Climate Noise Research Articles

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

Changing PM2.5 and related meteorology over India from 1950–2014: a new perspective from a chemistry-climate model ensemble

Surface PM2.5 concentrations in India have increased dramatically as emissions have risen in recent years. The role of meteorological factors in this increase is unclear, mainly due to a lack of long-term observations over the region. A 12-member ensemble of historical (1950–2014) simulations from the Community Earth System Model version 2-Whole Atmosphere Community Climate Model version 6 (CESM2-WACCM6) offers an unprecedented opportunity to examine simulated PM2.5 and meteorology for 20th century climates that can arise due to ‘climate noise’ under the same historical greenhouse gas and air pollutant emission trajectories. CESM2-WACCM6 includes interactive aerosol and gas-phase chemistry in the atmosphere coupled to ocean-sea ice-land models, and each ensemble member differs only in its initial conditions of the climate state. We systematically examine, decade-by-decade, the changes in PM2.5 and associated meteorology, including wind speed, surface temperature inversions, boundary layer height, precipitation, and relative humidity in four cities in India: Chennai, Kolkata, Mumbai, and New Delhi. Forced changes clearly emerge in meteorological variables from 1950 to 2014, including increases in both relative humidity and temperature inversion strength, and decreases in boundary layer height and average surface wind speed. The timing of these changes varies by city: boundary layer heights decrease most over New Delhi in the premonsoon season (ensemble average decrease of 400 m), but over Mumbai in the postmonsoon season (ensemble average decrease of 100 m). PM2.5 concentrations increase across India regardless of climate variability, with an almost threefold increase from 1950 to 2014 over New Delhi. Analysis of dimensionless variables shows that PM2.5 exhibits larger ensemble mean trends and smaller variability than the trends in the meteorological variables, enabling us to infer that the increase in PM2.5 is predominantly controlled by increases in anthropogenic emissions rather than climate variability. Overall, our simulations corroborate the dominant role of air pollutant emissions on poor air quality in India.

A probabilistic framework for quantifying the role of anthropogenic climate change in marine-terminating glacier retreats

Abstract. Many marine-terminating outlet glaciers have retreated rapidly in recent decades, but these changes have not been formally attributed to anthropogenic climate change. A key challenge for such an attribution assessment is that if glacier termini are sufficiently perturbed from bathymetric highs, ice-dynamic feedbacks can cause rapid retreat even without further climate forcing. In the presence of internal climate variability, attribution thus depends on understanding whether (or how frequently) these rapid retreats could be triggered by climatic noise alone. Our simulations with idealized glaciers show that in a noisy climate, rapid retreat is a stochastic phenomenon. We therefore propose a probabilistic approach to attribution and present a framework for analysis that uses ensembles of many simulations with independent realizations of random climate variability. Synthetic experiments show that century-scale climate trends substantially increase the likelihood of rapid glacier retreat. This effect depends on the timescales over which ice dynamics integrate forcing. For a population of synthetic glaciers with different topographies, we find that external trends increase the number of large retreats triggered within the population, offering a metric for regional attribution. Our analyses suggest that formal attribution studies are tractable and should be further pursued to clarify the human role in recent ice-sheet change. We emphasize that early-industrial-era constraints on glacier and climate state are likely to be crucial for such studies.

Detection of Forced Change Within Combined Climate Fields Using Explainable Neural Networks

AbstractAssessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These “indicator patterns” vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields.

A Review of Arctic Sea Ice Climate Predictability in Large-Scale Earth System Models

We provide a high-level review of sea ice models used for climate studies and of the recent advances made with these models to understand sea ice predictability. Models currently in use for the Coupled Model Intercomparison Project and new developments coming online that will enable enhanced predictions are discussed. Previous work indicates that seasonal sea ice can be predicted based on mechanisms associated with long-lived ice thickness or ocean heat anomalies. On longer timescales, internal climate variability is an important source of uncertainty, although anthropogenic forcing is sizable, and studies suggest that anthropogenic signals have already emerged from internal climate noise. Using new analysis from the Multi-Model Large Ensemble, we show that while models differ in the magnitude and timing of predictable signals, many ice predictability characteristics are robust across multiple models. This includes the reemergence of predictable seasonal signals in ice area and the sizable uncertainty in predictions of ice-free Arctic timing associated with internal variability.

