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This paper presents a methodological framework to evaluate the resilience, with the primary focus on performance degradation, of levee-protected electric power networks to flooding in a changing climate. To this end, a multi-disciplinary framework is established by integrating climate science, hydrology, and electric power network analysis. The framework quantifies the effect of climate change on flood hazard levels in a levee-protected area and the subsequent changes in the resilience of the electric power network. In the first step, the changes in the exposure of levee-protected regions to flooding hazard in a warming climate are calculated. Then, the probabilities of failure of power network components due to flooding under current and projected future climate are determined. Finally, the power system resilience index is used to assess the system resiliency for pre-flooding (baseline), historic flooding, and two projected future flooding scenarios. For demonstration, the proposed framework is applied to a levee-protected area in Northern California. The IEEE 118-bus standard test system is employed to represent the power network in the study area. Results reveal that climate change can considerably decrease the system resilience of the levee-protected electric power network. The findings of this study can contribute towards more resilient power network systems under a changing climate. [ABSTRACT FROM AUTHOR]

In this paper was calculated the return period of coastal floods for each of the coastal settlements that report this extreme event in the Cuban archipelago, based on the period 1901-2020. The chronology and classification of coastal floods for each province was used, as well as the Poisson distribution and the χ² goodness of fit test. It is concluded that the Poisson distribution was suitable in all cases for the significance level of 5%; while the greatest hazard is presented by Havana, Baracoa and the settlements of the Gulf of Batabanó in which the occurrence of a flood event is expected at least once every three to five years. [ABSTRACT FROM AUTHOR]

Background and objectives: Heat related mortality is of great concern for public health, and estimates of future mortality under a warming climate are important for planning of resources and possible adaptation measures. Papers providing projections of future heat-related mortality were critically reviewed with a focus on the use of climate model data. Some best practice guidelines are proposed for future research. Methods: The electronic databases Web of Science and PubMed/Medline were searched for papers containing a quantitative estimate of future heat-related mortality. The search was limited to papers published in English in peer-reviewed journals up to the end of March 2017. Reference lists of relevant papers and the citing literature were also examined. The wide range of locations studied and climate data used prevented a meta-analysis. Results: A total of 608 articles were identified after removal of duplicate entries, of which 63 were found to contain a quantitative estimate of future mortality from hot days or heat waves. A wide range of mortality models and climate model data have been used to estimate future mortality. Temperatures in the climate simulations used in these studies were projected to increase. Consequently, all the papers indicated that mortality from high temperatures would increase under a warming climate. The spread in projections of future climate by models adds substantial uncertainty to estimates of future heat-related mortality. However, many studies either did not consider this source of uncertainty, or only used results from a small number of climate models. Other studies showed that uncertainty from changes in populations and demographics, and the methods for adaptation to warmer temperatures were at least as important as climate model uncertainty. Some inconsistencies in the use of climate data (for example, using global mean temperature changes instead of changes for specific locations) and interpretation of the effects on mortality were apparent. Some factors which have not been considered when estimating future mortality are summarised. Conclusions: Most studies have used climate data generated using scenarios with medium and high emissions of greenhouse gases. More estimates of future mortality using climate information from the mitigation scenario RCP2.6 are needed, as this scenario is the only one under which the Paris Agreement to limit global warming to 2°C or less could be realised. Many of the methods used to combine modelled data with local climate observations are simplistic. Quantile-based methods might offer an improved approach, especially for temperatures at the ends of the distributions. The modelling of adaptation to warmer temperatures in mortality models is generally arbitrary and simplistic, and more research is needed to better quantify adaptation. Only a small number of studies included possible changes in population and demographics in their estimates of future mortality, meaning many estimates of mortality could be biased low. Uncertainty originating from establishing a mortality baseline, climate projections, adaptation and population changes is important and should be considered when estimating future mortality. [ABSTRACT FROM AUTHOR]

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally. [ABSTRACT FROM AUTHOR]

This paper examines search and rescue and backcountry medical response constraints in the Canadian Arctic and potential for unmanned aerial vehicles (UAV) to aid in response and preparedness. Semi-structured interviews (n = 18) were conducted with search and rescue responders, Elders, and emergency management officials to collect data on current emergency response and potential for UAV use. UAV test flights (n = 17) were undertaken with community members. We analyzed five years of weather data to examine UAV flight suitability. Numerous challenges face Arctic search and rescue and backcountry emergency response. Changing social and environmental conditions were described as increasing vulnerability to backcountry emergencies. Responders desired additional first aid and emergency training. Legal and weather restrictions were found to limit where, when and who could fly UAVs. UAVs were demonstrated to have potential benefits for hazard monitoring but not for SAR or medical response due to legal restrictions, weather margins, and local capacity. We find that communities are ill-prepared for ongoing SAR demands, let alone a larger disaster. There are numerous limitations to the use of consumer UAVs by Arctic communities. Prevention of backcountry medical emergencies, building resilience to disasters, and first responder training should be prioritized over introducing UAVs to the response system. [ABSTRACT FROM AUTHOR]

This paper offers new high-resolution oxygen and carbon isotope data from Stalagmite S1 from Mavri Trypa Cave, SW Peloponnese. Our data provide the climate background to the destruction of the nearby Mycenaean Palace of Nestor at Pylos at the transition from Late Helladic (LH) IIIB to LH IIIC, ~3150–3130 years before present (before AD 1950, hereafter yrs BP) and the subsequent period. S1 is dated by 24 U-Th dates with an averaged precision of ±26 yrs (2σ), providing one of the most robust paleoclimate records from the eastern Mediterranean for the end of the Late Bronze Age (LBA). The δ18O record shows generally wetter conditions at the time when the Palace of Nestor at Pylos was destroyed, but a brief period of drier conditions around 3200 yrs BP may have disrupted the Mycenaean agricultural system that at the time was likely operating close to its limit. Gradually developing aridity after 3150 yrs BP, i.e. subsequent to the destruction, probably reduced crop yields and helped to erode the basis for the reinstitution of a central authority and the Palace itself. [ABSTRACT FROM AUTHOR]

