Robust but weak winter atmospheric circulation response to future Arctic sea ice loss.

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models.

Role of Atmospheric Indices in Describing Inshore Directional Wave Climate in the United Kingdom and Ireland.

Improved understanding of how our coasts will evolve over a range of time scales (years-decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980-2017) and show that 73% are directionally bimodal. We find that winter-averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60-0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi-linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R 2  = 0.45-0.8; p < 0.05). We demonstrate for the first time the significant explanatory power of leading winter-averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions.

North Atlantic climate far more predictable than models imply.

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.

Possible impacts of a future grand solar minimum on climate: Stratospheric and global circulation changes.

A future decline in solar activity would not offset projected global warmingA future decline in solar activity could have larger regional effects in winterTop-down mechanism contributes to Northern Hemisphere regional response.

Removal of chlorofluorocarbons by increased mass exchange between the stratosphere and troposphere in a changing climate.

Chlorofluorocarbons (CFCs), along with bromine compounds, have been unequivocally identified as being responsible for most of the anthropogenic destruction of stratospheric ozone. With curbs on emissions of these substances, the recovery of the ozone layer will depend on their removal from the atmosphere. As CFCs have no significant tropospheric removal process, but are rapidly photolysed above the lower stratosphere, the timescale for their removal is set mainly by the rate at which air is transported from the troposphere into the stratosphere. Using a global climate model we predict that, in response to the projected changes in greenhouse-gas concentrations during the first half of the twenty-first century, this rate of mass exchange will increase by 3% per decade. This increase is due to more vigorous extra-tropical planetary waves emanating from the troposphere. We estimate that this increase in mass exchange will accelerate the removal of CFCs to an extent that recovery to levels currently predicted for 2050 and 2080 will occur 5 and 10 years earlier, respectively.