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opencc-by-4.0Jun 2021View details →

GFDL CM2.1 Partially-Coupled Simulations Data for "Understanding Lead Times of Warm-Water-Volumes to ENSO Sea Surface Temperature Anomalies"

GFDL CM2.1 partially-coupled idealized simulations: Two sets of idealized experiments with prescribed EP and CP ENSO SST anomaly patterns. Each set of experiments has a prescribed idealized sinusoidal ENSO oscillation with periodicities of 48, 36, and 24 months, respectively. For the details please refer to our paper; Zhao, S., Jin, F.-F., & Stuecker, M. F. (2021). Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies. Geophysical Research Letters, 48(19), e2021GL094366. <a href="https://doi.org/10.1029/2021GL094366">https://doi.org/10.1029/2021GL094366</a>

opencc-by-4.0Nov 2014View details →

Time projections of Sea Surface Temperature, for RCP 4.5 and RCP 8.5, for decades 2020, 2030, 2040 and 2040

The CMIP5 Sea Surface Temperature models projections correspond to the Coupled atmosphere-ocean general circulation models’ output named ‘tos’ (Temperature Of Surface) with a monthly time-step (12 values per year, from 2006 to 2100), for RCP 4.5 and RCP 8.5. For a given RCP, some models can have different sets of input parameters (called input ensemble), numbered r1i1p1, r1i1p2, etc., corresponding to different settings, resulting is an output for each rXiYpZ input. Variable ‘tos’ is provided by 86 combinations of models and input ensembles (see list in Annex). To compute an ensemble mean with equal weight for each model, the different outputs of a single model are first averaged. The resulting averaged models outputs, 1 average per model, are then regridded to a common grid, defined as a regular grid,  with a spatial resolution of ½ ° in latitude per ½ ° in longitude, from 0° to 360° in longitude, and -85° to 85° in latitude. Then, the regridded averages are averaged all together with the same weight. The averaging operations are grid-cell and time independent, which means that the averaging operator is not applied along the space and time dimensions, only in-between the different models values for the same place and time. The result of the operation is a time series of ocean surface temperature, from 2020 to 2050, at a grid resolution of 0.5°. Because of the difference in the spatial gridding, and difference in the land mass representation, some grid points did not used the same number of models averages to compute the final average: the number of model averages per grid cell is given in the final product, as well as the min-max amplitude between model averages.

opencc-by-4.0Sep 2022View details →

sea-surface temperature proxy data (TEX86 and UK'37) from Ocean Drilling Program Site 1168

These 2 data files contain the TEX86 and UK'37 sea surface temperature proxy data from Ocean Drilling Program Site 1168, covering the Eocene to recent (35–0 Ma). These were updated compared to previous versions, wherein some alkenone data was omitted.

opencc-by-4.0Nov 2023View details →

SST forcing files and Model Builds for "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions"

This repository provides archives of the Community Earth System Model version 2.2.0 (CESM2.2.0) and case directories for the simulations used in the "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions" manuscript. The repository includes:<ul><li>The original sea surface temperature forcing files used in each experiment (SST_Forcing Files) </li><li>The F2000CLIMO compset model builds forced for each experiment </li></ul>

opencc-by-4.0Nov 2023View details →

Climatological global-mean Sea Surface Temperature (SST) in AWI-CM-1-1-MR simulations for CMIP6, in preindustrial, present-day, +2°C, +3°C, and +4°C climates

Daily climatologies of global-mean sea surface temperature (SST, parameter 'tos') free-running simulations performed using the coupled climate models AWI-CM-1-1-MR. The unstructured grid-ocean component FESOM was conservatively remapped to the ERA5 grid. Data was averaged across the 5 ensemble members and temporally averaged over 10-year long time periods: 1850-1859 for preindustrial climate, 2015-2024 for present-day, 2034-2043 for +2°C climate, 2061-2079 for +3°C climate, and 2091-2100 for +4°C climate. Data is provided in .nc files, one for each climate.

Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.

Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.

Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 1. Среднемесячная температура воды в б. Новгородская на поверхности: 1 – За период 1931–1973 гг.; 2 – За 1977 г.; 3 – За 1978 г.; 4 – За 1979 г.; 5 – За 1980 г.; 6 – За 1981 г.; 7 – температура нереста (18ºС). Fig. 1. Average monthly sea surface water temperature in Novgorodskaya Bay: 1 – for the period 1931–1973; 2 – for 1977; 3 – for 1978; 4 – for 1979; 5 – for 1980; 6 – for 1981; 7 –spawning temperature (18ºC).

opencc-by-4.0Mar 2022View details →

Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)

Raw data (.nc) in NetCDF4 format, and formatted and rearranged data (.csv) in CSV format. With R Markdown document detailing the steps taken. All data obtained originally from NOAA's NCEP and NCDC data store systems.  Data used in developing and demonstrating explainable AI models for An Overview of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data, Wikle et al. (2022), for the Special Issue on Environmental Data Science for Environmetrics. See https://zenodo.org/record/6353636 for the corresponding model codebase.

opencc-by-4.0Jun 2022View details →

Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa

This dataset accompanies the publication by Wycech et al. "Multiproxy Reconstruction of Pliocene North Atlantic Sea Surface Temperatures and Implications for Rainfall in North Africa" in Paleoceanography and Paleoclimatology. The dataset is comprised of the raw paleo-proxy (Mg/Ca ratios and Uk’37) data and reconstructed sea surface temperatures (SSTs) from the early Pliocene (5 Ma) to modern. The provided data were input into the accompanying R codes, which executed principal component analysis and generated the results described in Wycech et al.

