The UK climate projections (UKCP09) comma separated value (CSV) archive consists of probabilistic data for various climate parameters. Two products are available: firstly, zip files of batch processed UKCP09 data outputs that were provided as an alternative to having to generate multiple requests on the UKCP09 website; and, secondly, additional products that were not available under from the UKCP09 website. These are provided as raw data files. List of products: 1. UK Probabilistic Projections of Climate Change over Land: Grouped by - variable and location and - variable and temporal average. 2. UK Probabilistic Projections of Climate Change over Marine Regions: Grouped by - emissions scenario, - location, - temporal average, - time period, - variable, - variable and location and - variable and temporal average. 3. Projections of Trend in Storm Surge for UK Waters: all data is grouped into one file. 4. Projections of Sea Level Rise for UK Waters: Grouped by - emissions scenario, - location and - emissions scenario and location. 5. Global average temperature change values for each time period and emissions scenario: - all cumulative distribution function (CDF) data in a single file - all sampled data in a single file. 6. UK Probabilistic Projections of Climate Change over Land conditioned by a given global average temperature change: Grouped by - probability level and - variable and probability level 7. Spatially Coherent Projections of UK Climate Change over Land: grouped by variable, temporal average and scenario The file naming convention is provided in the documentation.

The standardized precipitation index SPI (McKee et al. 1993) was designed to standardize precipitation timeseries across an observational record in order to compute precipitation anomalies in both time and space. The SPI is a widely used drought index that represents the number of standard deviations that observed precipitation deviates from the climatological average precipitation measured at the same location and time period. We calculated the SPI using monthly 4 kilometer resolution precipitation data from the Parameter-elevation with independent slopes model (PRISM; Daly et al. 1994) for the conterminous United States. Monthly PRISM data from 1948-2022 were aggregated to warm season (May-September) and annual (calendar year) totals. SPI anomalies were calculated for each seasonal time window relative to the 1948-2022 timeperiod.

Each timeseries of annual precipitation totals was fit to a Gamma probability distribution based on the L-moments of the data. We computed the cumulative distribution function (CDF) associated with the observations using the parameters from the aforementioned Gamma distribution. The CDF values were then evaluated within an inverse Gaussian function with a mean of zero and a standard deviation of one to obtain the final SPI value

Coordinate Reference System (CRS) = EPSG:4326

McKee, T.B., N.J. Doesken and J. Kleist, 1993: The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. Boston, American Meteorological Society, 179–184.

Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158.

The intended use of these data are for studies that require predictions of future temperature and precipitation conditions in the contiguous United States.

This data series contains 2868 temporal datasets. These data are climate model outputs that have been downscaled to 4-km spatial resolution using the Bias Corrected Statistical Downscaling (BCSD) method. Moore and Walden have modified the BCSD method described by Wood et al (2002), Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research-Atmospheres 107: 4429-4443 and Salathe (2005), Downscaling simulations of future global climate with application to hydrologic modeling. International Journal of Climatology 25: 419-436. The modifications include a different interpolation scheme between GCM grid cells and a different approach to dealing with extreme values (Z-scores versus CDF method). The spatial resolution of these data are determined by the historical dataset used to derive statisitcal relationships between the GCM and past measurements. The 4-km Parameter-elevation Relationships on Independent Slopes Model (PRISM) data are used here from Daly et al, (1994), A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33: 140-158.

Access constraints: Data will be provided to all who agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set. By downloading these data and using them to produce further analysis and/or products, users agree to appropriately acknowledge the National Science Foundation (NSF), Idaho EPSCoR and the individual investigators responsible for the data set.

Use constraints: Acceptable uses of data provided by Idaho EPSCoR include any academic, research, educational, governmental, recreational, or other not-for-profit activities. Any use of data provided by the Idaho EPSCoR must acknowledge Idaho EPSCoR and the funding source(s) that contributed to the collection of the data. Users are expected to inform the Idaho EPSCoR Office and the PI(s) responsible for the data of any work or publications based on data provided.

Liability: Although these data have been processed successfully on a computer system at the Idaho Geospatial Data Clearinghouse, no warranty, expressed or implied, is made regarding the utility of the data on any other system, nor shall the act of distribution constitute any such warranty.

