Constraints on regional projections of mean and extreme precipitation under warming.

The projected changes in the hydrological cycle under global warming remain highly uncertain across current climate models. Here, we demonstrate that the observational past warming trend can be utilized to effectively co1nstrain future projections in mean and extreme precipitation on both global and regional scales. The physical basis for such constraints relies on the relatively constant climate sensitivity in individual models and the reasonable consistency of regional hydrological sensitivity among the models, which is dominated and regulated by the increases in atmospheric moisture. For the high-emission scenario, on the global average, the projected changes in mean precipitation are lowered from 6.9 to 5.2% and those in extreme precipitation from 24.5 to 18.1%, with the inter-model variances reduced by 31.0 and 22.7%, respectively. Moreover, the constraint can be applied to regions in middle-to-high latitudes, particularly over land. These constraints result in spatially resolved corrections that deviate substantially and inhomogeneously from the global mean corrections. This study provides regionally constrained hydrological responses over the globe, with direct implications for climate adaptation in specific areas.

Notable impact of wildfires in the western United States on weather hazards in the central United States.

Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce (1) stronger moisture and aerosol transport to the central United States and (2) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.

Recent increases in tropical cyclone precipitation extremes over the US east coast.

The impacts of inland flooding caused by tropical cyclones (TCs), including loss of life, infrastructure disruption, and alteration of natural landscapes, have increased over recent decades. While these impacts are well documented, changes in TC precipitation extremes-the proximate cause of such inland flooding-have been more difficult to detect. Here, we present a latewood tree-ring-based record of seasonal (June 1 through October 15) TC precipitation sums (ΣTCP) from the region in North America that receives the most ΣTCP: coastal North and South Carolina. Our 319-y-long ΣTCP reconstruction reveals that ΣTCP extremes (≥0.95 quantile) have increased by 2 to 4 mm/decade since 1700 CE, with most of the increase occurring in the last 60 y. Consistent with the hypothesis that TCs are moving slower under anthropogenic climate change, we show that seasonal ΣTCP along the US East Coast are positively related to seasonal average TC duration and TC translation speed.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa.

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100 mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Human influence has intensified extreme precipitation in North America.

Precipitation extremes have implications for many facets of both the human and natural systems, predominantly through flooding events. Observations have demonstrated increasing trends in extreme precipitation in North America, and models and theory consistently suggest continued increases with future warming. Here, we address the question of whether observed changes in annual maximum 1- and 5-d precipitation can be attributed to human influence on the climate. Although attribution has been demonstrated for global and hemispheric scales, there are few results for continental and subcontinental scales. We utilize three large ensembles, including simulations from both a fully coupled Earth system model and a regional climate model. We use two different attribution approaches and find many qualitatively consistent results across different methods, different models, and different regional scales. We conclude that external forcing, dominated by human influence, has contributed to the increase in frequency and intensity of regional precipitation extremes in North America. If human emissions continue to increase, North America will see further increases in these extremes.

Spatial analysis of future climate risk to stormwater infrastructure.

Climate change is expected to result in more intense precipitation events that will affect the performance and design requirements of stormwater infrastructure. Such changes will vary spatially, and climate models provide a range of estimates of the effects on events of different intensities and recurrence. Infrastructure performance should be evaluated against the expected range of events, not just rare extremes. We present a national-scale, spatially detailed screening assessment of the potential effects of climatic change on precipitation, stormwater runoff, and associated design requirements. This is accomplished through adjustment relative to multiple future climate scenarios of precipitation intensity-duration-frequency analyses presented in NOAA Atlas 14, which are commonly used in infrastructure design. Future precipitation results are estimated for each Atlas 14 station (these currently omit the Pacific Northwest). Results are interpolated using a geographically conditioned regression kriging approach to provide information about potential climate change impacts in a format more directly useful to local stormwater managers. The intensity of 24-h events with 2-year or greater recurrence is likely to increase in most areas of the United States leading to increased runoff and potential need for increased storage volumes. Changes in more frequent events (e.g., the 90th percentile event) commonly used in design of green infrastructure are relatively less.

Compound hydroclimatic extremes in a semi-arid grassland: Drought, deluge, and the carbon cycle.

