Influence Of Anthropogenic Climate Change Research Articles

Future Changes in the Intensity and Duration of Marine Heat and Cold Waves: Insights from Coupled Model Initial-Condition Large Ensembles

Abstract The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

Anthropogenic climate change drives non-stationary phytoplankton internal variability

Abstract. Earth system models suggest that anthropogenic climate change will influence marine phytoplankton over the coming century with light-limited regions becoming more productive and nutrient-limited regions less productive. Anthropogenic climate change can influence not only the mean state but also the internal variability around the mean state, yet little is known about how internal variability in marine phytoplankton will change with time. Here, we quantify the influence of anthropogenic climate change on internal variability in marine phytoplankton biomass from 1920 to 2100 using the Community Earth System Model 1 Large Ensemble (CESM1-LE). We find a significant decrease in the internal variability of global phytoplankton carbon biomass under a high emission (RCP8.5) scenario and heterogeneous regional trends. Decreasing internal variability in biomass is most apparent in the subpolar North Atlantic and North Pacific. In these high-latitude regions, bottom-up controls (e.g., nutrient supply, temperature) influence changes in biomass internal variability. In the biogeochemically critical regions of the Southern Ocean and the equatorial Pacific, bottom-up controls (e.g., light, nutrients) and top-down controls (e.g., grazer biomass) affect changes in phytoplankton carbon internal variability, respectively. Our results suggest that climate mitigation and adaptation efforts that account for marine phytoplankton changes (e.g., fisheries, marine carbon cycling) should also consider changes in phytoplankton internal variability driven by anthropogenic warming, particularly on regional scales.

Assessing the future influence of the North Pacific trade wind precursors on ENSO in the CMIP6 HighResMIP multimodel ensemble

The El Niño Southern Oscillation (ENSO), as one of the largest coupled climate modes, influences the livelihoods of millions of people and ecosystems survival. Thus, how ENSO is expected to behave under the influence of anthropogenic climate change is a substantial question to investigate. In this paper, we analyze future predictions of specific traits of ENSO, in combination with a subset of well-established precursors—the Trade Wind Charging and North Pacific Meridional Mode (TWC/NPMM). We study it across three sets of experiments from a protocol-driven ensemble from CMIP6—the High Resolution Model Intercomparison Project (HighResMIP). Namely, (1) experiments at constant 1950’s radiative forcings, and (2) experiments of present (1950–2014) and (3) future (2015–2050) climate with prescribed increasing radiative forcings. We first investigate the current and predicted spatial characteristics of ENSO events, by calculating area, amplitude and longitude of the Center of Heat Index (CHI). We see that TWC/NPMM-charged events are consistently stronger, in both the presence and absence of external forcings; however, as anthropogenic forcings increase, the area of all ENSO events increases. Since the TWC/NPMM-ENSO relationship has been shown to affect the oscillatory behavior of ENSO, we analyze ENSO frequency by calculating CHI-analogous indicators on the Continuous Wavelet Transform (CWT) of its signal. With this new methodology, we show that across the ensemble, ENSO oscillates at different frequencies, and its oscillatory behavior shows different degrees of stochasticity, over time and across models. However, we see no consistent indication of future trends in the oscillatory behavior of ENSO and the TWC/NPMM-ENSO relationship.

The influence of anthropogenic climate change on meteorological drought in the Lancang-Mekong River basin

Frequent occurrence of droughts in the Lancang-Mekong River Basin (LMRB) has produced incalculable impacts on human livelihood and river ecosystem. However, the roles of natural and anthropogenic forcings in the spatio-temporal evolution and characteristics of droughts have yet to be comprehensively explored, nor being well disentangled. In this study, two reanalysis climate datasets and an ensemble of 9 CMIP6 model simulations were used to investigate the influences of natural and anthropogenic external forcings on drought evolutions in LMRB during 1951–2020, using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). The changes in drought frequency, duration, severity, intensity, and affected area and their Copula-based multi-dimensional drought probabilities were also analyzed at both gridded and sub-regional scales. Results reveal: (1) for SPI, a robust forcing from greenhouse gases is substantially detected in observational signals during dry season, especially in spring; (2) for SPEI, the anthropogenic signal is strongly attributable, especially over longer temporal scales and larger geographical areas, while the natural forcing is only detectable in dry season with underestimated degrees of changes; (3) for univariate drought characteristics, the human activities have exacerbated drought conditions throughout LMRB, especially the drought frequency in the lower Lancang River basin, and drought duration and severity in the middle and lower Mekong River basin; (4) for multi-dimensional drought characteristics, anthropogenic activities have general decreased the co-occurrence probability of multi-dimensional droughts in the Lancang Rivier basin, but increased the risk of multi-dimensional drought events associated with long duration in the Mekong River basin. These findings could be beneficial to better understand the impacts of anthropogenic climate change on droughts and provide a timely evidence base for drought mitigation in the important trans-boundary regions of LMRB under a changing environment.

