Description: Publication date: Available online 30 June 2018 Source: Weather and Climate Extremes Author(s): Mojtaba Khanian, Nadine Marshall, Kianoosh Zakerhaghighi, Marziyeh Salimi, Asadollah Naghdi When the social, economic, or ecological conditions under which socio-ecological systems are expected to adapt become untenable, a system may transform into a fundamentally new system. Within agricultural systems, farmers have the option of significantly transforming their practices, or migrating elsewhere in the search for a better lifestyle (and exiting the agricultural socio-ecological system). However, if governments, communities and industry leaders are to provide climate change impact support to agricultural regions, then it becomes critical to predict the conditions under which farmers, households and communities will choose to either transform their farming or migrate. We hypothesize that those conditions are likely to reflect environmental, economic and social conditions within a region, where some communities and/or some farmers may respond differently depending on their environmental, economic and social factors. Here, we examine how the perception of farmers to transformational change is influenced by social, environmental and economic factors in six villages within the Famenin County of western Iran, which have experienced a notable recent decrease in groundwater levels, an increase in temperature due to reduced rainfall and no support from government. Results show that environmental, economic and social factors were all important influences on the social transformations that were occurring within each village and across the region. In sum, environmental flexibility and local corporation eased the transformation process. Place attachment and social capital were critical to facilitate transformation during all parts of the transformation process. These results are discussed as two separate scenarios.

Description: Publication date: Available online 27 June 2018 Source: Weather and Climate Extremes Author(s): David L. Gobbett, Uday Nidumolu, Steven Crimp Frosts and extreme minimum temperature events cause significant damage to grapevines. In the Victorian vine growing regions of Australia, potentially causing complete wipe-out of a season's grape production, and millions of dollars of losses. Frost is an important risk to be managed by viticulturalists, and is likely to change under future climatic conditions with impacts on existing wine-grape growing regions. This case-study applies high spatial resolution analysis of minimum night-time temperatures to explore the impact of current and future frost risks. Multivariate Adaptive Regression Splines (MARS) are used to model high-resolution (30 m grid) minimum temperatures for a topographically diverse region in the Yarra Valley wine region in southeastern Australia. Remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) night-time temperatures, elevation, terrain indices and meteorological station data were used to develop the model of minimum night-time temperatures. The model is formally validated against independent vineyard minimum temperature records (R 2  = 0.68) as well as current distribution of viticulture land-use, which was compared with viticulturists' perceptions of a frost-line in the region. Historical temperature records were then adjusted to produce high-resolution maps of frost occurrence under a hot-dry future climate scenario, and a warm-wet scenario, for the years 2030 and 2050. All the future climate scenarios project down-elevation movement of the frost line of between 10 m and 30 m depending on scenarios. This work has implications for viticulturists who might plan to expand growing areas to lower elevations, or might consider changing to different grape varieties. The method developed here could be applied to other regions or used to explore other future climate scenarios.

Description: Publication date: Available online 22 June 2018 Source: Weather and Climate Extremes Author(s): Dave MacLeod Advanced warning of delayed onset or early cessation of the rainy seasons would be extremely valuable information for farmers in east Africa and is a common request from regional stakeholders. Such warnings are beginning to be provided, however forecast skill for these metrics has not been demonstrated. Here the forecast skill of the ECMWF seasonal hindcasts is evaluated for onset and cessation forecasts over east Africa. Correlation of forecast with observed long rains anomalies only above a 95% statistical significance level for a small part of the domain, whilst short rains are significance a large part of the region. The added value of updating the forecast outlook with the extended range 46 day forecast is assessed and this gives a small improvement. For the short rains detection of early onset is better near the coast, and late onset detection is better over northwestern Kenya. During exceptionally dry years the method to detect onset and cessation fails. Using this as a definition of a failed season, the model shows significant skill at anticipating long rains season failure in the northwest of Kenya, and short rains failure in Somalia and northeast Kenya. In addition the strength of the correlation between long rains cessation and seasonal total is shown to be particularly weak in observations but too strong in the hindcasts. Predictability of onset and cessation for both seasons appears to arise primarily from the link with seasonal total and it is unclear that the model represents variability in onset and cessation beyond this. This has important implications for operational forecasting: any forecast of season timing which is ‘inconsistent’ with seasonal total (e.g. an early onset but low total rainfall) must be treated with caution. Finally links with zonal winds are investigated. Late onset is correlated with easterly (westerly) anomalies during the long (short) rains, though the strength and spatial pattern of the relationship is not well represented in the model. Early cessation is correlated with easterly anomalies in both seasons for most of the region in both observations and hindcasts. However for the long rains the sign of the correlation is reversed along the coast in observations but not in the hindcasts. These dynamical inconsistencies may have a negative impact on forecast skill and have the potential to inform process-based development of climate modelling in the region.

