This study employed an arithmetic mean model with sea surface temperature and chlorophyll data (2-month lag) to determine the projected impact of climate change on the immature albacore tuna habitat in the Indian Ocean. Albacore tuna fishing data from Taiwanese longline fishery from 2000 to 2016 were used. Standardization of the nominal catch per unit effort data was performed to prevent bias and overestimation resulting from various temporal and spatial factors. In the Indian Ocean, potential immature albacore habitats exhibited significant habitat suitability index (HSI) changes in response to future climate change levels. As the water stratifies in a projected warm climate, low HSI areas were enlarged, and potential immature albacore habitats exhibited a net southward shift. Although the CMIP5 climate model sea surface temperature projections generated different HSI patterns for immature albacore, our results from the ensemble median immature albacore habitat forecasts provided information useful for assessing risks and adaptation strategy options for albacore fishery resources under climate change. The trends of the potential immature albacore habitat distribution could also be
cautiously used to inform resource stakeholders’ decisions.
Keywords: AMM, CPUE, Ensemble forecasting, HSI, PHH, IPCC, RCP, Indian Ocean
Due to global warming, precipitation concentration is expected to change, and extreme weather events are likely to occur more frequently. This study investigates the spatiotemporal variability of precipitation concentration in Iran. For this purpose, daily precipitation data with a spatial resolution of 0.25° × 0.25° from the Asfazari database are used. Three indices, i.e., the precipitation concentration index (PCI), precipitation concentration period (PCP), and precipitation concentration degree (PCD), are used to examine the variability of precipitation concentration in Iran. The results demonstrate that the central, southeastern, and eastern parts of Iran exhibit maximum temporal precipitation concentration, while the lowest precipitation concentration is observed over the Caspian coasts and the northern half of the country. The year 1998 is selected as the change point due to the considerable difference in the values of the examined indices, and the long-term statistical period is divided into two sub-periods before and after the change. During the sub-period after the change point (1999–2019), precipitation concentration increased in the western, central, eastern, and southeastern parts of Iran, according to PCI and PCD; on the other hand, it decreased in the north, northeast, and northern coastline of the Oman Sea. Furthermore, there are great spatial differences during the occurrence of precipitation along the northern coasts, according to PCP, varying from November (along the Caspian coast) to August (along the northern foothills of the Alborz Mountains). PCP increased during the sub-period after the change point along the northern coastlines of the Persian Gulf and Oman Sea and in the north parts of the country (along the Alborz Mountains), indicating a shift in the period of precipitation from the winter to the warm seasons of spring and summer. Moreover, a decrease in PCP in the northwest and northeast suggests that the period of occurrence of precipitation shifted from the second half of the winter toward the early winter and late fall. After the year of the change point, the frequency of rainy days and precipitation decreased, and PCI and PCD increased.
Farmers are front-line workers managing climatic change. As in many parts of the world, climate change in northern California is threatening natural resource-dependent communities by exacerbating droughts, heatwaves, and wildfires. This article draws on ethnographic methods, including 108 interviews with crop and livestock farmers and key informants, to query climate change experience, belief, and response in rural northeastern California. I find that farmers recognize and describe climate changes that match the meteorologic evidence of anthropogenic climate change, but attribute these changes to weather cycles and harsh geographies. However, irrespective of their belief in anthropogenic climate change, farmers implement climate adaptations—many of these practices with mitigation co-benefits, bolstering growing evidence that climate change belief and action are not tightly coupled. To accelerate farmer adaptation, this work suggests that policy and programming focus on actions and outcomes, rather than reshaping belief.
Velocity of climate change (VoCC), also known as climate velocity, has been widely used as a climate change metric to inform the past and projected impacts of climate change on biodiversity globally. It is a generalized climate-landscape metric that does not involve any biological assumptions and is beneficial for regions with a lack of extensive species presence/absence data. In the current study, the contemporary (1951–2018) climate velocity for India at the annual and seasonal timescales has been assessed using observational and reanalysis datasets for mean near-surface temperature. The associated coverage uncertainty and influence of the resolutions of the datasets have been identified and an attempt has been made to address them. The central, north-western, and southern peninsular regions, along with some parts of the Indo-Gangetic plains, were identified as having experienced the highest annual climate velocities, in the range of 3–8 km/year in the last five decades. The velocities in the post-monsoon (October and November) were found to be the highest (> 4 km/year) as compared to the other seasons owing to the higher temporal, lower spatial gradients and regional dynamics. Finer resolution dataset presents a more realistic estimate of climate velocities owing to better representation of local topographical features and associated microclimate.
Drought is a major natural disaster that has long-lasting effects on economic and social activities in northern China and has regional distinctions in duration, severity, and spatial extent. In this study, tree-ring chronologies and historical archives in the Yellow River Basin and its surrounding areas are collected to investigate the extreme drought history and dynamic process of the two extreme drought events for the past ~ 200 years. Instead of reconstructing climate indicators, the tree index is directly employed here to partly overcome the high-frequency information loss during tree-ring-based reconstruction. The results show that identified drought history is improved significantly relative to the single indicator reconstruction, and drought events are highly consistent with historical recorded ones. Two prominent drought events in modern Chinese history are analyzed, namely the Ding-Wu Great Famine (1876–1879) and the extreme drought in northern China during the late-1920s. Unexpectedly, the most prestigious Ding-Wu Great Famine is lower than the extreme drought in the late-1920s in terms of drought duration, spatial extent, and intensity. Our research further reveals that the drought events recorded in the historical records could be very different from actual events due to the influence of political and other factors. The analysis of spatial dynamics indicates that the potential mechanisms of the two drought events are also different. This is confirmed by research based on reanalysis data that the Ding-Wu Great Famine was caused by a typical strong ENSO event, while the mechanism of the extreme drought in the late-1920s was more complicated.