This dataset contains measurements of chemical depletion fraction (CDF), from three published articles, and estimates for each location of the dryness index (PET/P). To estimate the dryness index, long-term potential evapotranspiration (PET) was retrieved from climate data, while precipitation was provided by the three publication alongside the CDF measurements. The data reveal the strong nonlinear relation between CDF and wetness at the global scale.

Data are available at https://arcticdata.io/data/10.18739/A2542J95X This dataset contains raw chromatograms (in AIA format (*.CDF)) collected at a 3-hr time resolution with an automated gas chromatography and mass spectrometry with flame ionization detector (GC-MS/FID) system during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The backbone of MOSAiC was the year-round operation of the Research Vessel Polarstern that drifted with the sea ice across the Central Arctic from October 2019 to September 2020. Measurements were performed onboard Polarstern in the University of Colorado sea-container laboratory installed below deck in the forward cargo hold. A ~50 meter (m) long sampling line was deployed from the container to the bow crane to allow measurements forward of the vessel.

The TOMS (Total Ozone Mapping Spectrometer) dataset consists of daily gridded measurements of integrated column ozone, expressed in terms of the equivalent thickness the layer would have if brought to STP.

This thicknes is measured in Dobson Units (DU) where one DU is

equivalent to 0.01mm.

TOMS instruments have been flown on two satellites providing the

following temporal coverage: Nimbus-7, spanning 1st November 1978 - 6th May 1993 and Meteor-3, spanning 22nd August 1991 - 27th December 1994.

BADC holds all available data online.
Nimbus-7 data processed using software of Version 6 or greater, and all
Meteor-3 data, is stored in ASCII files.
Nimbus-7 Version 5 data is written in CDF format

All the ASCII data uses a grid with 288 longitude cells per one-degree

latitude zone.

Nimbus-7 Version 5 data uses a variable number of longitude cells

in order to give a roughly constant cell area over the globe. The number of cells ranges from 288 at the equator to 72 at the poles as follows: 0--50 deg. (288 cells), 50--70 deg. (144 cells) and 70--90 deg. (72 cells).

In the later (Version 6 onwards) Nimbus and Meteor data, groups of two

orfour cell values are repeated in order to fill the storage grid.

The TOMS instrument measures backscattered UV at 312.5, 317.5, 331.3,

339.9, 360.0 and 380.0 nm.

The first four wavelengths are sensitive to ozone while the two longer

wavelengths are used to estimate the scene reflectivity needed to derive ozone amounts.

The instrument views the whole globe once per day but ozone amounts

can only be derived for sunlit regions, ie there are no data above the winter poles.

Other experiments on the Nimbus-7 satellite:
Coastal Zone Colour Scanner - CZCS
Earth Radiation Budget - ERB
Limb Infrared Monitor of the Stratosphere - LIMS
Stratspheric Aerosol Measurement II - SAMII
Stratospheric and Mesospheric Sounder - SAMS
Solar Backscatter Ultraviolet - SBUV
Scanning Multichannel Microwave Radiometer - SMMR
Temperature Humidity Infrared Radiometer - THIR

Devils Hole, a fracture in the carbonate aquifer underlying the Death Valley Regional Groundwater Flow system, is home to the only extant population of Devils Hole pupfish (Cyprinodon diabolis). Since 1995, the population of C. diabolis has shown an unexplained decline, and a number of hypotheses have been advanced to explain this. Here, we examine the thermal regime of Devils Hole and its influence on the pupfish population. We present a computational fluid dynamic (CFD) model of thermal convection on the shallow shelf of Devils Hole, which provides critical habitat for C. diabolis to spawn and forage for food. Driven by meteorological data collected at Devils Hole, the model is calibrated with temperature data recorded in the summer of 2010 and validated against temperatures observed on the shallow shelf between 1999 and 2001.The shallow shelf experiences both seasonal and diel variations in water temperature, and the model results reflect these changes. A sensitivity analysis shows that the water temperatures respond to relatively small changes in the ambient air temperature (on the order of 1 8C), and a review of local climate data shows that average annual air temperatures in the Mojave Desert have increased by up to 2 8C over the past 30 years. The CFD simulations and local climate data show that climate change may be partially responsible for the observed decline in the population of C. diabolis that began in 1995.

