AYOUCHE, ADAM · Fox-Kemper, Baylor · Laxague, Nathan · et al.

Code and processed data reproducing the figures and analysis of Ayouche et al., "Sensitivity of Offshore Wind Farm Wakes and Ocean Coupling to Vertical Resolution in the Stable Marine Boundary Layer" (Wind Energy Science, 2025). The study uses WRF-Fitch mesoscale simulations of a large offshore wind farm over the Southern New England lease area during a stable marine boundary layer episode (2-8 May 2018), spanning five near-surface vertical resolutions (Δz = 5, 10, 25, 50, 100 m) and two ABL schemes (MYNN2, YSU), coupled to a 1-D Pollard-Rhines-Thompson ocean. Contents: runs/ - WRF namelists for the 15 simulations plus turbine configuration files figures_code/ - plotting scripts (figureN.py) and data-export scripts (figureN_save.py) figures_netcdf/ - the plotted arrays for Figs. 3, 4, 6, 7, 8, 9 (NetCDF) stats/ - figure statistics (CSV), including Fig. 5 shapefiles_masks_lease_areas/ - lease-area polygons used in Figs. 3, 8, 9 To run the scripts, edit the BASE and SHAPE_DIR paths at the top of each figure script. Raw WRF output (several TB) is available from the corresponding author on request; the archived namelists fully specify its regeneration. ERA5 (used for Figs. 1-2 and boundary conditions) is available from the Copernicus Climate Data Store ( https://doi.org/10.24381/cds.adbb2d47 ). Code is released under MIT; processed data (figures_netcdf/, stats/) under CC-BY-4.0.

Lu, Bin · Xin, Yi · Jin, Meng

8.0 MB

Jingwei-DO is a deep learning-based global ocean dissolved oxygen (DO) mapping dataset. The model is trained with quality-controlled dissolved oxygen observations from the World Ocean Database 2023 (WOD23) updated through 2025, and is applied to EN4.2.2 temperature and salinity fields for global inference. The product provides monthly dissolved oxygen fields on a 1° × 1° global grid from the surface to 5,500 m depth over the period 1965-2025. An open visualization and exploration platform is available at https://jingwei.acemap.info/ .

Gregory, Catherine · Hofmann Elizondo, Urs · Guinaldo, Thibault · et al.

2 files · 4.1 GB · zipdeclared

Unrealised extreme storylines of the 2023 North Atlantic marine heatwave revealed through ensemble boosting This Zenodo record accompanies the paper "Unrealised extreme storylines of the 2023 North Atlantic marine heatwave revealed through ensemble boosting" by Catherine H. Gregory, Urs Hofmann Elizondo, Thibault Guinaldo, and Thomas L. Frölicher. Overview This record contains the GitHub repository and associated simulation output used for the study. The project investigates the 2023 North Atlantic marine heatwave and explores physically plausible but unrealised extreme storylines using ensemble boosting experiments. Repository contents The GitHub repository contains analysis scripts, data products, and ensemble-boosting tools used in the study. It includes Jupyter notebooks for analysing and visualising marine heatwave evolution, sea-surface temperature anomalies, subsurface heatwave structure, and heat-budget diagnostics. It also contains filtered marine heatwave event datasets for boosted and double-boosted North Atlantic experiments. Simulation data The simulation output is provided as a compressed archive named Ensemble_boosting_simulations.zip . The model output in this archive has been reduced to the variables and broader study region relevant for the analyses presented in the paper. Ensemble boosting module A dedicated ensemble_boosting module provides tools to generate small, spatially distributed atmospheric temperature perturbations for climate-model ensemble branching. These perturbations are constructed with zero area-weighted global mean, enabling reproducible ensemble-boosting experiments while preserving global energy consistency. The module includes command-line utilities for creating perturbation masks, recording random seeds, and integrating perturbations into atmospheric restart files. Purpose Together, the repository and reduced simulation output support the analysis workflow used to identify, characterise, and visualise unrealised but physically plausible extreme storylines of the 2023 North Atlantic marine heatwave under ensemble-boosted climate-model simulations. Citation Gregory, C. H., Hofmann Elizondo, U., Guinaldo, T., & Frölicher, T. L. Unrealised extreme storylines of the 2023 North Atlantic marine heatwave revealed through ensemble boosting. Corresponding author: Catherine Gregory, catherine.gregory@unibe.ch

1 files · 17 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/Office of Satellite and Product Operations (OSPO) office from Visible and Infrared Imager/Radiometer Suite (VIIRS) sensor cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, degree_C) graphics (graphics overlay planes)

Wei, Ren · Li, Mingsong · Wang, Xiaomei · et al.