Drought is a major threat to banana production in Uganda, leading to large yield losses. This study documented drought effects on banana production and identified farmers’ drought mitigation strategies. Interviews were conducted in eight districts, randomly selected from banana-growing districts in Uganda’s cattle corridor, characterised by frequent droughts. Data were collected from 120 respondents/farms. Banana production in the study area was dominated by small-scale farmers, growing mostly a combination of cooking and dessert banana types. Among the 15 identified effects of drought stress on banana growth, reduced bunch weight, wilting and drying of leaves, reduced leaf production and reduced number of fingers and clusters were the most reported. ‘Mpologoma’ and ‘FHIA 17’ cultivars were reported as the most and least affected by drought stress, respectively. Although the cattle corridor is prone to recurrent droughts, the deployment of drought coping strategies was mostly low, with farmers using one to three strategies. A total of 12 drought mitigation practices were used across the cattle corridor, with mulching being the most common option. Irrigation was perceived as the most effective mitigation option though its deployment was limited by water scarcity and the high cost of water pumps. This study suggests the need for government support to mitigate drought through establishing infrastructure for irrigation, strengthening climate data collection and information systems and the development of drought-tolerant cultivars by breeders. Additionally, farmers need to prioritise preventive coping strategies like planting drought-tolerant cultivars, irrigation, mulching, and manure application and ensure timely of deployment of mitigation practices.
Climate change and indebtedness have been repeatedly highlighted as major causes of distress for rural households in India. However, despite the close connection between climate conditions and rural livelihoods, there has been little attempt to systematically examine the association between the two. To address this gap, we combine national-level longitudinal data from IHDS, MERRA-2, and the Indian Ministry of Agriculture to study the impact of climate anomalies on household indebtedness across rural India. Using a multilevel longitudinal approach that accounts for potential confounders at household, village, and district levels, we find pervasive effects of season-specific, 5-year climate anomalies on multiple dimensions of household debt, particularly in arid and semi-arid areas. Most notably, temperature anomalies in the winter cropping season in arid and semi-arid areas are associated with increasing household indebtedness. We further find that climate change interacts with existing socioeconomic differences—caste and landholding in particular—to deepen both the size and the depth of indebtedness for rural households.
Americans increasingly accept that global warming is happening and a serious threat. Using secondary data from national probability surveys of the US adult population and preregistered hypotheses, we explore how and why Americans self-report changing their minds about global warming. Common reasons included learning more about the issue, hearing or seeing the effects of global warming, and personally experiencing its effects. We tested these reasons, as well as additional factors known to influence global warming opinion, including perceptions of social norms, media attention, and exposure to extreme weather, to assess their relative strength in predicting self-reported opinion change. As expected, perceived experience with global warming—particularly vicarious experience or seeing/hearing others experience its effects—emerged as a top correlate, even while statistically controlling for perceptions of social norms and attention to partisan-leaning media like Fox News. Perceived personal experience was a stronger correlate of self-reported opinion change among Republicans, whereas learning more about global warming was a stronger correlate among Democrats. Also as expected, perceiving social norms supportive of climate action was associated with positive self-reported opinion change, particularly among Republicans. Further, attention to the Fox News Channel was associated with negative self-reported opinion change but only among Republicans. Although this research is exploratory and uses self-reported data, it suggests that personalizing and localizing the threat of climate change, and enhancing the norm that most people support action, may be important factors to investigate in future longitudinal research on public opinion change and communication strategies.
Beijing has implemented air pollution control policies and transitioned its energy system with lower carbon emissions to tackle severe air pollution. However, further advancing to a carbon–neutral future necessitates comprehensive measures far beyond the air-quality-oriented policies. This study aims to explore and compare different transition strategies of the Beijing energy system to achieve carbon neutrality and assess the associated air pollution reduction co-benefits by using an integrated modelling framework consisting of an energy system model MESSAGEix-Beijing and an air quality assessment model GAINS. Three scenarios are developed, namely, baseline (BS), indigenous (IND), and imported electricity-dependent (EIMP). The two distinct low-carbon pathways differ in cost-optimal technological solutions and the associated impacts of air pollution reduction. Compared to the BS scenario, the IND and EIMP scenarios could reduce the carbon dioxide emissions in Beijing by 94 to 96% in 2050 and achieve substantial air pollution co-benefits. In the IND scenario, the NOx, SO2, and PM2.5 emissions would decrease by 50%, 84%, and 30% in 2050, respectively. Importing full electricity from other provinces, as indicated by the EIMP scenario, would achieve even higher emissions reduction for air pollutants. The results highlight the necessity for concerted regional development of adjacent provinces to avoid the spillover of carbon emissions and air pollution.
To support equitable adaptation planning, quantitative assessments should consider the fairness of the distribution of outcomes to different people. What constitutes a fair distribution, however, is a normative question. In this study, we explore the use of different moral principles drawn from theories of distributive justice to evaluate fairness. We use adaptation planning in Vietnam Mekong Delta as a case study. We evaluate the preference ranking of six alternative policies for seven moral principles across an ensemble of scenarios. Under the baseline scenario, each principle yields distinctive preference rankings, though most principles identify the same policy as the most preferred one. Across the ensemble of scenarios, the commonly used utilitarian principle yields the most stable ranking, while rankings from other principles are more sensitive to uncertainty. The sufficientarian and the envy-free principles yield the most distinctive ranking of policies, with a median ranking correlation of only 0.07 across all scenarios. Finally, we identify scenarios under which using these two principles results in reversed policy preference rankings. Our study highlights the importance of considering multiple moral principles in evaluating the fairness of adaptation policies, as this would reduce the possibility of maladaptation.
Climate change threatens not only the material bases of human societies but also is likely to harm human psychological/emotional well-being. One aspect of this emotional harm may come from how the esthetic properties of environments—especially those stemming from the composition of predominant vegetative cover and cloud patterns—change in regions around the world with shifting climatic patterns. Research has established that humans respond to the fractal dimension of scenes, and that our innate “fractal fluency” leads us to prefer middle-range fractal complexity. Thus, the consequences of climate change for human emotional well-being may vary across regions depending on how the fractal character of landscapes and cloudscapes evolves under new climatic regimes.
Recently, the International Panel for Climate Change released the 6th Coupled Model Intercomparison Project (CMIP6) climate change scenarios with shared socioeconomic pathways (SSPs). The SSP scenarios result in significant changes to climate variables in climate projections compared to their predecessor, the representative concentration pathways from the CMIP5. Therefore, it is necessary to examine whether the CMIP6 scenarios differentially impact plant-disease ecosystems compared to the CMIP5 scenarios. In this study, we used the EPIRICE-LB model to simulate and compare projected rice blast disease epidemics in the Korean Peninsula using five selected family global climate models (GCMs) of the CMIP5 and CMIP6 for two forcing scenarios. We found a similar decrease in rice blast epidemics in both CMIP scenarios; however, this decrease was greater in the CMIP6 scenarios. In addition, distinctive epidemic trends were found in North Korea, where the rice blast epidemics increase until the mid-2040s but decrease thereafter until 2100, with different spatial patterns of varying magnitudes. Controlling devastating rice blast diseases will remain important during the next decades in North Korea, where appropriate chemical controls are unavailable due to chronic economic and political issues. Overall, our analyses using the new CMIP6 scenarios reemphasized the importance of developing effective control measures against rice blast for specific high-risk areas and the need for a universal impact and vulnerability assessment platform for plant-disease ecosystems that can be used with new climate change scenarios in the future.