Over the 20th century, urbanization has substantially shaped the surface of Earth. With population rapidly shifting from rural locations towards the cities, urban areas have dramatically expanded on a global scale and represent crystallization points of social, cultural and economic assets and activities. This trend is estimated to persist for the next decades, and particularly the developing countries are expected to face rapid urban growth. The management of this growth will require good governance strategies and planning. By threatening the livelihoods, assets and health as foundations of human activities, another major global change contributor, climate change, became an equally important concern of stakeholders. Based on the climate trends observed over the 20th century, and a spatially explicit model of urbanization, this paper investigates the impacts of climate change in relation to different stages of development of urban areas, thus evolving a more integrated perspective on both processes. As a result, an integrative measure of climate change trends and impacts is proposed and estimated for urban areas worldwide. We show that those areas facing major urban growth are to a large extent also hotspots of climate change. Since most of these hotspots are located in the Global South, we emphasize the need for stakeholders to co-manage both drivers of global change. The presented integrative perspective is seen as a starting point to foster such co-management, and furthermore as a means to facilitate communication and knowledge exchange on climate change impacts. [ABSTRACT FROM AUTHOR]

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months. [ABSTRACT FROM AUTHOR]

Climate change and land use change are two major elements of human-induced global environmental change. In temperate grasslands and woodlands, increasing frequency of extreme weather events like droughts and increasing severity of wildfires has altered the structure and dynamics of vegetation. In this paper, we studied the impact of wildfires and the year-to-year differences in precipitation on species composition changes in semi-arid grasslands of a forest-steppe complex ecosystem which has been partially disturbed by wildfires. Particularly, we investigated both how long-term compositional dissimilarity changes and species richness are affected by year-to-year precipitation differences on burnt and unburnt areas. Study sites were located in central Hungary, in protected areas characterized by partially-burnt, juniper-poplar forest-steppe complexes of high biodiversity. Data were used from two long-term monitoring sites in the Kiskunság National Park, both characterized by the same habitat complex. We investigated the variation in species composition as a function of time using distance decay methodology. In each sampling area, compositional dissimilarity increased with the time elapsed between the sampling events, and species richness differences increased with increasing precipitation differences between consecutive years. We found that both the long-term compositional dissimilarity, and the year-to-year changes in species richness were higher in the burnt areas than in the unburnt ones. The long-term compositional dissimilarities were mostly caused by perennial species, while the year-to-year changes of species richness were driven by annual and biennial species. As the effect of the year-to-year variation in precipitation was more pronounced in the burnt areas, we conclude that canopy removal by wildfires and extreme inter-annual variability of precipitation, two components of global environmental change, act in a synergistic way. They enhance the effect of one another, resulting in greater long-term and year-to-year changes in the composition of grasslands. [ABSTRACT FROM AUTHOR]

Presently, climate change has increased the frequency of extreme meteorological events such as tropical cyclones. In the western Pacific basin, these cyclones are called typhoons, and in this area, around Taiwan Island, their frequency has almost doubled since 2000. When approaching landmasses, typhoons have devastating effects on coastal vegetation. The increased frequency of these events has challenged the survival of coastal plant species and their posttyphoon recovery. In this study, a population of coastal gynodioecious Ficus pedunculosa var. mearnsii (Mearns fig) was surveyed for two years to investigate its recovery after Typhoon Morakot, which occurred in August 2009. Similar to all the Ficus species, the Mearns fig has an obligate mutualistic association with pollinating fig wasp species, which requires syconia (the closed Ficus inflorescence) to complete its life cycle. Moreover, male gynodioecious fig species produces both pollen and pollen vectors, whereas the female counterpart produces only seeds. The recovery of the Mearns fig was observed to be rapid, with the production of both leaves and syconia. The syconium:leaf ratio was greater for male trees than for female trees, indicating the importance of syconium production for the wasp survival. Pollinating wasps live for approximately 1 day; therefore, receptive syconia are crucial. Every typhoon season, few typhoons pass by the coasts where the Mearns fig grows, destroying all the leaves and syconia. In this paper, we highlight the potential diminution of the fig population that can lead to the extinction of the mutualistic pair of species. The effects of climate change on coastal species warrant wider surveys. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is predicted to have profound effects on species distributions over the coming decades. In this paper, we used maximum entropy modelling (Maxent) to estimate the effects of projected changes in climate on extent of climatically-suitable habitat for two Nepenthes pitcher plant species in Borneo. The model results predicted an increase in area of climatically-suitable habitat for the lowland species Nepenthes rafflesiana by 2100; in contrast, the highland species Nepenthes tentaculata was predicted to undergo significant loss of climatically-suitable habitat over the same period. Based on the results of the models, we recommend that research be undertaken into practical mitigation strategies, as approximately two-thirds of Nepenthes are restricted to montane habitats. Highland species with narrow elevational ranges will be at particularly high risk, and investigation into possible mitigation strategies should be focused on them. [ABSTRACT FROM AUTHOR]

The important topic of global climate change builds on a huge collection of scientific research. It is common for agencies releasing climate change information to be served with requests for all supporting materials resulting in a particular conclusion. Capturing and presenting global change provenance, linking to the research papers, data sets, models, analyses, observations, satellites, etc., that support the key research findings in this domain can increase understanding and aid in reproducibility of results and conclusions. The U.S. Global Change Research Program is now coordinating the production of a national climate assessment (NCA) that presents our best understanding of global change. We are now developing a global change information system that will present the content of that report and its provenance, including the scientific support for the findings of the assessment. We are using an approach that will present this information both through a human accessible Web site as well as a machine-readable interface for automated mining of the provenance graph. We plan to use the developing World Wide Web Consortium (W3C) PROV data model and ontology for this system. This paper will describe an overview of the process of developing the NCA and how the provenance trail of the report and each of the technical inputs can be captured and represented using the W3C PROV ontology. This will improve the visibility into the assessment process, increase understanding and possibility of reproducibility, and ultimately increase the credibility and trust of the resulting report. [ABSTRACT FROM AUTHOR]

