Wet Days Research Articles

Changes in the mean and variability of temperature and precipitation over global land areas

The frequency and intensity of extreme weather and climate events may change in response to shifts in the mean and variability of climate, which pose high risks to societies and natural ecosystems. Gridded near-surface temperature, precipitation, and the number of wet days from the Climatic Research Unit dataset were analyzed for two 30 year periods to explore changes in the mean and variability of temperature and precipitation over global land areas in the recent period (1991–2020) compared to the reference period (1951–1980). Global land areas are characterized by warmer and slightly wetter conditions in the recent period, while the variability of temperature and precipitation has remained nearly unchanged. Changes in the mean and variability of both temperature and precipitation are also analyzed over tropical, subtropical, and midlatitude land areas. The annual mean temperature over all these three latitudinal regions has increased in the recent period compared to the reference period, with the highest increase in subtropical and midlatitude land areas (0.7 ∘C), followed by tropical land areas (0.5 ∘C), while temperature variability has remained nearly unchanged. The annual precipitation has decreased over tropical, subtropical, and midlatitude land areas in the recent period compared to the reference period. Precipitation variability has not changed considerably over subtropical land areas. However, it has substantially increased over tropical land areas, which indicates a higher risk of droughts and periods of excess water in the recent period. In contrast, precipitation variability has decreased over midlatitude land areas, indicating narrower swings between wet and dry conditions, which decrease the risk of droughts and periods of excess water in the recent period.

Observed Changes in Interannual Precipitation Variability in the United States

AbstractCharacterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Prwet (%)), and 90th percentile (X90 (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

Recent Changes in Hydrometeorological Extremes in the Bilate River Basin of Rift Valley, Ethiopia

The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen’s slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days’ rate of change falls between +10.64 mm in the downstream to −10.67 mm in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin’s southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations’ average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring.

Changes in mean and extreme homogeneous precipitation in China during 1960–2020

This study aims to explore the observed national and regional changes in the seasonal mean and extreme precipitation in mainland China between 1960 and 2020 using data from 678 stations. Tests of homogeneity (e.g. standard normal homogeneity test, Pettitt's test and Buishand range test) are applied to identify homogeneous stations for the trend analysis. The non-parametric Mann-Kendall test and a modified Theil-Sen slope estimator were then used to examine changes in the precipitation characteristics. Our results show that despite a decreasing trend in the frequency of wet days, the mean precipitation has actually increased. Extreme precipitation has also increased, and in many locations, at greater rates than those of the mean. This is principally due to a greater rate of increase per degree of warming, of 8% for extreme precipitation compared to 7% for the mean. However, considerably regional and seasonal variability has been seen in the trends. Mean and extreme precipitation trends are positive throughout in western China for all seasons, but they vary in eastern China. Mean precipitation increased slightly in the southeast but decreased in the northeast of the country. Extreme precipitation increased in both regions, but the increases were greater in the southeast. Trends, additionally, also differed within regions, especially over eastern China. In southeast China, precipitation increased in the east and decreased in the west. In northeast China, precipitation increased in the north and decreased in the south. Using a multiple regression model, we found that changes during summer precipitation clearly dominate the overall annual changes in most regions. Most long-term changes over eastern China appear to be linked to those of the East-Asian and South-Asian Summer Monsoons.

Urbanization as a limiter and catalyst of watershed-scale sediment transport: Insights from probabilistic connectivity modeling

The conversion of rural lands to urban areas exerts considerable influence on the hydrologic processes governing sediment transport at the watershed scale. While the effects of urbanization on hydrology have been well-studied, the corresponding impact to the spatial and temporal variability of sediment detachment, transport, and connectivity is less certain. To address this knowledge gap, we apply process-based hydrologic simulation, probabilistic connectivity modeling, and in situ turbidity sensing to five watersheds positioned along a steep land use gradient in Kansas, USA. Connectivity modeling results show that urbanization systematically decreases the maximal extent of watershed-scale connectivity on the wettest days of the study period, from 51 % in the most rural watershed to 28 % in the most urban watershed. On the other hand, urbanization focuses sediment transport into fewer, more frequently wetted pathways, such as roadway drainage networks, which are activated 3.5 times more frequently than the equivalent pathways in rural basins. In this way, urbanization limits maximal connectivity as impervious surfaces indefinitely disconnect source zones from the sediment cascade, but also catalyzes hot spots of connectivity as these same impervious areas generate excess runoff and channel it to drainage systems. The 23.9 ± 4.2 % of days that exhibit watershed-scale functional connectivity account for 85.0 ± 9.5 % of sediment export with most of the export tied to a few highly connected days. Sensing results show that increases in watershed-scale connectivity only translate to larger fluvial sediment loads after a connectivity threshold (the median connected day) has been exceeded, suggesting a transition from functional to structural connectivity control on sediment dynamics after sufficient wetting. This study highlights the role of land use impacts on the sources and mechanisms of sediment transport, which will be an important consideration for land managers as urban areas continue to expand to accommodate global migration patterns.

