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Funders/Sponsors: EC BlueBridge & Blue-Cloud projects; UN-FAO, IRD; INRAE https://github.com/eblondel/OpenFairViewer/releases/tag/2.7.8

DIVAnd v2.7.9 Diff since v2.7.8 Closed issues: Bulls eye in region without data (#90) How to leverage other data while gridding? (Enhancement) (#96) Failed test (#104) Merged pull requests: CompatHelper: bump compat for Interpolations to 0.14, (keep existing compat) (#103) (@github-actions[bot])

DIVAnd v2.7.9 Diff since v2.7.8 Closed issues: Bulls eye in region without data (#90) How to leverage other data while gridding? (Enhancement) (#96) Failed test (#104) Merged pull requests: CompatHelper: bump compat for Interpolations to 0.14, (keep existing compat) (#103) (@github-actions[bot])

The Last Interglacial (LIG; ~129,000 to 116,000 years ago) is the most recent geologic period with a warmer-than-present climate. Consequently, climate reconstructions from this period provide an opportunity to relate current and projected climatic trends to natural climate variability from a warmer world. We established a ~4.800 year-long record of sea surface temperature (SST) variability from the Eastern Mediterranean Sea at 1-to-4-year resolution by applying mass spectrometry imaging of long-chain alkenones on the finely laminated sapropel deposited during the LIG. Reconstructed SSTs rapidly increased during the early part of sapropel deposition and subsequently remained at a level similar or above modern SSTs. In the early stage of sapropel deposition, the maximal amplitude of decadal variability reached higher values than recorded in the same region during the instrumental era, plausibly due to a highly stratified water column that led to reduced vertical mixing and thus different forcing of SST than today. The later stage of sapropel deposition is characterised by varve deposition, suggesting weakened stratification and its breakdown in the colder months; this implies the establishment of oceanographic conditions with SST forcing similar to modern. A comparison of the decadal SST variabilities from this varved part and the instrumental era reveals that the maximal amplitude of reconstructed decadal SST variability did not exceed the range of the recent period of warming climate. However, examining the more gradual SST trend reveals that the maximal centennial-scale SST increase from the varved sapropel section is below projected centennial-scale SST warming in the 21st century under medium-to-low-emissions scenarios, while the high-emissions scenarios suggest that future SST rise could even outpace the warming trends observed during early sapropel deposition underneath a highly stratified water column.

A CF-compliant Earth Science data analysis library

{"references": ["Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619\u20134646, https://doi.org/10.5194/gmd-10-4619-2017, 2017.", "Hassell et al., (2020). cfdm: A Python reference implementation of the CF data model. Journal of Open Source Software, 5(54), 2717, https://doi.org/10.21105/joss.02717"]} A CF-compliant Earth Science data analysis library

The Cage Aquaculture Particulate Output and Transport (CAPOT) model is an easy to use and flexible farm-scale model that can be used to rapidly estimate particulate waste deposition from cage production. CAPOT is a suitable initial, rapid assessment model to give an overview of potential impact of particulate waste from new or expanded fish cage farms, with little operator expertise by a wide range of stakeholders. The model is wholly encapsulated within standard spreadsheet format (.xlsm). Visualisation of the output is achieved using proprietary contour plotting software, such as SurferTM (Golden Software. CA, USA) and open-source Geographic Information System (GIS) software, such as QGIS, but can be achieved in a more limited form within the spreadsheet itself. Visual Basic for Applications (VBA) macro scripts within the spreadsheets perform calculations and data formatting to produce outputs in Surfer and ESRI (GIS) grid formats. The model should not be used for environment regulation/licensing purposes without checking with the relevant regulatory authority. Disclaimer: This model is distributed in good faith as a work in progress and potentially subject to error. As such, the authors and the University of Stirling do not accept any liability for inaccurate predictions or their consequences. This model is employed at the users own risk.

These tools were developed to validate the ResourceCode (https://resourcecode.ifremer.fr/) WaveWatch III hindcast data set against CMEMS InsituTAC wavebuoy archive data. The tools focus on validation against integrated wave parameters calculated from the wave spectra (e.g. Hm0, Tp, Tm02, etc.). The underlying data format supported is netCDF4, this is the standard used to archive the majority of oceanographic data, and has been used for the hindcast archive. The tools have been modularised to allow future development, and extension to other data formats.

This directory contains the datasets and python scripts necessary to reproduce the figures and tables in the main text of "Local drivers of marine heatwaves: A global analysis with an Earth system model" (Vogt et al. 2022). The .tar.lrz file was compressed on Linux using lrzip.

The data file `CENOGRID_Loess_20.txt` contains the astronomically tuned deep-sea benthic foraminifer carbon (������C) and oxygen (�������O) isotope reference records uniformly covering the entire Cenozoic. The first column is the tuned age in Ma, the second column the ������C, and the third column the �������O record. The original calculations were performed using the CRP Toolbox for MATLAB. In order to avoid installing the toolbox and for better performance, the functions for calculating RP and RQA were here reimplemented, providing identical result. To reproduce the RPs in Fig. 2, use the script `perform_rp.m`, for reproducing the determinism values and upper confidence bounds, use the script `perform_rqa.m`. {"references": ["T. Westerhold, N. Marwan, et al: An astronomically dated record of Earth's climate and its predictability over the last 66 million years, Science, 369(6509), 1383\u20131387 (2020)"]}