Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

Abstract The present paper introduces and illustrates methodological developments intended for so-called optimal fingerprinting methods, which are of frequent use in detection and attribution studies. These methods used to involve three independent steps: preliminary reduction of the dimension of the data, estimation of the covariance associated to internal climate variability, and, finally, linear regression inference with associated uncertainty assessment. It is argued that such a compartmentalized treatment presents several issues; an integrated method is thus introduced to address them. The suggested approach is based on a single-piece statistical model that represents both linear regression and control runs. The unknown covariance is treated as a nuisance parameter that is eliminated by integration. This allows for the introduction of regularization assumptions. Point estimates and confidence intervals follow from the integrated likelihood. Further, it is shown that preliminary dimension reduction is not required for implementability and that computational issues associated to using the raw, high-dimensional, spatiotemporal data can be resolved quite easily. Results on simulated data show improved performance compared to existing methods w.r.t. both estimation error and accuracy of confidence intervals and also highlight the need for further improvements regarding the latter. The method is illustrated on twentieth-century precipitation and surface temperature, suggesting a potentially high informational benefit of using the raw, nondimension-reduced data in detection and attribution (D&A), provided model error is appropriately built into the inference.

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Climate Change Detection and Attribution: Beyond Mean Temperature Signals

Journal of Climate ◽

10.1175/jcli3900.1 ◽

2006 ◽

Vol 19 (20) ◽

pp. 5058-5077 ◽

Cited By ~ 58

Author(s):

Gabriele C. Hegerl ◽

Thomas R. Karl ◽

Myles Allen ◽

Nathaniel L. Bindoff ◽

Nathan Gillett ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Climate Models ◽

Climate Extremes ◽

Global Ocean ◽

Sampled Data ◽

Free Atmosphere ◽

Ocean Heat Uptake ◽

Continental Scale ◽

Detection And Attribution

Abstract A significant influence of anthropogenic forcing has been detected in global- and continental-scale surface temperature, temperature of the free atmosphere, and global ocean heat uptake. This paper reviews outstanding issues in the detection of climate change and attribution to causes. The detection of changes in variables other than temperature, on regional scales and in climate extremes, is important for evaluating model simulations of changes in societally relevant scales and variables. For example, sea level pressure changes are detectable but are significantly stronger in observations than the changes simulated in climate models, raising questions about simulated changes in climate dynamics. Application of detection and attribution methods to ocean data focusing not only on heat storage but also on the penetration of the anthropogenic signal into the ocean interior, and its effect on global water masses, helps to increase confidence in simulated large-scale changes in the ocean. To evaluate climate change signals with smaller spatial and temporal scales, improved and more densely sampled data are needed in both the atmosphere and ocean. Also, the problem of how model-simulated climate extremes can be compared to station-based observations needs to be addressed.

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Detection and attribution of climate extremes in the observed record

Weather and Climate Extremes ◽

10.1016/j.wace.2016.01.001 ◽

2016 ◽

Vol 11 ◽

pp. 17-27 ◽

Cited By ~ 48

Author(s):

David R. Easterling ◽

Kenneth E. Kunkel ◽

Michael F. Wehner ◽

Liqiang Sun

Keyword(s):

Climate Extremes ◽

Detection And Attribution

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The Role of Human Activity in the Recent Warming of Extremely Warm Daytime Temperatures

Journal of Climate ◽

10.1175/2011jcli4150.1 ◽

2011 ◽

Vol 24 (7) ◽

pp. 1922-1930 ◽

Cited By ~ 88

Author(s):

Nikolaos Christidis ◽

Peter A. Stott ◽

Simon J. Brown

Keyword(s):

Extreme Value Distribution ◽

Value Theory ◽

Extreme Value ◽

Human Influence ◽

Fingerprinting Analysis ◽

Detection And Attribution ◽

External Forcings ◽

Global Optimal ◽

Optimal Fingerprinting

Abstract Formal detection and attribution analyses of changes in daily extremes give evidence of a significant human influence on the increasing severity of extremely warm nights and decreasing severity of extremely cold days and nights. This paper presents an optimal fingerprinting analysis that also detects the contributions of external forcings to recent changes in extremely warm days using nonstationary extreme value theory. The authors’ analysis is the first that attempts to partition the observed change in warm daytime extremes between its anthropogenic and natural components and hence attribute part of the change to possible causes. Changes in the extreme temperatures are represented by the temporal changes in a parameter of an extreme value distribution. Regional distributions of the trend in the parameter are computed with and without human influence using constraints from the global optimal fingerprinting analysis. Anthropogenic forcings alter the regional distributions, indicating that extremely warm days have become hotter.

