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This dataset contains coupled physical-biogeochemical ocean second generation Canadian Earth System Model (CanESM2) simulation outputs using the 1 degree NEMO-HadOCC model. The model output contains 3D Digital Image Correlation (DIC), alkalinity, temperature and salinity datasets at annualy-averaged frequency and monthly averaged surface ocean CO2 fugacities and fluxes. Job IDs included in this dataset: CanESM2 surface fluxes (started on 18th for first, 21st for second, and on the 19th for other 2): RCP85: u-ao419 RCP26: u-ao519 Constant atm CO2: RCP85: u-ao529 RCP26: u-ao531 (reduce walltime for nemo to test) This data was collected in support of CURBCO2: Carbon Uptake Revisited - Biases Corrected using Ocean Observations, a Natural Environment Research Council (NERC) funded project (NERC Grant NE/P015042/1). The overarching aim of this project was to provide UK and international governments with the best possible impartial information from which they can plan how best to work towards the global warming targets (the 'Paris Agreement') set at the Paris Climate Conference in December 2015.
FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. The objective of FireMAFS was to resolve limitations of fire modelling by developing a robust method to forecast fire activity (fire 'danger' indices, ignition probabilities, burnt area, fire intensity etc), via a process-based model of fire-vegetation interactions, tested, improved, and constrained. This used a state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. This dataset contains the MODIS Land Cover Type product multiple classification schemes, which describe land cover properties derived from observations spanning a year’s input of Terra and Aqua data. The data are stored in a 10 arc minute grid.
QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains soil data generated from ISLSCP II. The International Satellite Land Surface Climatology Project, Initiative II (ISLSCP II) is a follow on project from The International Satellite Land Surface Climatology Project (ISLSCP). ISLSCP II had the lead role in addressing land-atmosphere interactions - process modelling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.
The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. The data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.
Data from the Institut Pierre Simon Laplace IPSL-CM4 simulations
QUEST projects both used and produced an immense variety of global data sets that needed to be shared efficiently between the project teams. These global synthesis data sets are also a key part of QUEST's legacy, providing a powerful way of communicating the results of QUEST among and beyond the UK Earth System research community. This dataset contains decadal surface meteorology climatologies from CRU TS3.0 data 1901- 2000. Data includes parameters such as temperature, water vapour and precipitation.
Data from "The impact of climate change on the North Atlantic and European storm-track and blocking" project was a Natural Environment Research Council (NERC) RAPID Climate Change Research Programme project (Round 2 - NE/C509115/1 - Duration 14 Mar 2005 - 13 Mar 2008) led by Prof Sir Brian Hoskins of Imperial College London, Grantham Institute for Climate Change, with co-investigators also at the University of Reading. This dataset collection contains Unified Model climate temperature and humidity outputs from model run xctf. Rapid Climate Change (RAPID) was a £20 million, six-year (2001-2007) programme for the Natural Environment Research Council. The programme aimed to improve the ability to quantify the probability and magnitude of future rapid change in climate, with a main (but not exclusive) focus on the role of the Atlantic Ocean's Thermohaline Circulation.
Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR). This dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Average Surface Temperature (AST) Product. These data are the Level 2 spatially averaged geophysical product derived from Level 1B product and auxiliary data. There are two types of averages provided: 10 arcminute cells and 30 arcminute cells. All cells are present regardless of the surface type. Hence, the sea (land) cells would also have the land (sea) records even though these would be empty. Cells containing coastlines will have both valid land and sea records; the land (sea) record only contains averages from the land (sea) pixels. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files.
The AATSR Reprocessing for Climate (ARC) dataset consists of Advanced Along-Track Scanning Radiometer (AATSR) multimission data which has been reprocessed using various algorithms and in-situ contemporaneous measurements, to provide update retrievals of Sea Surface Temperature (SST) and assess their accuracy. This dataset contains version 1.1.1 of the Level 3 sea surface temperature data product produced by the ARC project team. The main ARC objective was to reduce regional biases in retrieved sea surface temperature (SST) to less than 0.1 K for all global oceans while creating a very homogeneous record with a stability (lack of drift in the observing system and analysis) of 0.05 K decade.
MarQUEST was led by Prof Andrew Watson (UEA), with 15 co-investigators at UEA/BAS, the Universities of Southampton, Essex, and Reading, and from the Plymouth Marine Laboratory and Proudman Oceanographic Laboratory. This dataset contains climatology and monthly measurements of phytoplankton Size Class from the SeaWiFS/SeaStar products. QUEST scientists cooperated in comparing various models, and examining more fundamental (physiological) approaches to understanding the planktonic ecoystem. MarQUEST also developed a module to simulate coastal ecosystems, usable in global ocean biogeochemical simulations. Finally, the project team generated an accurate physical simulation of the North Atlantic guided by data assimilation, into which ecosystem simulations can be embedded. This allows the variation in air-sea fluxes of gases (CO2, oxygen and dimethyl sulphide) from ocean to atmosphere to be quantified for the contemporary period. This data was produced by Takafumi Hirata, Plymouth Marine Laboratory, Plymouth, UK as part of NERC Programmes: Centre for the observation of Air-Sea Interaction and fluXes (CASIX), National Centre for Earth Observation (NCEO) and Quantifying and Understanding the Earth System (QUEST).