Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes

Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".

opencc-by-4.0Jul 2022View details →

Data files for figures in "Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming" by Bird et al. 2022

The data files for figures in Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming by Bird, Bodeker and Clem. The data files are provided either as self-describing netCDF files, or .csv files with column descriptors.

opencc-by-4.0Jul 2022View details →

Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"

Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in Nature Ecology & Evolution, 2022, https://doi.org/10.1038/s41559-022-01888-8). Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years. Scripts written by Tonke Strack DATA SOURCES * WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019). * LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021). * MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005). * planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv DATA 1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt 2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv 3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv CODE 1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2) 2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series 3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4) 4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5) *Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged Globigerinoides ruber ruber and Globigerinoides ruber albus, because some studies only reported them together as Globigerinoides ruber. Also, P/D intergrades (an informal category of morphological intermediates between Neogloboquadrina incompta and Neogloboquadrina dutertrei) were merged with Neogloboquadrina incompta. In total, 41 species of planktonic foraminifera were included in this study. Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Sci. Data 4, 170109, doi:10.1038/sdata.2017.109 (2017).

opencc-by-4.0Oct 2022View details →

Dataset Global Warming Forecast using Acceleration Factors

The dataset includes results of Global Warming forecast using four methods. The methods include a parabolic trendline of the last 61 years of global warming and cumulated CO2 emissions. Two other methods apply the velocity and the acceleration of global warming and cumulative CO2 emissions. The relation between the global surface temperature change and the change in the cumulative CO2 emissions was determined in previous publications as 0.000745°C/GtCO2. The average result from all four methods for the business as usual CO2 mitigation scenario is 4.4°C (4.1°C -5.0°C). According to this forecast, the global temperature change will reach 1.5°C in 2031 (9 years from now) and 2.0°C in 2047 (25 years from now).

opencc-by-4.0Oct 2022View details →

Hourly LC impacts - Global Warming - current mix and future scenarios, average demand

Dataset on LCA results of electricity generation and supply in Italy for 2018, 2019 and 2020 (current mix) and two future scenarios (2030) - Global Warming, average demand perspective. Modelling materials and methods are described in the paper "Life-cycle assessment of current and future electricity supply in Italy: addressing average and marginal hourly demand".

opencc-by-4.0Feb 2023View details →

Population-adjusted pseudo global warming simulations over the Great Lakes Region

Summaries of pseudo global warming simulations over the Great Lakes Region. Specifics below:   Each CSV file provides regional summaries from PGW simulations, either over land, for urban grids, or rural grids. This includes cumulative hours above critical heat stress threholds as well as their population-adjusted values. The 'Future_sensitivities_land.csv' file calculates heat stress in the future by keeping all variables except one the same value as the historical run. Among the geotiffs, _perc_land files include percentage of hours in summer above the National Weather Service heat index and wet bulb globe temperature thresholds, _pop_land incorporate population adjusted heat stress above those thresholds, the TEMP_contribution files estimate changes in heat stress if only air temperature changed and all variables remained the same as historical values and the HI_ and WBGT_pop rasters include summer average heat stress and their corresponding populations.  See Chakraborty et al. Under Review (will be updated on paper publication) for more details.

opencc-by-4.0Feb 2023View details →

Increased impact of heat domes on 2021-like heat extremes in North America under global warming

The key codes and processed data for the paper.

opencc-by-4.0Jul 2023View details →

Simulated discharge statistics in Central and Southwestern Europe considering water use under 2K global warming

We provide a novel, high-resolution hydrological modelling dataset using pseudo-global warming climate data as forcing to the Community Water Model (CWatM). CWatM is a state-of-the-art large-scale rainfall-runoff and channel routing water resources model that is process-based and used to quantify water supply, as well as human water withdrawals from different sectors (industry, domestic, agriculture) and multiple sources representing the effects of water infrastructure, including reservoirs, groundwater pumping and irrigation canals. CWatM is forced by a pseudo-global warming (PGW) experiment from 1981 to 2010. PGW simulations resemble historical weather patterns and events under globally warmer conditions (here, 2 K global warming) by perturbing historical, reanalysis-driven regional climate simulations. We performed simulations considering regular incremental adjustments of the historical water withdrawals (ranging between +/- 50% of historic water withdrawals) under PGW conditions. That range represents an ad hoc and simplified representation of multiple possible future water management scenarios across Southwestern and Central Europe. The approach allows us to investigate the effects of changing water withdrawals under 2 K global warming. Especially in Western and Central Europe, the projected impacts on low flows highly depend on the chosen water withdrawal assumption. The data highlights the importance of accounting for future water withdrawals in discharge projections.   Discharge statistics based on daily output from CWatM within 1981-2010: <ul> <li>Q1 - 1st percentile</li> <li>Q5 - 5th percentile</li> <li>Q10 - 10th percentile</li> <li>Qavg - average discharge</li> <li>Q90 - 90th percentile</li> <li>Q95 - 95th percentile</li> <li>Q99 - 99th percentile</li> </ul> Files: <ul> <li>Qxx_reference: CWatM considering historical water use forced by RACMO-ERA5</li> <li>Qxx_PGW: CWatM considering historical water use forced by RACMO-ERA5 + climate pertubations under 2 K global warming. In the reference experiment, RACMO is forced at the lateral and sea surface boundaries of the model domain by unperturbed ERA5 reanalysis data, while in the pseudo-global warming experiment, the forcing data consist of perturbed reanalysis data. Perturbations are added to the ERA5 reference data corresponding to climate change patterns of surface pressure and sea surface temperature, and atmospheric profiles of temperature, relative humidity, and wind speed components that are retrieved from a 16-member single model initial condition ensemble of EC-EARTH global climate simulations.</li> <li>Qxx_PGW_adjusted_demand: We have performed 11 additional hydrological simulations adjusting the historical water demand (ranging between +/- 50% of historic water withdrawals) to enable sensitivity assessments of low and high flows under 2 K global warming.</li> </ul> An upcoming publication will be made available and linked to this research very soon.

