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The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006-2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

Introduction. The wheat belt of southwest Western Australia (SWWA) experienced several severe frosts just before harvest in September 2016, leading to a loss of one million tonnes of grain crops [...]

There is an urgent need for the climate community to translate their meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities as we seek to understand how anthropogenic climate change has, and will, impact our society. This can be particularly challenging because there are often multiple specialized steps to model the hazard. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (~ 90 m) river flooding (fluvial) hazards. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be directly related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data-sets and thus can be applied anywhere in the world, but we use the Brahmaputra river in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. [ABSTRACT FROM AUTHOR]

ABSTRACT: In 2016 and continuing into 2017, Kenya experienced drought conditions, with over 3 million people in need of food aid due to low rainfall during 2016. Whenever extreme events like this happen, questions are raised about the role of climate change and how natural variability such as the El Niño ‐ Southern Oscillation influenced the likelihood and intensity of the event. Here we aim to quantify the relative contributions of different climate drivers to this drought by applying three independent methodologies of extreme event attribution. Analysing precipitation data for the South East and North West of Kenya we found no consistent signal from human‐induced climate change and thus conclude that it has not greatly affected the likelihood of low rainfall such as in 2016. However, 2016 was a La Niña year and we show that this event was indeed more likely because of the specific sea surface temperatures. There is a trend in temperatures in the region due to climate change that may have exacerbated the effects of this drought. By analysing precipitation minus evaporation and soil moisture, simulated by one climate model only, we did not see a reduction in moisture in simulations in the current climate compared with simulations without climate change. However, there are expected effects of higher temperatures that our simulations do not cover, such as increased demand on water resources and stress on livestock. Although we find no significant influence of climate change on precipitation, we cannot rule out that temperature‐related impacts of drought are linked to human‐induced climate change. [ABSTRACT FROM AUTHOR]

On 19 May 2016 the afternoon temperature reached 51.0 °C in Phalodi in the northwest of India, a new record for the highest observed maximum temperature in India. The previous year, a widely-reported very lethal heat wave occurred in the southeast, in Andhra Pradesh and Telangana, killing thousands of people. In both cases it was widely assumed that the probability and severity of heat waves in India are increasing due to global warming, as they do in other parts of the world. However, we do not find positive trends in the highest maximum temperature of the year in most of India since the 1970s (except spurious trends due to missing data). Decadal variability cannot explain this, but both increased air pollution with aerosols blocking sunlight and increased irrigation leading to evaporative cooling have counteracted the effect of greenhouse gases up to now. Current climate models do not represent these processes well and hence cannot be used to attribute heat waves in this area. The health effects of heat are often described better by a combination of temperature and humidity, such as a heat index or wet bulb temperature. Due to the increase in humidity from irrigation and higher SSTs these indices have increased over the last decades even when extreme temperatures have not. The extreme air pollution also exacerbates the health impacts of heat. From a health impact point of view, the severity of heat waves has increased in India. For the next decades we expect the trend due to global warming to continue, but the cooling effect of aerosols to diminish as air quality controls are implemented. The expansion of irrigation will likely continue, though at a slower pace, mitigating this trend somewhat. Humidity will probably continue to rise. The combination will give a strong rise of the temperature of heat waves. The high humidity will make health effects worse, whereas decreased air pollution would decrease the impacts. [ABSTRACT FROM AUTHOR]

Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes of such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows to generate very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of weather@home system (weather@home 2) with a higher resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid scale land surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 km to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM biases are overall reduced, in particular the warm and dry bias over eastern Europe, but large biases remain. The model is shown to represent main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales. [ABSTRACT FROM AUTHOR]

Volunteer or Crowd computing is becoming increasingly popular to solve complex research problems, from an increasing diverse range of areas. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. The BOINC system is ideal in those cases where not only does the research community involved need low cost access to massive computing resource but also that there is a significant public interest in the research done. We discuss the way in which Cloud services can help BOINC based projects to deliver results in a fast, on demand manner. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We show how this design can be used as an efficient distributed computing plat- form within the Cloud, and outline new approaches that could open up new possibilities in this field, using http://climateprediction.net as a case study. [ABSTRACT FROM AUTHOR]

Flood hazard is a global problem, which in some regions may be exacerbated byclimate change. In south Asia, where livelihoods are highly dependent on waterresources, people can be affected disproportionally. The 2017 monsoon flooding in theGanges-Brahmaputra-Meghna (GBM) basin, with record river levels observed,resulted in ∼1200 deaths, and dramatic loss of crops and infrastructure. Climatemodel scenarios representing the Paris Agreement targets were investigated andthe changes in extreme precipitation were quantified over the GBM region. Largeensembles of climate simulations using different models and experimental designs wereused to evaluate the range of possible changes. These estimates were linked toa high-resolution flood hazard model to determine the corresponding changes inflood hazard. The simulations of 1.5∘C and 2∘C warming indicate an increase inextreme precipitation and hence flood hazard over the GBM basin compared to thecurrent climate. So even with global warming limited to 1.5∘C, there is a detectableincrease in the likelihood in flooding for extreme precipitation events such as the southAsian crisis in 2017. The additional ∼0.6C warming needed to take us from currentclimate to 1.5∘C highlights the changed flood risk even with low levels of warming. [ABSTRACT FROM AUTHOR]