CMIP6 models simulate heavier future rainfall over the Highlands than across other agricultural areas of Yemen

The climate of Yemen remains largely unstudied, leading to major uncertainties and a lack of understanding of future extreme rainfall events and how they are likely to affect agriculture. Here, we examine future heavy rainfall events for the period 2025–2100 using the latest state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP 6). Based on differences in rainfall regimes, we divided the study area into two distinct parts: Highlands and the Red Sea Coast & Tihama Plain region. Analyzing annual total precipitation when daily precipitation exceeds the 95th percentile of wet day precipitation (R95p), we found that models predict 16–25% heavier future rainfall over the Highlands than across the Tihama Plain and other agricultural parts of Yemen. By mid-century, the Highlands will experience ~ 25% heavier rainfall than other agricultural areas under SSP2-4.5 and ~ 16% heavier under SSP5-8.5. In both cases, the risk of rejecting the null hypothesis (H0) when it is true is lower than 5%. These results suggest that whether the current status quo of emissions continues, or the radiative forcing level is stabilized at 4.5 W/m2, heavy rainfall events are projected to increase more over the Highlands than over other agricultural areas of Yemen. Considering the projected increase in heavy rainfall events, we argue that Yemen desperately needs investments and innovations that will foster readiness and overall climate disaster preparedness. Efforts and investments will need to be directed more toward bridging the gap between producers of climate information and end users. Ultimately, these efforts will contribute to shaping the future of farmer decision support across Yemen.

Equatorial Rossby waves on cold surge days and their impact on rainfall

AbstractThis study aims to understand the process that determines the regional rainfall distribution in the equatorial South China Sea (SCS) and its surrounding during the cold surge (CS) days by looking at the different vorticity phases of active equatorial Rossby (R1) waves and the dynamics of the CSs related to the R1 wave. The CS can be thought of as an anticyclonic outflow from midlatitude and behaves like a dispersive group of R1 meridional modes. From the study, it shows the vorticity of the R1 wave arranges itself in such a way that the anticyclonic phase is located to the west and the cyclonic phase is located to the east of the cold CS axis. The CS winds in the equatorial SCS occur early, remain longer, and possess a larger zonal component in the active cyclonic phase compared with the active anticyclonic phase. During the active cyclonic vorticity phase, the rainfall anomalies pattern in the equatorial SCS changes from a “tick‐mark‐like” pattern to a “V‐shape” pattern as a consequence of the westward propagation of the wave. The probability of rainfall increases near the centre of cyclonic vorticity, but for anticyclonic vorticity the probability increases in the southern part of the vorticity centre. There is both increased (decreased) intensity and probability of extreme rainfall events during the active cyclonic (anticyclonic) vorticity phase on CS days. Overall, the rainfall anomalies are largely dominated by changes in the intensity on wet days rather than the number of wet days. These changes indicate the roles of the R1 wave vorticity phase in the rainfall distribution during CS days. The timing of enhancement and suppression of rain will provide additional valuable information to help improve forecast extreme weather events.

Future changes in the mean and variability of extreme rainfall indices over the Guinea coast and role of the Atlantic equatorial mode

