Sign of Observed California Temperature Trends Depends on Data Set Homogenization: Implications for Weighting and Downscaling

• Published in: Geophysical research letters Vol. 49; no. 15
• Main Authors: Charn, Alexander B., O’Brien, Travis A., Risser, Mark D., Longmate, Julia M., Feldman, Daniel R.
• Format: Journal Article
• Language: English
• Published: Washington John Wiley & Sons, Inc 16-08-2022
• Subjects: Analogs; California; Climate; Climate models; coastal cooling; Cooling; End users; Estimates; Future climates; Homogenization; Inspection; LOCA; Local climates; Maximum temperatures; model weighting; Modelling; Records; summer temperature; Temperature; Temperature trends; Trends; Uncertainty; Weather stations; Weighting; United States > US; California
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2022GL099186

Because downscaling methods can yield substantially different projections of future climate, it is imperative to constrain these projections with information from existing observations, while also recognizing observational uncertainty. California is a natural test case to develop observational constraints on future projections given prior studies that have purportedly found contrasting spatial patterns in late‐20th‐century trends of summertime daily‐maximum temperature: cooling along the coast and warming inland. Revisiting this claim, we find that coastal cooling is largely confined to non‐homogenized temperature records while homogenized observations show either non‐significant cooling or warming trends throughout the state. This finding has implications for weighting historical and future climate simulations downscaled using localized constructed analogs. Failure to consider out‐of‐sample skill results in weighted and unweighted Representative Concentration Pathway 8.5 temperature trend estimates differing by 2 K/century or more in California. However, weighted mean estimates that properly account for trend uncertainty do not differ significantly from the unweighted mean.
Plain Language Summary
Downscaling methods and models yield climate projections on local scales relevant to entities such as cities and individual facilities. Weighted averages of future projections are needed so that the huge amount of existing climate model data is curated to a set that is compact enough to be widely‐accessible to end users. Observations are used to judge which models most faithfully represent historical climate. In California, previous work has ostensibly observed coastal cooling and inland warming over the later part of the 20th century. Upon closer inspection, we determine that this California coastal cooling finding is likely an artifact due to historical changes in observing practices at weather stations. We then evaluate and weight downscaled models using temperature records that show California coastal cooling and separately develop weights using temperature records that do not show such cooling. When we carefully consider the information in the observational data, we find that the two approaches to weighting produce about the same results. However, the weights can significantly differ when observational uncertainty is not considered. Together, these results highlight the importance, even over a heavily‐instrumented area like California, of carefully evaluating historical climate model simulations when producing future, localized climate projections for end users.
Key Points
Twentieth‐century California coastal cooling estimates are likely an artifact of historical changes in weather stations’ observing practices
Errors in measuring summertime extreme temperature trends have limited effect on model weights for localized extreme temperature projections
Failure to consider out‐of‐sample skill leads to underestimated uncertainty in future trend estimates of extreme temperature in California