Climate change predictions are wickedly, notoriously complex, requiring, assumptions about dozens of interrelated variables. Some variables drive self-reinforcing feedback loops, such as when sea ice melts and sunlight heats the now open, dark ocean instead of bouncing of bright reflective ice. The predictions referenced in this toolbox build from assumptions about these complex with all the uncertainty that implies.
The most important and difficult to predict variable is future emission of climate changing greenhouse gases. The primary global climate change models, downscaled to predict changes in Alaska, are classified based on assumptions (labeled B1, A1T, etc. see table below) about what humans will or won’t do to reduce greenhouse emissions.
A couple of points to keep in mind when thinking about or reviewing the results of climate change modeling.
- Predictions about the future rate and consequences of climate change fall across a spectrum: highly likely, probably, maybe
- Results are only as good as the inputs. And on many topics, Alaska lacks the detailed base information needed for more accurate and helpful climate change forecasts.
- “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know”. Donald Rumsfeld
Impacts in Alaska?
“Simulating the global ecosystem is complex, potentially involving infinite variables that describe and relate nature’s chemical, physical, and biological processes. The resulting range of possible climate scenarios has led to public confusion about the validity of climate prediction–and, more urgently, to delays in appropriate action.” http://thebulletin.org/uncertainty-climate-modeling