Climatic Change最新文献

The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.

Both full-fledged Earth system models (ESMs) and simple climate models (SCMs) have been used to investigate climate change for future representative CO₂ concentration pathways under the sixth phase of the Coupled Model Intercomparison Project. Here, we explore to what extent complex and simple models are consistent in their carbon cycle response in concentration-driven simulations. Although ESMs and SCMs exhibit similar compatible fossil fuel CO₂ emissions, ESMs systematically estimate a lower ocean carbon uptake than SCMs in the historical period and future scenarios. The ESM and SCM differences are especially large under low-concentration and overshoot scenarios. Furthermore, ESMs and SCMs deviate in their land carbon uptake estimates, but the differences are scenario-dependent. These differences are partly driven by a few model outliers (ESMs and SCMs) and the procedure of observational constraining that is present in the majority of SCMs but not applied in ESMs. The differences in land uptake arise from the difference in the way land-use change (LUC) emissions are calculated and different assumptions on how the carbon cycle feedbacks are defined, possibly reflecting the treatment of nitrogen limitation of biomass growth and historical calibration of SCMs. The differences in ocean uptake, which are especially large in overshoot scenarios, may arise from the faster mixing of carbon from the surface to the deep ocean in SCMs than in ESMs. We also discuss the inconsistencies that arise when converting CO₂ emissions from integrated assessment models (IAMs) to CO₂ concentrations inputs for ESMs, which typically rely on a single SCM. We further highlight the discrepancies in LUC emission estimates between models of different complexity, particularly ESMs and IAMs, and encourage climate modeling groups to address these potential areas for model improvement.

Climate action-based assumptions and tradable characteristics underpinned the development of climate change futures contracts, which are related to carbon and climate markets. Therefore, this paper examines return and volatility spillover between climate change futures and carbon allowance futures using dynamic conditional correlation (DCC) and asymmetric dynamic conditional correlation (ADCC) models with daily and weekly frequency data. Considering the emergence of US market-based carbon futures and climate futures, this study explores bivariate optimal hedging strategies and optimal portfolio strategies. Using daily data, this study discovers unidirectional and positive return and volatility spillover from the carbon futures market to the climate change futures market, implying opportunities for diversification and hedging. The weekly analysis shows bidirectional and negative return spillover between the carbon futures market and the climate change futures market, implying opportunities for risk hedging. In addition, it also reveals unidirectional and positive volatility spillovers from the carbon futures market to the climate change futures market. The carbon market dominates the climate change futures market. The study also reveals that optimal portfolio strategies will be preferred over optimal hedging strategies. Therefore, this study offers practical implications for investors and portfolio managers.

As global studies of climate change depict increasingly dire outcomes of extreme heat, there is an urgent need to understand the appropriateness of heatwave definitions and temperature datasets in different parts of the world. We carry out an intercomparison of the CHIRTS gridded station-satellite temperature dataset with three reanalysis products, ERA5, NCEP-DOE Reanalysis 2, and MERRA2, to assess biases in the absolute value of extreme heat events and the distribution of extreme events. We find close agreement between all four datasets in the magnitude and distribution of extreme temperatures, with a cold bias in the reanalyses over mountainous areas. However, there is little to no agreement between datasets on the timing of extreme heat events in the tropics, and the datasets do not even agree on which month is the hottest month climatologically in these regions. Second, we compare how the four datasets represent the frequency and timing of extreme heat events, using two different types of heatwave definitions: 5-day duration-based extremes and extreme temperature-humidity combinations (heat index). In the case of 5-day heatwaves, there are almost zero events recorded historically in tropical regions. In contrast, high absolute values of the heat index are most common in dry climates, likely due to the dominance of high temperature spikes in these regions, and high heat index events also occur in temperate and tropical regions. There is little agreement between datasets, however, on when these extreme heat index events have happened historically in the tropics. Given these results, we highlight the need for locally developed heatwave metrics for different parts of the world, and we urge against the use of a single heatwave definition in global studies. We also recommend that any studies assessing heat-health relationships in tropical regions beware of the lack of agreement between observational and reanalysis datasets and compare results from different gridded dataset products to estimate uncertainty in heat-health relationships.

Climate change impacts biodiversity through shifts in species distributions and changes in the composition of biological communities. However, the effects of these changes on the spatial association between species are poorly understood. In this study, we examined the effects of climate change on the distribution mismatch between Araucaria angustifolia (araucaria), a critically endangered keystone species in the Brazilian Atlantic Forest hotspot, and its community of seed dispersers and predators. Ecological niche models were employed to compare the distribution of A. angustifolia with the projected distributions of its dispersers and predators under two different climate change scenarios. Our projections revealed species-specific responses to climate change, resulting in varying levels of spatial mismatch between A. angustifolia and its dispersers and predators. Notably, significant changes in the spatial mismatch compared to current conditions were projected for 70% of the seed dispersers and 83% of the seed predators. Interestingly, our projections demonstrated a discernable pattern in the changes in richness of dispersers and predators within the distribution range of A. angustifolia, indicating a potential risk of imbalance in seed dispersal and predation in certain regions. Using ecological niche modeling techniques, our study highlights that the climate-driven decoupling of geographical distributions may contribute to the disruption of biotic interactions, with potential implications for the conservation of A. angustifolia and the fauna dependent on its seeds as a food source. These findings emphasize the importance of considering the indirect effects on biotic interactions when assessing the impacts of climate change on biodiversity.

