The metaphor of the Medawar zone describes the relationship between the difficulty of a scientific problem and the potential payoff of solving it. This zone represents the realm where questions offer high benefits relative to the effort required to address them. By harnessing the power of mechanistic modelling, scientists can navigate towards this zone, moving beyond known unknowns to discover unknown unknowns. This requires models to be realistic and reliable. Model usefulness, impact, and predictive power can be enhanced by achieving intermediate model complexity, where the trade-off between the realism and tractability of a model is optimised. To achieve these goals, we use the pattern-oriented modelling strategy (POM) to direct research into the Medawar zone by steering model structure towards intermediate complexity. We illustrate this strategy with a detailed conceptual process. Using example models from agri-ecological systems, we demonstrate how intermediate complexity can be attained through POM, and how pattern-oriented models of intermediate complexity that reproduce multiple patterns can uncover both known unknowns and unknown unknowns, which ultimately advances our understanding of complex systems and facilitates groundbreaking discoveries. In addition, we discuss the multidimensionality of the Medawar zone in the context of modelling philosophy and highlight the challenges and imperatives for achieving coherence in the modelling discipline. We emphasize the need for collaboration between end-users and modellers and the adoption of systematic modelling strategies such as POM.

BACKGROUND AND AIMS:Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible.METHODS:We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization.KEY RESULTS:POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns.CONCLUSIONS:Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Future climate change is predicted to lead to reduced winter chill accumulation in many temperate fruit-tree growing regions of the world. Reliable measures of chilling requirement (CR) are necessary for growers in mild winter production locations to determine phenotypic suitability of cultivars for profitable production under current and future climates. This study has produced a collection of CRs for apple (Malus domestica) in chill portions (CP), chill units (CU) and chill hours (CH) for the mild climatic region of Applethorpe, Queensland, Australia. Total seasonal chill accumulation (1 March to 31 August) for 2014 and 2015 in Applethorpe was 75 and 78 CP; 1341 and 1242 CU and; 899 and 882 CH, respectively. All cultivars met their CRs by 10 September (2014) and 2 September (2015). Cultivar CR was estimated using forced bud methods over two seasons. The range of CRs measured for each of the three chill models was 57 +/- 2.9 to 77 +/- 1.5 CP, 976 +/- 40.3 to 1307 +/- 86.6 CU and 662 +/- 44,5 to 908 +/- 23.3 CH. Regardless of chill model choice, the nine cultivars were ranked the same from lowest (`Cripps Red') to the highest requirement (Pup and Hi Early'). Analysis of historical climate data showed that the CRs of 'Cripps Pink' and 'Granny Smith' have been met in 56% and 58% of years respectively. By 2030, this was projected to fall to 2-25% of years for both varieties, and 0% of years by 2050 for 'Cripps Pink', and 0-2% by 2050 for 'Granny Smith'. The results emphasise the need for better understanding of bud progression through the phases of dormancy. In particular, well-defined methods for identifying the transition from endo- to ecodormancy, and improved capacity of chill models to capture the chill accumulation process are necessary to provide valuable CR information to temperate fruit industries for adaptation to climate change.

Background and Aims Functional-structural plant (FSP) models have been widely used to understand the complex interactions between plant architecture and underlying developmental mechanisms. However, to obtain evidence that a model captures these mechanisms correctly, a clear distinction must be made between model outputs used for calibration and thus verification, and outputs used for validation. In pattern-oriented modelling (POM), multiple verification patterns are used as filters for rejecting unrealistic model structures and parameter combinations, while a second, independent set of patterns is used for validation. Methods To test the potential of POM for FSP modelling, a model of avocado (Persea americana 'Hass') was developed. The model of shoot growth is based on a conceptual model, the annual growth module (AGM), and simulates photosynthesis and adaptive carbon allocation at the organ level. The model was first calibrated using a set of observed patterns from a published article. Then, for validation, model predictions were compared with a different set of empirical patterns from various field studies that were not used for calibration. Key Results After calibration, our model simultaneously reproduced multiple observed architectural patterns. The model then successfully predicted, without further calibration, the validation patterns. The model supports the hypothesis that carbon allocation can be modelled as being dependent on current organ biomass and sink strength of each organ type, and also predicted the observed developmental timing of the leaf sink-source transition stage. Conclusions These findings suggest that POM can help to improve the 'structural realism' of FSP models, i.e. the likelihood that a model reproduces observed patterns for the right reasons. Structural realism increases predictive power so that the response of an AGM to changing environmental conditions can be predicted. Accordingly, our FSP model provides a better but still parsimonious understanding of the mechanisms underlying known patterns of AGM growth.

