The purpose of the Arctic Environmental and Engineering Data and Design Support System (Arctic-EDS) is to apply downscaled climate model output to existing engineering methods. The Arctic-EDS simplifies and centralizes the process of finding, selecting, extracting, and formatting downscaled climate model output for engineering applications.
Design standards relying solely on historical observations may assume that the climate is stationary - but an Arctic climate warming four times faster than the rest of world (Rantanen et al., 2022) and numerous simulations of climate futures indicate that the past is no longer a reliable portrait of the conditions under which infrastructure will have to perform in the future. Moreover, historical data from climate stations is very limited. While actual historical datasets consisting of precise measurements for particular times and locations are preferred, such records are sparse for Alaska and the Arctic. Earth’s climate processes are continuous in time and space, but measurements of Earth’s climate are not.
Bias correction is the process of mathematically scaling climate model outputs to account for their systematic errors in order to improve their performance with respect to a modeled baseline or to actual historical observations. Bias correction methods can be mathematically simple, e.g. subtraction via the delta method, or more complex, e.g. quantile mapping or machine learning models.
No climate model or data processing technique entirely eliminates uncertainty, but bias correction can reduce mismatches between modeled baseline gridded data and measured historical point location data. Bias correction is applied to most downscaled climate model output.
The ultimate precision of bias correction required depends on the application. In some use cases, additional bias correction beyond what is already present in the Arctic-EDS may be necessary to seamlessly transition from engineering practices based on actual historical data to techniques using downscaled climate model output. High precision bias correction is less important when the application focuses on trends rather than actual values. Precision is most important when the application requires thresholds or exceedence values, or in relation to extreme events.
For example, if an engineering standard is based on mean annual temperature, a discrepancy between 6.0°C (measured historical) and 5.1°C (baseline modeled) may be less important if the projected modeled data have a warming trend of 1.0°C per decade leading to a mean annual temperature of 12.1°C by end of the time period relevant to the application. However, the same temperature discrepancy might yield an alarming divergence in values if the metric of interest is degree days above or below a particular threshold, or if the metric were dependent on a phase change such as permafrost thaw or dewpoint.
In a hypothetical case in which cooling degree days above 25°C were calculated for a location in which the actual historical measured temperature was 25°C each day of the year, but the modeled baseline temperature was 26°C each day of the year, the annual cumulative sum of the former would yield a value of 0 degree days while the latter would yield a value of 365 degree days—showing how the interaction of thresholds can impact calculation and interpretation of climate model output.
Peer reviewed frameworks for applying downscaled climate model output to engineering applications exist.
Cook et al. (2017) propose a six step framework:
The Arctic-EDS can enhance and perhaps even fulfill steps within this framework via pre-selection of models and scenarios, bias correction, and uncertainty regulation—but it is not a turnkey solution. Each engineering application may require additional steps (additional bias correction or spatial/temporal downscaling) to appropriately interpret downscaled climate model outputs and apply them to a specific engineering design.
Further tool updates will continue to refine and assist with techniques and guidance for data use. We suggest using multiple models, being transparent about assumptions, and assessing tradeoffs in risk, resiliency, performance, and costs while designing for low-regret, adaptability, and robustness.
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., ... & Laaksonen, A. (2022). The Arctic has warmed nearly four times faster than the globe since 1979. Communications earth & environment, 3(1), 168. https://doi.org/10.1038/s43247-022-00498-3
Cook, L. M., Anderson, C. J., & Samaras, C. (2017). Framework for incorporating downscaled climate output into existing engineering methods: Application to precipitation frequency curves. Journal of Infrastructure Systems, 23(4), 04017027. https://doi.org/10.1184/R1/5257867.v2