Decadal and Multidecadal Variability in ERSSTv5 Global SST during 1879–2018

AbstractDecadal and multidecadal variability in the ERSSTv5 global SST dataset are studied in terms of implicit fast (noise) and slow (signal) processes that affect variability on decadal time scales. Using a new method that better estimates the fast, or noise, component of decadal variability, estimates of the modes of variability in the slow component are possible. The fast component of decadal variability has a leading fast mode, which explains 62% of the variance, and it is shown that this fast variability, or decadal climate noise, is well represented by any of the indices associated with intradecadal or interannual variability in the tropical Pacific Ocean. Three slow modes are identified, representing 69% of the slow multidecadal variance, after removing the radiative forcing trend. These modes are shown to be related to variability in the Atlantic multidecadal oscillation (AMO) and SST multidecadal variability in the central western Pacific and in the Indian Ocean gyre region, respectively. The first and third slow modes represent two phases of a propagating mode with a period of about 80 years. The second slow mode represents multidecadal variability of the western Pacific warm pool, which is less robust than the other two and shown to be weakly related to the AMO with a lag of about 30 years; fast variability in this region is related to the leading fast mode. Three regions of significant slow variability are identified south of Australia, south of Africa, and near the Drake Passage in association with the Antarctic Circumpolar Current.

Main Challenges and Goals of the H2020 STRATOFLY Project

As eluded in previous studies, with special reference to those carried out in the European framework, some innovative high-speed aircraft configurations have now the potential to assure an economically viable high-speed aircraft fleet. They make use of unexploited flight routes in the stratosphere, offering a solution to the presently congested flight paths while ensuring a minimum environmental impact in terms of emitted noise and green-house gasses, particularly during stratospheric cruise. Only a dedicated multi-disciplinary integrated design approach could realize this, by considering airframe architectures embedding the propulsion systems as well as meticulously integrating crucial subsystems. In this context, starting from an in-depth investigation of the current status of the activities, the STRATOFLY project has been funded by the European Commission, under the framework of Horizon 2020 plan, with the aim of assessing the potential of this type of high-speed transport vehicle to reach Technology Readiness Level (TRL) 6 by 2035, with respect to key technological, societal and economical aspects. Main issues are related to thermal and structural integrity, low-emissions combined propulsion cycles, subsystems design and integration, including smart energy management, environmental aspects impacting climate change, noise emissions and social acceptance, and economic viability accounting for safety and human factors. This paper aims at summarizing the main challenges and goals of the STRATOFLY project, highlighting the steps forward with respect to the past European Projects and underlying the next planned goals.

Nonlinear time series models for the North Atlantic Oscillation

Abstract. The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models, including both short and long lags, perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution, and the different timescales of the two phases. As a spin-off of the modelling procedure, we can deduce that the interannual dependence of the NAO mostly affects the positive phase, and that timescales of 1 to 3 weeks are more dominant for the negative phase. Furthermore, the statistical properties of the model make it useful for the generation of realistic climate noise.

Indicator Patterns of Forced Change Learned by an Artificial Neural Network

AbstractMany problems in climate science require the identification of signals obscured by both the “noise” of internal climate variability and differences across models. Following previous work, we train an artificial neural network (ANN) to predict the year of a given map of annual‐mean temperature (or precipitation) from forced climate model simulations. This prediction task requires the ANN to learn forced patterns of change amidst a background of climate noise and model differences. We then apply a neural network visualization technique (layerwise relevance propagation) to visualize the spatial patterns that lead the ANN to successfully predict the year. These spatial patterns thus serve as “reliable indicators” of the forced change. The architecture of the ANN is chosen such that these indicators vary in time, thus capturing the evolving nature of regional signals of change. Results are compared to those of more standard approaches like signal‐to‐noise ratios and multilinear regression in order to gain intuition about the reliable indicators identified by the ANN. We then apply an additional visualization tool (backward optimization) to highlight where disagreements in simulated and observed patterns of change are most important for the prediction of the year. This work demonstrates that ANNs and their visualization tools make a powerful pair for extracting climate patterns of forced change.