opencc-by-4.0Sep 2023View details →

Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"

Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble". 1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>). 2. data_txt.tar contains the results used to obtain all the figures given in the article.

opencc-by-4.0Aug 2020View details →

Code and data for "Contrasting upper and deep ocean oxygen response to protracted global warming," by Frölicher et al., Global Biogeochemical Cycles, 34, e2020GB006601: https://doi.org/10.1029/2020GB006601

This file contains the data and python/NCL scripts that have been used for the analysis in this paper.

opencc-by-4.0May 2024View details →

Time-varying global energy budget since 1880 from a reconstruction of ocean warming

This dataset was produced as part of the research presented in the paper "Time-varying global energy budget since 1880 from a reconstruction of ocean warming" published in PNAS. For detailed methodology, analysis, and interpretation of the data, please refer to the original publication: 10.1073/pnas.2408839122.

dryad40/100

Data for: Curbing global solid waste emissions toward net-zero warming futures

No global analysis has considered the warming that could be averted through improved solid waste management and how much that could contribute to meeting the Paris Agreement's 1.5° and 2°C pathway goals or the terms of the Global Methane Pledge. With our estimated global solid waste generation of 2.56 to 3.33 billion tonnes by 2050, implementing abrupt technical and behavioral changes could result in a net-zero warming solid waste system relative to 2020, leading to 11 to 27 billion tonnes of carbon dioxide warming–equivalent emissions under the temperature limits. These changes, however, require accelerated adoption within 9 to 17 years (by 2033 to 2041) to align with the Global Methane Pledge. Rapidly reducing methane, carbon dioxide, and nitrous oxide emissions is necessary to maximize the short-term climate benefits and stop the ongoing temperature rise.

opencc-zeroNov 2023View details →

opencc-by-4.0Mar 2024View details →

Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming

Dataset used to analyize role of climate variability

F I G U R E 7 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 7 Model prediction of the proportion of juvenile Atlantic salmon choosing to smolt as 1-year-olds (full saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), 2-year-olds (medium saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), and 3-year-olds (low saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5). The red line is the point of reaction norm calibration to Piggins and Mills (1985).

F I G U R E 5 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 5 Ensemble average daily water temperature by day of year for the future projections under the three shared socioeconomic pathways and representative concentration pathways (SSP1-RCP2.6 left, SSP5-RCP7.0 middle, and SSP5-RCP8.5 right). Each line represents the day of year average temperature for the climate forcing ensemble with colors transitioning from blue to red toward the end of the century (starting with 2020 and ending with 2100). The lower dashed line represents the lower growth threshold temperature of 7 C, and the upper dashed line represents the upper growth threshold temperature for 23 C (Elliott & Hurley, 1997).

F I G U R E 6 Projected change between 1960 and 2100 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 6 Projected change between 1960 and 2100 in length-at-smoltification decision (a, b, and c), length-at-smoltification as 1-year-olds (d, e, and f), and length-at-smoltification as 2-year-olds (g, h, and i) under the three shared socioeconomic pathways and representative concentration pathways: SSP1-RCP2.6 (green), SSP3-RCP7.0 (orange), and SSP5-RCP8.5 (red) for juvenile Atlantic salmon in the Burrishoole. The gray-shaded area represents the historical reference (2000 to 2020), and the red vertical line represents the historical average.

F I G U R E 4 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 4 Generalized linear model of body length (mm) as a function of cumulative growing degree days (CGDD, C day) for the 23 observed cohorts of juvenile Atlantic salmon in the Burrishoole watershed. The solid line represents the mean length, and the gray bands represent the 95% prediction interval. The outer lines represent the sample density.

F I G U R E 3 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar

F I G U R E 3 The residual error between observed and predicted water temperature (top panel), and the in-situ water temperature (black line) and long short-term memory neural network water temperature prediction (red crosses) for the training (1961–1994) and validation (1995–2019) dataset in the Mill Race (bottom panel). Years excluded due to accumulation of internal sate (green), prolonged periods of missing data (blue shaded), and measurement error (red shaded) are shown in the top panel, and the delineation of the training and validation period is shown by the vertical dashed line in both panels.