Evaluating model performance and constraining uncertainty using a processed-based framework for Southern African precipitation in historical and future climate projections

This thesis develops an innovative process-based analysis of contemporary model performance of precipitation over southern Africa. This region is typically understudied and not fully understood due to the complexity of various influences and drivers of precipitation. Historical simulations of precipitation are assessed including principal drivers, sources of biases and dominant modes of interannual variability. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the south-west Indian Ocean, is evaluated as the feature of interest in historical simulations. Most CMIP5 models simulate an SIOCZ feature, but are typically too zonally oriented and discontinued between land and the adjacent Indian Ocean. Excessive precipitation over the continent is likely associated with excessively high low-level moisture flux around the Angola Low, which is almost entirely due to model circulation biases. Drivers of precipitation over southern Africa include three dominant moisture flux transport pathways which originate from flow around the SIOHP and SAOHP and monsoon winds. Interannual variability in the SIOCZ is shown by a clear dipole pattern, indicative of a northeast-southwest movement of the SIOCZ. Drivers of this shift are significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However models do not capture these teleconnections well, limiting confidence in model representation of variability. A large majority of the population rely heavily on precipitation over southern Africa for agricultural purposes. Therefore spatial and temporal changes in precipitation are crucial to identify and understand with intentions to ultimately provide useful climate information regarding water security over the region. Key climate change signals over southern Africa are established in this thesis (OND and DJF), in which the dominant regional mechanisms of precipitation change over southern Africa are quantified. Robustness and credibility of these changes are additionally quantified. The most notable projected change in precipitation over southern Africa is the distinct drying signal evident in the pre-summer season (OND). This has the implication of delaying the onset of the rainy season affecting planting and harvesting times. Future projections of the SIOCZ are determined, which indicate a northward shift of approximately 200km. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, hence implying a northward shift of the ITCZ, consistent with the SIOCZ shift. Using a decomposition method it is established that ΔP’s dipole pattern emerges largely from the dynamic component, which holds most uncertainty, particularly over the south-west Indian Ocean. Changes in precipitation over land are not solely driven by dynamical changes but additionally driven by thermodynamic contributions, implying projected changes over land and ocean regions require different approaches. SST patterns of warming over the Indian Ocean corroborate the warmest-get-wetter mechanism driving wetting over the south-west Indian Ocean, which is robust in both key seasons. Coherent model behaviour is understood via across model correlation plots of principal components, whereby patterns of coherent warming patterns are identified. Composite analyses of diagnostic variables across models illustrate patterns driving projected precipitation changes. Drying is more robust over land than over the south-west Indian Ocean. Clear robust drying signal in OND, however magnitude is uncertain. Drivers of uncertainty include SST pattern changes, which modulate atmospheric circulation patterns. Therefore reductions in uncertainty rely on the accurate representation of these processes within climate models to become more robust. There is a desire from both climate scientists and policy-makers to reduce uncertainty in future projections. No one particular methodology is unanimously agreed upon, however one approach is analysed in this thesis. Uncertainties of future precipitation projections are addressed using a process-based model ranking framework. Several metrics most applicable to southern African climate are selected and ranked, which include aspects of both mean state and variability. A sensitivity test via a Monte Carlo approach is performed for various sub-samples of “top” performing models within the CMIP5 model dataset. Uncertainty is significantly reduced when particular sub-sets of “top” performing models are selected, however only for austral summer over the continent. The result has the implication that potential value is established in performing a process-based model ranking over southern Africa. However additional investigation is required before such an approach may become viable and sufficiently credible and robust. Reductions in model spread are additionally established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore model process convergence and coherence is established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases

Climate model simulation of the South Indian Ocean Convergence Zone: mean state and variability

Evaluation of climate model performance at regional scales is essential in determining confidence in simulations of present and future climate. Here we developed a process-based approach focussing on the South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the southwest Indian Ocean. Simulation of the SIOCZ was evaluated for the Coupled Model Intercomparison Project (CMIP5). Comparison was made between CMIP5 and Atmospheric Model Intercomparison Project (AMIP) models to diagnose sources of biases associated with coupled ocean-atmosphere processes. Models were assessed in terms of mean SIOCZ characteristics and processes of interannual variability. Most models simulated a SIOCZ feature, but were typically too zonally oriented. A systematic bias of excessive precipitation was found over southern Africa and the Indian Ocean, but not particularly along the SIOCZ. Excessive precipitation over the continent may be associated with excessively high low-level moisture flux around the Angola Low found in most models, which is almost entirely due to circulation biases in models. AMIP models represented precipitation more realistically over the Indian Ocean, implying a potential coupling error. Interannual variability in the SIOCZ was evaluated through empirical orthogonal function analysis, where results showed a clear dipole pattern, indicative of a northeast-southwest movement of the SIOCZ. The drivers of this shift were significantly related to the El Niño Southern Oscillation and the subtropical Indian Ocean dipole in observations. However, the models did not capture these teleconnections well, limiting our confidence in model representation of variability

