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47 results for “climate forcing”
Data from: Sporadic sampling not climatic forcing drives observed early hominin diversity
The role of climate change in the origin and diversification of early hominins is hotly debated. Most accounts of early hominin evolution link observed fluctuations in species diversity to directional shifts in climate or periods of intense climatic instability. None of these hypotheses, however, have tested whether observed diversity patterns are distorted by variation in the quality of the hominin fossil record. Here, we present a detailed examination of early hominin diversity dynamics, including both taxic and phylogenetically corrected diversity estimates. Unlike past studies, we compare these estimates to sampling metrics for rock availability (hominin-, primate-, and mammal-bearing formations) and collection effort, in order to assess the geological and anthropogenic controls on the sampling of the early hominin fossil record. Taxic diversity, primate-bearing formations, and collection effort show strong positive correlations, demonstrating that observed patterns of early hominin taxic diversity can be explained by temporal heterogeneity in fossil sampling rather than genuine evolutionary processes. Peak taxic diversity at 1.9 million years ago (Ma) is a sampling artefact, reflecting merely maximal rock availability and collection effort. In contrast, phylogenetic diversity estimates imply peak diversity at 2.4 Ma and show little relation to sampling metrics. We find that apparent relationships between early hominin diversity and indicators of climatic instability are, in fact, driven largely by variation in suitable rock exposure and collection effort. Our results suggest that significant improvements in the quality of the fossil record are required before the role of climate in hominin evolution can be reliably determined.
Response of the Snowball Earth Climate to Orbital Forcing at High CO2 Level
<p>CAM3&CLM3 simulation results that support the findings of this study are archived here.</p>
Deglacial climate changes as forced by different ice sheet reconstructions - model ouputs
<p>This dataset contains the model output corresponding to the paper entitled "Deglacial climate changes as forced by different ice sheet reconstructions" submitted to Climate of the Past. For the description of the model and simulations we refer to this article.</p> <p> </p> <p><strong>Simulations:</strong><br> degla_P_bathy_500yr_is_SH_nobathy = with ICE_6G_C, fixed bathymetry<br> degla_P_bathy_500yr_is_SH = with ICE_6G_C, evolving bathymetry<br> degla_P_bathy_500yr_is_SH_bis = with ICE_6G_C, evolving bathymetry, mask modified<br> degla_T_bathyT_100yr_is_SH_nobathy = with GLAC-1D, fixed bathymetry<br> degla_T_bathyT_100yr_is_SH = with GLAC-1D, evolving bathymetry<br> degla_T_bathyT_100yr_is_SH_FWF = with GLAC-1D, evolving bathymetry, fresh water flux<br> degla_T_bathyT_100yr_is_SH_FWFtest3 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 3<br> degla_T_bathyT_100yr_is_SH_FWFtest4 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 4</p> <p> </p> <p><strong>Variables and corresponding files:</strong><br> <em>Evolution of ocean volume (m3):</em><br> volume_ocean_degla_P_bathy_500yr_is_SH.txt<br> volume_ocean_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Evolution of ocean surface area (1e6 km2):</em><br> surface_area_degla_P_bathy_500yr_is_SH.txt<br> surface_area_degla_T_bathyT_100yr_is_SH.txt</p> <p><em>Sea land masks for time slices:</em><br> tmask_bathy_P_0yr_SH_CC_PI.nc<br> tmask_degla_P_bathy_500yr_is_SH_21ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_21ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_12ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_12ka.nc<br> tmask_degla_P_bathy_500yr_is_SH_9ka.nc<br> tmask_degla_T_bathyT_100yr_is_SH_9ka.nc</p> <p><em>Evolution of global mean temperature (degree C):</em><br> Temperature_evolution_degla_P_bathy_500yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH.txt<br> Temperature_evolution_degla_P_bathy_500yr_is_SH_bis.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt<br> Temperature_evolution_degla_T_bathyT_500yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWF.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest3.txt<br> Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest4.txt</p> <p><em>Temperature maps for time slices:</em><br> temp_degla_P_bathy_500yr_is_SH_nobathy_21ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_21ka.nc<br> temp_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> temp_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc</p> <p><em>Evolution of salinity:</em><br> iLOVECLIM_salinity_ICE-6G_C.nc<br> iLOVECLIM_salinity_GLAC-1D.nc</p> <p><em>Temperature evolution at NGRIP location:</em><br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Temperature evolution at EDC location:</em><br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Evolution of surface albedo (all globe):</em><br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of surface albedo (Northern Hemisphere):</em><br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_bis.nc</p> <p><em>Evolution of surface albedo (Southern Hemisphere):</em><br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_bis.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Northern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Evolution of sea ice area in the Southern Hemisphere (1e12 km2):</em><br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH.nc</p> <p><em>Winter sea ice fraction and mixed layer depth (m) at time slices:</em><br> iLOVECLIM_sea_ice_mld_bathy_P_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_bathy_T_21000yr_SH_21ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_10ka.nc<br> iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_10ka.nc</p> <p><em>Evolution of the maximum strength of AMOC:</em><br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_nobathy.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWF.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc<br> iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc</p> <p><em>Meridional overtunring circulation at time slices:</em><br> MOC_degla_P_bathy_500yr_is_SH_21ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_10ka.nc<br> MOC_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_21ka.nc<br> MOC_degla_T_bathyT_100yr_is_SH_10ka.nc</p>