Raw project data is available by contacting ctemps@unr.edu

This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium spp. presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of Alexandrium spp. presence in present time (1997 – 2016) in SW Ireland. The analyses were performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_probability/ ). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime – Probability of phytoplankton (Alexandrium species) presence, Southwest Ireland – present time prediction (1997 – 2016). Marine Institute, Ireland. doi:10/hvsw.

This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of Alexandrium in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_abundance/). None

Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime - Abundance of phytoplankton (Alexandrium species), Southwest Ireland - present time prediction (1997 - 2016). Marine Institute, Ireland. doi:10/hvs9.

The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. Aerosol data obtained by Colorado State University during May 1998 on the NCAR C-130 research flights as part of the First ISCCP Regional Experiment (FIRE3) Arctic Cloud Experiment (ACE) flown onboard the NCAR C-130 aircraft during the FIRE ACE field campaign. The data are in ASCII format. The primary measurements were of ice nuclei and condensation nuclei.

This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of Alexandrium in the future (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_abundance/). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime - Abundance of phytoplankton (Alexandrium species), Southwest Ireland - future prediction (2017 - 2035). Marine Institute, Ireland. doi:10/hvtb.

This is the output of GBM (Gradient Boosting Model) analyses of P. seriata presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of P. seriata presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/P_seriata_probability/). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Clarke, Dave; Nolan, Glenn. (2022) CoClime - Probability of phytoplankton (“Pseudo-nitzschia seriata” complex ) presence, Southwest Ireland - present time prediction (1997 - 2016). Marine Institute, Ireland. doi:10/hvsp.

This is the output of GBM (Gradient Boosting Model) analyses of D. acuta, abundance data done by MI OCIS and CoCliME project. This output dataset shows abundance of D. acuta in present time (2017 - 2035) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuta_abundance/). None

Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime - Abundance of phytoplankton (Dinophysis acuta), Southwest Ireland - future prediction (2017 - 2035). Marine Institute, Ireland. doi:10/hvs6.

The Chilean Coastal Cordillera features a spectacular climate and vegetation gradient, ranging from arid and unvegetated areas in the north to humid and forested areas in the south. The DFG Priority Program "EarthShape" (Earth Surface Shaping by Biota) uses this natural gradient to investigate how climate and biological processes shape the Earth's surface. We explored the critical zone, the Earth's uppermost layer, in four key sites located in desert, semidesert, mediterranean, and temperate climate zones of the Coastal Cordillera, with the focus on weathering of granitic rock. Here, we present first results from four ~2m-deep regolith profiles to document: (1) architecture of weathering zone; (2) degree and rate of rock weathering, thus the release of mineral-derived nutrients to the terrestrial ecosystems; (3) denudation rates; and (4) microbial abundances of bacteria and archaea in the saprolite.

From north to south, denudation rates from cosmogenic nuclides are ~10 t km-2 yr-1 at the arid Pan de Azúcar site, ~20 t km-2 yr-1 at the semi-arid site of Santa Gracia, ~60 t km-2 yr-1 at the mediterranean climate site of La Campana, and ~30 t km-2 yr-1 at the humid site of Nahuelbuta. A and B horizons increase in thickness and elemental depletion or enrichment increases from north (~26 °S) to south (~38 °S) in these horizons. Differences in the degree of chemical weathering, quantified by the chemical depletion fraction (CDF), are significant only between the arid and sparsely vegetated site and the other three sites. Differences in the CDF between the sites, and elemental depletion within the sites are sometimes smaller than the variations induced by the bedrock heterogeneity. Microbial abundances (bacteria and archaea) in saprolite substantially increase from the arid to the semi-arid sites.

With this study, we provide a comprehensive dataset characterizing the Critical Zone geochemistry in the Chilean Coastal Cordillera. This dataset confirms climatic controls on weathering and denudation rates and provides prerequisites to quantify the role of biota in future studies. The data are supplementary material to Oeser et al. (2018).

