Human-caused long-term changes in global aridity

Widespread aridification of the land surface causes substantial environmental challenges and is generally well documented. However, the mechanisms underlying increased aridity remain relatively underexplored. Here, we investigated the anthropogenic and natural factors affecting long-term global aridity changes using multisource observation-based aridity index, factorial simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6), and rigorous detection and attribution (D&A) methods. Our study found that anthropogenic forcings, mainly rising greenhouse gas emissions (GHGE) and aerosols, caused the increased aridification of the globe and each hemisphere with high statistical confidence for 1965–2014; the GHGE contributed to drying trends, whereas the aerosol emissions led to wetting tendencies; moreover, the bias-corrected CMIP6 future aridity index based on the scaling factors from optimal D&A demonstrated greater aridification than the original simulations. These findings highlight the dominant role of human effects on increasing aridification at broad spatial scales, implying future reductions in aridity will rely primarily on the GHGE mitigation.

New Zealand weather extremes and climate-related events; a model-based assessment

<p>Extreme weather and climate-related events can have pronounced environmental, economic and societal impacts, yet large natural variability within Earth’s constantly evolving climate system challenges the understanding of how these phenomena are changing. Increasingly powerful climate models have made it possible to study how certain factors, including anthropogenic forcings, have modified the likelihood and magnitude of extreme events. This study examines climate observations, reanalysis fields and model output to assess how weather extremes and climate-related events have changed. Part 1 investigates the detection and attribution of surface climate changes in relation to ozone depletion. Part 2 uses probabilistic event attribution and storyline frameworks to evaluate the role of anthropogenic forcings in altering the risk of extreme 1-day rainfall (RX1D) events for Christchurch, New Zealand in light of an unprecedented rainfall event that occurred in March 2014. Extremely large simulations of possible weather generated by the weather@home Australia-New Zealand (w@h ANZ) model found ozone forcings induced significant changes globally (< 3 hPa) in simulations of mean sea level pressure for 2013. A clear seasonal response was detected in the Southern Hemisphere (SH) circulation that was consistent with prior studies. Ozone-induced changes to average monthly rainfall were not significant in New Zealand with large natural variability and the limitation of one-year simulations challenging attribution to this climate forcing. In Christchurch, model and observational data give evidence of human activity increasing the likelihood and magnitude (+17%) of RX1D events despite significant drying trends for mean total rainfall (-66%) in austral summer. For events similar to that observed during March 2014, the fraction of attributable risk (FAR) is estimated to be 27.4%. This result was robust across different spatial averaging areas though is sensitive to the rainfall threshold examined. Unique meteorological conditions in combination with anomalously high sea surface temperatures (SSTs) in the tropical South Pacific were likely important to the occurrence of this extreme event. These results demonstrate how human influence can be detected in present-day weather and climate events.</p>

New Zealand weather extremes and climate-related events; a model-based assessment

<p>Extreme weather and climate-related events can have pronounced environmental, economic and societal impacts, yet large natural variability within Earth’s constantly evolving climate system challenges the understanding of how these phenomena are changing. Increasingly powerful climate models have made it possible to study how certain factors, including anthropogenic forcings, have modified the likelihood and magnitude of extreme events. This study examines climate observations, reanalysis fields and model output to assess how weather extremes and climate-related events have changed. Part 1 investigates the detection and attribution of surface climate changes in relation to ozone depletion. Part 2 uses probabilistic event attribution and storyline frameworks to evaluate the role of anthropogenic forcings in altering the risk of extreme 1-day rainfall (RX1D) events for Christchurch, New Zealand in light of an unprecedented rainfall event that occurred in March 2014. Extremely large simulations of possible weather generated by the weather@home Australia-New Zealand (w@h ANZ) model found ozone forcings induced significant changes globally (< 3 hPa) in simulations of mean sea level pressure for 2013. A clear seasonal response was detected in the Southern Hemisphere (SH) circulation that was consistent with prior studies. Ozone-induced changes to average monthly rainfall were not significant in New Zealand with large natural variability and the limitation of one-year simulations challenging attribution to this climate forcing. In Christchurch, model and observational data give evidence of human activity increasing the likelihood and magnitude (+17%) of RX1D events despite significant drying trends for mean total rainfall (-66%) in austral summer. For events similar to that observed during March 2014, the fraction of attributable risk (FAR) is estimated to be 27.4%. This result was robust across different spatial averaging areas though is sensitive to the rainfall threshold examined. Unique meteorological conditions in combination with anomalously high sea surface temperatures (SSTs) in the tropical South Pacific were likely important to the occurrence of this extreme event. These results demonstrate how human influence can be detected in present-day weather and climate events.</p>

The Influence of Natural and Anthropogenic Forcing on Water and Energy Balance and on Photosynthesis

Land surface processes are rarely studied in Detection and Attribution Model Inter-comparison Project (DAMIP) experiments on climate change. We analyzed a CMIP6 DAMIP historical experiment by using multi-linear regression (MLRM) and analysis of variance methods. We focused on energy and water budgets, including gross primary productivity (GPP). In MLRM, we estimated each forcing’s contribution and identified the role of natural forcing, which is usually ignored. Contributions of the forcing factors varied by region, and high-ranked variables such as net radiation could receive multiple influences. Greenhouse gases (GHG) accelerated energy and water cycles over the global land surface, including evapotranspiration, runoff, GPP, and water-use efficiency. Aerosol (AER) forcing displayed the opposite characteristics, and natural forcing accounted for short-term changes. A long-term analysis of total soil moisture and water budget indicated that as the AER increases, the available water on the global land increases continuously. In the recent past, an increase in net radiation (i.e., a lowered AER) reduced surface moisture and hastened surface water cycle (GHG effect). The results imply that aerosol emission and its counterbalance to GHG are essential to most land surface processes. The exception to this is GPP, which was overdominated by GHG effects.

A Method for Detection and Attribution of Regional Precipitation Change Using Granger Causality 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 propose a novel approach to regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is minimized. In a single framework, we 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. We use output from global climate models in a perfect-data sense 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). In doing so, we turn an apparent limitation into an opportunity by using the diversity of responses to short-lived climate forcers across the CMIP6 multi-model ensemble to ensure our D&A is insensitive to structural uncertainty. Our framework is developed using a Pearl-causal perspective, but forthcoming research now underway will apply the framework to in situ measurements using a Granger-causal perspective. 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.

Game-Theoretic Decision Support for Cyber Forensic Investigations

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.