Recasting Economics as if the Climate and Global Ecology Really Mattered

Peer reviewedPublisher PD

A probabilistic analysis of human influence on recent record global mean temperature changes

December 2013 was the 346th consecutive month where global land and ocean average surface temperature exceeded the 20th century monthly average, with February 1985 the last time mean temperature fell below this value. Even given these and other extraordinary statistics, public acceptance of human induced climate change and confidence in the supporting science has declined since 2007. The degree of uncertainty as to whether observed climate changes are due to human activity or are part of natural systems fluctuations remains a major stumbling block to effective adaptation action and risk management. Previous approaches to attribute change include qualitative expert-assessment approaches such as used in IPCC reports and use of 'fingerprinting' methods based on global climate models. Here we develop an alternative approach which provides a rigorous probabilistic statistical assessment of the link between observed climate changes and human activities in a way that can inform formal climate risk assessment. We construct and validate a time series model of anomalous global temperatures to June 2010, using rates of greenhouse gas (GHG) emissions, as well as other causal factors including solar radiation, volcanic forcing and the El Niño Southern Oscillation. When the effect of GHGs is removed, bootstrap simulation of the model reveals that there is less than a one in one hundred thousand chance of observing an unbroken sequence of 304. months (our analysis extends to June 2010) with mean surface temperature exceeding the 20th century average. We also show that one would expect a far greater number of short periods of falling global temperatures (as observed since 1998) if climate change was not occurring. This approach to assessing probabilities of human influence on global temperature could be transferred to other climate variables and extremes allowing enhanced formal risk assessment of climate change. © 2014

In a changing world, climate adaptation researchers play a key role in addressing risk and ethical responsibilities.

The uncertainties related to climate science present some unique challenges for policymakers and researchers alike. Drawing on lessons from the health care domain, where there are established mechanisms and processes in place for managing risk, Justine Lacey, Mark Howden and Chris Cvitanovic look at ways researchers can proactively support decision-makers. Could a similar ethics system to the one used by frontline medical professionals be implemented by climate scientists to enhance decision-making

Integrated assessments of climate variability and change for Australian agriculture - Connecting the islands of knowledge

Key clients for regional or national assessment capabilities are government and industry policymakers, who must deal with constantly changing policy questions. For instance, adaptation to climate change has relatively recently come onto the policy agenda, as has the interaction between adaptation and greenhouse gas mitigation. 'Integrated assessment' has therefore become a common approach that attempts to demonstrate the policy relevance of science. It is intended to inform policies that ultimately lead to better risk management of agro-ecosystems (amongst other objectives). Increasingly policy stakeholders also demand realistic assessments of uncertainties that are associated with the scenarios underpinning such integrated assessments. This requires quantitative, probabilistic evaluation of risks and opportunities associated with specific scenarios that need to supplement the overall, qualitative assessments. Such evaluations can help to cut through the complexity of policy related issues without sacrificing the holistic perspective needed to maintain policy relevance. Using climate change as an example, we explore the role of quantitative models for integrated assessments and argue that a nested modelling approach (eg. climate model - biophysical model - socio-economic model - engagement model) to address all relevant disciplines, stakeholders and scales not only provides the quantitative information needed, but is also a valuable process to negotiate the complexities of the policy domain. This process might help us move more quickly from impact assessments (ie. unadapted responses) to well-structured scenario planning with adaptation, a process that is both policy and response informing

Impacts of Climate Change on Livestock Systems: What We Know and What We Don’t Know

Climate changes and the associated increases in atmospheric carbon dioxide concentration are just two of many possible future drivers of change in grassland systems and whilst there are significant uncertainties around these, they are probably more effectively characterised than many other drivers. The challenge for grasslands systems research is not so much trying to precisely predict future climate in the face of unresolvable uncertainty but rather to work with decision-makers to enhance their decisions for a range of possible climates, build their capacity to make sound risk-based and informed decisions and increase the array of options available for adaptation. There are many adaptations possible to address key climate impacts such as increased heat stress, altered pests and disease risk, vegetation change, increased risk of soil degradation and changes in forage quantity, quality and the variability of these. Many of these adaptations are extensions of existing best management practice. However, it is important to explore adaptations that are beyond incremental change to existing systems to be inclusive of more substantial systems change and even transformational changes. There is a need also to consider adaptations beyond the farm scale including in relation to value chains, institutional change and policy development. It is these areas in particular where there are likely to be increasing demands for research

Adapting the Australian grassland & livestock industry to climate change by systemic adaptation: value of adaptation at cross-regional scale

