Uncertainty in geomorphological responses to climate change Abstract Successful adaptation to climate change at regional scales can often depend on understanding the nature of geomorphological responses to climate change at those scales. Here we use evidence from landscapes which are known to be environmentally sensitive to show that geomorphological change in response to shifts in climate can be highly nonlinear. Our study sites are two mountain massifs on the western coast of Ireland. Both sites have similar geological and Pleistocene glacial histories and are similar topographically, geomorphologically and in their climate histories. We show that despite these similarities their response to late Holocene, climate change has differed. Both massifs have responded to short-term climate changes over the last 4500 years that are considered to have been uniform across the region, but these climate changes have resulted in highly differentiated and nonlinear landscape responses. We argue this reflects nonlinearity in the forcing–response processes at such scales and suggests that current approaches to modelling the response of such systems to future climate change using numerical climate models may not accurately capture the landscape response. We end by discussing some of the implications for obtaining decision-relevant predictions of landscape responses to climatic forcing and for climate change adaptation and planning, using regional climate models.

Real options analysis of climate-change adaptation: investment flexibility and extreme weather events Abstract Investments in climate-change adaptation will have to be made while the extent of climate change is uncertain. However, some important sources of uncertainty will fall over time as more climate data become available. This paper investigates the effect on optimal investment decision-making of learning that reduces uncertainty. It develops a simple real options method to value options that are found in many climate-change adaptation contexts. This method modifies a binomial tree model frequently applied to climate-change adaptation problems, incorporating gradual learning using a Bayesian updating process driven by new observations of extreme events. It is used to investigate the timing, scale, or upgradable design of an adaptation project. Recognition that we might have more or different information in the future makes flexibility valuable. The amount of value added by flexibility and the ways in which flexibility should be exploited depend on how fast we learn about climate change. When learning will occur quickly, the value of the option to delay investment is high. When learning will occur slowly, the value of the option to build a small low-risk project instead of a large high-risk one is high. For intermediate cases, the option to build a small project that can be expanded in the future is high. The approach in this paper can support efficient decision-making on adaptation projects by anticipating that we gradually learn about climate change by the recurrence of extreme events.

Not all carbon dioxide emission scenarios are equally likely: a subjective expert assessment Abstract Climate researchers use carbon dioxide emission scenarios to explore alternative climate futures and potential impacts, as well as implications of mitigation and adaptation policies. Often, these scenarios are published without formal probabilistic interpretations, given the deep uncertainty related to future development. However, users often seek such information, a likely range or relative probabilities. Without further specifications, users sometimes pick a small subset of emission scenarios and/or assume that all scenarios are equally likely. Here, we present probabilistic judgments of experts assessing the distribution of 2100 emissions under a business-as-usual and a policy scenario. We obtain the judgments through a method that relies only on pairwise comparisons of various ranges of emissions. There is wide variability between individual experts, but they clearly do not assign equal probabilities for the total range of future emissions. We contrast these judgments with the emission projection ranges derived from the shared socio-economic pathways (SSPs) and a recent multi-model comparison producing probabilistic emission scenarios. Differences on long-term emission probabilities between expert estimates and model-based calculations may result from various factors including model restrictions, a coverage of a wider set of factors by experts, but also group think and inability to appreciate long-term processes.

Climate change increases potential plant species richness on Puerto Rican uplands Abstract Modeling climate change effects on species and communities is critical especially in isolated islands. We analyzed the potential effects of climate change on 200 plant species in Puerto Rico under two emission scenarios and in four periods over the twenty-first century. Our approach was based on ensemble bioclimatic modeling using eight modeling algorithms and community richness analysis. Our findings showed that the probabilities of environmental suitability decline for wet climate species and increase for drier and warm climate species in the future periods under both emission scenarios, with stronger effects under the higher emission scenario. Expansion of dry climate species to higher elevations appears to be a prominent response of species to climatic change in the island based on changes in environmental suitability but the actual species redistribution will be influenced by their life histories, potential adaptation, dispersal abilities, species introductions, and species interactions. This potential movement leads to a spatial pattern of species richness at site level that shows a positive relationship with elevation, which becomes stronger in the later periods of the century. The spatial pattern of species richness, if combined with single species projections, can provide critical information for conservation management in the island. Conservation management can support island-wide biological diversity by protecting the wet climate species on the uplands.

