By Jennifer Hushaw
In the January bulletin, we focused on global trends in the modern temperature record. Now we look ahead to what climate models can tell us about the future of global temperature.
Climate Modeling Explained
From economic forecasting to predicting solar eclipses, we are familiar with the use of computer models to describe economic, social, biological, and physical systems – we use them in all realms of science, to improve our understanding of the mechanics of a system and allow for prediction of the future under different conditions. While they necessarily involve simplifications of complex systems, they do have utility – hence the familiar adage “all models are wrong, but some are useful.”
In some respects, global climate models (GCMs) are a fairly straightforward type of model because they describe physical processes, i.e. the basic physics of the climate system, which can be much simpler to accurately characterize than phenomena like biology or human behavior. However, it is the sheer complexity of the system that presents the biggest challenge and leads to some potential limitations:
- There may be unknown processes that are not included in the models because we are not aware of them.
- There may be processes that are not understood well enough to model accurately.
- The models may be utilizing a coarse spatial or temporal scale that cannot capture certain processes.
For example, most GCMs have grid cells of 60 to 100 miles per side, so they cannot directly simulate processes that occur on smaller spatial scales, such as cloud formation. Instead, they account for these within-cell processes using parameterization (NCA 2014).
Today’s comprehensive GCMs have many coupled components, including atmosphere, land surface, ocean and sea ice, aerosols, the carbon cycle, dynamic vegetation, atmospheric chemistry, and land ice. Their ‘performance’ is tested by evaluating how well they can reproduce the actual temperature variations we have observed in the past, which helps validate that they are effectively simulating the most important mechanisms in the system. Climate models perform particularly well with simulations of average global temperature, where they demonstrate good agreement with the decade to decade changes we have observed (IPCC 2012a).
Future Global Temperature
In the near-term (2016-2035), it is expected that the average global surface temperature will warm between 0.5 and 1.3⁰F (0.3 – 0.7⁰C), compared to the 1986-2005 reference period. However, much larger changes are projected for the latter half of this century (2081-2100), when it is likely that we will exceed 2.7⁰F (1.5⁰C) of warming under all IPCC scenarios except those with the most significant emissions reductions. The greatest warming is projected under the highest emissions scenario, which is likely to exceed 5.4⁰F (3⁰C) (Figure 1; IPCC 2013).
There will also be changes in temperature extremes, specifically “more frequent hot and fewer cold temperature extremes over most land areas on daily and seasonal timescales, as global mean surface temperature increases” (IPCC 2014). It is also likely that heat waves will increase in length, frequency, and/or intensity over most land areas (IPCC 2012b, Table 3-1).
Note: Attributing extreme weather events, like heat waves, to climate change and predicting exactly how the frequency and intensity of these events will change in the future is an important and emerging field of research that will be discussed in more detail in a future bulletin.
As we have seen in the last 100+ years, we do not anticipate that these changes will occur uniformly across the globe. Instead, we expect the pattern of greater warming over land and at high latitudes to continue into the future, as shown below for the late 21st century (Figure 2). These regional differences will be far more important for land managers than changes in the average global temperature, but additional analysis is needed to bring large-scale projections down to a scale that is relevant for local decision-making.
There have been efforts in many regions to achieve higher resolution information (typically 6 to 30 miles per grid cell) through the use of downscaled climate models. The two most common approaches are dynamical and statistical models. Dynamical models directly simulate how regional climate processes respond to global change, whereas statistical models use observed relationships between large-scale weather features and local climate to translate future projections to a smaller scale (information on the merits of each approach can be found HERE). The regional projections used in the U.S. National Climate Assessment are an example of outputs from a statistical downscaling model.
Why so many possible futures?
Simulation of Feedbacks
If we kept everything in the climate system constant and only doubled the amount of CO2 in the atmosphere, we would eventually expect to see about 2.2⁰F (1.2⁰C) warming from those emissions (Manabe and Wetherald 1967; Hansen et al. 1985). That value is derived from an understanding of the fundamental physics of the greenhouse effect, which has been well-understood since the mid-1800’s (American Institute of Physics 2015).
All climate models simulate the greenhouse effect the same way, but they diverge in their projections because they have slightly different ways of simulating feedbacks, from sources such as clouds and albedo (reflectivity). These feedbacks can amplify or dampen the warming signal. The IPCC suggests that the likely range of warming is slightly higher – between 2.7 and 8.1⁰F (1.5-4.5⁰C) (IPCC 2013), which is not surprising, given that we live in a world with many positive feedbacks that tend to magnify warming over time.
Different Emissions Scenarios
We know that cumulative CO2 emissions will determine the total average surface warming by the end of this century (IPCC 2014), but uncertainty regarding future emissions levels is one of the reasons why we have such a spread of possible trajectories for the future. This is why it is necessary to utilize different emissions scenarios for climate model projections, such as the Representative Concentration Pathways (RCPs) outlined by the IPCC.
The RCPs layout different CO2 emission pathways based on assumptions about future global economic activity, population growth, the types of energy we will use, and how efficiently we will use it – from RCP2.6 that assumes our emissions will peak in the next five years and then decline, to RCP8.5, which is a business-as-usual scenario where emissions continue to rise throughout the century. The level of change depends on the RCP and time frame in question (Figure 3).
