The SRES data sets were published by the IPCC in 2000 and classified into four different scenario families (A1, A2, B1, B2). SRES_B2 storyline describes a world in which the emphasis is on local solutions to economic, social and enviromental sustainability. The global population is increasing at a lower rate than A2. It has an intermediate level of economic development and a less rapid and more diverse technological change than in A1 and B1. The Hadley Centre Circulation Model is a 3-dim AOGCM described by (Gordon et al.,2000 and Pope et al.,2000). The atmospheric component has a 19 levels horizontal resolution, comparable with spectral resolution of T42, while the ocean component has a 20 levels resolution. HADCM3 (http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadcm3 ). The changes of anthropogenic emissions of CO2, CH4, N2O and sulphur dioxide are prescribed according to the above mentioned scenario.

The SRES data sets were published by the IPCC in 2000 and classified into four different scenario families (A1, A2, B1, B2). SRES_B2 storyline describes a world in which the emphasis is on local solutions to economic, social and enviromental sustainability. The global population is increasing at a lower rate than A2. It has an intermediate level of economic development and a less rapid and more diverse technological change than in A1 and B1. The atmospheric component AGCM2 is a spectral model with triangular truncation at wave no. 32 and 10 vertical levels. The ocean model component based on the GFDL MOM 1.1 code with 29 vertical levels and has a iospycnal / eddy stirring parameterization (Gent and McWilliams,1990). CGCM2 (http://ec.gc.ca/ccmac-cccma/default.asp?lang=En&n=40D6024E-1 ). The changes of anthropogenic emissions of CO2, CH4, N2O and sulphur dioxide are prescribed according to the above mentioned scenario. These data belongs to a set of three ensemble runs, with the CCCma-model, using the SRES_B2 scenario. They provide monthly averaged values of selected variables for the IPCC-DDC.

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets. These data includes all datasets published for 'CMIP6.DAMIP.CSIRO-ARCCSS.ACCESS-CM2' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. Experiments include the hist-GHG, hist-aer, and hist-nat simulations.

These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and are being used by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ. Lineage: The model used in climate research named Australian Community Climate and Earth System Simulator Climate Model Version 2, released in 2019, includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run by the CSIRO (Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia), ARCCSS (Australian Research Council Centre of Excellence for Climate System Science) (CSIRO-ARCCSS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Immersion Data Centre Cooling Market Outlook

The global immersion data centre cooling market size was valued at approximately USD 0.5 billion in 2023 and is expected to grow significantly, reaching around USD 2.7 billion by 2032, registering a CAGR of 21% during the forecast period. This impressive growth can be attributed to several factors, including the increasing demand for high-efficiency cooling solutions, the rising global data traffic, and the growing environmental concerns driving the adoption of greener technologies.

One of the major growth factors propelling the immersion data centre cooling market is the escalating demand for data storage and processing capabilities. With the proliferation of the Internet of Things (IoT), artificial intelligence, and big data analytics, data centres are under immense pressure to enhance their cooling efficiencies. Immersion cooling, which submerges IT hardware in non-conductive liquids, offers a highly effective solution by significantly reducing the energy consumption associated with traditional air-based cooling methods. This technological advancement is crucial in meeting the rising data demands while simultaneously adhering to environmental regulations.

Another significant driver for the market is the increasing energy costs and the need for sustainable cooling solutions. Traditional air cooling systems consume vast amounts of electricity and are often less efficient in transferring heat away from high-density data centres. Immersion cooling systems, however, provide a more energy-efficient alternative. By directly immersing the electronic components in a cooling fluid, these systems can drastically cut down on energy consumption, leading to substantial cost savings in the long term. Additionally, as global awareness regarding climate change intensifies, the push towards greener and more sustainable technologies has become a compelling factor for many organizations.