A forecast-model-based extreme weather event attribution system developed for Aotearoa New Zealand

A largely automated extreme weather event (EWE) attribution system has been developed that uses the Weather Research and Forecast numerical weather prediction model to simulate EWEs under current and pre-industrial climate conditions. The system has been applied to two extreme precipitation events in Aotearoa New Zealand with the goal of quantifying the effect of anthropogenic climate change on the severity of these events. The forecast simulation of the target event under current climate conditions constitutes the first scenario (ALL). We then apply a climate change signal in the form of delta fields in sea-surface temperature, atmospheric temperature and specific humidity, creating a second ‘naturalised’ scenario (NAT) which is designed to represent the weather system in the absence of human interference with the climate system. A third scenario, designed to test for coherence, is generated by applying deltas of opposite sign compared to the naturalised scenario (ALL+). Each scenario comprises a 22-member ensemble which includes one simulation that was not subject to stochastic perturbation. Comparison of the three ensembles shows that: (1) the NAT ensemble develops an extreme event which resembles the observed event, (2) the severity, i.e. maximum intensity and/or the size of area affected by heavy precipitation, changes when naturalising the boundary conditions, (3) the change in severity is consistently represented within the three scenarios and the signal is robust across the different ensemble members, i.e. it is typically shown in most of the 22 ensemble members. Thus, the attribution system presented here can be used to provide information about the influence of anthropogenic climate change on the severity of specific extreme events.

Climate change amplified the 2009 extreme landslide event in Austria

Landslides are an important natural hazard in mountainous regions. Given the triggering and preconditioning by meteorological conditions, it is known that landslide risk may change in a warming climate, but whether climate change has already affected individual landslide events is still an open question, partly owing to landslide data limitations and methodological challenges in climate impact attribution. Here, we demonstrate the substantial influence of anthropogenic climate change on a severe event in the southeastern Alpine forelands with some estimated 952 individual landslides in June 2009. Our study is based on conditional event attribution complemented by an assessment of changes in atmospheric circulation. Using this approach, we simulate the meteorological event under observed and a range of counterfactual conditions of no climate change and explicitly predict the landslide occurrence probability for these conditions. We find that up to 10%, i.e., 95 landslides, can be attributed to climate change.

Increased tropical cyclone intensification time in the western North Pacific over the past 56 years

It has been projected that the influence of anthropogenic climate change on tropical cyclone (TC) intensity could be detected by the end of the century although significant increasing trends in TC intensity metrics have been found based on the currently available historic records. The human influences on TC intensity have been debated for about two decades because of a lack of quantitative assessment of the contributions of large-scale environmental factors and track shifting. As an extension of a previous study, we show that the observed rise in the percentage of intense TCs in the western North Pacific basin over the past 56 years resulted from the combined influence of the track shifting and temporary changes in environmental factors. The influence of environmental factors was primarily owing to the decrease of environmental vertical wind shear and the warming of sea surface temperature (SST). While a small part of the observed rise in the percentage of intense TCs resulted from SST warming, the track shifting increased the TC intensification time by 18.2% (11.3 h) over the past 56 years, accounting for more than one-third of the observed percentage increase in intense TCs. Since track shifting is also projected in the global warming experiments, this study suggests that anthropogenic climate change may intensify TCs by shifting TC prevailing tracks.