Abstract. The occurrence of climate extremes could have dramatic impacts on various sectors such as agriculture, water supply, and energy production. This study aims to understand part of the variability in the extreme rainfall indices over Guinea coast that can be related to the Atlantic equatorial mode (AEM), whose positive phases are associated with an increase in the intensity and frequency of rainfall events. We use six extreme indices computed from six observed rainfall databases and historical and SSP5-8.5 simulations from 24 general circulation models (GCMs) that participate in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to study changes in extreme rainfall events over Guinea coast during July–September. Under present-day conditions, we found that current GCMs clearly overestimate the frequency of wet events and the maximum number of consecutive wet days. The magnitude of the other extreme indices simulated is within the range of the observations which, moreover, present a large spread. Our results confirm the existing studies. However, less attention has been paid to the evaluation of the modelled rainfall extremes associated with the AEM under different climate conditions, while the variability of the AEM is expected to decrease in the future, with a potentially significant impact on the extreme events. Here, we use six (one) observed rainfall (sea surface temperature) data and 24 GCM outputs to investigate the present-day, near-term, mid-term, and long-term future links between the AEM and the extreme rainfall events over the Guinea coast. The biases in the extreme rainfall responses to the AEM are subject to a large spread across the different models and observations. For the long-term future (2080–2099), less frequent and more intense rainfall events are projected. As an illustration, the multimodel ensemble median (EnsMedian) maximum rainfall during 5 consecutive wet days (RX5day) would be 21 % higher than under present-day conditions. Moreover, the variability of the majority of the extreme indices over the Guinea coast is projected to increase (48 % for RX5day in the long-term future). By contrast, the decreased variability of the AEM in a warmer climate leads to a reduced magnitude of the rainfall extreme responses associated with AEM over the Guinea coast. While under present-day conditions the AEM explains 18 % of the RX5day variance in the EnsMedian, this value is reduced to 8 % at the end of 21st century. As a consequence, in absolute, there is a projected increase in the total variability of most of the extreme rainfall indices, but the contribution of the AEM to this variability weakens in a warmer future climate.

Unevenness of temporal distribution of precipitation over the Yangtze River basin and its main influencing factors

AbstractUnderstanding the unevenness of precipitation distribution within a year is important for water resources management and agricultural production. In this study, spatiotemporal variations of precipitation unevenness in the Yangtze River basin (YRB) during 1961–2020 and its main influencing factors are investigated by the relative entropy (RE) and rotated empirical orthogonal function. The timings of different percentiles of annual total precipitation show that the temporal distribution of precipitation in the YRB is uneven. The unevenness of precipitation distribution exhibits a large spatial variability with a high‐value centre in the northeast and a low value centre in the northwest of the YRB, and an upward trend in almost the entire YRB. Precipitation becomes more uneven with the decrease of annual number of wet days and low‐intensity precipitation amount and the increase of high‐intensity precipitation amount during the study period. Precipitation unevenness of different regions is modulated by the different combinations of different seasonal climate indices with specific time lags. For example, in the south‐central YRB, precipitation unevenness generally tends to be enhanced by the positive Tropical Northern Atlantic Index in current summer and sea surface temperature anomalies over South China Sea (SCS) in previous summer, and negative North Atlantic Oscillation in current winter, while in the northeast YRB, be weakened by negative Quasi‐Biennial Oscillation in current spring, Atlantic Meridional Mode in current summer and positive SCS in current autumn. Besides, the identified significant climate indices can partly explain the temporal changes of precipitation unevenness, and their explanatory ability is more powerful in the south‐central, southeast and northwest YRB than in the north‐central and northeast YRB, which reveals a strong negative correlation with the degree of precipitation unevenness.

Response of fatal landslides to precipitation over the Chinese Loess Plateau under global warming

Rain-induced loess landslides are especially prevalent in the Chinese Loess Plateau (CLP). Some became fatal landslide disasters, leading to numerous casualties and significant socioeconomic losses. Extreme precipitation is the main cause of landslide occurrence. Therefore, in this study we discuss the correlation between seven extreme precipitation indices, single continuous precipitation events and fatal landslides in the CLP using Pearson correlation analysis. We also predict future precipitation under climate changes using five optimal CMIP6 models. During the period 2004–2016, fatal landslides in the CLP increased at a rate of 0.6 per year, with frequent landslide events occurring especially in the central and southwestern parts of the CLP. We find that SDII (simple daily intensity precipitation index) and R×5day (max 5-day precipitation amount) show spatial distribution that are consistent with fatal landslides. Extreme precipitation events were frequent after year 2000; and several extreme precipitation indices show an increasing trend with a higher magnitude since 2000 than before 2000. In particular, in 2013 when the number of fatal landslides was as high as 17, SDII, R95pTOT (extremely wet days), R25mm (very heavy precipitation days), and R×5day all showed abrupt increases. Single continuous precipitation events have profound effects on fatal landslides. We show that single continuous precipitation events with cumulative precipitation of 185–235 mm and duration of 6 days or longer have the highest correlation with fatal landslides. As the increasing occurrence of extreme rainfall events by the global warming, the CLP may face more fatal landslides in the future, especially in the high emission scenario of greenhouse gases (GHGs).

