Search

We present a thorough analysis of the scientific production in climate change and adaptation in Brazil. We conducted a bibliometric and bibliographic review to reveal and discuss how climate change research has been carried out. We compared a broader climate change research dataset with a more specific climate adaptation research dataset, to understand the main differences and convergence points, and how science conducted from a mitigation and adaptation perspective demonstrates potential to confront the climate change challenges and drivers in Brazil. Four main clusters of investigation within climate change were detected: (1) impacts on forest and plant development, (2) land use and ecology, (3) adaptation/governance, and (4) climate/atmospheric studies. Only about 5% of the total studies on climate change address adaptation, for which three main clusters of research were identified: (1) adaptation actions and policies; (2) urban environment, vulnerability, and health and (3) food and coastal impacts. Although there are strong research trends in climate change for the Amazon Forest, there was less evidence of studies concerning climate adaptation for this and other Brazilian biomes, smaller cities, rural and traditional communities, and poorer regions. Our results shed a light on the more commonly chosen research topics, their strongest points and potential gaps and trends. This can contribute to the scientific communication and implementation of climate actions in Brazil, and a better understanding of the climate science knowledge from the perspective of a middle-income country. [ABSTRACT FROM AUTHOR]

El Niño Modoki events can be further classified into El Niño Modoki I and II in terms of their opposite impacts on southern China rainfall (Wang and Wang, J Clim 26:1322-1338, 2013) and the Indian Ocean dipole mode (Wang and Wang, Clim Dyn 42:991-1005, 2014). The present paper develops an index to identify the types of El Niño events. The El Niño Modoki II (MII) index is defined as the leading principle component of multivariate empirical orthogonal function analysis of the normalized El Niño Modoki index, Niño4 index and 850 hPa relative vorticity anomalies averaged near the Philippine Sea during autumn. The MII index exhibits dominant variations on interannual (2-3 and 4-5 years) and decadal (10-20 years) timescales. El Niño Modoki II events can be well identified by using the MII index value being larger than 1 standard deviation. Further analyses and numerical model experiments confirm that the MII index can portray the major oceanic and atmospheric features of El Niño Modoki II events. The constructed MII index along with previous ENSO indices can be used for classifying and identifying all types of El Niño events. Because of distinct impacts induced by different types of El Niño events, the implication of the present study is that climate prediction and future climate projection under global warming can be improved by using the MII index and other indices to identify the types of El Niño events. [ABSTRACT FROM AUTHOR]

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)-two contrasting SD methods-in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector. [ABSTRACT FROM AUTHOR]

The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980-2006 tropical cyclone frequency and rainfall intensity for the months of August-October are compared against results from previous studies and observation-based datasets. The 1980-2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8-17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius-Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300-500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones. [ABSTRACT FROM AUTHOR]

This paper starts from the observation that development projects that create various forms of environmental injustice in Europe are an integral part of the process of biospheric expulsions, that is of pushing out groups from adequate land, water or air, as described by Saskia Sassen (2014). Apart from the environmental, socio-economic and health-impacts of ecologically destructive projects, there is an added dimension of concern that has been less obvious in the past, but tends to become increasingly pronounced in a warming world. Is it possible that accumulating environmental inequalities and forms of injustice can create new and "unnatural" vulnerabilities to the projected climate change impacts? The fi rst question that we tackle is whether environmental justice confl icts in Europe tend to take place disproportionately in climate hotspot areas, which are geographic spaces with above-average social sensitivity, potential vulnerability, potential social impact, potential environmental impacts or response capacity (ESPON, 2011). The second question concerns the distribution of diff erent characteristics of projects and of their associated confl icts in climate hotspot vs. non-hotspot areas. The fi nal goal is to establish, at a preliminary level, the emergence of a climate edge in Europe, a spatial confi guration in which vulnerability to climate change impacts is shaped by processes of biospheric expulsion, as postulated at a general level by Sassen. For the analysis, the most current data on environmental justice confl icts (444) from the Environmental Justice Atlas and ESPON climate impact projections, mapped on the Climate Adapt platform, are used. The expected result is to provide a preliminary description of the postulated climate edge. [ABSTRACT FROM AUTHOR]

