Abstract. We present the first analysis of global and hemispheric surface warming trends that attempts to quantify the major sources of uncertainty. We calculate global and hemispheric annual temperature anomalies by combining land surface air temperature and sea surface temperature (SST) through an optimal averaging technique. The technique allows estimation of uncertainties in the annual anomalies resulting from data gaps and random errors. We add independent uncertainties due to urbanisation, changing land-based observing practices and SST bias corrections. We test the accuracy of the SST bias corrections, which represent the largest source of uncertainty in the data, through a suite of climate model simulations. These indicate that the corrections are likely to be fairly accurate on an annual average and on large space scales. Allowing for serial correlation and annual uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 ñ 0.16øC between 1861 and 2000. Estimating Uncertainties in Temperature DataLand surface-air temperature (LAT) and SST observations are taken from a new global data set (HadCRUTv, Jones et al., 2001) whose variance is homogenised for local temporal variations in data density. To assess the uncertainties in annual global and hemispheric average surface temperature anomalies due to data gaps, random data representivity errors and measurement errors, we employ a two-step optimal averaging (OA) method. The OA method provides a better estimate of the true mean than does a simple average and a consistent flamework on which to add independent uncertainties due to other factors described below.Step 1

Hydrological model sensitivity to climate change can be defined as the response of a particular hydrological model to a known quantum of climate change. This paper estimates the hydrological sensitivity, measured as the percentage change in mean annual runoff, of two lumped parameter rainfall-runoff models, SIMHYD and AWBM and an empirical model, Zhang01, to changes in rainfall and potential evaporation. These changes are estimated for 22 Australian catchments covering a range of climates, from cool temperate to tropical and moist to arid. The results show that the models display different sensitivities to both rainfall and potential evaporation changes. The SIMHYD, AWBM and Zhang01models show mean sensitivities of 2.4%, 2.5% and 2.1% change in mean annual flow for every 1% change in mean annual rainfall, respectively. All rainfall sensitivities have a lower limit of 1.8% and show upper limits of 4.1%, 3.4% and 2.5%, respectively. The results for potential evaporation change are-0.5%,-0.8% and-1.0% for every 1% increase in mean annual potential evaporation, respectively, with changes rainfall being approximately 3 to 5 times more sensitive than changes in potential evaporation for each 1% change in climate. Despite these differences, the results show similar correlations for several catchment characteristics. The most significant relationship is between percent change in annual rainfall and potential evaporation to the catchment runoff coefficient. The sensitivity of both A and B factors decreases with an increasing runoff coefficient, as does the uncertainty in this relationship. The results suggest that a firstorder relationship can be used to give a rough estimate of changes in runoff using estimates of change in rainfall and potential evaporation representing small to modest changes in climate. Further work will develop these methods further, by investigating other regions and changes on the subannual scale.

Droughts have significant environmental and socio-economic impacts in Australia. This emphasizes Australia's vulnerability to climate variability and limitations of adaptive capacity. Two drought indices are compared for their potential utility in resource management. The Rainfall Deciles-based Drought Index is a measure of rainfall deficiency while the Soil-Moisture Deciles-based Drought Index is a measure of soil-moisture deficiency attributed to rainfall and potential evaporation. Both indices were used to assess future drought events over Australia under global warming attributed to low and high greenhouse gas emission scenarios (SRES B1 and A1F1 respectively) for 30-year periods centred on 2030 and 2070. Projected consequential changes in rainfall and potential evaporation were based on results from the CCCma1 and Mk2 climate models, developed by the Canadian Climate Center and the Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) respectively. A general increase in drought frequency associated with global warming was demonstrated by both indices for both climate models, except for the western part of Australia. Increases in the frequency of soil-moisture-based droughts are greater than increases in meteorological drought frequency. By 2030, soil-moisture-based drought frequency increases 20-40% over most of Australia with respect to 1975-2004 and up to 80% over the Indian Ocean and southeast coast catchments by 2070. Such increases in drought frequency would have major implications for natural resource management, water security planning, water demand management strategies, and drought relief payments.

This paper discusses the role and relevance of the shared socioeconomic pathways (SSPs) and the new scenarios that combine SSPs with representative concentration pathways (RCPs) for climate change impacts, adaptation, and vulnerability (IAV) research. It first Climatic Change (2014) 122:481-494 DOI 10.1007 This article is part of the Special Issue on "A Framework for the Development of New Socio-economic Scenarios for Climate Change Research" edited by Nebojsa Nakicenovic, Robert Lempert, and Anthony Janetos. provides an overview of uses of social-environmental scenarios in IAV studies and identifies the main shortcomings of earlier such scenarios. Second, the paper elaborates on two aspects of the SSPs and new scenarios that would improve their usefulness for IAV studies compared to earlier scenario sets: (i) enhancing their applicability while retaining coherence across spatial scales, and (ii) adding indicators of importance for projecting vulnerability. The paper therefore presents an agenda for future research, recommending that SSPs incorporate not only the standard variables of population and gross domestic product, but also indicators such as income distribution, spatial population, human health and governance.

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