Why we need easy–to–use decision–support tools for
Assessments of climate-change effects on Northern ecosystems are
hindered by a lack of decision-support tools that can visualize
possible future landscapes under different scenarios.
Model output products are publicly available, but often in formats
requiring considerable expertise to access and process. Repackaging
these outputs as climate reports moves relevant climate information
out of storage and into decision–making spaces—thus
helping to gauge future fire hazard, reduce future wildfire risk,
promote ecological resilience and manage wildlife habitat.
What’s changing in the Arctic?
Several climate-related ecosystem features are driving rapid change in
the North. Using ecosystem data to envision how these changes may
progress in the near-term and more distant future is crucial for land
managers, planners, and communities.
These ecosystem drivers—temperature, permafrost, and
fire—are closely linked in terms of what causes them and their
effects on ecosystems.
- Warming temperatures are driving some changes in
species composition—particularly warmer summers, earlier
springs, later autumns, and less severe winters.
Thawing of permafrost, also driven by warming
temperatures, alters surface drainage and increases possible rooting
depths, yielding distinct ecological shifts.
Increases in fire frequency—again indirectly
driven by warming—also contributes to broad
landscape–level vegetation change, as early-succession species
increase in frequency and late-success species decrease.
These connections are complex, and the uncertainty associated with
them is high. Nonetheless, by studying and modeling each factor and
connecting model outputs when possible, we can produce a range of
projections that explore possible changes locally, regionally, or
landscape-wide across the North.
Models, projections, uncertainties
Climate models help us imagine possible climate futures.
We use weather forecasts for short-term planning. Climate projections
can be used for long-term planning—but they are not the same as
forecasts. Weather varies day to day, whereas climate refers to the
average or typical conditions over much longer time periods. However,
there are uncertainties based on model limitations and unknown future
human behavior that make long-range forecasting very different from
predicting tomorrow's weather.
Climate projections look much further into the future than weather
forecasts. They address uncertainties by focusing on the range of
future conditions that would likely occur, given what we currently
know about the climate system and how it will change in response to
changes in the factors that affect it.
How do we make these projections? We use climate models.
Climate models are simplified versions of reality that try to explain
climatic processes with just the most necessary parts of the system.
Their usefulness is evident when we compare observed historical
climate and simulated data—the models capture the most important
Climate models use data to calculate how the global climate varies.
These data include:
- initial climate conditions
“forcings” such as atmospheric greenhouse gas, solar and
- ocean and atmosphere variability
- land surface conditions
- feedbacks such as the carbon cycle and the water cycle
The end product is a simulation of future climate. Because the end
product is based on statistical probabilities, the data are most
reliable when averaged across time or space, such as the projected
average of 30 years of winter precipitation for your community, or the
likely hottest temperature that might occur on the North Slope.
Climate models aren’t perfect, though.
Between now and about 20–30 years from now, current climate
change is the best predictor of the rate of change, but year to year
variability is the largest source of uncertainty. Although long-term
climate change shows clear trends, those can be masked by natural ups
and downs in the short term. Climate models do simulate this kind of
variability, but they cannot predict it precisely.
Best practices for making projections
- Use multiple decades. Averages over 20–30
years are more resistant to transient variability in climate models
and to natural variations in regional climate. Compare a future
(like 2030–2069) to a historical reference frame (like
1970–1999), and keep in mind that the later the historical
reference frame, the more climate change is already in it!
- Use multiple climate models. Picking one model is
not good practice because all the models are at least plausible, if
not equally likely. Use several separate models if the full range of
possibilities is important to your work, or use the average of
multiple models if you are more interested in the most likely
outputs. This is especially critical between now and about the 2050s
- Use multiple emission scenarios. Given the
uncertainty around future human behavior, you should pick at least
two scenarios that bracket the likely range, unless you are only
interested in looking at the “best case” or “worst
case”. RCP 4.5 and RCP 8.5 are good choices.. This is
especially critical after about 2050.
- Look at medium–term and longer–term futures.
A comprehensive assessment would consider a historical, a mid-21st
century future, and a late–21st century future. The
two futures should have a high, low and middle range each, possibly
with multiple models and multiple emission scenarios in each future
- Don’t make your assessment area too small.
The more local your assessment, the more likely it is that local
factors like elevation, vegetation, etc. and the process used to
downscale the climate model to local resolution contribute to the
uncertainty. Larger areas are probably more resistant to this local
variation, so a watershed, planning unit, or responses across
several of these are perhaps more useful to consider.
- For fire projections and post-fire vegetation, look at averages
across many model runs.
The ALFRESCO fire and vegetation model cannot predict the precise
behavior of future fires, but it can simulate the likelihood that
fires will start and spread across broad landscapes across long
periods of time, causing shifts in the age and type of vegetation.
When looking at outputs, consider either a typical model run
(“best replicate”) or an average of many possible
burning scenarios. Outputs are helpful for assessing
large–scale long–term shifts, but are not meaningful at
a pixel by pixel level.
How will the “real” climate compare to any projection?
The future climate we experience will not look exactly like any of
these projections, but it will look like a lot of them. There are a
range of future climates we may experience, and best practices are to
plan for the likely range of climates, impacts, and associated risks
for the time frame and region you’re planning for.
Projections are always improving incrementally. Don’t wait
for a better projection—you’ll always be waiting and the
costs of waiting will increase. In general, plan for the range of
historical variability plus the range of climates described by a
less warm (none show cooling!) climate model under a lower emissions