By synthesizing recent studies employing a wide range of approaches (modern observations, paleo reconstructions, and climate model simulations), this paper provides a comprehensive review of the linkage between multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability and Atlantic Multidecadal Variability (AMV) and associated climate impacts. There is strong observational and modeling evidence that multidecadal AMOC variability is a crucial driver of the observed AMV and associated climate impacts and an important source of enhanced decadal predictability and prediction skill. The AMOC‐AMV linkage is consistent with observed key elements of AMV. Furthermore, this synthesis also points to a leading role of the AMOC in a range of AMV‐related climate phenomena having enormous societal and economic implications, for example, Intertropical Convergence Zone shifts; Sahel and Indian monsoons; Atlantic hurricanes; El Nino–Southern Oscillation; Pacific Decadal Variability; North Atlantic Oscillation; climate over Europe, North America, and Asia; Arctic sea ice and surface air temperature; and hemispheric‐scale surface temperature. Paleoclimate evidence indicates that a similar linkage between multidecadal AMOC variability and AMV and many associated climate impacts may also have existed in the preindustrial era, that AMV has enhanced multidecadal power significantly above a red noise background, and that AMV is not primarily driven by external forcing. The role of the AMOC in AMV and associated climate impacts has been underestimated in most state‐of‐the‐art climate models, posing significant challenges but also great opportunities for substantial future improvements in understanding and predicting AMV and associated climate impacts.

A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts

We review the growing evidence for a widespread inconsistency between the low strength of predictable signals in climate models and the relatively high level of agreement they exhibit with observed variability of the atmospheric circulation. This discrepancy is particularly evident in the climate variability of the Atlantic sector, where ensemble predictions using climate models generally show higher correlation with observed variability than with their own simulations, and higher correlations with observations than would be expected from their small signal-to-noise ratios, hence a ‘signal-to-noise paradox’. This unusual behaviour has been documented in multiple climate prediction systems and in the response to a number of different sources of climate variability. However, we also note that the total variance in the models is often close in magnitude to the observed variance, and so it is not a simple matter of models containing too much variability. Instead, the proportion of Atlantic climate variance that is predictable in climate models appears to be too weak in amplitude by a factor of two, or perhaps more. In this review, we provide a range of examples from existing studies to build the case for a problem that is common across different climate models, common to several different sources of climate variability and common across a range of timescales. We also discuss the wider implications of this intriguing paradox.

/pdf/a-signal-to-noise-paradox-in-climate-science-3fh0v98wgj.pdf

A signal-to-noise paradox in climate science

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1–3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7–9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. Current models are too noisy to predict climate usefully on decadal timescales, but two-stage post-processing of model outputs greatly improves predictions of decadal variations in North Atlantic winter climate.

/pdf/north-atlantic-climate-far-more-predictable-than-models-2r2l8n5hiy.pdf

North Atlantic climate far more predictable than models imply

The recent levelling of global mean temperatures after the late 1990s, the so-called global warming hiatus or slowdown, ignited a surge of scientific interest into natural global mean surface temperature variability, observed temperature biases, and climate communication, but many questions remain about how these findings relate to variations in more societally relevant temperature extremes. Here we show that both summertime warm and wintertime cold extreme occurrences increased over land during the so-called hiatus period, and that these increases occurred for distinct reasons. The increase in cold extremes is associated with an atmospheric circulation pattern resembling the warm Arctic-cold continents pattern, whereas the increase in warm extremes is tied to a pattern of sea surface temperatures resembling the Atlantic Multidecadal Oscillation. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature.

/pdf/increasing-occurrence-of-cold-and-warm-extremes-during-the-23t50pzd3p.pdf

Increasing occurrence of cold and warm extremes during the recent global warming slowdown

This paper reviews recent progress made by Chinese scientists on the pathways of influence of the Northern Hemisphere mid-high latitudes on East Asian climate within the framework of a “coupled oceanic-atmospheric (land-atmospheric or sea-ice-atmospheric) bridge” and “chain coupled bridge”. Four major categories of pathways are concentrated upon, as follows: Pathway A—from North Atlantic to East Asia; Pathway B—from the North Pacific to East Asia; Pathway C—from the Arctic to East Asia; and Pathway D—the synergistic effects of the mid-high latitudes and tropics. In addition, definitions of the terms “combined effect”, “synergistic effect” and “antagonistic effect” of two or more factors of influence or processes and their criteria are introduced, so as to objectively investigate those effects in future research.