With projected climate change, it is increasingly important to understand the current role of weather in everyday life and how it may change in the future. This paper evaluates two qualitative research approaches for their ability to generate insight into weather-related experiences and everyday practices. As these mundane day-to-day weather interactions often escape memory over time, the paper calls into question the ability of conventional methodological approaches that rely on recall to accurately excavate these events. The research examined serial one-on-one, semi-structured interviews with an adapted short-term focus, and daily diary-photographs. Each approach was applied to one of two Australian cities with sharply contrasting climates - Melbourne and Darwin. Both approaches were able to generate insight into weather-related experiences and practices. However, both presented problems. In interviews, these problems related to retrospective accounts beyond 'yesterday', where physical experience recollections became limited. For diary-photographs, inspiring participants to continually complete and submit contributions proved problematic. I argue that interviews are better for documenting routine practice and generalised experiences, while diary-photographs are better at showing fine-grained detail of physical experiences. Where both aspects are sought, as in this study, a combination is possible. However, using dual methods increases the risk of participant fatigue. [ABSTRACT FROM AUTHOR]

Climate models project an increase in mean annual air temperatures and a reduction in the depth and duration of winter snowpack for many mid and high latitude and high elevation seasonally snow-covered ecosystems over the next century. The combined effects of these changes in climate will lead to warmer soils in the growing season and increased frequency of soil freeze-thaw cycles (FTCs) in winter due to the loss of a continuous, insulating snowpack. Previous experiments have warmed soils or removed snow via shoveling or with shelters to mimic projected declines in the winter snowpack. To our knowledge, no experiment has examined the interactive effects of declining snowpack and increased frequency of soil FTCs, combined with soil warming in the snow-free season on terrestrial ecosystems. In addition, none have mimicked directly the projected increase in soil FTC frequency in tall statured forests that is expected as a result of a loss of insulating snow in winter. We established the Climate Change Across Seasons Experiment (CCASE) at Hubbard Brook Experimental Forest in the White Mountains of New Hampshire in 2012 to assess the combined effects of these changes in climate on a variety of pedoclimate conditions, biogeochemical processes, and ecology of northern hardwood forests. This paper demonstrates the feasibility of creating soil FTC events in a tall statured ecosystem in winter to simulate the projected increase in soil FTC frequency over the next century and combines this projected change in winter climate with ecosystem warming throughout the snow-free season. Together, this experiment provides a new and more comprehensive approach for climate change experiments that can be adopted in other seasonally snow-covered ecosystems to simulate expected changes resulting from global air temperature rise. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). [ABSTRACT FROM AUTHOR]

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)—weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)-Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. [ABSTRACT FROM AUTHOR]

The analysis of the tree radial growth response to climate is crucial for dendroclimatological research. However, the response relationships between tree-ring indices and climatic factors at different timescales are not yet clear. In this study, the tree-ring width of Huashan pine (Pinus armandii) from Huashan in the Qinling Mountains, north-central China, was used to explore the response differences of tree growth to climatic factors at daily, pentad (5 days), dekad (10 days) and monthly timescales. Correlation function and linear regression analysis were applied in this paper. The tree-ring width showed a more sensitive response to daily and pentad climatic factors. With the timescale decreasing, the absolute value of the maximum correlation coefficient between the tree-ring data and precipitation increases as well as temperature (mean, minimum and maximum temperature). Compared to the other three timescales, pentad was more suitable for analysing the response of tree growth to climate. Relative to the monthly climate data, the association between the tree-ring data and the pentad climate data was more remarkable and accurate, and the reconstruction function based on the pentad climate was also more reliable and stable. We found that the major climatic factor limiting Huashan pine growth was the precipitation of pentads 20–35 (from April 6 to June 24) rather than the well-known April–June precipitation. The pentad was also proved to be a better timescale for analysing the climate and tree growth in the western and eastern Qinling Mountains. The formation of the earlywood density of Chinese pine (Pinus tabulaeformis) from Shimenshan in western Qinling was mainly affected by the maximum temperature of pentads 28–32 (from May 16 to June 9). The maximum temperature of pentads 28–33 (from May 16 to June 14) was the major factor affecting the ring width of Chinese pine from Shirenshan in eastern Qinling. [ABSTRACT FROM AUTHOR]

This paper introduces the average predictability time (APT) for characterizing the overall predictability of a system. APT is the integral of a predictability measure over all lead times. The underlying predictability measure is based on the Mahalanobis metric, which is invariant to linear transformation of the prediction variables and hence gives results that are independent of the (arbitrary) basis set used to represent the state. The APT is superior to some integral time scales used to characterize the time scale of a random process because the latter vanishes in situations when it should not, whereas the APT converges to reasonable values. The APT also can be written in terms of the power spectrum, thereby clarifying the connection between predictability and the power spectrum. In essence, predictability is related to the width of spectral peaks, with strong, narrow peaks associated with high predictability and nearly flat spectra associated with low predictability. Closed form expressions for the APT for linear stochastic models are derived. For a given dynamical operator, the stochastic forcing that minimizes APT is one that allows transformation of the original stochastic model into a set of uncoupled, independent stochastic models. Loosely speaking, coupling enhances predictability. A rigorous upper bound on the predictability of linear stochastic models is derived, which clarifies the connection between predictability at short and long lead times, as well as the choice of norm for measuring error growth. Surprisingly, APT can itself be interpreted as the “total variance” of an alternative stochastic model, which means that generalized stability theory and dynamical systems theory can be used to understand APT. The APT can be decomposed into an uncorrelated set of components that maximize predictability time, analogous to the way principle component analysis decomposes variance. Part II of this paper develops a practical method for performing this decomposition and applies it to meteorological data. [ABSTRACT FROM AUTHOR]

Vulnerability is an emerging concept for climate science and policy. Over the past decade, efforts to assess vulnerability to climate change triggered a process of theory development and assessment practice, which is reflected in the reports of the Intergovernmental Panel on Climate Change (IPCC). This paper reviews the historical development of the conceptual ideas underpinning assessments of vulnerability to climate change. We distinguish climate impact assessment, first- and second-generation vulnerability assessment, and adaptation policy assessment. The different generations of assessments are described by means of a conceptual framework that defines key concepts of the assessment and their analytical relationships. The purpose of this conceptual framework is two-fold: first, to present a consistent visual glossary of the main concepts underlying the IPCC approach to vulnerability and its assessment; second, to show the evolution of vulnerability assessments. This evolution is characterized by the progressive inclusion of non-climatic determinants of vulnerability to climate change, including adaptive capacity, and the shift from estimating expected damages to attempting to reduce them. We hope that this paper improves the understanding of the main approaches to climate change vulnerability assessment and their evolution, not only within the climate change community but also among researchers from other scientific communities, who are sometimes puzzled by the unfamiliar use of technical terms in the context of climate change. [ABSTRACT FROM AUTHOR]