Global warming impact on confined livestock in buildings: efficacy of adaptation measures to reduce heat stress for growing-fattening pigs Abstract Pigs and poultry are raised predominantly at high stocking densities in confined, insulated livestock buildings with mechanical ventilation systems. These systems are quite sensitive to heat stress, which has increased in recent decades from anthropogenic warming. A dataset of hourly meteorological data from 1981 to 2017 was used to drive a steady-state balance model for sensible and latent heat that simulates the indoor climate of a conventional reference system, and this model was used to predict the effect of global warming on growing-fattening pigs housed in such livestock confinement buildings. Seven adaptation measures were selected to investigate the effect on the indoor climate; these measures included three energy-saving air preparation systems, a doubling of the maximum ventilation rate, a reduction in the stocking density, and a shift in the feeding and resting time patterns. The impact of heat stress on animals was calculated with the following three heat stress metrics: a threshold of the indoor temperature, the temperature-humidity index, and a body mass–adapted temperature. The seven adaptation measures were quantified by a reduction in factors of the heat stress parameters. The highest reduction of heat stress in comparison with the conventional reference system was achieved by the three air preparation systems in the range of 54 to 92% for adiabatic systems and 65 to 100% for an earth-air heat exchanger, followed by an increase in the ventilation rate and the time shift. The reduction in the stocking density showed the lowest improvement. In addition to the reduction in the heat stress, a temporal trend over three decades was also used to quantify the resilience of pig confinement systems. The efficacy of some of the adaptation measures is great enough to mitigate the increase of heat stress that occurs due to global warming.

Decarbonization and its discontents: a critical energy justice perspective on four low-carbon transitions Abstract Low-carbon transitions are often assumed as positive phenomena, because they supposedly reduce carbon emissions, yet without vigilance, there is evidence that they can in fact create new injustices and vulnerabilities, while also failing to address pre-existing structural drivers of injustice in energy markets and the wider socio-economy. With this in mind, we examine four European low-carbon transitions from an unusual normative perspective: that of energy justice. Because a multitude of studies looks at the co-benefits of renewable energy, low-carbon mobility, or climate change mitigation, we instead ask in this paper what are the types of injustices associated with low-carbon transitions? Relatedly, in what ways do low-carbon transitions worsen social risks or vulnerabilities? Lastly, what policies might be deployed to make these transitions more just? We answer these questions by first elaborating an “energy justice” framework consisting of four distinct dimensions—distributive justice (costs and benefits), procedural justice (due process), cosmopolitan justice (global externalities), and recognition justice (vulnerable groups). We then examine four European low-carbon transitions—nuclear power in France, smart meters in Great Britain, electric vehicles in Norway, and solar energy in Germany—through this critical justice lens. In doing so, we draw from original data collected from 64 semi-structured interviews with expert participants as well as five public focus groups and the monitoring of twelve large internet forums. We document 120 distinct energy injustices across these four transitions, including 19 commonly recurring injustices. We aim to show how when low-carbon transitions unfold, deeper injustices related to equity, distribution, and fairness invariably arise.

The impact of future urban scenarios on a severe weather case in the metropolitan area of São Paulo Abstract In this work, convective parameters are applied, based on numerical simulations made with Brazilian Regional Atmospheric Modeling System (BRAMS) model, to a severe weather case which occurred in the metropolitan area of São Paulo (MASP). Scenarios of future urban area growth and increase of building heights were made to evaluate changes in convective parameters and rainfall for the study region. Using factorial planning and factor separation methods, we found that the urban area growth predicted for 2030 is capable of increasing the amount of precipitation, mainly due to the land use change from rural to urban. In the scenario of building heights increasing, it was found a tendency for rainfall suppression. The urban area for 2030 is the major factor contributing to increasing atmospheric instability and wind shear. Vertical urban growth causes an increase in atmospheric instability and a decrease in wind shear. The interaction between urban area and building height factors increases the amount of precipitation and storm motion over the MASP.

