Utilizing GCM Projections

Given that there is no easy alternative to producing future projections based on GCMs, we now delve deeper into the way uncertainty manifests itself in GCM projections, and what the attempts are at reducing it or at least characterizing and quantifying it in robust ways. Figure 3.2 shows changes along the twentieth and twenty-first centuries in global temperature from the climate models (21 of them) used in IPCC AR4 that performed their simulations under the emissions scenario SRES A1B (the set of models that have contributed experiments to IPCC AR4 is also known as the World Climate Research Program Coupled Model Intercomparison Phase 3, a.k.a. CMIP3, Meehl et al. 2007a). Clearly, some models warm more rapidly than others. There is consensus across models that the future will be warmer but even at the global average scale the difference in the magnitude of warming is large, with up to a factor of 2 between the two extremes of the range. The same observation applies to the trajectories of global mean precipitation (not shown).

The uncertainty increases when we consider regional changes. Two maps in Figs. 3.3 and 3.4 show geographic patterns of change by the end of this century

Box 3.1: Sources of uncertainty in climate change projections

1) Initial conditions: slight changes in the starting point of the simulation change where the wiggles in the trajectory happen, due to the natural variability of the system (i.e., the chaotic nature of weather). In order to account for this, ensemble of runs by the same model/under the same scenario in which only the initial state of the system is varied are used to characterize the range of natural variability, and are averaged when looking at climate statistics, making the dependence from initial conditions disappear.

2) External forcings: different pathways of greenhouse gas and aerosol emissions cause very different evolutions (trajectories) of the perturbed climate system. As a consequence numerous different pathways need to be explored and adaptation policies tested against possible alternative futures. Notice though that for short-term projections the outcome is very similar no matter what the emission scenario is. Most of what will happen in the next two or three decades is the result of "commitment", based on what we have emitted so far.

3) Unresolved or poorly understood system behavior: certain climate processes are not perfectly understood (i.e. ice sheet collapse mechanisms are still beyond our scientific grasp) or are not perfectly modeled (cloud behavior is not resolved and thus directly simulated by GCM, local weather patterns are not reproduced because of the coarse topography represented in these models). Increasing the resolution of models (which goes hand in hand with increasing computing power) and ultimately the progress of our scientific understanding will ameliorate this problem. Meanwhile, perturbed physics and multi-model ensembles help span the range of possible answers, and quantify this kind of uncertainty.

computed as the ensemble mean of the same set of models run under the same A1B scenario. The stippling in the figures marks points in space where 90% or more of the models in the ensemble agree on the direction of change (quite a lenient condition). Again, models agree that our planet will get warmer everywhere, but the agreement on the sign of precipitation change is not as strong, except for the high latitudes of the northern hemisphere and some areas of the tropics, expected to become wetter, and limited areas of the subtropics, expected to become drier.

What that means for a specific region is that a histogram of the average precipitation change (for a given season, or annually) may straddle the zero line. In fact for many areas the ensemble mean change is very close to zero, hiding a range of possibilities that go all the way from significant increases to significant decreases in the average quantity of rain falling in the future.

Change in glob. ave. temperature under A1B (1980-1999 baseline)

Change in glob. ave. temperature under A1B (1980-1999 baseline)

1960 1980 2000 2020 2040 2060 2080 2100

Fig. 3.2 Global average temperature changes along the twentieth and twenty-first centuries from a set of GCMs (18 models from the CMIP3 archive used in IPCC AR4) that performed their simulations under the emissions scenario SRES A1B and made available both temperature and precipitation output (for consistency with Figs. 3.2 and 3.3). Units are degrees Celsius, changes are with respect to the two decadal average 1981-2000. Each line corresponds to a different GCM. The trajectories are connecting 15 decadal averages

1960 1980 2000 2020 2040 2060 2080 2100

Fig. 3.2 Global average temperature changes along the twentieth and twenty-first centuries from a set of GCMs (18 models from the CMIP3 archive used in IPCC AR4) that performed their simulations under the emissions scenario SRES A1B and made available both temperature and precipitation output (for consistency with Figs. 3.2 and 3.3). Units are degrees Celsius, changes are with respect to the two decadal average 1981-2000. Each line corresponds to a different GCM. The trajectories are connecting 15 decadal averages

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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