Detecting the response characteristics and thresholds of grassland spring phenology to climatic factors in the Mongolian Plateau

Understanding the response characteristics and thresholds of grassland spring phenology (GSP) to different climatic factors in arid and semi-arid regions is of great significance to deal with global climate change. The Mongolian Plateau is one of the largest plateaus located in arid and semi-arid regions of eastern Asia, extremely sensitive to climate change. At present, the response characteristics and thresholds of the grassland spring phenology to climate factors in the Mongolian Plateau remain uncertain. Here, we used statistical analysis, random forest and geographical detector to explore these uncertainty. We found that 1) 76.67% of study area showed a significant advancing trend. GSP has significantly advanced by −0.656 days/decade (P < 0.05) over the past three decades. 2) The vapor pressure (VAP) shows a more important impact on the GSP than other climate factors both in strength and range. Especially when the VAP value was between 2 and 5 hPa, the GSP advanced as the VAP increased. The near-surface temperature minimum (TMN), near-surface temperature (TMP), near-surface temperature maximum (TMX) and diurnal temperature range (DTR) have the greatest impact on the GSP in June, and the six-month cumulative effects of precipitation (PRE), wet day frequency (WET) and cloud cover (CLD) are greater than those in a single month. The interaction between VAP and PRE could largely explain GSP changes (69.35%). 3) The GSP have different response threshold to climate factors. The GSP advances as the temperature increases when TMP < 0 °C, DTR < 14 °C, TMX > 10 °C and TMN > −5 °C, and it delays as the FRS, WET and CLD increase. This study is of great significance to reveal the impact characteristics and threshold of climate change on the GSP, which could do help in understanding climate change.

Assessing Rainfall Variability in Jamaica Using CHIRPS: Techniques and Measures for Persistence, Long and Short-Term Trends

Jamaica, as a Small Island Developing State (SIDS), is highly vulnerable to weather extremes. As precipitation persistence is a critical factor in determining the susceptibility of an area to risks, this work assesses the spatial and temporal variations of rainfall persistence in Jamaica from 1981 to 2020, using satellite-based information. The Hurst exponent (H) and the serial correlation coefficient (SCC) are used to evaluate the long-term persistence of precipitation and the Persistence Threshold (PT) concept is introduced to provide a description of rainfall characteristics over short periods, specifically, the number of consecutive days with precipitation above or below a set threshold value. The PT method is a novel concept that expands upon the Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) methods that only consider a threshold of 1 mm. Results show notable temporal and spatial variations in persistence over the decades, with an overall increasing trend in high precipitation persistence and a decreasing trend in low precipitation persistence. Geographically, the northern mountainous area of Jamaica received the most persistent rainfall over the study period with an observed increase in extreme rainfall events. The excess rainfall of the 2001–2010 decade is remarkable in this study, coinciding with the global unprecedented climate extremes during this time. We conclude that the data used in this study is viable for understanding and modeling rainfall trends in SIDS like Jamaica, and the derived PT method is a useful tool for short-term rainfall trends, but it is just one step toward determining flood or drought risk. Further research will focus on developing drought and flood indices.

Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye

As hydrological disasters become more common, the need for monitoring extreme precipitation events is growing globally. Remote sensing technologies developed in recent years help to meet this demand with satellite-based precipitation products, particularly for ungauged basins. However, due to limitations and uncertainties, these products must be validated before being used in hydrometeorological/climatological applications. Validation studies also help improve retrieval algorithms by identifying errors and biases in the data that can be corrected or adjusted. One of NASA's Global Precipitation Measurement products, called Integrated Multi-satellitE Retrievals for GPM (IMERG), has been extensively validated for its ability to detect extreme precipitation events in drastically different climate regions across the world. However, such studies remain limited for Turkiye. In this study, we examined the ability of IMERG Early (E), Late (L), and Final (F) products to detect extreme precipitation in Turkiye using rain gauge data over an eleven-year period (2010−2020). We used extreme precipitation indices recommended by the Expert Team on Climate Change Detection Indices (ETCCDI) to evaluate the ability of IMERG products to detect extreme precipitation. For the stations selected across Turkiye, the Pearson correlation coefficients (R) between daily ground-based precipitation measurements and the three IMERG precipitation products (IMERG-E, IMERG-L, and IMERG-F) were found to be 0.54, 0.55, and 0.64, respectively. IMERG products performed better in detecting the precipitation extreme based on the 85% percentile compared to the 95% and 99% percentiles. IMERG underestimates consecutive dry days by about 25% while overestimating consecutive wet days at the same rate. The performance of IMERG Early, Late, and Final products is inversely proportional to the daily precipitation totals. The annual intensity of precipitation outperformed in the Final run compared to other products across the country. Light and moderate rainfall was detected well, while the detection capability of IMERG decreased for heavy and heavy torrential precipitation. Geographically, the Aegean, Mediterranean, and Southeastern Anatolia regions generally performed better, while Eastern Anatolia and interior parts of the Black Sea region exhibited poorer performance. The low performance in Eastern Anatolia may be attributed to the region's mountainous terrain, while the low performance in the Black Sea region may be due to orographic forcing and land-sea interaction. Overall, the findings of this study showed that the detection capability of IMERG products performed better with increasing latency.