Computational modelling is becoming increasingly significant in improving our understanding of natural systems, and in making predictions to manage them. Many computational models have been used in various areas for these purposes. However, it has been suggested that models should not be either too simple or too complex, if they are to be useful. Thus, it is of importance to construct models with an optimised model structure that sufficiently well represents their real world counterparts. To do that, better modelling strategies are needed. The pattern-oriented modelling (POM) strategy is an approach that has been proposed to address these issues. It has been used widely to develop agent-based models (ABMs), aiming to make the models more comprehensive and rigorous, and increasing their predictive power. Functional-structural plant models (FSPMs) can be identified as ABMs, if organs/growth units of a plant are considered as agents. To test the feasibility and demonstrate the value of using the POM strategy for functional-structural plant modelling, this study focuses on modelling of avocado (Persea americana, cv. Hass), because of its clear modular construction and its economic significance to subtropical and tropical horticulture world-wide. Our study focuses on the systematic development of techniques to apply the POM strategy to functional-structural plant modelling. The overall objective was to determine whether the POM strategy could be used to construct FSPMs in order to increase their predictive power. In the present study, a functional-structural plant model of the annual growth module of avocado was constructed using the POM strategy. The model was able to reproduce multiple observed patterns of architecture and shoot growth simultaneously, and to make independent predictions providing insights into branching architecture, which were consistent with independently generated findings of other studies. Comparison of model outcomes to multiple observed patterns of modular construction at different scales, e.g. metamer level, growth unit level and branch level, increases our confidence that the model performed well. Those independent predictions can be strong indicators that the model is structurally realistic.

A self-organising model of macadamia, expressed using L-Systems, was used to explore aspects of canopy management. A small set of parameters control the basic architecture of the model, with a high degree of self-organisation occurring to determine the fate and growth of buds. Light was sensed at the leaf level and used to represent vigour and accumulated basipetally. Buds also sensed light so as to provide demand in the subsequent redistribution of the vigour. Empirical relationships were derived from a set of 24 completely digitised trees after conversion to multiscale tree graphs (MTG) and analysis with the OpenAlea software library. The ability to write MTG files was embedded within the model so that various tree statistics could be exported for each run of the model. To explore the parameter space a series of runs was completed using a high-throughput computing platform. When combined with MTG generation and analysis with OpenAlea it provided a convenient way in which thousands of simulations could be explored. We allowed the model trees to develop using self-organisation and simulated cultural practices such as hedging, topping, removal of the leader and limb removal within a small representation of an orchard. The model provides insight into the impact of these practices on potential for growth and the light distribution within the canopy and to the orchard floor by coupling the model with a path-tracing program to simulate the light environment. The lessons learnt from this will be applied to other evergreen, tropical fruit and nut trees.

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework.

Timeseries of observed and projected sea level changes for the 20th and 21st century are analyzed at various coastal locations around the world that are vulnerable to climate change. Observed time series are from tide gauges and altimetry, as well as from reconstructions over the last 50 years. CMIP5 coupled atmosphere-ocean model output of regional sea-level and associated uncertainty estimates are merged with scenario-independent contributions from GIA and dynamic ice to provide time series of coastal sea-level projections to the end of the 21st century. We focus on better quantifying the regional departure of coastal sea level rise from its global average, identify the reasons for the regional departure, and quantify the reasons for the uncertainty in these regional projections. Many of these coastal sea level projections are lower than the global mean change in sea level due to glacial isostatic adjustment, and gravitational changes from loss of land ice and terrestrially stored ground water. In most coastal regions, local deviations from the global mean vary up to ±20 cm which, depending on the location, differ substantially in their underlying causes.

This study identifies and corrects instrumental drift for satellite altimeter missions, which affects estimates of the rates of sea-level rise. Corrected data show an acceleration in the rate of rise, counter to previous estimates and in line with projections.