8.0 MB

This repository provides the processed CESM diagnostics and reproducible analysis scripts for the study "Orbital gating of monsoon peat-carbon burial during the late Paleozoic Ice Age". The dataset is organized around fixed-angular calendar-corrected monthly CESM outputs and a sequential Python workflow used to reproduce the main hydroclimate diagnostics, peat-burial emulator calculations, sensitivity analyses and figure products. The folder data_calendar_corrected_monthly/ contains 21 fixed-angular calendar-corrected monthly NetCDF files. Each file corresponds to one orbital configuration defined by eccentricity state, obliquity state and longitude of perihelion, for example Emax_Omax_90_monthly.nc, Emean_Omean_180_monthly.nc and Emin_Omin_180_monthly.nc. These files include the monthly climate fields used in the analyses, including near-surface temperature, precipitation components, evaporation, wind fields, land fraction and associated grid information. The folder scripts/ contains the Python scripts required to reproduce the analysis in the order used in the manuscript. The workflow begins with compute_monsoon_domain_corrected.py, which derives land-monsoon masks and area-weighted hydroclimate diagnostics from the corrected monthly NetCDF files. The figure scripts then reproduce the main manuscript analyses from Fig. 2 to Fig. 7, including monsoon precipitation and humid-window diagnostics, eccentricity-obliquity response surfaces, study-area peat-burial forcing extraction, nonlinear peat-carbon burial simulations, the orbital-coupling burial schematic, Sobol sensitivity analysis and spatial extrapolation of monsoon-belt peat-carbon burial over a 1.2 Myr orbital window. The repository also includes intermediate and output products generated by the scripts, such as monsoon masks, study-area forcing parameter files, peat-emulator diagnostics, Sobol sensitivity indices, orbital-trajectory burial fields and figure-ready outputs. The scripts are designed to be run sequentially from the project root. A README file describes the directory structure, required Python packages, processing order, expected inputs and generated outputs.

1 files · 17 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/Office of Satellite and Product Operations (OSPO) office from Visible and Infrared Imager/Radiometer Suite (VIIRS) sensor cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, kelvin) graphics (graphics overlay planes)

Weiskittel, Aaron R.

