Project description:Projected changes of future precipitation extremes exhibit substantial uncertainties among climate models, posing grand challenges to climate actions and adaptation planning. Practical methods for narrowing the projection uncertainty remain elusive. Here, using large model ensembles, we show that the uncertainty in projections of future extratropical extreme precipitation is significantly correlated with the model representations of present-day precipitation variability. Models with weaker present-day precipitation variability tend to project larger increases in extreme precipitation occurrences under a given global warming increment. This relationship can be explained statistically using idealized distributions for precipitation. This emergent relationship provides a powerful constraint on future projections of extreme precipitation from observed present-day precipitation variability, which reduces projection uncertainty by 20-40% over extratropical regions. Because of the widespread impacts of extreme precipitation, this has not only provided useful insights into understanding uncertainties in current model projections, but is also expected to bring potential socio-economic benefits in climate change adaptation planning.
Project description:The latest climate models project widely varying magnitudes of future extreme precipitation changes, thus impeding effective adaptation planning. Many observational constraints have been proposed to reduce the uncertainty of these projections at global to sub-continental scales, but adaptation generally requires detailed, local scale information. Here, we present a temperature-based adaptative emergent constraint strategy combined with data aggregation that reduces the error variance of projected end-of-century changes in annual extremes of daily precipitation under a high emissions scenario by >20% across most areas of the world. These improved projections could benefit nearly 90% of the world's population by permitting better impact assessment and adaptation planning at local levels. Our physically motivated strategy, which considers the thermodynamic and dynamic components of projected extreme precipitation change, exploits the link between global warming and the thermodynamic component of extreme precipitation. Rigorous cross-validation provides strong evidence of its reliability in constraining local extreme precipitation projections.
Project description:Global warming is anticipated to intensify the hydrological cycle. However, this is neither expected to be globally uniform nor is the relationship between temperature increase and rainfall intensities expected to be linear. The objective of this study is to assess changes in annual rainfall extremes, total annual precipitation, and their relationship in the larger Mediterranean region. We use an up-to-date ensemble of 33 regional climate simulations from the EURO-CORDEX initiative at 0.11° resolution. We analyse the significance of trends for 1951-2000 and 2001-2100 under a 'business-as-usual' pathway (RCP8.5). Our future projections indicate a strong north/south Mediterranean gradient, with significant, decreasing trends in the magnitude of daily precipitation extremes in the south and the Maghreb region (up to -10 mm/decade) and less profound, increasing trends in the north. Despite the contrasting future trends, the 50-year daily precipitation extremes are projected to strongly increase (up to 100%) throughout the region. The 100-year extremes, derived with traditional extreme value approaches from the 1951-2000 simulations, underestimate the magnitude of these extreme events in the 2001-2100 projections by 30% for the drier areas of the Mediterranean (200-500 mm average annual rainfall) and by up to 20-30% for the wetter parts of the region. These 100-year extremes can occur at any time in any Mediterranean location. The contribution of the wettest day per year to the annual total precipitation is expected to increase (5-30%) throughout the region. The projected increase in extremes and the strong reductions in mean annual precipitation in the drier, southern and eastern Mediterranean will amplify the challenges for water resource management.
Project description:The intensity of the heaviest extreme precipitation events is known to increase with global warming. How often such events occur in a warmer world is however less well established, and the combined effect of changes in frequency and intensity on the total amount of rain falling as extreme precipitation is much less explored, in spite of potentially large societal impacts. Here, we employ observations and climate model simulations to document strong increases in the frequencies of extreme precipitation events occurring on decadal timescales. Based on observations we find that the total precipitation from these intense events almost doubles per degree of warming, mainly due to changes in frequency, while the intensity changes are relatively weak, in accordance to previous studies. This shift towards stronger total precipitation from extreme events is seen in observations and climate models, and increases with the strength - and hence the rareness - of the event. Based on these results, we project that if historical trends continue, the most intense precipitation events observed today are likely to almost double in occurrence for each degree of further global warming. Changes to extreme precipitation of this magnitude are dramatically stronger than the more widely communicated changes to global mean precipitation.
Project description:The remnants of Hurricane Ida caused major damage and death in the United States on September 1st, 2021, and 11 people drowned in flooded basement apartments within New York City (NYC). It was catastrophic because the maximum hourly precipitation intensity, recorded as 3.47 inches (88.1 mm) per hour at Central Park, was unprecedentedly high for the NYC region. The stormwater infrastructure in NYC is built for 1.75 inches (44.5 mm) per hour, and so understanding the dynamic risk associated with Ida can inform city planning efforts for climate change's impact on short duration extreme precipitation events. We contextualize this storm's record-breaking hourly intensity within the historical record as well as project its risk in the near- to medium-term future using nonstationary stochastic models. These models are conditioned on average temperature (Tavg) and cooling degree day (CDD) projections from three climate models as a covariate, each with a SSP 126 and SSP 370 scenario. The likelihood of such a storm was slowly increasing even before Ida happened, but the projected aggregate reoccurrence risk of an event of Ida's magnitude over time from the non-stationary models ranges from 4 to 52 times higher than the risk given by the stationary model. Using CDD as a covariate resulted in risks that were more than twice the magnitude than when using Tavg. Presenting both covariates provides a broader envelope of uncertainty, which highlights the importance and nuances in the choice of a regionally appropriate covariate for non-stationary risk analysis.
