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

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 on Western Australian mixed farm systems

Primary enterprises are expected to contend with more frequent climate crises, environmental degradation and even climate-related regulatory change (IPCC, 2014). These stressors occur against an existing backdrop of conventional drivers including economic, biophysical, institutional, cultural and political pressures (Marshall et al., 2012). Australia’s primary industries have historically operated in a highly variable climate and this has posed significant challenges to production, requiring sound and responsive risk management practices. Climate change, brings with it a number of new challenges not yet accounted for by Australian primary producers, and so understanding the scale of these impacts is of importance in undertsanding the changing nature of agricultural risk in the near future. Western Australia with about 4 million ha of wheat production is a major contributor to the Australian agrifood sector and economy. Like cereal production, pastures in WA play a major role in agricultural enterprises and contribute over $3 billion annually through animal production, improvements to crop rotations and conserved fodder (The Department of Agriculture and Food, 2014). Farming profitably in the Western Australia in recent years has been a challenge due in part to declines in annual rainfall as well as exposure to both heat and cold teperature extremes (McConnell & O’Hare, 2013), although lower production might be still profitable. Climate drives the productivity, profitability and environmental health of these systems as they often have to respond to low and variable rainfall. Here we identify the likely effect of climate change in 2030 on mixed farm systems of the Western Australia across a climate transect in terms of production, profit, and environmental impacts for projected climate scenarios in 2030 relative to the baseline of 1980-1999. This work will give insight for designing strategies to respond to changes in climate such as optimized shift towards more intensive livestock systems, dual-purpose cropping, etc

Systemic adaptations to climate change in Western Australian mixed farm systems

Australia’s primary industries have historically operated in a highly variable climate. This has posed significant challenges to production, requiring sound and responsive risk management practices. Climate change has, and will, introduce even greater challenges. This means that there is a clear need to continue to assess the opportunities for farmers to improve how they respond to climate variability and changes. We built representative mixed farm systems (using AusFarm) across climate gradients to investigate likely effect of climate change and variability and systemic adaptations to explore system’s resilience, to enhance productivity under climate variability, and change by 2030. We used AusFarm to build mixed farm systems. Model inputs were derived by consulting with producers and models performance was validated against survey data. For a climate gradient of 335-215 mm rainfall (Apr-Oct) in Western Australia, we evaluated long term average effectiveness of changes in planting date, fertilizer application rate, crop and stubble grazing, and stocking rates (SR) for 2002-2012 as baseline. To assess the impact of climate change, we used two high-emissions CMIP3 scenarios (A1FI and A2) with high and medium sensitivity and six global climate models projected climate for 2030. In 2030 and in a relatively medium rainfall region (MR) of the climate gradient, wheat, barley, canola production changed by +6%, +2%, and -2% on average while meat and wool production increased by 1% and 2%. In 2030, and in lower rainfall (LR) end of gradient, wheat, barley, canola, and lupine production changed by -8%, -2%, -11% and -16% while meat and wool production changed by -2% and -4%. In 2030, GHG emissions changed by -10% for LR and -5% for MR under current management. In addition to systemic combination of options described above, we evaluated a range of climate adaptation packages, which were determined in collaboration with stakeholders. These adaptation packages designed specifically for each region to reduce negative impact and risk of climate change and benefit from likely opportunities. Alteration of the crop-livestock balance is an adaptation that can compensate negative impact of climate change by reduction in business risk. These were evaluated through a package with elements of optimizing area proportions of cropping and pasture either by changing the relative areas of existing crop & pasture sequences or the relative length of crop & pasture phases, optimizing stocking rate, and adjustments in livestock joining and sale dates. We designed low-variability to high-intensity mixed farming as adaptation packages optimised for different risk and return management approaches. Overall, financially optimal systemic adaptations were projected to offset negative impact of climate change on production and profitability of whole farm system in 2030 at majority of sites. This would require for practice and land use change to cope with changes in climate

Linking adaptation science to action to build food secure Pacific Island communities

Climate change is a major threat to food security in Pacific Island countries, with declines in food production and increasing variability in food supplies already evident across the region. Such impacts have already led to observed consequences for human health, safety and economic prosperity. Enhancing the adaptive capacity of Pacific Island communities is one way to reduce vulnerability and is underpinned by the extent to which people can access, understand and use new knowledge to inform their decision-making processes. However, effective engagement of Pacific Island communities in climate adaption remains variable and is an ongoing and significant challenge. Here, we use a qualitative research approach to identify the impediments to engaging Pacific Island communities in the adaptations needed to safeguard food security. The main barriers include cultural differences between western science and cultural knowledge, a lack of trust among local communities and external scientists, inappropriate governance structures, and a lack of political and technical support. We identify the importance of adaptation science, local social networks, key actors (i.e., influential and trusted individuals), and relevant forms of knowledge exchange as being critical to overcoming these barriers. We also identify the importance of co-ordination with existing on-ground activities to effectively leverage, as opposed to duplicating, capacity

