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617 results for “Climate models”
High Mountain Asia COAWST Daily 4km Regional Climate Model Simulations V001
This data product contains either daily averaged or daily accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.
High Mountain Asia COAWST 6-Hourly 4km Regional Climate Model Simulations V001
This data product contains either 6-hourly accumulated or 6-hourly snapshots of modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.
TRACKING CLIMATE MODELS
CLAIRE MONTELEONI*, GAVIN SCHMIDT**, AND SHAILESH SAROHA*** Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate scientists to simulate and predict climate. Given temperature predictions from the top 20 climate models worldwide, and over 100 years of historical temperature data, we track the changing sequence of which model currently predicts best. We use an algorithm due to Monteleoni and Jaakkola that models the sequence of observations using a hierarchical learner, based on a set of generalized Hidden Markov Models (HMM), where the identity of the current best climate model is the hidden variable. The transition probabilities between climate models are learned online, simultaneous to tracking the temperature predictions. On historical data, our online learning algorithm’s average prediction loss nearly matches that of the best performing climate model in hindsight. Moreover its performance surpasses that of the average model prediction, which was the current state-of-the-art in climate science, the median prediction, and least squares linear regression. We also experimented on climate model predictions through the year 2098. Simulating labels with the predictions of any one climate model, we found significantly improved performance using our online learning algorithm with respect to the other climate models, and techniques.
High Mountain Asia COAWST Monthly 4km Regional Climate Model Simulations V001
This data product contains either monthly averaged or monthly accumulated modeled data in the High Mountain Asia region, generated by the Coupled-Ocean-Atmosphere-Waves-Sediment Transport (COAWST) modeling system (operated as a regional climate model). These modeled data span 15 years and have been used by the NASA High Mountain Asia Team (HiMAT) to research water resource use.
Database of the integrated model, used for the material and climate implication of car transition in China
<p>Database of the integrated model, used for the material and climate implication of China car transition. All excel files are the parameter settings used in that case. All csv files are results under all scenarios.</p>
Permafrost model for the Argentinian Andes - Results and climatic scenarios
<p><strong>Supplementary information to the following publication: </strong></p> <p><strong>Tapia Baldis C, Trombotto Liaudat D. 2020. Permafrost debris-model in Central Andes of Argentina (28°-33° S). Cuadernos de Investigación Geográfica 46, <a href="http://doi.org/10.18172/cig.3802">http://doi.org/10.18172/cig.3802</a></strong></p> <p>To predict regional-scale spatial patterns of permafrost occurrence, especially over remote environments with limited data, empiric-statistical models are widely used. This kind of approach correlates permafrost occurrence with topo-climatic factors (altitude, geographic position, slope, aspect, air temperature, ground temperature, solar radiation, etc.) easily available, in some cases. Different combinations of empiric-statistical models were tested to evaluate the permafrost spatial distribution in the study area.</p> <p>The study area (28° to 33°S and 70°30’ to 69°W) comprises the middle portion of the South American (Argentinian side) Central Andes (17°30’ to 35°S), named Dry Andes. The landscape is expressed as mountain ranges and valleys with 50% of the terrain surface above 3000 m a.s.l. The highest elevations are represented by mountain peaks such us Mercedario (6850 m a.s.l.) or La Ramada (6400 m a.s.l.). The Dry Andes could be further separated into Desert Andes (17°30’ to 31°S) and Central Andes (31° to 35°S), according to precipitation rates and landscape geomorphological characteristics. </p> <p>Models were trained in a calibration area to evaluate the correlation between geomorphological permafrost indicators (named explanatory variable) and the topoclimatic parameters (predictive variable). A logistic regression model with a logit link function was chosen as a mathematical approach.</p> <p>Data for model calibration was obtained from the Bramadero river basin, located at 31°50’ S and 70°00’ W in the Central Andes. From a geomorphological point of view, the landscape of the Dry Andes is characterized by the interdigitation of glacial, periglacial, alluvial, fluvial, and gravitational processes. The Bramadero river basin was largely glaciated during the LGM, even today it is possible to recognize erosive forms and glacial deposits all over the main valley and subordinated creeks. Even though Quaternary glacial stages modeled the landscape; periglacial features prevail today. Currently, periglacial processes are active in elevations exceeding 2700 m a.s.l. (lowest limit of seasonal freezing), however, a wide variety of periglacial deposits and permafrost indicating cryoforms occur between 3400 and >4500 m a.s.l. (permafrost periglacial belt).</p> <p>The complete geomorphological characterization of the Bramadero river basin and the geomorphometric data extracted from every kind of landform were used to set up the permafrost predictive categories. The first predictive category (presence) includes geoforms that indicate current permafrost, such as; active rock glaciers, inactive rock glaciers, protalus lobes, cryoplanation surfaces, and perennial snow patches. The second category (absence) includes geoforms without current permafrost (relict or fossil rock glaciers, bedrock outcrops, glacial abrasion surfaces, debris/mud flows, and Andean wetlands/peatlands types). It also includes geoforms where the presence of permafrost could not be certainly assessed such us: frozen and unfrozen talus slopes, glaciers and covered glaciers, moraines and morainic complexes, debris/snow avalanches, rock avalanches, and rock slides.</p> <p><strong>The following link can accede data from the calibration area: </strong></p> <p><strong>Tapia Baldis, Carla. (2018). Permafrost model for the Argentinian Andes - Calibration data set [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7229569">https://doi.org/10.5281/zenodo.7229569</a></strong></p>
