Learn about the most current global climate model data, the 6th Coupled Model Intercomparison Project (CMIP6).
Learn about the latest phase of the Coupled Model Intercomparison Project (CMIP6), the most current global climate data now available on ClimateData.ca. Explore topics including differences between CMIP5 and CMIP6 model experiments and Shared Socio-Economic Pathway (SSP) emissions scenarios.
CMIP6 is the latest phase of collaboration under the Coupled Model Intercomparison Project (CMIP). CMIP6 data are the most current global climate model data available. The data are scientifically robust and provide the foundation for the Intergovernmental Panel on Climate Change’s Sixth Assessment Reports.
CMIP is an international scientific collaboration under the United Nations World Climate Research Programme. This initiative involves climate modelling teams from around the world. CMIP sets the experimental design protocol and recommends core sets of experiments for the climate modelling community to undertake. The goal of CMIP is to better understand past, present and future climate changes through assessments of model performance during the historical period and quantification of the causes of the range in future projections. The CMIP collaboration designs a set of standard simulations to allow for cross-comparison of results to detect where models agree and disagree. The CMIP process furthers our understanding of climate modelling, enables modelling improvements, and ensures scientifically robust future climate projections.
Forty-nine climate modelling groups, running 100 climate models, are taking part in this latest phase of CMIP. In comparison, CMIP5 included 40 global climate models created by 20 climate-modelling groups.
CMIP6 models generally have increased complexity (more components) and spatial resolution, representing the atmosphere, oceans and small-scale processes (such as clouds, water vapour, and aerosols) in more detail. This increase in spatial resolution means, among other things, that the representation of temperature and precipitation in mountainous areas is improved compared to CMIP5 simulations.
Another notable difference between CMIP5 and CMIP6 is the emissions scenarios used to project future levels of global climate change. Representative Concentration Pathways (RCPs) were used to drive the CMIP5 generation of models. CMIP6 utilizes an enhanced set of emissions scenarios, which are based on Shared Socio-Economic Pathways (SSPs). SSPs complement RCPs by exploring the socio-economic conditions behind various emissions levels in a standardized manner. While some RCPs and SSPs exhibit approximately the same increase in radiative forcing by the end of the century (e.g., RCP8.5 and SSP5-8.5), there are differences in the GHG emissions associated with each of these emissions pathways.
Finally, some models included in CMIP6 have a higher equilibrium climate sensitivity than those in CMIP5. See below for a description of climate sensitivity.
Different climate models have different spatial resolutions. Over time, resolution has generally increased as climate modelling techniques have improved and computing technology has advanced. For example, for the scenario experiments exploring the evolution of future climate in response to changing greenhouse gas (GHG) emissions, the French model IPSL-CM5-LR (used in CMIP5) had a resolution of 1.9° latitude x 3.75° longitude. The latest version of this model (IPSL-CM6A-LR) used in CMIP6 has a resolution of 1.25° latitude x 2.5° longitude. However, some climate modelling centres chose to keep the same resolution, e.g., CanESM2 (CMIP5) and CanESM5 (CMIP6) from the Canadian Centre for Climate Modelling and Analysis both have a spatial resolution of 2.8° latitude x 2.8° longitude.
Both the CMIP5 and CMIP6 datasets on ClimateData.ca have been downscaled to the same spatial resolution, approximately 6 x 10 km (0.0833° latitude x 0.0833° longitude).
The future modelling in CMIP6 is based on an updated set of future emissions scenarios. CMIP5 model runs were based on four Representative Concentration Pathways (RCPs) and their associated greenhouse gas (GHG) concentrations starting in 2006. CMIP6 model runs use GHG concentrations stemming from Shared Socioeconomic Pathways (SSPs) which relate emissions levels with socio-economic conditions starting in 2015.
CMIP5 models served as the basis for IPCC’s Fifth Assessment Report (AR5) published in 2013; however, these model experiments were started a number of years before being reported on in AR5. Modelling efforts for CMIP6 began shortly after AR5 was published, and were reported on in the Sixth Assessment Report (AR6; 2021). The earlier CMIP5 experiments, therefore, followed observed GHG emissions until 2005, while the more recent CMIP6 experiments followed observed GHG emissions until 2014.
A recent Canadian study has shown that while the CMIP5 and CMIP6 future climate simulations are qualitatively similar for Canada, CMIP6 simulations exhibit larger temperature (mean and extreme) and precipitation (extreme) changes by the end of this century. In another global-scale study, extreme high temperature and precipitation events are projected to become more frequent and intense in CMIP6 simulations.