Climate change is predicted to increase the frequency and intensity of extreme events including droughts and large precipitation events or "deluges." While many studies have focused on the ecological impacts of individual events (e.g., a heat wave), there is growing recognition that when extreme events co-occur as compound extremes, (e.g., a heatwave during a drought), the additive effects on ecosystems are often greater than either extreme alone. In this study, we assessed a unique type of extreme-a contrasting compound extreme-where the extremes may have offsetting, rather than additive ecological effects, by examining how a deluge during a drought impacts productivity and carbon cycling in a semi-arid grassland. The experiment consisted of four treatments: a control (average precipitation), an extreme drought (<5th percentile), an extreme drought interrupted by a single deluge (>95th percentile), or an extreme drought interrupted by the equivalent amount of precipitation added in several smaller events. We highlight three key results. First, extreme drought resulted in early senescence, reduced carbon uptake, and a decline in net primary productivity relative to the control treatment. Second, the deluge imposed during extreme drought stimulated carbon fluxes and plant growth well above the levels of both the control and the drought treatment with several additional smaller rainfall events, emphasizing the importance of precipitation amount, event size, and timing. Third, while the deluge's positive effects on carbon fluxes and plant growth persisted for 1 month, the deluge did not completely offset the negative effects of extreme drought on end-of-season productivity. Thus, in the case of these contrasting hydroclimatic extremes, a deluge during a drought can stimulate temporally dynamic ecosystem processes (e.g., net ecosystem exchange) while only partially compensating for reductions in ecosystem functions over longer time scales (e.g., aboveground net primary productivity).

Towards Quantifying the Uncertainty in Estimating Observed Scaling Rates.

Short-duration precipitation extremes (PE) increase at a rate of around 7%/K explained by the Clausius-Clapeyron relationship. Previous studies show uncertainty in the extreme precipitation-temperature relationship (scaling) due to various thermodynamic/dynamic factors. Here, we show that uncertainty may arise from the choice of data and methods. Using hourly precipitation (PPT) and daily dewpoint temperature (DPT) across 2,905 locations over the United States, we found higher scaling for quality-controlled data, all locations showing positive (median 6.2%/K) scaling, as compared to raw data showing positive (median 5.3%/K) scaling over 97.5% of locations. We found higher scaling for higher measurement precision of PPT (0.25 mm: median 7.8%/K; 2.54 mm: median 6.6%/K). The method that removes seasonality in PPT and DPT gives higher (with seasonality: median 6.2%/K; without seasonality: median 7.2%/K) scaling. Our results demonstrate the importance of quality-controlled, high-precision observations and robust methods in estimating accurate scaling for a better understanding of PE change with warming.

Association between sequential extreme precipitation-heatwaves events and hospitalizations for schizophrenia: The damage amplification effects of sequential extremes.

OBJECTIVES: In the context of frequent global extreme weather events, there are few studies on the effects of sequential extreme precipitation (EP) and heatwaves (HW) events on schizophrenia. We aimed to quantify the effects of the events on hospitalizations for schizophrenia and compare them with EP and HW alone to explore the amplification effect of successive extremes on health loss. METHODS: A time-series Poisson regression model combined with a distributed lag non-linear model was applied to estimate the association between sequential EP and HW events (EP-HW) and schizophrenia hospitalizations. The effects of EP-HW with different intervals and intensities on the admission of schizophrenia were compared. In addition, we calculated the mean attributable fraction (AF) and attributable numbers (AN) per exposure of extreme events to reflect the amplification effect of sequential extreme events on health hazards compared with individual extreme events. RESULTS: EP-HW increased the risk of hospitalization for schizophrenia, with significant effects lasting from lag0 (RR and 95% CI: 1.150 (1.041-1.271)) to lag11 (1.046 (1.000-1.094)). Significant associations were found in the subgroups of male, female, married people, and those aged≥ 40 years old. Shorter-interval (0-3days) or higher-intensity EP-HW (both precipitation ≥ P97.5 and mean temperature ≥ P97.5) had a longer lag effect compared to EP-HW with longer intervals or lower intensity. We found that the mean AF and AN caused by each exposure to EP-HW (AF: 0.074% (0.015%-0.123%); AN: 4.284 (0.862-7.118)) were higher than those induced by each exposure to HW occurring alone (AF:0.032% (0.004%-0.058%); AN:1.845 (0.220-3.329)). CONCLUSIONS: Sequential extreme precipitation-heatwaves events significantly increase the risk of hospitalizations for schizophrenia, with greater impact and disease burden than independently occurring extremes. The impact of consecutive extremes is supposed to be considered in local sector early warning systems for comprehensive public health decision-making.

Effects of extreme precipitation on hospital visit risk and disease burden of depression in Suzhou, China.