Ecological niche models for British Columbia's rare species (Red-, Blue-, and SARA-listed) -- Climate normal and future (2050s & 2080s) distributions
<p>Maps of Maxent ecological niche models for Red-listed, Blue-listed and SARA Schedule 1 species in British Columbia. Models were calibrated for species' ranges across Turtle Island under climate normal conditions (1961-1990) and projected into future periods (2050s and 2080s) based on an ensemble of GCMs under SSP 2-4.5.</p>
Energy Consumption Reduction in Historic Urban Residential Sectors: A Case Study of Kyoto City Using Bottom-Up Modeling and Future Climate Scenarios
<p>This dataset is a detailed simulation result of 21 scenarios in the paper. The results include:</p> <ol> <li>Annual daily energy consumption data divided by energy source and residential type.</li> <li>Photovoltaic power generation data, direct photovoltaic use, battery use, and photovoltaic power generation consumed by apartment houses and Kyomachiya through P2C systems. </li> <li>Annual daily net energy consumption data divided by energy source and residential type.</li> </ol>
Global SPEI over the last millennium calculated from monthly climate variables of isotope-enabled climate model simulations
<p>This data is Standardized precipitation evapotranspiration index (SPEI) for different time scales from 851 to 2000. The time scale is partially omitted due to the upload capacity, but it is from 1 month to 48 months at maximum.</p> <p>[Structure]</p> <p>Spatial resolution: 1.9(Treated the earth as a 94x192 grid)</p> <p>Time resolution: 1 month(1150 year = 13800 month)</p> <p>This data is one-dimensional. When using, please slice to (13800,94,192) using python, etc.</p> <p>The method for obtaining the grid for the survey area is as follows.</p> <p>north latitude: (94/180)*(90+lat)</p> <p>south latitude: (94/180)*(90-lat)</p> <p>east longitude: (192/360)*longitude</p> <p>west longitude: (192/360)*(360-longitude)</p> <p> </p> <p>This SPEI is calculated using the SPEI package in R. The settings for this package are as follows.</p> <p>Setting</p> <p>kernel: type = rectangular, shift =0</p> <p>distribution: log-Logistic</p> <p>fit: ub-pwm</p> <p> </p> <p>The data used for this SPEI calculation are climate data reconstructed by data assimilation using isotope ratios. Please refer to the following page for details of the data.</p> <p>Details</p> <p>[Title]</p> <p>Data assimilation products by using multiple climate model simulations and different combinations of proxies</p> <p>[url]</p> <p>https://zenodo.org/record/5760209#.ZAs2qxXP1D9</p>
Supporting data case study, ensemble climate-impact modelling
<p>Data supporting the case study in 'Ensemble climate-impact modelling: extreme impacts from moderate meteorological conditions', publication under review.</p>
Data supporting the findings of "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale"
Open the record for dataset details and reuse information.
Climate models simulations archive for analysis of ENSO influence on Arctic stratosphere
<p>CMIP5 model (CCSM4, CMCC-CMS, CNRM-CM5, IPSL-CM5B-LR, MRI-CGCM3, MIROC5) data of historical simulation (1950-2005): daily zonal mean temperature averaged over 70-90N, zonal mean zonal wind averaged over 60-62N, and geopotential height averaged over 50-70N at hPa levels from 1000 to 10 hPa (29 February removed)</p>
Thesis: Locate and understand the mortality risk of forest tree under climate change. A multi-scale approach combining statistical and mechanistic modeling
<p>Data and tables in chapters 2 and 3 of the thesis</p>
Complementary Data of Groundwater model for the Publication: "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate"
<p>This repository provides the resources related to the publication "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate." It includes the groundwater model data sets.</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes daily basin average forcing data (consisting of precipitation, temperature, potential evapotranpiration, air pressure, relative humidity, and wind speed), as well as simulated daily runoff (mm/d) of five models during 1960‒2019 at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p>
EAMv1 outputs Macquarie Island- Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations
<p>EAMv1 outputs for the ACP publication </p> <p>https://acp.copernicus.org/articles/23/5735/2023/acp-23-5735-2023.pdf</p> <p>We have archived the outputs from the EAMv1 control simulations. </p>
Complementary Data and Model Repository for the Publication: "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate"
<p>This repository provides the resources related to the publication "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate." It includes the data sets used in the study, the SWAT model and python script for evaluation of the different methods compared in this study.</p>
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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.