The Climateprediction.net project is harnessing the spare CPU cycles of tens of thousands of individual users' PCs to run a massive ensemble of climate simulations using the Met Office's Unified Model. A multi-thousand member ensemble of simulation results from the perturbed physics climate sensitivity experiment is available for research purposes.

The Climateprediction.net project is harnessing the spare CPU cycles of tens of thousands of individual users' PCs to run a massive ensemble of climate simulations using the Met Office's Unified Model. A multi-thousand member ensemble of simulation results from the perturbed physics climate sensitivity experiment is available for research purposes.

This dataset contains data for the plots in Figures 3 and 4 in the article: Effective rheology across the fragmentation transition for sea ice and ice shelves, Åström, and D.I. Benn, GRL, 2019. The data is produced with the numerical simulation code HiDEM, which is an open source code that can be found at: https://github.com/joeatodd/HiDEM. The data plots in the paper contain the data used as benchmarks for testing the reliability of the simulations (Fig.3), and the main results (Fig. 4), the effective rheology of sea ice across the fragmentation transition. Funding was provided by the NERC grant NE/P011365/1 Calving Laws for Ice Sheet Models CALISMO.

Model simulations undertaken by the Quantifying variability of the El Nino Southern Oscillation on adaptation-relevant time scales using a novel palaeodata-modelling approach (QPENSO) project. These are coupled ocean-atmosphere experiments with a modified version of the HadCM3 (UM version 4.5) climate model. The model has been modified to include stable isotopes of oxygen in both the ocean and atmosphere sub-models, after Tindall et al., 2009. The simulations are grouped into two experiments: 1) 'picontrol', comprising a single 750 year duration unforced pre-industrial boundary condition simulation; 2) 'forced', comprising a suite of six historical simulations of the interval 1160-1360 AD and including changes in solar, volcanic and greenhouse gas forcing. The six simulations represent an initial-condition ensemble over this interval. This project was funded by NERC under grant NE/H009957/1.

Model simulations undertaken by the Quantifying variability of the El Nino Southern Oscillation on adaptation-relevant time scales using a novel palaeodata-modelling approach (QPENSO) project. These are coupled ocean-atmosphere experiments with a modified version of the HadCM3 (UM version 4.5) climate model. The model has been modified to include stable isotopes of oxygen in both the ocean and atmosphere sub-models, after Tindall et al., 2009. The simulations are grouped into two experiments: 1) 'picontrol', comprising a single 750 year duration unforced pre-industrial boundary condition simulation; 2) 'forced', comprising a suite of six historical simulations of the interval 1160-1360 AD and including changes in solar, volcanic and greenhouse gas forcing. The six simulations represent an initial-condition ensemble over this interval. This dataset contains the forced experiment data. This project was funded by NERC.

Model simulations undertaken by the Quantifying variability of the El Nino Southern Oscillation on adaptation-relevant time scales using a novel palaeodata-modelling approach (QPENSO) project. These are coupled ocean-atmosphere experiments with a modified version of the HadCM3 (UM version 4.5) climate model. The model has been modified to include stable isotopes of oxygen in both the ocean and atmosphere sub-models, after Tindall et al., 2009. The simulations are grouped into two experiments: 1) 'picontrol', comprising a single 750 year duration unforced pre-industrial boundary condition simulation; 2) 'forced', comprising a suite of six historical simulations of the interval 1160-1360 AD and including changes in solar, volcanic and greenhouse gas forcing. The six simulations represent an initial-condition ensemble over this interval.

Simulated 15-min discharge time-series (1/10/2015-17/1/2016) for the River Kent at Sedgwick following a Natural Flood Management intervention of ‘Enhanced Hillslope Storage’ plus the baseline simulations are presented. To derive these data, the observed 15-minute discharge River Kent measured at the Environment Agency (EA) Sedgwick gauging station (https://nrfa.ceh.ac.uk/data/station/info/73005) through the 1 Oct 2015 to 17 Jan 2016 period were modelled using the latest version of Lancaster University’s Dynamic TOPMODEL (https://cran.r-project.org/web//packages/dynatop/index.html). The spatially distributed rainfall field used as input to TOPMODEL was derived from a new direction-dependent and topographically controlled interpolation using observed rainfall data for the Cumbrian Mountains (Page et al., 2022. Hydrological Processes 36: e14758, https://doi.org/10.1002/hyp.14758). Lack of perfect understanding of the hydrological processes routing rainfall for stream channels and then along stream channels to the Sedgwick gauge was represented by using a very wide range of model parameters applied randomly within 10,000 simulations. Using the approach detailed in Beven et al. (2022a. Hydrological Processes 36(10): e14703, https://doi.org/10.1002/hyp.14703), the resultant wide range of simulated discharge time-series was reduced by rejecting all but 67 simulations that passed the prescribed criteria. These 67 baseline simulations of observed behaviour through the +3 month period at Sedgwick are presented here. To represent the effect of adding surface storage distributed across this 209 sq km River Kent catchment, the Digital Elevation Model (DEM) used in the baseline simulations according to Hankin et al (2018. Technical report SC150005/R6. Environment Agency, Bristol. 77pp, https://www.gov.uk/flood-and-coastal-erosion-risk-management-research-reports/working-with-natural-processes-to-reduce-flood-risk) to represent bunds placed on hillslopes in rural areas. The bunds are a type of flood mitigation measure known as Natural Flood Management or NFM. These are known formally as ‘Enhanced Hillslope Storage’ or EHS features (Beven et al 2022b. Hydrological Processes 36: e14752, https://doi.org/10.1002/hyp.14752). The TOPMODEL parameter sets producing the 67 ‘acceptable’ baseline simulations were then re-run with the modified DEM. These results are also presented here. Full details about this dataset can be found at https://doi.org/10.5285/af081a90-b014-43f7-9399-c948a8b7672f