807 KB

This dataset quantifies the uncertainty in mapping late-successional and old-growth (LSOG) forest across the approximately 4.2 million hectares of Maine's unorganized townships, and tests whether LSOG is rapidly disappearing. Three to four independent, credible mapping methods are compared on a common 100 m grid: (M1) a reproduction of the Hagan et al. (2026) airborne-LiDAR canopy random forest, rebuilt from their public Zenodo deposit; (M2) a logistic model of the FIA field-structure LSOG class on Potapov (GEDI-calibrated) canopy height; (M3) a direct canopy-height threshold; and (M4) the FIA structural class imputed to every pixel via USFS TreeMap (2016, 2020, 2022). Version 1.2.0 additions. This version adds the materials behind the formal Ecosphere Comment on Hagan et al. (2026): (a) a cross-validated accuracy assessment (AUC) of each mapping approach on the original authors' own training plots, showing that high training accuracy does not transfer to agreement among independent maps; (b) an FIA design-based estimate of older forest with sampling-error confidence intervals, the unbiased ground reference the original analysis lacked, putting older forest at about 3.9 percent (3.3 to 4.6) and rising, including on private commercial timberland; (c) a threshold-sensitivity sweep and a 20-seed reproduction ensemble; (d) an ownership-resolved breakdown (private commercial versus public); (e) a hex-scale (8 km) summary of cross-method disagreement; and (f) the Comment manuscript and Supporting Information. Headline findings. Credible methods disagree by roughly 2.8 times on how much LSOG exists and on the location of most LSOG hectares, while agreeing closely on the rare, well-defined old-growth core. Protecting the top 5 to 20 percent of hectares by one map versus another overlaps on only 16 to 30 percent of the ground, so single-map patch-level prioritization for large expenditures is fragile. The design-based FIA estimate and TreeMap imputation both show older forest stable to increasing rather than rapidly declining; the apparent loss reported elsewhere is a gross harvest flux, not a net stock decline. Contents. Derived 100 m GeoTIFFs (reproduced Hagan class, v5.1-GEDI probability, TreeMap class, a per-cell method-consensus layer), summary tables (area by method, pairwise agreement, concordance, prioritization fragility, AUC by approach, design-based older-forest trend with CIs, ownership breakdown, and FIA validation), the analysis R scripts, quick-look figures, the Ecosphere Comment manuscript and Supporting Information, and a full methods-and-findings report (PDF). Privacy. No FIA plot coordinates are included; all products are derived rasters or aggregate summary tables. Caveats: the robust temporal signal is direction rather than precise rate; FIA stand age is modeled, so a structural large-tree domain is reported alongside the age domain; cross-validated intervals are best read as lower bounds because plots are spatially dispersed but not independent. See the README and report for full methods, provenance, and limitations. Version 1.12.0 additions. The cross-map comparison is refined to independent remote-sensing operationalizations only. (a) A three-map remote-sensing ensemble over Maine on a common 100 m grid: reproduced Hagan airborne-LiDAR (any-LSOG 21.9 percent), an FIA-structure class on Potapov GEDI-calibrated spaceborne canopy height (14.0 percent), and the ORNL/Bruening national old-growth stratum (36.1 percent); the three span a 2.6-fold range and agree on only 2.7 percent of flagged hectares, with the ORNL stratum spatially uncorrelated with the structure maps. The USFS TreeMap imputation is reclassified as a second FIA-anchored accounting, reported with the design-based estimate rather than as an independent map. (b) A design-based estimate of LSOG itself: integrated any-LSOG 14.1 percent (12.9 to 15.3) and strict four-axis true LSOG 3.1 percent (2.5 to 3.7) of Maine forestland, the airborne map exceeding even the inclusive ground estimate. (c) A balanced-model LSOG probability surface at 100 m, with a binary class calibrated to the design-based area to bound over-prediction. (d) A multi-objective support vector regression pilot tracing the Pareto front of total versus systematic (attenuation) error. Derived rasters, the three-map agreement layer, the ORNL stratum reprojected to the study grid, tables, R scripts, the updated Comment, and the companion manuscript are included. No FIA plot coordinates are included. Version 1.13.0. Final consolidated release. Adds: a rare-class remedy menu for the reproduced random forest (default vs class weighting vs balanced sub-sampling vs voting-threshold; old-growth detection 0.24 to 0.82, mapped old-growth area 1.0 to 2.3 percent); the definitive five-model LSOG probability map for Maine with across-model uncertainty and reference reserves (MNAP/TNC network, Baxter, Big Reed) over real state and county boundaries; design-based 95 percent confidence intervals for forest-type and ecoregion representation and for disturbance shares; a full robustness/stress-test matrix; and the copy-edited, sole-authored Comment, companion manuscript, and Maine Forest Products Council technical report. Authorship updated to Aaron R. Weiskittel.

1 files · 17 KB · netcdf

Daily Merge seasurfacetemperature from NOAA-20 Visible and Infrared Imager/Radiometer Suite (VIIRS) cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, degree_C) graphics (graphics overlay planes)

1 files · 18 KB · netcdf

Daily Merge seasurfacetemperature from NOAA-20 Visible and Infrared Imager/Radiometer Suite (VIIRS) cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sea_surface_temperature (sea surface sub-skin temperature, kelvin) graphics (graphics overlay planes)

Wang, Ting · Tang, Chao-Sheng · Zeng, Zhixiong

8.0 MB

This dataset supports the study: Dominant drivers and projected increases in soil cracking risk across China The repository contains a global soil cracking database compiled from laboratory experiments and field observations, together with seasonal prediction maps of soil cracking across China for 2022. The database is provided as Soil_Cracking_Database.xlsx . The workbook contains two worksheets: l 01ratio: observations of soil surface crack ratio and associated environmental variables. l 02width: observations of soil crack width and associated environmental variables. Variables include location, plasticity index, soil texture fractions (sand, silt, and clay), temperature, relative humidity, initial water content, final water content, and soil cracking characteristics. The repository also includes seasonal prediction maps for 2022: Soil crack ratio : l Ratio_Spring_2022.tif l Ratio_Summer_2022.tif l Ratio_Autumn_2022.tif l Ratio_Winter_2022.tif Soil crack width : l Width_Spring_2022.tif l Width_Summer_2022.tif l Width_Autumn_2022.tif l Width_Winter_2022.tif All raster datasets are provided in GeoTIFF format using the WGS 1984 Albers Equal Area Conic projection. The prediction maps were generated using machine-learning models trained on the compiled soil cracking database. Detailed information on data collection, model development, model validation, and future climate projections can be found in the associated publication.