There has been significant progress in describing and understanding global-mean sea-level rise, but the regional departures from this global-mean rise are more poorly described and understood. Here, we present a comprehensive analysis of Australian sea-level data from the 1880s to the present, including an assessment of satellite-altimeter data since 1993. Sea levels around the Australian coast are well sampled from 1966 to the present. The first Empirical Orthogonal Function (EOF) of data from 16 sites around the coast explains 69% of the variance, and is closely related to the El Niño Southern Oscillation (ENSO), with the strongest influence on the northern and western coasts. Removing the variability in this EOF correlated with the Southern Oscillation Index reduces the differences in the trends between locations. After the influence of ENSO is removed and allowing for the impact of Glacial Isostatic Adjustment (GIA) and atmospheric pressure effects, Australian mean sea-level trends are close to global-mean trends from 1966 to 2010, including an increase in the rate of rise in the early 1990s. Since 1993, there is good agreement between trends calculated from tide-gauge records and altimetry data, with some notable exceptions, some of which are related to localised vertical-land motions. For the periods 1966 to 2009 and 1993 to 2009, the average trends of relative sea level around the coastline are 1.4±0.3mmyr−1 and 4.5±1.3mmyr−1, which become 1.6±0.2mmyr−1 and 2.7±0.6mmyr−1 after removal of the signal correlated with ENSO. After further correcting for GIA and changes in atmospheric pressure, the corresponding trends are 2.1±0.2mmyr−1 and 3.1±0.6mmyr−1, comparable with the global-average rise over the same periods of 2.0±0.3mmyr−1 (from tide gauges) and 3.4±0.4mmyr−1 (from satellite altimeters). Given that past changes in Australian sea level are similar to global-mean changes over the last 45years, it is likely that future changes over the 21st century will be consistent with global changes. A generalised additive model of Australia's two longest records (Fremantle and Sydney) reveals the presence of both linear and non-linear long-term sea-level trends, with both records showing larger rates of rise between 1920 and 1950, relatively stable mean sea levels between 1960 and 1990 and an increased rate of rise from the early 1990s.

Climate change and on-going water policy reforms will likely contribute to on-farm and regional structural adjustment in Australia. This paper gathers empirical evidence of farm-level structural adjustments and integrates these with a regional equilibrium model to investigate sectoral and regional impacts of climate change and recent water use policy on rice industry. We find strong evidence of adjustments to the farming system, enabled by existing diversity in on-farm production. A further loss of water with additional pressures to adopt less intensive and larger-scale farming, will however reduce the net number of farm businesses, which may affect regional rice production. The results from a regional CGE model show impacts on the regional economy over and above the direct cost of the environmental water, although a net reduction in real economic output and real income is partially offset by gains in rest of the Australia through the reallocation or resources. There is some interest within the industry and from potential new corporate entrants in the relocation of some rice production to the north. However, strong government support would be crucial to implement such relocation. (C) 2013 Elsevier Ltd. All rights reserved.

Sea-level changes on a wide range of time and space scales. This article considers changes in mean sea level, that is, sea level averaged over a sufficient period of time to remove fluctuations associated with surface waves, tides, and individual storm surge events. Changes in sea level changes over the last hundred years or so and on how it might change over the next one hundred years are or particular interest. However, to understand these changes requires consideration of sea level since the last glacial maximum 20,000 years ago. The longer-term implications of changes in the Earth's climate arising from changes in atmospheric greenhouse gas concentrations are also considered.

Allowances have been developed for future rise of relative sea-level (i.e. sea level relative to the land) based on the projections of regional sea-level rise, its uncertainty, and the statistics of tides and storm surges (storm tides). An ‘allowance’ is, in this case, the vertical distance that an asset needs to be raised under a rising sea level, so that the present likelihood of flooding does not increase. This continues the work of Hunter (2012), which presented allowances based on global-average sea level and local storm tides. The inclusion of regional variations of sea-level rise (and its uncertainty) significantly increases the global spread of allowances. For the period 1990–2100 and the A1FI emission scenario (which the world is broadly following at present), these range from negative allowances caused by land uplift (in the northern regions of North America and Europe) to the upper 5-percentile which is greater than about 1m (e.g. on the eastern coastline of North America).

