Peter R. Briggs

Indian and Pacific Ocean Influences on Southeast Australian Drought and Soil Moisture

AbstractThe relative influences of Indian and Pacific Ocean modes of variability on Australian rainfall and soil moisture are investigated for seasonal, interannual, and decadal time scales. For the period 1900–2006, observations, reanalysis products, and hindcasts of soil moisture during the cool season (June–October) are used to assess the impacts of El Nino–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) on southeastern Australia and the Murray–Darling Basin, two regions that have recently suffered severe droughts. A distinct asymmetry is found in the impacts of the opposite phases of both ENSO and IOD on Australian rainfall and soil moisture. There are significant differences between the dominant drivers of drought at interannual and decadal time scales. On interannual time scales, both ENSO and the IOD modify southeastern Australian soil moisture, with the driest (wettest) conditions over the southeast and more broadly over large parts of Australia occurring during years when an El Nino...

OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models

We describe results of a project known as OptIC (Optimisation InterComparison) for comparison of parameter estimation methods in terrestrial biogeochemical models. A highly simplified test model was used to generate pseudo-data to which noise with different characteristics was added. Participants in the OptIC project were asked to estimate the model parameters used to generate this data, and to predict model variables into the future. Ten participants contributed results using one of the following methods: Levenberg-Marquardt, adjoint, Kalman filter, Markov chain Monte Carlo and genetic algorithm. Methods differed in how they locate the minimum (gradient-descent or global search), how observations are processed (all at once sequentially), or the number of iterations used, or assumptions about the statistics (some methods assume Gaussian probability density functions; others do not). We found the different methods equally successful at estimating the parameters in our application. The biggest variation in parameter estimates arose from the choice of cost function, not the choice of optimization method. Relatively poor results were obtained when the model-data mismatch in the cost function included weights that were instantaneously dependent on noisy observations. This was the case even when the magnitude of residuals varied with the magnitude of observations. Missing data caused estimates to be more scattered, and the uncertainty of predictions increased correspondingly. All methods gave biased results when the noise was temporally correlated or non-Gaussian, or when incorrect model forcing was used. Our results highlight the need for care in choosing the error model in any optimization.

Fire in Australian savannas: from leaf to landscape

Savanna ecosystems comprise 22% of the global terrestrial surface and 25% of Australia (almost 1.9 million km2) and provide significant ecosystem services through carbon and water cycles and the maintenance of biodiversity. The current structure, composition and distribution of Australian savannas have coevolved with fire, yet remain driven by the dynamic constraints of their bioclimatic niche. Fire in Australian savannas influences both the biophysical and biogeochemical processes at multiple scales from leaf to landscape. Here, we present the latest emission estimates from Australian savanna biomass burning and their contribution to global greenhouse gas budgets. We then review our understanding of the impacts of fire on ecosystem function and local surface water and heat balances, which in turn influence regional climate. We show how savanna fires are coupled to the global climate through the carbon cycle and fire regimes. We present new research that climate change is likely to alter the structure and function of savannas through shifts in moisture availability and increases in atmospheric carbon dioxide, in turn altering fire regimes with further feedbacks to climate. We explore opportunities to reduce net greenhouse gas emissions from savanna ecosystems through changes in savanna fire management.

How did ocean warming affect Australian rainfall extremes during the 2010/2011 La Niña event?

Extreme rainfall conditions in Australia during the 2010/2011 La Nina resulted in devastating floods claiming 35 lives, causing billions of dollars in damages, and far-reaching impacts on global climate, including a significant drop in global sea level and record terrestrial carbon uptake. Northeast Australian 2010/2011 rainfall was 84% above average, unusual even for a strong La Nina, and soil moisture conditions were unprecedented since 1950. Here we demonstrate that the warmer background state increased the likelihood of the extreme rainfall response. Using atmospheric general circulation model experiments with 2010/2011 ocean conditions with and without long-term warming, we identify the mechanisms that increase the likelihood of extreme rainfall: additional ocean warming enhanced onshore moisture transport onto Australia and ascent and precipitation over the northeast. Our results highlight the role of long-term ocean warming for modifying rain-producing atmospheric circulation conditions, increasing the likelihood of extreme precipitation for Australia during future La Nina events.