BACKGROUND: The purpose of this study was to explore the impact of extreme precipitation on the risk of outpatient visits for depression and to further explore its associated disease burden and vulnerable population. METHODS: A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was used to investigate the exposure-lag-response relationship between extreme precipitation (≥95th percentile) and depression outpatient visits from 2017 to 2019 in Suzhou city, Anhui Province, China. RESULTS: Extreme precipitation was positively associated with the outpatient visits for depression. The effects of extreme precipitation on depression firstly appeared at lag4 [relative risk (RR): 1.047, 95% confidence interval (CI): 1.005-1.091] and lasted until lag7 (RR = 1.047, 95% CI: 1.009-1.087). Females, patients aged ≥65 years and patients with multiple outpatient visits appeared to be more sensitive to extreme precipitation. The attributable fraction (AF) and numbers (AN) of extreme precipitation on outpatient visits for depression were 5.00% (95% CI: 1.02-8.82%) and 1318.25, respectively. CONCLUSIONS: Our findings suggested that extreme precipitation may increase the risk of outpatient visits for depression. Further studies on the burden of depression found that females, aged ≥65 years, and patients with multiple visits were priority targets for future warnings. Active intervention measures against extreme precipitation events should be taken to reduce the risk of depression outpatient visits.

Technology for Position Correction of Satellite Precipitation and Contributions to Error Reduction-A Case of the '720' Rainstorm in Henan, China.

In July 2021, an extreme precipitation event occurred in Henan, China, causing tremendous damage and deaths; so, it is very important to study the observation technology of extreme precipitation. Surface rain gauge precipitation observations have high accuracy but low resolution and coverage. Satellite remote sensing has high spatial resolution and wide coverage, but has large precipitation accuracy and distribution errors. Therefore, how to merge the above two kinds of precipitation observations effectively to obtain heavy precipitation products with more accurate geographic distributions has become an important but difficult scientific problem. In this paper, a new information fusion method for improving the position accuracy of satellite precipitation estimations is used based on the idea of registration and warping in image processing. The key point is constructing a loss function that includes a term for measuring two information field differences and a term for a warping field constraint. By minimizing the loss function, the purpose of position error correction of quantitative precipitation estimation from FY-4A and Integrated Multisatellite Retrievals of GPM are achieved, respectively, using observations from surface rain gauge stations. The errors of different satellite precipitation products relative to ground stations are compared and analyzed before and after position correction, using the '720' extreme precipitation in Henan, China, as an example. The experimental results show that the final run has the best performance and FY-4A has the worse performance. After position corrections, the precipitation products of the three satellites are improved, among which FY-4A has the largest improvement, IMERG final run has the smallest improvement, and IMERG late run has the best performance and the smallest error. Their mean absolute errors are reduced by 23%, 14%, and 16%, respectively, and their correlation coefficients with rain gauge stations are improved by 63%, 9%, and 16%, respectively. The error decomposition model is used to examine the contributions of each error component to the total error. The results show that the new method improves the precipitation products of GPM primarily in terms of hit bias. However, it does not significantly reduce the hit bias of precipitation products of FY-4A while it reduces the total error by reducing the number of false alarms.

Evaluation of extreme sub-daily precipitation in high-resolution global climate model simulations.

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius-Clapeyron (CC) rate (6-7% K-1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014.

BACKGROUND: Infections with nontyphoidal Salmonella cause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence of Salmonella in soil and food. However, the impact of extreme weather events on Salmonella infection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions. METHODS: To address this knowledge gap, we obtained Salmonella case data for S. Enteriditis, S. Typhimurium, S. Newport, and S. Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95th percentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates. RESULTS: We observed that extreme heat exposure was associated with increased rates of infection with S. Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates of S. Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate of S. Newport infections in Maryland associated with extreme precipitation events. CONCLUSIONS: Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection with Salmonella serovars that persist in environmental or plant-based reservoirs, such as S. Javiana and S. Newport, appear to be of particular significance regarding increased heat and rainfall events.

Dynamic amplification of extreme precipitation sensitivity.

