Find research datasets worth reusing

opencc-zeroSep 2019View details →

Data from: Effects of multiple climate change stressors on gene expression in blue rockfish (Sebastes mystinus)

Global climate change is predicted to increase the co-occurrence of high pCO2 and hypoxia in upwelling zones worldwide. Yet, few studies have examined the effects of these stressors on economically and ecologically important fishes. Here, we investigated short-term responses of juvenile blue rockfish (Sebastes mystinus) to independent and combined high pCO2 and hypoxia at the molecular level, using changes in gene expression and metabolic enzymatic activity to investigate potential shifts in energy metabolism. Fish were experimentally exposed to conditions associated with intensified upwelling under climate change: high pCO2 (1200 μatm, pH~7.6), hypoxia (4.0 mg O2/L), and a combined high pCO2/hypoxia treatment for 12 h, 24 h or two weeks. Muscle transcriptome profiles varied significantly among the three treatments, with limited overlap among genes responsive to both the single and combined stressors. Under elevated pCO2, blue rockfish increased expression of genes encoding proteins involved in the electron transport chain and muscle contraction. Under hypoxia, blue rockfish up regulated genes involved in oxygen and ion transport and down regulated transcriptional machinery. Under combined high pCO2 and hypoxia, blue rockfish induced a unique set of ionoregulatory and hypoxia responsive genes not expressed under the single stressors. Thus, high pCO2 and hypoxia exposure appears to induce a non-additive transcriptomic response that cannot be predicted from single stressor exposures alone, further highlighting the need for multiple stressor studies at the molecular level. Overall, lack of a major shift in cellular energetics indicates that blue rockfish may be relatively resistant to intensified upwelling conditions in the short term.

opencc-by-4.0Jun 2017View details →

Probabilistic state-level estimates of US coastal storm property damages from climate change

Probabilistic estimates of property damage (2010 USD) by damage mechanism, sea level measure, region, and time period. Values are derived from Estimates of US coastal damages by local sea level (doi: https://doi.org/10.5281/zenodo.820149) using the code at https://github.com/ClimateImpactLab/acp-impacts Direct (surge + wind) and business-interruption storm damage estimates as well as estimated property below sea level are projected by estimating damages as a function of local sea level (LSL). These damage functions are then applied to probabilistic estimates of local sea level from Kopp et al (2014) using a Monte Carlo simulation. Percentiles of the resulting distributions are presented in the included files. Estimates of property below sea level are calculated for both mean sea level (MSL) as well as mean higher high water (MHHW). In each period, estimates of the cumulative value of inundated property, or property below sea level, are removed from the exposure data set when computing future storm damages; therefore, projections of future storm damages differ between the two measures of sea level.

