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Climate scenarios

About the analysis

Content:
Climate models
Scenarios
Climate scenarios
Global warming levels
Scenarier are not forecasts
Climate indices
Why are different reference periods used?
About ensembles
References
Literature


This page uses scenarios and observations to explain the development of the climate in Sweden up until last year, and to predict how the climate could continue to develop in Sweden during the 21st century.

The material is based on weather observations together with calculations with climate models that use information about future changes in the atmosphere. The climate models manage the interaction between the physical processes in the atmosphere-land-water system. The results of the climate model calculations have been further processed for Swedish counties, river basins and meteorological forecast districts. The calculations cover the period 1961-2100. The climate model calculations are a part of the international research project CORDEX (Jones et al., 2011).

The historical, observation-based data that are presented up until last year are based on average values for areas of 4x4 km, while the climate scenarios correspond to averages for areas of 50x50 km. All available data within an area is used to calculate the average for the area.

Climate models

Climate models are needed in order to estimate the future climate. These models include a 3-dimensional representation of the atmosphere, land surface, sea, lakes and ice. The atmosphere is divided up into a 3-dimensional grid over and above the earth’s surface. In order to obtain a good result, the models have to take the whole atmosphere into consideration, covering the entire surface of the earth as well as up into the air above it. These models are called global climate models.

For each grid point the change over time is calculated for a number of meteorological, hydrological and climatological parameters.

A climate model requires a lot of computer power, which means that the 3-dimensional grid is restricted. In a global climate model the grid spacing is usually quite large, which means that the level of detail at regional level is low. In order to study a smaller part of the earth in detail, regional climate models are used. A grid is placed over a smaller area, for example Europe. This means a smaller grid spacing can be used without requiring too much computer power, giving a more detailed model.

Events that happen outside the calculation area in a regional climate model are regulated by the results from a global climate model. In this way, changes outside the regional model area are still taken into account. In this study the Rossby Centre’s regional atmospheric model RCA has been used. The model covers Europe and the grid resolution over the land surface is about 50x50 km.

Scenarios

The climate model calculations are based on emission scenarios or radiation scenarios. Emission scenarios are assumptions about future emission of greenhouse gases, based on estimates of the development of the world economy, population growth, globalisation, increasing use of green technology, etc. The amount of greenhouse gases that are emitted depends on global evolution. These scenarios are called SRES scenarios (Special Report on Emission Scenarios (Nakićenović, 2000)).

Radiation scenarios are based on assumptions about how the greenhouse effect will increase in the future, known as radiative forcing (measured in W/m²). If there is an increased emission of greenhouse gases, then there will be more radiative forcing. These scenarios are called RCP scenarios (Representative Concentration Pathways (Moss et al., 2010)).

This analysis uses four scenarios:
  • RCP2.6: Powerful climate politics cause greenhouse gas emissions to peak in 2020. The radiative forcing will reach 2.6 W/m² by the year 2100 (used by IPCC, AR5). This scenario is closest to the ambition of the Paris Agreement.
  • RCP4.5: Strategies for reducing greenhouse gas emissions cause radiative forcing to stabilise at 4.5 W/m² before the year 2100 (used by IPCC, AR5).
  • RCP8.5: Increased greenhouse gas emissions mean that radiative forcing will reach 8.5 W/m² by the year 2100 (used by IPCC, AR5). This scenario is closest to the currently measured trends in greenhouse gas concentrations.
  • SRES A1B: Moderate population growth, rapid global development towards more efficient technologies and a balanced use of fossil fuels and renewable energy (used by IPCC, AR4).

The RCP scenarios are newer than SRES A1B, which is mainly used as a reference to the RCP scenarios. More information on RCP scenarios can be found on this page (in Swedish).

