# Proposed PhD Research Outline: Investigating Potential Predictability of the Southern Hemispheric Westerly-Wave and its Impact on Southern African Seasonal Climate on Decadal Timescales.

## Background

In the sections below I provide a brief motivation for considering the features and questions that I plan to investigate further in this project. In \ref{Intro:Wave}, some background to studies of the dynamics and predictability of the Southern Hemisphere circumglobal Westerly Wave is provided. This is followed in \ref{Intro:Predict} by a general discussion on the importance of considering initial condition (IC) predictability and uncertainty in climate change and variability assessments.

### Dynamics of and projected changes in the Southern Hemisphere Westerly Wave

\label{Intro:Wave}

Indications are that greenhouse gas (GHG) related warming will tend to result in a poleward shift in the Southern Hemisphere (SH) mid-latitude cyclone (MLC) tracks (IPCC, 2013)—in part because the zones of subtropical subsidence are expected to expand zonally, thus extending the tropical Hadley Cell circulation (Lu et al., 2007). However, this influence is likely to be counteracted, to some extent, by stratospheric ozone recovery (Fogt et al., 2009; Thompson et al., 2011), implying that the net change remains somewhat uncertain (IPCC, 2013). Additionally, as in the Northern Hemisphere (NH), low frequency internal variability may dominate any predictable forced signal in storm track shift for some decades to come (IPCC, 2013). The IPCC (2013) conclude that blocking frequency in both hemispheres will likely remain relatively unchanged.

The IPCC (2013) suggests that there is substantial evidence that summer monsoon systems will strengthen in response to anthropogenic climate change. If the hypothesised coupling between summer monsoon systems in the NH and the subsidence associated with the South Atlantic High Pressure system (Lee et al., 2013) indeed operates—and continues to do so under rapidly evolving climate forcings—this may further enhance shifts in the Westerly Wave over the Southern African sector.

Evidence has been presented that there is a significant influence exerted on SH MLC track—and consequently winter rainfall over south-western South Africa and summer synoptic patterns associated with the Westerly Wave—by sea ice distribution and concentration around Antarctica (Blamey et al., 2007; Raphael et al., 2010; Hudson et al., 2001). Hudson (1998) finds that the winter time response to a change in Antarctic sea ice distribution in the South Atlantic sector is opposite to parts of the Pacific sector, suggesting a complex, intricate relationship between slowly varying climate state variables and atmospheric response.

### Importance of understanding initial condition uncertainty and predictability

\label{Intro:Predict}

Stainforth et al. (2007) suggest that uncertainty in future climate change can be regarded as consisting of the following five components:

1. 1.

”forcing uncertainty”—uncertainty regarding the state and evolution of climate drivers external to Earth’s climate system;

2. 2.

”model uncertainty”—uncertainty regarding the optimal set-up and formulation of various climate processes in existing climate models;

3. 3.

”model inadequacy”—uncertainty arising from the unavoidable shortcomings of models in simulating Earth’s climate, the impact of which on model fidelity is unknown and difficult to quantify;

4. 4.

”microscopic IC uncertainty”—IC uncertainty, a reduction in which does not significantly further constrain future climate variable distributions (see also Lorenz, 1969); and

5. 5.

”macroscopic IC uncertainty”—IC uncertainty related to slowly varying components of the climate system, which, if better observed, could potentially help better constrain future climate distributions.

Quantifying both levels of IC uncertainty and their influence on subsequent climate system trajectories is of great importance (Palmer, 2000; Stainforth et al., 2007; Daron et al., 2013; Daron et al., 2015; Hawkins et al., 2015).For microscopic IC uncertainty, this importance lies therein that it is an intrinsic, fundamentally irreducible uncertainty; it represents an absolute lower bound to total uncertainty in climate projection (Slingo et al., 2011; Smith, 2000; Smith, 2000a; Smith, 2002). No possible future improvement in observations, model resolution, model set-up and parameterisation, or progress or in any other aspect of climate model development could significantly alter the magnitude of microscopic IC uncertainty. Macroscopic IC uncertainty is important to consider, because constraining the present state of slowly varying components of the climate system could help allow one to constrain future climatic probability distributions and quantify the uncertainty associated with incomplete knowledge of the system state on various time scales (Stainforth et al., 2007; Daron et al., 2013; Daron et al., 2015). Hence, understanding IC uncertainty is necessary for meaningful quantification of total uncertainty in future projections.

Both microscopic and macroscopic IC uncertainty relate to internal climate variability (climate system variability with characteristic time scales beyond the weather scale, such that predictability of the second—rather than first—kind is of relevance (Lorenz, 1975)), and consequently also to climate predictability (Daron et al., 2013; Daron et al., 2015). Unpredictable internal variability can be quantified using IC ensembles, produced by choosing a single starting climate system state, to which small perturbations are applied. Such ensembles allow one to establish the spread of possible states consistent with a given forcing trajectory and macroscopic initial state in a given model set-up. Kay et al. (2015) and Deser et al. (2012) demonstrate using large initial condition ensembles that—especially on regional and ocean-basin scales—internal variability is responsible for a substantial proportion of the spread in multi-model ensemble climate change response, and this may persist for multiple decades (Hawkins et al., 2015). Using multiple ensembles with ICs corresponding to different states of slowly varying climate system components (such as sea ice state, or ocean temperatures at intermediate depth), allows one to quantify the associated potential predictability for a given ”model world” (see, e.g., Hawkins et al., 2015; Daron et al., 2015; Conradie, 2015).

## Working Aim

To explore the relative contribution of initial condition predictability to total potential predictability of quantifiable characteristics of the Southern Hemispheric westerly wave over