The online version contains supplementary material available at 10.1007/s10584-022-03410-2.
Assessing the potential impacts of climate change on river flows is critically important for adaptation. Data from global or nested regional climate models (GCMs/RCMs) are frequently used to drive hydrological models, but now there are also very high-resolution convection-permitting models (CPMs). Here, data from the first CPM climate ensemble for the UK, along with the RCM ensemble within which the CPM is nested, are used to drive a grid-based hydrological model. The performance for simulating baseline (1981–2000) river flows is compared between the RCM and the CPM, and the projections of future changes in seasonal mean flows and peak flows are compared across Britain (1981–2000 to 2061–2080). The baseline performance assessment shows that (before bias correction) the CPM generally performs better than the RCM, and bias correction of precipitation makes both the RCM and CPM perform more similarly to use of observation-based driving data. The analysis of future changes in flows shows that the CPM almost always gives higher flow changes than the RCM. If reliable, these differences in flow projections suggest that adaptation planning for high flows based on use of regional data may be insufficient, although planning for low flows may be slightly over-cautious. However, the availability of CPM data only for one RCM/GCM is a limitation for use in adaptation as it under-samples the uncertainty range. There are significant challenges to the wider application of CPM ensembles, including the high computational and data storage demands.
Most people in the United States recognize the reality of climate change and are concerned about its consequences, yet climate change is a low priority relative to other policy issues. Recognizing that belief in climate change does not necessarily translate to prioritizing climate policy, we examine psychological factors that may boost or inhibit prioritization. We hypothesized that perceived social norms from people's own political party influence their climate policy prioritization beyond their personal belief in climate change. In Study 1, a large, diverse sample of Democratic and Republican participants (N = 887) reported their prioritization of climate policy relative to other issues. Participants' perceptions of their political ingroup's social norms about climate policy prioritization were the strongest predictor of personal climate policy prioritization-stronger even than participants' belief in climate change, political orientation, environmental identity, and environmental values. Perceptions of political outgroup norms did not predict prioritization. In Study 2 (N = 217), we experimentally manipulated Democratic and Republican descriptive norms of climate policy prioritization. Participants' prioritization of climate policy was highest when both the political ingroup and the outgroup prioritized climate policy. Ingroup norms had a strong influence on personal policy prioritization whereas outgroup norms did not. These findings demonstrate that, beyond personal beliefs and other individual differences, ingroup social norms shape the public's prioritization of climate change as a policy issue.
The online version contains supplementary material available at 10.1007/s10584-022-03396-x.
The notion of climate anxiety has gained traction in the last years. Yet uncertainty remains regarding the variations of climate anxiety across demographic characteristics (e.g., gender, age) and its associations with adaptive (i.e., pro-environmental) behaviors. Moreover, the point-estimate proportion of people frequently experiencing climate anxiety has seldom been probed. In this study, we assessed climate anxiety (including its related functional impairments), along with demographic characteristics, climate change experience, and pro-environmental behaviors, in 2080 French-speaking participants from eight African and European countries. 11.64% of the participants reported experiencing climate anxiety frequently, and 20.72% reported experiencing daily life functional consequences (e.g., impact on the ability to go to work or socialize). Women and younger people exhibited significantly higher levels of climate anxiety. There was no difference between participants from African and European countries, although the sample size of the former was limited, thus precluding any definite conclusion regarding potential geographic differences. Concerning adaptation, climate anxiety was associated with pro-environmental behaviors. However, this association was significantly weaker in people reporting frequent experiences of climate anxiety (i.e., eco-paralysis) than in those with lower levels. Although this observation needs to be confirmed in longitudinal and experimental research, our results suggest that climate anxiety can impede daily life functioning and adaptation to climate change in many people, thus deserving a careful audit by the scientific community and practitioners.
The online version contains supplementary material. Available at: 10.1007/s10584-022-03402-2.
The objective of this paper is to analyze the impact of climate change adaptation strategies on maize productivity of farms focusing on gender diferences. To do so, a selection model generalizing the Heckman (1979) approach and the Oaxaca and Blinder decomposition
procedure are specifed and estimated. The empirical analysis is based on intra-agricultural household data from the 2018 Harmonized Household Living Conditions Survey (EHCVM) of Togo with a total sample of 8622 maize plots disaggregated by seasonThe results reveal that the average maize productivity of men is about 23.5% higher than that of women. Similarly, the average maize productivity of married women is about 28.5% higher than that of unmarried women. This suggests that married men and women have greater adaptive capacity than their counterparts and are also more likely to improve their productivity. The factors that contribute to the performance of married men and women at the expense of their counterparts are secondary education, producer assets, and climate information. We also fnd that the use of improved seeds, of-season cropping, and a combination of both strategies are the types of coping strategies adopted by men and married women to increase their productivity. These results have implications for the direction of development policies. These development policies can be more targeted at unmarried women.
Keywords Adaptation · Climate change · Heckman generalized selection model · Maize productivity · Oaxaca and Blinder decomposition
JEL Classifcation J16 · Q18 · Q54
Statistical crop models, using observational data, are widely used to analyze and predict the impact of climate change on crop yields. But choices in model building can drastically influence the outcomes. Using India as a case study, we built multiple crop models (rice, wheat, and pearl millet) with different climate variables: from the simplest ones containing just space and time dummy variables, to those with seasonal mean temperature and total precipitation, to highly complex ones that accounted for within-season climate variability. We observe minimal improvement in overall model performance with increasing model complexity using standard accuracy metrics like the root mean square error and adjusted R², suggesting the simplest models, also the most parsimonious, are often the best. However, we find that simpler models, such as those including only seasonal climate variables, fail to fully capture impacts of climate change and extreme events as they can confound the influence of climate on crop yields with space and time. Automated model and variable selection based on parsimony principles can produce predictions that are not fit for purpose. Statistical models for estimating the impacts of climate change on crop yields should therefore be based on a conjunctive use of domain theory (for example plant physiology) with accuracy and performance metrics.
