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Penelope Maher and Steven C. Sherwood

Abstract

Precipitation is influenced by multiple large-scale natural processes. Many of these large-scale precipitation “drivers” are not independent of one another, which complicates attribution. Moreover, it is unclear whether natural interannual drivers alone can explain the observed longer-term precipitation trends or account for projected precipitation changes with global warming seen in climate models. Separating the main interannual drivers from processes that may prevail on longer time scales, such as a poleward circulation shift or increased specific humidity, is essential for an improved understanding of precipitation variability and for making longer-term predictions.

In this study, an objective approach to disentangle multiple sources of large-scale variability is applied to Australian precipitation. This approach uses a multivariate linear independence model, involving multiple linear regressions to produce a partial correlation matrix, which directly links variables using significance thresholds to avoid overfitting. This is applied to regional winter precipitation in Australia as a test case, using the ECMWF Interim Re-Analysis (ERA-Interim) and Australian Water Availability Project datasets. Traditional drivers and several drivers associated with the width of the tropics are assessed.

The results show that the web of interactions implied by correlations can be simplified using this multivariate linear independence model approach: the total number of apparent precipitation drivers was reduced in each region studied, compared to correlations meeting the same statistical significance. Results show that the edge of the tropics directly influences regional precipitation in Australia and also has an indirect influence, through the interaction of the subtropical ridge and atmospheric blocking. These results provide observational evidence that changes associated with an expansion of the tropics reduce precipitation in subtropical Australia.

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Penelope Maher and Steven C. Sherwood

Abstract

Expansion of the tropics will likely affect subtropical precipitation, but observed and modeled precipitation trends disagree with each other. Moreover, the dynamic processes at the tropical edge and their interactions with precipitation are not well understood. This study assesses the skill of climate models to reproduce observed Australian precipitation variability at the tropical edge. A multivariate linear independence approach distinguishes between direct (causal) and indirect (circumstantial) precipitation drivers that facilitate clearer attribution of model errors and skill. This approach is applied to observed precipitation and ERA-Interim reanalysis data and a representative subset of four models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and their CMIP3 counterparts. The drivers considered are El Niño–Southern Oscillation, southern annular mode, Indian Ocean dipole, blocking, and four tropical edge metrics (position and intensity of the subtropical ridge and subtropical jet). These models are skillful in representing the covariability of drivers and their influence on precipitation. However, skill scores have not improved in the CMIP5 subset relative to CMIP3 in either respect. The Australian precipitation response to a poleward-located Hadley cell edge remains uncertain, as opposing drying and moistening mechanisms complicate the net response. Higher skill in simulating driver covariability is not consistently mirrored by higher precipitation skill. This provides further evidence that modeled precipitation does not respond correctly to large-scale flow patterns; further improvements in parameterized moist physics are needed before the subtropical precipitation responses can be fully trusted. The multivariate linear independence approach could be applied more widely for practical model evaluation.

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Andrew D. King, Nicholas P. Klingaman, Lisa V. Alexander, Markus G. Donat, Nicolas C. Jourdain, and Penelope Maher

Abstract

Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.

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