Spatio-Temporal Evolution of Inland Lakes and Their Relationship with Hydro-Meteorological Factors in Horqin Sandy Land, China

In the inland closed area of Horqin Sandy Land, China, lakes are the most important source of water, and they maintain the regional hydrological balance and ecosystem health. Clarifying long-term continuous changes of inland lake surface area and water storage in the sandy land is thus of great significance to the management of water resources in arid and semi-arid areas. This study estimated changes in the surface area and water storage of small lakes in the sandy land during 1984–2021 using a multiple index threshold method and an empirical equation based on Shuttle Radar Topography Mission (SRTM) DEM (digital elevation model) data and Landsat 5/7/8 images. Hydro-meteorological variables were also incorporated to explore their potential relationship with changes in the lake elements. The lakes in the sandy land resemble stars or dots, with distinct inhomogeneity. Permanent lakes account for the majority of the total lake area, mostly distributed in the center and east of the study area, whereas most seasonal lakes are small water bodies, with surface areas of 0.1–0.5 km2 and irregularly distributed. Lake surface area and water storage underwent frequent changes during the 38 years, and could be divided into three characteristic fluctuation phases. From 1984 to 1999, the lake surface area fluctuated greatly, and the water storage increased by approximately 0.3732 Gt, with an average expansion rate of 0.0572 Gt/y. Specifically, lakes with a surface area &gt;2 km2 primarily accounted for the expansion. From 1999 to 2004, the lake area shrank sharply to the lowest point, and the water storage capacity decreased by approximately 0.4003 Gt. From 2004 to 2021, the lake surface area and water storage tended to be stable. Annual fluctuations of lake surface area and water storage were mostly affected by precipitation and evapotranspiration, followed by vapor pressure deficit, wet day frequency, and temperature, which have significant periodicity and hysteresis.

Climate change over Indonesia and its impact on nutmeg production: An analysis under high‐resolution CORDEX‐CORE regional simulation framework

AbstractNutmeg is an important spice and contributes significantly to the gross domestic product of Indonesia. The study examines the impact of climate change on Indonesia and the production of nutmeg over Banda Neira Island. For this, the outputs from high‐resolution regional climate models under different representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5), within the framework of Coupled Model Intercomparison Project 5/Coordinated Regional Climate Down‐scaling Experiment‐Coordinated Output for Regional Evaluations (CMIP5/CORDEX‐CORE) over southeast Asia domain are employed. The representation of the spatial structure of rainfall and temperature climatology is found to be much better in ensembles as compared to individual model experiments. The behaviour of simulated rainfall and temperature from the model experiments is examined to check whether the projected climate favours an increase in nutmeg production. The study found that projected changes in the number of wet days (ideal 100–160 wet days), heavy rainfall events, rising mean temperature above the optimum temperature range (25–26°C), increasing heatwave spells, and prolongation of the warm period led to an unfavourable climate for nutmeg production. Based on the analysis of dynamically produced (model simulated) climate variables, the study rejects the forecasted increase in nutmeg production using a statistical regression model due to the unfavourable climatic conditions that are likely to affect the growth, production and quality of nutmeg. The changing climate not only reduces regional agricultural production but also impacts the socio‐economic condition of the people of Indonesia. Therefore, the study emphasizes implementing stringent climate laws and policies at global, regional and local levels to curb greenhouse gas emissions and reduce the negative impact of climate change.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

Fingerprinting of rainfall over semi-arid region, Western India, using MATLAB and GIS.

The present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981-2020 using computational and spatial analysis tools. Daily gridded rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann-Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991-2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters.

Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios

Abstract. Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved.