• Energy Research

Understanding of extreme precipitation change in response to CO2 forcing and associated socioeconomic exposure is limited. In this study, a comprehensive analysis is conducted to explore the response of global extreme precipitation to CO2 forcing in terms of hysteresis and reversibility effect and associated population exposure. In this regard, climate outputs under two idealized CO2 scenarios such as ramp-up (RU; about +1% annually until quadrupling of present level) and ramp-down (RD; around −1% annually set back to present level) from Community Earth System Model version 1.2, and the projected population data from the five shared Socioeconomic Pathways (SSPs) are used. Extreme precipitation events are evaluated using the number of heavy precipitation days (R30 mm), maximum consecutive 5-day precipitation (Rx5day), and the precipitation of very wet days (R95pTOT) indices. Results show that the magnitude of extreme precipitation change and associated population exposure is higher in the CO2 reduction period (RD) than in RU. All the indices show substantial irreversible and hysteresis effects, ∼69% of the global land is expected to experience irreversible changes in extreme precipitation. Further, the hotspots of irreversibility (the region with irreversible change and a large hysteresis) will emerge in >20% of the global area. Spatially, strong hysteresis and irreversibility are particularly concentrated over global land monsoon regions. The leading exposure is estimated under SSP3 combined with both RU and RD periods. Under the SSP3-RD combination, the highest population exposure is estimated at ∼67.1% (globally averaged), and ∼72% (averaged over hotspots) higher than that of the present day. The exposed population is prominent in South Africa and Asia. Notably, the population change effect is the principal factor in global exposure change, while it is the climate change effect over the hotspots of irreversibility. These findings provide new insight into policymaking that only CO2 mitigation effort is not enough to cope with extreme precipitation, rather advanced adaptation planning is a must to have more socio-economic benefits.

Observed long-term variations in summer season timing and length in the Northern Hemisphere (NH) continents and their subregions were analyzed using temperature-based indices. The climatological mean showed coastal–inland contrast; summer starts and ends earlier inland than in coastal areas because of differences in heat capacity. Observations for the past 60 years (1953–2012) show lengthening of the summer season with earlier summer onset and delayed summer withdrawal across the NH. The summer onset advance contributed more to the observed increase in summer season length in many regions than the delay of summer withdrawal. To understand anthropogenic and natural contributions to the observed change, summer season trends from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations forced with the observed external forcings [anthropogenic plus natural forcing (ALL), natural forcing only (NAT), and greenhouse gas forcing only (GHG)] were analyzed. ALL and GHG simulations were found to reproduce the overall observed global and regional lengthening trends, but NAT had negligible trends, which implies that increased greenhouse gases were the main cause of the observed changes. However, ALL runs tend to underestimate the observed trend of summer onset and overestimate that of withdrawal, the causes of which remain to be determined. Possible contributions of multidecadal variabilities, such as Pacific decadal oscillation and Atlantic multidecadal oscillation, to the observed regional trends in summer season length were also assessed. The results suggest that multidecadal variability can explain a moderate portion (about ±10%) of the observed trends in summer season length, mainly over the high latitudes.

Abstract Recent studies showed that anthropogenic greenhouse gas (GHG) increase is a major driver of the observed increases in extreme temperatures at global and regional scales using an optimal fingerprint (OF) method, which is a frequentist approach based on linear regression. Here, a Bayesian decision method is employed, which finds the most probable cause of the observed changes by comparing likelihoods of different forcings in view of observations. To quantify individual forcing contributions, a new modified attribution procedure based on Bayesian decision is proposed, i.e., computing the likelihood ratio [Bayes factor (BF)] between different forcings. First, the contribution of anthropogenic forcing (ANT) is measured by BF between anthropogenic-plus-natural forcing (ALL) and natural forcing (NAT) using a threshold for “substantial” evidence (lnBF ≥ 1). Similarly, the NAT contribution is assessed by BF between ALL and ANT. Further, the GHG contribution to the detected ANT is quantified by BF between ANT and anthropogenic aerosols (AA), and the AA contribution is evaluated by BF between ANT and GHG. The devised Bayesian approach is applied to HadEX3 observations and CMIP6 multimodel simulations for extreme temperature intensities (warmest day/night and coldest day/night) for global, continental, and regional domains following previous studies. Bayesian attribution results indicate that the ANT signal is detected in many continental and subregions for all extremes indices. This is generally consistent with OF-based results but with less frequent detection, indicating that the Bayesian method is slightly stricter than the OF method. However, GHG contributions to the detected ANT are identified over more subregions in the Bayesian attribution, suggesting its potential advantage over conventional methods in case of low signal-to-noise ratio and high collinearity.

AbstractFor the comprehensive estimation of regional climate change over East Asia (EA) at the 2 and 3 °C global warming levels (GWLs), the Köppen‐Trewartha climate‐type change is assessed with ensemble regional climate change projections in line with Coordinated Regional Climate Downscaling Experiment (CORDEX)‐EA phase 2. Under the 2 °C (3 °C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate‐type changes. Tropical and subtropical climate types will expand northward, accompanied by increasing hydroclimatic intensity. Limiting GWL to 2 °C shows benefits by preventing subtropical‐type expansion around far EA. Desertification over inland regions of EA exhibits scenario dependency. Boreal and tundra climate types over high‐latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau. The results are expected to be a baseline assessment of climate change over EA under the 2 and 3 °C GWLs above the preindustrial level.

