, and wave power density","authors":"Sonia Ponce de León,Maria Panfilova,Andrés F. Orejarena-Rondón,Marco Restano,Roberto Sabia,Jérôme Benveniste","doi":"10.5194/essd-18-7391-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7391-2026","url":null,"abstract":"Accurate assessment of wave power density (WPD) is essential for marine renewable energy development and for validating numerical wave models, particularly in coastal and nearshore regions where in situ observations are sparse. This study presents a high-resolution, multi-region wave dataset generated within the WAPOSAL (Wave Power & Satellite Altimetry) project, derived from Synthetic Aperture Radar (SAR) altimetry data acquired by the Sentinel-3A/B and CryoSat-2 missions. Significant wave height and normalized radar cross-section were obtained using the SAMOSA+ retracker, and zero-crossing wave period was estimated based on an empirical regression method calibrated with in situ buoy and ERA5 data. Wave power density was then computed along altimeter tracks across eleven regions: Norway & Baltic Sea, UK & North Sea, French façade, Spain Atlantic, Portugal, Mediterranean sea, Madeira, Canary Islands, Azores Archipelago, French Guiana and French Polynesia. The temporal coverage spans 2011–2023, depending on the region and the satellite mission considered. The present paper describes the database and illustrates its use through simple application examples. The dataset in its current format and version can be discovered, shared and cited via the following link: https://doi.org/10.57780/ESA-1AB8CF3 (Ponce de León et al., 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"44 1","pages":"7391-7402"},"PeriodicalIF":11.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149151085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved estimation of net ecosystem CO\u0000 2\u0000 exchange over North America using LSTM-based flux upscaling (2001–2021)","authors":"Chengcheng Huang,Wei He,Brendan Byrne,Ngoc Tu Nguyen,Jingfeng Xiao,Hua Yang,Philippe Ciais,Songhan Wang,Xing Li,Han Ma,Peipei Xu,Mengyao Zhao,Hui Chen,Weimin Ju","doi":"10.5194/essd-18-7367-2026","DOIUrl":"https://doi.org/10.5194/essd-18-7367-2026","url":null,"abstract":"Accurate estimation of regional-scale terrestrial carbon budgets is of great importance but remains challenging. With particular advantages, the Long Short-Term Memory (LSTM) network method shows potential for improving regional carbon budget upscaling estimations. Here, using LSTM, we upscaled regional net ecosystem carbon exchange (NEE) with available flux tower measurements and satellite land surface observations in North America. With well-established ecosystem-specific LSTMs, we produced monthly NEE at a spatial resolution of 0.1 ° ×0.1 ° over 2001–2021 (labelled as MemoryFlux). Unlike existing upscaling estimates, our dataset properly identified the Midwest Corn Belt as a region of large seasonal carbon uptake during the peak growing season, a feature revealed by previous top-down studies and recognized as a model benchmark. Moreover, the seasonal variations in NEE estimated by MemoryFlux were strongly correlated with those from independent atmospheric inversions, including the ensemble mean of the Orbiting Carbon Observatory-2 Model Intercomparison Project (OCO-2 v10 MIP; r =0.96, p <0.001) and CarbonTracker2022 (CT2022) ( r =0.97, p <0.001). The mean annual NEE was estimated at −1.27 ± 0.12 Pg C yr −1 , which was closer in magnitude to inversions (−0.83 to −0.70 Pg C yr −1 ) than existing upscaling estimates (−3.30 to −1.68 Pg C yr −1 ). In addition, MemoryFlux captured spatial NEE anomaly patterns associated with six selected severe drought and flood events. We further found that explicitly incorporating historical predictor information improved the representation of NEE interannual variability and spatial anomalies associated with climate extremes. MemoryFlux provides an improved bottom-up estimation of North American NEE and shows greater consistency in magnitude with independent atmospheric inversion estimates than several existing EC-based upscaling products. The MemoryFlux dataset is available at https://doi.org/10.5281/zenodo.20482274 (Huang and He, 2026).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"18 1","pages":"7367-7390"},"PeriodicalIF":11.4,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}