opencc-by-4.0Jun 2022View details →

Tightly linked zonal and meridional sea surface temperature gradients over the past five million years

Climatologies for the climate model simulations performed by Fedorov et al., Nature Geoscience, <a href="https://www.nature.com/articles/ngeo2577">https://www.nature.com/articles/ngeo2577</a>. This table shows how the names of the simulation files provided in this dataset relate to the experiment names provided in Table S2 of Fedorov et al., (2015, Nature Geoscience). Note that experiments 1-26 are from Burls and Fedorov (2014) and published in <a href="https://doi.org/10.5281/zenodo.6762450">https://doi.org/10.5281/zenodo.6762450</a> <table> <tbody> <tr> <td>Experiment # in Article (Table S2)</td> <td>Name of Files</td> </tr> <tr> <td>27</td> <td> abrupt2xCO2_T31_gx3v7*.nc </td> </tr> <tr> <td>28</td> <td> abrupt4xCO2_T31_gx3v7*.nc </td> </tr> <tr> <td>29</td> <td> abrupt8xCO2_T31_gx3v7*.nc </td> </tr> <tr> <td>30</td> <td> abrupt16xCO2_T31_gx3v7*.nc </td> </tr> <tr> <td>Extended Exp 11</td> <td>40p_ILWP_1590deg_tropx2_T31_gx3v7*.nc</td> </tr> <tr> <td>Extended Exp 16</td> <td>60p_ILWP_1590deg_tropx4_T31_gx3v7*.nc</td> </tr> </tbody> </table> Article abstract: The climate of the tropics and surrounding regions is defined by pronounced zonal (east–west) and meridional (equator to mid-latitudes) gradients in sea surface temperature. These gradients control zonal and meridional atmospheric circulations, and thus the Earth’s climate. Global cooling over the past five million years, since the early Pliocene epoch, was accompanied by the gradual strengthening of these temperature gradients. Here we use records from the Atlantic and Pacific oceans, including a new alkenone palaeotemperature record from the South Pacific, to reconstruct changes in zonal and meridional sea surface temperature gradients since the Pliocene, and assess their connection using a comprehensive climate model. We find that the reconstructed zonal and meridional temperature gradients vary coherently over this time frame, showing a one-to-one relationship between their changes. In our model simulations, we systematically reduce the meridional sea surface temperature gradient by modifying the latitudinal distribution of cloud albedo or atmospheric CO2 concentration. The simulated zonal temperature gradient in the equatorial Pacific adjusts proportionally. These experiments and idealized modelling indicate that the meridional temperature gradient controls upper-ocean stratification in the tropics, which in turn controls the zonal gradient along the equator, as well as heat export from the tropical oceans. We conclude that this tight linkage between the two sea surface temperature gradients posits a fundamental constraint on both past and future climates.

opencc-by-4.0Aug 2022View details →

CICE6 standalone sea ice surface temperature output

CICE6 standalone sea ice concentration and sea ice surface temperature data for July-August and October-November 2019. JRA55-do 1.4.0 (2010-2018) and JRA55-do 1.5.0 (2019) was used for atmospheric forcing, ocean forcing was taken from an ACCESS-OM2 (1deg_jra55_iaf_omip2_cycle6) output. A wave propagation and attenuation model (Meylan et al., 2014) was implemented into CICE6 to enable wave forcing from WaveWatch III (CAWCR).

Рис. 7. Сумма температур перед нерестом приморского гребешка (а) и количество спата на коллекторах (б). Fig. 7. The sum of temperatures before spawning of the Japanese scallop (a) and the number of spat on collectors (б). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 7. Сумма температур перед нерестом приморского гребешка (а) и количество спата на коллекторах (б). Fig. 7. The sum of temperatures before spawning of the Japanese scallop (a) and the number of spat on collectors (б).

opencc-by-4.0Jul 2022View details →

Fig. 2 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery

Fig. 2 — The four sections of the sampling site Table 1 — Landsat images features (29) Product Type Pixel size (collected) Pixel size (resampled) Thermal band Landsat 4-5 TM L1 120-meters 30-meters Band 6 Landsat 7 ETM+ L1 60-meters 30-meters Band 6 Landsat 8 OLI/TIRS L1 100-meters 30-meters Band 10/ Band11

opencc-by-4.0Jul 2022View details →

Fig. 3 in Retrieving climate change dependent Sea Surface Temperature (SST) in Southern Turkey by using Landsat thermal imagery

Fig. 3 — SST anomalies: a) T1 Cross-section, b) T2 Cross-section, c) T3 Cross-section, and d) T4 Cross-section

Fig. 6 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 6: Maxiumum growth rate determination of all temperatures, light intensities and nutrient concentrations.

Fig. 5 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 5: Entomoneis sp biomass (Chl a, µg /L) under different N/P ratios and light intensities (a) representing growth under T1°C (b) T2°C (c) and T3°C.

Fig. 2 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 2: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on chlorophyll a concentration (µg/L) of Entomoneis sp. using an N/P ratio of 11.

Fig. 3 in Effect of Nutrient, Light Intensity and Temperature on the Growth Rates and Metabolism of a Stress-Resistant Bacillariophyta Species Entomoneis sp. - in Izmir Bay (Aegean Sea) Abstract

Fig. 3: 3D response surface plot and contour line of Box– Behnken Design showing the mutual effect of temperature and light intensity on growth rate (day-1) of Entomoneis sp. using an N/P ratio of 4.4.