opencc-by-4.0Oct 2024View details →

Data - Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages

This repository contains the data and scripts required to reproduce the results of the manuscript "Sustainable Development Key to Limiting Climate Change-Driven Wildfire Damages" submitted to the Environmental Research Climate Journal (ERCL).  Brief description of project This project has two main goals: <ol> <li>Examine the key factors influencing global economic wildfire damages </li> <li>Projecting future damages under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP370)</li> </ol> Repository structure <ul> <li>/data directory: contains the data to reproduce the regression analyses and plot the figures presented in the manuscript <ul> <li>/data/historical: contains the historical (training) data that was used for fitting the linear regression model </li> <li>/data/ssp: contains the SSP projection data for all predictors, as well as the projected model output for future wildfire damages</li> <li>/data/source: contains all raw data used in this study</li> </ul> </li> <li>/scripts directory: contains the python scripts to run the regression model and to plot the figures presented in the manuscript <ul> <li>/scripts/linregress: contains the scripts for running the linear regression model and to conduct various model validation steps <ul> <li>run_linregress.py: script to run the linear regression model </li> <li>run_nonlinregress.py: script to run the nonlinear models (preliminary)</li> <li>run_plm.py: script to run the supplementary panel regression model (plm)</li> <li>run_gdp_linregress.py: script to run the alternative linear regression model using absolute damages as outcome variable and GDP as additional independent predictor</li> <li>inspect_model.py: script to conduct model validation</li> </ul> </li> <li>/scripts/plotting: contains the scripts to plot all figures presented in the manuscript <ul> <li>plot_map_y_X_hist.py: script to plot Figure 1 (world maps of historical wildfire damage and predictors used in this study)</li> <li>plot_residual_plots.py: script to plot Figure 2 (residual and partial residual plots of the fitted regression model)</li> <li>plot_beta_coef_model_prediction.py: script to plot Figure 3 (standardized beta coefficients of the fitted regression model and the scatterplots for reported vs. model-estimated wildfire damages)</li> <li>plot_predictor_ssp_timeseries_global.py: script to plot Figure 4 (time-series of the SSP projections of the predictors)</li> <li>plot_map_X_ssp.py: script to plot Figure 5 (world maps of predictor values for the three SSPs explored in this study)</li> <li>plot_ssp_damage_projection_by_region.py: script to plot Figure 6 (projected wildfire damages under the three SSPs and for the six IPCC AR6 regions)</li> <li>plot_ssp_damage_projection_per_predictor.py: script to plot Figure 7 (time-series of global mean projected wildfire damage with all predictors changing and only individual predictors changing)</li> <li>plot_ssp3_ssp1_difference.py: script to plot Figure 8 (time-series of mean avoided wildfire damage in SSP126 compared to SSP370)</li> <li>SI_plot_ssp_damage_projection_lin_vs_nonlin.py: script to plot Figure S1 (comparison of time-series of mean projected wildfire damage with the linear and nonlinear models)</li> <li>SI_plot_ssp_damage_projection_xterm.py: script to plot Figure S2 (comparison of time-series of mean projected wildfire damages using models with and without interaction terms)</li> <li>SI_plot_beta_coef_pop_wui.py: script to plot Figure S3 (same as Figure 3 but for the model using pop_wui instead of PDforest)</li> <li>SI_plot_ssp_population.py: script to plot Figure S4 (population projection under the three SSP scenarios)</li> <li>SI_plot_ssp_map_pop_wui.py: script to plot Figure S5 (world maps of the pop_wui predictor under three SSP scenarios)</li> <li>SI_plot_ssp_map_damage.py: script to plot Figure S6 (world maps of projected wildfire damages under the three SSP scenarios and for the years 2030, 2050 and 2070)</li> <li>SI_plot_ssp_damage_projection_pop_wui.py: script to plot Figure S7 (comparison of the time-series of projected wildfire damage using pop_wui vs PDforest as predictor)</li> <li>SI_plot_predictor_ssp_trend_by_dev_region.py: script to plot Figure S8 (time-series of the SSP projections of the predictors by developmental regions)</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0May 2024View details →

Indicators of Global Climate Change 2024

This release contains the indicators of global climate change updated to the end of 2024. Datasets included are: <ul> <li>Attribution of historical warming 1850-2024</li> <li>Earth's energy imbalance 1971-2024</li> <li>Effective radiative forcing 1750-2024</li> <li>Global mean surface temperature anomalies 1850-2024</li> <li>Global temperature extreme anomalies 1950-2024</li> <li>Greenhouse gas concentrations 1750-2024</li> <li>Greenhouse gas emissions 1750-2023</li> <li>Remaining carbon budgets in 0.1°C increments</li> <li>Sea level rise 1880-2024 (corrected time bounds)</li> </ul>

opencc-by-4.0Apr 2024View details →

CMIP6 Climate Change indicators

Paneuropean maps of climate change indicators (e.g. heating degree days) for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (reference, short time-horizon, medium time-horizon, long time-horizon) derived from CMIP6 climate data. This v2 includes the metadata.

SSH CENTRE - Mini-reports : Focus groups on "Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030"

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU's transition to carbon neutrality.  SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages –  including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU's journey to a sustainable future.The aim of the focus groups was to gather citizen's perspectives, their hopes, concerns and ideas related to the Horizon Mission of Adaptation to Climate Change: support at least 150 European regions and communities to become climate resilient by 2030. The focus group discussion topics while remaining close to the Mission, avoid specific technical references to allow citizens to contribute based on their differing levels of understanding. As part of the SSH CENTRE project, in total, four focus group series will be conducted relating to Adaptation to Climate Change; Restore our Ocean and Waters by 2030; 100 Climate-Neutral and Smart Cities by 2030; A Soil Deal for Europe.  Notes were taken during each focus groups and turned into mini-reports. These mini-reports sum up the essence of the discussion: the participants' main ideas and some interesting quotes.

SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018. Paper ". Impacts of climate change on water quality, benthic mussels and suspended mussel culture in a shallow, eutrophic estuary by Maar et al. Heliyon,

SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018

Datasets used for "Heat Pump - Heating Electrification and Climate Change - Grid Impact Studies"