Absence of internal multidecadal and interdecadal oscillations in climate model simulations

For several decades the existence of interdecadal and multidecadal internal climate oscillations has been asserted by numerous studies based on analyses of historical observations, paleoclimatic data and climate model simulations. Here we use a combination of observational data and state-of-the-art forced and control climate model simulations to demonstrate the absence of consistent evidence for decadal or longer-term internal oscillatory signals that are distinguishable from climatic noise. Only variability in the interannual range associated with the El Niño/Southern Oscillation is found to be distinguishable from the noise background. A distinct (40–50 year timescale) spectral peak that appears in global surface temperature observations appears to reflect the response of the climate system to both anthropogenic and natural forcing rather than any intrinsic internal oscillation. These findings have implications both for the validity of previous studies attributing certain long-term climate trends to internal low-frequency climate cycles and for the prospect of decadal climate predictability.

Klimat akustyczny polskich uzdrowisk = The acoustic climate of Polish health resorts

ilence and a good acoustic climate are important in every environment where human beings live (at home or the place of work, in the streets, on transport, etc.). Special attention needs to be paid to health resorts at which patients come to regenerate their health. However, the acoustic climate of spas is under the great impact of noise generated by local transport, neighbours, bars and restaurants, as well as by other communal sources. According to the legislative Act of the Ministry of Environment (the Dziennik Ustaw Official Journal of Laws of 2012, item 1109) in the core area of a health resort (the so-called zone A), the level of noise level cannot exceed 45 dB during daytime and 40 dB during night hours. In places close to roads the limits are slightly higher, at 50 and 45 dB respectively. These limits are very restrictive. However, we must remember that noise is very destructive and can involve many health disturbances; and health resorts are places in which sick and aged people come to improve their health status and to regenerate organisms. In spite of the great importance of an appropriate acoustic climate noise measurements are undertaken periodically and not monitored by public institutions. Acoustic climate studies are usually carried out by research institutes (e.g. the Institute of Geography and Spatial Organization of the Polish Academy of Sciences) every ten years, as a part of investigations to assess the quality of climatic conditions in health resorts. The aim of the paper is a general assessment of the acoustic climate in Polish health resorts and of trends as regards possible changes over the last 10 years (2009–2019) in 20 spas located in different bioclimatic regions of Poland. More detailed analysis is done for 2 coastal resorts (Świnoujście and Kołobrzeg). There are analysed factors influencing the acoustic climate and the effectiveness of actions undertaken by local authorities to reduce noise levels. Attention is paid to the spatial distribution of resort objects (sanatoria, hospitals etc.), the organisation of local transport, and the quality of roads and of the neighbourhood of cities in which tourist and administrative functions are pursued. As the most important factors influencing the acoustic climate are transport and activity of a multifunctional nature (with health-resort and tourist functions having contrasting expectations).

НЕОПРЕДЕЛЕННОСТЬ ЕСТЕСТВЕННЫХ ЭМИССИЙ МЕТАНА ИЗ ВЛАЖНЫХ ЭКОСИСТЕМ ВЫСОКИХ ШИРОТ

Current estimates of methane emissions from high latitude wetlands have significant intermodel variations, one reason for which may be internal atmospheric circulation variability. This paper analyzes the impact of internal atmospheric variability on variations in methane emissions from the Hudson Bay Lowland (HBL) area. The ensemble of numerical experiments with a joint model of the methane cycle and heat and moisture transport in soil was performed. An ensemble of 45 realizations of the multi-year data of meteorological variables at the land surface, calculated by the ECHAM5 for different initial and identical boundary conditions for a 34-year period (from 1.01.1979 to 31.12.2012) was specified as space-distributed input data. Necessary physical characteristics of the soil were calculated in the module of heat and moisture transport, supplemented with an interactive scheme for calculating the area of the model cell occupied by the wetlands. Wetlands are estimated to occupy 8–20 % (with 11.4 % ensemble mean) of HBL region area. The ensemble average of annual emissions for HBL over the estimated period equals 6.1 TgCH4/yr with – 0.1 TgCH4/yr trend. The obtained uncertainty associated with climate noise equals 32 % for average annual emissions, and 31–38 % for individual months. It is shown that a significant contribution to the high uncertainty of methane emissions is made by the climatically determined variability of the wetland area.