We quantify the rate of sea level rise around the Australian continent from an analysis of tide gauge and Global Positioning System (GPS) data sets. To estimate the underlying linear rates of sea level change in the presence of significant interannual and decadal variability (treated here as noise), we adopt and extend a novel network adjustment approach. We simultaneously estimate time-correlated noise as well as linear model parameters and realistic uncertainties from sea level time series at individual gauges, as well as from time-series differences computed between pairs of gauges. The noise content at individual gauges is consistent with a combination of white and time-correlated noise. We find that the noise in time series from the western coast of Australia is best described by a first-order Gauss-Markov model, whereas east coast stations generally exhibit lower levels of time-correlated noise that is better described by a power-law process. These findings suggest several decades of monthly tide gauge data are needed to reduce rate uncertainties to < 0.5 mm yr(-1) for undifferenced single site time series with typical noise characteristics. Our subsequent adjustment strategy exploits the more precise differential rates estimated from differenced time series from pairs of tide gauges to estimate rates among the network of 43 tide gauges that passed a stability analysis. We estimate relative sea level rates over three temporal windows (1900-2011, 1966-2011 and 1993-2011), accounting for covariance between time series. The resultant adjustment reduces the rate uncertainty across individual gauges, and partially mitigates the need for century-scale time series at all sites in the network. Our adjustment reveals a spatially coherent pattern of sea level rise around the coastline, with the highest rates in northern Australia. Over the time periods beginning in 1900, 1966 and 1993, we find weighted average rates of sea level rise of 1.4 +/- 0.6, 1.7 +/- 0.6 and 4.6 +/- 0.8 mm yr(-1), respectively. While the temporal pattern of the rate estimates is consistent with acceleration in sea level rise, it may not be significant, as the uncertainties for the shorter analysis periods may not capture the full range of temporal variation. Analysis of the available continuous GPS records that have been collected within 80 km of Australian tide gauges suggests that rates of vertical crustal motion are generally low, with the majority of sites showing motion statistically insignificant from zero. A notable exception is the significant component of vertical land motion that contributes to the rapid rate of relative sea level change (> 4 mm yr(-1)) at the Hillarys site in the Perth area. This corresponds to crustal subsidence that we estimate in our GPS analysis at a rate of -3.1 +/- 0.7 mm yr(-1), and appears linked to groundwater withdrawal. Uncertainties on the rates of vertical displacement at GPS sites collected over a decade are similar to what we measure in several decades of tide gauge data. Our results motivate continued observations of relative sea level using tide gauges, maintained with high-accuracy terrestrial and continuous co-located satellite-based surveying.

The ocean has the largest heat capacity in the climate system and as a result the ocean plays a critical role in the climate. Changes in ocean heat content dominate the Earth’s energy storage; and the ocean’s thermal expansion has been a major contributor to sea-level rise in the twentieth century and likely to be the largest contributor in the twenty-first century. The agreement between changes in ocean heat storage over recent decades and changes in the Earth’s radiative balance, within uncertainties, provides strong support for current understanding of anthropogenic climate change. As a result of improvements in observations and modeling of sea level and components contributing to sea-level change, there is now an improved explanation for twentieth century sea-level rise. Models project a continuing sea-level rise during the twenty-first century and beyond. However, a number of uncertainties remain in our understanding of the global mean and regional distribution of sea-level rise resulting from changes in ocean circulation and changes in the Earth’s gravitational field. Ocean-ice-sheet interactions are important for quantitatively estimating future ice-sheet contributions to sea-level rise.

Development of new agricultural industries in northern Australia is seen as a way to provide food security in the face of reduced water availability in existing regions in the south. It is envisaged that this relocation of the rice industry to north could offset decreases in the irrigated area and output of the Murray Darling Basin (MDB) as a result of decreased inflows and buybacks of environmental water under the Murray Darling Basin Plan. This paper provides information to support decisions on the incremental and transformational changes by considering the net effects of shifting agricultural production from southern rice areas (Riverina) to sugar dominated areas in northern Queensland (Burdekin) using a dynamic regional Computable General Equilibrium (CGE) model. Three rice relocation scenarios under two time periods, 2030 and 2070, are considered. The results demonstrate that in all cases there is a net reduction in real economic output and real income, although a rice-sugarcane rotation in the Burdekin partly offsets some of the negative impact. We conclude that there is unlikely to be a rapid and spontaneous increase in rice production in the north, because of a lack of infrastructure, wariness in relation to the agronomic issues and the opportunity cost of turning away from sugar. However, even if such relocationsstart to occur, strong government support would be crucial to sustaining a northern rice industry.