Future Precipitation Research Articles

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Robust future intensification of winter precipitation over the United States

We investigate 21st-century hydroclimate changes over the United States (US) during winter and the sources of projection uncertainty under three emission scenarios (SSP2–4.5, SSP3–7.0, and SSP5–8.5) using CMIP6 models. Our study reveals a robust intensification of winter precipitation across the US, except in the Southern Great Plains, where changes are very small. By the end of the 21st century, winter precipitation is projected to increase by about 2–5% K−1 over most of the US. The frequency of very wet winters is also expected to increase, with 6–7 out of 30 winters exceeding the very wet threshold under the different scenarios. Our results suggest that the enhancement of future winter precipitation is modulated largely by coupled dynamic and thermodynamic responses, though partly offset by thermodynamic responses. Overall, our results highlight a high likelihood of increasing impacts from winter precipitation due to climate change.

Centennial‐Scale Intensification of Wet and Dry Extremes in North America

AbstractDrought and pluvial extremes are defined as deviations from typical climatology; however, background climatology can shift over time in a non‐stationary climate, impacting interpretations of extremes. This study evaluated trends in meteorological drought and pluvial extremes by merging tree‐ring reconstructions, observations, and climate‐model simulations spanning 850–2100 CE across North America to determine whether modern and projected future precipitation lies outside the range of natural climate variability. Our results found widespread and spatially consistent exacerbation of drought and pluvial extremes, especially summer drought and winter pluvials, with drying in the west and south, wetting trends in the northeast, and intensification of both extremes across the east and north. Our study suggests that climate change has already shifted precipitation climatology beyond pre‐Industrial climatology and is projected to further intensify ongoing shifts.

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (>0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Global and regional hydrological impacts of global forest expansion

Abstract. Large-scale reforestation, afforestation, and forest restoration schemes have gained global support as climate change mitigation strategies due to their significant carbon dioxide removal (CDR) potential. However, there has been limited research into the unintended consequences of forestation from a biophysical perspective. In the Community Earth System Model version 2 (CESM2), we apply a global forestation scenario, within a Paris Agreement-compatible warming scenario, to investigate the land surface and hydroclimate response. Compared to a control scenario where land use is fixed to present-day levels, the forestation scenario is up to 2 °C cooler at low latitudes by 2100, driven by a 10 % increase in evaporative cooling in forested areas. However, afforested areas where grassland or shrubland are replaced lead to a doubling of plant water demand in some tropical regions, causing significant decreases in soil moisture (∼ 5 % globally, 5 %–10 % regionally) and water availability (∼ 10 % globally, 10 %–15 % regionally) in regions with increased forest cover. While there are some increases in low cloud and seasonal precipitation over the expanded tropical forests, with enhanced negative cloud radiative forcing, the impacts on large-scale precipitation and atmospheric circulation are limited. This contrasts with the precipitation response to simulated large-scale deforestation found in previous studies. The forestation scenario demonstrates local cooling benefits without major disruption to global hydrodynamics beyond those already projected to result from climate change, in addition to the cooling associated with CDR. However, the water demands of extensive forestation, especially afforestation, have implications for its viability, given the uncertainty in future precipitation changes.

Plant functional trait is a strong predictor of ecosystem productivity under altered precipitation in desert steppes

AbstractThe relationship between biodiversity and ecosystem function has always been one of the hot issues in the field of ecology. With the acceleration of global warming, the precipitation pattern has become one of the main drivers of biodiversity loss, which has a profound impact on ecosystem functional services and stability. However, the studies on the effects and mechanisms of plant community diversity and ecosystem productivity under precipitation changes in desert steppe are still unclear. According to the change rate (−41.1% to 39.2%) of precipitation in the study area in recent 50 years, five precipitation gradients (i.e., −40%, −20%, CK, +20% and +40%) were set to simulate the possible future precipitation pattern changes. Aboveground biomass increased with the increase of precipitation. Compared with CK, the aboveground biomass increased by 22.81% with +40% and decreased by 80.71% with −40%, and the negative impact of precipitation decrease on aboveground biomass was more significant. Through multiple stepwise regression analyses, species diversity, functional diversity and phylogenetic diversity were identified as the best models of aboveground biomass. The results showed that the aboveground biomass changes could be explained by 51.3%, 81.6%, 32.6% and 60% respectively. Combined with plant community diversity, the final index model was obtained through multiple stepwise regression analyses, which could explain 88.3% of changes in aboveground biomass. In this model, The average coefficient of specific leaf area and leaf thickness had a very high significance level, and these two functional traits of dominant species had a greater explanatory power for ecosystem system function. There was a nonlinear correlation between precipitation and aboveground biomass, and drought had a more significant negative effect on aboveground biomass. Compared with species diversity and phylogenetic diversity, plant functional traits can better explain ecosystem productivity. Selection effects are the main maintenance mechanism of desert steppe community productivity under the background of precipitation change.