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Changes in climate extremes in Vietnam

Vietnam Journal of Science Technology and Engineering ◽

10.31276/vjste.59(1).79 ◽

2017 ◽

Vol 59 (1) ◽

pp. 79-87

Author(s):

Van Thang Nguyen ◽

Van Khiem Mai ◽

Van Thang Vu ◽

Dang Mau Nguyen ◽

Ngoc Bich Phuong Nguyen ◽

...

Keyword(s):

Climate Extremes

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The Rise of Climate-Driven Sediment Discharge in the Amazonian River Basin

Atmosphere ◽

10.3390/atmos11020208 ◽

2020 ◽

Vol 11 (2) ◽

pp. 208 ◽

Cited By ~ 3

Author(s):

Nazzareno Diodato ◽

Naziano Filizola ◽

Pasquale Borrelli ◽

Panos Panagos ◽

Gianni Bellocchi

Keyword(s):

River Basin ◽

Amazon Basin ◽

Global Climate ◽

Climate Extremes ◽

Temporal Variations ◽

Sediment Discharge ◽

Storm Events ◽

Discharge Data ◽

Magdalena River ◽

Sediment Export

The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood in this region. With the aim of examining the interactions between precipitation and landscape-scale controls of sediment export from the Amazon basin, we developed a parsimonious hydro-climatological model on a multi-year series (1997–2014) of sediment discharge data taken at the outlet of Óbidos (Brazil) watershed (the narrowest and swiftest part of the Amazon River). The calibrated model (correlation coefficient equal to 0.84) captured the sediment load variability of an independent dataset from a different watershed (the Magdalena River basin), and performed better than three alternative approaches. Our model captured the interdecadal variability and the long-term patterns of sediment export. In our reconstruction of yearly sediment discharge over 1859–2014, we observed that landscape erosion changes are mostly induced by single storm events, and result from coupled effects of droughts and storms over long time scales. By quantifying temporal variations in the sediment produced by weathering, this analysis enables a new understanding of the linkage between climate forcing and river response, which drives sediment dynamics in the Amazon basin.

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The Role of Extension and Advisory Services in Strengthening Farmers’ Innovation Networks to Adapt to Climate Extremes

Sustainability ◽

10.3390/su13041941 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1941

Author(s):

Md Kamruzzaman ◽

Katherine Anne Daniell ◽

Ataharul Chowdhury ◽

Steven Crimp

Keyword(s):

Information Access ◽

Significant Risk ◽

Climate Extremes ◽

Rice Cultivation ◽

Innovation Networks ◽

Flash Flooding ◽

Research Organizations ◽

Local Input ◽

Local Weather

There is anecdotal evidence of the effectiveness of Extension and Advisory Service (EAS) agencies for strengthening innovation networks to adapt to extreme events that impact agricultural production and productivity. In Bangladesh, the Department of Agricultural Extension (DAE) is responsible for ensuring sustainable rice farming, which is damaged by flash flooding every year. This study investigates how EAS can strengthen farmers’ innovation networks by examining DAE’s efforts to adapt rice cultivation to flash flooding. Using surveys and interviews from farmers affiliated with DAE (DAE-farmers) and farmers independent of DAE (non-DAE farmers), the effectiveness of innovation networks was examined. One of the key findings of this paper is that DAE’s efforts to strengthen the innovation networks of farmers to adapt rice cultivation to flash flooding focused on the facilitation of the agronomic network development. The organization missed the opportunity to enable the harvesting networks’ efficacy. As the harvesting activities are highly exposed to flash flooding, the absence of adequate support from the DAE and timely updates of local weather and flash flooding information indicates that farmers are still at significant risk. This study also shows the value of including both formal (e.g., EAS agencies, research organizations) and informal actors (e.g., relatives, local input dealers) in the innovation network as a way of ensuring diversity of information access.

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Game-Theoretic Decision Support for Cyber Forensic Investigations

Sensors ◽

10.3390/s21165300 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5300

Author(s):

Antonia Nisioti ◽

George Loukas ◽

Stefan Rass ◽

Emmanouil Panaousis

Keyword(s):

Cyber Security ◽

Strategic Decisions ◽

Bayesian Games ◽

Bayesian Game ◽

Cyber Forensics ◽

Detection And Attribution ◽

Different Types ◽

Game Theoretic ◽

The Cost

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players’ actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker’s type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.

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Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Remote Sensing ◽

10.3390/rs13071230 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1230

Author(s):

Simeng Wang ◽

Qihang Liu ◽

Chang Huang

Keyword(s):

Arid Region ◽

Vegetation Change ◽

Vegetation Index ◽

Pearson Correlation ◽

Time Lag ◽

Climate Extremes ◽

Northwest China ◽

Precipitation Extremes ◽

Temperature Extremes ◽

Increasing Trend

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

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