These responses can be partially explained by differences in GHG forcing between the high emissions scenarios – SSP5-8.5 (CMIP6) and RCP8.5 (CMIP5) and partly by the fact that some of the CMIP6 models exhibit higher climate sensitivity, as explained below.
For more details, see:
Sobie SR, Zwiers FW, Curry CL (2021): Climate model projections for Canada: A comparison of CMIP5 and CMIP6. Atmosphere-Ocean 59: 269-284. https://doi.org/10.1080/07055900.2021.2011103
Li C, Zwiers F, Zhang X, Li G, Sun Y, Wehner M (2021): Changes in annual extremes of daily temperature and precipitation in CMIP6 models. Journal of Climate, 34(9), 3441–3460. https://doi.org/10.1175/JCLI-D-19-1013.1
Equilibrium climate sensitivity refers to the amount of warming after the climate system has stabilized in response to an instantaneous doubling of CO2 in the atmosphere. The increase in global mean temperature when this equilibrium state is reached is used as a metric to describe how models respond to changes in forcing. Models with higher climate sensitivity project more warming in response to the same forcing than those with lower climate sensitivity.
Some of the CMIP6 models (including the Canadian Model, CanESM5) have a higher equilibrium climate sensitivity than the models used in CMIP5. This is partially due to modelling enhancements in CMIP6, such as higher spatial resolution and improved cloud physics. Further research is underway to investigate these findings, which is a normal part of scientific process.
For more details, see:
Sobie SR, Zwiers FW, Curry CL (2021): Climate model projections for Canada: A comparison of CMIP5 and CMIP6. Atmosphere-Ocean 59: 269-284. https://doi.org/10.1080/07055900.2021.2011103
Climate model simulations of the historical period do not match actual observations because climate models are imperfect representations of the Earth system. Each model simulation will be different, and it is important to consider multiple model runs as each run is a possible representation of the climate and its natural variability.
Importantly, however, monthly, seasonal and annual climatological averages and variations are similar between simulated and observed historical climates when regional or larger scale averages are considered.
Because models and meteorological observations do not generally represent information at the same spatial scales, it is important to use modelled historical data when making direct comparisons with modelled future data.
Yes, the same method used to downscale CMIP5 data (known as BCCAQv2) has been used to downscale CMIP6 data. More information on the downscaling of global climate model data can be found at the Pacific Climate Impacts Consortium website here.
That depends. Although CMIP6 climate models generally include more climate processes, have been run at higher spatial resolutions, and use updated emissions scenarios, there is no such thing as a “perfect” global climate model. Nearly all CMIP6 models build off those used in CMIP5, and CMIP6 model results exhibit many similarities to those of CMIP5.
CMIP5 data are still valid, and can be used to explore possible future climates. Both CMIP5 and CMIP6 rely on emissions scenarios with comparable levels of radiative forcing at the end of the century (2100).
It is recommended, however, that you use CMIP6 for new work requiring future climate projections and that you examine the similarities/differences with CMIP5. Comparing CMIP5 and CMIP6 will indicate if there are significant differences between the two sets of projections and if you need to update any of the work which used projections from CMIP5.
Presently there is no date set to remove access to the CMIP5 data on ClimateData.ca. However, at some point CMIP5 data will be removed and CMIP6 data will be used exclusively. CMIP5 data will remain available through the Climate Research Division (ECCC) and DataMart websites as well as the GeoMet API.
The CMIP datasets are a part of a collaborative process involving many climate modelling groups and researchers from around the world and take several years to develop. New CMIP data are generally updated and released every 5 to 8 years.
The datasets are developed in phases to further contribute, coordinate and enhance our understanding of climate modelling but also to support national and international assessments of climate change. This includes the Assessment Reports produced by the United Nations Intergovernmental Panel on Climate Change (IPCC), as well as Canada’s Changing Climate reports.
Shared Socioeconomic Pathways (or SSPs) were used to develop the latest generation of GHG emissions scenarios. SSPs systematically explore potential socio-economic trends over the next century and quantify the effect of these developments on greenhouse gas (GHG) concentrations. The emissions that result under these pathways serve as input to drive climate models and project resultant levels of climate change.
More information on the SSPs can be found below and here.
The set of future scenarios used in CMIP6 encompass a broader range of potential greenhouse gas (GHG) emissions trajectories than those used in CMIP5.
It is possible for multiple emissions scenarios to be associated with each SSP. Due to the extensive resources required to run multiple climate model experiments, only a few SSPs are actually used to drive climate models. These are selected in an effort to span the range of possible emissions scenarios.
