The material is based on calculations made with climate models that use information about future changes in the atmosphere and a regional coupled atmosphere-ice-ocean climate model. The calculations cover the period 1961-2100.
Climate models are needed in order to estimate the future climate. These models include a 3-dimensional representation of the atmosphere, land surface, ocean, lakes and ice. The atmosphere is divided 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.
The latest generation of global climate models are models that also calculate the dynamics of the oceans. In a similar manner as for the atmosphere, the oceans are filled with a 3-dimensional grid covering the full volume from the ocean surface to the sea bottom.
All changes over time are in each grid point calculated for a number of meteorological, hydrological and oceanographic parameters. Due to that a climate model requires a lot of computer power, and the size of the 3-dimensional grid becomes the limiting factor. 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 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 regional climate model RCA4-NEMO has been used (Wang et al., 2015). The model covers the Baltic Sea and the North Sea and the horizontal grid resolution is about 4x4 km. This model system provides a better potential than a global ocean model to describe the processes that are important in the smaller geographic area.
The climate model calculations used are based on radiation scenarios. These scenarios are based on assumptions of 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)) and used by IPCC AR5 (IPCC, 2014).
This analysis uses three 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. This scenario is closest to the ambition of the Paris Agreement (UNFCCC, 2015).
- RCP4.5: Strategies for reducing greenhouse gas emissions cause radiative forcing to stabilise at 4.5 W/m² before the year 2100.
- RCP8.5: Increased greenhouse gas emissions mean that radiative forcing will reach 8.5 W/m² by the year 2100. This scenario is closest to the currently measured trends in greenhouse gas concentrations.
A climate scenario is a combination of a 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 table in the section about ensembles give more information about the global climate models that are used.
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 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 used when the atmospheric 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.
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.
For the regional climate model, a number of studies have investigated different aspects of the regional climate system using RCA4-NEMO: Wang et al., 2015 (Model Description), Gröger et al., 2015. (Added Value), Schimanke et al., 2014 (Major Baltic Inflows), Ganske et al., 2016 (Changes in Wind Speed), Jeworrek et al., 2017 (Snowbands), Pätsch et al., 2017 (Model Intercomparison), Dieterich et al., 2013 (Model Validation).
Scenarios are not forecasts
The results that are presented from the climate model calculations are usually called climate scenarios. Climate scenarios are not weather or ocean forecasts. Climate scenarios are based on assumptions about the future and represent the statistical behaviour of the weather and ocean, i.e. the climate. Climate scenarios do not recreate the actual “weather” for a specific location at a particular point in time. A weather or ocean forecast however provides information about what will happen at the local scale during a shorter time period.
Why different reference periods are used?
SMHI uses the reference period 1961-1990 to define the current atmospheric 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 atmospheric reference periods, and the next reference period will be 1991-2020 which will start to be used in 2021.
For the oceans, there is no generally defined reference period for the current climate and different periods are used in different studies. In this study the period 1970-1999 is used.
Climate scenarios are often presented as changes compared to the current climate.
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 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 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 in the ensemble analysis
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.
Dieterich, C., Schimanke, S., Wang, S., Väli, G. Liu, Y., Hordoir, R. Axell, L., Höglund, A., Meier, H. E. M.: Evaluation of the SMHI coupled atmosphere-ice-ocean model RCA4-NEMO, SMHI Report Oceanography, RO 47, 2013.
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.
Ganske, A., Tinz, B., Rosenhagen, G., Heinrich, H.: Interannual and Multidecadal Changes of Wind Speed and Directions over the North Sea from Climate Model Results, Meteorologische Zeitschrift, 25, 463-478, 2016.
Gröger, M., Dieterich, C., Meier, H. E. M., Schimanke, S.: Thermal air-sea coupling in hindcast simulations for the North Sea and Baltic Sea on the NW European shelf, Tellus A, 67, 586-599, 2015.
Hazeleger, W. and Coauthors: EC-Earth: A seamless Earth-system prediction approach in action. Bull. Amer. Meteor. Soc., 91, 1357-1363, 2010.
IPCC, 2014: Climate Change 2014: 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.
Jeworrek, J., Wu, L., Dieterich, C., Rutgersson, A.: Characteristics of convective snow bands along the Swedish east coast, Earth Syst. Dynam., 8, 163-175, 2017.
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.
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.
Pätsch, J., Burchard, H., Dieterich, C., Gräwe, U., Gröger, M., Mathis, M., Kapitza, H., Bersch, M., Moll, A., Pohlmann, T., Su, J., Ho-Hagemann, H.T.M., Schulz, A., Elizalde, A., Eden, C.: An evaluation of the North Sea circulation in global and regional models relevant for ecosystem simulations, accepted in Ocean Modelling.
Schimanke, S., Dieterich, C., Meier, H. E. M.: An algorithm based on sea-level pressure fluctuations to identify major Baltic inflow events, Tellus A, 66, 2421-2441, 2014.
UNFCCC: The Paris Agreements. United Nations Framework Convention on Climate Change http://unfccc.int/paris_agreement/items/9485.php, 2015.
Wang, S., Dieterich, C., Döscher, R., Höglund, A., Hordoir, R., Meier, H. E. M., Samuelsson, P., Schimanke, S.: Development and evaluation of a new regional coupled atmosphere-ocean model in the North Sea and Baltic Sea, Tellus A, 67, 1867–1883, 2015.
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 (in Swedish)
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. (in Swedish)