A useful starting hypothesis for predictions of changes in precipitation extremes with climate is that those extremes increase at the same rate as atmospheric moisture does, which is [Formula: see text] following the Clausius-Clapeyron (CC) relation. This hypothesis, however, neglects potential changes in the strengths of atmospheric circulations associated with precipitation extremes. As increased moisture leads to increased precipitation, the increased latent heating may lead to stronger large-scale ascent and thus, additional increase in precipitation, leading to a super-CC scaling. This study investigates this possibility in the context of the 2015 Texas extreme precipitation event using the Column Quasi-Geostrophic (CQG) method. Analogs to this event are simulated in different climatic conditions with varying surface temperature ([Formula: see text]) given the same adiabatic quasigeostrophic forcing. Precipitation in these events exhibits super-CC scaling due to the dynamic contribution associated with increasing ascent due to increased latent heating, an increase with importance that increases with [Formula: see text] The thermodynamic contribution (attributable to increasing water vapor; assuming no change in vertical motion) approximately follows CC as expected, while vertical structure changes of moisture and diabatic heating lead to negative but secondary contributions to the sensitivity, reducing the rate of increase.

The response of soil respiration to precipitation change is asymmetric and differs between grasslands and forests.

Intensification of the Earth's hydrological cycle amplifies the interannual variability of precipitation, which will significantly impact the terrestrial carbon (C) cycle. However, it is still unknown whether previously observed relationship between soil respiration (Rs ) and precipitation remains applicable under extreme precipitation change. By analyzing the observations from a much larger dataset of field experiments (248 published papers including 151 grassland studies and 97 forest studies) across a wider range of precipitation manipulation than previous studies, we found that the relationship of Rs response with precipitation change was highly nonlinear or asymmetric, and differed significantly between grasslands and forests, between moderate and extreme precipitation changes. Response of Rs to precipitation change was negatively asymmetric (concave-down) in grasslands, and double-asymmetric in forests with a positive asymmetry (concave-up) under moderate precipitation changes and a negative asymmetry (concave-down) under extreme precipitation changes. In grasslands, the negative asymmetry in Rs response was attributed to the higher sensitivities of soil moisture, microbial and root activities to decreased precipitation (DPPT) than to increased precipitation (IPPT). In forests, the positive asymmetry was predominantly driven by the significant increase in microbial respiration under moderate IPPT, while the negative asymmetry was caused by the reductions in root biomass and respiration under extreme DPPT. The different asymmetric responses of Rs between grasslands and forests will greatly improve our ability to forecast the C cycle consequences of increased precipitation variability. Specifically, the negative asymmetry of Rs response under extreme precipitation change suggests that the soil C efflux will decrease across grasslands and forests under future precipitation regime with more wet and dry extremes.

Multi-model projections of precipitation extremes in Southeast Asia based on CORDEX-Southeast Asia simulations.

This study examines the projected precipitation extremes for the end of 21st century (2081-2100) over Southeast Asia (SEA) using the output of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment - Southeast Asia (SEACLID/CORDEX-SEA). Eight ensemble members, representing a subset of archived CORDEX-SEA simulations at 25 km spatial resolution, were examined for emission scenarios of RCP4.5 and RCP8.5. The study utilised four different indicators of rainfall extreme, i.e. the annual/seasonal rainfall total (PRCPTOT), consecutive dry days (CDD), frequency of extremely heavy rainfall (R50mm) and annual/seasonal maximum of daily rainfall (RX1day). In general, changes in extreme indices are more pronounced and covering wider area under RCP8.5 than RCP4.5. The decrease in annual PRCPTOT is projected over most of SEA region, except for Myanmar and Northern Thailand, with magnitude as much as 20% (30%) under RCP4.5 (RCP8.5) scenario. The most significant and robust changes were noted in CDD, which is projected to increase by as much as 30% under RCP4.5 and 60% under RCP8.5, particularly over Maritime Continent (MC). The projected decrease in PRCPTOT over MC is significant and robust during June to August (JJA) and September to November (SON). During March to May (MAM) under RCP8.5, significant and robust PRCPTOT decreases are also projected over Indochina. The CDD changes during JJA and SON over MC are even higher, more robust and significant compared to the annual changes. At the same time, a wetting tendency is also projected over Indochina. The R50mm and RX1day are projected to increase, during all seasons with significant and robust signal of RX1day during JJA and SON.

Responses of phytoremediation in urban wastewater with water hyacinths to extreme precipitation.