1 files · 13 KB · netcdf

Sea surface temperature retrievals produced by NOAA/NESDIS/Office of Satellite and Product Operations (OSPO) office from Visible and Infrared Imager/Radiometer Suite (VIIRS) sensor cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][rows][cols]): l2p_flags swath_latitude (Latitude, degrees_north) swath_longitude (Longitude, degrees_east) sea_surface_temperature (sea surface subskin temperature, degree_C) sses_bias (SSES bias estimate, degree_C) sses_standard_deviation (degree_C) graphics (graphics overlay planes)

Fang, Jianing · Lian, Xu · Ryu, Youngryel · et al.

8.0 MB

Version 3.3 update (June 2026): This version extends LCREF-MODIS (BRDF-normalized red and near-infrared reflectance) from the original 2001-01-2023-12 record through 2026-05-31 (new observations 2024-01-01 to 2026-05-31; biweeks 202401a-202605b) and refines the global land mask. The v3.2 baseline production (Fang et al. 2025) is unchanged: MCD43C1.061 BRDF → biweekly max-NDVI composite normalized to SZA=45° → snow masking → HANTS gap-fill → red/NIR reflectance. Updates in v3.3: Temporal extension : new MODIS observations (MCD43C1.061) for 2024-01 through 2026-05. Gap-filling : HANTS is now applied over a 9-year moving window (±4 years, 216 biweeks) instead of a single full-series fit, better capturing low-frequency variability and accommodating the open-ended record. Windows are best-effort centered and clamped to complete years at the record end; the incomplete 2026 is isolated to one shifted window preserving full annual periodicity. The QA layers (red_qa, nir_qa) are unchanged: 0 = observation, 1 = high-quality HANTS gap-fill, 2 = climatology fill, 3 = no data. Snow ancillary layer : a snow_masked layer (1 = snow-contaminated pixel masked before gap-fill) is provided for the 2024-onward biweeks (0 over the 2001-2023 record). Land mask : all pixels are clamped to the MODIS IGBP land mask (MCD12C1, unioned with the LCREF-v3.2 land footprint), removing ocean/coastal bleed. Validation (v3.3 vs v3.2, 2001-2023 overlap) : observed (QA=0) pixels reproduce v3.2 essentially exactly; pixelwise spatial correlation r ≈ 0.998. Differences are confined to gap-filled pixels, plus an expected increase in trailing-window climatology (QA=2) coverage during 2020-2023. The original version 3.2 description follows below. Paper Reference: Fang, J., Lian, X., Ryu, Y., Jeong, S., Jiang, C., & Gentine, P. (2025). A long-term reconstruction of a global photosynthesis proxy over 1982-2023. Scientific data , 12 (1), 372. https://doi.org/10.1038/s41597-025-04686-6 Usage Notes : This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark. Key updates in version 3.2 include: Quality Flags : New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses. Extension: to include observations from the year of 2023. Snow mask: we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. The LCREF-AVHRR v3.2 (1982-2023) is available at 10.5281/zenodo.11905959 The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at: LCSPP-AVHRR v3.2 (1982-2000): 10.5281/zenodo.7916850 LCSPP-AVHRR v3.2 (2001-2023): 10.5281/zenodo.11906675 LCSPP-MODIS v3.2(2001-2023): 10.5281/zenodo.11657458 A paper describing the technical details is available at https://doi.org/10.1038/s41597-025-04686-6 , which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file "a" representative of the 1 st day to the 15 th day of a month, and the second file "b" representative of the 16 th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.

