Climate data and scenarios: synthesis of recent observation and modelling results, chapter 3
3. Future climate
The climate of the future will continue to experience natural variability, much as it has in the past. However, the background change in mean climate, already being driven by human activities, will continue at a rate that is determined primarily by current and future emissions of greenhouse gases and aerosols. Because future emissions are difficult to predict, it is necessary to use plausible scenarios, ranging from low to high emission pathways, to project future climate change. Global Earth System Models--which produce comprehensive computer simulations of the global climate system and the related carbon-cycle processes (see: Flato, 2011)--provide scientifically-based tools to make projections of future climate by simulating the response to atmospheric greenhouse gases and aerosols, land-use change, and other external forcings. Owing to uncertainties in the detailed representation of many complex climate processes, individual Earth System Models vary in their representation of these processes and will have biases of various kinds. Because of this, it is preferable to make use of a multi-model ensemble of projections for many applications. The average of a multi-model ensemble generally produces smaller historical errors than any individual model (Flato, et al., 2013) and the spread amongst models allows some quantification of uncertainty. The World Climate Research Programme,Footnote1 (WCRP) coordinates multi-model climate projections via its Working Group on Coupled Modelling (WGCM) and the Coupled Model Intercomparison Project (CMIPFootnote2). The results presented in the following sections are based on the CMIP5 results that were also featured in the Working Group I contribution to the IPCC Fifth Assessment Report (IPCC, 2013: see chapters 9, 11, and 12, and Annex I).
The CMIP5 projections make use of Representative Concentration Pathways (RCPs), which are designed to provide plausible future scenarios of anthropogenic forcing spanning a range from a low emission scenario characterized by active mitigation (RCP 2.6), through two intermediate scenarios (RCP 4.5 and RCP6), to a high emission scenario (RCP 8.5).Footnote3 Figure 4 illustrates some of the assumptions underlying these scenarios. These scenarios make use of various combinations of projected population growth, economic activity, energy intensity, and socio-economic development. These, in turn, lead to calculations of energy consumption and related emissions and finally atmospheric concentrations of greenhouse gases and other climate forcings. These RCP scenarios serve as input to the Earth System Models, which simulate the climate system response and resulting climate conditions.
Figure 4 - Socioeconomic (top row), energy intensity (second row), greenhouse gas emission (third row), and ultimately greenhouse gas concentration (bottom row) assumptions underlying the representative concentration pathways (RCPs) used to drive future climate projections. From van Vuuren, et al., 2011, reproduced with permission.
Long description of Figure 4
This figure consists of 10 panels each depicting a graph showing projections from 2000 to 2100 showing how the projections are built up from socioeconomic to greenhouse gas concentrations. Each graph consists of four solid lines representing the four different forcing scenarios, known as Representative Concentration Pathways, used in the IPCC Fifth Assessment Report (RCP2.6, RCP4.5, RCP6, and RCP8.5). The top line of two panels represents socioeconomic variables. The upper left panel depicts population (in millions), starting at 6000 in 2000. RCP8.5 shows the greatest increase by the year 2100, from 6000 to 12000, and RCP4.5 shows the least growth from 6000 to about 8500. The upper right panel represents GDP ($2000), starting at 35 in the year 2000 for all RCPs, increasing to 300 for RCP2.6 (which is on the high end of the scale for the four RCPs), and about 150 for RCP6 (which is on the low side). The second row consists of two panels and shows projections of energy intensities, which are derived from the projections shown in the top row. The left panel in the second row represents primary energy consumption (EJ), starting around 400 in the year 2000, and increasing to 1750 in 2100 for RCP8.5 representing the high end of change, and about 750 for RCP6 on the low end of change. The right graph on the second row represents oil consumption (EJ), starting at 150 in 2000, peaking at 350 around 2070 then falling back to 150 by 2100 for RCP8.5, whereas the RCP2.6 trends fall throughout the period to around 50 by 2100. The third row consists of three panels showing greenhouse gas emissions projections, which are derived from the projections in the second row. The first panel shows CO2 emissions (GtC), which starts at 7.5 for all RCPs in 2000. The high end of the scale shows an increase to around 27.5 by 2100 for RCP8.5, and drops to 0 by 2080 for RCP2.6 on the low end of the scale. The second panel shows CH4 concentrations (TgCH4), starting at around 310 for all RCPs in 2000 and increasing to around 900 by 2100 for RCP8.5 on the high end of the scale, and around 130 for RCP2.6 on the low side. The third panel shows N2O emissions (TgN) starting around 7.5 in 2000 for all RCPs, and increasing to 15 for RCP8.5 by 2100, and decreasing to 5 for RCP2.6. The fourth row consists of three panels and represents the projected greenhouse gas concentrations, which are derived from the projections in the previous row. The first panel represents CO2 concentration (ppm), starting at around 370 in 2000 for all RCPs, and increasing to around 950 by 2100 for RCP8.5 (at the high end), or leveling out to around 400 for RCP2.6 by 2100 (at the low end). The second panel is CH4 concentration (ppb), starting around 1750 in 2000 for all RCPs, increasing to around 4000 by 2100 for RCP8.5, and decreasing to 1250 by 2100 for RCP2.6. The third panel represents the N2O concentration (ppb), starting at around 320 in 2000 for all RCPs, and increasing to 450 for RCP8.5 in 2100, or leveling off at 325 for RCP2.6 by 2100.
