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Rivers play an important role in the macroplastics transport and retention dynamics in the environment. Therefore, gaining an understanding of these processes is vital in order to help determine the fate of macroplastic in the environment. During flood events the transport of floating macroplastic is known to increase. We compared plastic accumulation and flushing on sixteen riverbanks along the Dutch Meuse river during the period that includes the July 2021 extreme flood to plastic accumulation on the same riverbanks as during normal discharge conditions between 2018 and 2021. Here we show that following the flood small macroplastic categories ( $\lt\!2.5$ cm) were flushed from the riverbanks, while larger soft plastic fragments (2.5–50 cm) and wet tissues were deposited. We found that for all plastic categories and for all riverbanks averaged, the plastic accumulation rate during the July 2021 flood was higher than that which would be expected for that time of year. However, the average accumulation rate for all locations during the period that included the flood was lower than the average accumulation rate over the Dutch winter (Oct/Nov–Feb/Mar). Our results show that litter category composition following the July 2021 flood differed from normal conditions. This indicates that plastic deposition and remobilization processes on riverbanks differ between extreme flood and annually average conditions. These observations contribute to a better understanding of the fate of macroplastic in the environment in terms of the drivers of both its transport and deposition.

An unseasonal dust storm hit large parts of Central Asia on 4–5 November 2021, setting records for the column aerosol burden and fine particulate concentration in Tashkent, Uzbekistan. The dust event originated from an agropastoral region in southern Kazakhstan, where the soil erodibility was enhanced by a prolonged agricultural drought resulting from La Niña-related precipitation deficit and persistent high atmospheric evaporative demand. The dust outbreak was triggered by sustained postfrontal northerly winds during an extreme cold air outbreak. The cold air and dust outbreaks were preceded by a chain of processes consisting of recurrent synoptic-scale transient Rossby wave packets over the North Pacific and North Atlantic, upper-level wave breaking and blocking over Greenland, followed by high-latitude blocking over Northern Europe and West Siberia, and the equatorward shift of a tropopause polar vortex and cold pool into southern Kazakhstan. Our study suggests that the historic dust storm in Uzbekistan was a compound weather event driven by cold extreme, high winds, and drought precondition.

The extreme precipitation during August 2018 in Kerala, India was catastrophic, triggering one of the worst floods in history. There is growing evidence of human-induced climate change in driving hydroclimatic extremes across the globe. However, whether and to what degree the 2018 flooding event in Kerala was influenced by climate change has yet to be fully understood. To this end, we present the first formal attribution analysis of the event, using the probabilistic event attribution (PEA) framework. Three methods using (i) Historical and HistoricalNat runs from CMIP6 (general circulation models-based method), (ii) observed records from 1901–2018 for two periods, split at 1950 (time-slice method) and (iii) observations that are scaled to 1901 and 2018 climates (scaling method), are considered for quantifying the risk ratio (RR) of the event. Using an objective approach, the 2018 precipitation event is defined by the return period of the 4 day cumulative precipitation over the Periyar River Basin (PRB), during 15–18 August, 2018. The subsequent flood event is characterized by the return period of the 1 day maximum streamflow at one of the outlets of the PRB, where maximum impact during the event was reported. The results from multiple methods are consistent, suggesting that the event is exceptionally less likely to have been caused by anthropogenic climate change, with RR for the precipitation and flood events ranging from 0.31 to 0.82 and 0.55 to 0.8, respectively. The role of wet antecedent soil moisture conditions, which is found to be the primary driving factor of floods in the PRB, is also found to be unchanged between simulations with and without climate change. Our results highlight the challenges in unequivocal discerning of the climate change signal on regional hydrological events and emphasize the importance of better consideration of local confounding interventions in PEA studies.

Hurricane Harvey made landfall in August 2017 as the first land-falling category 4 hurricane to hit the state of Texas since Hurricane Carla in September 1961. While its intensity at landfall was notable, most of the vast devastation in the Houston metropolitan area was due to Harvey stalling near the southeast Texas coast over the next several days. Harvey’s long-duration rainfall event was reminiscent of extreme flooding that occurred in the neighboring state of Louisiana: both of which were caused by a stalled tropical low-pressure system producing four days of intense precipitation. A quantitative attribution analysis of Harvey’s rainfall was conducted using a mesoscale atmospheric model forced by constrained boundary and initial conditions that had their long-term climate trends removed. The removal of the various trends of the boundary and initial conditions minimizes the effects of warming in the air and the ocean surface on Harvey. The 60 member ensemble simulations suggest that post-1980 climate warming could have contributed to the extreme precipitation that fell on southeast Texas during 26–29 August 2017 by approximately 20%, with an interquartile range of 13%–37%. While the attribution outcome could be model dependent, this downscaling approach affords the closest means possible of a case-to-case comparison for event attribution, complementing other statistics-based attribution studies on Harvey. Further analysis of a global climate model tracking Harvey-like stalling systems indicates an increase in storm frequency and intensity over southeast Texas through the mid-21st century.

An active debate has been underway on the magnitude and duration of carbon (C) emissions from hydroelectric reservoirs, yet little attention has been paid to stochastic C emissions from reservoir sediments during extreme climatic events. A rare opportunity for field measurements of CO _2 efflux from a hydroelectric reservoir in Korea during an extreme drought event was used to examine how prolonged droughts can affect microbial organic matter processing and the release of CO _2 , CH _4 and N _2 O from exposed sediments. Chamber measurements of CO _2 efflux along an exposed sediment transect, combined with high-frequency continuous sensor measurements of the partial pressure of CO _2 (pCO _2 ) in the reservoir surface water, exhibited extraordinary pulses of CO _2 from exposed sediments and the turbulent inflowing water in contrast to a small CO _2 sink in the main water body of the reservoir and a low efflux of CO _2 from the flooded sediment. Significant increases in the production of CO _2 , CH _4 and N _2 O observed in a laboratory incubation of sediments, together with enhanced activities of phenol oxidase and three hydrolases, indicate a temporary activation of microbial organic matter processing in the drying sediment. The results suggest that drought-triggered pulses of greenhouse gas emission from exposed sediments can offset the C accumulation in reservoir sediments over time scales of years to decades, reversing the trend of declining C emissions from aging reservoirs.

Climatic extreme events strongly affect forest growth and thus significantly influence the inter-annual terrestrial carbon balance. As we are facing an increase in frequency and intensity of climate extremes, extensive empirical archives are required to assess continental scale impacts of temperature and precipitation anomalies. Here we divide a tree-ring network of approximately 1000 sites into fifteen groups of similar high-frequency growth variability to reconstruct regional positive and negative extreme events in different parts of Europe between 1500 and 2008. Synchronized growth maxima or minima within and among regions indicate eighteen years in the pre-instrumental period and two events in the 20th century (1948, 1976) with extensive radial growth fluctuations. Comparisons with instrumental data showed that the European tree-ring network mirrors the spatial extent of temperature and precipitation extremes, but the interpretation of pre-instrumental events is challenged by lagged responses to off-growing season climate extremes. We were able to attribute growth minima in subsequent years to unfavourable August–October conditions and to mild climate during winter months associated with respiratory carbon losses. Our results emphasize the importance of carry-over effects and species-specific growth characteristics for forest productivity. Furthermore, they promote the use of regional tree-ring chronologies in research related to climate variability and terrestrial carbon sink dynamics.

Studies that quantify the spatial and temporal variability of carbon sources and sinks provide process-level information for the prediction of future levels of atmospheric carbon dioxide as well as verification of current emission agreements. Assessments of carbon sources and sinks for North America that compare top-down atmospheric constraints with bottom-up inventories find particularly large carbon sinks in the southeastern US. However, this southeastern US sink may be impacted by extreme land-use disturbance events due to mountaintop coal mining (MCM). Here we apply ecosystem modeling and field experiment data to quantify the potential impact of future mountaintop coal mining on the carbon budget of the southern Appalachian forest region. For projections based on historical mining rates, grassland reclamation, and the continued regrowth of un-mined forests, we find that the southern Appalachian forests switch from a net carbon sink to a net carbon source by year 2025–33 with a 30%–35% loss in terrestrial carbon stocks relative to a scenario with no future mining by the year 2100. Alternatively, scenarios of forest sequestration due to the effect of CO _2 fertilization result in a 15%–24% loss in terrestrial carbon stocks by the year 2100 for mining scenarios relative to scenarios with no future mining. These results suggest that while power plant stack emissions are the dominant life-cycle stage in coal-fired electricity, accounting for mountaintop coal mining in bottom-up inventories may be a critical component of regional carbon budgets.