Description: Publication date: Available online 22 June 2018 Source: Weather and Climate Extremes Author(s): Gabriela Narcizo de Lima, Víctor Orlando Magaña Rueda The Metropolitan Area of Sao Paulo (MASP) has undergone a rapid urban growth process over the years, and this has been reflected in major climate change. Thus, through the documentation of the urban evolution process of MASP associated with the analysis of historical series of meteorological data, this research tries to show that changes in land use, evidenced mainly by the loss of green areas in the process of urbanization growth over the years, may result in climatic changes characterized by different states of equilibrium. The climatic changes caused by urban growth are better observed in the temperatures, however, the variable precipitation, although with a high level of uncertainty, can also reflect these changes in particular episodes, which in the case of MASP occur mainly during the rainy season. The results of this research indicate that both precipitation and temperature data show a tendency to increase the frequency of days with extreme values, especially since the mid 70's. Knowing the results that intense changes in land use can generate, the population and urban planners should consider the possibility of applying actions to control and reduce this disturbance in addition to adapting to the changes that have already occurred.

Description: Publication date: Available online 9 June 2018 Source: Weather and Climate Extremes Author(s): Andrea J. Dittus, David J. Karoly, Markus G. Donat, Sophie C. Lewis, Lisa V. Alexander The oceans are a well-known source of natural variability in the climate system, although their ability to account for inter-annual variations of temperature and precipitation extremes over land remains unclear. In this study, the role of sea-surface temperature (SST)-forcing is investigated for variability and trends in a range of commonly used temperature and precipitation extreme indices over the period 1959 to 2013. Using atmospheric simulations forced by observed SST and sea-ice conditions (SIC) from three models participating in the Climate of the Twentieth Century Plus (C20C+) Project, results show that oceanic boundary conditions drive a substantial fraction of inter-annual variability in global average temperature extreme indices, as well as, to a lower extent, for precipitation extremes. The observed trends in temperature extremes are generally well captured by the SST-forced simulations although some regional features such as the lack of warming in daytime warm temperature extremes over South America are not reproduced in the model simulations. Furthermore, the models simulate too strong increases in warm day frequency compared to observations over North America. For extreme precipitation trends, the accuracy of the simulated trend pattern is regionally variable, and a thorough assessment is difficult due to the lack of locally significant trends in the observations. This study shows that prescribing SST and SIC holds potential predictability for extremes in some (mainly tropical) regions at the inter-annual time-scale.

Description: Publication date: Available online 8 June 2018 Source: Weather and Climate Extremes Author(s): Seyni Salack, Inoussa A. Saley, Namo Z. Lawson, Ibrahim Zabré, Elidaa K. Daku In the field of climate services, characterization of rainfall extremes is useful to identify and quantify rain rates that can trigger floods, on-farm water stagnation, excess run-off causing arable soil depletion and other natural hazards. Delving through multiple sources of observational uncertainties, we define extreme rain events (EREs) using the 99th percentile thresholds of daily accumulated rainfall, extracted from historical data records (1960–2016) of manual and tipping bucket gauges. The results of the analysis show that the average amplitude of these threshold values has been increasing in the recent years. Meanwhile, the three categories of heavy rains exhibit an intra-seasonal timing that follows different phases of the West African monsoon. Category 1 & 2 occur mostly in the northern Sahel, between weeks 27 and 35 of the year with an accumulated daily amount varying in the 37–65 mm range and less than or equal to 85 mm/day respectively. In category 3, rain rates are greater than 85 mm/day, observed between 28th and 38th week-of-the-year predominantly in the southern and western of the Sahelsub-region. For each category of ERE, high risk areas are mapped using the relative probability of occurrence at local scale. This classification can be exploited for forecasts verification, climate model evaluations and operational early warning services against high impact rainfall events.

