My project aimed to better understand the response of the atmospheric circulation to climate change, including how to best characterize the uncertainties associated with that response.  The project thoroughly addressed all three of its main objectives, represented in the three WPs which represented parallel strands that together achieved the overall aim of the project.

All publications can be found on my main University web page, in the University of Reading’s publicly accessible archive.

WP1: Diagnosing model error

The sensitivity in climate models of key features of atmospheric circulation to poorly constrained aspects of parameterized drag processes — a comparatively unexplored aspect of model error — was extensively examined. Using free-running models, the team explored the role of boundary-layer roughness (Polichtchouk & Shepherd 2016), orographic blocking (Pithan et al. 2016; van Niekerk et al. 2017) and non-orographic gravity-wave drag (Polichtchouk et al. 2018a,b). In a collaboration with ECMWF, the model errors associated with the first two of these processes were shown to evolve differently between weather and climate timescales because of dynamical feedbacks (Sandu et al. 2016). A novel nudging technique was used to suppress these feedbacks to obtain a cleaner understanding of the coupling between the different processes leading to surface drag (van Niekerk et al. 2016). Our work has stimulated a renewed focus on surface drag processes within weather and climate modelling centres around the world (Sandu et al. 2019). The team also elucidated the links between cloud processes and atmospheric circulation, another underexplored area of research (Ceppi & Shepherd 2017).

WP2: Calibrating model projections

The standard approach to using multi-model ensembles of climate projections is to take the ensemble mean as the central estimate, and the spread as a measure of uncertainty. This is widely admitted to be unfounded, and the team has sought better ways of identifying the real information contained within the ensemble. A new approach of optimal seasonal averaging was developed to increase the signal-to-noise ratio of the circulation response (Zappa et al. 2015a), and used to show that virtually all the uncertainty associated with the projected cold-season Mediterranean drying — which will have profound socio-economic consequences for Europe — is traceable to the circulation response (Zappa et al. 2015b). The team developed a novel, non-probabilistic ‘storyline’ approach to characterizing the uncertainty associated with atmospheric circulation (Zappa & Shepherd 2017; Mindlin et al. 2020). This identified the crucial role of the polar vortex breakdown date for Southern Hemisphere (SH) midlatitude circulation changes (Ceppi & Shepherd 2019). We further identified distinct timescales of the sea-surface temperature response to climate change, which help reconcile otherwise puzzling aspects of the circulation response (Ceppi et al. 2018) and its implications for water security in water-stressed climates (Zappa et al. 2020).

WP3: Understanding natural variability

Climate noise is highly structured in space and time, with long-memory effects, which can confound the detection of signals. Two broad approaches were followed in the project. The first aimed to understand the implications of interannual variations in the seasonal cycle on widely used statistical methods of causal inference, which focus on anomalies rather than full fields and generally assume stationarity. The work had a very strong focus on stratosphere-troposphere coupling in the SH, because of its importance for SH regional climate (Byrne et al. 2016, 2017, 2019; Byrne & Shepherd 2018; Saggioro & Shepherd 2019). The second approach developed theoretical models for the interaction between different components of the atmospheric circulation, to provide a physical basis for analysis of causal inference (Boljka & Shepherd 2018, 2020; Boljka et al. 2018), and for the understanding of skewness of temperature extremes (Tamarin-Brodsky et al. 2019, 2020).

Novel and/or unconventional methodologies

Diagnosing climate model biases is hampered by strong dynamical feedbacks that can either amplify or obscure the source of the biases. WP1 overcame this limitation for surface drag processes by developing a novel nudging methodology, where the free atmosphere is constrained by observations whilst the boundary layer is free to respond to model perturbations (van Niekerk et al. 2016). The method has been proposed as an experimental protocol within the World Meteorological Organization, and has been applied by groups elsewhere to look at the role of land-surface processes such as soil moisture in heat waves.

The traditional approach to extreme-event attribution is to assess if the risk of the event has been increased by climate change. This is very challenging for extremes involving the atmospheric circulation or storms, generally leading to no result. Trenberth, Fasullo & Shepherd (2015) advocated an alternative and novel framing of the question, taking the meteorological event as given and asking whether its impacts were increased by known thermodynamic aspects of climate change, concerning which there is reasonably high confidence. Shepherd (2016) cast the approach within a mathematical framework, calling it a ‘storyline’ approach, and it is increasingly being applied in extreme-event attribution studies. We have applied the nudging methodology developed in WP1 (see above) to storyline event attribution, which provides a novel way of following long-term, complex extremes (van Garderen et al. 2020).