The rapid advancements in technology and continuous innovation in immersion cooling solutions are also boosting market growth. Leading companies are investing heavily in research and development to enhance the performance, reliability, and cost-effectiveness of their immersion cooling products. For instance, developments in specialized cooling fluids and the design of better-optimized systems are making immersion cooling a viable and attractive option for a broader range of applications. These innovations are not only catering to current market demands but are also paving the way for future growth and widespread adoption of immersion cooling technologies.

From a regional outlook perspective, North America currently dominates the immersion data centre cooling market, driven by the presence of numerous data centres and a high rate of technology adoption. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, fueled by the rapid expansion of digital infrastructure, increasing investments in data centres, and supportive government policies. Europe and other regions are also witnessing steady growth due to the increasing emphasis on energy efficiency and sustainable practices.

Component Analysis

The immersion data centre cooling market can be segmented by component into hardware, software, and services. Each of these components plays a crucial role in the effective implementation and operation of immersion cooling systems. The hardware segment includes the physical equipment and infrastructure required for immersion cooling, such as cooling tanks, pumps, heat exchangers, and specialized cooling fluids. These components are essential for the physical immersion of IT hardware and efficient heat dissipation within the data centre environment.

The software segment encompasses the control systems, monitoring tools, and management software that facilitate the seamless operation of immersion cooling solutions. Advanced software solutions enable real-time monitoring of temperature, fluid levels, and system performance, ensuring optimal cooling efficiency and preventing potential malfunctions. These tools are critical for maintaining the reliability and longevity of immersion cooling systems, as they allow for proactive maintenance and prompt response to any anomalies.

Services, the third component in this segmentation, include the design, installation, maintenance, and consulting services offered by various providers. Designing an immersion cooling system requires specialized knowledge to ensure compatibility with existing data centre infrastructure and to meet the spec

Data sets derived from paleo proxy evidence, including ice cores, tree rings, ocean and lake sediments, corals, cave deposits, loess, climate models. Includes raw data and reconstructions of past climate and environment.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of India, Southcentral Asia. The time period coverage is from 450 to -44 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Data Center Environment Sensor Market Outlook

According to our latest research, the global Data Center Environment Sensor market size in 2024 stands at USD 2.15 billion, reflecting the rapidly increasing adoption of advanced monitoring solutions in data centers worldwide. The market is projected to expand at a robust CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 6.09 billion by 2033. This growth is primarily driven by the escalating demand for real-time environmental monitoring to ensure optimal operational efficiency, minimize downtime, and enhance the security of mission-critical data center infrastructure.

One of the key growth factors propelling the Data Center Environment Sensor market is the exponential rise in global data traffic and cloud computing adoption. As organizations migrate their workloads to cloud and hybrid environments, the need for robust, scalable, and automated monitoring systems has intensified. Environmental sensors such as temperature sensors, humidity sensors, and smoke detectors play a crucial role in maintaining data center uptime and performance by providing real-time alerts and actionable insights. Additionally, the growing complexity of data center architectures, with increased rack densities and power consumption, necessitates advanced sensor deployment to proactively manage thermal loads, prevent equipment failure, and support energy-efficient operations. The integration of IoT and AI-based analytics further enhances the value proposition of environment sensors, enabling predictive maintenance and smarter facility management.

Another significant driver is the increasing emphasis on sustainability and regulatory compliance in data center operations. With rising energy costs and mounting pressure to reduce carbon footprints, data center operators are leveraging environment sensors to optimize cooling, manage airflow, and detect water leaks early, thereby minimizing energy waste and environmental impact. Regulatory frameworks such as the European Union’s Energy Efficiency Directive and emerging green data center standards in Asia Pacific and North America are compelling operators to invest in advanced sensor-based monitoring solutions. Furthermore, the proliferation of edge data centers and hyperscale facilities is expanding the addressable market for environment sensors, as these decentralized architectures require granular monitoring to ensure resilience and compliance across geographically dispersed sites.