Climate scenarios

A climate scenario is a combination of an emission or radiation scenario, a global climate model, a regional climate model and the modelled time period. The following figure illustrates the climate scenarios that are used in this analysis. The tables in the section about ensembles give more information about the global climate models that are used.

klimatscenario

The models have been run from 1961 to 2100. Since the models have been run from 1961 for each climate scenario, the results can differ right from the start, even before the effects of the emission and radiation scenarios. This is because the data from the global models that are used does not always reflect the current climate in exactly the same way for each global model run. The meteorological normal period 1961-1990 is first used when the model is validated. The model results from 1961-1990 can be compared with observations from the same period to show how well the models can represent the current climate. The period 1961-1990 is then used as a reference to show how the climate has changed. Future results are often compared to the mean values for the period 1961-1990.
Normal mean annual temperature for Sweden
Normal annual precipitation for Sweden

Since the calculated results are in the form of a grid, it is difficult to directly compare the model results with the observations. Observations describe the situation at a particular place, while the model describes a situation evenly distributed over a grid square. For example a large amount of rainfall could be measured locally at monitoring station, while other stations nearby register little or no rainfall. If the same precipitation volume is calculated by the model, it is distributed evenly over the grid square. This should correspond to the same amount of precipitation at all stations, but the volume would be much less than that measured by one of the monitoring stations in reality.

Global warming levels

During the climate negotiations in Cancún 2010 there was an agreement in an ambition to limit the increase in global average temperature to below 2 degrees compared to pre-industrial (1881-1910) levels. A temperature increase of more than 2 degrees is a limit that is considered to bee too costly on society and environment (e.g. IPCC, 2007; UNFCC, 2010), but still possible to be below (IPCC, 2014). In 2015 the countries of the world agreed on the so called the Paris Agreement. This states that the global temperature rise should be kept well under 2 degrees and that efforts should be made to limit the temperature increase even further to 1.5 degrees above pre-industrial levels.

Since the global temperature increase of 1.5 or 2 degrees are just averages it is interesting to look at the temperature increase at regional scale in Europe and Sweden. For each climate model the time when 1.5 and 2 degrees warming is reached according to scenario RCP8.5 is calculated. A 30-year period centred around that time is used in an ensemble. Since the sensitivity to changes in the amount of greenhouse gases is different in different models a certain warming level will be reached at different times (see figure below). Focus is on the average climate in the 30-year period when a certain warming level is reached in the different models and not at a specific point in time.

Note that in the maps shown here future warming is compared to the period 1971-2000. Some of the warming occurs before year 1971. To be precise, the global average temperature has already increased with 0.46°C from pre-industrial time until 1971. A warming of 2 degrees compared to pre-industrial levels corresponds therefore to a warming of 1.54°C compared to 1971-2000 (Vautard et al., 2014).

Two degrees warming
Global temperature increase compared to 1881-1910 according to nine different climate models according to scenario RCP8.5 (coloured lines) and the average of the model ensemble (black line). The 30-year periods representing 2 degrees warming are shown as horizontal lines of the same colours as the respective models.
Enlarge image

Scenarios are not forecasts

The results that are presented from the climate model calculations are usually called climate scenarios. Climate scenarios are not weather forecasts. Climate scenarios are based on assumptions about the future and represent the statistical behaviour of the weather, i.e. the climate. Climate scenarios do not recreate the actual weather for a specific location at a particular point in time. A weather forecast however provides information about what will happen at the local scale during a shorter time period.

Climate indices

Apart from temperature and precipitation, a number of climate indices are also calculated, with the help of the general meteorological parameters generated by the model. This could be the number of warm or cold days, accumulated precipitation during a week, or the length of the vegetation period. Since many indices are based on threshold values (for example when the temperature rises above a certain level), they are sensitive to systematic errors in the climate models. For example, if a model is generally too cold it can make a big difference to the predicted number of warm days. This does not need to be a problem when analysing the future climate since we are mainly interested in how much the index will have changed in the future.

Why are different reference periods used?