Climate change not only intensifies eutrophication and enhances the rainfall, but also elevates the contents of greenhouse gases, which can further increase the intensity and frequency of extreme precipitation events. The effectivity of phytoremediation of urban wastewaters by water hyacinths under an extreme rainfall event (up to 380 mm d-1) was investigated using self-designed fabrications with six flow rates (2-15 m3 d-1) in situ on pilot scale for 30 days. The results suggest that water hyacinths had high N and P removal capacities even under adverse conditions such as low dissolved oxygen concentrations (DO, <1 mg L-1) and high ammonium concentrations (NH4+-N, >7 mg L-1). Specifically, the highest removal yields of N and P were 13.14 ± 0.47 g N·m-2·d-1 and 2.12 ± 0.04 g P·m-2·d-1, respectively. The results indicate that water hyacinths can be used for water treatment to reduce the amounts of NH4+-N, dissolved organic nitrogen (DON) and phosphate (PO43-) even during extreme precipitation events. Moreover, DO increased due to wet deposition, runoff and surface flows during the extreme rainfall event, resulting in shifts between nitrification and denitrification processes which significantly altered nitrogen forms in urban wastewater. Results of this study suggest that water hyacinths could be recommended as a cost-effective and eco-friendly technology for urban wastewater phytoremediation in areas suffering from frequent extreme precipitation events.

Asymmetric responses of plant community structure and composition to precipitation variabilities in a semi-arid steppe.

Changing precipitation regimes can profoundly affect plant growth in terrestrial ecosystems, especially in arid and semi-arid regions. However, how changing precipitation, especially extreme precipitation events, alters plant diversity and community composition is still poorly understood. A 3-year field manipulative experiment with seven precipitation treatments, including - 60%, - 40%, - 20%, 0% (as a control), + 20%, + 40%, and + 60% of ambient growing-season precipitation, was conducted in a semi-arid steppe in the Mongolian Plateau. Results showed total plant community cover and forb cover were enhanced with increased precipitation and reduced under decreased precipitation, whereas grass cover was suppressed under the - 60% treatment only. Plant community and grass species richness were reduced by the - 60% treatment only. Moreover, our results demonstrated that total plant community cover was more sensitive to decreased than increased precipitation under normal and extreme precipitation change, and species richness was more sensitive to decreased than increased precipitation under extreme precipitation change. The community composition and low field water holding capacity may drive this asymmetric response. Accumulated changes in community cover may eventually lead to changes in species richness. However, compared to control, Shannon-Weiner index (H) did not respond to any precipitation treatment, and Pielou's evenness index (E) was reduced under the + 60% treatment across the 3 year, but not in each year. Thus, the findings suggest that plant biodiversity in the semi-arid steppe may have a strong resistance to precipitation pattern changes through adjusting its composition in a short term.

Assessing the magnitude and uncertainties of the burden of selected diseases attributable to extreme heat and extreme precipitation under a climate change scenario in Michigan for the period 2041-2070.

BACKGROUND: Extreme heat (EH) and extreme precipitation (EP) events are expected to increase with climate change in many parts of the world. Characterizing the potential future morbidity and mortality burden of EH and EP and associated costs, as well as uncertainties in the estimates, can identify areas for public health intervention and inform adaptation strategies. We demonstrate a burden of disease and uncertainty assessment using data from Michigan, USA, and provide approaches for deriving these estimates for locations lacking certain data inputs. METHODS: Case-crossover analysis adapted from previous Michigan-specific modeling was used to characterize the historical EH-mortality relationship by county poverty rate and age group. Historical EH-associated hospitalization and emergency room visit risks from the literature were adapted to Michigan. In the U.S. Environmental Protection Agency's BenMAP software, we used a novel approach, with multiple spatially-varying exposures, to estimate all non-accidental mortality and morbidity occurring on EH days (EH days; days where maximum temperature 32.2-35 C or > 35 C) and EP days. We did so for two time periods: the "historical" period (1971-2000), and the "projected" period (2041-2070), by county. RESULTS: The rate of all non-accidental mortality associated with EH days increased from 0.46/100,000 persons historically to 2.9/100,000 in the projected period, for 240 EH-attributable deaths annually. EH-associated ED visits increased from 12/100,000 persons to 68/100,000 persons, for 7800 EH-attributable emergency department visits. EP-associated ED visits increased minimally from 1.7 to 1.9/100,000 persons. Mortality and morbidity were highest among those aged 65+ (91% of all deaths). Projected health costs are dominated by EH-associated mortality ($280 million) and EH-associated emergency department visits ($14 million). A variety of sources contribute to a moderate-to-high degree of uncertainty around the point estimates, including uncertainty in the magnitude of climate change, population composition, baseline health rates, and exposure-response estimates. CONCLUSIONS: The approach applied here showed that health burden due to climate may significantly rise for all Michigan counties by midcentury. The costs to health care and uncertainties in the estimates, given the potential for substantial attributable burden, provide additional information to guide adaptation measures for EH and EP.