Data homogeneity has become a significant issue in the study of tropical cyclones (TCs) and climate change. In this study, three historical datasets for the western North Pacific TCs from the Joint Typhoon Warning Center (JTWC), Japan Meteorological Agency (JMA), and China Meteorological Administration (CMA) are compared with a focus on TC intensity. Over the past 55 years , significant discrepancies are found among the three datasets, especially between the CMA and JTWC datasets.The TC intensity in the CMA dataset was evidently overestimated in the 1950s and from the late 1960s to the early 1970s, while it was overestimated after 1988 in the JTWC dataset, especially during 1993-2003. Large discrepancies in TC tracks exist in two periods of 1951-early 1960s and 1988-1990s. Further analysis reveals that the discrepancies are obviously related to the TC observational techniques. Before the era of meteorological satellites (1951-the early 1960s), and after the termination of aircraft reconnaissance (since 1988), large discrepancies exist in both TC intensity and track. That the intensity discrepancy was smallest during the period (1973-87) when aircraft reconnaissance data and the Dvorak technique were both available suggests that availability of the aircraft reconnaissance and the Dvorak method helps in reducing the TC intensity discrepancy. For those TCs that were included in all the three datasets, no significant increasing or decreasing trend was found over the past 50 years. Each of the three TC datasets has individual characteristics that make it difficult to tell which one is the best. For TCs that affect China, the CMA dataset has obvious advantages such as more complete and more accurate information.

To better understand the causes of climate change in the tropical Pacific on decadal and longer time scales, the authors delineate the rectification effect of ENSO events into the mean state by contrasting the time-mean state of a low-order model for the Pacific with its equilibrium state. The model encapsulates the essential physics of the ENSO system, but remains simple enough to allow for the obtaining of its equilibrium state. The model has an oscillatory regime that resembles the observations. In this oscillatory regime, the time-mean SST in the eastern equatorial Pacific is found to be significantly different from the corresponding equilibrium SST, with the former being warmer than the latter. The difference is found to be proportional to the amplitude of ENSO. In addition, the zonal SST contrast of the time-mean state is found to be less sensitive to increases in external forcing than that of the equilibrium state, due to warming effect of ENSO events on the eastern Pacific. It is further shown that this rectification effect of ENSO events results from the nonlinear advection term in the heat budget equation. The study elucidates the role of ENSO events in shaping the tropical mean climate state and suggests that decadal warming in the recent decades in the eastern tropical Pacific may be more a consequence than a cause of the elevated ENSO activity during the same period. The results also provide a simple explanation for why it is difficult to detect an anthropogenically forced trend in the zonal SST contrast in the observations.

Despite the gradually increasing emphasis on assessing the skill of dynamical seasonal climate predictions from the probabilistic perspective, there is a lack of in‐depth understanding that an inherent relationship may exist between the probabilistic and deterministic seasonal forecast skills. In this study, we focus on investigating this relationship, through theoretical consideration based on an analytical approach and diagnostic analysis of the historical forecasts produced by multiple dynamical models. The probabilistic forecast skill is gauged in terms of its two different attributes: resolution and reliability, while the deterministic forecast skill is measured in terms of anomaly correlation (AC). Through the theoretical consideration under certain simplified assumptions, a nonlinear, monotonic relationship is analytically derived between the resolution and the AC. Subsequent diagnostic analysis shows that the resolution and AC skills of both the multimodel ensemble and its member single models indeed appear to be approximately monotonically and nonlinearly related, specifically when they are calculated in a zonally aggregated manner by which the impact of finite sample size is reduced. This observed relationship has a specific form that is consistent with what the theory predicts. In short, the theoretical result is well verified by the dynamical model forecasts. Diagnostic analysis also shows that no good relationship exists between the reliability and the AC, signifying the difference of reliability and resolution in nature. A specific application of the proven resolution‐AC coherence is also demonstrated. The proved resolution‐AC relationship can facilitate comparisons among various assessments of seasonal climate prediction skill from the deterministic or probabilistic perspective alone.