Find research datasets worth reusing

opencc-by-4.0Oct 2017View details →

Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia

Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.

Fig. 1 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 1. Methodology used to predict morning emergence time of Gambelia sila. Emergence was predicted as the time of day immediately preceding a distinct upward slope in the lizard's T b (triangles and dotted line) based on the assumption that it would take several minutes for the radio transmitter to heat in the sun. The rising Tb was also typically associated with the departure from the burrow physical model temperatures (diamonds and long-dashed line) and the approach of the open (sun) physical model temperatures (squares and short-dashed line). In each case, the predicted time was then compared to the observed emergence time when the lizard's head first appeared outside its burrow. The average difference between observed and predicted emergence times was 11 minutes and 37 seconds.

Fig. 4 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 4. Proportions of correctly predicted observations of microhabitat use of Gambelia sila using temperature-based activity estimation based on physical model temperatures. Lizard microhabitat use was predicted correctly most often when they were in the open, but overall microhabitat use was not accurately predicted with TBAE in this heliothermic lizard.

Fig. 3 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 3. Temperature-based activity estimation resulted in accurate prediction of above-ground activity by Gambelia sila more often than accurate prediction of below-ground (burrow) occupation. Using air temperature (T air) was superior to using physical model temperatures when predicting below-ground occupation. For both methods, ~93% of observations predicted to be above ground were correct, whereas 62% (using T air) and 51% (using physical models) were correct for below-ground predictions.

Fig. 2 in Temperature-based activity estimation accurately predicts surface activity, but not microhabitat use, in the Endangered heliothermic lizard Gambelia sila

Fig. 2. Proportions of correct predictions using air temperature to predict surface activity versus below-ground refuge use of Gambelia sila. This method resulted in accurate predictions 64–76% of the time among the various temperature differentials shown on the x-axis. Predictions were maximized (76% correct) using the criterion that lizards are above ground when their body temperatures (T b) are at least 6 °C above the air temperature (T ).

Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations: data and code

This is the data and code used for a journal paper entitled "Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations", written by Mu-Ting Chien and Daehyun Kim in 2024. This paper is in minor revision in the Journal of Advances in Modeling Earth System. The submitted paper is here: (https://essopenarchive.org/doi/full/10.22541/essoar.171322728.86206700/v1).

CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL

CESM2 Experiment names: <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> Details for files cesm2.[experiment name].SST.noise.nc <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207 For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374

opencc-by-4.0Sep 2018View details →

Road surface temperature forecast study HKI-TKU 0708

This dataset includes road weather station measurements, radar and HARMONIE forecast data related to manuscript entitled "Verification of road surface temperature forecasts utilizing data from mobile sensors". The manuscript will be submitted to a scientific journal for publication. Road weather model output data is also included. Each folder contains ReaMe file for the folder's data.

opencc-by-4.0Feb 2019View details →

Data assimilation-based surface temperature reconstructions over the last two millennia over Antarctica

