Can we trust simple marine DMS parameterisations within complex climate models?

Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted inter-annual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed inter-annual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high inter-annual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed

The mechanisms of North Atlantic CO2 uptake in a large Earth System Model ensemble

The oceans currently take up around a quarter of the carbon dioxide (CO2) emitted by human activity. While stored in the ocean, this CO2 is not influencing Earth's radiation budget; the ocean CO2 sink therefore plays an important role in mitigating global warming. CO2 uptake by the oceans is heterogeneous, with the subpolar North Atlantic being the strongest CO2 sink region. Observations over the last 2 decades have indicated that CO2 uptake by the subpolar North Atlantic sink can vary rapidly. Given the importance of this sink and its apparent variability, it is critical that we understand the mechanisms behind its operation. Here we explore the combined natural and anthropogenic subpolar North Atlantic CO2 uptake across a large ensemble of Earth System Model simulations, and find that models show a peak in sink strength around the middle of the century after which CO2 uptake begins to decline. We identify different drivers of change on interannual and multidecadal timescales. Short-term variability appears to be driven by fluctuations in regional seawater temperature and alkalinity, whereas the longer-term evolution throughout the coming century is largely occurring through a counterintuitive response to rising atmospheric CO2 concentrations. At high atmospheric CO2 concentrations the contrasting Revelle factors between the low latitude water and the subpolar gyre, combined with the transport of surface waters from the low latitudes to the subpolar gyre, means that the subpolar CO2 uptake capacity is largely satisfied from its southern boundary rather than through air-sea CO2 flux. Our findings indicate that: (i) we can explain the mechanisms of subpolar North Atlantic CO2 uptake variability across a broad range of Earth System Models; (ii) a focus on understanding the mechanisms behind contemporary variability may not directly tell us about how the sink will change in the future; (iii) to identify long-term change in the North Atlantic CO2 sink we should focus observational resources on monitoring lower latitude as well as the subpolar seawater CO2; (iv) recent observations of a weakening subpolar North Atlantic CO2 sink may suggest that the sink strength has peaked and is in long-term decline.This work was supported by the EU FP7 Collaborative Project CarboOcean (Grant Agreement Number 264879), the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), and the NERC directed research programme RAGNARoCC (NE/K002473/1)

The HadGEM2 family of Met Office Unified Model climate configurations

We describe the HadGEM2 family of climate configurations of the Met Office Unified Model, MetUM. The concept of a model "family" comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family of configurations includes atmosphere and ocean components, with and without a vertical extension to include a well-resolved stratosphere, and an Earth-System (ES) component which includes dynamic vegetation, ocean biology and atmospheric chemistry. The HadGEM2 physical model includes improvements designed to address specific systematic errors encountered in the previous climate configuration, HadGEM1, namely Northern Hemisphere continental temperature biases and tropical sea surface temperature biases and poor variability. Targeting these biases was crucial in order that the ES configuration could represent important biogeochemical climate feedbacks. Detailed descriptions and evaluations of particular HadGEM2 family members are included in a number of other publications, and the discussion here is limited to a summary of the overall performance using a set of model metrics which compare the way in which the various configurations simulate present-day climate and its variability