Linking Future Precipitation Changes to Weather Features in CESM2‐LE

AbstractWeather features, such as extratropical cyclones, atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally and are associated with different precipitation characteristics. However, future changes in these characteristics, as well as their representation in climate models, remain uncertain. We attribute 6‐hourly accumulated precipitation to cyclones, moisture transport axes (AR‐like features), fronts, and cold air outbreaks, and the combinations thereof in 10 ensemble members of the CESM2‐LE between 1960 and 2100 under the SSP3‐7.0 scenario. We find that, despite some biases in both precipitation and weather features, CESM2‐LE adeptly represents the precipitation characteristics associated with the different combinations of weather features. The combinations of weather features that contribute most to precipitation in the present climate also contribute the most to future changes, both due to changes in intensity as well as frequency. While the increase in precipitation intensity dominates the overall response for total precipitation in the storm track regions, the precipitation intensity for the individual weather features does not necessarily change significantly. Instead, approximately half of the increase in precipitation intensity in the storm track regions can be attributed to a higher occurrence of the more intensely precipitating combinations of weather features, such as the co‐occurrence of extratropical cyclones, fronts, and moisture transport axes.

Ancient wisdom: A new perspective on the past and future Chinese precipitation patterns based on the twenty-four solar terms

Precipitation, as a crucial component of the hydrological cycle, exhibits intense responsiveness to climatic changes. Accurately and finely describing precipitation patterns on a temporal scale within complex climatic regions is a matter that has consistently garnered attention. China, with its intricate geographical environment spanning five climatic zones, has seen its laboring populace develop the unique Twenty-Four Solar Terms (TFST) calendar from ancient times to macroscopically describe climatic shifts and to guide their agricultural activities. Here, based on the chronological framework of the TFST, we revisited and validated the response of typical solar terms to conventional precipitation indicators over the past 40 years in China. By comparing the accumulated precipitation volume and the number of effective precipitation days 30 days before and after the typical solar terms, we established the statistical credibility of this ancient calendar system’s summarization of precipitation patterns. Furthermore, the study examines the response of various future extreme precipitation events to the TFST and their spatial distribution patterns. We identified that more than 70 % of regions in China experienced a concentration of extreme precipitation events during the XiaoShu and DaShu solar terms. Under four different future scenario models, there is prediction that over 55.2 % of the areas in China will also have extreme precipitation events predominantly during the same two solar terms. Additionally, in the Qinghai-Tibet region, there is a trend indicating an increased risk of extreme precipitation frequency during these two critical solar terms associated with rising carbon emissions, with a significance level averaging over 95 %. This research deeply integrates modern meteorological and hydrological science with traditional culture, allowing for a nuanced understanding of the periodicity of extreme precipitation. It also offers a novel perspective and theoretical basis to guide flood prevention efforts and studies on the regularities of extreme precipitation.

Precipitation changes alter plant dominant species and functional groups by changing soil salinity in a coastal salt marsh

Specific mechanisms of precipitation change due to global climate variability on plant communities in coastal salt marsh ecosystems remain unknown. Hence, a field manipulative precipitation experiment was established in 2014 and 5 years of field surveys of vegetation from 2017 to 2021 to explore the effects of precipitation changes on plant community composition. The results showed that changes in plant community composition were driven by dominant species, and that the dominance of key species changed significantly with precipitation gradient and time, and that these changes ultimately altered plant community traits (i.e., community density, height, and species richness). Community height increased but community density decreased with more precipitation averaged five years. Furthermore, changes in precipitation altered dominant species composition and functional groups mainly by influencing soil salinity. Salinity stress caused by decreased precipitation shifted species composition from a dominance of taller perennials and grasses to dwarf annuals and forbs, while the species richness decreased. Conversely, soil desalination caused by increased precipitation increased species richness, especially increasing in the dominance of grasses and perennials. Specifically, Apocynaceae became dominance from rare while Amaranthaceae decreased in response to increased precipitation, but Poaceae was always in a position of dominance. Meanwhile, the dominance of grasses and perennials has the cumulative effect of years and their proportion increased under the increased 60% of ambient precipitation throughout the years. However, the annual forb Suaeda glauca was gradually losing its dominance or even becoming extinct over years. Our study highlights that the differences in plant salinity tolerance are key to the effects of precipitation changes on plant communities in coastal salt marsh. These findings aim to provide a theoretical basis for predicting vegetation dynamics and developing ecological management strategies to adapt to future precipitation changes.