A currently popular idea is that El Niño–Southern Oscillation (ENSO) can be viewed as a linear deterministic system forced by noise representing processes with periods shorter than ENSO. Also, there is observational evidence to suggest that the Madden–Julian oscillation (MJO) acts to trigger and/or amplify the warm phase of ENSO in this way. The feedback of the slower process, ENSO, to higher-frequency atmospheric phenomena, of which a large part of the variability in the intraseasonal band is due to the MJO, has received little attention. This paper considers the hypothesis that the probability of an El Niño event is modified by high MJO activity and that, in turn, the MJO is regulated by ENSO activity. If this is indeed the case, then viewing ENSO as a low-frequency oscillation forced by additive stochastic noise would not present a complete picture. This paper tests the above hypothesis using a stochastically forced intermediate coupled model by allowing ENSO to directly influence the stochastic forcing. The model response to a variety of stochastic forcing types is found to be sensitive to the type of forcing applied. When the model is operated beyond its intrinsic Hopf bifurcation, its probability distribution function (PDF) is fundamentally altered when the stochastic forcing is changed from additive to multiplicative. The model integration period also influences the shape of the PDF, which is also compared to the PDF derived from observations. It is found that multiplicative stochastic forcing reproduces some measures of the observations better than the additive stochastic forcing. [ABSTRACT FROM AUTHOR]

A number of recent papers have argued that the mechanical energy budget of the ocean places constraints on how the thermohaline circulation is driven. These papers have been used to argue that climate models, which do not specifically account for the energy of mixing, potentially miss a very important feedback on climate change. This paper reexamines the question of what energetic arguments can teach us about the climate system and concludes that the relationship between energetics and climate is not straightforward. By analyzing the buoyancy transport equation, it is demonstrated that the large-scale transport of heat within the ocean requires an energy source of around 0.2 TW to accomplish vertical transport and around 0.4 TW (resulting from cabbeling) to accomplish horizontal transport. Within two general circulation models, this energy is almost entirely supplied by surface winds. It is also shown that there is no necessary relationship between heat transport and mechanical energy supply. [ABSTRACT FROM AUTHOR]

Barry Saltzman was a giant in the fields of meteorology and climate science. A leading figure in the study of weather and climate for over 40 yr, he has frequently been referred to as the “father of modern climate theory.” Ahead of his time in many ways, Saltzman made significant contributions to our understanding of the general circulation and spectral energetics budget of the atmosphere, as well as climate change across a wide spectrum of time scales. In his endeavor to develop a unified theory of how the climate system works, he played a role in the development of energy balance models, statistical dynamical models, and paleoclimate dynamical models. He was a pioneer in developing meteorologically motivated dynamical systems, including the progenitor of Lorenz’s famous chaos model. In applying his own dynamical-systems approach to long-term climate change, he recognized the potential for using atmospheric general circulation models in a complimentary way. In 1998, he was awarded the Carl-Gustaf Rossby medal, the highest honor of the American Meteorological Society “for his life-long contributions to the study of the global circulation and the evolution of the earth’s climate.” In this paper, the authors summarize and place into perspective some of the most significant contributions that Barry Saltzman made during his long and distinguished career. This short review also serves as an introduction to the papers in this special issue of the Journal of Climate dedicated to Barry’s memory. [ABSTRACT FROM AUTHOR]

Adaptation, as a strategy to respond to climate change, has limits: there are conditions under which adaptation strategies fail to alleviate impacts from climate change. Research has primarily focused on identifying absolute bio-physical limits. This paper contributes empirical insight to an emerging literature on the social limits to adaptation. Such limits arise from the ways in which societies perceive, experience and respond to climate change. Using qualitative data from multi-stakeholder workshops and key-informant interviews with representatives of the fisheries and tourism sectors of the Great Barrier Reef region, we identify psycho-social and structural limits associated with key adaptation strategies, and examine how these are perceived as more or less absolute across levels of organisation. We find that actors experience social limits to adaptation when: i) the effort of pursuing a strategy exceeds the benefits of desired adaptation outcomes; ii) the particular strategy does not address the actual source of vulnerability, and; iii) the benefits derived from adaptation are undermined by external factors. We also find that social limits are not necessarily more absolute at higher levels of organisation: respondents perceived considerable opportunities to address some psycho-social limits at the national-international interface, while they considered some social limits at the local and regional levels to be effectively absolute. [ABSTRACT FROM AUTHOR]

Since 1987, this paper has presented the yearly report of the meteorological observations at the Geophysical Observatory of Modena and Reggio Emilia University, which is located inside the eastern tower of the Dukes' Palace of Modena. The Observatory was founded the 26th January 1826 and ever since its instruments have regularly recorded data. For the year 2015, we present a summary of the annual average values, total and extreme weather of the main parameters, plots with annual and monthly trends of several parameters compared with average climatological values. In addition, "the extremes of 2015", i.e. the average and extreme values of all data collected by the Observatory and at the meteorological stations "Campus DIEF", located in the Department of Engineering in Modena, and at the Campus San Lazzaro in Reggio Emilia are presented. In 2015, there were also anomalies, in line with climate change taking place at a global scale. In particular, July 2015 with an average temperature of 29.4 °C, equals the hottest temperature record of August 2003 and was the hottest July ever recorded. The year 2015, with an average temperature of 16.1 °C, is the second warmest year on record. The amount of precipitation is close to climatic mean, but with some anomalies in monthly distribution. [ABSTRACT FROM AUTHOR]

In the first part of this paper existing evidence (mostly from earth and atmospheric sciences) on global climatic changes and their dependence (and effect) upon human action is briefly reviewed. Reference is made to existing major international programmes of research focusing on these climatic changes. The main part of the paper concentrates on the study of global change within the framework of the social sciences. Five psychological characteristics of global environmental change are explained which would account for the human inability to respond adequately without the supportive means of information presentation and processing. Future research on psychological dimensions of global change should explore these in greater detail and study modes of social communication that will become instrumental in implementing such support systems. [ABSTRACT FROM AUTHOR]