Project description:Increasing atmospheric moisture content is expected to intensify precipitation extremes under climate warming. However, extreme precipitation sensitivity (EPS) to temperature is complicated by the presence of reduced or hook-shaped scaling, and the underlying physical mechanisms remain unclear. Here, by using atmospheric reanalysis and climate model projections, we propose a physical decomposition of EPS into thermodynamic and dynamic components (i.e., the effects of atmospheric moisture and vertical ascent velocity) at a global scale in both historical and future climates. Unlike previous expectations, we find that thermodynamics do not always contribute to precipitation intensification, with the lapse rate effect and the pressure component partly offsetting positive EPS. Large anomalies in future EPS projections (with lower and upper quartiles of -1.9%/°C and 8.0%/°C) are caused by changes in updraft strength (i.e., the dynamic component), with a contrast of positive anomalies over oceans and negative anomalies over land areas. These findings reveal counteracting effects of atmospheric thermodynamics and dynamics on EPS, and underscore the importance of understanding precipitation extremes by decomposing thermodynamic effects into more detailed terms.
Project description:Anthropogenic forcing is increasing the likelihood and severity of certain extreme weather events, which may catalyze outbreaks of climate-sensitive infectious diseases. Extreme precipitation events can promote the spread of mosquito-borne illnesses by creating vector habitat, destroying infrastructure, and impeding vector control. Here, we focus on Cyclone Yaku, which caused heavy rainfall in northwestern Peru from March 7th - 20th, 2023 and was followed by the worst dengue outbreak in Peru's history. We apply generalized synthetic control methods to account for baseline climate variation and unobserved confounders when estimating the causal effect of Cyclone Yaku on dengue cases across the 56 districts with the greatest precipitation anomalies. We estimate that 67 (95% CI: 30 - 87) % of cases in cyclone-affected districts were attributable to Cyclone Yaku. The cyclone significantly increased cases for over six months, causing 38,209 (95% CI: 17,454 - 49,928) out of 57,246 cases. The largest increases in dengue incidence due to Cyclone Yaku occurred in districts with a large share of low-quality roofs and walls in residences, greater flood risk, and warmer temperatures above 24°C. Analyzing an ensemble of climate model simulations, we found that extremely intense March precipitation in northwestern Peru is 42% more likely in the current era compared to a preindustrial baseline due to climate forcing. In sum, extreme precipitation like that associated with Cyclone Yaku has become more likely with climate change, and Cyclone Yaku caused the majority of dengue cases across the cyclone-affected districts.
Project description:AbstarctConstraints on soil moisture can guide agricultural practices, act as input into weather, flooding and climate models and inform water resource policies. Space-based interferometric synthetic aperture radar (InSAR) observations provide near-global coverage, even in the presence of clouds, of proxies for soil moisture derived from the amplitude and phase content of radar imagery. We describe results from a 1.5 year-long InSAR time series spanning the March, 2015 extreme precipitation event in the hyperarid Atacama desert of Chile, constraining the immediate increase in soil moisture and drying out over the following months, as well as the response to a later, smaller precipitation event. The inferred temporal evolution of soil moisture is remarkably consistent between independent, overlapping SAR tracks covering a region ~100 km in extent. The unusually large rain event, combined with the extensive spatial and temporal coverage of the SAR dataset, present an unprecedented opportunity to image the time-evolution of soil characteristics over different surface types. Constraints on the timescale of shallow water storage after precipitation events are increasingly valuable as global water resources continue to be stretched to their limits and communities continue to develop in flood-prone areas.
Project description:Complex adaptive systems - such as critical infrastructures (CI) - are defined by their vast, multi-level interactions and emergent behaviors, but this elaborate web of interactions often conceals relationships. For instance, CI is often reduced to technological components, ignoring that social and ecological components are also embedded, leading to unintentional consequences from disturbance events. Analysis of CI as social-ecological-technological systems (SETS) can support integrated decision-making and increase infrastructure's capacity for resilience to climate change. We assess the impacts of an extreme precipitation event in Phoenix, AZ to identify pathways of disruption and feedback loops across SETS as presented in an illustrative causal loop diagram, developed through semi-structured interviews with researchers and practitioners and cross-validated with a literature review. The causal loop diagram consists of 19 components resulting in hundreds of feedback loops and cascading failures, with surface runoff, infiltration, and water bodies as well as power, water, and transportation infrastructures appearing to have critical roles in maintaining system services. We found that pathways of disruptions highlight potential weak spots within the system that could benefit from climate adaptation, and feedback loops may serve as potential tools to divert failure at the root cause. This method of convergence research shows potential as a useful tool to illustrate a broader perspective of urban systems and address the increasing complexity and uncertainty of the Anthropocene.Supplementary informationThe online version contains supplementary material available at 10.1186/s43065-023-00085-6.