Using quantile mapping and random forest for bias‐correction of high‐resolution reanalysis precipitation data and CMIP6 climate projections over Iran

AbstractClimate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS‐ESM1‐5, BCC‐CSM2‐MR, CanESM5, CMCC‐ESM2 and MRI‐ESM2‐0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias‐correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). First, bias‐correction was performed on ERA5‐Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5‐Land reanalysis data was considered as measured data. Based on the corrected ERA5‐Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias‐corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5‐Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM‐RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC‐ESM2 revealed the highest RMSE in both SSP2‐4.5 and SSP3‐7.0 amounting to 331.74 and 201.84 mm·year−1, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5‐8.5, notably in the case of ACCESS‐ESM1‐5 (RMSE = 431.39 mm·year−1), while the RMSE reduced after using QM‐RF method (197.75 mm·year−1). Obviously, a significant enhancement in results was observed upon implementing the QM‐RF combination method in CMCC‐ESM2 under both SSP2‐4.5 (RMSE = 139.30 mm·year−1) and SSP3‐7.0 (RMSE = 151.43 mm·year−1) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year−1, respectively. Although each bias‐corrected model output was evaluated individually, multi‐model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM‐RF method revealed the lower errors in correcting model outputs, we used the QM‐RF technique to create the MME. Based on SSP2‐4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3‐7.0. Moreover, MME projected dramatic declines under SSP5‐8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5.

The atmospheric effect of aerosols on future tropical cyclone frequency and precipitation in the Energy Exascale Earth System Model

This study uses experiments from the Energy Exascale Earth System Model (E3SM) to compare the influence on tropical cyclone (TC) activity of: (i) the atmospheric effect of aerosols under specified sea-surface temperatures (SSTs); and (ii) the net effect of greenhouse gases (GhGs) (including changes in SSTs). The experiments were performed using the CMIP6 Shared Socioeconomic Pathway SSP5-8.5 emissions scenario with GhG-induced SST warming specified and atmospheric aerosol effects simulated but without explicit ocean coupling. Insignificant changes in global TC frequency are found in response to the atmospheric effect of future aerosols and GhGs, as significant regional responses in TC frequency counteract each other. Future GhGs contribute to more frequent TCs in the North Atlantic, and reductions over the Northwestern Pacific and Southern Indian Ocean. The atmospheric effect of future aerosols drives more frequent TCs over the Northwestern Pacific and reductions over the Northeast Pacific and North Atlantic. Along with increases in TC intensity, global TC precipitation (TCP) is projected to increase by 52.8% (14.1%/K) due to the combined effect of future aerosols and GhGs. Although both forcings contribute to TCP increases (14.7–19.3% from reduced aerosols alone and 28.1–33.3% from increased GhGs alone), they lead to different responses in the spatial structure of TCP. TCP increases preferentially in the inner-core due to increased GhGs, whereas TCP decreases in the inner-core and increases in the outer-bands in response to the atmospheric effects of decreased aerosols. These changes are distinct from those caused by aerosol-induced SST changes, which have been considered in other studies.

Ecohydrological and hydrogeological dynamics of groundwater springs in Eastern Himalaya, India