This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to 1) quantify the climate change commitment of different radiative forcing trajectories and 2) explore the extent to which climate change is reversible on human time scales. All commitment simulations follow the four representative concentration pathways (RCPs) and their extensions to year 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near-preindustrial values in most models for RCPs 2.6-6.0. The MOC weakening is more persistent for RCP8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs for RCPs 4.5-8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination of CO2 emissions in all EMICs. Restoration of atmospheric CO2 from RCP to preindustrial levels over 100-1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to preindustrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2. [ABSTRACT FROM AUTHOR]

Mountain observatories around the world are unique sites for monitoring and investigating variations, trends, forcings and feedbacks in the climate system, which are of utmost interest for understanding global climate change. From the small number of these research platforms, Sonnblick Observatory (Austrian Alps) stands out because of its long time series dating back to 1886 at an elevation of 3,100 m a.s.l. The paper describes the contribution of mountain observatories to climatology by the example of Sonnblick Observatory. The observatory's scientific evolution is summarised, starting from the original idea of upper air atmospheric measurements to its recent role as an atmospheric background station and interdisciplinary research site. [ABSTRACT FROM AUTHOR]

This paper evaluates with numerical computations the respective merits of two competing notions of coalition stability in the standard global public goods model of climate change. To this effect it uses the CWS integrated assessment model. After a reminder of the two game theoretical stability notions involved--core-stability and internal-external stability--and of the CWS model, the former property is shown to hold for the grand coalition if resource transfers of a specific form between countries are introduced. The latter property appears to hold neither for the grand coalition nor for most large coalitions whereas it is verified for most small coalitions in a weak sense that involves transfers. Finally, coalitions, stable in either sense, that perform best in terms of carbon concentration and global welfare are always heterogeneous ones. Therefore, if coalitional stability is taken as an objective, promoting small or homogeneous coalitions is not to be recommended. [ABSTRACT FROM AUTHOR]

This paper develops a methodology for eliciting shifts in preference across future scenarios in the performance assessment of infrastructure policies and investments. The methodology quantifies the robustness of alternative portfolios across a variety of scenarios and identifies the scenarios that greatly affect the assessments. An innovation of the methodology is to elicit, for each scenario, only a few relative increases or decreases in importance of selected terms of the value function, which is more efficient than a full elicitation of the value function for each scenario. The identification of critical scenarios via our methodology can be used to focus resource-intensive and potentially costly modeling activities. The methodology integrates preference orders, centroid weights, and the Borda method. In a demonstration, the methodology assesses the relative sea level and other climate-change scenarios that could affect the performance of coastal protections. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for diagnosing predictability on multiple time scales without time averaging. The method finds components that maximize the average predictability time (APT) of a system, where APT is defined as the integral of the average predictability over all lead times. Basing the predictability measure on the Mahalanobis metric leads to a complete, uncorrelated set of components that can be ordered by their contribution to APT, analogous to the way principal components decompose variance. The components and associated APTs are invariant to nonsingular linear transformations, allowing variables with different units and natural variability to be considered in a single state vector without normalization. For prediction models derived from linear regression, maximizing APT is equivalent to maximizing the sum of squared multiple correlations between the component and the time-lagged state vector. The new method is used to diagnose predictability of 1000-hPa zonal velocity on time scales from 6 h to decades. The leading predictable component is dominated by a linear trend and presumably identifies a climate change signal. The next component is strongly correlated with ENSO indices and hence is identified with seasonal-to-interannual predictability. The third component is related to annular modes and presents decadal variability as well as a trend. The next few components have APTs exceeding 10 days. A reconstruction of the tropical zonal wind field based on the leading seven components reveals eastward propagation of anomalies with time scales consistent with the Madden–Julian oscillation. The remaining components have time scales less than a week and hence are identified with weather predictability. The detection of predictability on these time scales without time averaging is possible by virtue of the fact that predictability on different time scales is characterized by different spatial structures, which can be optimally extracted by suitable projections. [ABSTRACT FROM AUTHOR]

Cut-off low pressure systems (COLs) are defined as closed lows in the upper troposphere that have become completely detached from the main westerly current. These slow-moving systems often affect the weather conditions at the earth's surface and also work as a mechanism of mass transfer between the stratosphere and the troposphere, playing a significant role in the net flow of tropospheric ozone. In the first part of this work we provide a comprehensive summary of results obtained in previous studies of COLs. Following this, we present three long-term climatologies of COLs. The first two climatologies are based on the conceptual model of a COL, using European Centre for Medium-range Weather Forecasts (ECMWF) analyses (1958–2002) and National Centers for Environmental Prediction–National Center for Atmospheric Research (1948–2006) reanalysis data sets. The third climatology uses a different method of detection, which is based on using potential vorticity as the physical parameter of diagnosis. This approach was applied only to the ECMWF reanalysis data. The final part of the paper is devoted to comparing results obtained by these different climatologies in terms of areas of preferential occurrence, life span, and seasonal cycle. Despite some key differences, the three climatologies agree in terms of the main areas of COL occurrence, namely (1) southwestern Europe, (2) the eastern north Pacific coast, and (3) the north China–Siberian region. However, it is also shown that the detection of these areas of main COL occurrence, as obtained using the potential vorticity approach, depends on the level of isentropic analysis used. [ABSTRACT FROM AUTHOR]