This study examines the impacts of 1.5 °C and 2.0 °C global warming levels on the characteristics of four major drought modes over Eastern Africa in the future under two climate forcing scenarios (RCP4.5 and RCP8.5). The droughts were quantified using two drought indices: the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI) at 12-month scale. Four major drought modes were identified with the principal component analysis (PCA). Multi-model simulation datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) were analysed for the study. The skill of the models to reproduce the spatial distribution and frequency of past drought modes over Eastern Africa was examined by comparing the simulated results with the Climate Research Unit (CRU) observation. The models give realistic simulations of the historical drought modes over the region. The correlation between the simulated and observed spatial pattern of the drought modes is high (r ≥ 0.7). Over the hotspot of the drought modes, the observed drought frequency is within the simulated values, and the simulations agree with the observation that the frequency of SPI-12 droughts is less than that of SPEI-12 droughts. For both RCP4.5 and RCP8.5 scenarios, the simulation ensemble projects no changes in the spatial structure of the drought modes but suggests an increase in SPEI-12 drought intensity and frequency over the hotspots of the drought modes. The magnitude of the increase, which varies over the drought mode hotspots, is generally higher at 2 °C than at 1.5 °C global warming levels. More than 75% of the simulations agree on these projections. The projections also show that the increase in drought intensity and frequency is more from increased potential evapotranspiration than from reduced precipitation. Hence, the study suggests that to reduce impacts of global warming on future drought, the adaptation activities should focus on reducing evaporative loss surface water.

Water temperature is a key variable affecting fish habitat in rivers. The Sockeye salmon (Oncorhynchus nerka), a keystone species in north western aquatic ecosystems of North America, is profoundly affected by thermal regime changes in rivers, and it holds a pivotal role in ecological and economic contexts due to its life history, extensive distribution, and commercial fishery. In this study, we explore the effects of climate change on the thermal regime of the Nechako River (British Columbia, Canada), a relatively large river partially controlled by the Skins Lake Spillway. The CEQUEAU hydrological-thermal model was calibrated using discharge and water temperature observations. The model was forced using the Fifth generation of ECMWF Atmospheric Reanalysis data for the past and meteorological projections (downscaled and bias-corrected) from climate models for future scenarios. Hydrological calibration was completed for the 1980–2019 period using data from two hydrometric stations, and water temperature calibration was implemented using observations for 2005–2019 from eight water temperature stations. Changes in water temperature were assessed for two future periods (2040–2069 and 2070–2099) using eight Coupled Model Intercomparison Project Phase 6 climate models and using two Shared Socioeconomic Pathway scenarios (4.5 and 8.5 W/m² by 2100) for each period. Results show that water temperatures above 20°C (an upper threshold for adequate thermal habitat for Sockeye salmon migration in this river) at the Vanderhoof station will increase in daily frequency. While the frequency of occurrence of this phenomenon is 1% (0–9 days/summer) based on 2005–2019 observations, this number range is 3.8–36% (0–62 days/summer) according to the ensemble of climate change scenarios. These results show the decreasing habitat availability for Sockeye salmon due to climate change and the importance of water management in addressing this issue.

Climate despair—a sense of hopelessness about humanity’s ability to pursue a sustainable future—is emerging as a psychosocial threat. Psychological science conceptualizes hopelessness as a cognitive schema characterized by negative expectancies. Climate hopelessness, then, may be conceptualized as a mental model that represents climate change as a massive problem with futile response options. It manifests in negative expectancies about the future. Here we show that learning about eco-innovations—novel climate-response options—can decrease climate hopelessness. Across 11 experiments (N = 3224), we found that adults (mostly from the USA) reported lower climate hopelessness after viewing videos that depicted eco-innovations (such as a high-tech, net-zero-energy city) than they did in various control conditions, including those that were unrelated to climate (such as a no-video control) and those that depicted more familiar, schema-consistent climate responses (such as living in a rural, intentional community). This research provides causal evidence that thinking about novel climate responses can contribute to a more hopeful outlook, and it identifies technological innovation as one possible seed for such messaging.

Fossil fuels remain undertaxed worldwide despite the accelerating threat of climate change. While subscribing to the existing literature on that domestic politics matters, I contend that policy diffusion is yet another mechanism here: risk-averse policy makers carefully follow their counterparts abroad, so incrementalism in taxing fossil fuels is self-perpetuating transnationally. Using data from 29 OECD countries, 1990–2019, various spatial econometric analyses lend support to my argument: the excise tax on gasoline in one country positively correlates to that in other countries, with this interdependence being more pronounced between geographic or linguistic “neighbors” and trade or political partners. Substantively, a gasoline tax cut by one unit in a single country could spread over 60% of its negative effect elsewhere. This Letter concludes with its contribution to the literature, possible avenues for future research, as well as important policy implications.