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As climate change continues to alter local weather patterns, it is important to understand how people are experiencing such changes because personal experience may affect mitigation and adaptation policy preferences and behaviors. Local weather conditions are also an easily accessible source of information that, aggregated over time, may enable people to detect long-term climate trends and update their beliefs about global warming. However, motivated reasoning—the tendency to fit information to conclusions that correspond with a preexisting belief—may limit the accuracy of local weather perceptions. This paper focuses on perceptions of seasonal weather in Norway and examines evidence for motivated reasoning consistent with pre-existing beliefs about climate change, using a national panel survey combined with high-resolution seasonal climate observations. Respondents’ perceptions are sensitive to observed differences in both temperature and precipitation, but respondents are more likely to accurately perceive local precipitation than local temperature. Controlling for observed conditions, beliefs about global climate change had a large effect on perceptions of seasonal temperature, and smaller effects on perceptions of seasonal precipitation. These findings provide evidence that individual perceptions of seasonal weather are related to local conditions, but they are also likely to be motivated by beliefs about global climate change. [ABSTRACT FROM AUTHOR]

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44 resolution and five Statistical Downscaling Methods (SDMs) -analog resampling, weather typing and generalized linear models- trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices -mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days- taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison. [ABSTRACT FROM AUTHOR]

This paper examines whether experience of extreme weather events-such as excessive heat, droughts, flooding, and hurricanes-increases an individual's level concern about climate change. We bring together micro-level geospatial data on extreme weather events from NOAA's Storm Events Database with public opinion data from multiple years of the Cooperative Congressional Election Study to study this question. We find evidence of a modest, but discernible positive relationship between experiencing extreme weather activity and expressions of concern about climate change. However, the effect only materializes for recent extreme weather activity; activity that occurred over longer periods of time does not affect public opinion. These results are generally robust to various measurement strategies and model specifications. Our findings contribute to the public opinion literature on the importance of local environmental conditions on attitude formation. [ABSTRACT FROM AUTHOR]

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the sensitivity of fuel moisture to changes in temperature and precipitation and explore the implications under a future climate. We use the Canadian Forest Fire Weather Index System components to represent the moisture content of fine surface fuels (Fine Fuel Moisture Code, FFMC), upper forest floor (duff) layers (Duff Moisture Code, DMC) and deep organic soils (Drought Code, DC). We obtained weather data from 12 stations across Canada for the fire season during the 1971-2000 period and with these data we created a set of modified weather streams from the original data by varying the daily temperatures by 0 to +5 °C in increments of 1 °C and the daily precipitation from −40 to 40 % in increments of 10 %. The fuel moistures were calculated for all the temperature and precipitation combinations. When temperature increases we find that for every degree of warming, precipitation has to increase by more than 15 % for FFMC, about 10 % for DMC and about 5 % for DC to compensate for the drying caused by warmer temperatures. Also, we find in terms of the number of days equal to or above an FFMC of 91, a critical value for fire spread, that no increase in precipitation amount alone could compensate for a temperature increase of 1 °C. Results from three General Circulation Models (GCMs) and three emission scenarios suggest that this sensitivity to temperature increases will result in a future with drier fuels and a higher frequency of extreme fire weather days. [ABSTRACT FROM AUTHOR]

This paper is among the first to report on the full integration of basin-scale models that include projections of the demand and supply of water and energy for residential, commercial, industrial, and agricultural sector users. We link two widely used regional planning models that allow one to study the impact of rising climate variability on water and electricity use in Sacramento, California. Historic data combined with the current energy and water system configuration was used to assess the implications of changes in temperature and precipitation. Climate simulations suggest that electricity imports to the region would increase during hot dry spells, when regional power production is most constrained. In particular, regional imports of electricity would increase over 35 % in hot dry years, assuming a 4 °C increase in average temperature and a 25 % decrease in average precipitation. [ABSTRACT FROM AUTHOR]

The African Development Bank has called for $40 Billion USD per year over the coming decades to be provided to African countries to address development issues directly related to climate change. The current study addresses a key component of these issues, the effect of climate change on the road infrastructure of Malawi, Mozambique, and Zambia. The study incorporates a stressor-response approach to estimate the effects of projected precipitation, temperature, and flooding changes on the paved and unpaved road infrastructure of these countries. The paper highlights the result of running 425 climate scenarios for each road type and policy option from 2010 to 2050. Based on this broad analysis, it is estimated that the three southern African countries are facing a potential $596 million price tag based on median climate scenarios to maintain and repair roads as a result of damages directly related to temperature and precipitation changes from potential climate change through 2050. The challenge for policy makers is to determine the potential risk that a country is facing based on the uncertainties associated with the multiple aspects of climate change modeling. [ABSTRACT FROM AUTHOR]

Worldwide climate is likely to become more variable or extreme with increases in intense precipitation. In Mediterranean areas, climate change will increase the risks of droughts, flash floods and soil erosion. Despite rainfall intensity being a key factor in erosive processes, in these areas information on extreme rainfall intensity and the associated erosivity, based on high-temporal resolution data, is either non homogeneous or scarce. These parameters thus need to be assessed in order to highlight suitable adaptation strategies. In this paper, an hourly rainfall intensity (RI) data series is analyzed together with the corresponding 1-min rainfall intensity maximum (RIm) of 23 rainfall gauges located in Tuscany, Italy, in an area highly vulnerable to erosion. The aim is to look for temporal trends (1989-2010) in extreme rainfall intensity and erosivity. Fixed effect logistic regression shows statistically significant temporal increases in the number of RI and RIm exceedances over the 95th percentile threshold. Winter is shown to be the season with the strongest increasing trend in coastal and inland rainfall gauge groups, followed by spring for the coastal group and autumn for the inland group. Linear regressions show that in the inland group there is a temporal increase in rainfall erosivity and on a seasonal basis, the highest increase is observed in autumn. By contrast, for the coastal group this increasing trend is only detectable for spring and winter. Such an increase in rainfall erosivity and its potential continuation could have a strong adverse effect on Mediterranean land conservation. [ABSTRACT FROM AUTHOR]

This paper analyzes the meteorological drought events in Paraguay in the period of 1964 to 2011, using the Standardized Precipitation Index (SPI). The objective is to determine if the frequency and/or severity of droughts has increased or decreased in the last years, in response of climate change. They The southernmost parts of the country are affected by severe droughts producing damage to soybean and corn crop during the rainy season (October-March) especially during the summer of 2008-2009. The years of 1967, 1968, 1978, 1979, 2000 and 2008 was identified as severe to extreme drought events and coincides with La Niña event. However, the relationship between all drought events, especially those agricultural droughts and La Niña is not clear, suggesting the necessity of new research, focusing on new drivers to explain the cause of the droughts. The economy of Paraguay, based for good part on agriculture, is clearly vulnerable to droughts. Even though no undeniable increasing trend in drought frequency/severity was detected, contingency plans to diminish drought impacts ought to be elaborated. [ABSTRACT FROM AUTHOR]