In climate science, an anomaly is the deviation of a quantity from its annual cycle. There are many ways to define annual cycle. Traditionally, this annual cycle is taken to be an exact repeat of itself year after year. This stationary annual cycle may not reflect well the intrinsic nonlinearity of the climate system, especially under external forcing. In this paper, we re-examine the reference frame for anomalies by re-examining the annual cycle. We propose an alternative reference frame for climate anomalies, the modulated annual cycle (MAC) that allows the annual cycle to change from year to year, for defining anomalies. In order for this alternative reference frame to be useful, we need to be able to define the instantaneous annual cycle: we therefore also introduce a new method to extract the MAC from climatic data. In the presence of a MAC, modulated in both amplitude and frequency, we can then define an alternative version of an anomaly, this time with respect to the instantaneous MAC rather than a permanent and unchanging AC. Based on this alternative definition of anomalies, we re-examine some familiar physical processes: in particular SST re-emergence and ENSO phase locking to the annual cycle. We find that the re-emergence mechanism may be alternatively interpreted as an explanation of the change of the annual cycle instead of an explanation of the interannual to interdecadal persistence of SST anomalies. We also find that the ENSO phase locking can largely be attributed to the residual annual cycle (the difference of the MAC and the corresponding traditional annual cycle) contained in the traditional anomaly, and, therefore, can be alternatively interpreted as a part of the annual cycle phase locked to the annual cycle itself. In addition to the examples of reinterpretation of physics of well known climate phenomena, we also present an example of the implications of using a MAC against which to define anomalies. We show that using MAC as a reference framework for anomaly can bypass the difficulty brought by concepts such as “decadal variability of summer (or winter) climate” for understanding the low-frequency variability of the climate system. The concept of an amplitude and frequency modulated annual cycle, a method to extract it, and its implications for the interpretation of physical processes, all may contribute potentially to a more consistent and fruitful way of examining past and future climate variability and change. [ABSTRACT FROM AUTHOR]

Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds. [ABSTRACT FROM AUTHOR]

Abstract: When planning the acquisition of new palaeo proxy data for reconstructing past climates, many factors influence the decision of where the proxies are to be collected. One such influence is the likelihood of recording a significant climate change relative to the modern. Another consideration, which is less often considered, is the desire to target regions in which there is significant uncertainty in numerical model predictions of past climate change. New proxy data in these regions enables a more rigorous test of model simulations, and results in better constraints on the various boundary conditions used to force the models. In this paper we present a methodology for targeting new palaeo proxy data, based on model simulations. We use the terrestrial late Miocene (11.6 to 5.3 Ma) as a case study. The late Miocene climate provides insights into future climate behavior, as it is believed to have been considerably warmer than the modern. We carry out a suite of late Miocene atmosphere-only General Circulation Model simulations, which we initially evaluate relative to an example dataset of terrestrial temperature and precipitation. In terms of targeting future data acquisition, we locate those regions in the model which exhibit the largest change in temperature and/or precipitation relative to the pre-industrial. We also locate those regions which display the largest variability between the model simulations, because new data in these regions are most likely to provide a strict test of the reliability of the model results. Among other regions, the Amazon basin, the Chad basin, central Canada, and northern India are identified as potentially useful areas to collect data. [Copyright &y& Elsevier]

Climate changes affect the abundance, geographic extent, and floral composition of vegetation, which are reflected in the pollen rain. Sediment cores taken from lakes and peat bogs can be analysed for their pollen content. The fossil pollen records provide information on the temporal changes in climate and palaeo-environments. Although the complexity of the variables influencing vegetation distribution requires a multi-dimensional approach, only a few research projects have used GIS to analyse pollen data. This paper presents a new approach to palynological data analysis by combining GIS and spatial modelling. Eastern Colombia was chosen as a study area owing to the migration of the forest–savanna boundary since the last glacial maximum, and the availability of pollen records. Logistic regression has been used to identify the climatic variables that determine the distribution of savanna and forest in eastern Colombia. These variables were used to create a predictive land-cover model, which was subsequently implemented into a GIS to perform spatial analysis on the results. The palynological data from the study area were incorporated into the GIS. Reconstructed maps of past vegetation distribution by interpolation showed a new approach of regional multi-site data synthesis related to climatic parameters. The logistic regression model resulted in a map with 85.7% predictive accuracy, which is considered useful for the reconstruction of future and past land-cover distributions. The suitability of palynological GIS application depends on the number of pollen sites, the distribution of the pollen sites over the area of interest, and the degree of overlap of the age ranges of the pollen records. [ABSTRACT FROM AUTHOR]

This research begins with the recognition that climate change researchers have become interested in Inuit ecological observations because of the importance of northern changes on the global climate system, but this research often marginalizes Inuit understandings of these climate-related changes. The direct translation of the Inuktitut term Sila in much of this research is a case study in this methodological problem. In contrast to climate research translations of Sila as weather, ethnographies of Inuit conceive of Sila as a spiritual power that is related to the weather. These diverging interpretations of Sila reflect the difficulty of conducting cross-cultural research. To mitigate this difficulty a dialogue was engaged with Inuit concerning their experience of climate change in relation to the different Inuit and Western understandings of Sila. The central goals of this paper are to clearly define Sila so as to identify implications it may have for Western approaches in conducting cross-cultural climate research, and, in the process, to begin a cross-cultural dialogue that aims at an agreed upon understanding of climate change that is respectful of different knowledges. [ABSTRACT FROM AUTHOR]

The radiative feedback from clouds remains the largest source of variation in climate sensitivity amongst general circulation models (GCMs). A cloud clustering methodology is applied to six contemporary GCMs in order to provide a detailed intercomparison and evaluation of the simulated cloud regimes. By analysing GCMs in the context of cloud regimes, processes related to particular cloud types are more likely to be evaluated. In this paper, the mean properties of the global cloud regimes are evaluated, and the cloud response to climate change is analysed in the cloud-regime framework. Most of the GCMs are able to simulate the principal cloud regimes, however none of the models analysed have a good representation of trade cumulus in the tropics. The models also share a difficulty in simulating those regimes with cloud tops at mid-levels, with only ECHAM5 producing a regime of tropical cumulus congestus. Optically thick, high top cloud in the extra-tropics, typically associated with the passage of frontal systems, is simulated considerably too frequently in the ECHAM5 model. This appears to be a result of the cloud type persisting in the model after the meteorological conditions associated with frontal systems have ceased. The simulation of stratocumulus in the MIROC GCMs is too extensive, resulting in the tropics being too reflective. Most of the global-mean cloud response to doubled CO2 in the GCMs is found to be a result of changes in the cloud radiative properties of the regimes, rather than changes in the relative frequency of occurrence (RFO) of the regimes. Most of the variance in the global cloud response between the GCMs arises from differences in the radiative response of frontal cloud in the extra-tropics and from stratocumulus cloud in the tropics. This variance is largely the result of excessively high RFOs of specific regimes in particular GCMs. It is shown here that evaluation and subsequent improvement in the simulation of the present-day regime properties has the potential to reduce the variance of the global cloud response, and hence climate sensitivity, amongst GCMs. For the ensemble of models considered in this study, the use of observations of the mean present-day cloud regimes suggests a potential reduction in the range of climate sensitivity of almost a third. [ABSTRACT FROM AUTHOR]