The “Bumpy” Road Ahead
Anyone who has lived through the past few winters in the eastern U.S. may begin to think ‘global warming’ sounds like wishful thinking – between severe cold snaps and monumental snowfall – but this is short-term, local variability that doesn’t necessarily reflect how average global conditions are changing. We will also see short-term variability at the global scale, despite the long-term warming trend. In fact, we expect the future global temperature trajectory to be much ‘bumpier’ than the smooth curves you see in Figure 1, due to short-term (sub-decade) temperature changes driven by internal climate variability, such as the El Niño-Southern Oscillation (ENSO). The simple curves are an artifact of averaging multiple climate model simulations, which removes the ‘noise’ of internal variability. Rather than one year always being warmer than the next, we anticipate short-term variability in global temperature that is similar to what we might observe in a single model simulation (e.g. see colored lines in Figure 4).
Future temperature changes will impact forest resources in both positive and negative ways, through the direct effect of temperature or the indirect effect of temperature on other stressors.
More frequent and intense heat waves will exacerbate periodic drought conditions, adding to physiological tree stress and potential mortality. However, there is ample evidence that extreme heat alone can have a wide variety of effects on tree function from the molecular level to the entire tree. Heat waves are of particular concern because they can have negative effects on growth that are more severe than the same amount of heat applied as a change in average temperature.
A recent paper by Teskey et al. (2014) reviewed the current science regarding tree response to heat waves and extreme heat events. Their review highlights the many physiological and morphological responses that help them cope with extreme heat stress. For example, some will cool themselves through transpiration by keeping their stomata open, even when reduced photosynthesis would typically cause them to close and in even conditions when this causes more water loss. Research suggests that this is an especially important mechanism for seedlings, which can experience extreme temperatures in the early stages when the soil is exposed to full sun. It is not known how many tree species have the capacity to avoid heat stress this way, but it has been experimentally observed in loblolly pine, northern red oak, red maple, and ponderosa pine (Teskey et al. 2014).
Another temperature-relevant adaptive response is the ability to tolerate heat stress more effectively after becoming acclimated to warmer temperatures. For each tree species, there is a critical temperature for the stability of important proteins involved in photosynthesis. When temperatures rise above that critical threshold, those proteins will generally be damaged. However, experimental evidence has shown that that critical temperature can be increased if the plant is exposed to higher temperatures and allowed to acclimate for a period of time – the warmer the acclimation temperature, the higher the temperature at which those important proteins can function normally and continue photosynthetic reactions (Teskey et al. 2014). Although the implications of this response were not discussed in the Teskey et al. paper, it is particularly relevant for considering how trees might adapt to warmer ambient conditions in the future. Consequently, this is an active area of research that we will continue to monitor.
Longer Growing / Frost-Free Season*
One of the most important effects of temperature change on forest productivity will be future change in the length of the growing season. As the first fall frost has happened later in the year and the last spring frost has happened earlier, we have seen a corresponding increase in the overall length of the frost-free season in the U.S. (Figure 6). In fact, frost-free season length increased by approximately two weeks during the last century and the increase was much greater in the western part of the country (Kunkel et al. 2004).
This increase is projected to continue in the future, with the U.S. growing season lengthening by as much as 30 to 80 days by the end of the century (2070-2099), as compared to the 1971-2000 base period. The largest changes are expected in the mountainous regions of the western U.S. and smaller changes expected in the Midwest, Northeast, and Southeast (NCA 2014) (Figure 7).
Growing season length ties directly to forest productivity and these projected changes may increase growth and productivity in forests, especially where moisture is not a limiting factor. There are also a number of operational considerations that are directly affected by frost-free and growth season length, including planting, herbicide applications, and timber harvesting – the timing of planting and herbicide use may change as plants react to temperature and, in wetter regions, some sites that were only accessible in winter may dry out and be accessible in the summer months as well.
A potential downside is that a longer warm season is also beneficial to other organisms, such as competing invasive plants and both native and invasive forest pests, so the net effect on desirable species is not guaranteed to be a positive one. A great example of this has been seen in the Rocky Mountains, where bark beetles enjoyed greater over-winter survival and faster reproduction rates as a result of warmer temperatures and milder winters (Funk et al. 2014).
* Changes in the length of the frost-free season and growing season are related and expected to be similar, so we use the terms interchangeably here.
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American Institute of Physics. 2015. “The Discovery of Global Warming: The Carbon Dioxide Greenhouse Effect.” February. http://www.aip.org/history/climate/co2.htm.
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Intergovernmental Panel on Climate Change (IPCC). 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II, III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Kunkel, Kenneth E., David R. Easterling, Kenneth Hubbard, and Kelly Redmond. 2004. “Temporal Variations in Frost-Free Season in the United States: 1895–2000.” Geophysical Research Letters 31 (3): n/a – n/a. doi:10.1029/2003GL018624.
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National Climate Assessment (NCA). 2014. Climate Change Impacts in the United States: The Third National Climate Assessment. doi: 10.7930/J0Z31WJ2. U.S. Global Change Research Program.
Teskey, Robert, Timothy Wertin, Ingvar Bauweraerts, Maarten Ameye, Mary Anne McGuire, and Kathy Steppe. 2014. “Responses of Tree Species to Heat Waves and Extreme Heat Events.” Plant, Cell & Environment, doi:10.1111/pce.12417.