The evolving threat landscape, characterized by increasing risks of fire, water leaks, and unauthorized access, is also fueling demand for comprehensive environment monitoring in data centers. Environment sensors are integral to the implementation of multi-layered security strategies, enabling early detection of anomalies and rapid incident response. The convergence of environmental monitoring with physical security systems—such as surveillance, access control, and smoke detection—creates a holistic risk management framework that is essential for safeguarding sensitive digital assets. Moreover, advancements in wireless sensor technology and cloud-based monitoring platforms are making it easier for data center operators to deploy, scale, and manage sensor networks with minimal disruption and lower total cost of ownership.

From a regional perspective, North America currently leads the Data Center Environment Sensor market due to the presence of major cloud service providers, a mature data center ecosystem, and stringent regulatory standards. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitization, expanding internet penetration, and significant investments in hyperscale and edge data centers across China, India, and Southeast Asia. Europe remains a strong market, underpinned by a focus on energy efficiency and data sovereignty. Meanwhile, Latin America and the Middle East & Africa are emerging as attractive markets, supported by rising demand for digital infrastructure and government-led smart city initiatives. This regional diversification is expected to intensify competition and spur innovation in sensor technologies and deployment models over the forecast period.

This simulation was performed with the first version of the coupled global model CGCMI with the standard concentration of CO2. Details of the model and an analysis of the simulation are given in Flato et al. (1998). Note: Due to data archival problems, data for April 2041 in the CONTROL run were lost. In order to provide continuous time series, this missing month was filled by linear interpolation between adjacent months. These data represent monthly averaged surface values of selected variables for the IPCC-Data Distribution Centre. (see also https://www.ipcc-data.org/ )

The Intergovernmental Panel on Climate Change (IPCC) has been established by WMO und UNEP to assess scientific, technical and socio-economic information, relevant for the understanding of climate change, its potential impacts and option for adaption and migration. Projection of future trends for a number of key variables are provided through this section of the DDC (http://ipcc-data.org/sim/gcm_clim/SRES_TAR ). This information contained in either IS92 emission scenarios (IPCC 1992), the Special Report on Emission Scenarios (IPCC 2000, SRES) or published model studies using data from these scenarios. Six alternative IPCC scenarios (IS92a to f) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumption affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adoped. The SRES scenarios have been constructed to explore future developments in the global enviromental with special reference to the production of greenhouse gases and aerosol precursor emission. A set of four scenario families (A1, A2, B1, B2) have been developed that each of this storylines describes one possible demographic, polito-economic, societal and technological future. Model experiments, also using different forcing scenarios, were calculated at other modeling centres. Emissions Scenarios. 2000 ,Special Report of the Intergovernmental Panel on Climate Change Nebojsa Nakicenovic and Rob Swart (Eds.) Cambridge University Press, UK. pp 570

This collection includes historical oceanographic biological, biochemical, chemical, physical, meteorological, and other data. The data includes barometric pressure, cloud amount and frequency, current, wave, conductivity, nutrients, pH, salinity, temperature, turbidity, transmissivity, biomass measurements, nutrients, fluorescence, species and subspecies identification, phaeophytin, zooplankton, chlorophyll, dissolved oxygen, nitrate, nitrite, phosphate, silicate, alkalinity, and other measurements. These data were collected by bottle, net, CTD, XBT, MBT, BT, and other instruments from drifting buoy, ships, and other platforms in oceans and seas around the world.

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets. These data includes all datasets published for 'CMIP6.RFMIP.CSIRO.ACCESS-ESM1-5' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. Experiments include the piClim-control, piClim-4xCO2, piClim-ghg, piClim-aer, piClim-anthro, and piClim-lu simulations.

These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and are being used by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ.

Lineage: The model used in climate research named Australian Community Climate and Earth System Simulator Earth System Model Version 1.5, released in 2019, includes the components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: none, ocnBgchem: none, seaIce: none. The model was run by the Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia (CSIRO) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km.