Future precipitation projections over central and southern Africa and the adjacent Indian Ocean: what causes the changes and the uncertainty?

Future projections of precipitation at regional scales are vital to inform climate change adaptation activities. Therefore, is it important to quantify projected changes and associated uncertainty, and understand model processes responsible. This paper addresses these challenges for Southern Africa and adjacent Indian Ocean focusing on the local wet season. Precipitation projections for the end of the 21st century indicate a pronounced dipole pattern in the CMIP5 multi-model mean. The dipole indicates future wetting (drying) to the north (south) of the climatological axis of maximum rainfall, implying a northward shift of the ITCZ and South Indian Ocean Convergence Zone, and therefore not consistent with a simple ‘wet-get-wetter’ pattern. This pattern is most pronounced in early Austral summer suggesting a later and shorter wet season over much of southern Africa. Using a decomposition method we determine physical mechanisms underlying this dipole pattern of projected change, and the associated inter-model uncertainty. The projected dipole pattern is largely associated with the dynamical component of change indicative of shifts in the location of convection. Over the Indian Ocean, this apparent northward shift in the ITCZ may reflect the response to changes in the north-south SST gradient over the Indian Ocean, consistent with a ‘warmest-get-wetter’ mechanism. Over land subtropical drying is relatively robust, particularly in the early wet season. This has contributions from dynamical shifts in location of convection, which may be related to regional SST structures in the Southern Indian Ocean, and the thermodynamic decline in relative humidity. Implications for understanding and potentially constraining uncertainty in projections are discussed

Seasonal temperature prediction skill over Southern Africa and human health

An assessment of probabilistic prediction skill of seasonal temperature extremes over southern African is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December to February, January to March and February to April over a 15- year retroactive period. Comparisons are drawn between downscaled seasonal 850 hPa geopotential height field forecasts of a two-tiered system versus downscaled height forecasts from a coupled ocean-atmosphere system. The ECHAM4.5 atmospheric general circulation model is used for both systems; in the one-tiered system the ECHAM4.5 is directly coupled to the ocean model MOM3, and the two-tiered system the ECHAM4.5 is forced with Van den Dool SST hindcasts. Model output statistic equations are developed using canonical correlation analysis to reduce system deficiencies. Probabilistic verification is conducted using the relative operating characteristic (ROC) and reliability diagram. The coupled model performs best in capturing seasonal maximum temperature extremes. Seasons demonstrating the highest ROC scores coincide with the period of highest seasonal temperatures found over southern Africa. The above-normal category of the one-tiered system indicates the highest skill in predicting maximum temperature extremes, implying the coupled model skilfully predicts when there is a high likelihood of experiencing extremely high seasonal maximum temperatures during mid to late summer. The downscaled coupled maximum temperature hindcasts are additionally evaluated in terms of their monetary value and quality to the general public. The seasonal forecast system presented here should be able to reduce risks in decision making by the health industry in southern Africa.http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1469-80802015-10-31hb201

Engrailed‐1 Promotes Pancreatic Cancer Metastasis

Engrailed-1 (EN1) is a critical homeodomain transcription factor (TF) required for neuronal survival, and EN1 expression has been shown to promote aggressive forms of triple negative breast cancer. Here, it is reported that EN1 is aberrantly expressed in a subset of pancreatic ductal adenocarcinoma (PDA) patients with poor outcomes. EN1 predominantly repressed its target genes through direct binding to gene enhancers and promoters, implicating roles in the activation of MAPK pathways and the acquisition of mesenchymal cell properties. Gain- and loss-of-function experiments demonstrated that EN1 promoted PDA transformation and metastasis in vitro and in vivo. The findings nominate the targeting of EN1 and downstream pathways in aggressive PDA