A formidable El Niño in the equatorial Pacific is a decisive catalyst for heavily ENSO-reliant seasonal forecasting systems, providing an opportunity to predict the upcoming winter with heightened reliability. Nevertheless, any anticipation of universal accuracy in these forecasts is unwarranted. Despite ENSO being the most robust natural forcing, its influence does not consistently conform to expectations across every region. This unreliability is particularly pronounced in California, starkly evident in the lingering memory of the 2015/16 winter. Despite forecasts of substantial precipitation fueled by one of the most potent El Niños ever recorded, parts of California and the southwest remained drier than average in the 2015/16 December-January-February average.
NOAA's seasonal outlook for December-January-February at the 0.5-month lead. California is leaning above average. Stronger colors on the map show higher confidence and vice versa.
NOAA's seasonal outlook for California is "leaning above average" with 40-50% above normal probability. This underconfident seasonal outlook for California is unsurprising, given the inherent challenges of seasonal predictability in Mediterranean climates. Oceanic forcing, specifically variations in sea surface temperatures, can only account for a third or less of California's winter precipitation variations, according to current understandings. Knowledge about how modes of atmospheric variability impact California's winter is even scarcer. Some attribute the varied influence of ENSO to atmospheric variability, such as the Arctic Oscillation. Regardless, seasonal forecasting systems have difficulty predicting variations in atmospheric modes. Therefore, even if we could establish a meaningful relationship between atmospheric variability and precipitation in California, it might not benefit the current generation of seasonal forecasting systems.
The lack of explainability surrounding precipitation variations in California, particularly episodes of multi-year droughts and those years when ENSO teleconnection deviated from climatological norms, leaves a conspicuous void in our comprehension. This void strengthens the perspective that climate change is pivotal in shaping California's 21st-century winters. However, it is imperative to note that while climate change can exacerbate wet/dry precipitation extremes, it is less likely to be their primary cause at the current anthropogenic forcing level.
Physical explanations must underpin instances where ENSO teleconnection fails to register its expected influence or when the region experiences severe dry or wet conditions without ENSO forcing. As noted earlier, some link the inconsistency of ENSO with Northern Hemisphere atmospheric modes, as witnessed in the case of 2015/2016. However, the puzzle deepens when considering extreme variations in California's winter during non-ENSO years, exemplified by the drought of 2013/2014 and the wet winter of 2016/2017, both occurring without significant ENSO presence. This prompts a fundamental question: Is California's winter predictability as limited as commonly perceived, particularly the part dependent on oceanic forcings?
Answering this question or explaining the unexplained California’s winters is far from impossible, but it demands a bold departure from traditional understanding and a foray beyond the confines of the conventional framework of Earth system predictability. To support this claim, I present an empirical model for predicting California and southwest winters (December-January-February) at one month lead (November pre-conditioning). It is a simple multi-linear regression model that does not involve using any known indexes of oceanic forcings.
The coefficient of determination (R-squared) of the multi-linear regression model.
This model elucidates 60% of California's spatial winter precipitation variability, starkly contrasting the prevailing understanding that only a third of California’s winter precipitation variance can be traced back to oceanic forcing. Reproducing the general patterns of 2015/16 winter precipitation anomalies, the model asserts that the Arctic or North Atlantic Oscillation was not the primary impediment preventing the strong 2015 ENSO's forcing from delivering its anticipated wet punch. Furthermore, without any extratropical forcing, it adeptly reproduces the drought of 2013/14 and the wet winters of 2016/17, even when ENSO forcing was weak.
In totality, over 80% of winters between 1985 and 2022 are reasonably reproduced with above-average or high accuracy, with the most notable exceptions being the 2007/08 and 2008/09 winters. Overall, it decisively affirms that California's winters are reasonably predictable with a one-month lead, utilizing oceanic forcings as predictors.
The animation shows the predicted and observed average December-January-February precipitation anomalies between 1985 and 2022.
Since this empirical formulation is based on unpublished work, its technical explanation will have to wait for another blog post in the coming months. However, drawing on the model, we can boldly forge a potential seasonal outlook for the winters of 2023/24 in California. The model posits that, despite the presence of one of the most formidable El Niños this year, the ensuing effect won't be as wet as it was in response to the 1997/98 El Niño. Instead, we anticipate the development of a dipole pattern, characterized by above-average precipitation in northern California and below-average conditions in central to southern California. This emerging pattern bears a resemblance to the weather conditions observed during the 2015/16 El Niño event.
It's crucial to note that actual precipitation anomalies may differ from these predictions if any of the predictors used in the empirical model significantly deviate from their November conditions. Moreover, although known atmospheric variability does not consistently impact year-to-year precipitation variability in California's winters, substantial sub-seasonal variations in one of the relevant modes still have the potential to compound or offset the impact of oceanic predictors.
The prediction for the 2023/24 winter using the empirical model.