SMHI uses the reference period 1961-1990 to define the current climate. New observations are compared to the mean value for 1961-1990 to measure how they differ. For example, if the summer is warmer than normal, it means that it is warmer than the average value of the summers of 1961-1990. The World Meteorological Organization, WMO, defines the reference periods, and the next reference period will be 1991-2020 which will start to be used in 2021.

Climate scenarios are often presented as changes compared to the current climate. Often the reference period 1961-1990 is used, just as for observations. Since the climate is changing, the period 1961-1990 is not fully representative for what we consider to be the current climate. Therefore, later reference periods have started to be used, and many projects are now using the years 1971-2000.

About ensembles

What is an ensemble?
An ensemble is a collection of climate scenarios (estimates of the future climate) where the individual scenarios are different from each other. The climate scenarios can for example differ with respect to the climate model used, or the emission or radiation scenario. A climate scenario that is part of an ensemble is called a member.

Why use ensembles?
An ensemble gives a good overview of the spread of the difference between the members, and highlights some of the uncertainties associated with simulating the future climate. The ensemble is a measure of the reliability of the results. If many different climate scenarios give similar results, then the results are relatively more reliable than if they all pointed in different directions.

The significance of the global climate model
One type of ensemble has members which are calculated based on different global climate models but with the same emission or radiation scenario. There is a difference in the results because the climate models use different ways to describe the physical processes in the climate system that is simulated. This illustrates the uncertainty of our understanding of how the climate system works. It is not easy to select which climate models should be included in an ensemble. A model can perform well in some parts of the world and less well in other areas. Another model maybe describes the temperature well but is not as good for precipitation. It can therefore be worth using large ensembles since they are better at capturing the uncertainty of the results. In practice the choice of ensemble depends very much on how many model simulations can feasibly be run.

Another type of ensemble is obtained by using one single global climate model where the different model calculations are made using different initial conditions; small but plausible differences in the starting conditions for the model. Since climate models and climate systems are chaotic by nature, a small difference at a certain point in time can lead to a significant difference later on. In this way the climate system’s natural variability can be studied. This is explained in more detail below.

Reliability can be better described with ensembles
When an ensemble run has been carried out, the spread of the result gives an idea about the reliability of the results. Depending on the type of ensemble that has been produced, the significance of the choice of climate models and start values can be studied.

Significance of the time period
The number of models and scenarios used within an ensemble partly depends on the time period of the climate study. In general, the need for a large number of different combinations of models and initial model conditions is greater for queries closer in time (a few decades) or for more extreme situations. If a query instead has a longer time perspective (a century) then there is a greater need for more scenarios (that represent the various possible forms of global development).

Natural variability is important in the short term
In addition to human impact on the climate, the climate system has its own natural variations. These natural fluctuations from year to year, or from decade to decade, complicate the analysis of the climate scenarios. In particular this applies when changes to the climate are studied over shorter periods of time. By the year 2100 the change in the climate is assumed to be so significant that there are clear trends, even if the figures vary widely from year to year. The natural variation of the climate cannot be predicted for an exact date with the knowledge we have today. However the natural variability can be studied by building an ensemble of several climate scenarios based on a radiation scenario with different initial conditions. By the end of the century the uncertainties mainly depend on which global climate model and which radiation scenario was used.