AbstractRecent climate change is characterized by rapid global warming, but the goal of the Paris Agreement is to achieve a stable climate where global temperatures remain well below 2°C above pre‐industrial levels. Inferences about conditions at or below 2°C are usually made based on transient climate projections. To better understand climate change impacts on natural and human systems under the Paris Agreement, we must understand how a stable climate may differ from transient conditions at the same warming level. Here we examine differences between transient and quasi‐equilibrium climates using a statistical framework applied to greenhouse gas‐only model simulations. This allows us to infer climate change patterns at 1.5°C and 2°C global warming in both transient and quasi‐equilibrium climate states. We find substantial local differences between seasonal‐average temperatures dependent on the rate of global warming, with mid‐latitude land regions in boreal summer considerably warmer in a transient climate than a quasi‐equilibrium state at both 1.5°C and 2°C global warming. In a rapidly warming world, such locations may experience a temporary emergence of a local climate change signal that weakens if the global climate stabilizes and the Paris Agreement goals are met. Our research demonstrates that the rate of global warming must be considered in regional projections.

Heavily industrialized East Asia, with its high greenhouse gas emissions, must inevitably increase renewable energy production to achieve the goals of the Paris Agreement. Photovoltaics (PV), a widely utilized renewable energy source, is directly affected by the weather and climate. This study conducted the first analysis of current and future PV potential (PVpot) changes over East Asia using the ERA5 reanalysis and multiple high-resolution regional climate model simulations. The recent PVpot over East Asia did not exhibit any notable changes, but the future PVpot of the multi-model ensemble is predicted to decrease by-4.3% (winter) to-1.5% (summer) on average with excellent inter-model agreements. Results demonstrated that the widespread increase in near-surface air temperature causes the overall PVpot decrease (around-2.0%) over East Asia across all seasons. Interestingly, surface down-welling shortwave radiation increases in summer, offsetting temperature-induced PVpot decreases (by about 0.7%) while it declines in winter and spring, intensifying the warming-driven PVpot decrease (by approximately-1.4% to-2.3%). Further, the changes in the number of rainy days are associated with the changing patterns of surface down-welling shortwave radiation, indicating the importance of reliable projections of precipitation. Wind speed exerts a negligible effect on the future PVpot change. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; 1 ; 1 ; N ; scie ; scopus

AbstractUsing Generalized Extreme Value analysis, this study details the independent seasonal impacts of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on rainfall extremes that cause many hydro‐meteorological hazards and affect vulnerable populations in Indonesia, based on indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), for the period 1981–2019. Gridded Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is used to calculate maximum consecutive 5‐day precipitation (Rx5d), total precipitation from days above 95 percentile (R95p), and maximum number of consecutive dry days (CDD). Consistent with previous studies, the ENSO and IOD impacts on rainfall extremes are shown to be strongest during the dry seasons (JJA‐SON) and weaker in the wet seasons (DJF‐MAM). Rainfall extremes appear to be widely influenced throughout Indonesia by ENSO, whereby extremes become drier (wetter) during El Niño (La Niña). Similarly, positive (negative) phases of the IOD lead to more extreme dry (wet) conditions. However, distinct from previous studies, as ENSO and IOD often co‐occur, we also provide independent influences of the two climate modes. Low‐level circulation northeast and southwest of Indonesia, both previously suggested as main drivers of impacts on Maritime Continent rainfall, are more closely associated with independent ENSO and IOD, respectively. For example, ENSO, independent of IOD, impacts rainfall extremes more in the northern and eastern regions of Indonesia, and the IOD, independent of ENSO, modulates rainfall extremes more over southern and western regions. Despite independent ENSO and IOD impacts understandably being found more eastward and westward of the country, respectively, details provided here help explain regional differences between rainfall extremes and ENSO and IOD, such as Jakarta in west Java, which is predominantly influenced by local forcing associated with the IOD.

Abstract Spatial climate analogs effectively illustrate how a location’s climate may become more similar to that of other locations from the historical period to future projections. Also, novel climates (emerging climate conditions significantly different from the past) have been analyzed as they may result in significant and unprecedented ecological and socioeconomic impacts. This study analyzes historical to future spatial climate analogs across East Asia and Europe, in the context of climatic impacts on ecology and human health, respectively. Firstly, the results of climate analogs analysis for ecological impacts indicate that major cities in East Asia and Europe have generally experienced novel climates and climate shifts originating from southern/warmer regions from the early 20th century to the current period, primarily attributed to extensive warming. In future projections, individual cities are not expected to experience additional significant climate change under a 1.5 °C global warming (warming relative to pre-industrial period), compared to the contemporary climate. In contrast, robust local climate change and climate shifts from southern/warmer regions are expected at 2.0 °C and 3.0 °C global warming levels. Specially, under the 3.0 °C global warming, unprecedented (newly emerging) climate analogs are expected to appear in a few major cities. The climate analog of future projections partially align with growing season length projections, demonstrating important implications on ecosystems. Human health-relevant climate analogs exhibit qualitatively similar results from the historical period to future projections, suggesting an increasing risk of climate-driven impacts on human health. However, distinctions emerge in the specifics of the climate analogs analysis results concerning ecology and human health, emphasizing the importance of considering appropriate climate variables corresponding to the impacts of climate change. Our results of climate analogs present extensive information of climate change signals and spatiotemporal trajectories, which provide important indicators for developing appropriate adaptaion plans as the planet warms.