A synoptic climatology of heavy rain events in the Lake Eyre and Lake Frome catchments

The rare occasions when Lake Eyre in central, southern Australia fills with water excite great interest and produce major ecological responses. The filling of other smaller lakes such as Lake Frome, have less impact but can contribute important information about the current and past climates of these arid regions. Here, the dominant synoptic systems responsible for heavy rainfall over the catchments of Lake Eyre and Lake Frome since 1950 are identified and compared. Heavy rain events are defined as those where the mean catchment rainfall for 24 hours reaches a prescribed threshold. There were 25 such daily events at Lake Eyre and 28 in the Lake Frome catchment. The combination of a monsoon trough at mean sea level and a geopotential trough in the mid-troposphere was found to be the synoptic system responsible for the majority of the heavy rain events affecting Lake Eyre and one in five of the events at Lake Frome. Complex fronts where subtropical interactions occurred with Southern Ocean fronts also contributed over 20% of the heavy rainfall events in the Frome catchment. Surface troughs without upper air support were found to be associated with 10% or fewer of events in each catchment, indicating that mean sea level pressure analyses alone do not adequately capture the complexity of the heavy rainfall events. At least 80% of the heavy rain events across both catchments occurred when the Southern Oscillation Index (SOI) was in its positive phase, and for Lake Frome, the SOI exceeded +10 on 60% of occasions, suggesting that the background atmospheric state in the Pacific Ocean was tilted towards La Nina. Hydrological modeling of the catchments suggests that the 12-month running mean of the soil moisture in a sub-surface layer provides a low frequency filter of the precipitation and matches measured lake levels relatively well.

Operational Delivery of Hydro-Meteorological Monitoring and Modeling Over the Australian Continent

The Australian Water Availability Project (AWAP) is a system that operationally delivers weekly estimates of soil moisture stores and water fluxes at continental scale over Australia. The highly modularized system implements a miniature spatial data infrastructure by exploiting a simple data format standard and metadata scheme to enable the flexible ingestion of a variety of input data types, including gridded meteorological fields, land surface parameterizations and, optionally, remote sensing data. The use of these standards, together with a client-server architecture and portable coding, enable the system to function across multiple interchangeable computers, leading to a robust system with a high degree of redundancy. Through a well-defined interface, the framework supports the development and testing of multiple models. Thorough model and data version-control and log file capture also allows automated operational runs in the same environment as that in which models are built and tested. The system includes a web portal (http://www.csiro.au/awap) that provides a variety of ways for data users to dynamically explore and examine output (which currently includes over a century of data for the Australian continent at monthly intervals, in addition to weekly near-real-time products) in summary or extended forms.

Block-Entropy Analysis of Climate Data

Abstract We explore the use of block entropy as a dynamics classifier for meteorological timeseries data. The block entropy estimates define the entropy growth curve H(L) with respect to block length L. For a finitary process, the entropy growth curve tends to an asymptotic linear regime H(L) = E + hμL, with entropy rate hμ and excess entropy E. These quantities apportion the systems information content into ‘memory’ (E) and ‘randomness’ (hμ). We discuss the challenges inherent in analyzing weather data using symbolic techniques, identifying the pitfalls associated with alphabet size, finite sample timeseries length, and stationarity. We apply the block entropy-based techniques in the form of a wet/dry partition to Australian daily precipitation data from the Patched Point Dataset station record collection and version 3 of the Australian Water Availability Project analysis dataset. Preliminary results demonstrate hμ and E are viable climatological classifiers for precipitation, with station records from similar climatic regimes possessing similar values of hμ and E. The resultant clustering reflects expected characteristics of local climatic memory and randomness. The AWAP results show weaker clustering than their PPD counterparts, with different E- and hμ-values reflecting respectively the relative biases and truncation errors in the AWAP analysis system. The entropy rates of convergence analysis rules out finite order Markov processes for orders falling within the range of block sizes considered.