Huang, Yibin · Chen, Xiao

12,692 rows × 20 cols · 1.2 MB

18 categorical · 1 numeric · 1 text

This archive contains a global compilation of marine net primary productivity (NPP) measurements derived from bottle incubation experiments using radioactive or stable carbon tracers ( 14 C or 13 C). All NPP estimates are reported as depth-integrated values within the euphotic zone. The database includes sampling date, geographic coordinates, integration depth, depth-integrated NPP, measurement method, data source, and reference information. The dataset was compiled to support the evaluation and development of satellite-based and data-driven marine productivity models and to improve observational constraints on the global distribution of ocean primary productivity. Chen, X., Huang, Y*., Liu, H., Cassar, N., Liu, X., Wang, W., Chai, F., Kang, J., & Huang, B. (2026). Reduced estimate of global marine primary productivity and hemispheric redistribution over the satellite era revealed by an expanded global observational database. Submitted to Communications Earth & Environment .

1 files · 18 KB · netcdf

U.S. West Coast Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua High Resolution Sea Surface Temperature (SST) Climatology Fields (July 2002 - March 2014). This suite of Chlorophyll-a (CHLA) and SST climatology and anomaly data products are derived from daily, 0.0125 degree x 0.0125 degree, MODIS Aqua CHLA and SST fields that cover the California Current System (22N - 51N, 155W - 105W) for the 11-year period July 2002 through June 2013. These daily fields, obtained from the NOAA CoastWatch West Coast Regional Node website, were processed using a successive 3x3, 5x5 and 7x7 grid cell hybrid median filtering technique. This technique was found to effectively reduce noise in the daily fields while maintaining features and detail in important regions such as capes and headlands. The resulting median filtered daily fields were then linearly interpolated to a 0.025 degree x 0.025 degree grid and averaged to create 132 monthly mean fields. The seasonal cycles at each 0.025 degree x 0.025 degree grid cell were obtained by fitting each multiyear time series of monthly means to a nine-parameter regression model consisting of a constant plus four harmonics (frequencies of N/(1-year), N-1,4; Risien and Chelton 2008, Journal of Physical Oceanography (JPO)). Even with the median filtering and the temporal averaging of the daily fields, the highly inhomogeneous nature of the MODIS fields still resulted in regression coefficients that were excessively noisy. We therefore applied the same successive 3x3, 5x5 and 7x7 hybrid median filtering technique, described above, to the regression coefficients before finally spatially smoothing the coefficients using a loess smoother (Schlax et al. 2001, JTECH) with filter cutoff wavelengths of 0.25 degree latitude by 0.25 degree longitude. The seasonal cycles were then calculated from the filtered regression coefficients for each 0.025 degree x 0.025 degree grid cell using the mean and all four harmonics. It is important to note that THIS SUITE OF DATA PRODUCTS IS HIGHLY EXPERIMENTAL and is strictly intended for scientific evaluation by experienced marine scientists. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sst (Sea Surface Temperature, degree_C)