Description: Publication date: Available online 4 June 2018 Source: Weather and Climate Extremes Author(s): Uday Nidumolu, Lilly Lim-Camacho, Estelle Gaillard, Peter Hayman, Mark Howden Climate variability is a key source of livelihood risks faced by smallholder farmers in drier environments in many developing countries. Climate information provided on seasonal time-scales can sometimes improve agricultural decision-making. However, there are many barriers to the effective dissemination, communication and use of such information on farm and across the value chain. We used a case study in southern India to explore ways of overcoming some of these barriers such as those limiting access to information, and effective communication of probabilistic forecast information. Firstly, we used social network analysis at the village level to identify particular individuals, groups or/and institutions who are central in information networks so as to be able to support them to increase the efficiency, effectiveness, equity and robustness of information transfers. This allowed us to identify potential opportunities and challenges around access, communication and forecast use. Close linking of formal and informal networks appeared to be a common, positive influencing factor. Secondly, we used value chain analysis to assess how pre and post-farm decision-makers could use seasonal climate forecasts (SCF) in their own businesses and how this may propagate up and down the value chain. We found that the motivation for using SCF varied across the value chain and was likely of limited use to smaller, off-farm value chain players who take a short-term adaptive management approach to planning. However, it was seen as having significant potential for larger businesses who take a more strategic approach. This identified a possible risk of increased competitive inequality between businesses of different sizes. Thirdly, we addressed the challenge of translating probabilistic climate forecast information into support for decision making by using decision analysis with intermediaries enabling them to structure clearly problems with embedded climate probabilities. The construction of decision-trees enabled farm advisers and local researchers to explore the potential value of SCF by using the decision trees as a “boundary object” around which farmers and other decision makers, agricultural scientists, climate scientists, economists, social scientists and policy-makers could have thoughtful discussion leading to useful strategies to better manage climate risk. This paper outlines approaches and outcomes associated with these three activities as a way of exploring effective application of seasonal climate information and identifies additional research to enhance applicability.

Description: Publication date: June 2018 Source: Weather and Climate Extremes, Volume 20

Description: Publication date: Available online 30 April 2018 Source: Weather and Climate Extremes Author(s): Andrew Ciavarella, Nikos Christidis, Martin Andrews, Margriet Groenendijk, John Rostron, Mark Elkington, Claire Burke, Fraser C. Lott, Peter A. Stott We present a substantial upgrade of the Met Office system for the probabilistic attribution of extreme weather and climate events with higher horizontal and vertical resolution (60 km mid-latitudes and 85 vertical levels), the latest Hadley Centre atmospheric and land model (ENDGame dynamics with GA6.0 science and JULES at GL6.0) as well as an updated forcings set. A new set of experiments designed for the evaluation and implementation of an operational attribution service are described which consist of pairs of multi-decadal stochastic physics ensembles continued on a season by season basis by large ensembles that are able to sample extreme atmospheric states possible in the recent past. Diagnostics from these experiments form the HadGEM3-A contribution to the international Climate of the 20th Century Plus (C20C+) project and were analysed under the European Climate and Weather Events: Interpretation and Attribution (EUCLEIA) event attribution project as well as contributing to the Climate Science for Service Partnership (CSSP)-China programme. After discussing the framing issues surrounding questions that can be asked with our system we construct a novel approach to the evaluation of atmosphere-only ensembles intended for event attribution, in the process highlighting and clarifying the distinction between hindcast skill and model performance. A framework based around assessing model representation of predictable components and ensuring exchangeability of model and real world statistics leads to a form of detection and attribution to boundary condition forcing as a means of quantifying one degree of freedom of potential model error and allowing for the bias correction of event probabilities and resulting probability ratios. This method is then applied systematically across the globe to assess contributions from anthropogenic influence and specific boundary conditions to the changing probability of observed and record seasonal mean temperatures of four recent 3-month seasons from March 2016–February 2017.

Description: Publication date: Available online 7 April 2018 Source: Weather and Climate Extremes Author(s): Christopher J. Paciorek, Dáithí A. Stone, Michael F. Wehner Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, either specific events or classes of events. A common approach to event attribution uses climate model output under factual (real-world) and counterfactual (world that might have been without anthropogenic greenhouse gas emissions) scenarios to estimate the probabilities of the event of interest under the two scenarios. Event attribution is then quantified by the ratio of the two probabilities. While this approach has been applied many times in the last 15 years, the statistical techniques used to estimate the risk ratio based on climate model ensembles have not drawn on the full set of methods available in the statistical literature and have in some cases used and interpreted the bootstrap method in non-standard ways. We present a precise frequentist statistical framework for quantifying the effect of sampling uncertainty on estimation of the risk ratio, propose the use of statistical methods that are new to event attribution, and evaluate a variety of methods using statistical simulations. We conclude that existing statistical methods not yet in use for event attribution have several advantages over the widely-used bootstrap, including better statistical performance in repeated samples and robustness to small estimated probabilities. Software for using the methods is available through the climextRemes package available for R or Python. While we focus on frequentist statistical methods, Bayesian methods are likely to be particularly useful when considering sources of uncertainty beyond sampling uncertainty.