Understanding Climate Change
Climate models
Pour simuler le climat futur, les climatologues utilisent des modèles climatiques. Il s’agit de logiciels complexes représentant les principales interactions physiques dans l’atmosphère, l’océan, la glace et la surface de la Terre. Celles-ci sont modélisées par les équations mathématiques fondées sur les lois physiques de la mécanique des fluides.
Typiquement, les modélisations climatiques couvrent la période 1850-2100. On fournit aux modèles les valeurs de concentrations de gaz à effet de serre (GES) observées dans le passé et estimées sur les prochaines décennies afin de voir comment les GES influencent le climat. Des conditions virtuelles, de température, précipitation, humidité, vent, etc. à différents points dans l’espace et dans le temps sont ainsi obtenues. Ces données aident à comprendre les processus climatiques et alimentent les portraits du climat futur. Elles fournissent également de l’information sur laquelle s’appuient les efforts en adaptation aux changements climatiques ainsi qu’en atténuation.
Il existe deux principales catégories de modèles climatiques : les modèles globaux du climat (MGC) (ou plus récemment appelés des « modèles du système terrestre ») qui représentent le climat sur l’ensemble du globe et les modèles régionaux du climat (MRC) qui se concentrent sur une partie du globe.
On appelle « simulation climatique » le résultat de l’exécution du modèle sur le passé et le futur alors qu’une « projection climatique » réfère au futur seulement.
To simulate the future climate, climatologists use climate models, which are complex software that takes into account the main physical interactions in the atmosphere, the ocean, ice and the surface of the Earth. These are modelled by mathematical equations based on the physical laws of fluid mechanics.
Climate models use data on greenhouse gas concentrations observed in the past and estimated over the coming decades to see how these concentrations influence the climate. This creates a virtual modeled conditions of the temperature, precipitation, humidity, wind, etc., at different points in space and time. The result of running the model on the past and the future is called a “climate simulation,” while a “climate projection” refers only to the future.
There are two main categories of climate models: global climate models (GCMs) (or more recently called "earth system models") that represent the climate over the entire globe (Figure 1) and regional climate models (RCMs) that focus on one part of the globe (Figure 2).
Figure 1 and Figure 2
Some fifty organizations around the world are developing climate models and providing simulation results in the public domain. This data helps us understand climate processes and what the future climate will be like. It also provides information to assist with climate change adaptation and mitigation efforts.
Each climate model gives different results, depending on its strengths and weaknesses. For more reliable results, it is recommended to use an ensemble of climate models to obtain a portrait of the future climate.
The Canadian Regional Climate Model
Since 1991, UQAM and the Centre pour l’étude et la simulation du climat à l’échelle régionale (centre for regional climate studies and simulation) have collaborated with Environment and Climate Change Canada and other research centres to develop the Canadian Regional Climate Model (CanRCM). Ouranos contributes to these efforts by producing climate simulations with CanRCM. These simulations are usually done over North America. These collaborations produce high-level research and give access to valuable climate information to improve understanding of the North American climate and support adaptation to climate change at fine spatial scales and for more complex phenomena.
Greenhouse gas scenarios
Since climate change is attributable to the increase in greenhouse gases, climate modelling aims to assess the impact of this increase on different climate variables such as temperature and precipitation. However, we cannot accurately predict the amount of greenhouse gases that will be emitted into the atmosphere in the future, which is why we use different emission scenarios. These scenarios describe different plausible futures in terms of the emission of greenhouse gases, aerosols and other gases into the atmosphere and are based on assumptions about changing socio-economic factors, environmental policies, demographics, energy and the technological choices of the world’s population.
Emission scenarios are updated when new data or methods emerge, often prior to the publication of IPCC reports. In 2013, when the fifth IPCC report was published, four main scenarios, called Representative Concentration Pathways (RCPs), were adopted. Figure 6 shows the projected global temperature change ranges for the four most common RCPs. It shows:
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Few differences depending on the emission scenario until 2040
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A divergence that increases over time
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Much more warming for high-emission scenarios at the end of the century
In other words, the range of values in the plausible futures grows over time. Since it is impossible to choose the most likely emission scenario, it is recommended that more than one greenhouse gas emission scenario be used in climate change impact and adaptation studies.
Figure 3
Uncertainty
The term “uncertainty” in climate science does not refer to confidence or lack of confidence in climate projections. The fact that the climate is changing is certain and undeniable. However, there is uncertainty about the magnitude of the change due to:
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Natural climate variability—the climate varies naturally with some years being warmer than others;
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Greenhouse gas emissions—they fluctuate according to populations’ behaviours and cannot be accurately predicted in the long term ;
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Climate models—they are imperfect representations of reality.
These sources of uncertainty lead to projections of different plausible futures—all warmer, but to different extents and warming at different speeds. To make informed adaptation decisions, it is therefore valuable to know the relative importance of these three sources of uncertainty depending on the region, the variable and the future period under study.
For example, for the coming decades, natural climate variability is generally the greatest source of uncertainty, while emission scenarios are similar. The use of a set of simulations produced with a single emission scenario may therefore be appropriate. If we are looking at the end of the century, it would be more appropriate to include several scenarios for greenhouse gas emissions since the divergences increase after 2040.