Liu, Yanlan · Kumar, Mukesh · Bisht, Gautam

8.0 MB

Data archive supporting the manuscript " Multilayer canopy model outperforms big-leaf model for evapotranspiration predictions under high water and heat stress conditions " (Authors: Raghav, Liu, Kumar, Bisht) This archive provides, for each of 28 eddy-covariance sites, the hourly time series of modeled and observed evapotranspiration (ET) together with the meteorological drivers used to force the models. ET is reported as latent heat flux (LE), in W m⁻² , the native model and tower-measurement unit (to convert to a water flux, divide by the latent heat of vaporization &lambda; ≈ 2.45 × 10⁶ J kg⁻&sup1;: 1 W m⁻² ≈ 0.00147 mm h⁻&sup1;). The two model configurations are run from the same CLM-ml code and differ only in the number of within-canopy layers; in this archive both configurations use identical below-ground (root) and soil parameters , so that differences reflect the above-ground canopy representation alone. Contents File Description <SITE>_ET_hourly.csv` Per-site hourly time series (28 files; columns below). all_sites_ET_hourly.csv All 28 sites concatenated (same columns, plus `Site`). sites_metadata.csv Site list with latitude, longitude, number of hours, and date range. model_forcing_netcdf/<SITE>_forcing.nc Complete model forcing for each site (all driver variables and the gap-filled/closure-corrected flux products; see below) Sites (28) CA-Cbo, CA-Gro, CA-TP3, CA-TPD, CH-Lae, CZ-Lnz, CZ-RAJ, CZ-Stn, DE-Hai, FR-Bil, FR-Hes, IT-Cp2, IT-SR2, US-Bar, US-Me2, US-Me6, US-NC1, US-NR1, US-Oho, US-UMB, US-UMd, US-xAB, US-xBR, US-xDL, US-xHA, US-xJE, US-xTA, US-xTR. Coordinates and record lengths are in sites_metadata.csv . Columns in <SITE>_ET_hourly.csv Column Definition Units TIMESTAMP Time at the start of the hour, as provided in the model forcing (site local-standard-time convention) YYYY-MM-DD HH:MM:SS Site Site identifier - ET_obs_LE_gapfilled_Wm2 Observed latent heat flux, gap-filled by the marginal-distribution-sampling (MDS) method (FLUXNET/ONEFlux `LE_F_MDS`). W m⁻² ET_obs_LE_corrected_Wm2 Observed latent heat flux after energy-balance-closure correction (Bowen-ratio-preserving). This is the target used to evaluate the models. W m⁻² ET_MLCAN_Wm2 Modeled latent heat flux from the multilayer canopy (MLCAN) configuration. W m⁻² ET_1L_Wm2 Modeled latent heat flux from the single-layer / big-leaf (1L) configuration. W m⁻² H_obs_gapfilled_Wm2 Observed sensible heat flux, MDS gap-filled (`H_F_MDS`). W m⁻² H_obs_corrected_Wm2 Observed sensible heat flux after energy-balance-closure correction. W m⁻² SW_IN_Wm2 Incoming shortwave radiation (model driver, `FSDS`). W m⁻² TA_degC Air temperature (model driver, `TBOT`, converted from K). °C VPD_kPa Vapor pressure deficit, computed from observed relative humidity and air temperature. kPa SWC_m3m3 Volumetric soil water content (same across all soil layers). m³ m⁻³ LAI_m2m2 Effective leaf area index used to drive the models (`ELAI`). m² m⁻² Note: Missing values are written as empty fields. The fraction of finite, energy-balance-corrected observed ET per site is given in `sites_metadata.csv` (`ET_obs_corrected_pct_finite`). Model forcing NetCDF files (`model_forcing_netcdf/`) Each `<SITE>_forcing.nc` contains the complete set of driver variables used to run both configurations and the full observed-flux products, at the same temporal resolution. Key variables (units as stored): - Meteorology: `TBOT` (K), `RH` (%), `WIND` (m s⁻&sup1;), `FSDS` (incoming shortwave, W m⁻²), `FLDS` (incoming longwave, W m⁻²), `PSRF` (surface pressure, Pa), `PRECTmms` (precipitation, mm s⁻&sup1;), `CO2MF` (CO₂ mole fraction), `ZBOT` (reference height, m). - Vegetation / soil: `ELAI`, `ESAI` (effective leaf/stem area index), `SWC` (volumetric soil water), `H2OSOI`, `TSOI` (soil-profile moisture and temperature). - Observed fluxes: `LE_F_MDS`, `H_F_MDS` (MDS gap-filled), `LE_c`, `H_c` (energy-balance corrected), `GPP_DT`, `GPP_NT` (daytime/nighttime partitioned gross primary productivity). - Temporal coverage: each site's record covers 1 July - 31 December of each year (the study's analysis window). Methods (brief) Eddy-covariance processing . Half-hourly fluxes were computed with EddyPro and quality-controlled; latent and sensible heat were gap-filled using the MDS algorithm and then corrected for the surface-energy-balance closure gap (Bowen-ratio-preserving). Models. CLM-ml (Bonan et al., 2021; https://doi.org/10.1016/j.agrformet.2021.108435) was run in two configurations viz. multilayer (MLCAN) and single-layer (1L) that share identical code, leaf-level formulations, and soil/root parameters and differ only in the number of within-canopy layers. Each configuration was independently calibrated to the energy-balance-corrected observed ET. Provenance, license, and citation The **original** half-hourly eddy-covariance observations for each site are distributed by AmeriFlux, the ICOS Drought-2018 and Warm-Winter-2020 collections, and NEON; the per-site dataset DOIs are listed in the Supplementary Information file of the associated manuscript. The non-gap-filled (raw) observed LE can be obtained from those original datasets. The modeled ET, the processed (gap-filled and corrected) observed ET, and the assembled forcing in this archive are the data generated by this study . - License: Creative Commons Attribution 4.0 (CC-BY-4.0).

Fox-Kemper, Baylor · DeRepentigny, Patricia · Treguier, Anne Marie · et al.