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'decadal1968' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 ( https://pcmdi.llnl.gov/mips/cmip5 ). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. decadal1968 (10-year hindcast/prediction initialized in year 1968) - Version 2: The atmospheric composition (and other conditions) should be prescribed as in the historical run (expt. 3.2) and the RCP4.5 scenario (expt. 4.1) of the long-term suite of experiments. Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date. Land, sea-ice and atmosphere initial conditions are left to the discretion of each group. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax ( https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf ) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc .

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'rcp45' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 4.1 rcp45 (4.1 RCP4.5) - Version 1: Future projection (2006-2100) forced by RCP4.5. RCP4.5 is a representative concentration pathway which approximately results in a radiative forcing of 4.5 W m-2 at year 2100, relative to pre-industrial conditions. RCPs are time-dependent, consistent projections of emissions and concentrations of radiatively active gases and particles. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

This additional information contains code and a folder structure that can be used to reproduce or update the HadCRU_MLE_v1.3 dataset. The zip file contains a README text file with instructions on how to use and run the code. The code was written in MATLAB and requires the use of a graphics processing unit.

The SPECMAP Archive No.1 was compiled by Brown University under the direction of A. Duffy and J. Imbrie with the assistance of A.C. Mix and A. Mcintyre, and funded by the National Science Foundation. The SPECMAP Archive No.1 contains climate times series of the past 400,000 years, and basic downcore and core-top data from which these time series were derived for 17 sediment cores from the Atlantic Ocean. Downcore records include (1) quantitative data on planktonic species and assemblages which reflect conditions in the surface waters of the Atlantic ocean; (2) measurements of 0-18, C-13 difference (planktic and benthic), and Cd/Ca. The age model used to transform each downcore record into a time series by correlation of its O-18 record with the published O-18 chronology of Imbrie et al. (1984) is given. Time series with uniformly spaced samples may be calculated by linear interpolation. The Archive No. 1 contains 101 files, including documentation and bibliographic information. The SPECMAR Archive No. 1 is available for direct ftp download from NCDC. The SPECMAP Archive No. 1 was originally submitted to NOAA's National Geophysical Data Center (NGDC) for archive and was subsequently transferred to NOAA's National Climatic Data Center (NCDC) Paleoclimatology Group for stewardship..

Data from the Bjerknes Centre for Climate Research Bergen Climate Model (BCM) Version 2 simulations

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: '1pctCo2' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 6.1 1pctCo2 (6.1 1 percent per year CO2) - Version 1: Idealized 1% per year increase in atmospheric CO2 to quadrupling. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

Data from the Bjerknes Centre for Climate Research, Canadian Centre for Climate Modelling and Analysis, Centre National de Recherches Meteorologiques, Meteorological Institute of the University of Bonn, Geophysical Fluid Dynamics Laboratory, Institute of Numerical Mathematics, Institut Pierre Simon Laplace, LASG, Institute of Atmospheric Physics, Max Planck Institute for Meteorology, NASA Goddard Institute for Space Studies, National Center for Atmospheric Research and the Met Office Hadley Centre.

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'rcp45' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (https://pcmdi.llnl.gov/mips/cmip5). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 4.1 rcp45 (4.1 RCP4.5) - Version 1: Future projection (2006-2100) forced by RCP4.5. RCP4.5 is a representative concentration pathway which approximately results in a radiative forcing of 4.5 W m-2 at year 2100, relative to pre-industrial conditions. RCPs are time-dependent, consistent projections of emissions and concentrations of radiatively active gases and particles. Experiment design: https://pcmdi.llnl.gov/mips/cmip5/experiment_design.html List of output variables: https://pcmdi.llnl.gov/mips/cmip5/datadescription.html Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects:1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and2) to the Coupled Model Intercomparison Project No 5 (CMIP5).CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI.The project embraces the simulations with about 30 climate models of about 20 institutes worldwide.