Multi-model chain for climate change scenario analysis to support coastal erosion and water quality risk management for the Metropolitan city of Venice

Under the influence of anthropogenic climate change, hazardous climate and weather events are increasing in frequency and severity, with wide-ranging impacts across ecosystems and landscapes, especially fragile and dynamic coastal zones. The presented multi-model chain approach combines ocean hydrodynamics, wave fields, and shoreline extraction models to build a Bayesian Network-based coastal risk assessment model for the future analysis of shoreline evolution and seawater quality (i.e., suspended particulate matter, diffuse attenuation of light). In particular, the model was designed around a baseline scenario exploiting historical shoreline and oceanographic data within the 2015–2017 timeframe. Shoreline erosion and water quality changes along the coastal area of the Metropolitan city of Venice were evaluated for 2021–2050, under the RCP8.5 future scenario. The results showed a destabilizing trend in both shoreline evolution and seawater quality under the selected climate change scenario. Specifically, after a stable period (2021–2030), the shoreline will be affected by periods of erosion (2031–2040) and then accretion (2041–2050), with a simultaneous decrease in seawater quality in terms of higher turbidity. The decadal analysis and sensitivity evaluation of the input variables demonstrates a strong influence of oceanographic variables on the assessed endpoints, highlighting how the factors are strongly connected.The integration of regional and global climate models with Machine Learning and satellite imagery within the proposed multi-model chain represents an innovative update on state-of-the-art techniques. The validated outputs represent a good promise for better understanding the varying impacts due to future climate change conditions (e.g., wind, wave, tide, and sea-level). Moreover, the flexibility of the approach allows for the quick integration of climate and multi-risk data as it becomes available, and would represent a useful tool for forward-looking coastal risk management for decision-makers.

Attributing the driving mechanisms of the 2015–2017 drought in the Western Cape (South Africa) using self-organising maps

The Southwestern Cape (SWC) region in South Africa experienced a severe rainfall deficit between 2015–2017. The resulting drought caused the City of Cape Town to almost run out of water during the summer of 2017–2018. Using the self-organising maps approach, we identify and classify the synoptic circulation states over Southern Africa known to influence the local climate in the SWC into three groups (dry, intermediate, and wet circulation types) using large ensembles of climate model simulations with anthropogenic forcing and natural forcing. We then assessed the influence of anthropogenic climate change on the likelihood of these circulation types and associated rainfall amounts over the SWC during the drought. Our findings suggest that during the drought, the frequency of dry (wet) circulation types increases (decreases) across all models under anthropogenic forcing relative to the natural forcing. While there was no clear direction in the associated rainfall change in the dry circulation types, rainfall decreased across most models in wet nodes. All models agree that anthropogenic climate change has increased the likelihood of dry circulation types (median probability ratio (PR): 0.93–0.96) and decreased that of wet circulation types (median PR: 1.01 and 1.12), indicating a shift towards lesser (more) wet (dry) synoptic circulation states and associated rainfall during the drought. The long-term climatology also depicts similar patterns indicating the drought may result from long-term changes in the frequency of wet circulations and their associated rainfall. This study further explains the anthropogenic influence on the dynamic (synoptic circulation states) and thermodynamic (rainfall) factors that influenced the SWC 2015–2017 drought.

When don’t we need a new extreme event attribution study?

The influence of anthropogenic climate change on the frequency and intensity of extreme weather events is becoming increasingly well understood. Extreme event attribution studies now exist for hundreds of events over the past few decades. However, there remain large heterogeneities in the number of attribution studies across the world and for different hazards, as well as limited capacity to conduct new studies. In this study, we suggest that there is more information to draw from past attribution studies about recent events. This is because, even though anthropogenic forcing continues to increase, many new events share meteorological characteristics with previously attributed ones. Here, we explore the possibility of using related studies and other lines of existing evidence such as projections and trend analysis to create rapid, low-resource attribution statements. To do this, we discuss the potential use cases for attribution results, including raising awareness of climate risks, preparing adaptation measures and attributing climate loss and damage. Then we discuss the considerations necessary to fulfil these uses in three cases studies, including a heatwave in the UK, a tropical storm in the Caribbean and a drought in East Africa. To conclude, we highlight the regions and hazards for which information can be drawn without new quantitative analysis, and those in which it remains urgent. This could aid prioritisation of limited resources for research into less well understood regions and hazards.