The global climate models (GCM) in the ensemble analysis for the RCP scenarios
Driving GCM Institute,
Country
References Europe
+
Sweden
World
+
Africa
Southwestern
Asia
+
South
America
Arctic
RCP
2,6
RCP
4,5
RCP
8,5
RCP
4,5
RCP
8,5
RCP
4,5
RCP
8,5
RCP
4,5
RCP
8,5
CanESM2 CCCMA,
Canada
Chylek
et al., 2011
X X X X X X X X
CNRM-CM5 CNRM-CERFACS,
France
Voldoire
et al., 2012
X X X X X X
EC-EARTH EC-EARTH,
EU
Hazeleger
et al., 2010
X X X X X X X X X
IPSL-CM5A-MR IPSL,
France
Dufresne
et al., 2013
X X X X X X
MIROC5 MIROC,
Japan
Watanabe
et al., 2011
X X X X X X
HadGEM2-ES Hadley Centre,
UK
Collins
et al., 2011
X X X X X X X
MPI-ESM-LR MPI-M,
Germany
Popke
et al., 2013
X X X X X X X X X
NorESM1-M NCC,
Norway
Bentsen
et al., 2013
X X X X X X X X
GFDL-ESM2M NOAA GFDL,
USA
Dunne
et al., 2012
X X X X X X
CSIRO-Mk3-6-0 CSIRO,
Australia
Gordon
et al., 2010
X X


The global climate models (GCM) in the ensemble analysis for the SRES A1B emission scenario
Driving GCM Institute, Country References
CCSM3 NCAR, USA Collins et al., 2006
HadCM3-Q0 Hadley Centre, UK Gordon et al., 2000; Collins et al., 2010
ECHAM5-r3 MPI-met, Germany Jungclaus et al., 2006; Roeckner et al., 2006
BCM Bjerknes Centre, Norway Furevik et al., 2003
IPSL IPSL, France Hourdin et al., 2006
CNRM CNRM, France Voldoire et al., 2012


References

Read more about Rossby Centre and climate models under "Research" at smhi.se.

Bentsen, M., Bethke, I., Debernard, J.B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I.A., Hoose, C. and Kristjánsson, J.E..: The Norwegian earth system model, NorESM1-M. Part 1: Description and basic evaluation, Geosci. Mod. Dev., 6, 687-720, doi:10.5194/gmd-6-687-2013, 2013.

Chylek, P., Li, J., Dubey, M. K., Wang, M. and Lesins. G.: Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos. Chem. Phys. Discuss., 11, 22893-22907, 2011

Collins, W.D., Bitz, C.M. Blackmon, M.L., Bonan, G.B., Bretheron, C.S., Carton, J.a., Chang, S., Doney, C., Hack, J.J., Henderson, T.B., Kielh, J. T., Large, W.G., McKenna, D.S., Santer, B.D. and Smith, R.D.: The Community Climate System Model Version 3 (CCSM3), Journal of Climate, 19: 2122-2143, 2006.

Collins, M., Booth, B.B., Bhaskaran, B., Harris, G.R., Murphy, J.M. and co-authors: Climate model errors, feedbacks, and forcings: a comparison of perturbed physics and multi-model ensembles. Climate Dynamics, doi:10.1007/s00382-010-0808-0, 2010.

Collins, W.J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C.D., Joshi, M., Liddicoat, S., et al.: Development and evaluation of an Earth-System model-HadGEM2. Geosci. Model Dev., 4, 1051-1075, 2011.

Dufresne, J.-L.; Foujols, M.-A.; Denvil, S.; Caubel, A.; Marti, O.; Aumont, O.; Balkanski, Y.; Bekki, S.; Bellenger, H.; Benshila, R.; et al.: Climate change projections using the IPSL-CM5 Earth system model: From CMIP3 to CMIP5. Clim. Dyn., 40, 2123-2165, 2013.

Dunne et al.: GFDL's ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics. Journal of Climate Vol. 25. DOI: 10.1175/JCLI-D-11-00560.1, 2012.

Furevik, T., Bentsen, M., Drange, H., Kindem, I. K. T., Kvamstø, N. G., and Sorteberg, A.: Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM, Clim. Dynam., 21, 27-51, doi:10.1007/s00382-003-0317-5, 2003.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.F.B., and Wood, R.A.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn., 16: 147-168, 2000.

Gordon, H. et al.: The CSIRO Mk3.5 Climate Model. CAWCR Technical Report, 21, 1–74, 2010.

Hazeleger, W. and Coauthors: EC-Earth: A seamless Earth-system prediction approach in action. Bull. Amer. Meteor. Soc., 91, 1357-1363, 2010.