1 files · 483 KB · pdfdeclared

The ocean and sea ice are central to Earth's climate system, influencing global heat and carbon cycles, weather patterns, and sea level rise. Recent decades have seen rapid advances in Earth System Models (ESMs), but limitations remain in simulating and comparing key oceanic and cryospheric processes across models. A recurring challenge in model intercomparison efforts like the Coupled Model Intercomparison Project (CMIP) is determining the output variables that best represent essential mechanisms while remaining manageable in volume and complexity. Here we present the CMIP7 ocean and sea ice data request, developed through an international, community-based process to prioritize variables for model output. We identify seven opportunities -science-based use cases spanning ocean and cryosphere drivers and responses, paleoclimate, polar amplification, extremes, wind waves, and rapid model evaluation-to guide variable selection and temporal resolution. To address these opportunities we request new high-frequency and depth-integrated variables, support improved diagnostics of ocean heat uptake, sea ice processes, and model-observation comparison, and build on lessons from CMIP6. Our approach enables targeted, efficient, and transparent data curation to support a wide range of users, from model developers to policymakers. This effort reflects a growing need for more sophisticated, integrative model outputs that address pressing climate questions, including regional extremes and tipping points, while laying the groundwork for future modeling developments.

Voet, Gunnar

1 files · 317 KB · zipdeclared

Processing toolbox for moored thermistor chains: clock and in-situ temperature calibration, gridding, and shared-fluctuation drift correction.

Razdar, Babak · Reder, Alfredo

8.1 MB

This dataset provides a curated and ready-to-use collection of gridded climate indicator maps for the ADRION domain, derived from the Copernicus Interactive Climate Atlas/C3S Atlas. It includes reanalysis-based climatological indicators for the 1991-2020 reference period and climate-projection change anomalies for selected future periods relative to the 1981-2010 baseline. Data are provided as GeoTIFF (*.tif) files, with each file representing one climate indicator map for a specific combination of spatial domain, input dataset, temporal aggregation, climate scenario, and, where applicable, future time horizon. The files are organised to support local climate hazard and risk evaluation, climate-service applications, and adaptation-related assessments over the ADRION area. This dataset does not replace the original Copernicus source data, but offers a harmonised subset extracted and structured for easier use within the ADRION context. Users should acknowledge the Copernicus Climate Change Service (C3S) and the Copernicus Interactive Climate Atlas / C3S Atlas when using these files.

1 files · 18 KB · netcdf

U.S. West Coast Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua High Resolution Sea Surface Temperature (SST) Climatology Fields (July 2002 - March 2014). This suite of Chlorophyll-a (CHLA) and SST climatology and anomaly data products are derived from daily, 0.0125 degree x 0.0125 degree, MODIS Aqua CHLA and SST fields that cover the California Current System (22N - 51N, 155W - 105W) for the 11-year period July 2002 through June 2013. These daily fields, obtained from the NOAA CoastWatch West Coast Regional Node website, were processed using a successive 3x3, 5x5 and 7x7 grid cell hybrid median filtering technique. This technique was found to effectively reduce noise in the daily fields while maintaining features and detail in important regions such as capes and headlands. The resulting median filtered daily fields were then linearly interpolated to a 0.025 degree x 0.025 degree grid and averaged to create 132 monthly mean fields. The seasonal cycles at each 0.025 degree x 0.025 degree grid cell were obtained by fitting each multiyear time series of monthly means to a nine-parameter regression model consisting of a constant plus four harmonics (frequencies of N/(1-year), N-1,4; Risien and Chelton 2008, Journal of Physical Oceanography (JPO)). Even with the median filtering and the temporal averaging of the daily fields, the highly inhomogeneous nature of the MODIS fields still resulted in regression coefficients that were excessively noisy. We therefore applied the same successive 3x3, 5x5 and 7x7 hybrid median filtering technique, described above, to the regression coefficients before finally spatially smoothing the coefficients using a loess smoother (Schlax et al. 2001, JTECH) with filter cutoff wavelengths of 0.25 degree latitude by 0.25 degree longitude. The seasonal cycles were then calculated from the filtered regression coefficients for each 0.025 degree x 0.025 degree grid cell using the mean and all four harmonics. It is important to note that THIS SUITE OF DATA PRODUCTS IS HIGHLY EXPERIMENTAL and is strictly intended for scientific evaluation by experienced marine scientists. cdm_data_type = Grid VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]): sst (Sea Surface Temperature, degree_C)