Atmospheric rivers drive flood damages in the western United States

Atmospheric rivers over eastern US affected by Pacific/North America pattern

Atmospheric rivers (ARs) play important roles in various extreme weather events across the US. While AR features in western US have been extensively studied, there remains limited understanding of their variability in the eastern US (EUS). Using both observations and a state-of-the-art climate model, we find a significant increase (~10% dec-1) in winter AR frequency in the EUS during the past four decades. This trend is closely linked to recent changes in the Pacific/North America (PNA) teleconnection pattern, accompanied by a poleward shift of the mid-latitude jet stream. We further reveal a strong correlation (R = 0.8; P < 0.001) between interannual variations in AR occurrence and the PNA index. This linkage has been verified in various model simulations. A statistical model, built on this linkage, has proven effective in predicting the AR frequency using the PNA index at both monthly and seasonal scales. These promising results have important implications for addressing concerns related to AR-associated extreme precipitation and flooding in this region.

Temporal compounding increases economic impacts of atmospheric rivers in California

Temporally compounding atmospheric river (AR) events cause severe flooding and damage in California. However, the contribution of temporal compounding to AR-induced loss has yet to be systematically quantified. We show that the strongest ARs are more likely to be part of sequences, which are periods of elevated hydrologic hazard associated with temporally clustered ARs. Sequences increase the likelihood of flood-related impacts by 8.3% on AR days and 5.4% on non-AR days, and across two independent loss datasets, we find that ARs within sequences have over three times higher expected losses compared to ARs outside of sequences. Expected losses also increase when the preceding AR is higher intensity, when time since the preceding AR is shorter, and when an AR is the second or later event within a sequence. We conclude that temporal compounding is a critical source of information for predicting an AR's potential consequences.

Learning skillful medium-range global weather forecasting

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning-based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems.

Long-term elevated precipitation induces grassland soil carbon loss via microbe-plant-soil interplay

Global climate models predict that the frequency and intensity of precipitation events will increase in many regions across the world. However, the biosphere-climate feedback to elevated precipitation (eP) remains elusive. Here, we report a study on one of the longest field experiments assessing the effects of eP, alone or in combination with other climate change drivers such as elevated CO2 (eCO2 ), warming and nitrogen deposition. Soil total carbon (C) decreased after a decade of eP treatment, while plant root production decreased after 2 years. To explain this asynchrony, we found that the relative abundances of fungal genes associated with chitin and protein degradation increased and were positively correlated with bacteriophage genes, suggesting a potential viral shunt in C degradation. In addition, eP increased the relative abundances of microbial stress tolerance genes, which are essential for coping with environmental stressors. Microbial responses to eP were phylogenetically conserved. The effects of eP on soil total C, root production, and microbes were interactively affected by eCO2 . Collectively, we demonstrate that long-term eP induces soil C loss, owing to changes in microbial community composition, functional traits, root production, and soil moisture. Our study unveils an important, previously unknown biosphere-climate feedback in Mediterranean-type water-limited ecosystems, namely how eP induces soil C loss via microbe-plant-soil interplay.

Watershed memory amplified the Oroville rain-on-snow flood of February 2017

Mountain snowpacks are transitioning to experience less snowfall and more rainfall as the climate warms, creating more persistent low- to no-snow conditions. This precipitation shift also invites more high-impact rain-on-snow (ROS) events, which have historically yielded many of the largest and most damaging floods in the western United States. One such sequence of events preceded the evacuation of 188,000 residents below the already-damaged Oroville Dam spillway in February 2017 in California's Sierra Nevada. Prior studies have suggested that snowmelt during ROS dramatically amplified reservoir inflows. However, we present evidence that snowmelt may have played a smaller role than previously documented (augmenting terrestrial water inputs by 21%). A series of hydrologic model experiments and subdaily snow, soil, streamflow, and hydrometeorological measurements demonstrate that direct, "passive" routing of rainfall through snow, and increasingly efficient runoff driven by gradually wetter soils can alternatively explain the extreme runoff totals. Our analysis reveals a crucial link between frequent winter storms and a basin's hydrologic response-emphasizing the role of soil moisture "memory" of within-season storms in priming impactful flood responses. Given the breadth in plausible ROS flood mechanisms, this case study underscores a need for more detailed measurements of soil moisture along with in-storm changes to snowpack structure, extent, energy balance, and precipitation phase to address ROS knowledge gaps associated with current observational limits. Sharpening our conceptual understanding of basin-scale ROS better equips water managers moving forward to appropriately classify threat levels, which are projected to increase throughout the mid-21st century.