Pathways of Influence of the Northern Hemisphere Mid-high Latitudes on East Asian Climate: A Review

Massive Riverbed Erosion Induced by Inappropriate Grade Control: A Case Study in a Large-scale Compound Channel

Recent work suggests that the boreal autumn Southern Hemisphere annular mode (SAM) favors a tripole pattern of winter precipitation anomalies in the Northern Hemisphere This study focuses on the abilities of climate models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce the physical processes involved in this observed cross-seasonal connection A systematic evaluation suggested that 16 out of 25 models were essentially capable of reproducing this cross-seasonal connection Two categories of models were selected to explore the underlying reasons for these successful simulations Models that successfully simulated the cross-seasonal relationship were placed in the type-I category, and these performed well in reproducing the related physical mechanism, known as the “coupled ocean–atmosphere bridge,” in terms of the SST variability associated with the SAM and response of the meridional circulation to these SST anomalies In contrast, the type-II category

Cross-Seasonal Relationship between the Boreal Autumn SAM and Winter Precipitation in the Northern Hemisphere in CMIP5

The North Atlantic Oscillation (NAO) is one of the most prominent teleconnection patterns in the Northern Hemisphere and has recently been found to be both an internal source and useful predictor of the multidecadal variability of the Northern Hemisphere mean surface temperature (NHT). In this study, we examine how well the variability of the NAO and NHT are reproduced in historical simulations generated by the 40 models that constitute Phase 5 of the Coupled Model Intercomparison Project (CMIP5). All of the models are able to capture the basic characteristics of the interannual NAO pattern reasonably well, whereas the simulated decadal NAO patterns show less consistency with the observations. The NAO fluctuations over multidecadal time scales are underestimated by almost all models. Regarding the NHT multidecadal variability, the models generally represent the externally forced variations well but tend to underestimate the internal NHT. With respect to the performance of the models in reproducing the NAO-NHT relationship, 14 models capture the observed decadal lead of the NAO, and model discrepancies in the representation of this linkage are derived mainly from their different interpretation of the underlying physical processes associated with the Atlantic Multidecadal Oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC). This study suggests that one way to improve the simulation of the multidecadal variability of the internal NHT lies in better simulation of the multidecadal variability of the NAO and its delayed effect on the NHT variability via slow ocean processes.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD025979

NAO and its relationship with the Northern Hemisphere mean surface temperature in CMIP5 simulations

This paper evaluates the simulation performance of the 37 coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the East Asian summer monsoon (EASM) and the Indo-Pacific warm pool and North Pacific Ocean dipole (IPOD) and also the interrelationships between them. The results show that the majority of the models are unable to accurately simulate the interannual variability and long-term trends of the EASM, and their simulations of the temporal and spatial variations of the IPOD are also limited. Further analysis showed that the correlation coefficients between the simulated and observed EASM index (EASMI) is proportional to those between the simulated and observed IPOD index (IPODI); that is, if the models have skills to simulate one of them then they will likely generate good simulations of another. Based on the above relationship, this paper proposes a conditional multi-model ensemble method (CMME) that eliminates those models without capability to simulate the IPOD and EASM when calculating the multi-model ensemble (MME). The analysis shows that, compared with the MME, this CMME method can significantly improve the simulations of the spatial and temporal variations of both the IPOD and EASM as well as their interrelationship, suggesting the potential for the CMME approach to be used in place of the MME method.

Simulating the IPOD, East Asian summer monsoon, and their relationships in CMIP5

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Massive Riverbed Erosion Induced by Inappropriate Grade Control: A Case Study in a Large-scale Compound Channel

Cross-Seasonal Relationship between the Boreal Autumn SAM and Winter Precipitation in the Northern Hemisphere in CMIP5

NAO and its relationship with the Northern Hemisphere mean surface temperature in CMIP5 simulations

Simulating the IPOD, East Asian summer monsoon, and their relationships in CMIP5