Groundwater springs are critical to achieving Sustainable Development Goals (SDG 6, access to clean water) in the Himalaya and remain highly vulnerable to climate change and land-use and land cover change. In a first from Eastern Himalaya, we analysed the relative controls of land-use, precipitation, soil properties, and hydrogeology on the diel and seasonal variability in three representative springs using high-frequency discharge monitoring. Kamrang spring is a high-discharge depression spring fed by a homogenous aquifer, whereas Mamley and Gaddi show dual-flow characteristics attributed to primary matrix-based flows and secondary conduit (karst) or unconsolidated storage-based flows, respectively. The first reports of strong diel fluctuations in springflows show significantly higher amplitude in the depression spring (22 ± 41 l min−1) than the fracture (15 ± 26 l min−1) and karst springs (12 ± 24 l min−1), attributed to evapotranspiration and hydrogeology, respectively. The forest spring (Gaddi, low soil hydraulic conductivity, Ksat) showed a faster response at intense precipitation (>30 mm h−1), whereas the agriculture springs (Kamrang and Mamley, high Ksat) showed the lowest lags at low-moderate intensities (<20 mm h−1). The depression spring showed high recharge potential, whereas the karst and fracture springs were constrained by their relatively smaller recharge area and low Ksat, respectively. The per capita daily water availability was barely sufficient to support the minimum (20 l) and mandated (55 l) requirements for 30–70% and 2–47% of days a year, respectively. Thus, future precipitation intensification and land-use change will disproportionately impact the >5th-order karst and fracture springs. The study provides an integrated analytical framework for understanding Himalayan springs, which are critical for achieving SDG 6 (access to clean water) and a baseline for developing appropriate springshed models for effective management of freshwater ecosystems (SDG 15) against future climate change impacts (SDG 13), as well as informing the water security assessment in the Himalaya.

Downscaling future precipitation with shared socioeconomic pathway (SSP) scenarios using machine learning models in the North-Western Himalayan region

Downscaling future precipitation with shared socioeconomic pathway (SSP) scenarios using machine learning models in the North-Western Himalayan region

Projected Climate Change Impacts on the Number of Dry and Very Heavy Precipitation Days by Century’s End: A Case Study of Iran’s Metropolises

This study explores the impacts of climate change on the number of dry days and very heavy precipitation days within Iran’s metropolises. Focusing on Tehran, Mashhad, Isfahan, Karaj, Shiraz, and Tabriz, the research utilizes the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Circulation Models (GCMs) to predict future precipitation conditions under various Shared Socioeconomic Pathways (SSPs) from 2025 to 2100. The study aims to provide a comprehensive understanding of how climate change will affect precipitation patterns in these major cities. Findings indicate that the SSP126 scenario typically results in the highest number of dry days, suggesting that under lower emission scenarios, precipitation events will become less frequent but more intense. Conversely, SSP585 generally leads to the lowest number of dry days. Higher emission scenarios (SSP370, SSP585) consistently show an increase in the number of very heavy precipitation days across all cities, indicating a trend towards more extreme weather events as emissions rise. These insights are crucial for urban planners, policymakers, and stakeholders in developing effective adaptation and mitigation strategies to address anticipated climatic changes.

Unsupervised deep learning bias correction of CMIP6 global ensemble precipitation predictions with cycle generative adversarial network

Abstract Climate change significantly impacts agricultural production, ecosystem stability, and socioeconomic development. Global climate models (GCMs) serve as the primary tool for simulating historical and future precipitation patterns. However, due to issues such as coarse resolution, boundary condition, and parameterization, model outputs require bias correction (BC). With the evolution of deep learning techniques, supervised convolutional neural network (CNN) frameworks have gained popularity in the area of climate model BC but face limitations in spatial correlation assumptions and data sparsity, particularly for extreme precipitation This study proposed an unsupervised learning approach using cycle generative adversarial network (CycleGAN) to correct the ensemble mean bias of models and compare its performance with CNN and Quantile Mapping methods. The results demonstrate that the proposed CycleGAN approach outperforms both CNN and Quantile Mapping in ensemble mean BC. It effectively learns the overall distribution of precipitation through an adversarial process and yields better extreme precipitation predictions.

Quantile Delta-Mapped Spatial Disaggregation Analysis for Climate Variations over the Yangtze River Basin

Abstract Significant increases in temperature and precipitation due to global warming affect socioeconomics. Accurate analysis is needed for future temperature and precipitation variations across the Yangtze River basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time. The evaluation results show that the QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, while it shows improvement in reproducing precipitation. Projections indicate the annual-mean temperature will increase from 2020 to 2080 under shared socioeconomic pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest range of differences in summer. Conversely, annual-mean temperatures significantly decrease from 2081 to 2100 under SSP2-4.5. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual-mean precipitation will increase from 2020 to 2080 but decrease from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policymaking to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry).

