In recent decades, many research efforts focused on global climate change, multidecadal, decadal, interannual variability, and the increasing extreme events of sea surface temperature. In contrast, the continuous evolution of the reference frame, the annual cycle of SST used to quantify the aforementioned variability and changes, has long been overlooked, resulting in difficulties in understanding the underlying physical mechanisms responsible for these variability and changes. In this study, we strive to bridge this gap on the phase changes in SST annual cycle. By devising a running correlation-based method, we can now quantify the non-sinusoidal shape of the evolving SST annual cycle, such as the advancing or delaying of summer and winter peaking times. It is revealed that the varying phases of summer or winter are more closely linked to multidecadal SST variability than to long-term climate change. Both the systematic shift of the phase and alterations in the annual cycle shape contribute to the phase changes, which explain 0.4~1.0 °C of monthly SST anomaly with respect to the climatological annual cycle in a multidecadal timescale. Furthermore, it is evident that the SST phases in historical simulations are better captured in winter than in summer and exhibit stronger variation compared with observation.
Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle. However, an often overlooked feature of the climate seasonal cycle is the semi‐annual harmonic, which can account for a significant portion of the variance of the seasonal cycle and varies in amplitude and phase across space. Together, the spatial variation in the annual and semi‐annual harmonics can play an important role in driving processes that are tied to seasonality (e.g., ecological and agricultural processes). We propose a multivariate spatiotemporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi‐annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally varying coefficients. We apply the model to minimum and maximum temperature over North American for the years 1979–2018. Formal model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in minimum and maximum temperature seasonal cycles due to the relative effects of changes in the two harmonics.
more » « less- PAR ID:
- 10449980
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Environmetrics
- Volume:
- 32
- Issue:
- 6
- ISSN:
- 1180-4009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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