All samples are assigned with International Geo Sample Numbers (IGSN), a globally unique and persistent Identifier for physical samples. The IGSNs are provided in the data tables and link to a comprehensive sample description in the internet.

The content of the eight data tables is:

Table S1: Catena properties of the four primary EarthShape study areas. Table S2: Major and selected trace element concentration for bedrock samples. Table S3 Normative modal abundance of rock-forming minerals. Table S4: Major and selected trace element concentration for regolith samples and dithionite and oxalate soluble pedogenic oxides. Table S5: Weathering indices CDF and CIA, and the mass transfer coefficients (τ) for major and trace elements along with volumetric strain (ɛ). Table S6: Chemical weathering and physical erosion rates Table S7: Relative microbial abundances in saprolite of the four study areas. Table S8: Uncorrected major and trace element concentration.

The data tables are provided as one Excel file with eight spreadsheets, as individual tables in .csv format in a zipped archive and as printable PDF versions in a zipped archive.

ABSTRACT The thermal efficiency of naturally ventilated greenhouses is limited due to the permanent exchange of air through the vents, especially during the night hours. The objective of the work consisted in evaluating a system of inflatable air ducts that close the roof vents during the night as a strategy to reduce the energy loss during these hours. For the development of this work, we applied the computational fluid dynamics (CFD) method to a passive multi span greenhouse operating under the dominant nocturnal climatic conditions of the Bogota savannah (Colombia). The results indicated that the use of the ducts system reduces the value of the negative thermal gradient between the interior and exterior of the greenhouse. The CFD model used was validated by comparing experimental data and simulated data and by calculating goodness-of-fit parameters, finding that the numerical model predicts satisfactorily and with an adequate degree of fit the actual thermal behavior of the greenhouse evaluated.

This is the output of GBM (Gradient Boosting Model) analyses of Alexandrium spp. presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of Alexandrium spp. presence in the future (2017 – 2035) in SW Ireland. The analyses were performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/Alexandrium_probability/ ). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime – Probability of phytoplankton (Alexandrium species) presence, Southwest Ireland – future prediction (2017 – 2035). Marine Institute, Ireland. doi:10/hvsx.

This is the output of GBM (Gradient Boosting Model) analyses of D. acuminata presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of D. acuminata presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuminata_probability/). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime - Probability of phytoplankton (Dinophysis acuminata) presence, Southwest Ireland - present time prediction (1997 - 2016). Marine Institute, Ireland. doi:10/hvst.

Average air temperature above the water surface in the workshop from 1 to 12 o’clock in a typical summer climate.

This is the output of GBM (Gradient Boosting Model) analyses of P. seriata presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows a prediction of probability of P. seriata presence in the future (2017 – 2035) in SW Ireland. The analyses were performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/P_seriata_probability/). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime – Probability of phytoplankton (“Pseudo-nitzschia seriata” complex) presence, Southwest Ireland – future prediction (2017 – 2035). Marine Institute, Ireland. doi:10/hvhg.

This is the output of GBM (Gradient Boosting Model) analyses of D. acuta presence and absnece data done by MI OCIS and CoCliME project. This output dataset shows prediction of probability of D. acuta presence in present time (1997 - 2016) in SW Ireland. The analyses was performed in R 3.6.3, with the packages tidyverse 1.3.0 for data handling and visualisation, and xgboost 1.2.0.1 for boosted regression analyses. The dataset used for analyses is available on MI data catalogue (http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.4445) and the dataset used for prediction is requested to be included in MI data catalogue ([20]. CDF-t Climate run (Climate_run_corr.rds) in CoCliME model SOP). This dataset is used to visualise the prediction on R Shiny application for present period (https://marine-institute-ireland.shinyapps.io/D_acuata_probability/). None Suggested Citation: Yamanaka, Tsuyuko; Cusack, Caroline; Nolan, Glenn; Clarke, Dave. (2022) CoClime - Probability of phytoplankton (Dinophysis acuta) presence, Southwest Ireland - present time prediction (1997 - 2016). Marine Institute, Ireland. doi:10/hvsr.