<h2>                                        Summary</h2>   In this work, we explore long term patterns in electricity demand driven by the dual effects of space heating electrification and climate change. We use an open source nodal power system model of the Electric Reliability Council of Texas (ERCOT) system to investigate a wide range of future climate and technology scenarios that evolve over time, and report results in terms of market prices, reliability and corresponding relative capacity requirements                               <h2>                                        About </h2> The technical analysis aimed to: <h3>1) Understand the Long-Term Patterns:</h3> We aim to analyze patterns in peak load, total load, loss of load, and the seasonality of these phenomena, driven by widespread heat pump adoption alongside climate change. <h3>2) Use Extensive Scenario Analysis:</h3> Explore a wide range of future scenarios, including variations in climate pathways, to capture the uncertainty associated with these long-term changes. In total, 1280 simulation years. <h3>3) Use a validated open source DC OPF model(reproducibility)</h3> Use an open-source nodal power system model of the ERCOT system to simulate and understand the potential impacts on market prices, reliability, and relative capacity requirements. Similar models are available for all interconnections of the conterminous US. <h3>4) Assess Grid Vulnerability:</h3> Assess the vulnerability of the grid to these simultaneous changes, identify potential vulnerability. <h3>5) Provide Insights for System Planners:</h3> Offer results that can assist long-term system planners in anticipating and preparing for potential shifts in grid reliability.

Code and data for publication "Assessing carbon cycle projections from complex and simple models under SSP scenarios" published in "Climatic Change"

Data and scripts for the article "Assessing carbon cycle projections from complex and simple models under SSP scenarios" by I. Melnikova, P. Ciais, O. Boucher and K. Tanaka was accepted for publication in Climatic Change (https://doi.org/10.1007/s10584-023-03639-5) We use bash, CDO, and python.SSP2.xlsx contains preprocessed annual estimates of climate and carbon cycle variables from ESMs and SCMs used in the paper.Two bash scripts contain preprocessing cdo commands for ESM output.s SCMs were preprocessed directly in python.Jupyter notebook (python) contains preprocessing of data and plotting of all figures of the manuscript. The folder "additional" contains some more Excel files needed to run Jupyter-Notebook. Please adapt the folder names.If you have any questions, please contact the corresponding author Irina MELNIKOVA at melnikova . irina@nies.go.jp

opencc-by-4.0Dec 2023View details →

Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?

Supplementary Figures for: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv.Figure S1. Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with p = 0.0889 and p = 0.7139, respectively.Figure S2. Distinctive RFLP patterns obtained with CpnClassiPhyR from in silico digestion of cpn60UT from SbGPQ clones and AY-Col. Lanes labelled MW in in silico RFLP represent HaeIII-digested phage ϕX174 DNA.Figure S3. Phylogenetic tree using neighbour-joining method of the 16S, secY, nusA, rp, secA, cpn60 and tuf sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. Acholeplasma laidlawii PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. Fig. S3 Panel 1: cpn60UT, tuf, and secY trees.Fig. S3 Panel 2: nusA, rp, and secA trees.Fig. S3 Panel 3: 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.Figure S4. Leafhopper feeding-associated damages observed in strawberry plants. A, in the field. B, in the greenhouse after incubation with leafhoppers.Figure S5. Alpha diversity indexes were calculated to study Macrosteles quadrilineatus microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.Figure S6. Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with p = 0.8488.Figure S7. Effect of insecticides on Macrosteles quadrilineatus and Empoasca fabae population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with p = 0.1781 for the aster leafhopper M. quadrilineatus and p = 0.6540 for the potato leafhopper E. fabae.Figure S8. Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.

opencc-zeroDec 2023View details →

Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years

Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by P. australis, which has displaced native marsh vegetation at Piermont Marsh. Although P. australis has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.

opencc-zeroDec 2023View details →

Competitors alter selection on alpine plants exposed to experimental climate change

Investigating how climate change alters selection regimes is a crucial step towards understanding the potential of populations to evolve in the face of changing conditions. Previous studies have mainly focused on understanding how changing climate directly influences selection, while the role of species' interactions has received little attention. Here, we used a transplant experiment along an elevation gradient to estimate how climate warming and competitive interactions lead to shifts in directional phenotypic selection on morphology and phenology of four alpine plants. We found that warming generally imposed novel selection, with the largest shifts in regimes acting on specific leaf area and flowering time across species. Competitors instead weakened the selection acting on traits that was imposed directly by warming. Weakened or absent selection in the presence of competitors was largely associated with the suppression of absolute means and variation of fitness. Our results suggest that although climate change can impose strong selection, competitive interactions within communities might act to limit selection and thereby stymie evolutionary responses in alpine plants facing climate change.

opencc-zeroJan 2024View details →

Modelling the carbon balance in bryophytes and lichens: Presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects

Premise Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO2 exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 exchange rates of these poikilohydric organisms change through time as a function of weather conditions. Methods PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2-exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects. Results Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration. Conclusions Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation.

opencc-zeroJan 2024View details →

Data from: A species' response to spatial climatic variation does not predict its response to climate change

The dominant paradigm for assessing ecological responses to climate change assumes that future states of individuals and populations can be predicted by current, species-wide performance variation across spatial climatic gradients. However, if the fates of ecological systems are better predicted by past responses to in situ climatic variation through time, this current analytical paradigm may be severely misleading. Empirically testing whether spatial or temporal climate responses better predict how species respond to climate change has been elusive, largely due to restrictive data requirements. Here we leverage a newly collected network of ponderosa pine tree-ring time series to test whether statistically inferred responses to spatial versus temporal climatic variation better predict how trees have responded to recent climate change. When compared to observed tree growth responses to climate change since 1980, predictions derived from spatial climatic variation were wrong in both magnitude and direction. This was not the case for predictions derived from climatic variation through time, which were able to replicate observed responses well. Future climate scenarios through the end of the 21st century exacerbated these disparities. These results suggest that the currently dominant paradigm of forecasting the ecological impacts of climate change based on spatial climatic variation may be severely misleading over decadal to centennial timescales.

opencc-zeroJan 2024View details →

Projected trophic changes in species-carrying capacities under climate change scenarios

Climate controls the amount of energy available for plants, which in turn determines the quantity of resources available for animals. It follows that when climate changes, so should trophic communities. Using a novel modeling approach, we investigate how bird and mammal trophic communities might disassemble and reassemble under 21st century climate changes. We show that trophic structures are expected to undergo profound changes globally, chiefly in the tropics and across high latitudes in the northern hemisphere. This trophic reorganization of communities is characterized by shifts in species richness within trophic guilds. While some guilds might face population collapses, others are projected to find new opportunities to maintain stable populations in previously inhospitable areas. The proposed models offer a tool for projecting and understanding the trophic ramifications of climate change, highlighting their potential in guiding future research and conservation efforts.

opencc-zeroFeb 2024View details →

Geographic distribution change and climatic niche change of Odonates in Great Britain

Species are largely thought to maintain broadly static niches over time, an assumption underpinning much theoretical ecology including the implementation of ecological models to project species' current and future distributions. Here, we assess niche conservatism in odonates in Great Britain over the past six decades by simultaneously quantifying changes in species geographic distribution and evaluating temporal trends in species realised climatic niche.

opencc-zeroFeb 2024View details →

Data for: Dissolved organic matter (DOM) offsets the detrimental effects of climate change in the nitrogen fixing cyanobacterium Crocosphaera

<div> <div> <div> <div> Diazotrophs provide a significant reactive nitrogen source in the ocean. Increased warming and stratification may decrease nutrient availability in the future, forcing microbial communities to use dissolved organic matter (DOM). Not depending on reactive nitrogen availability, diazotrophs may be "winners" in a nutrient depleted ocean. However, their ability to exploit DOM may influence this success. We exposed cultures of the widespread Crocosphaera to low (26°C, pH 8.1), moderate (28°C, pH 8.0), and extreme (30°C, pH 7.9) climate change scenarios, under control or DOM-amended conditions. Growth was suboptimal in the low and extreme treatments, and favoured in the moderate treatment. DOM was preferred as a carbon source regardless of the treatment, and promoted N2 fixation in extreme conditions. This was reflected in the increased expression of photosynthesis genes to obtain energy. DOM provides Crocosphaera with a key ecological advantage, possibly dictating diazotroph-derived nitrogen inputs in the future ocean. </div> </div> </div> </div>

opencc-by-4.0Aug 2023View details →

High quality figures of "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"

This repository provides the figures for the publication "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China" in their original resolution, ensuring clarity and high-quality visual representations for readers.

opencc-zeroFeb 2024View details →

Major changes in fish thermal habitat diversity in Canada's Arctic lakes due to climate change

Climate warming is a major disruptor of fish community structure globally. We use large-scale geospatial analyses of 447,077 Canadian Arctic lakes to predict how climate change would impact lake thermal habitat diversity across the Arctic landscape. Increases in maximum surface temperature (+2.4–6.7 °C), ice-free period (+14–38 days), and thermal stratification presence (+4.2–18.9%) occur under all climate scenarios. Lakes, currently fishless due to deep winter ice, open up; many thermally uniform lakes become thermally diverse. Resilient coldwater habitat supply is predicted; however, thermally diverse lakes shift from providing almost exclusively coldwater habitat to providing substantial coolwater habitat and previously absent warmwater habitat. Across terrestrial ecozones, most lakes exhibit major shifts in thermal habitat. The prevalence of thermally diverse lakes more than doubles, providing refuge for coldwater taxa. Ecozone-specific differences in the distribution of thermally diverse and thermally uniform lakes require different management strategies for adapting fish resource use to climate change.