Geomorphic response of outburst floods: Insight from numerical simulations and observations--The 2018 Baige outburst flood in the upper Yangtze River

Outburst floods related to glacial or landslide damming are a major agent of geomorphic change in mountain rivers. Although the evidence between outburst flooding and riverine landscapes has been gradually recognized, the lack of hydraulics to the extent that there has still not been quantified on the relationship of how the amount and spatial distribution of these changes relate quantitatively to the hydraulic conditions and durations of these catastrophic events. This study combined remote and field observations of the 2018 Baige outburst flood with two-dimensional numerical simulation using the diffusive wave equation. By feeding the measured dam-breach hydrograph and comparing three different Manning coefficients in numerical experiments, the simulation results show that when n = 0.055, the time of peak flow was only 0.5 h different from that indicated by measured data in Yebatan, 54 km downstream of the Baige landslide dam. Under high shear stress over several hours at sustained ~20 m water depth, lateral erosion caused by these outburst floods contributed to the adjacent landslide, which was activated in association with intermittent water velocity waves of approximately 17 m/s. Sustained high stream power (>50 kW m²) from the outburst flood eroded slope toes and accelerated slippage of six slopes. Combining simulation and observations, we also developed a physical model related to hillslope instability caused by high hydrodynamic erosion of riverbanks generated by flow waves lasting several hours, which explained the hydrodynamic response of the outburst flood to the canyon geomorphology. Furthermore, we suggest that the pattern of channel widening erosion and deposition is governed by the variation in shear stress and Froude number as the high-energy flood flows from a wide channel into a narrow river valley. Our findings highlight that the hydraulics of high-magnitude outburst floods and sediment transport play crucial roles in reshaping canyon geomorphology.

Shifting in the global flood timing

Climate change will have an impact on not only flood magnitude but also on flood timing. This paper studies the shifting in flood timing at 6167 gauging stations from 1970 to 2010, globally. The shift in flood timing and its relationship with three influential factors (maximum 7-day precipitation, soil moisture excess, and snowmelt) are investigated. There is a clear global pattern in the mean flooding date: winter (Dec-Feb) across the western Coastal America, western Europe and the Mediterranean region, summer (Jun-Aug) in the north America, the Alps, Indian Peninsula, central Asia, Japan, and austral summer (Dec-Feb) in south Africa and north Australia area. The shift in flood timing has a trend from - 22 days per decade (earlier) to 28 days per decade (delayed). Earlier floods were found extensively in the north America, Europe and northeast Australia while delayed floods were prevailing in the Amazon, Cerrado, south Africa, India and Japan. Earlier flood timing in the north America and Europe was caused by earlier snowmelt while delayed extreme soil moisture excess and precipitation have jointly led to delayed floods around the monsoon zone, including south Africa, India and Japan. This study provides an insight on the shifting mechanism of flood timing, and supports decisions on the global flood mitigation and the impact from future climate change.

Climate change is increasing the risk of a California megaflood

Despite the recent prevalence of severe drought, California faces a broadly underappreciated risk of severe floods. Here, we investigate the physical characteristics of "plausible worst case scenario" extreme storm sequences capable of giving rise to "megaflood" conditions using a combination of climate model data and high-resolution weather modeling. Using the data from the Community Earth System Model Large Ensemble, we find that climate change has already doubled the likelihood of an event capable of producing catastrophic flooding, but larger future increases are likely due to continued warming. We further find that runoff in the future extreme storm scenario is 200 to 400% greater than historical values in the Sierra Nevada because of increased precipitation rates and decreased snow fraction. These findings have direct implications for flood and emergency management, as well as broader implications for hazard mitigation and climate adaptation activities.

Climate change contributions to future atmospheric river flood damages in the western United States

Atmospheric rivers (ARs) generate most of the economic losses associated with flooding in the western United States and are projected to increase in intensity with climate change. This is of concern as flood damages have been shown to increase exponentially with AR intensity. To assess how AR-related flood damages are likely to respond to climate change, we constructed county-level damage models for the western 11 conterminous states using 40 years of flood insurance data linked to characteristics of ARs at landfall. Damage functions were applied to 14 CMIP5 global climate models under the RCP4.5 "intermediate emissions" and RCP8.5 "high emissions" scenarios, under the assumption that spatial patterns of exposure, vulnerability, and flood protection remain constant at present day levels. The models predict that annual expected AR-related flood damages in the western United States could increase from $1 billion in the historical period to $2.3 billion in the 2090s under the RCP4.5 scenario or to $3.2 billion under the RCP8.5 scenario. County-level projections were developed to identify counties at greatest risk, allowing policymakers to target efforts to increase resilience to climate change.

Routine Measurement of Water Vapour Using GNSS in the Framework of the Map-Io Project