Climatic forcing and individual heterogeneity in a resident mountain bird: legacy data reveal effects on reproductive strategies
<p><span>Optimization of clutch size and timing of reproduction </span><span>have substantial effects on lifetime reproductive success in vertebrates, and b</span><span>oth </span><span>individual quality and </span><span>environmental variation may impact </span><span>life history strategies</span><span>. </span><span>We tested hypotheses related to maternal investment and timing of reproduction</span><span>, using 17 years (1978-1994) of individual-based life history data on willow ptarmigan (<em>Lagopus l. lagopus</em>, </span><span>n=290 breeding females with n=319 breeding attempts</span><span>) in central Norway. We analysed whether climatic variation and individual state variables (age and body mass) affected number of offspring and timing of reproduction, and individual repeatability in strategies</span><span>. The results suggest that willow ptarmigan share a common optimal clutch size that is largely independent of measured individual states. While we found no clear direct weather effects on clutch size, higher spring temperatures advanced onset of breeding, and early breeding was followed by an increased number of offspring. Warmer springs were positively related to maternal mass, and mass interacted with clutch size in production of hatchlings. Finally, clutch size and timing of reproduction were highly repeatable within individuals, indicating that individual quality guided trade-offs in reproductive effort. Our results demonstrate how climatic forcing and individual heterogeneity in combination influenced life history traits in a resident montane keystone species.</span></p>
Future land cover and land cover dynamic trajectories in China under anthropogenic and climate forcing in 8 SSP-RCP scenarios
<p>The projection of future land cover in 21st century in China and land cover dynamic trajectories in 8 SSP-RCP scenarios</p>
Data from: Sporadic sampling not climatic forcing drives observed early hominin diversity
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Data from: Phase synchronization between culture and climate forcing
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Data from: Climate warming prolongs the time interval between leaf-out and flowering in temperate trees: effects of chilling, forcing and photoperiod
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Data from: The Relationship between the Southern Ocean and the Eastern tropical Pacific in unforced and forced climate model simulations
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Climatic forcing and individual heterogeneity in a resident mountain bird: legacy data reveal effects on reproductive strategies
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Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)
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Data from: Climatic forcing of Quaternary deep-sea benthic communities in the North Pacific Ocean
There is a growing evidence that changes in deep-sea benthic ecosystems are modulated by climate changes, but most evidence to date comes from the North Atlantic Ocean. Here we analyze new ostracod and published foraminiferal records for the last 250,000 years on Shatsky Rise in the North Pacific Ocean. Using linear models, we evaluate statistically the ability of environmental drivers (temperature, productivity, and seasonality of productivity) to predict changes in faunal diversity, abundance and composition. These microfossil data show glacial-interglacial shifts in overall abundances and species diversities that are low during glacial intervals and high during interglacials. These patterns replicate those previously documented in the North Atlantic Ocean, suggesting that the climatic forcing of the deep-sea ecosystem is widespread, and possibly global in nature. However, these results also reveal differences with prior studies that probably reflect the isolated nature of Shatsky Rise as a remote oceanic plateau. Ostracod assemblages on Shatsky Rise are highly endemic but of low diversity, consistent with the limited dispersal potential of these animals. Benthic foraminifera, by contrast, have much greater dispersal ability and their assemblages at Shatsky Rise show diversities typical for deep-sea faunas in other regions. Statistical analyses also reveal ostracod–foraminferal differences in relationships between environmental drivers and biotic change. Rarefied diversity is best explained as a hump-shaped function of surface productivity in ostracods, but as having a weak and positive relationship with temperature in foraminifera. Abundance shows a positive relationship with both productivity and seasonality of productivity in foraminifera, and a hump-shaped relationship in ostracods. Finally, species composition in ostracods is influenced by both temperature and productivity, but only a temperature effect is evident in foraminifera. Though complex in detail, the global-scale link between deep-sea ecosystems and Quaternary climate changes underscores the interaction between the physical and biological components of paleoceanographical research to better understand the history of the biosphere.
Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)
<p>## Introduction<br>This folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.</p> <p>## Files Description</p> <p>1. **DL_Model_test.py**<br> - Description: Loads the trained deep learning algorithms featured in the paper.<br> - Functionality: Demonstrates the use of the model with example data points.</p> <p>2. **SIC_class_contribution.py**<br> - Description: Calculates the class contribution for Sea Ice Concentration (SIC).<br> - Note: The procedure can be adapted for other fields in a similar manner.</p> <p>## Requirements<br>the requred linrary are listed in the code header</p>
Analysis code in support of "The Interaction Between Climate Forcing and Feedbacks"
<p>Analysis code in support of Manuscript "The Interaction Between Climate Forcing and Feedbacks"</p> <p>Data is already available through DOI: https://doi.org/10.26024/bzne-yf09</p> <p>read_ppe2map.ipynb will create data files from the PPE output (if the files at the DOI do not work), and then the different codes herein are used to create figures in the manuscript.</p>
Top-down effects override climate forcing on reproductive success in a declining sea duck
Population performance is predicted to be more strongly influenced by detrimental species interactions such as predation under benign climatic conditions, and by climate forcing under harsh conditions, reflected in geographical gradients in biotic interaction strength. Less appreciated is the potential for site-specific changes in drivers with the advent of anthropogenic alteration of predator-prey relationships, including apex predator restoration and spread of invasive predators. Particularly interesting is the relative impact of climate and biotic interactions on population performance when these conflict. In this 31-year study (1990-2020), we revisit a common eider (Somateria mollissima) population from SW Finland, Baltic Sea, fifteen years on from an earlier study showing that climate warming positively affected reproductive parameters and performance. However, the population is simultaneously exposed to increasing predation by the rapidly recovering native apex predator and invasive mammals. Based on the current population trend, we predicted (i) a weakening of the previously documented positive effects of a warming climate on vital rates, (ii) intensified predation, and (iii) increasing top-down control of vital rates and accompanying population decline. Five out of seven breeding parameters (annual spread in female body condition, breeding phenology and synchrony, interval between arrival and breeding, fledgling production) were best explained by predation indices, whereas climate signals (winter NAO, Baltic Sea maximum ice cover) on breeding parameters have weakened. Particularly intriguing is that the previous positive association between mild ice winters and subsequent reproductive output has disappeared during the past 15 years, highlighting the non-linear nature of climate change responses. Indirect predation effects (selective disappearance, changed reproductive strategies, nest-site selection and population age distribution) can potentially explain also the remaining breeding parameters (annual mean body condition and clutch size). The observed regime shift in predation risk appears to prevent this now endangered population from reaping the potential benefits of a warming climate.
A stylized study of the climate response to longwave and shortwave forcing at the altitude of aviation-induced cirrus
<p>Data and figures including MATLAB code used for plotting figures and creating tables of this study.<br>Case Deatils.xlsx: Details of all cases analysed in this study, which can be used to reproduce the results of this study. <br><br></p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces
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Repository associated with "Quantifying CO2 forcing effects on lightning, wildfires, and climate interactions"
<p>Results of CESM2 simulation runs.<br>See readme file for details.</p>
Last glacial cycle simulations forced by PMIP3 climate with a matrix and index method using a 3D thermodynamical ice-sheet model IMAU-ICE
<p>IMAU-ICE 2.0 model output of the ice evolution during the last glacial cycle at a 10 ka temporal resolution, as described in Scherrenberg at al., 2023.</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.