Simulating sea-surface temperature effects on Southern African rainfall using a mesoscale numerical model

Dissertation submitted to the Faculty of Science, University of the Witwatersrand, for completion of the Degree of' Master of ScienceThe atmospheric response of the Colorado State University Regional Atmospheric Modelling System (RAMS) to sea-surface temperature anomaliesis investigated. A period of four days was chosen from 21 to 24 January 1981, where focus was placed on the development and dissipation of a tropical-temperate trough across Southern Africa. Previous experimenting this mesoscalenumerical model have detemined the kinematic, moisture, and thermodynamic nature of these synoptic features. The research in this dissertation focuses specifically on the sensitivity of the numerical model's simulated responses to positive sea-surface temperature anomalies. Three separate experiments were devised, in which positive anomalous temperatures were added to the ocean surface north of Madagascar (in the tropical Indian Ocean), at the region of the Agulhas Current retroflection, and along the tropical African west coast (in the Northern Benguela and Angola currents). The circulation aspects of each sensitivity test were investigated through the comparison of simulated variables such as vapour and cloud mixing ratios, temperature, streamlines and vertical velocity, with the same variables created by a control simulation. The results indicate that for the first sensitivity test, (the Madagascar anomaly), cyclogenesis was initiated over the area of modified sea temperatures which resulted in a marginal decrease in continental precipitation. The second sensitivity test (over the Agulhas retroflection) produced a much smaller simulated response to the addition of anomalously warm sea temperatures than the tropical Indian Ocean anomaly. Instability and precipitation values increased over the anomalously warm retroflection region, and were slowly transferred along the westerly wave perturbation and the South African east coast. The third sensitivity experiment showed a predominantly localised simulated increase in precipitation over Gabon and the Congo, with the slow southward progression of other simulated circulation differences taking place. The small perturbations in each of the simulated meteorological responses are consistent with the expected climate response to anomalously warm sea-surface temperatures in those areas.AC 201

Understanding frost risk in a variable and changing climate - the southern frost paradox

This Thesis examines whether Australia has experienced spatially coherent changes in frost risk, driven by variations in synoptic scale weather patterns, leading to increases and persistent production losses for winter cereal producers. This research has included examination of the trends in Australian minimum temperatures to determine if trends are localised or more spatially coherent; examination of the links between minimum temperature extremes and synoptic drivers; statistical modelling of the spatial and temporal changes in minimum temperatures to identify changes in dominant synoptic drivers and to determine if there was an anthropogenic signal related to this change; use of the modelling approach to project possible future (2030) changes in minimum temperature extremes; and examination of the impacts of recent changes in minimum temperature extremes on Australian crop production

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

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, according this report. Abstract 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

Improved point scale climate projections using a block bootstrap simulation and quantile matching method

Statistical downscaling methods are commonly used to address the scale mismatch between coarse resolution Global Climate Model output and the regional or local scales required for climate change impact assessments. The effectiveness of a downscaling method can be measured against four broad criteria: consistency with the existing baseline data in terms of means, trends and distributional characteristics; consistency with the broader scale climate data used to generate the projections; the degree of transparency and repeatability; and the plausibility of results produced. Many existing downscaling methods fail to fulfil all of these criteria. In this paper we examine a block bootstrap simulation technique combined with a quantile prediction and matching method for simulating future daily climate data. By utilising this method the distributional properties of the projected data will be influenced by the distribution of the observed data, the trends in predictors derived from the Global Climate Models and the relationship of these predictors to the observed data. Using observed data from several climate stations in Vanuatu and Fiji and out-of-sample validation techniques, we show that the method is successful at projecting various climate characteristics including the variability and auto-correlation of daily temperature and rainfall, the correlations between these variables and between spatial locations. This paper also illustrates how this novel method can produce more effective point scale projections and a more credible alternative to other approaches in the Pacific region. © 2013 Springer-Verlag Berlin Heidelberg

Greenhouse gas implications of replacing fish protein with beef in the lower Mekong Basin

At least 88 new hydropower dams are planned between 2010 and 2030 in the lower Mekong River basin in Southeast Asia as a source of electricity with lower greenhouse gas emissions. Dams result in declines in fish populations that will need to be replaced with other sources of protein for food security. We make the first assessment of emissions should beef production substitute for lost fish in Cambodia and Laos. We assessed two sources of emissions. Replacing lost fish with beef would require as much as 12 million hectares of new pasture. Forest clearing for pastures in Cambodia and Lao PDR would initially emit between 0.859 and 3.015 giga-tonnes of carbon dioxide equivalents (Gt CO2-eq.). Methane emissions from additional cattle would add at least 0.0013 Gt CO2-eq./year to Cambodia�s total greenhouse gas emissions, equivalent to a 20% increase. In Laos at least 0.0005 Gt CO2-eq./year would be released, a 4�12% increase in annual emissions. We demonstrate that activities displaced by hydropower developments could significantly increase emissions. It shows how enclosure of commons at local scales impacts upon other common pool resources at different scales, raising questions for sustainable and equitable transboundary governance