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.

FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. This dataset collection 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. Fire was the most important disturbance agent worldwide in terms of area and variety of biomass affected, a major mechanism by which carbon is transferred from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Despite such clear coupling between fire, climate, and vegetation, fire was not modelled as an interactive component of the climate/earth systems models of full complexity or intermediate complexity, that are used to model terrestrial ecosystem processes principally for simulating CO2 exchanges. The objective of FireMAFS was to resolve these limitations 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. Much of the activity of FireMAFS was shaped by the research and technical priorities of QUESTESM (earth system model). Key activities included the progressive development of the JULES-ED and SPITFIRE submodels. Fire is now very well represented in QESM (Quest Earth System Model), making progress towards a modelling capability for fire risk forecasting in the context of global change.

The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30' E-119°45' E, 39°10' N-42°40'N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes: (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012 (3) the flood inundation prediction under different development strategies and climate scenarios to 2030 (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168

This data contains the time series flow discharge results of hydrological simulation of the River Trent at Colwick using UKCP09 Weather Generator inputs for a variety of time slices and emissions scenarios. The Weather Generator (WG) inputs were run on a hydrological model (Leathard et al., unpublished), calibrated using the observed record 1961-2002. Each simulation is derived from 100 30-year time series of weather at the WG location 4400355 for Control, Low, Medium and High emissions scenarios for the 2020s, 2030s, 2040s, 2050s and 2080s time slices. The datasets include the relevant accompanying input WG data. Full details about this dataset can be found at https://doi.org/10.5285/986d3df3-d9bf-42eb-8e18-850b8d54f37b

This data set represents the model results plotted in the figures in Bett et al. (2020). Data portrays Amundsen Sea freshwater fluxes and freshwater passive tracer results, along with the results on the effect of grounded icebergs and iceberg melt on sea ice and oceanic heat content. These results are derived from Amundsen Sea regional model simulations over the period 1979-2018, with the first 10 years regarded as model spin up. For full descriptions of the results plotted in each figure see Bett et al. (2020).

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. LWdown or surface incident longwave radiation (also known as downwards long-wave radiation flux ) is the surface incident longwave radiation averaged over the next six hours, measured in W/m2 at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.

These files represent the model build used to generate postcode level concentrations to estimate Aspergillus fumigatus exposure from outdoor composting activities in England between 2005 and 2014. Each file, named after the nearest SCAIL-Agriculture validated meteorological station used to model the outputs, contains modelled concentrations at composting sites within 4km of each composting site. These files, presented as.txt, are the .APL files used to model bioaerosol dispersion from every composting site in England, using ADMS 5. To use this file, please convert the .txt file extension to .APL and upload into ADMS. From there, press run. Model runs are likely to generate over 40GB of data per model run. The work was supported by the Natural Environment Research Council grants ((NE/P010806/1 and NE/M011631/1). Full details about this dataset can be found at https://doi.org/10.5285/9f1b307b-9b47-4a11-8e5b-e14008ad0032

This dataset includes the PROTECH validation output against a yearlong monitoring study conducted during 2016 in the lake and catchment of Rostherne Mere and the PROTECH output files following changes in internal and external nutrient loads and future climate scenarios based on the UK Climate Projections (UKCP09) data. These data were collected to demonstrate the future possible trajectories of change with alterations in air temperature, internal nutrient loads and external nutrient loads. Validation data is presented as daily model outputs, while all future projection data is presented as collated annual average model output data for each future change scenario. The PROTECH model (Phytoplankton RespOnses To Environmental CHange) simulates the in situ dynamics of phytoplankton in lakes and reservoirs, specialising in predicting phytoplankton species, particularly Cyanobacteria (blue-green algae) The work was carried out as part of a Natural Environment Research Council (NERC) funded PhD [grant number NE/L002493/1]. Full details about this dataset can be found at https://doi.org/10.5285/2f0eae1c-1512-4823-9cbe-cb54f05ee996

This dataset provides model output for 20th and 21st-century ice-ocean simulations in the Amundsen Sea. The simulations are performed with the MITgcm model at 1/10 degree resolution, including components for the ocean, sea ice, and ice shelf thermodynamics. Atmospheric forcing is provided by the CESM1 climate model for the historical period (1920-2005) and four future scenarios (2006-2100), using 5-10 ensemble members each. The open ocean boundaries are forced by either the corresponding CESM1 simulation or a present-day climatology. The simulations were completed in 2022 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). UKRI Fund for International Collaboration NE/S011994/1

The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three/six-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. To understand how the data grid is formed it is necessary to read the attached WFD-land-long-lat-z files either in NetCDF or DAT formats. The data covers land points only and excludes the Antarctica. Qair or 2m specific humidity (instantaneous) is the instantaneous specific humidity at 2m measured in kg/kg at 6 hourly resolution and 0.5 x 0.5 degrees spatial resolution.