Dan Bernie

A review of recent developments in climate change science. Part II: The global-scale impacts of climate change:

This article presents a review of recent developments in studies assessing the global-scale impacts of climate change published since the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Literature covering six main impact sectors is reviewed: sea-level rise (SLR) and coastal impacts, ocean acidification, ecosystems and biodiversity, water resources and desertification, agriculture and food security, and human health. The review focuses on studies with a global perspective to climate change impacts assessment, although in the absence of global studies for some sectors or aspects of impacts, national and regional studies are cited. The review highlights three major emerging themes which are of importance for the policy- and decision-making process: (1) a movement towards probabilistic methods of impacts assessment and/or the consideration of climate modelling uncertainty; (2) a move towards assessing potential impacts that could be avoided under different climate change mitigation scenarios relative to a business-as-usual reference scenario; and (3) uncertainties that remain in understanding the relationship between climate and natural or human systems. Whether recent impact assessments show a changed risk of damage to human or natural systems since the AR4 depends upon the impact sector; whether the assessments are robust or not (i.e. will stand the test of time) requires additional expert judgement. However, using this judgement, overall we find an increased risk to natural systems, and in some components of human systems.

A review of recent developments in climate change science. Part I: Understanding of future change in the large-scale climate system:

This article reviews some of the major lines of recent scientific progress relevant to the choice of global climate policy targets, focusing on changes in understanding since publication of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Developments are highlighted in the following major climate system components: ice sheets; sea ice; the Atlantic Meridional Overturning Circulation; tropical forests; and accelerated carbon release from permafrost and ocean hydrates. The most significant developments in each component are identified by synthesizing input from multiple experts from each field. Overall, while large uncertainties remain in all fields, some substantial progress in understanding is revealed.

Computing the distribution of return levels of extreme warm temperatures for future climate projections

In order to take into account uncertainties in the future climate projections there is a growing demand for probabilistic projections of climate change. This paper presents a methodology for producing such a probabilistic analysis of future temperature extremes. The 20- and 100-years return levels are obtained from that of the normalized variable and the changes in mean and standard deviation given by climate models for the desired future periods. Uncertainty in future change of these extremes is quantified using a multi-model ensemble and a perturbed physics ensemble. The probability density functions of future return levels are computed at a representative location from the joint probability distribution of mean and standard deviation changes given by the two combined ensembles of models. For the studied location, the 100-years return level at the end of the century is lower than 41°C with an 80% confidence. Then, as the number of model simulations is low to compute a reliable distribution, two techniques proposed in the literature (local pattern scaling and ANOVA) have been used to infer the changes in mean and standard deviation for the combinations of RCM and GCM which have not been run. The ANOVA technique leads to better results for the reconstruction of the mean changes, whereas the two methods fail to correctly infer the changes in standard deviation. As standard deviation change has a major impact on return level change, there is a need to improve the models and the different techniques regarding the variance changes.

Do differences in future sulfate emission pathways matter for near-term climate? A case study for the Asian Monsoon

Anthropogenic aerosols could dominate over greenhouse gases in driving near-term hydroclimate change, especially in regions with high present-day aerosol loading such as Asia. Uncertainties in near-future aerosol emissions represent a potentially large, yet unexplored, source of ambiguity in climate projections for the coming decades. We investigated the near-term sensitivity of the Asian summer monsoon to aerosols by means of transient modelling experiments using HadGEM2-ES under two existing climate change mitigation scenarios selected to have similar greenhouse gas forcing, but to span a wide range of plausible global sulfur dioxide emissions. Increased sulfate aerosols, predominantly from East Asian sources, lead to large regional dimming through aerosol-radiation and aerosol-cloud interactions. This results in surface cooling and anomalous anticyclonic flow over land, while abating the western Pacific subtropical high. The East Asian monsoon circulation weakens and precipitation stagnates over Indochina, resembling the observed southern-flood-northern-drought pattern over China. Large-scale circulation adjustments drive suppression of the South Asian monsoon and a westward extension of the Maritime Continent convective region. Remote impacts across the Northern Hemisphere are also generated, including a northwestward shift of West African monsoon rainfall induced by the westward displacement of the Indian Ocean Walker cell, and temperature anomalies in northern midlatitudes linked to propagation of Rossby waves from East Asia. These results indicate that aerosol emissions are a key source of uncertainty in near-term projection of regional and global climate; a careful examination of the uncertainties associated with aerosol pathways in future climate assessments must be highly prioritised.

The Impact of Bias Correction and Model Selection on Passing Temperature Thresholds

Knowledge of when specific global or local temperature levels are reached is important for decision makers in that it provides a time frame over which adaptation strategies for temperature related climate impacts need to be put in place. The time frame varies depending on the adaptation strategy but can range from a few years to the order of decades. Climate models, however, show a high degree of uncertainty in the timing of passing specific warming levels, limiting their use in adaptation policy development. This study examines the impact of two approaches, which may reduce the uncertainty in modeled timing of reaching specific warming levels. Firstly, the use of different performance metrics to preferentially weight model ensembles and secondly, the application of four bias correction approaches. Using the CMIP5 simulations of the RCPs, our results show that selecting models based on performance or bias correcting model data both reduce the spread in timing of specific warming levels reached in the first half of the century by up to 50% in some regions. This implies the potential of these approaches to support adaptation planning.

Performance of Pattern-Scaled Climate Projections under High-End Warming. Part I: Surface Air Temperature over Land

Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to ‘high-end’ warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favourable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-squared errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to ~3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing RCP8.5 scenario. Assessments of climate change impacts under ‘high-end’ warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above pre-industrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs.