Greenhouse Gas Forcing a Necessary Causation for Marine Heatwaves Over the Northeast Pacific Warming Pool

Over the last decade, the northeast Pacific (NP) experienced strong marine heatwaves (MHWs) that produced devastating marine ecological impacts and received major societal concerns. Here, we assess the link between the well-mixed greenhouse gas (GHG) forcing and the occurrence probabilities of the duration and intensity of the NP MHWs. To begin with, we apply attribution technique on the SST time series, and detect a region of systematically and externally-forced SST increase -- the long-term warming pool -- co-located with the past notably Blob-like SST anomalies. The anthropogenic signal has recently emerged from the natural variability of SST over the warming pool, which we attribute primarily to increased GHG concentrations, with anthropogenic aerosols playing a secondary role. With extreme event attribution technique, we further show that GHG forcing is a necessary, but not a sufficient, causation for the multi-year persistent MHW events in the current climate, such as that happened in 2019/2020 over the warming pool. However, the occurrence of the 2019/2020 MHW was extremely unlikely in the absence of GHG forcing. Thus, as GHG emissions continue to firmly rise, it is very likely that GHG forcings will become a sufficient cause for events of the magnitude of the 2019/2020 record event.

Assessing Spatial Vulnerability of Bangladesh to Climate Change and Extremes: A Geographic Information System Approach

Regarding climate change, the world’s most discussed issue for the last few decades, countries like Bangladesh are always noteworthy due to its susceptibility resulting from its geography, hazard proneness, and socioeconomic condition. Thus, this aimed to justify the hypothesis that Bangladesh has spatial diversity in sectors of Climate Change Vulnerability (CCV) by identifying the sectors of vulnerability and visualizing the spatial distribution of vulnerability through multivariate geospatial analysis in the GIS environment. For an integrated assessment of CCV, 38 indicators (socio-economic and biophysical) have been incorporated in the IPCC framework in raster form. Test statistics have shown Kiser-Meyer-Olkin (KMO) value is 0.73 and the p-value of Bartlett’s sphericity is 0. The principal component analysis resulted in 6 principal components with 73.52% total explained variance. Sectors of CCV are the Climatic extreme event vulnerability (PC1), Meteorological shift vulnerability (PC2), Infrastructure and demographic vulnerability (PC3), Ecological vulnerability (PC4), Flood vulnerability (PC5), and Economic vulnerability (PC6) with Cronbach’s alpha 0.90, 0.81, 0.88, 0.72, 0.72, and 0.66 respectively. Among 3 clusters (Jenk’s Natural break) of weighted averaged indices, the highly vulnerable cluster has shown that the PC1 has the highest magnitude with a score of 0.53–0.87, while the PC5 has the highest spatial coverage with 24 districts. The present study however is a new edition in climate vulnerability assessment in Bangladesh since it encompasses multivariate spatial analysis to demonstrate countrywide CCV. This study should be an important tool for setting adaptation and mitigation strategies from the root level to policymaking platforms of Bangladesh.

The Impact of Covid-19 on the Automobile and Allied Sectors

This paper examines the impact of Covid-19 outbreak on the automobile and allied sector. The role of the automobile sector is significant in the overall economy in India. We have used event study methodology to capture the price impact on account of the Covid-19 outbreak. We found that automobile sector and allied sector have witness the negative impact on the event of the pandemic. We have presented the daily and period wise results to provide clear cut understanding about the impact of Covid-19 outbreak on the automobile and allied sectors. This paper contributes in the extreme event literature and help decision makers to hedge their position during the extreme events.

Land area increase in Ukrainian part of the Danube delta

The quantitative indicators of land growth in the Ukrainian part of the Danube delta are considered. Comparison of Landsat satellite images in three key areas of the delta showed that for the period 1975-2020 the area of wetlands at the mouth of the Сhilia channel increased by 1448 hectares due to the accumulation of sediments between the Starostambul and Limba branches and their overgrowth with vegetation. In the area of the Bystroe channel, the area of new lands increased by 1037 hectares due to the artificial deepening of this channel for the Ukrainian ships passage into the Danube River and the deposition of sediments along the coast. A slightly smaller increase in land cover (797 ha) was found in the northern part of the coast of the Ukrainian part of the delta, where saline and carbonate soils are formed. In the future, active land growth is expected in the Musura bay between the mouths of the Starostambul and Sulina branches, ie at the contact of Ukraine and Romania. Some changes in these parameters are expected after a powerful flood in 2021, which will become known after the establishment of a relative equilibrium between the processes of accumulation and erosion after this extreme event.

Floods and droughts in association with cold and warm ENSO events and related circulation features

For the Indian subcontinent. the occurrence of floods and droughts is closely linked with the summer monsoon activity. The phenomenon of El Nino-Southern Oscillation (ENSO) has been established to be one of the major teleconnections of Indian Summer Monsoon. Also the relationship between the circulation features and summer monsoon activity is well documented in the literature. The interaction of F.NSO with monsoon system was known to the seasonal forecasters in India from the days of G. Walker. Northland (1953) summarising these results has remarked that ‘Monsoon has a prolonged influence on the global weather rather than global weather parameters influencing the monsoon’. 1990-94 was a prolonged period of warm ENSO producing weather anomalies in different regions of the globe. Yet during the same period all India rainfall was very close to normal and in fact. 1994 was a year of abundant rainfall for India. The aim of the study is to examine some of these features more critically. It is observed that ENSO has a modifying effect on the regional scale circulation pattern and possible interactions and/or phase-Locking with the planetary scale circulation pattern. which results into the occurrence or non-occurrence of an extreme event. Also, a qualitative analysis is carried for a period 1960-90 to assess how far the mid-season rainfall deficiency is made up at the end of the season. It is observed that even during drought years, the mid-season rainfall deficiency is made up at the end of the season for a considerable percentage of the total number of cases.

A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, policy making, or educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Several support methods and metrics are then implemented in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Based on these, we conduct three empirical studies on the U.S. power systems and markets to introduce some new solutions and unexpected findings. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

Open-Access Data and Toolbox for Tracking COVID-19 Impact on Power Systems

Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. With this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, policy making, or educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Several support methods and metrics are then implemented in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Based on these, we conduct three empirical studies on the U.S. power systems and markets to introduce some new solutions and unexpected findings. This conveys a more complete picture of the pandemic's impacts, and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.

Contrasting biophysical and social impacts of hydro-meteorological extremes

Extreme hydrological and meteorological conditions can severely affect ecosystems, parts of the economy, and consequently society. These impacts are expected to be aggravated by climate change. Here we analyze and compare the impacts of multiple types of extreme events across several domains in Europe, to reveal corresponding impact signatures. We characterize the distinct impacts of droughts, floods, heat waves, frosts and storms on a variety of biophysical and social variables at national level and half-monthly time scale. We find strong biophysical impacts of droughts, floods, heat waves and frosts, while public attention and property damage are more affected by storms and floods. We show unexpected impact patterns such as reduced human mortality during floods and storms. Comparing public attention anomalies with impacts across all other considered domains we find that attention on droughts is comparatively low despite the significant overall impacts. Resolving these impact patterns highlights large-scale vulnerability and supports regional extreme event management to consequently reduce disaster risks.