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Figure 4 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 4. Changes in the monthly average temperature of the Barents Sea (red) and their linear trend (blue) at depths of 5 meters (above) and 105 meters (below) for the period 1948-2016.

Figure 3 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 3. Fields of changes in the average monthly temperature of the Barents Sea (°C over 10 years) at depths of 5 meters (above) and 105 meters (below) calculated from the linear trends of its monthly average anomalies for 1948 - 2016.

Figure 9 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 9. Interannual variability of the North Atlantic Oscillation Index (red) and monthly average temperature anomalies of the Barents Sea (blue) at a depth of 5 meters for the period 1948-2016 after applying the Butterworth bandpass filter from 7 to 10 years (above), and the cross-correlation picture of their material transformations without filtering (below). Preliminary removal of linear trends, centering and normalization of the series to their standard deviations were performed.

Figure 2 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 2. Fields of average temperature of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for 1948-2016.

Figure 6 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 6. Energy spectra (blue) of the monthly average temperature anomalies of the Barents Sea at depths of 5 meters (above) and 105 meters (below) for 1948-2016. A confidence level from 5% (black line at the bottom) to 95% (black line at the top) and a spectrum of red noise (red line between them) were drown. The preliminary normalization of the series to their standard deviations was made.

Figure 1 in Seasonal and Interannual Variability of the Barents Sea Temperature

Figure 1. Series of monthly average temperature anomalies for the Barents Sea at depths of 5, 55, 105, 154 and 222 meters for the period 1948-2016 after applying the 2-year low-frequency Butterworth filter.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part06

This dataset is part six of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format.  Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part05

This dataset is part five of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format.  Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part02

This dataset is part two of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format.  Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part03

This dataset is part three of the FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format.  Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-part04

This dataset is part one of the? FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format.  Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

model data for "Observed and Model-Simulated Dramatic Bottom Temperature Variations During a Weakened Typhoon in the Northern Yellow Sea"-Part01

This dataset is part one of the  FVCOM model data of typhoon Lekima during August 2019. The data is in netcdf format. Each nc file contained varaibles including sea level elevation, current, temperature, salinity and mixing parameters of 24 hours dring one day.

North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations

This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x. The data are provided as netcdf files. The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245). Each file contains data for the following one dimensional variables: <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul>

opencc-by-4.0Oct 2024View details →

Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures

Data repository for How unusual was Australia's 2017–2019 Tinderbox Drought? This repository contains LIM data underpinning the paper How unusual was Australia's 2017–2019 Tinderbox Drought? [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in Weather and Climate Extremes 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details).  The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. In all cases, please see the paper for important details on the data and how they were produced.  Global LIMs <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> Tropical Pacific Ocean LIMs <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> Indian Ocean LIMs <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> How to cite this repository If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a>

opencc-by-4.0Aug 2022View details →

Data and code to accompany 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record'

Data and analysis code to accompany the manuscript 'One hundred years of daily sea surface temperature from the Hopkins Marine Station in Pacific Grove, California: A review of the history, acquisition, and significance of the record', published in Oceanography and Marine Biology: An Annual Review (<a href="https://doi.org/10.1201/9781003363873-2">https://doi.org/10.1201/9781003363873-2</a>). Data files include records of sea surface temperature (SST) collected in Pacific Grove, California, USA from  January 20, 1919 to the end of 2020. The analysis code produces a continuous 100+ year record with adjustments made for time of day the data were collected, and filling gaps in the data set where necessary.  This dataset makes use of an earlier 83-year version of the sea surface temperatures produced by Breaker et al. 2005 available at <a href="https://aquadocs.org/handle/1834/20890">https://aquadocs.org/handle/1834/20890</a>, with the data file available at <a href="https://purl.stanford.edu/rc833pc4972">https://purl.stanford.edu/rc833pc4972</a>

opencc-by-4.0May 2023View details →

Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle

This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC).    This dataset includes two .csv files 2022-10-14_NORDACC_Turbidity.csv This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to:  Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS) 2022-10-14_NORDACC_UnderwayData.csv This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to: Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS) About NORDACC: The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization. NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1  For more information about NORDACC see the accompanying paper in HardwareX.