Publisher Correction: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors.

'In the original HTML version of this Article, ref.12 was incorrectly cited in the first sentence of the first paragraph of the Introduction. The correct citation is ref. 2. This has now been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.'

Critical Southern Ocean climate model biases traced to atmospheric model cloud errors.

The Southern Ocean is a pivotal component of the global climate system yet it is poorly represented in climate models, with significant biases in upper-ocean temperatures, clouds and winds. Combining Atmospheric and Coupled Model Inter-comparison Project (AMIP5/CMIP5) simulations, with observations and equilibrium heat budget theory, we show that across the CMIP5 ensemble variations in sea surface temperature biases in the 40-60°S Southern Ocean are primarily caused by AMIP5 atmospheric model net surface flux bias variations, linked to cloud-related short-wave errors. Equilibration of the biases involves local coupled sea surface temperature bias feedbacks onto the surface heat flux components. In combination with wind feedbacks, these biases adversely modify upper-ocean thermal structure. Most AMIP5 atmospheric models that exhibit small net heat flux biases appear to achieve this through compensating errors. We demonstrate that targeted developments to cloud-related parameterisations provide a route to better represent the Southern Ocean in climate models and projections.

High risk of unprecedented UK rainfall in the current climate.

In winter 2013/14 a succession of storms hit the UK leading to record rainfall and flooding in many regions including south east England. In the Thames river valley there was widespread flooding, with clean-up costs of over £1 billion. There was no observational precedent for this level of rainfall. Here we present analysis of a large ensemble of high-resolution initialised climate simulations to show that this event could have been anticipated, and that in the current climate there remains a high chance of exceeding the observed record monthly rainfall totals in many regions of the UK. In south east England there is a 7% chance of exceeding the current rainfall record in at least one month in any given winter. Expanding our analysis to some other regions of England and Wales the risk increases to a 34% chance of breaking a regional record somewhere each winter.A succession of storms during the 2013-2014 winter led to record flooding in the UK. Here, the authors use high-resolution climate simulations to show that this event could have been anticipated and that there remains a high chance of exceeding observed record monthly rainfall totals in many parts of the UK.

Mixing and transport in urban areas.

Concern over terrorist releases of harmful material has generated interest in short-range air-borne dispersion in urban areas. Here, we review the important fluid dynamical processes that control dispersion in the first kilometre, the neighbourhood scale, when much of the material remains within the urban canopy. Dispersion is then controlled by turbulent mixing and mean flow transport through the network of streets. We consider mixing and transport in a long straight street, street intersections and then a network of streets connected by intersections. The mixing and transport in these systems are illustrated with results from recent fine-resolution numerical simulations and laboratory models, which then inform simpler scaling estimates and modelling schemes. Finally, we make some tentative steps to pull the process studies together to begin to understand results from full-scale observations. In particular, it is shown that the positions of 'shear layers' and 'dividing streamlines' largely control the patterns of mixing and transport. It is also shown that neighbourhood-scale dispersion follows one scaling in the near field and another in the far field after passage through many intersections. The challenge for the future is to bring these threads together into a coherent mathematical model.