Uncertainty is inherent in SNAP’s work
There are many sources of uncertainty in future climate projections, ranging from the uncertainty of predicting economic and social choices to the sparseness of historical climate data with which to calibrate models. Because these sources are so disparate, the uncertainty in projections cannot reliably be captured in a single number or probability.
Sources of uncertainty in SNAP’s climate projections
- Uncertainty in future economic and social choices and behavior, which will dramatically affect the amount of greenhouse gases that will be emitted, and thus the amount of excess heat trapped in the atmosphere. This is best shown and understood by studying the differences between the Representative Concentration Pathways (RCPs) we model. Learn more about RCPs in this overview.
- Uncertainty stemming from the complexity of Global Circulation Models (GCMs) that simulate the movement of this excess heat through Earth’s atmosphere and oceans. This is best accounted for via the differences between the five GCMs we downscale.
- Inherent variability in weather and short-term climate that can temporarily obscure long-term climate trends. Decadal averaging or other smoothing of model outputs can make trends clearer.
- Uncertainty stemming from the challenges of downscaling coarse data to the local level, particularly when the accurate historical weather and climate data that are needed for model calibration are so sparse. Local knowledge of historical and current conditions can aid in interpretation. Learn more about SNAP’s downscaling methods.
How uncertain? It depends
The different sources of uncertainty can be more or less important for different questions or at different times in the climate projection. For example, emissions scenarios are fairly similar over the next few decades. It might not be important to study all of the emissions scenarios if you have a question about just the 2030s, but it would be critical for a question about the end of this century. On the other hand, the uncertainty from natural variability is particularly important over the next couple of decades.
Data analysis and interpretations are always important, and must be understood in order to effectively and appropriately use our products.
Scenario planning (allowing for more than one possible future) allows for greater flexibility in the face of high uncertainty, and is an important part of all SNAP model interpretation.
Fleming MD, Chapin FS, Cramer W et al. 2000. Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record. Global Change Biology 6 (Suppl. 1): 49-58.
Sluiter R. 2009. Interpolation methods for climate data: a literature review. KNMI Internal Report, De Bilt, The Netherlands.
Uncertainty in our ecosystem modeling
ALFRESCO Fire Model
Uses SNAP input to project fire's effects on the landscape. Calibrated to match historical climate conditions to historical fire records.
Uncertainty: Projections are inherently uncertain because they depend on assumptions and estimates regarding the frequency and location of fire starts as well as the calculated relationship between climate, forest age and type, and fire spread.
GIPL Permafrost Model
SNAP permafrost modeling has been done in conjunction with experts at the Geophysical Institute Permafrost Lab (GIPL). Algorithms to determine the depth of active layer depend on calculations of the insulating properties of varying ground cover and soil types, as well as on climate variables.
Uncertainty: Although GIPL researchers have used the best available data for all inputs, some datasets are incomplete.
SNAP works with partners in the U.S. and Canada to project potential landscape shifts driven by climate change. Projections depend on links between vegetation and climate, as well as the ability of various species to adapt to change.
Uncertainty: incomplete data on existing species ranges, behaviors, and dispersal, and incomplete data on the relationship between climate and habitat variables.