Modeling the sensitivity of wheat yield and yield gap to temperature change with two contrasting methods in the North China Plain Abstract Wheat productivity in the North China Plain (NCP) is highly sensitive to climate change and varies greatly in spatial-temporal scale. Contrasting responses of wheat productivity to climate change were reported with different assessment methods. In this study, the impacts of climate warming (+ 2 °C) on wheat yields and yield gaps in the NCP were compared under rainfed, irrigated, and potential conditions using climatic resource utilization model (CRUM) and APSIM. Average potential yield increased 289 kg ha−1 per decade (P < 0.01) simulated by CRUM but decreased 219 kg ha−1 per decade (P < 0.01) simulated by APSIM across the NCP during 1961–2010. Under the + 2 °C scenario compared with current climate (1961–2010), wheat yields under potential, two irrigations, one irrigation, and rainfed conditions increased 27%, 23%, 28%, and 13% simulated by CRUM but decreased 7%, 8%, 10%, and 17% simulated by APSIM. Simulated yield gaps between potential yield and yields under rainfed and one and two irrigations by CRUM increased 33%, 27%, and 32%, respectively. Simulated yield gap between potential and rainfed yields by APSIM increased 9% while the gaps between potential yield and yields under one and two irrigations by APSIM decreased 12% and 10%. Without cultivar change, simulated shortened growth period by APSIM due to increased temperature would decrease wheat yields. By contrast, increased temperature under a constant growth period assumed by CRUM would increase yields especially potential yield. This suggested that wheat yields could be maintained by effective utilization of crop growth duration, such as breeding new cultivars under warming climate in the NCP.

Pitfalls in comparing Paris pledges Abstract The Paris pledges are unique documents in climate governance that outline what each country intends to do to combat climate change. Often, these documents contain headline greenhouse gas percentage reduction targets that appear to summarize countries’ contributions to mitigation. This is a boon for comparative climate policy research. However, I show in this paper that the Paris pledges require detailed interpretation to be comparable. I demonstrate the risks in comparing these targets by re-visiting a recent studying linking national public opinion to the stringency of countries’ mitigation goals. I develop new indicators that better account for the structure of the targets and show in replications that the original finding is inconsistent with the underlying data. I conclude by drawing lessons for studying the Paris pledges.

The local dependency of precipitation on historical changes in temperature Abstract Globally, mean and extreme precipitation will increase with climate change. This is largely controlled by moisture and energy availability which is linked to temperature. Therefore, changes in precipitation are regularly presented proportional to a change in mean global temperature, and temperature is often proposed as a covariate for projecting precipitation with climatic change. However, studies which investigate the association between precipitation and temperature largely focus on the day-to-day association between precipitation and temperature fluctuations at a gauged location, which is not necessarily equivalent to changes in precipitation at climatic spatial and time scales. To assess whether temperature changes may help inform changes in precipitation with climatic change, we evaluate the historical relationship between precipitation and annual temperature fluctuations. We find positive correlations between precipitation and mean annual dew point temperature. These associations are strongest for annual average precipitation and weakest for the shortest, most extreme precipitation. We find that the strength of this correlation is more strongly linked to the number of rain days, rather than the precipitation depth itself. When dry-bulb temperatures are used in place of dew point temperature, the association between precipitation and temperature is either negative or zero. As a strong association between wet day dry-bulb and dew point temperatures exists, changes in temperature may aid in understanding the changes to precipitation as global temperatures increase. However, as the precipitation-dew point correlation is not necessarily physically related to the precipitation depth but rather to precipitation occurrence; precipitation-temperature sensitivities need to be interpreted with caution.