KU-Product of regional climate projection

Objective weather types of Deutscher Wetterdienst derived from different Reanalysis and Global Climate Model simulations for the control run (1951-2000) and the projection period (2000-2100). On the one hand, the dataset is useful for evaluation of representative circulation statistics in Central Europe, on the other hand, for the analysis of future weather types due to climate change. Added temperature and precipitation data allow to study the weather type effectiveness for these important climate parameters.

Past, present and future rainfall erosivity in Northwestern Europe calculated from convection-permitting climate simulations in CNRM-AROME (Lucas-Picher et al., 2022; https://doi.org/10.1007/s00382-022-06637-y) using emission scenario RCP 8.5. A description of the methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (https://doi.org/10.5194/hess-28-87-2024).

Objective weather types of Deutscher Wetterdienst derived from different Reanalysis and Global Climate Model simulations for the control run (1951-2000) and the projection period (2000-2100). Furthermore, the NAO-index is also provided. On the one hand, the dataset is useful for evaluation of representative circulation statistics in Central Europe, on the other hand, for the analysis of future weather types due to climate change. Added temperature and precipitation data allow to study the weather type effectiveness for these important climate parameters.

Objective weather types of Deutscher Wetterdienst derived from different Reanalysis and Global Climate Model simulations for the control run (1951-2000) and the projection period (2000-2100). Forthermore, the NAO-index is also provided. On the one hand, the dataset is useful for evaluation of representative circulation statistics in Central Europe, on the other hand, for the analysis of future weather types due to climate change. Added temperature and precipitation data allow to study the weather type effectiveness for these important climate parameters.

‘Heat stored in the Earth system: Where does the energy go?’ contains a consistent long-term Earth system heat inventory over the period 1960-2018. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. This Earth Energy Imbalance (EEI) is the most critical number defining the prospects for continued global warming and climate change. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory, and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2018. Changes in version 2: a) uncertainties have been added and updated in the netcdf file b) Ocean heat content > 2000m depth: update of one time series, and thus revised ensemble mean c) Atmospheric heat content: update of the time series as received by experts on the 29/05/2020 d) Available heat cyropshere: update of the time series as received by experts on the 27/05/2020. e) some attributes have been added for more details.

The Bias Corrected CESMv1 data for current (2006-2015) and future (2091-2100) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature, relative humidity, wind speed, total precipitation, mean surface shortwave flux, top-of-atmosphere outgoing longwave radiation, mean surface latent and sensible heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 60 files (10 variables x 3 temporal resolutions x 2 periods packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x120, and 369x369x12 data points, respectively. The entire dataset is about 100 GB in size. The WRF version used for this project is WRF 3.8.1. . The WRF-ARW source codes and suitable tutorials are available free to users as an open-source model in the NCAR’s https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html website.

This experiment collects the datasets created by the "Weather Data" group of the IEA EBC Annex 80 “Resilient Cooling for Buildings” project. These are datasets of current and future weather files for building energy performance simulation covering 15 locations in ten climate zones worldwide. The datasets contain ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets include typical and extreme weather years in the EnergyPlus weather file (EPW) format and multi-year projections in comma-separated value (CSV) format for three periods: historical (2001-2020), future mid-term (2041-2060), and future long-term (2081-2100). The weather files were generated based on the climate projections from the Regional Climate Model (RCM) MPI-RCA4, then bias-corrected using multiyear observational data for each city. The weather files are ready to be used in building energy simulations and systems design for adaptation and resilience studies. The EPW is a weather file format used to run simulations in EnergyPlus. EPWs are text files and can be opened and edited in any text editor, spreadsheet tools or open-source software tools for creating and editing customised weather files, such as the Element software developed by Big Ladder Software (https://bigladdersoftware.com/projects/elements/). The EnergyPlus Auxiliary Programmes document (attached Additional Info) describes EPW weather data and provides general information on weather data for energy simulations and weather file conversion. Funder: - The Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Office, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 - The Horizon 2020 project 0E-BUILDINGS, Grant agreement ID: 101024627 - The Marie Skłodowska-Curie grant agreement Nº 101024627 - The Fraunhofer Internal Programs under Grant No. Attract 003-695033 - Det Energiteknologisk Udviklingsog Demonstrations Program (EUDP) under grant 64018-0578

The dataset ‘Heat stored in the Earth system: Where does the energy go?’ contains a consistent long-term Earth system heat gain over the past 58 years. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. This Earth Energy Imbalance (EEI) is a fundamental metric of climate change. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system - is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This dataset is based on a study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory, and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960-2018.