Understanding of how best to support those most vulnerable to climate stress is imperative given expected changes in climate variability. This paper investigates local adaptation strategies to climate variability, focusing on agricultural decision-making in a communal irrigation scheme in Vhembe District, Limpopo Province, South Africa. Research done through interviews, surveys and participatory methods demonstrates that adaptation strategies within a community are socially differentiated and present differing objectives and priorities. These results highlight the need for intervention and policy that support a heterogeneous response to a wide range of stresses. Evidence for climate change is clear and the need for adaptation is urgent. However, adaptation measures have to be sensitively integrated with ongoing development pathways to ensure they are sustainable and relevant to local priorities. [ABSTRACT FROM AUTHOR]

Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations. [ABSTRACT FROM AUTHOR]

Abstract: This paper offers an alternative to the use of geomorphological and sedimentological evidence for the reconstruction of flood and low flow frequencies. It is based on a technique developed to estimate the hydrological impact of future climate change and it uses either observed or calculated meteorological parameters. It is possible to use this method directly without modification to ‘hindcast’ events within the period of regional meteorological records, which in areas like the UK extend back over the last 150 years. It should also be possible to extend the approach to earlier periods using reconstructed meteorological parameters based on surrogate data, such as ships'' logs. The resulting extreme flow sequences may be used to study past hydrological regimes or to improve estimates of present-day risks by extending the flow records. The technique uses an airflow index-based stochastic weather generator to create hydrometeorological parameters to input into a physically based hydrological simulation model. The method is illustrated here in a reconstruction of daily flow series for the River Wye catchment above Rhayader, mid-Wales, for the period 1889–1998. The method makes use of observed climatic variables for the entire period, with the aim of capturing actual climate variability occurring over the 110-year period. Changes in high flow characteristics are assessed using the mean annual flood (MAF), Q 5 flow and peaks-over-threshold (POT) calculated from the 110-year simulated daily flow series. This particular application shows evidence of a possible trend towards increasing magnitude and frequency of high flow events, which, if continued, would have implications for flood and water resource management. Looking at evidence from the last 110 years helps to place possible future trends within the context of past variations in high flow extremes due to both natural and anthropogenically influenced fluctuations in climate. [Copyright &y& Elsevier]

Isoprene is emitted from vegetation to the atmosphere in significant quantities, and it plays an important role in the reactions that control tropospheric oxidant concentrations. As future climatic and land-cover changes occur, the spatial and temporal variations, as well as the magnitude of these biogenic isoprene emissions, are expected to change. This paper presents a study of the change in biogenic isoprene emissions that would result from both anthropogenic land-cover and climate-driven changes. Annual global isoprene emissions were estimated to be 522 Tg yr-1 under current climatological and land-cover conditions. When climate-driven land-cover changes are predicted, but climate does not change, total global emissions did not change significantly, although regional impacts were important. However, the use of future temperature and land-cover drivers to estimate isoprene emissions produced a global estimate of 889 Tg yr-1. Anthropogenic land-cover changes, such as urbanization and changes of natural vegetation to plantation forests, can also have substantial impacts on isoprene emissions (i.e., up to 717 Tg yr-1, a 37% increase, when land-cover changes but temperature remains at current-day values). The Model for Ozone and Related Tracers, version 2 (MOZART-2) was run with the different isoprene emission scenarios to simulate the potential changes in global atmospheric chemical composition. The simulated regional surface ozone concentrations changed as much as -9 to 55 ppbv under certain emission and climate scenarios. These results were used to evaluate changes in the ozone production chemistry under different emission scenarios. As expected, the impacts of changing isoprene emissions are regionally dependent, with large changes in China, the Amazon, and the United States. [ABSTRACT FROM AUTHOR]

Tropical warm pools appear as the primary mode in the distribution of tropical sea surface temperature (SST). Most previous studies have focused on the role of atmospheric processes in homogenizing temperatures in the warm pool and establishing the observed statistical SST distribution. In this paper, a hierarchy of models is used to illustrate both oceanic and atmospheric mechanisms that contribute to the establishment of tropical warm pools. It is found that individual atmospheric processes have competing effects on the SST distribution: atmospheric heat transport tends to homogenize SST, while the spatial structure of atmospheric humidity and surface wind speeds tends to remove homogeneity. The latter effects dominate, and under atmosphere-only processes there is no warm pool. Ocean dynamics counter this effect by homogenizing SST, and it is argued that ocean dynamics is fundamental to the existence of the warm pool. Under easterly wind stress, the thermocline is deep in the west and shallow in the east. Because of this, poleward Ekman transport of water at the surface, compensated by equatorward geostrophic flow below and linked by equatorial upwelling, creates a cold tongue in the east but homogenizes SST in the west, creating a warm pool. High clouds may also homogenize the SST by reducing the surface solar radiation over the warmest water, but the strength of this feedback is quite uncertain. Implications for the role of these processes in climate change are discussed. [ABSTRACT FROM AUTHOR]

The relative importance of the processes responsible for the annual cycle in the upper-ocean heat content in the Peru Current, in the southeastern tropical Pacific, was diagnosed from an oceanic analysis dataset. It was found that the annual cycle of heat content is forced mainly by insolation. However, the ocean dynamical processes play an important role in producing different regional budget characteristics. In a band 500 km from the coast of Peru, the annual heat content changes in this region are relatively large and can be approximated as sea surface temperature (SST) changes in a fixed-depth mixed layer. The annual cycle of the albedo associated with low-level clouds enhances the annual cycle in insolation, which explains the relatively strong annual cycle of heat content. These clouds, to a large extent, act as a feedback to SST, but a small additional forcing, which is proposed to be cold air advection in this paper, is needed to explain the fact that the maximum cloudiness leads the lowest SST by around a month. Ocean dynamics is important closer to the coast, where upwelling acts partly as damping of the heat content changes and forces it to peak earlier than farther offshore. In a band farther to the southwest, locally wind-forced thermocline motions, which become shallower (deeper) in the warm (cool) season, partially cancel the effect of net surface heat fluxes, whose annual cycle is comparable to that in the region previously mentioned, producing a relatively small annual cycle of heat content. The local forcing appears to be associated with the annual meridional displacements of the South Pacific anticyclone. The annual cycle in SST is also relatively small, which is probably due to the changes in the temperature of the water entrained into the mixed layer associated with the thermocline motions, but also to a mixed layer deeper than that closer to the coast. [ABSTRACT FROM AUTHOR]

