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 a single number or probability.
Major 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 uncertainty can best be represented and understood by examining the differences between the Representative Concentration Pathways (RCPs) we model. Learn more about RCPs in this overview.
- Uncertainty stemming from the complexity of General Circulation Models (GCMs) that simulate the movement of this excess heat through Earth’s atmosphere and oceans. This uncertainty is best represented and accounted for via the differences between the five GCMs we downscale. Learn more about how GCMs fit into SNAP’s model selection strategy.
- 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, so it might not be important to look at all of the emissions scenarios if you have a question about 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. Learn more about SNAP’s work in scenario planning.
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 current ecosystem modeling efforts by SNAP and collaborators
Uses SNAP input to project fire on the landscape and is well calibrated to match historical climate conditions to historical fire records.
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.
SNAP permafrost modeling has been performed 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.
Although GIPL researchers have used the best available data for all inputs, some datasets are incomplete.
SNAP has worked with multiple partners in the US and Canada to predict potential landscape shifts driven by climate change. Projections depend on links between vegetation and climate, as well as the ability of various species to shift across the landscape under either gradual or threshold-driven change.
Uncertainties stem from incomplete data on existing species ranges, behaviors, and dispersal, and incomplete data on the relationship between climate and habitat variables.