This dataset contains data assimilation-based temperature and δ18O reconstructions in 10 Antarctic regions over the last two millennia, presented in : <blockquote> <a href="https://www.clim-past-discuss.net/cp-2018-90/">Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018. </a> </blockquote> We use a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al., 2017) to constrain model ensembles derived from two simulations: one performed using ECHAM5-MPI-OM that covers the period 800-1999 CE with a horizontal resolution of 3.75° by 3.75° (Sjolte et al., 2018), and the other performed with ECHAM5-wiso, spanning 1871-2011 CE at 1.125° spatial resolution (Steiger et al., 2017). This latter simulation is available <a href="https://zenodo.org/record/1249604#.XHa824Uo_RY">here</a>. Four netCDF files are available: <ol> <li>d18O_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based δ18O reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>ts_DA_ECHAM5-MPI-OM_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-MPI-OM</li> <li>d18O_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based δ18O reconstructions using the model ensemble derived from ECHAM5-wiso</li> <li>ts_DA_ECHAM5-wiso_1-2015.nc: data assimilation-based surface temperature reconstructions using the model ensemble derived from ECHAM5-wiso</li> </ol> The variables included in the NetCDF files are: <ul> <li>region: integers from 1 to 10 corresponding to the ID of the ten reconstructions targets, that were defined in Stenni et al. (2017): <ul> <li>1: East Antarctic Plateau</li> <li>2: Wilkes Land Coast</li> <li>3: Weddell Sea Coast</li> <li>4: Antarctic Peninsula</li> <li>5: West Antarctic Ice Sheet</li> <li>6: Victoria Land Coast-Ross Sea</li> <li>7: Dronning Maud Land Coast</li> <li>8: West Antarctica</li> <li>9: East Antarctica</li> <li>10: Antarctica</li> </ul> </li> <li>time: integers from 1 to 2015, corresponding to the years CE covered by the reconstructions</li> <li>DA_ts (or DA_d18O): data assimilation-based reconstructed surface temperature (or δ18O). The values are annual means and are given in anomalies computed over full period. The units are degrees celsius (or permil). </li> <li>DA_ts_std (or DA_d18O_std): Weighted standard deviation of the particles used for reconstructing temperature (or δ18O). The units are degrees celsius (or permil).</li> </ul> For a detailed description of the experimental design, please see the associated publication (Klein et al., 2018). Don't hesitate to contact <a href="mailto:francois.klein@uclouvain.be">François Klein</a> for more information. References Klein, F., Abram, N. J., Curran, M. A. J., Goosse, H., Goursaud, S., Masson-Delmotte, V., Moy, A., Neukom, R., Orsi, A., Sjolte, J., Steiger, N., Stenni, B., and Werner, M.: Assessing the robustness of Antarctic temperature reconstructions over the past two millennia using pseudoproxy and data assimilation experiments, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-90, in review, 2018. Sjolte, J., Sturm, C., Adolphi, F., Vinther, B. M., Werner, M., Lohmann, G., and Muscheler, R.: Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, Climate of the Past, 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018, 2018. Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H., and Hakim, G. J.: Climate reconstruction using data assimilation of water isotope ratios from ice cores, Journal of Geophysical Research: Atmospheres, 122, 1545–1568, https://doi.org/10.1002/2016JD026011, 2017. Stenni, B., Curran, M. A. J., Abram, N. J., Orsi, A., Goursaud, S., Masson-Delmotte, V., Neukom, R., Goosse, H., Divine, D., van Ommen, T., Steig, E. J., Dixon, D. A., Thomas, E. R., Bertler, N. A. N., Isaksson, E., Ekaykin, A., Werner, M., and Frezzotti, M.: Antarctic climate variability on regional and continental scales over the last 2000 years, Climate of the Past, 13, 1609–1634, https://doi.org/10.5194/cp-13-1609-2017, 2017.

opencc-by-4.0Sep 2019View details →

Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet"

Source data for "Discovery of topological Weyl fermion lines and drumhead surface states in a room temperature magnet" by I. Belopolski et al., Science 365, 1278-1281 (2019).

opencc-by-4.0Sep 2019View details →

ECMWF ERA5 Monthly surface air temperature anomalies (Celcius) relative to 1981-2010

Monthly global-mean and European-mean surface air temperature anomalies relative to 1981-2010, from January 1979 to August 2019.  Data source: ERA5. Credit: Copernicus Climate Change Service/ECMWF. See <a href="https://climate.copernicus.eu/surface-air-temperature-august-2019">https://climate.copernicus.eu/surface-air-temperature-august-2019</a> for more information.

opencc-by-4.0Aug 2019View details →

A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003–2017

The LSTC dataset contains land surface temperature data in China (about 9.6 million square kilometers of land) during the period of 2003-2017, in monthly temporal and 5600 m spatial resolution. It combines MODIS daily data, monthly data and meteorological station data to reconstruct the true LST under cloud coverage, and then the data performance is further improved by establishing a regression analysis model. The accuracy analysis shows that the  reconstruction result is closely correlated with the in-situ measurements, with an average RMSE is 1.39 °C, an average MAE of 1.30 ° C and an R2 of 0.97.  The dataset can be used for the spatiotemporal evaluation of LST and will be useful for high temperature and drought studies and food security.

Figure 8 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 8. The relationship between the temperature of the sea surface layer obtained from drifters and SST according to Landsat-5, -7 Level-2 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.

Figure 4 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 4. An example of the absence in the archives of images over the central part of the Caspian Sea (flight track N 166 of the Landsat-7 satellite on 22 July 2008.

Figure 2 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 2. Drifter tracks in the Caspian Sea: (a) from 4 October 2006 to 20 February 2007, and (b) from 19 July 2008 to 10 October 2008.

Figure 10 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 10. Dependence between SST from the drifter and according to data from Landsat-5, -7 sensors having different levels of processing.

Figure 7 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 7. Histogram of temperature determination error values according to Landsat Level-1 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.

Figure 3. A in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 3. A mosaic of Landsat-7 images in the Caspian Sea: (a) from 4 October 2006 to 20 February 2007, and (b) from 19 July 2008 to 10 October 2008. Drifter tracks are superimposed on satellite images.

Figure 9 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 9. Histogram of temperature determination error values according to Landsat Level-2 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.

Figure 6 in Assessment of the accuracy of determining the Caspian Sea surface temperature by Landsat-5, -7 satellites based on the measurements of drifters

Figure 6. The relationship between the temperature of the sea surface layer obtained from drifters and SST according to Landsat-5, -7 Level-1 data: (a) measurements that have a time difference of no more than two hours with the flight of the satellite; (b) all measurements on the day of the satellite flyby.