A framework for detection and attribution of regional precipitation change: Application to the United States historical record

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

New York State Hurricane Hazard: History and Future Projections

Abstract This study addresses hurricane hazard to the state of New York in past, present, and future using synthetic storms generated by the Columbia Hazard model (CHAZ) and climate inputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5), in conjunction with historical observations. The projected influence of anthropogenic climate change on future hazard is quantified by the normalized differences in statistics of hurricane hazard between the recent historical period (1951–2005) and two future periods under the representative concentration pathway 8.5 warming scenario: the near future (2006–40) and the late-twenty-first century (2070–99). Changes in return periods of storms affecting the state at given intensities are computed, as are wind hazards for individual counties. Other storm characteristics examined include hurricane intensity, forward speed, heading, and rate of change of the heading. The 10th, 25th, 50th, 75th, and 90th percentiles of these characteristics mostly change by less than 3% from the historical to the near future period. In the late-twenty-first century, CHAZ projects a clear upward trend in New York hurricane intensity as a consequence of increasing potential intensity and decreasing vertical wind shear in the vicinity. CHAZ also projects a decrease in translation speed and an increasing probability of approach from the east. Changes in hurricane wind hazard, however, are epistemically uncertain because of a fundamental uncertainty in CHAZ projections of New York State hurricane frequency in which frequency either increases or decreases depending on which humidity variable is used in the environmental index that controls genesis in the model. Thus, projected changes in the wind hazards are reported separately under storylines of increasing or decreasing frequency.

Observed influence of anthropogenic climate change on tropical cyclone heavy rainfall

Observed influence of anthropogenic climate change on tropical cyclone heavy rainfall

Operational extreme weather event attribution can quantify climate change loss and damages

“It is now well established that the influence of anthropogenic climate change on certain individual extreme weather events can be quantified by event attribution techniques. It is time that these activities move from the research community to the operational centers. Such routine evaluation of the human influence on extreme weather increases our scientific understanding and informs the public of climate change impacts. Furthermore, quantification of the human influence on extreme weather can be used to fairly evaluate climate change induced loss and damages”.

Stairway to extinction? Influence of anthropogenic climate change on distribution patterns of montane Strigiformes in Mesoamerica

Although anthropogenic climate change (ACC) is a global phenomenon affecting all ecosystems, its effects are especially relevant in certain ecosystems, such as tropical montane forests. Responses of montane species to ACC in Mesoamerica remain unclear, limiting our ability to assess their vulnerability and the impacts on these ecosystems overall. To understand mechanisms underlying the distribution and vulnerability of montane faunas, we analyzed the influence of ACC on the geographic distribution of owls (order Strigiformes), which are a group of top avian predators distributed in montane forests. Using ecological niche models, we estimated the potential distributions of 35 species at present and under projected future climates (2050 and 2070) and analyzed changes in distributional patterns in terms of range size and altitudinal distribution for each species, as well as spatio-temporal patterns of species richness. Most of our projections (~86%) were consistent with the widely accepted hypothesis of species range shift to higher altitudes combined with reduction in distribution, as species try to track their climatic preferences. Interestingly, the mid-elevation species emerge as the most strongly affected by ACC, showing the highest rates of change. All climate scenarios produced a similar pattern of change in owl species richness, but they differed in the total number of species, a loss of 11 species and a maximum gain of seven species. Species richness remained relatively constant at mid elevations, whereas the greatest losses were in the highlands and the contiguous lowlands. Overall, our results suggest a severe impact of ACC in the coming decades for most owls of Mesoamerican montane forests.

Anthropogenic climate change has slowed global agricultural productivity growth

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.

The influence of anthropogenic climate change on wet and dry summers in Europe

Understanding the role of anthropogenic forcings in regional hydrological changes can help communities plan their adaptation in an informed manner. Here we apply attribution research methods to investigate the effect of human influence on historical trends in wet and dry summers and changes in the likelihood of extreme events in Europe. We employ an ensemble of new climate models and compare experiments with and without the effect of human influence to assess the anthropogenic contribution. Future changes are also analysed with projections to year 2100. We employ two drought indices defined relative to the pre-industrial climate: one driven by changes in rainfall only and one that also includes the effect of temperature via changes in potential evapotranspiration. Both indices suggest significant changes in European summers have already emerged above variability and are expected to intensify in the future, leading to widespread dryer conditions which are more extreme in the south. When only the effect of rainfall is considered, there is a distinct contrast between a shift towards wetter conditions in the north and dryer in the south of the continent, as well as an overall increase in variability. However, when the effect of warming is also included, it largely masks the wet trends in the north, resulting in increasingly drier summers across most of the continent. Historical index trends are already detected in the observations, while models suggest that what were extremely dry conditions in the pre-industrial climate will become normal in the south by the end of the century.