To enable comparison between CMIP5 and CMIP6, the three SSP scenarios which correspond to the most widely-used RCPs (2.6, 4.5, and 8.5) have been adopted by the climate modelling community (RCP2.6 – SSP1-2.6, RCP4.5 – SSP2-4.5, and RCP8.5 – SSP5-8.5). Others have been added to explore how climate may change in response to different social and economic conditions. For example, SSP1-1.9 was introduced to explore a world in which social and economic factors mean that there is a good chance of meeting the “aspirational” goal of the Paris Agreement (i.e., a global temperature increase below 1.5 °C), while SSP3-7.0 was selected to represent the medium to high end of the range of future forcing pathways.
Both SSPs and RCPs are attempts to describe greenhouse gas emissions over the next century.
SSPs further refine the previous greenhouse gas concentration scenarios known as RCPs. RCPs were explicitly designed for the climate modelling community to explore the effects of different emissions trajectories or emissions concentrations (resulting in various radiative forcing values). The socio-economic characteristics used to define RCPs are not standardized, making it difficult to map societal changes like population, education, and government policies to climate targets, such as keeping global warming well below 2°C. SSPs address this by defining how societal choices can lead to changes in GHG emissions, to their concentrations in the atmosphere, and thus to radiative forcing by the end of the century (2100). As such, SSPs expand on RCPs to allow for a standardized comparison of society’s choices and their resulting levels of emissions.
ClimateData.ca provides climate projections at a spatial scale more suitable for decision-making than global climate models. To achieve this, CMIP6 data must first be downscaled to a finer spatial resolution, which requires time and computing resources. At present, downscaling efforts have focused on SSPs that are comparable with the RCPs currently available on ClimateData.ca (from CMIP5). This will make it easier to compare results between the CMIP5 and CMIP6 climate model projections.
Some modelling centres did not provide runs for certain SSPs, meaning that the sample of available results is not considered to sufficiently represent the range of possible future climates resulting from that particular SSP. In instances such as this, results were not downscaled to a finer resolution.
Additional downscaled projections for other SSPs may be made available at a later date.
While the SSPs were in development beginning at the same time as the RCPs, they were not ready in time for CMIP5, but have been used in CMIP6. Downscaling of the CMIP6 runs to a finer spatial resolution more suited to decision-making has also taken time.
SSPs systematically consider a wide range of factors that affect global emissions (including population growth, global levels of education and economic development), allowing for comparison of risks and impacts to society, as well as challenges to adaptation and mitigation, in a standardized manner. Importantly, mitigation actions can be superimposed within the overall socio-economic narrative of an SSP to gauge its effectiveness in reducing global emissions.
Yes, RCPs are still valid.
However, SSPs provide an enhanced understanding of the relationship between socio-economic factors and climate change. Since our knowledge of the climate system is constantly evolving, using the most current set of emissions scenarios means that practitioners can ensure their work reflects the most up-to-date socio-economic and climate information available.
The complex nature of the climate system, climate models and human factors makes it difficult to determine exactly how the climate will change in future. What is known for certain is that the future climate will be different from the past. While action to mitigate climate change is essential, a certain amount of warming has already been “locked-in”. By assessing more than one possible future, planners and decision-makers can better prepare for a range of possible outcomes.
It is important to consider two questions prior to selecting SSPs: First, what components of my project are vulnerable to climate change? Second, what level of risk am I comfortable taking?
For example, the consequences of a rare but damaging environmental hazard could be very high, with the power to impact things such as local food security, national GDP, and public safety. In a situation such as this, the costs of adapting to SSP5-8.5 (the high emissions scenario) up until the end of the century may be considered worthwhile. In other circumstances, where the consequences are lower and/or the likelihood of damaging events is low, adapting to SSP5-8.5 may not be necessary or economically viable. Regardless of the project and the rationale, this question of “how much risk am I comfortable taking” is complex, undoubtedly requiring conversations with diverse partner and stakeholder groups to understand the broad range of potential impacts and implications.
A project’s planning horizon is another important consideration during this process. Over relatively short time periods (i.e. the next decade), the range of climate changes between different SSPs is small, meaning it matters less which SSP is selected. However, at mid-century the scenarios quickly diverge revealing diverse levels of climate change.
In applications not directly related to adaptation, where determining vulnerability and risk is not an important component of a project, the SSP selection process may consider additional factors. For example, SSP5-8.5 describes the most global warming and, as such, it contains the greatest “climate change signal to climate variability noise ratio”. In research applications, where the goal is to find a correlation between climate change and some other event, SSP5-8.5 might be the best pick as the climate signal is strongest under this emissions scenario.