The traditional approach to quantifying uncertainty in climate model projections, e.g. in the IPCC Assessment Reports, is to take the ensemble mean as the central estimate, and the spread as a measure of uncertainty. This is widely admitted to be unfounded but is still the standard practice. WP2 pioneered new approaches to this problem, in order to extract more information from the multi-model ensemble. Zappa & Shepherd (2017) developed a ‘storyline’ approach to representing the circulation-related aspects of the uncertainty in climate change as a discrete set of physically-coherent possibilities. This advantage of this approach is that it allows for the treatment of correlated and compound risk. Shepherd (2019) showed how storylines could be embedded within causal networks, thus providing a link to probabilistic representations and establishing a solid epistemological framework for the storyline approach. These innovations appear to be strongly influencing the approach taken by the IPCC in its current Assessment Report.

To understand natural internal variability, the standard approach has been to examine the behaviour of anomalies relative to the long-term average, assuming statistical stationarity. WP3 has shown the pitfalls of this approach (Byrne et al. 2016), and developed novel statistical approaches that take proper account of interannual variations in the seasonal cycle (Byrne et al. 2017; Byrne & Shepherd 2018). Causal networks can also be applied to the internal variability, and provide a way to causally connect different aspects of the circulation response to climate change (Saggioro & Shepherd 2019; Kretschmer et al. 2020).

Most of the understanding of how temperature variability will respond to climate change is based on the first two moments of the temperature distribution: the mean and the variance. Indeed, Chapter 1 of the IPCC AR5 report even stated that other moments were not important. Moreover, much of the focus in the literature has been on thermodynamic mechanisms. Yet in a weather context, people generally associate temperature extremes with the transport of air masses from warmer or colder regions. WP3 developed robust ways of quantifying temperature variability due to transport, which bridges the weather and climate perspective in a novel way, and also provides important insight into the role of skewness changes for temperature extremes (Tamarin-Brodsky et al. 2019, 2020).

Inter and cross disciplinary developments

I raised the awareness of the importance of atmospheric circulation as a source of uncertainty in climate change — the focus of this project — in many seminars and invited talks at conferences. One such talk led to an invitation to write a peer-reviewed Perspective for Nature Geoscience (Shepherd 2014), a high-impact inter-disciplinary journal. The article was accordingly aimed at non-specialists, and seems to have struck a chord as it is being very highly cited.

Related to this, I was involved in visioning within the World Climate Research Programme, leading to two more of these peer-reviewed Perspectives in inter-disciplinary Nature family journals (though in these cases, they are community papers). One concerns the coupling between clouds, circulation and climate sensitivity, and was published in Nature Geoscience (Bony et al. 2015); the other concerns dynamical linkages between Arctic warming and midlatitude weather, and was published in Nature Climate Change (Overland et al. 2016). A third piece of this type arose from my involvement in a COST action on bias correction and downscaling, and was published in Nature Climate Change (Maraun et al. 2017).

Extreme-event attribution is becoming a very important topic in climate science, both for public communication and for adaptation planning. I became very interested in extreme events connected with atmospheric circulation, because they challenge the robustness of our understanding of the physical mechanisms of climate change. This led to a number of inter-disciplinary activities.

I was invited to write a short article (1000 words) on extreme events by the two highest-profile inter-disciplinary science journals in the world: a News & Views piece on heat waves in Nature (published in July 2015), and a Perspective on Arctic-midlatitude weather linkages in Science (published in September 2016). Both pieces are non-technical, non-peer-reviewed, and aimed at a broad audience.

I was asked to join a Committee of the US National Academy of Sciences writing a peer-reviewed assessment of Extreme Event Attribution in the Context of Climate Change. This was a major, high-profile undertaking, with the report released in March 2016 including briefings on Capitol Hill and at the White House. My role in the report mainly had to do with the chapter on Framing (of the question), which was the most inter-disciplinary subject of the entire report.

Because of my storylines approach, I was asked by a philosopher of science (who has a background on evolutionary biology) to co-author an invited review on the attribution of extreme ecosystem and environmental catastrophes to climate change, which was published in a prestigious inter-disciplinary journal (Lloyd & Shepherd 2020).

In order to situate my `storyline’ approach to climate-change uncertainty within the wider uncertainty landscape, I organized a workshop through the auspices of the Royal Society, including economists. It led to a group-authored paper in the inter-disciplinary journal Climatic Change (Shepherd et al. 2018), which is being highly cited.

I become very interested in how the language used to convey scientific uncertainty affects people’s perception of climate change, and collaborated with a team of psychologists, leading to two publications (Løhre et al. 2019; Juanchich et al. 2020). I also embedded a research assistant (Max Leighton) within a climate research project at the University of Cape Town exploring perceptions of climate change across cities in southern Africa, leading to a joint inter-disciplinary publication with that team (Steynor et al. 2020).

Finally, as an outcome of an inter-disciplinary workshop at Columbia University, I published a paper on `localness' of climate change as part of a special issue of the humanities journal Comparative Studies of South Asia, Africa and the Middle East (Shepherd & Sobel 2020).

WP1: Diagnosing model error