Inventories of extreme weather events and impacts: Implications for loss and damage from and adaptation to climate extremes

Extreme and impactful weather events of the recent past provide a vital but under-utilised data source for understanding present and future climate risks. Extreme event attribution (EEA) enables us to quantify the influence of anthropogenic climate change (ACC) on a given event in a way that can be tailored to stakeholder needs, thereby enhancing the potential utility of studying past events. Here we set out a framework for systematically recording key details of high-impact events on a national scale (using the UK and Puerto Rico as examples), combining recent advances in event attribution with the risk framework. These ‘inventories’ inherently provide useful information depending on a user’s interest. For example, as a compilation of the impacts of ACC, we find that in the UK since 2000, at least 1500 excess deaths are directly attributable to human-induced climate change, while in Puerto Rico the increased intensity of Hurricane Maria alone led to the deaths of up to 3670 people. We also explore how inventories form a foundation for further analysis, learning from past events. This involves identifying the most damaging hazards and crucially also vulnerabilities and exposure characteristics over time. To build a risk assessment for heat-related mortality in the UK we focus on a vulnerable group, elderly urban populations, and project changes in the hazard and exposure within the same framework. Without improved preparedness, the risk to this group is likely to increase by ~50% by 2028 and ~150% by 2043. In addition, the framework allows the exploration of the likelihood of otherwise unprecedented events, or 'Black Swans’. Finally, not only does it aid disaster preparedness and adaptation at local and national scales, such inventories also provide a new source of evidence for global stocktakes on adaptation and loss and damage such as mandated by the Paris Climate Agreement.

Conditional and residual trends of singular hot days in Europe

The influence of anthropogenic climate change on both mean and extremely hot temperatures in Europe has been demonstrated in a number of studies. There is a growing consensus that high temperature extremes have increased more rapidly than the regional mean in central Europe, while the difference between extreme and mean trends is not significant in other European regions. However, it is less clear how to quantify the changes in different processes leading to heat extremes. Extremely hot temperatures are associated to a large extent with specific types of atmospheric circulation. Here we investigate how the temperature associated with atmospheric patterns leading to extremely hot days in the present could evolve in the future. We propose a methodology to calculate conditional trends tailored to the circulation patterns of specific days by computing the evolution of the temperature for days with a similar circulation to the day of interest. We also introduce the concept of residual trends, which compare the conditional trends to regional mean temperature trends. We compute these trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single coupled model (CESM), in order to account for different sources of uncertainty. We also evaluate how bias correction of climate simulations influences the results.

The Pacific Meridional Mode and ENSO: a Review

This paper reviews recent progress in understanding of the North Pacific Meridional Mode (NPMM) and its influence on the timing, magnitude, flavor, and intensity of the El Nino-Southern Oscillation (ENSO). The NPMM is a seasonally evolving mode of coupled climate variability and features several distinct opportunities to influence ENSO. They include: (1) A Wind-Evaporation-SST (WES) feedback-driven propagation of surface anomalies onto the equator during boreal spring, (2) Trade Wind Charging (TWC) of equatorial subsurface heat content by NPMM-related surface wind stress curl anomalies in boreal winter and early spring, (3) The reflection of NPMM-forced ocean Rossby waves off the western boundary in boreal summer, and (4) A Gill-like atmospheric response associated with anomalous deep convection in boreal summer and fall. The South Pacific Meridional Mode (SPMM) also significantly modulates ENSO, and its interactions with the NPMM may contribute to ENSO diversity. Together, the NPMM and SPMM are also important components of Tropical Pacific Decadal Variability; however, future research is needed to improve understanding on these timescales. Since 1950, the boreal spring NPMM skillfully predicts about 15–30% of observed winter ENSO variability. Improving simulated NPMM-ENSO relationships in forecast models may reduce ENSO forecasting error. Recent studies have begun to explore the influence of anthropogenic climate change on the NPMM-ENSO relationship; however, the results are inconclusive.