A new feature of the IPCC Fifth Assessment Report (AR5) is the Atlas of Global and Regional Climate Projections (Annex 1--IPCC, 2013), which provides a synthesis of results from the CMIP5 multi-model ensemble. For application to Canadian impact studies and adaptation planning, the regional boundaries of the Atlas are less than optimal: western Canada is combined with the western United States and Alaska, and eastern Canada is combined with Greenland and Iceland (but separated from western Canada). We have therefore generated multi-model ensemble results specific to Canada, using output from 29 CMIP5 models from which results were available for historical simulations, RCP2.6, RCP4.5, and RCP8.5 (results for RCP6.0 are also available, but from fewer models; so this scenario is not illustrated here). Further details on the models used in this document are presented in Table 2.
The ensemble climate model results include output representing a broad range of climate variables. For example, model output includes temperature, precipitation, snow depth, ocean pH and salinity, soil moisture, downwelling solar radiation, and many other quantities. As an example, a full listing of results from the Canadian model (CanESM2) is available. In this document we focus on temperature and precipitation changes in the Canadian context.
|Model Name||Place of Origin||Institution|
|BCC-CSM1-1||China||Beijing Climate Centre, China Meteorological Administration|
|BCC-CSM1-1-m||China||Beijing Climate Centre, China Meteorological Administration|
|BNU-ESM||China||Beijing Normal University|
|CanESM2||Canada||Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada|
|CCSM4||USA||National Centre for Atmospheric Research|
|CESM1-CAM5||USA||National Centre for Atmospheric Research|
|CESM1-WACCM||USA||National Centre for Atmospheric Research|
|CNRM-CM5||France||Centre National de Recherches Météorologiques and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique|
|CSIRO-Mk3.6.0||Australia||Queensland Climate Change Centre of Excellence and Commonwealth Scientific and Industrial Research Organisation|
|EC-Earth||Europe||A consortium of European institutions|
|FGOALS-g2||China||State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics|
|FIO-ESM||China||First Institute of Oceanography, State Oceanographic Administration|
|GFDL-CM3||USA||NOAA Geophysical Fluid Dynamics Laboratory|
|GFDL-ESM2G||USA||NOAA Geophysical Fluid Dynamics Laboratory|
|GFDL-ESM2M||USA||NOAA Geophysical Fluid Dynamics Laboratory|
|GISS-E2-H||USA||NASA Goddard Institute for Space Studies|
|GISS-E2-R||USA||NASA Goddard Institute for Space Studies|
|HadGEM2-AO||UK||UK Met Office Hadley Centre|
|HadGEM2-ES||UK||UK Met Office Hadley Centre|
|IPSL-CM5A-LR||France||Institut Pierre Simon Laplace|
|IPSL-CM5A-MR||France||Institut Pierre Simon Laplace|
|MIROC-ESM||Japan||University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology|
|MIROC-ESM-CHEM||Japan||University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology|
|MIROC5||Japan||University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology|
|MPI-ESM-LR||Germany||Max Planck Institute for Meteorology|
|MPI-ESM-MR||Germany||Max Planck Institute for Meteorology|
|MRI-CGCM3||Japan||Meteorological Research Institute|
|NorESM1-M||Norway||Norwegian Climate Centre|
|NorESM1-ME||Norway||Norwegian Climate Centre|
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