The annual net primary production of temperate grasslands and livestock industries is predicted to decrease in southern Australia with future climate change. By using biophysical modelling, we addressed productivity and profitability relative to geography, enterprise, and time, while considering various grassland management and animal genetic improvement adaptations individually or as combinations. Grazing systems were modeled at a daily time step for a historical reference period and under future climates projected for the SRES A2 scenario. We predicted that single incremental adaptations will not completely avert declines in productivity and profitability; hence, combinations of adaptations are needed. Upscaling over all southern Australia, GCMs and enterprises, the most profitable systemic combination could increase profit by +188%, +196% and +241% in 2030, 2050, and 2070, compared to no adaptation. Changes in meat production were estimated to be +24%, +25%, and +14% in 2030, 2050, and 2070 compared to average production of recent decades. The potential value of adaptation across southern Australia was estimated as 2.7, 2.5, and 2.9 billion AU$ in 2030, 2050, and 2070, respectively. Financially-motivated changes to grazing systems may affect the environmental outcomes which their tradeoffs with adaptation could inhibit the implementation of adaptations. We estimated that a full adaption of optimal systemic adaptation will result in improvement in soil environment and water use efficiency. However, it will lead to greater ruminant CH4 emissions from 70 kg ha-1 yr-1 in baseline to 84, 83, and 75 kg ha-1 yr-1 in 2030, 2050, and 2070. Greater intensification and ruminant CH4 emissions are likely to occur, as increasing future demand of meat has been projected and we predicted that there is capacity for higher and profitable production to respond to this demand. Future food market projections have shown great demand to meat even under higher price effects

Climate change impact, adaptation, and mitigation in temperate grazing systems: a review

Managed temperate grasslands occupy 25% of the world, which is 70% of global agricultural land. These lands are an important source of food for the global population. This review paper examines the impacts of climate change on managed temperate grasslands and grassland-based livestock and effectiveness of adaptation and mitigation options and their interactions. The paper clarifies that moderately elevated atmospheric CO2 (eCO2) enhances photosynthesis, however it may be restiricted by variations in rainfall and temperature, shifts in plant’s growing seasons, and nutrient availability. Different responses of plant functional types and their photosynthetic pathways to the combined effects of climatic change may result in compositional changes in plant communities, while more research is required to clarify the specific responses. We have also considered how other interacting factors, such as a progressive nitrogen limitation (PNL) of soils under eCO2, may affect interactions of the animal and the environment and the associated production. In addition to observed and modelled declines in grasslands productivity, changes in forage quality are expected. The health and productivity of grassland-based livestock are expected to decline through direct and indirect effects from climate change. Livestock enterprises are also significant cause of increased global greenhouse gas (GHG) emissions (about 14.5%), so climate risk-management is partly to develop and apply effective mitigation measures. Overall, our finding indicates complex impact that will vary by region, with more negative than positive impacts. This means that both wins and losses for grassland managers can be expected in different circumstances, thus the analysis of climate change impact required with potential adaptations and mitigation strategies to be developed at local and regional levels

Water availability and agricultural demand:An assessment framework using global datasets in a data scarce catchment, Rokel-Seli River, Sierra Leone

Study region: The proposed assessment framework is aimed at application in Sub-Saharan Africa, but could also be applied in other hydrologically data scarce regions. The test study site was the Rokel-Seli River catchment, Sierra Leone, West Africa. Study focus: We propose a simple, transferable water assessment framework that allows the use of global climate datasets in the assessment of water availability and crop demand in data scarce catchments. In this study, we apply the assessment framework to the catchment of the Rokel-Seli River in Sierra Leone to investigate the capabilities of global datasets complemented with limited historical data in estimating water resources of a river basin facing rising demands from large scale agricultural water withdrawals. We demonstrate how short term river flow records can be extended using a lumped hydrological model, and then use a crop water demand model to generate irrigation water demands for a large irrigated biofuels scheme abstracting from the river. The results of using several different global datasets to drive the assessment framework are compared and the performance evaluated against observed rain and flow gauge records. New hydrological insights: We find that the hydrological model capably simulates both low and high flows satisfactorily, and that all the input datasets consistently produce similar results for water withdrawal scenarios. The proposed framework is successfully applied to assess the variability of flows available for abstraction against agricultural demand. The assessment framework conclusions are robust despite the different input datasets and calibration scenarios tested, and can be extended to include other global input datasets

Changing perceptions and practices regarding aspirin, nonsteroidal anti-inflammatory drugs, and cyclooxygenase-2 selective nonsteroidal anti-inflammatory drugs among US primary care providers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74775/1/j.1365-2036.2008.03836.x.pd