Recent observed climate trends result from a combination of external radiative forcing and internally generated variability. To better contextualize these trends and forecast future ones, it is necessary to properly model the spatiotemporal properties of the internal variability. Here, a statistical model is developed for terrestrial temperature and precipitation, and global sea level pressure, based upon monthly gridded observational datasets that span 1921–2014. The model is used to generate a synthetic ensemble, each member of which has a unique sequence of internal variability but with statistical properties similar to the observational record. This synthetic ensemble is combined with estimates of the externally forced response from climate models to produce an observational large ensemble (OBS-LE). The 1000 members of the OBS-LE display considerable diversity in their 50-yr regional climate trends, indicative of the importance of internal variability on multidecadal time scales. For example, unforced atmospheric circulation trends associated with the northern annular mode can induce winter temperature trends over Eurasia that are comparable in magnitude to the forced trend over the past 50 years. Similarly, the contribution of internal variability to winter precipitation trends is large across most of the globe, leading to substantial regional uncertainties in the amplitude and, in some cases, the sign of the 50-yr trend. The OBS-LE provides a real-world counterpart to initial-condition model ensembles. The approach could be expanded to using paleo-proxy data to simulate longer-term variability.

Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.

The wintertime atmospheric circulation responses to observed patterns of North Atlantic sea surface temperature and sea ice cover trends in recent decades are studied by means of experiments with an atmospheric general circulation model. Here the relationship between the forced responses and the dominant pattern of internally generated atmospheric variability is focused on. The total response is partioned into a portion that projects onto the leading mode of internal variability (the indirect response) and a portion that is the residual from that projection (the direct response). This empirical decomposition yields physically meaningful patterns whose distinctive horizontal and vertical structures imply different governing mechanisms. The indirect response, which dominates the total geopotential height response, is hemispheric in scale with resemblance to the North Atlantic Oscillation or Northern Hemisphere annular mode, and equivalent barotropic in the vertical from the surface to the tropopause. In contrast. the direct response is localized to the vicinity of the surface thermal anomaly (SST or sea ice) and exhibits a baroclinic structure in the vertical, with a surface trough and upper-level ridge in the case of a positive heating anomaly, consistent with theoretical models of the linear baroclinic response to extratropical thermal forcing. Both components of the response scale linearly with respect to the amplitude of the forcing but nonlinearly with respect to the polarity of the forcing. The deeper vertical penetration of anomalous heating compared to cooling is suggested to play a role in the nonlinearity of the response to SST forcing. © 2004 American Meteorological Society.

Abstract Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

The role of sampling variability in ENSO composites of winter surface air temperature and precipitation over North America during the period 1920–2013 is assessed for observations and ensembles of coupled model simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged to those of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14 La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is, aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude of the estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates an approach to model assessment that considers not only the model’s forced response to ENSO, given by its ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Such an approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.

Application of random sampling techniques to composite differences between 18 El Niño and 14 La Niña events observed since 1920 reveals considerable uncertainty in both the pattern and amplitude of the Northern Hemisphere extratropical winter sea level pressure (SLP) response to ENSO. While the SLP responses over the North Pacific and North America are robust to sampling variability, their magnitudes can vary by a factor of 2; other regions, such as the Arctic, North Atlantic, and Europe are less robust in their SLP patterns, amplitudes, and statistical significance. The uncertainties on the observed ENSO composite are shown to arise mainly from atmospheric internal variability as opposed to ENSO diversity. These observational findings pose considerable challenges for the evaluation of ENSO teleconnections in models. An approach is proposed that incorporates both pattern and amplitude uncertainty in the observational target, allowing for discrimination between true model biases in the forced ENSO response and apparent model biases that arise from limited sampling of non-ENSO-related internal variability. Large initial-condition coupled model ensembles with realistic tropical Pacific sea surface temperature anomaly evolution during 1920–2013 show similar levels of uncertainty in their ENSO teleconnections as found in observations. Because the set of ENSO events in each of the model composites is the same (and identical to that in observations), these uncertainties are entirely attributable to sampling fluctuations arising from internal variability, which is shown to originate from atmospheric processes. The initial-condition model ensembles thus inform the interpretation of the single observed ENSO composite and vice versa.

The discrepancy between the observed lack of surface warming in the eastern equatorial Pacific and climate model projections of an El Niño-like warming pattern confronts the climate research community. While anthropogenic aerosols have been suggested as a cause, the prolonged cooling trend over the equatorial Pacific appears in conflict with Northern Hemisphere aerosol emission reduction since the 1980s. Here, using CESM, we show that the superposition of fast and slow responses to aerosol emission change-an increase followed by a decrease-can sustain the La Niña-like condition for a longer time than expected. The rapid adjustment of Hadley Cell to aerosol reduction triggers joint feedback between low clouds, wind, evaporation, and sea surface temperature in the Southeast Pacific, leading to a wedge-shaped cooling that extends to the central equatorial Pacific. Meanwhile, the northern subtropical cell gradually intensifies, resulting in equatorial subsurface cooling that lasts for decades.

In the boreal spring of 2023, an extreme coastal El Niño struck the coastal regions of Peru and Ecuador, causing devastating rainfalls, flooding, and record dengue outbreaks. Observations and ocean model experiments reveal that northerly alongshore winds and westerly wind anomalies in the eastern equatorial Pacific, initially associated with a record-strong Madden-Julian Oscillation and cyclonic disturbance off Peru in March, drove the coastal warming through suppressed coastal upwelling and downwelling Kelvin waves. Atmospheric model simulations indicate that the coastal warming in turn favors the observed wind anomalies over the far eastern tropical Pacific by triggering atmospheric deep convection. This implies a positive feedback between the coastal warming and the winds, which further amplifies the coastal warming. In May, the seasonal background cooling precludes deep convection and the coastal Bjerknes feedback, leading to the weakening of the coastal El Niño. This coastal El Niño is rare but predictable at 1 month lead, which is useful to protect lives and properties.