The "Marion Dufresne Atmospheric Program - Indian Ocean" (MAP-IO) project is a research program that aims to collect long-term atmospheric observations in the under-instrumented Indian and Austral Oceans. As part of this project, a Global Navigation Satellite System (GNSS) antenna was installed on the research vessel (R/V) Marion Dufresne in October 2020. GNSS raw data is intended to be used to retrieve Integrated Water Vapour (IWV) content along the Marion Dufresne route, which cruises more than 300 days per year in the tropical and austral Indian Ocean. This paper presents a first assessment of this GNSS-based IWV retrieval, based on the analysis of 9 months of GNSS raw data acquired along the route of the R/V Marion Dufresne in the Indian Ocean. A first investigation of GNSS raw data collected during the first 5 months of operation has highlighted the bad positioning of the antenna on the R/V that makes it prone to interference. Changing the location of the antenna has been shown to improve the quality of the raw data. Then, ship-borne GNSS-IWV are compared with IWV estimates deduced using more conventional techniques such as European Centre for Medium-range Weather Forecasts (ECMWF) fifth reanalysis (ERA5), ground-launched radiosondes and permanent ground GNSS stations operating close to the route of the R/V Marion Dufresne. The rms difference of 2.79 kg m−2 shows a good match with ERA5 and subsequently improved after the change in location of the GNSS antenna (2.49 kg m−2). The match with ground-based permanent GNSS stations fluctuates between 1.30 and 3.63 kg m−2, which is also shown to be improved after the change in location of the GNSS antenna. However, differences with ground-launched radiosondes still exhibit large biases (larger than 2 kg m−2). Finally, two operational daily routine analyses (at day+1 and day+3) are presented and assessed: the rms of the differences are shown to be quite low (1 kg m−2 for the day+1 analyses, 0.7 kg m−2 for the day+3 analysis), which confirms the quality of these routine analysis. These two routine analyses are intended to provide a continuous monitoring of water vapour above the Indian Ocean and deliver ship-borne IWV with a low latency for the entire scientific community.

Climate change and extreme weather: A review focusing on the continental United States

Anthropogenic emissions of greenhouse gases are warming the Earth. It is likely that the greatest impacts of climate change on human and natural systems will come from increasingly frequent and severe extreme weather and climate events. Some increases in such extremes are already being detected, and this trend is projected to continue as Earth warms. Here we review the overarching climate drivers of increases in extreme weather and address the context in which extremes occur and the challenges of projecting future changes. The observational evidence for climate-driven increases in extremes and the implications of model projections are reviewed for heat and drought and several types of storms: tropical cyclones, midlatitude storms, and severe local weather, focusing on those changes most relevant to the continental United States. We emphasize the overall observed and modeled trends in extreme weather in which we have the greatest confidence, because they are consistent with our fundamental understanding of weather and climate. Despite remaining uncertainty about many details, especially in model-based projections, the signal of increasing extremes is sufficiently clear that it demands a robust human response, in limiting future emissions of greenhouse gases and in making our human systems more resilient to further changes that are inevitable as Earth continues to warm.Implications: By placing observed and projected changes in extreme weather in the context of our fundamental understanding of physics and statistics, this review makes it clear that these are significant and impactful changes that demand a robust human response.

Significant increase of global anomalous moisture uptake feeding landfalling Atmospheric Rivers

One of the most robust signals of climate change is the relentless rise in global mean surface temperature, which is linked closely with the water-holding capacity of the atmosphere. A more humid atmosphere will lead to enhanced moisture transport due to, among other factors, an intensification of atmospheric rivers (ARs) activity, which are an important mechanism of moisture advection from subtropical to extra-tropical regions. Here we show an enhanced evapotranspiration rates in association with landfalling atmospheric river events. These anomalous moisture uptake (AMU) locations are identified on a global scale. The interannual variability of AMU displays a significant increase over the period 1980-2017, close to the Clausius-Clapeyron (CC) scaling, at 7 % per degree of surface temperature rise. These findings are consistent with an intensification of AR predicted by future projections. Our results also reveal generalized significant increases in AMU at the regional scale and an asymmetric supply of oceanic moisture, in which the maximum values are located over the region known as the Western Hemisphere Warm Pool (WHWP) centred on the Gulf of Mexico and the Caribbean Sea.

Increasing atmospheric temperatures are expected to have various impacts on the global water cycle. Here, the authors show that there is an intensification of atmospheric rivers, that causes enhanced evapotranspiration and thus atmospheric moisture uptake in many regions of the world.

A multimodel evaluation of the water vapor budget in atmospheric rivers

Atmospheric rivers (ARs) are narrow regions of strong horizontal water vapor transport that play important roles in the global water cycle, weather, and hydrology. Motivated by challenges in simulating ARs with state-of-the-art global models, this paper diagnoses model errors with a focus on relative contributions of moisture convergence, evaporation, and precipitation to AR column-integrated water vapor (IWV) budget. Using 20-year simulations by 24 global weather/climate models, budget terms are calculated for four AR sectors: postfrontal, frontal, prefrontal, and pre-AR, with biases assessed against two reanalysis products. The results indicate that each sector is unique in terms of the dominant water vapor balance, and that the terms exhibiting the largest intermodel spread are the same terms dominating the water vapor balance in each sector. Overall, simulated bulk AR characteristics (e.g., geometry, frequency, and intensity) are more sensitive to biases in IVT convergence and IWV tendency than to biases in evaporation and precipitation, although evaporation/precipitation biases do affect key AR bulk characteristics in selected sectors. The large intermodel spread (particularly for precipitation) and, in certain cases, discrepancies between the reanalysis references themselves (particularly for precipitation types) highlight the need for observational efforts that target better constraining AR processes in weather/climate models and reanalyses.