Climate Change Impact Assessment
Part 1: Global Climate Models and Emission Scenarios
Introduction
In its latest Emission Gap Report, the UN Environment Program starts with a cold hard truth that we kind of knew but refused to believe till now:
There’s no credible pathway to 1.5°C in place. Implementation of the current pledges will only reduce this to a 2.4–2.6°C temperature rise by the end of the century.
This does not mean that the battle is over and that we are doomed (although we kind of are). It’s just that we now have twice the work: while pushing for a systematic transition to a net-zero world, we also need to prepare our society for the impacts of climate change.
The first step to preparing for what’s to come is apprehending what is coming. Climate Change Impact Assessment (CCIA) is an active field of research that seeks to answer that question. It is a framework to quantify the risks of environmental change on people, communities, infrastructure, and ecosystems. There are three main steps in a typical CCIA project:
- Global climate modeling: What is the climate projection of the world in the future?
- Downscaling: Given the global climate projection, what can we deduce about the physical state of the region of interest?
- Impact modeling: Given the estimated climate projection of the region of interest, what are the impacts on the use case at hand?
In this series, we will focus on the global climate models and downscaling techniques as they are the basis of all impact assessment projects.
Climate projection
A climate projection is a description of Earth’s climate system in future decades in response to a specific emission scenario of increasing greenhouse gas, as simulated by a global climate model (GCM).
Climate Projection = Global Climate Model + Emission Scenario
The combination of these two factors creates a large number of unique projections of future climate. The latest IPCC collects a massive amount of around 100 climate models along with five possible emission scenarios called Shared Socioeconomic Pathways (SSP).
In the following sections, we will take a closer look at these two components of a climate projection, starting with the Global Climate Models.
Global Climate Model
The most fundamental principle of a climate model is to simulate the exchange of energy between the Earth system and space and its effects on average surface temperature.
One of the first climate models was developed by Svante Arrhenius in 1896 by combining simple radiative transfer principles with a zero-dimensional model (it only considers energy conservation). His study showed that human emissions of carbon dioxide could warm the Earth:
Any doubling of the percentage of carbon dioxide in the air would raise the temperature of the Earth’s surface by 4.
The Probable Cause of Climate Fluctuations - Arrhenius (1906)
Yes, the year was 1906.
As time went on, more components were added to the simulations. Nowadays, GCMs are extremely complex 3-D numerical systems representing physical, chemical, and biological processes in the atmosphere, ocean, cryosphere, and land surface. It models many different climate variables:
(Usually, what interests us most in impact studies is the atmospheric surface climate variable: air temperature, precipitation, and water vapor.)
GCMs divide the Earth into discrete cell grids representing computational units. To give you an idea of the scale of the problem, using 100-kilometer spatial resolution, or about 500 000 grid points, with 100 vertical levels and ten physical variables in the equations, produces a model with a dimension of 500 million. And that’s for only one timestep.
Why do scientists need to build models on a global scale?
Simply put, many fundamental mechanisms that influence Earth's climate operate on a vast spatial scale (El Niño effects on thousands of kilometers) as well as a temporal scale (ice-sheet processes over millennia).
However, there are also many essential mechanisms that operate on a much much smaller scale (for example, local precipitation patterns).
It is thus impossible to simulate all processes. This is where raises the differences between GCMs. Different research groups make other choices as to which physical aspects to emphasize and at which granularity. As the Earth’s climate system is incredibly complicated, there is no one best model, thus a need to collect and analyze as many models as possible to understand the whole picture, as done in the IPCC reports.
In general, GCMs are good at simulating the state of the climate system for larger time slices and regions. Special care has to be taken to assess whether they can (and should) be used to study events occurring on small temporal and spatial scales, e.g., when analyzing the state of the climate system for a particular location (a single grid cell) or a short period (a single storm event). This is where model downscaling comes into play, but we will reserve another article for that topic.
Next, we will discuss the remaining piece of a climate projection: emission scenarios.
Emission Scenario
Emission scenarios describe how greenhouse gas emissions could evolve on various hypotheses of anthropogenic activity like socio-economic, technological, demographic, and environmental development.
Each scenario is then translated into equivalent changes in greenhouse gas concentrations. This information is used as input data in the GCMs, resulting in the physical reaction of the climate system to that particular hypothetical forcing.
Due to this forcing-dependent character, climate model outcomes are not interpreted as forecasts (an initial value problem) but as projections based on a specific scenario (a boundary value problem).
As mentioned above, the latest IPCC report employs five emission scenarios derived from the Shared Socio-economic Pathways [3] (SSP X-Y in the below figure). These scenarios span a wide range of futures, from a business-as-usual world where little or no mitigation actions are taken (SSP 5–8.5) to a world where aggressive mitigation limits warming to no more than 1.5°C (SSP 1–1.9).
Using these standardized scenarios as drivers of GCMs has facilitated comparisons among the models’ responses to these future emissions. This is where Coupled Model Intercomparison Project (CMIP) [4] comes in. It is a framework to compare climate model experiments from different research groups by putting them through the same forcing scenarios. CMIP data are available online and used extensively in many downstream research and impact assessment projects.
Conclusion
This article discusses the two main components of a climate project: the Global Climate Model and the Emission Scenario.
Emission scenarios provide the forcing condition for the GCMs, which will then generate a projection of the future climate state based on its simulations.
Finally, it was through historical climate projections that scientists concluded that human effects caused the observed increase in global average temperature since 1950. When they employed an emission scenario without human activities, the simulations generated by GCMs could not explain the observed temperature trend. Only by adding man-made forcing into the models could they replicate the historical tendency.
References
- Trenberth, Kevin E., and John T. Fasullo. “Tracking Earth’s energy: From El Niño to global warming.” Surveys in geophysics 33.3 (2012): 413–426.
- Alley, R., Berntsen, T., Bindoff, N. L., Chen, Z., Chidthaisong, A., Friedlingstein, P., … & Zwiers, F. (2007). Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. IPCC Secretariat, Geneva, Switzerland. 21p.
- Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., … & Tavoni, M. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global environmental change, 42, 153–168.
- Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958.