• The majority of climate impact studies focus on changes in yields for a four staple crops. • There has been little research into climate change and farm labor productivity. • Research into climate impacts on knowledge capital is needed. • Climate change impacts on people in the tropics outweigh that on crops. • Ignoring labor impacts understates losses in the world's poorest countries. This paper provides a critical assessment of the literature estimating the consequences of climate impacts in agriculture and the food system. This literature focuses overwhelmingly on the impact of elevated CO2 concentrations in the atmosphere, higher temperatures and changing precipitation on staple crop yields. While critically important for food security, we argue that researchers have gravitated to measuring impacts 'under the streetlight' where data and models are plentiful. We argue that prior work has largely neglected the vast majority of potential economic impacts of climate change on agriculture. A broader view must extend the impacts analysis to inputs beyond land, including the consequences of climate change for labor productivity, as well as for purchased intermediate inputs. Largely overlooked is the impact of climate change on the rate of total factor productivity growth and the potential for more rapid depreciation of the underlying knowledge capital underpinning this key driver of agricultural output growth. This broader view must also focus more attention on non-staple crops, which, while less important from a caloric point of view, are critically important in redressing current micronutrient deficiencies in many diets around the world. The paper closes with numerical simulations that demonstrate the extent to which limited input and output coverage of climate impacts can lead to considerable underestimation of the consequences for food security and economic welfare. Of particular significance is the finding that humans in the humid tropics are likely more vulnerable to heat stress than are many of the well-adapted crops, such as rice. By omitting the impact of heat stress on humans, most studies of climate impacts greatly understate the welfare losses in the world's poorest economies. [ABSTRACT FROM AUTHOR]

British pre-industrial economic growth has traditionally been analysed from the Malthusian point of view and other more optimistic approaches, but in many cases, ignoring environmental factors. This article explores the inclusion of the climate in this general debate, focusing on one of the colder periods of the last 500 years, known as the Maunder Minimum. The provisional results suggest that climate change and the resulting adaptations may have influenced the start of the English Agricultural Revolution, the Energy Transition and the European Divergence. However, from an econometric point of view these results are not fully conclusive, making it necessary to continue working with better primary sources and other alternative methodologies. [ABSTRACT FROM AUTHOR]

Climate variability and human activities are considered to be the most likely reasons for negative trends in river inflow and the water level of some lakes and wetlands in the world. To quantify the uncertain impacts of climate variations and anthropogenic activities on Ajichay River flow in Iran, a multi-model ensemble approach based on the Bayesian model averaging (BMA) method is applied. Several statistical and simulation-based methods are used to distinguish the impacts of climatic and anthropogenic factors on river flow. The results show that almost all the methods identified human activities as the dominant impact on streamflow (about 73–85% of the change). The between-model and within-model uncertainty analyses using BMA showed that the 95% uncertainty intervals of the individual approaches have relatively large deviation ranges. The BMA mean prediction could reduce the range of between-model uncertainties to 14–27% for climate impacts and 74–80% for human impacts. This approach provides a way to better understand the contributions of climatic and anthropogenic impacts on river flow change. [ABSTRACT FROM AUTHOR]

Climate science seeks to make statements of confidence about what has happened, and what will happen (conditional on scenario). The approach is effective for the global, thermodynamic aspects of climate change, but is ineffective when it comes to aspects of climate change related to atmospheric circulation, which are highly uncertain. Yet, atmospheric circulation strongly mediates climate impacts at the regional scale. In this way, the confidence framework, which focuses on avoiding type 1 errors (false alarms), raises the prospect of committing type 2 errors (missed warnings). This has ethical implications. At the regional scale, however, where information on climate change has to be combined with many other factors affecting vulnerability and exposure--most of which are highly uncertain--the societally relevant question is not 'What will happen?' but rather 'What is the impact of particular actions under an uncertain regional climate change?' This reframing of the question can cut the Gordian knot of regional climate change information, provided one distinguishes between epistemic and aleatoric uncertainties--something that is generally not done in climate projections. It is argued that the storyline approach to climate change--the identification of physically self-consistent, plausible pathways--has the potential to accomplish precisely this. [ABSTRACT FROM AUTHOR]