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Abstract The eastern tropical Pacific has defied the global warming trend. There has been a debate about whether this observed trend is forced or natural (i.e., the Interdecadal Pacific Oscillation; IPO) and this study shows that there are two patterns, one that oscillates along with the IPO, and one that is emerging since the mid-1950s, herein called the Pacific Climate Change (PCC) pattern. Here we show these have distinctive and distinguishable atmosphere-ocean signatures. While the IPO features a meridionally broad wedge-shaped SST pattern, the PCC pattern is marked by a narrow equatorial cooling band. These different SST patterns are related to distinct wind-driven ocean dynamical processes. We further show that the recent trends during the satellite era are a combination of IPO and PCC. Our findings set a path to distinguish climate change signals from internal variability through the underlying dynamics of each.

Abstract We present new climate field reconstructions (CFR) of tropical Pacific ENSO sea surface temperatures (HadISST) for the boreal winter season using a circum‐Pacific tree‐ring network from known El Niño rainfall impact regions. We use two different CFR methods: Point‐by‐Point Regression (PPR) and reduced‐space Orthogonal Spatial Regression (OSR). Both methods produce reconstructions with high validation skill, but OSR is preferred because it has less spatial noise and is more efficient. Only the leading EOF of the SST field (EOF1) can be skillfully reconstructed by either method; EOF2 does not validate. The success of EOF1 reflects its importance for ENSO rainfall impacts over land; the failure with EOF2 is from the lack of these impacts. EOF1 allows for the reconstruction of many ENSO indices, including the ENSO Longitudinal Index (ELI). We also find evidence in our reconstructions for a recent increase in ENSO activity.

Abstract The North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

Exceptionally wet growing seasons, compared to the past 700 years, preceded the European Great Famine of 1315-1317 and are associated with a newly identified hydroclimate pattern, according to analyses of Europe-wide tree ring data.

This study examines coupled relationships among clouds, atmospheric circulation, and sea ice in Antarctic winter. We find that the wave-3 pattern dominates the leading covariability mode among cloud, atmospheric circulation, and sea ice. Both horizontal transport and vertical motion contribute to cloud formation, resulting in maximum cloud anomalies spatially between maximum meridional wind and pressure anomalies in the coupled system. The radiative effect of the clouds related to the wave-3 pattern can generate sea ice anomalies up to 12 cm thick in one month in the Amundsen Sea. It also strengthens the sea ice anomalies that are directly induced by low-level atmospheric circulation anomalies. In addition, the radiative forcing of the leading cloud mode in the lower troposphere is suppressed by the dynamic and thermodynamic effects of the circulation anomalies. These discoveries provide a better understanding of Antarctica’s interactive processes, and also offer physical evidence for climate model validations.

Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.