Dominant policy approaches have failed to generate action at anywhere near the rate, scale or depth needed to avert climate change and environmental disaster. In particular, they fail to address the need for a fundamental cultural transformation, which involves a collective shift in mindsets (values, beliefs, worldviews and associated inner human capacities). Whilst scholars and practitioners are increasingly calling for more integrative approaches, knowledge on how the link between our mind and the climate crisis can be best addressed in policy responses is still scarce. Our study addresses this gap. Based on a survey and in-depth interviews with high-level policymakers worldwide, we explore how they perceive the intersection of mind and climate change, how it is reflected in current policymaking and how it could be better considered to support transformation. Our findings show, on the one hand, that the mind is perceived as a victim of increasing climate impacts. On the other hand, it is considered a key driver of the crisis, and a barrier to action, to the detriment of both personal and planetary wellbeing. The resultant vicious cycle of mind and climate change is, however, not reflected in mainstream policymaking, which fails to generate more sustainable pathways. At the same time, there are important lessons from other fields (e.g. education, health, the workplace, policy mainstreaming) that provide insights into how to integrate aspects of mind into climate policies. Our results show that systematic integration into policymaking is a key for improving both climate resilience and climate responsiveness across individual, collective, organisational and system levels and indicate the inner human potential and capacities that support related change. We conclude with some policy recommendations and further research that is needed to move from a vicious to a virtuous cycle of mind and climate change that supports personal and planetary wellbeing.
The online version contains supplementary material available at 10.1007/s10584-022-03398-9.
Climate change has played a crucial role in the subrogation of Chinese dynasties. In particular , the Ming-Qing transition coincided with the rapid decrease in precipitation and the sharp deterioration of agroecological conditions in northern China under the cold conditions brought on by the Little Ice Age. Here, we present a new precipitation reconstruction (June-April) for northern Chinese Loess Plateau since 1590 CE. The reconstruction was derived from a tree-ring width chronology of Platycladus orientalis, and made it possible to quantitatively assess the period of megadroughts during the late Ming Dynasty, with high resolution. Our analysis showed that these extreme drought events have been unprecedented in China for the last 500 years, and precipitation variation could be linked to ENSO activities. The environmental imbalance caused by these megadroughts magnified the negative impacts of the climate on agriculture and society, an important reason for considering these phenomena as catalysts for the demise of the Ming Dynasty.
Property-level flood risk adaptation (PLFRA) has received significant attention in recent years, as flood resilience has become increasingly important in flood risk management. Earlier studies have indicated that learning from flood experiences can affect flood risk perception and the adoption of PLFRA measures; however, it remains unclear whether and how this learning process can be affected by flood control infrastructure-specifically, the level of flood resistance it offers. This study attempts to answer the question: Do people living in environments with different levels of flood resistance learn different lessons from flood experience, manifested in flood risk perception and PLFRA? We present a comparative study of the rural village of Xinnongcun and the urban community of Nanhuyayuan in Central China. In-person interviews with a total of 34 local residents were conducted to understand how flood experiences affect flood risk perception and PLFRA. We find that learning from flood experiences in the highly flood-resistant environment (Nanhuyayuan) does not contribute to flood risk perception but further enhances flood resistance, whereas learning in a less flood-resistant environment (Xinnongcun) leads to a better understanding of flood risk and promotes PLFRA. We argue that flood resistance can affect the learning from flood experiences. High flood resistance can suppress PLFRA through a different learning process that involves learning inertia and path dependency. In the search for flood resilience, this begs society to re-examine the widespread assertion that both structural and nonstructural measures are important in flood risk management.
The online version contains supplementary material available at 10.1007/s10584-022-03401-3.
In many parts of the world, wildfires have become more frequent and intense in recent
decades, raising concerns about the extent to which climate change contributes to the
nature of extreme fire weather occurrences. However, studies seeking to attribute fire
weather extremes to climate change are hitherto relatively rare and show large
disparities depending on the employed methodology. Here, an empirical-statistical
method is implemented as part of a global probabilistic framework to attribute recent
changes in the likelihood and magnitude of extreme fire weather. The results show that
the likelihood of climate-related fire risk has increased by at least a factor of four in
approximately 40% of the world’s fire-prone regions as a result of rising global
temperature. In addition, a set of extreme fire weather events, occurring during a
recent 5-year period (2014-2018) and identified as exceptional due to the extent to
which they exceed previous maxima, are, in most cases, associated with an increased
likelihood resulting from rising global temperature. The study’s conclusions highlight
important uncertainties and sensitivities associated with the selection of indices and
metrics to represent extreme fire weather and their implications for the findings of
attribution studies. Among the recommendations made for future efforts to attribute
extreme fire weather events is the consideration of multiple fire weather indicators and
communication of their sensitivities.
The impacts of wildfires are increasing in the Mediterranean Basin due to more extreme fire seasons featuring increasingly fast and high-intensity fires, which often overwhelm the response capacity of fire suppression forces. Fire behaviour is expected to become even more severe due to climate change. In this study, we quantified the effect of climate change on fire danger (components of the Canadian FWI System) and wildfire behaviour characteristics (rate of spread and fireline intensity) for the four major Mediterranean forest ecosystems located in the Transboundary Biosphere Reserve of Meseta Ibérica under RCP4.5 and RCP8.5 scenarios. The effect of climate change on wildfire behaviour was supplemented by taking into account net primary production (NPP), hence fuel load. Our results show that the meteorological fire season will start earlier and end later, leading to a significant increase in the number of days with weather conditions that promote high-intensity wildfires, for both climate scenarios. Fuel type shapes how wildfire spread characteristics will unfold. The most relevant changes are projected to occur in pine forests, where a wildfire with median fireline intensity will offer serious resistance to control from spring to autumn. The severity of fire behaviour in shrublands also increases substantially when considering climate change, with high-intensity wildfires potentially occurring in any time of the year. Both deciduous and evergreen broadleaf forests are predicted to typically generate wildfires with low enough intensity to remain within suppression capability. By adjusting fuel load to future climate conditions, our results highlight that fireline intensity in deciduous and evergreen broadleaf forests may not increase during summer, and can even be significantly reduced in shrublands. This study suggests that improved fire planning and management of wildfire-prone landscapes will counteract the effect of climate change on fire behaviour and impacts.