The 1976 Pacific climate shift is examined, and its manifestations and significance in Alaskan climatology during the last half-century are demonstrated. The Pacific Decadal Oscillation index shifted in 1976 from dominantly negative values for the 25-yr time period 1951–75 to dominantly positive values for the period 1977–2001. Mean annual and seasonal temperatures for the positive phase were up to 3.1°C higher than for the negative phase. Likewise, mean cloudiness, wind speeds, and precipitation amounts increased, while mean sea level pressure and geopotential heights decreased. The pressure decrease resulted in a deepening of the Aleutian low in winter and spring. The intensification of the Aleutian low increased the advection of relatively warm and moist air to Alaska and storminess over the state during winter and spring. The regime shift is also examined for its effect on the long-term temperature trends throughout the state. The trends that have shown climatic warming are strongly biased by the sudden shift in 1976 from the cooler regime to a warmer regime. When analyzing the total time period from 1951 to 2001, warming is observed; however, the 25-yr period trend analyses before 1976 (1951–75) and thereafter (1977–2001) both display cooling, with a few exceptions. In this paper, emphasis is placed on the importance of taking into account the sudden changes that result from abrupt climatic shifts, persistent regimes, and the possibility of cyclic oscillations, such as the PDO, in the analysis of long-term climate change in Alaska. [ABSTRACT FROM AUTHOR]

In this paper a system for in situ measurement of H216O/H218O in air based on tunable diode laser (TDL) absorption spectroscopy is described. Laboratory tests showed that its 60-min precision (one standard deviation) was 0.21‰ at a water vapor volume mixing ratio of 2.67 mmol mol-1 (dewpoint temperature -10.8°C at sea level) and improved to 0.09 at 15.3 mmol mol-1 (dewpoint temperature 13.4°C). The TDL measurement of the vapor generated by a dewpoint generator differed from the equilibrium prediction by -0.11 ± 0.43‰ (mean ± one standard deviation). Its measurement of the ambient water vapor differed from the cold-trap/mass spectrometer method by -0.36 ± 1.43‰. The larger noise of the latter comparison was caused primarily by the difficulty in extracting vapor from air without altering its isotope content. In a 1-week test in Logan, Utah, in August 2003, the isotope ratio of water vapor in ambient air was positively correlated with the water vapor mixing ratio and also responded to wetting events (rain and irrigation) in an expected manner. This system has been in continuous operation in New Haven, Connecticut, since December 2003. It is suggested that such uninterrupted measurement may open a new window on the hydrologic cycle, particularly processes involving phase changes of water, and can increase the power of the isotope method in ecological applications. [ABSTRACT FROM AUTHOR]

This paper reviews recent research that assesses evidence for the detection of anthropogenic and natural external influences on the climate. Externally driven climate change has been detected by a number of investigators in independent data covering many parts of the climate system, including surface temperature on global and large regional scales, ocean heat content, atmospheric circulation, and variables of the free atmosphere, such as atmospheric temperature and tropopause height. The influence of external forcing is also clearly discernible in reconstructions of hemispheric-scale temperature of the last millennium. These observed climate changes are very unlikely to be due only to natural internal climate variability, and they are consistent with the responses to anthropogenic and natural external forcing of the climate system that are simulated with climate models. The evidence indicates that natural drivers such as solar variability and volcanic activity are at most partially responsible for the large-scale temperature changes observed over the past century, and that a large fraction of the warming over the last 50 yr can be attributed to greenhouse gas increases. Thus, the recent research supports and strengthens the IPCC Third Assessment Report conclusion that “most of the global warming over the past 50 years is likely due to the increase in greenhouse gases.” [ABSTRACT FROM AUTHOR]

The paper analyses the public perception of climate variability and change on both local and global scales by residents of Ibadan, the largest truly indigenous city in sub-Saharan Africa. Data for the survey was collected by the administration of a questionnaire to 453 respondents of different social and demographic strata within the city. The questions were aimed at eliciting how citizens perceived climatic experiences in their local environment and globally; its dynamics and citizens explanations for the changes identified. Sources of climatic information of respondents were explored and the social and demographic specific difference of respondents on the issues analysed. The outcome of the study indicates that a significant proportion (92%) of the public is aware of the dynamics of the local climate. In addition, almost 70% of the respondents have heard of global climate change but less than 25% know the causes. [ABSTRACT FROM AUTHOR]

In order for a scientific innovation to reach a wide audience it needs to travel through diverse networks and be understandable to a variety of people. This paper focuses on networks of stakeholders involved in the diffusion of seasonal climate forecasts. It is argued that understanding stakeholder networks is key to determining the opportunities and barriers to the flow of forecast information, which could enable more focused forecast dissemination. Lesotho is used as a case study where Stakeholder Thematic Networks (STNs) are used as a novel method for investigating forecast dissemination. STNs enable qualitative information to be analysed through semi-quantitative mapping of relationships that enable the networks and scales of linkages to be visualised. This illustrates the types of nodes and channels of seasonal forecast information flow and so enables existing or emerging patterns of dissemination to be uncovered. Sub-networks that exist for purposes other than climate information dissemination are identified as salient sub-networks for focusing development of future forecast dissemination. These existing sub-networks enable stakeholder needs to be addressed and decrease the need for new networks to be established. By using these sub-networks, information relating to climate variability can be mainstreamed into existing development pathways. This is critical to recognise if innovations relating to climate information are to be used to improve climate change adaptation. [ABSTRACT FROM AUTHOR]