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Relative contribution of dynamic and thermodynamic components on Southeast Asia future precipitation changes from different multi-GCM ensemble members

To address the gap in understanding precipitation changes in Southeast Asia and to enhance the reliability of climate projections for the region through moisture budget analysis, this study examines the differences among six multi-model ensembles of CMIP6 simulated precipitation in term of moisture budget analysis. It investigates the relative contributions of thermodynamic and dynamic components to seasonal precipitation changes over Southeast Asia under the highest emission scenario, SSP5-8.5. The comparison between ensembles indicates that Good performance model ensembles slightly outperform the combination of all resolution and all category ensembles in reducing the biases. There is no strong evidence showing that good category ensembles outperform the combination of all model ensemble groups in simulating the spatial pattern of historical seasonal precipitation. From the perspective of moisture budget, regions receiving seasonal high rainfall intensity are mainly influenced by the moisture convergence during the monsoon seasons: northeast monsoon (December‒January‒February) and southwest monsoon (June–July–August). By the late 21st century (2081–2100), all model ensemble projections show an increase in December‒January‒February precipitation over the northern Southeast Asia and decreased June‒July‒August rainfall in the southern regions. The moisture budget analysis explained that the seasonal mean rainfall change in Southeast Asia is largely influenced by evaporation and followed by moisture flux convergence. The changes in moisture flux convergence are contributed by both the dynamic and thermodynamic components. Greater inter-model uncertainty was found in the precipitation dynamic component compared to the thermodynamic component suggesting the existence of large discrepancy between the various approaches used by GCMs in describing atmospheric dynamics. The study highlights that the Good model ensemble with middle to low resolution is able to narrow the inter-model uncertainties in terms of the moisture budget analysis compared to the combination of all Good model ensembles.

Effect of simulated extreme rainfall on the vegetative phenology of perennial and annual herbaceous plants from a Brazilian dry forest.

Detecting changes in the phenological responses of herbaceous species as a function of predicted climate change is important for forecasting future scenarios for the functioning of dry tropical forests, especially when predicting an increase in the frequency and intensity of extreme droughts. Because of the sensitivity of plants to water availability, our study hypothesizes that if years become drier or wetter, herbaceous plants will synchronously change the onset, duration, and intensity of their vegetative phenophases. We used a historical series of 60 years of precipitation observations for the Caatinga vegetation to define daily average of precipitation for rainy (Twet), median (Tcontrol), and dry (Tdry) years. We simulated past average daily rainfall (Twet, Tcontrol, and Tdry) while growing two herbaceous perennials and two herbaceous annuals. We monitored plant growth and measured the activity (absence or presence) and intensity of vegetative phenophases. We used circular statistical analysis to assess differences between treatments. Our results revealed that leaf production was seasonal but relatively uniform for perennial species and highly seasonal (wet season) for annual species. Simulated dry years induced lower leaf emergence concentrated over a few months in annual species, but this effect was more strongly significant in one of the two perennial species. Both annual and perennial species can experience delayed and less intense leaf abscission during the rainy season in years with below-average precipitation. In contrast, large voluminous rains in years with above-average precipitation can accelerate and intensify the process of leaf renewal. If future precipitation reductions occur, the changes in phenological response indicate that the cover of annual and perennial herbaceous species in this study will likely decrease, altering the landscape and functioning of dry tropical forests. However, the potential trade-offs observed may help populations of these species to persist during years of severe drought in the Caatinga.

Summer Convective Precipitation Changes Over the Great Lakes Region Under a Warming Scenario

AbstractTo understand future summer precipitation changes over the Great Lakes Region (GLR), we performed an ensemble of regional climate simulations through the Pseudo‐Global Warming (PGW) approach. We found that different types of convective precipitation respond differently to the PGW signal. Isolated deep convection (IDC), usually concentrated in the southern domain, shows an increase in precipitation to the north of the GLR. Mesoscale convective systems (MCSs), usually concentrated upwind of the GLR, shift to the downwind region with increased precipitation. Thermodynamic variables such as convective available potential energy (CAPE) and convective inhibition energy (CIN) are found to increase across almost the entire studied domain, creating a potential environment more favorable for stronger convection systems and less favorable for weaker ones. Meanwhile, changes in the lifting condensation level (LCL) and level of free convection (LFC) show a strong correlation with variations in convective precipitation, highlighting the significance of these thermodynamic factors in controlling precipitation over the domain. Our results indicate that the decrease in LCL and LCF in areas with increased convective precipitation is mainly due to increased atmospheric moisture. In response to the prescribed warming perturbation, MCSs occur more frequently downwind, while localized IDCs exhibit more intense rain rates, longer durations, and larger rainfall area.