Scientific assessments, such as those by the Intergovernmental Panel on Climate Change (IPCC), inform policymakers and the public about the state of scientific evidence and related uncertainties. We studied how experts from different scientific disciplines who were authors of IPCC reports, interpret the uncertainty language recommended in the Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. This IPCC guidance note discusses how to use confidence levels to describe the quality of evidence and scientific agreement, as well likelihood terms to describe the probability intervals associated with climate variables. We find that (1) physical science experts were more familiar with the IPCC guidance note than other experts, and they followed it more often; (2) experts’ confidence levels increased more with perceptions of evidence than with agreement; (3) experts’ estimated probability intervals for climate variables were wider when likelihood terms were presented with “medium confidence” rather than with “high confidence” and when seen in context of IPCC sentences rather than out of context, and were only partly in agreement with the IPCC guidance note. Our findings inform recommendations for communications about scientific evidence, assessments, and related uncertainties.
Evaluating the strategies fishermen have used to respond to short-term climate variability in the past can help inform our understanding of the adaptive capacity of a fishery in the face of anticipated future change. Using historic fishery landings, climate records, and fishermen surveys, we document how market squid fishermen respond to high seasonal and interannual climate variability associated with the El Niño Southern Oscillation (ENSO) and responses to hypothetical future scenarios of low abundance and range shift. Overall, fishermen have been able to adapt to dramatic shifts in the geographic range of the fishery given their high mobility, with fishermen with larger vessels expressing a willingness to travel greater distances than those with smaller vessels. Nearly half of fishermen stated that they would switch fisheries if market squid decreased dramatically in abundance, although fishermen who were older, had been in the fishery longer, were highly dependent on squid for income, and held only squid and/or coastal pelagic finfish permits were less likely to switch to another fishery in a scenario of lower abundance. While market squid fishermen have exhibited highly adaptive behavior in the face of past climate variability, recent (and likely future) range shifts across state boundaries, as well as closures of other fisheries, constrain fishermen's choices and emphasize the need for flexibility in management systems. Our study highlights the importance of considering connectivity between fisheries and monitoring and anticipating trans-jurisdictional range shifts to facilitate adaptive fishery management.
The online version contains supplementary material available at 10.1007/s10584-022-03394-z.
Extensive use of groundwater in the rice–wheat cropping system of northwest India has resulted in groundwater depletion at an alarming rate of 33–88 cm per year over the past 2–3 decades. Projected climate change is likely to affect crop water demand, groundwater withdrawal, and replenishment in future. A modeling study was undertaken to simulate the impact of climate change on groundwater resources under existing rice–wheat cropping system and with revised crop management strategies in the Karnal district of Northwest India. Different cop management strategies considered are marginal shift in sowing dates of rice and wheat, and fractional diversification of rice area to maize. MODFLOW software driven by the projected climate change scenarios under four representative concentration pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) were used for simulating groundwater behavior in the study area under business as usual and proposed crop management strategies. Simulation results indicated 4.3–61.5 m (28.9–291.2%) additional decline in groundwater levels in different zones of the study area under different RCPs by the end century (2070–2099) period in relation to the reference groundwater level of year 2015 under the existing sowing dates of 15 June for rice and 15 November for wheat. Maintaining rice sowing date at 15 June but advancing wheat sowing date by 10 days can reduce groundwater decline by 9.8–14.4%, 14.4–19.6%, and 18.1–25.8% under different RCPs by the end of early (2010–2039), mid (2040–2069), and end (2070–2099) century periods, respectively, vis-à-vis prevailing sowing dates. Replacing 20%, 30%, and 40% rice area with maize in rice–wheat system is likely to reduce groundwater decline by 7.1 (24.9%), 10.1 (35.3%), and 13.8 m (48.5%), respectively, in comparison to projected end century (2099) decline of 28.5 m under the prevailing sowing dates of rice–wheat. However, declining groundwater trend of rice–wheat would be reversed with the replacement of 80% rice area under maize crop. Simulation results suggest that specific crop management strategies can potentially moderate groundwater decline in the study area under the envisaged climate change.
The ocean is the main driver of internal climate variability. However, the relatively short length of the historical record limits our ability to study the impact of large-scale oscillations on the regional climate. This work uses the output from the Canadian Earth System Model large ensemble (CanESM2-LE) to study the impact of three large-scale oscillations on temperature and precipitation anomalies over North America. The 50-member ensemble provides data series covering 2500 years to study the superpositions between ENSO, AMO, and PDO over the 1961–2010 period. The main characteristics of all three oscillations are well reproduced by CanESM2-LE. The impact of each oscillation is considered independently (with the others in their neutral phases), as well as combined with the other two (e.g., all three in non-neutral phases). The results outline a dominant role of ENSO in annual precipitation and of AMO in temperature over most of North America. PDO has a minimal impact on precipitation and temperature. The dominant roles of ENSO on precipitation and AMO on temperature are enhanced by superpositions between these patterns. These combined impacts are consistent with their independent ones. Even though this study is conducted in a model world, the superpositions are mostly consistent with our understanding derived from observations. The results therefore extend our understanding of the relationship between large-scale oscillations and climate variability over North America and highlight the importance of considering the superpositions between oscillations to better understand internal hydroclimatic variability.
Quantifying which nations are culpable for the economic impacts of anthropogenic warming is central to informing climate litigation and restitution claims for climate damages. However, for countries seeking legal redress, the magnitude of economic losses from warming attributable to individual emitters is not known, undermining their standing for climate liability claims. Uncertainties compound at each step from emissions to global greenhouse gas (GHG) concentrations, GHG concentrations to global temperature changes, global temperature changes to country-level temperature changes, and country-level temperature changes to economic losses, providing emitters with plausible deniability for damage claims. Here we lift that veil of deniability, combining historical data with climate models of varying complexity in an integrated framework to quantify each nation’s culpability for historical temperature-driven income changes in every other country. We find that the top five emitters (the United States, China, Russia, Brazil, and India) have collectively caused US$6 trillion in income losses from warming since 1990, comparable to 14% of annual global gross domestic product; many other countries are responsible for billions in losses. Yet the distribution of warming impacts from emitters is highly unequal: high-income, high-emitting countries have benefited themselves while harming low-income, low-emitting countries, emphasizing the inequities embedded in the causes and consequences of historical warming. By linking individual emitters to country-level income losses from warming, our results provide critical insight into climate liability and national accountability for climate policy.
The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.
In this paper, we consider climate scepticism in the Russian context. We are interested in whether this has been discussed within the social scientific literature and ask first whether there is a discernible climate sceptical discourse in Russia. We find that there is very little literature directly on this topic in either English or Russian and we seek to synthesise related literature to fill the gap. Secondly, we consider whether Russian climate scepticism has been shaped by the same factors as in the USA, exploring how scientists, the media, public opinion, the government and business shaped climate scepticism in Russia. Climate scepticism in the USA is understood as a ‘conservative countermovement’ that seeks to react against the perceived gains of the progressive environmental movement, but we argue that this is not an appropriate framework for understanding Russian climate scepticism. Articulated within a less agonistic environment and situated within an authoritarian regime, Russian expressions of climate scepticism balance the environmental, political and economic needs of the regime under the constraints of a strong ‘carbon culture’ and closed public debate.
Tropical cyclones (TCs) are amongst the costliest natural hazards for southwest Pacific (SWP) Island nations. Extreme winds coupled with heavy rainfall and related coastal hazards, such as large waves and high seas, can have devastating consequences for life and property. Effects of anthropogenic climate change are likely to make TCs even more destructive in the SWP (as that observed particularly over Fiji) and elsewhere around the globe, yet TCs may occur less often. However, the underpinning science of quantifying future TC projections amid multiple uncertainties can be complex. The challenge for scientists is how to turn such technical knowledge framed around uncertainties into tangible products to inform decision-making in the disaster risk management (DRM) and disaster risk reduction (DRR) sector. Drawing on experiences from past TC events as analogies to what may happen in a warming climate can be useful. The role of science-based climate services tailored to the needs of the DRM and DRR sector is critical in this context. In the first part of this paper, we examine cases of historically severe TCs in the SWP and quantify their socio-economic impacts. The second part of this paper discusses a decision-support framework developed in collaboration with a number of agencies in the SWP, featuring science-based climate services that inform different stages of planning in national-level risk management strategies.
Many hydrological models use the concept of potential evapotranspiration (PE) to simulate actual evapotranspiration (AE). PE formulations often neglect the effect of carbon dioxide (CO2), which challenges their relevance in a context of climate change and rapid changes in CO2 atmospheric concentrations. In this work, we implement three options from the literature to take into account the effect of CO2 on stomatal resistance in the well-known Penman–Monteith PE formulation. We assess their impact on future runoff using the Budyko framework over France. On the basis of an ensemble of Euro-Cordex climate projections using the RCP 4.5 and RCP 8.5 scenarios, we show that taking into account CO2 in PE formulations largely reduces PE values but also limits projections of runoff decrease, especially under an emissive scenario, namely, the RCP 8.5, whereas the classic Penman–Monteith formulation yields decreasing runoff projections over most of France, taking into account CO2 yields more contrasting results. Runoff increase becomes likely in the north of France, which is an energy-limited area, with different levels of runoff response produced by the three tested formulations. The results highlight the sensitivity of hydrological projections to the processes represented in the PE formulation.
Changes in precipitation pattern can lead to widespread impacts across natural and human systems. This study assesses precipitation variability as well as anthropogenic and natural factors responsible for the precipitation variability over India in the twentieth and twenty-first centuries using the Coupled Model Intercomparison Project (CMIP5) and observational data sets. We specifically quantify the impact of natural (volcanic and solar radiation) (NAT), greenhouse gas (GHG), anthropogenic aerosol (AA), and land use (LU) forcings on precipitation changes over India. In the observational record, a decrease in precipitation is observed during monsoon and winter seasons, whereas an increase is noticed in pre- and post-monsoon seasons. These observed changes are likely dominated by changes to GHGs and a reduction in AA during the twentieth century compared to those of the LU and NAT forcings. Anthropogenic aerosol plays a dominant role in reducing rainfall in the summer monsoon, leading to drying trends over the second half of the twentieth century in India. The projected simulations indicate an increasing trend during the twenty-first century. The temporal and spatial variability of precipitation displays substantial changes over India during different seasons at different periods under the Representative Concentration Pathway (RCP) 2.6 and 8.5 emission scenarios. Overall, model simulations based on RCP 8.5 show increases in future precipitation throughout the twenty-first century over India.
Lower tax revenues and greater government spending result in higher deficits and public debt. As a result, determining the degree of budgetary effects is vital, but important to assess the persistence of these effects. We aim to investigate the impact of climate change on the fiscal balance and public debt in the countries of the Middle East and North Africa. The empirical analysis relies on panel data in the period 1990-2019 and employs various models. The findings show that temperature changes adversely affect the government budget and increase debt, but we find no significant impact of changes in rainfall. The average temperature decreases fiscal balance by 0.3 percent and increases debt by 1.87 percent. Using projections of temperature and rainfall over the years 2020 to 2099, we find a significant decrease in the fiscal balance at 7.3 percent and an increase in the public debt at 16 percent in 2060-2079 and 18 percent in 2080-2099 under the assumption of a high greenhouse gas (GHG) emission scenario. On the contrary, under the low GHG emission scenario, the fiscal balance deteriorates by 1.7 percent in 2020-2039 and 2.2 percent in 2080-2099, while public debt rises by 5 percent in 2020-2039 and 6.3 percent in 2080-2099.
The online version contains supplementary material available at 10.1007/s10584-022-03388-x.
Rising temperatures are likely to boost residential demand for electricity in warm locations for reasons including increased use of air conditioners, fans, and refrigeration. Yet precise effects may vary by geographical area and with socio-economic conditions. Knowledge on these effects in developing countries is limited due to data availability and reliability issues. Using a high-quality provincial-level monthly dataset for China and fixed-effect panel methods, we find a U-shaped and asymmetrical relationship between ambient temperature and monthly residential electricity use. An additional day with a maximum temperature exceeding 34 °C is on average associated with a 1.6% increase in that month’s per capita residential electricity use relative to if that day’s maximum temperature had been in the 22–26 °C range. The effect of an additional cold day is smaller. There are differences in effects for the south versus the north of China and for urban versus rural areas. Under a high global carbon dioxide emission trajectory, we estimate that expected temperature increases would lead to more than a 25% increase in residential electricity use in July in some provinces by the end of the century, holding other factors constant.
This paper analyzes the framing of the leading state-level climate change mitigation policy in the USA, renewable portfolio standards, in top newspapers from all fifty states. From a corpus of 1522 state newspaper articles which mention renewable portfolio standards, our analysis uses structural topic modeling to identify common frames by region, time period, and state partisanship. Interviews with activists in Michigan and Nevada who were involved in framing renewable portfolio standard legislation provides additional context as to how social movement organizations (SMOs) make framing choices. We find that in newspaper reporting economic frames about business development and utility costs strongly predominate over other frames about emissions and public health. Despite some evidence that a public health frame is effective at increasing support for climate mitigation policies and its being advanced by activists, the frame is almost nonexistent in newspaper coverage.
The exposure of sovereigns to climate risks is priced and can affect credit ratings and debt servicing costs. I argue that the climate risks to fiscal stability are not receiving adequate attention and discuss how to remedy the situation. After providing evidence of divergent climate risks to advanced economies, I describe the transmission channels from climate change to public finance. Then, I suggest how integrated assessment models (IAMs) can be linked with stochastic debt sustainability analysis (DSA) to inform our understanding of climate risks to sovereign debt dynamics and assess the available fiscal space to finance climate policies. I argue for adopting the narrative scenario architecture developed within the IPCC to bring structure and transparency to the analysis. The analysis is complicated by deep uncertainty -risks, ambiguity, and mis-specifications- of climate change. Using scenario trees, narrative scenarios, and ensembles of models, respectively, we can deal with these three challenges. I illustrate using two prominent IAMs to generate the debt dynamics of a high-debt country under climate risks to economic growth and find adverse effects from as early as 2030. I conclude with the policy implications for fiscal stability authorities.
The online version contains supplementary material available at 10.1007/s10584-022-03373-4.
The impacts of the growing population in Lebanon including Lebanese, Palestinian, and Syrian refugees, together with the changing climate, are putting the Bekaa Valley’s water resources in a precarious situation. The water resources are under significant stress limiting the water availability and deteriorating the water quality in the Upper Litani River Basin (ULRB) within the Bekaa Valley. Here, the impacts on water balance and water quality for a 2013 baseline and future scenarios are simulated using the Water Evaluation And Planning model, served by the Watershed Modeling System which provides flows throughout the ungauged zones of the Litani River and its tributaries. The output from a General Circulation Model is used to project the future climate up to 2100 under several emissions’ scenarios which shows a critical situation in the high emission scenario where the precipitation will be reduced by about 87 mm from 2013 to 2095. The research highlights the need to reduce the water pollution that limits the availability of usable water, and to minimize the gap between the demand and supply of water within the ULRB to maintain water supply and quality, even after 80 years. This may be achieved by removing encroachments on the river, adding wastewater treatment plants, reducing the amount of lost water in damaged water network, and avoiding the overconsumption of groundwater.
Though the magnitude of any climate change is important, regions which have a larger signal of climate change relative to the background variations will potentially face greater risks than other regions, as they will see unusual or novel climate conditions more quickly as reported by Frame et al. (Nat Clim Chang 7(6):407–411, 2017). Providing more information about signal and noise on regional scales, and the associated attribution to particular causes, is therefore important for adaptation planning as discussed by Chen et al. (2021). However, whether a detectable signal in temperature extremes emerges in China at the local or regional level during 850–2005 has not been discussed. Based on six selected and bias-corrected global models under the Coupled Model Intercomparison Project Phase 5, relative to 1850–1900, we show that the temporal information of signal-to-noise ratio (S/N) in annual temperature extremes are consistent with annual mean temperature variations in China during 850–2005. Before 1850, absolute values of regional mean S/N in temperature extremes under cold climatic conditions are generally larger than those under warm climatic conditions. At the level of S/N > 1, local increasing signals of cold extremes emerge in the second half of thirteenth century and in the early nineteenth century after large volcanic eruptions in 1257 and 1815 in most part of China, especially in southern China and Tibet Plateau. Over the past 150 years under global warming, absolute values of regional mean S/N in temperature extremes have increasing trends. The regional mean increasing signals of warm extremes over China begin to exceed natural variability in 1963 at the level of S/N > 1, and local warm signals first occur in 1924 in Tibet Plateau. These warming signals are related to greenhouse gas forcing.
Wetlands provide many important ecosystem functions and services worldwide and are hotspots of biological diversity. However, depressional wetlands are particularly vulnerable to effects of climate change due to the significant role that precipitation and surface runoff play in shaping their hydrology. In the Southern Great Plains of North America, climate projections predict more extreme storm events, higher temperatures, and severe droughts, which could threaten natural hydrological patterns of depressional wetlands in this region. Regional hydrological models that accurately predict water dynamics are critical for developing effective climate change adaptation strategies. We developed a model to predict wetland inundation status for depressional wetlands in the Pleistocene Sand Dunes Ecoregion of Oklahoma, USA, that evaluated effects of weather variables, wetland characteristics , and landscape-level variables. We then predicted numbers of inundated wetlands and frequency of wetland inundation under three climate change scenarios for the middle and end of the century (2036-2050 and 2084-2099, respectively). Total precipitation measured in the 2 months prior to an inundation event and average daily temperature were the most important variables predicting wetland inundation status, and land use and wetland characteristics explained relatively little variation in water dynamics. Projections of wetland inundation status indicate numbers of inundated wetlands will decrease in spring and summer by as much as 42% and 79%, respectively, by midcentury. Future inundation patterns during fall and winter were less clear but will likely be similar to current, highly variable conditions. These results suggest climate change may threaten persistence of wetlands during key seasonal periods when humans, plants, and wildlife depend on them for crucial resources and services.
This study aims to project the compound impacts of climate change and human activities, including agriculture expansion and hydropower generation, on the future water availability in the Sre Pok River Basin. The five regional climate models (RCMs): ACESS, REMO2009, MPI, NorESM, CNRM were selected for the future climate projection under two scenarios, i.e., RCP 4.5 and RCP 8.5. Our results reveal that the future annual rainfall is expected to decrease by 200 mm, whereas the average temperature is expected to increase by 0.69 to 4.16 °C under future scenarios. The future water availability of Sre Pok River Basin was projected using soil and water assessment tool (SWAT). Next, the CROPWAT model was used to examine the irrigation water requirement and the HEC-ResSim model to simulate the hydropower generation of Buon Tuar Sarh reservoir. The future simulation indicates the decrease in future water availability, increasing demand for irrigation water and decreases in hydropower generation for the future periods. The irrigated areas are increased from 700 to 1500 ha as per the provincial development plan. This study also examines the present and future drought conditions of Sre Pok River via streamflow drought index (SDI). Our results expect to contribute toward supporting the planning and management of water resources for agriculture and to efficiently cope with drought conditions in the studied basin and beyond.
Considerable interest has been shown in evaluating methodologies to calculate current enteric methane emissions and using those that produce the most precise results. The objectives of this study were (1) to calculate the emission factors (EFs) for enteric methane produced by different livestock systems in the Mexican tropics using the Tier-2 methodology of the 2006 IPCC and 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (2019 IPCC); (2) to calculate the Tier-2 EFs using both IPCC versions with the methane conversion factor (Ym) estimated with emission data specific to the Mexican tropics (denoted as Tier-2MX), and (3) to compare the EFs from (2) and (1) based on the Ym specific to the Mexican tropics and the default Ym for the 2006 and 2019 IPCC, respectively. To calculate the EFs and Ym using the Tier-2 methodology, three models of meat production in the tropics were selected: a monoculture system (MC, 6 farms), an intensive silvopastoral system (ISP, 6 farms), and a native silvopastoral system (NSP, 6 farms). Twelve of the selected farms were dual-purpose (meat and milk production), and 6 were used for calf production. The EFs were estimated using two main steps: (1) classification of livestock into subcategories: bulls, lactating cows, dry cows, and replacement heifers; and (2) calculation of the gross energy (MJ day⁻¹) intake as prescribed in Chapter 10, Volume 4 of the IPCC (2006 and 2019). The data showed that high and low productivity could be distinguished using the 2019 IPCC but not the 2006 IPCC. Higher average EFs were generated by Tier-1 than by Tier-2. The Tier-2 EFs were higher than the Tier-2MX EFs. These results confirm that Tier-2 methodologies can enhance existing differences. Additionally, the Tier-2MX EFs for each type of cattle were lower than the Tier-2 and Tier-1 EFs. These results show that it is advisable to use methane yields determined for a particular country or region.
We analyse the expected characteristics of drought events in northern Italy for baseline (1971–2000), near (2021–2050), and far (2071–2100) future conditions, estimating the drought spatial extent and duration, the percentage of affected area, and the frequency of drought episodes. To this end, daily ensembles of precipitation and temperature records from Global Climate Models (GCMs) and Regional Climate Models (RCMs) pairs, extracted from EURO-CORDEX and MED-CORDEX for the RCP 4.5 and 8.5 scenarios, are collected at spatial resolution of 0.11 degrees. Before the analysis, model outputs are validated on daily weather station time series, and scaling factors for possible use in bias correction are identified. Annual temperature and precipitation anomalies for near and far future conditions are investigated; drought events are identified by the standardized precipitation evapotranspiration index and standardized precipitation index at the 12-, 24-, and 36-month timescales. This study highlights the importance of using multiple drought indicators in the detection of drought events, since the comparison reveals that evapotranspiration anomaly is the main triggering factor. For both scenarios, the results indicate an intensification of droughts in northern Italy for the period 2071–2100, with the Alpine chain being especially affected by an increase of drought severity. A North-to-South spatial gradient of drought duration is also observed.
The consequences of climate change are particularly noticeable through extreme events, which have already changed in intensity and frequency worldwide. This study aims to evaluate the ability of 33 CMIP6 models to simulate the observed trends of four extreme temperature indices in South America during the period 1979–2014. We use daily minimum and maximum temperatures from an observational database, ERA5 reanalysis, and CMIP6 models to estimate the international indices: cold nights, warm nights, cold days, and warm days. Trends are calculated using Sen’s slope for different seasons and spatial scales (continental, sub-regional, and at each grid point) and tested with the Mann–Kendall test. All databases agree on an increase (decrease) in the frequency of warm (cold) extremes in South America, with the most intense changes in the austral spring. In particular, the warm nights index and the northern sub-regions of South America show the most pronounced trends. In contrast, in the southern sub-regions of South America, the observations do not indicate significant trends of the minimum temperature indices, which differ from the trends estimated by the CMIP6 ensemble median and most of the individual models. In general, the ensemble median simulates significant long-term changes at almost all grid points, unlike the observations and reanalysis. Finally, the simulated trends related to minimum temperature are slightly better represented than those related to maximum temperatures. Nevertheless, neither model stands out as the best, and all of them have difficulty simulating trends, especially for cold days.
Assessing the impact of future climate on the severity of ice jam floods (IJFs) is an essential component of a flood mitigation strategy for many ice jam-prone northern communities. The general circulation model (GCM) outputs are used to derive hydrological conditions under future climate scenarios. Although GCMs are often downscaled to the point of interest, there can still be significant differences between modelled climate scenarios and historically observed climate scenarios. Therefore, the changes between the model simulated baseline and future scenarios are applied to observed baseline values to derive projected future values. In IJF modelling, such projected values are provided as input (boundary conditions) to assess the frequency and severity of future IJF events. Different methods can be used to calculate the climate change signal (difference between model-simulated future vs model-simulated historical conditions), and depending upon the method employed, results can vary. In this study, we evaluate the impact of using different delta change methods (i.e., absolute vs relative) to directly bias-correct the hydrological model output on the assessment of frequency and severity of IJFs under future climate. The method was tested in the Athabasca River at Fort McMurray in western Canada, an ice jam-prone location, to assess the IJF probabilities and intensities in the 2041–2070 period. Our results indicate that there is a notable difference in the projected frequency and severity of IJFs between absolute and relative delta change approaches, suggesting the methods should be carefully selected and results cautiously interpreted.
To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5–5.7% per °C), but to increase with increasing CO2 concentration (4.6–8.3% per 100-ppm), rainfall (2.1–6.1% per 10% increase), and nitrogen rates (1.3–6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments.
Visualisations are often the entry point to information that supports stakeholders’ decision- and policy-making processes. Visual displays can employ either static, dynamic or interactive formats as well as various types of representations and visual encodings, which differently affect the attention, recognition and working memory of users. Despite being well-suited for expert audiences, current climate data visualisations need to be further improved to make communication of climate information more inclusive for broader audiences, including people with disabilities. However, the lack of evidence-based guidelines and tools makes the creation of accessible visualisations challenging, potentially leading to misunderstanding and misuse of climate information by users. Taking stock of visualisation challenges identified in a workshop by climate service providers, we review good practices commonly applied by other visualisation-related disciplines strongly based on users’ needs that could be applied to the climate services context. We show how lessons learned in the fields of user experience, data visualisation, graphic design and psychology make useful recommendations for the development of more effective climate service visualisations. This includes applying a user-centred design approach, using interaction in a suitable way in visualisations, paying attention to information architecture or selecting the right type of representation and visual encoding. The recommendations proposed here can help climate service providers reduce users’ cognitive load and improve their overall experience when using a service. These recommendations can be useful for the development of the next generation of climate services, increasing their usability while ensuring that their visual components are inclusive and do not leave anyone behind.