Hourdin, F., I. Musat, S. Bony, P. Braconnot, F. Codron, J.L. Dufresne, L. Fairhead, M.A. Filiberti, P. Friedlingstein, J.Y. Grandpeix, G. Krinner, P. Levan, Z.X. Li & F. Lott: The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Climate Dynamics, 27: 787-813, 2006.

IPCC: Summary for policymakers Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change ed M L Parry, O F Canziani, J P Palutikof, P J van der Linden and C E Hanson (Cambridge: Cambridge University Press) pp 7-22, 2007.

IPCC: Climate Change 2014: Summary for Policymakers, Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp, 2014.

Jones, C. et al.: The Coordinated Regional Downscaling Experiment: CORDEX, An international downscaling link to CMIP5: CLIVAR Exchanges, No 56, Vol 16, No 2, 34-40, 2011.

Jungclaus, J.H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.J., Latif, M., Marotzke, J., Mikolajewicz, U. and Roeckner, E.: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. Journal of Climate, 19: 3952-3972, 2006.

Moss, R. H. et al.: The next generation of scenarios for climate change research and assessment. Nature, Vol 463, 11 February 2010, doi:10.1038/nature08823, 2010.

Nakićenović, N. & Swart, R. (ed.): Special report on emissions scenarios. A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press. 612 pp., 2000.

Popke, D., Stevens, B. and Voigt, A.: Climate and climate change in a radiative-convective equilibrium version of ECHAM6. Journal of Advances in Modeling Earth Systems, Vol.. 5, 1-14, doi:10.1029/2012MS000191, 2013

Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh, L., Manzini, E., Schlese, U. and Schulzweida, U.: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. Journal of Climate, 19: 3771-3791, 2006.

UNFCCC: The Cancun Agreements. United Nations Framework Convention on Climate Change http://unfccc.int/meetings/cancunnov2010/meeting/6266.php, 2010.

Vautard, R., Gobiet, A., Sobolowski, S., Kjellström, E., Stegehuis, A., Watkiss, P., Mendlik. T., Landgren, O., Nikulin, G., Teichmann, C. and Jacob, D.: The European climate under a 2°C global warming. Environ. Res. Letters. Environ. Res. Lett. 9, 034006, doi:10.1088/1748-9326/9/3/034006, 2014.

Voldoire, A., Sánchez-Gómez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., Chauvin, F.: The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dyn. doi:10.1007/s00382-011-1259-y, 2012.

Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., et al.: MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev., 4, 845-872, 2011.


Literature

Bernes, C., 2007. En ännu varmare värld. Växthuseffekten och klimatets förändringar. Monitor 20. Naturvårdsverket. 176 s. En populärvetenskaplig bok som kan beställas från www.naturvardsverket.se/bokhandeln

Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G. and Ullerstig, A. 2011: 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations. Tellus 63A. DOI: 10.1111/j.1600-0870.2010.00475.x

Rummukainen, M.: State-of-the-art with regional climate models. Wiley Interdisciplinary Rev.: Clim. Change, 1, 82-96, doi:10.1002/wcc.8, 2010.

Samuelsson, P., Jones, C. G., Willen, U., Ullerstig, A., Gollvik, S., Hansson, U., Jansson, C., Kjellström, E., Nikulin, G., and Wyser, K.: The Rossby Centre regional climate model RCA3: model description and performance, Tellus A, 63, 4-23, doi:10.1111/j.1600-0870.2010.00478.x, 2011.

SMHI Faktablad nr 29. Klimat i förändring. En jämförelse av temperatur och nederbörd 1991-2005 med 1961-1990.


Software

The maps on these pages were made with NCL
The NCAR Command Language (Version 6.2.0) [Software]. (2014). Boulder, Colorado: UCAR/NCAR/CISL/TDD. http://dx.doi.org/10.5065/D6WD3XH5