Linking ITCZ Migrations to the AMOC and North Atlantic/Pacific SST Decadal Variability

AbstractWe examine the link between migrations in the intertropical convergence zone (ITCZ) and changes in the Atlantic meridional overturning circulation (AMOC), Atlantic multidecadal variability (AMV), and Pacific decadal oscillation (PDO). We use a coupled climate model that allows us to integrate over climate noise and assess underlying mechanisms. We use an ensemble of ten 300-yr-long simulations forced by a 50-yr oscillatory North Atlantic Oscillation (NAO)-derived surface heat flux anomaly in the North Atlantic, and a 4000-yr-long preindustrial control simulation performed with GFDL CM2.1. In both setups, an AMV phase change induced by a change in the AMOC’s cross-equatorial heat transport forces an atmospheric interhemispheric energy imbalance that is compensated by a change in the cross-equatorial atmospheric heat transport due to a meridional ITCZ shift. Such linkages occur on decadal time scales in the ensemble driven by the imposed forcing, and internally on multicentennial time scales in the control. Regional precipitation anomalies differ between the ensemble and the control for a zonally averaged ITCZ shift of similar magnitude, which suggests a dependence on time scale. Our study supports observational evidence of an AMV–ITCZ link in the twentieth century and further links it to the AMOC, whose long-time-scale variability can influence the phasing of ITCZ migrations. In contrast to the AMV, our calculations suggest that the PDO does not drive ITCZ migrations, because the PDO does not modulate the interhemispheric energy balance.

Climate Noise Influences Ice Sheet Mean State

AbstractEvidence from proxy records indicates that millennial‐scale abrupt climate shifts, called Dansgaard‐Oeschger events, happened during past glacial cycles. Various studies have been conducted to uncover the physical mechanism behind them, based on the assumption that climate mean state determines the variability. However, our study shows that the Dansgaard‐Oeschger events can regulate the mean state of the Northern Hemisphere ice sheets. Sensitivity experiments show that the simulated mean state is influenced by the amplitude of the climatic noise. The most likely cause of this phenomenon is the nonlinear response of the surface mass balance to temperature. It could also cause the retreat processes to be faster than the buildup processes within a glacial cycle. We propose that the climate variability hindered ice sheet development and prevented the Earth system from entering a full glacial state from Marine Isotope Stage 4 to Marine Isotope Stage 3 about 60,000 years ago.

Effect of Health-Related Uncertainty and Natural Variability on Health Impacts and Cobenefits of Climate Policy.

Climate policy can mitigate health risks attributed to intensifying air pollution under climate change. However, few studies quantify risks of illness and death, examine their contribution to climate policy benefits, or assess their robustness in light of natural climate variability. We employ an integrated modeling framework of the economy, climate, air quality, and human health to quantify the effect of natural variability on U.S. air pollution impacts under future climate and two global policies (2 and 2.5 °C stabilization scenarios) using 150 year ensemble simulations for each scenario in 2050 and 2100. Climate change yields annual premature deaths related to fine particulate matter and ozone (95CI: 25 000-120 000), heart attacks (900-9400), and lost work days (3.6M-4.9M) in 2100. It raises air pollution health risks by 20%, while policies avert these outcomes by 40-50% in 2050 and 70-88% in 2100. Natural variability introduces "climate noise", yielding some annual estimates with negative cobenefits, and others that reach 100% of annual policy costs. This "noise" is three times the magnitude of uncertainty (95CI) in health and economic responses in 2050. Averaging five annual simulations reduces this factor to two, which is still substantially larger than health-related uncertainty. This study quantifies the potential for inaccuracy in climate impacts projected using too few annual simulations.

Potential predictability of Australian seasonal rainfall

The potential predictability of Australian seasonal mean rainfall at 800 stations is estimated using an analysis of variance method for the period 1957-2015 and for all twelve three-month seasons. The method estimates the contribution of the slow, potentially predictable, signal of the rainfall to the total inter-annual variance, after removing the climate noise due to intra-seasonal and weather variability. The results show that there are stations, in all seasons, where the potential predictability is relatively high, and can be greater than half of the total inter-annual variance. Largest potential predictability, coherent over eastern Australia, occurs during the transition to spring, and in spring seasons. Large and coherent potential predictability also occurs during the autumn seasons over Queensland and south-eastern Australia. For summer and the northern wet seasons, the potential predictability is larger over the northeast coastal stations, in the southeast, central east and central west of the continent. During winter, relatively large and coherent potential predictability occurs over the southeast, the central east, and in an implied northwest-southeast band across the continent. Patterns of seasonal forecast skill from the coupled Predictive Ocean Atmosphere Model for Australia are shown to be highly consistent with our estimates of the potential predictability. Factors that may influence the potential predictability are briefly discussed in the light of previous studies that have considered the relationships between the slow, potentially predictable, components of rainfall and the atmospheric and oceanic circulations. Prominent among these are the El Nino-Southern Oscillation, the Southern Annular mode and the meridional Indian Ocean Dipole.

Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble

AbstractA warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

Anchovy dynamics and the colour of climate noise: Assessing climatic scenarios and exploitation strategies in the Gulf of Cádiz

AbstractThis manuscript implements stochastic modeling to investigate the response of a European anchovy (Engraulis encrasicolus) stock to climatic forcing under different exploitation strategies. The model implements realistic parametrizations for defining the key environmental variables forcing recruitment in the Gulf of Cádiz. Despite the tight control the environment exerts on this population, model outputs and historical records indicate that stock signal is not the same as the forcing signal; instead, the time series of the stock signal is less red (more variance at higher frequencies) than the forcing variables. The response is also very sensitive to the exploitation strategy and to the colour (white versus red noise) assumed for the climatic forcing. These results indicate that establishing a safe management framework for this stock demands not only an assessment of an adequate exploitation strategy, but also an understanding of how this strategy interacts with the different colours of the climatic forcing.

Potential predictability of Australian seasonal rainfall

The potential predictability of Australian seasonal mean rainfall at 800 stations is estimated using an analysis of variance method for the period 1957-2015 and for all twelve three-month seasons. The method estimates the contribution of the slow, potentially predictable, signal of the rainfall to the total inter-annual variance, after removing the climate noise due to intra-seasonal and weather variability.The results show that there are stations, in all seasons, where the potential predictability is relatively high, and can be greater than half of the total inter-annual variance. Largest potential predictability, coherent over eastern Australia, occurs during the transition to spring, and in spring seasons. Large and coherent potential predictability also occurs during the autumn seasons over Queensland and south-eastern Australia. For summer and the northern wet seasons, the potential predictability is larger over the northeast coastal stations, in the southeast, central east and central west of the continent. During winter, relatively large and coherent potential predictability occurs over the southeast, the central east, and in an implied northwest-southeast band across the continent. Patterns of seasonal forecast skill from the coupled Predictive Ocean Atmosphere Model for Australia are shown to be highly consistent with our estimates of the potential predictability.Factors that may influence the potential predictability are briefly discussed in the light of previous studies that have considered the relationships between the slow, potentially predictable, components of rainfall and the atmospheric and oceanic circulations. Prominent among these are the El Niño-Southern Oscillation, the Southern Annular mode and the meridional Indian Ocean Dipole.

Leadership and Management Commitment towards Logistics Performance Improvement in Malaysia with the Adoption of Green Logistics as the Moderating Variable

This study is to look into the leadership and management commitment of logistics service firms in Malaysia towards improving their logistics service performance by adopting the green logistics practices. As concern for many environmental issues that arises nowadays, logistics service firms must look more seriously of the logistics associated external costs mainly with the climate change, air pollution, noise pollution, land use, vibration, accidents and wastes. Apart from reducing the logistics costs as their focus, the world is concern about the issue of climate change and the increasing of carbon dioxide (CO2) emission from the operations of various transportation modes, especially on the road freight. Several researches conducted over a decade found out that human activities are releasing huge amounts of greenhouse gases into the atmosphere which had increase the greenhouse effect and warming the climate. In facing global warming and limited amount of energy resources, initiatives on green saving and low carbon emission of road transport service are among the major policy objectives that need to be implemented in Malaysia. Considering these externalities not only help the logistics service firms to reduce their operational costs but also helps to keep the globe green. The logistics services provided must be able to satisfy customer’s demand and most importantly, to come out with better solutions and new ideas to cope with the pace evolution in the manufacturing industry. This study adds to the existing literature by explaining conceptually the linkage between leadership and management commitment towards logistics performance improvement by adopting the green logistics practices.