March April May Research Articles

Concurrent heat stress and air pollution episodes by considering future projection of climate change

The simultaneous effect of heat stress and air pollutants such as ozone can cause many health issues in cities. The situation exacerbates in the context of climate change and temperature rise. Furthermore, ground-level ozone, worsened by climate change, needs investigation for effective management. Therefore, this study projects heat stress and ozone levels in two Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios, SSP245 and SSP585. Results indicate heightened heat stress with increased ozone levels, especially in severe climate scenarios like SSP585. Besides, the study shows that in the SSP245 and SSP585 scenarios, the seasonal shift of high Heat Index (HI) values as well as high ozone concentrations is happening toward the previous months of June-July-August (JJA). High values in the HI classification and the Maximum Daily 8-hour average (MDA8) ozone happen sooner in the March-April-May (MAM) months than the expected JJA months. Furthermore, in the SSP585 scenario, the HI classification above 105 (very hot equal to danger category) is 10% high in all months in comparison to the SSP245 scenario. The study emphasizes the importance of understanding the interactions between heat stress and ozone pollution for implementing effective adaptation and mitigation measures.

Distinct impacts of the El Niño–Southern Oscillation and Indian Ocean Dipole on China's gross primary production

Abstract. Gross primary production (GPP), a crucial component in the terrestrial carbon cycle, is strongly influenced by large-scale circulation patterns. This study explores the influence of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's GPP, utilizing long-term GPP data generated by the Boreal Ecosystem Productivity Simulator (BEPS). Partial correlation coefficients between GPP and ENSO reveal substantial negative associations in most parts of western and northern China during the September–October–November (SON) period of ENSO development. These correlations shift to strongly positive over southern China in December–January–February (DJF) and then weaken in March–April–May (MAM) in the following year, eventually turning generally negative over southwestern and northeastern China in June–July–August (JJA). In contrast, the relationship between GPP and IOD basically exhibits opposite seasonal patterns. Composite analysis further confirms these seasonal GPP anomalous patterns. Mechanistically, these variations are predominantly controlled by soil moisture during ENSO events (except MAM) and by temperature during IOD events (except SON). Quantitatively, China's annual GPP demonstrates modest positive anomalies in La Niña and negative IOD years, in contrast to minor negative anomalies in El Niño and positive IOD years. This outcome is due to counterbalancing effects, with significantly larger GPP anomalies occurring in DJF and JJA. Additionally, the relative changes in total GPP anomalies at the provincial scale display an east–west pattern in annual variation, while the influence of IOD events on GPP presents an opposing north–south pattern. We believe that this study can significantly enhance our understanding of specific processes by which large-scale circulation influences climate conditions and, in turn, affects China's GPP.

Synchronization of the Recent Decline of East African Long Rains and Northwestern Eurasian Warming

AbstractThe East African March–April–May (MAM, “long rains”) precipitation decline in recent decades remains a puzzle marked by various proposed large‐scale drivers. Here, the interannual variability of the long rains and their recent drying trend are examined using global model simulations and observations. Comparison of a control simulation and re‐initialized simulations in which land‐surface feedback is suppressed shows that much of the long rains deficit experienced between 1980 and 2014 is synchronized with the warming of the Northwestern Eurasian landmass. In agreement with the modeling results, multiple observational data sets reveal a strong negative correlation between MAM mean East African rainfall amount and the surface temperature over Northwestern Eurasia. Idealized simulations further indicate that warming in Northwestern Eurasia weakens the regional Hadley Cell and diverts the monsoonal transport of moisture away from Eastern Africa toward Europe and southern Africa, highlighting the role of remote land surface warming on the observed precipitation decline.

Simulating springtime extreme rainfall over Southern East Asia: unveiling the importance of synoptic-scale activities

This study investigates the influence of synoptic-scale activities on extreme precipitation during March–April–May (MAM) over Southern East Asia (SEA) using observational data and compares the results with the outputs from 20 Coupled Model Intercomparison Project phase 6 (CMIP6) historical runs. Observations show that SEA intense daily precipitation in MAM is linked to enhanced upper-level synoptic-scale waves; these disturbances are associated with significant anomalous temperature advection as well as moisture flux convergence, creating favorable conditions for extreme rainfall. Furthermore, it is found that a temperature advection index (TAI) can be utilized to characterize such synoptic-scale activities. Inspection of CMIP6 historical runs reveals that, among 20 models, 13 models perform well in accurately capturing the observed SEA rainfall pattern; such extreme events are also closely linked to TAI in the model environment. Overall, observed (simulated) results show that 78% (75%) of extreme events in the Yangtze River Basin–South Korea–south of Japan region can be attributed to positive TAI. Additionally, the related circulation anomalies such as the upper-level synoptic-scale wave feature, temperature advection, and moisture anomalies from these models closely resemble those observed during extreme precipitation days in SEA. Our findings suggest that TAI can effectively indicate both the frequency and intensity of extreme rainfall events in SEA, along with the associated synoptic-scale activities. Further study reveals a close lead-lag correlation between TAI and rainfall patterns over SEA. This correlation is characterized by eastward-propagating wave trains across the entire troposphere. Consequently, TAI not only acts as a benchmark for quantifying synoptic-scale extreme rainfall in SEA but also shows potential in predicting SEA rainfall linked to synoptic-scale disturbances.

Assessing teleconnection influences on the spatial and temporal patterns of meteorological drought in Northwest China

ABSTRACT This study delves into the complex relationship between teleconnection patterns and meteorological drought in Northwest China, highlighting the crucial influence of global circulation indices on regional drought dynamics. By analyzing precipitation and temperature data from 1962 to 2022 using the CN05.1 datasets and incorporating various global circulation indices, the study employs the Standardized Precipitation Evapotranspiration Index (SPEI) to delineate drought conditions. Utilizing the Modified Mann-Kendall test and segmented models for trend and change point detection, along with cross wavelet transforms (XWT) and wavelet coherence (WTC) analysis to examine the impact of 15 global circulation indices, the study uncovers significant spatial and temporal climatic variations. Findings indicate a significant increase in temperature and precipitation, with March-April-May (MAM) season showing pronounced drought severity mainly due to a significant temperature rise with a value of 0.0420 °C/year. Change point analysis reveals pivotal shifts in climate, highlighting the region’s susceptibility to climate change. The study identified strong correlations between drought occurrences and global circulation indices like the Artic Oscillation (AO), Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO) and the Southern Oscillation Index (SOI), especially within a 4–8 year timeframe, pointing to the significant role of teleconnections in affecting local drought conditions. These insights are vital for formulating effective drought management and climate adaptation strategies in arid and semi-arid regions, offering valuable guidance for policymakers and researchers focused on improving water resource management and enhancing climate resilience in such vulnerable environments.

Projected near-future changes in precipitation extremes over Anambra-Imo River Basin inferred from CMIP6 HighResMIP

The southeastern region of Nigeria is susceptible to flood disasters primarily triggered by extreme precipitation with localized impacts. This study uses the output of High-Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate seasonal dependent changes in precipitation extremes in the near future (2031–2050) in the Anambra-Imo River Basin, in the southeastern region of Nigeria. Evaluating the models against observation for the 1995–2014 period, it is found that models reproduced the spatial pattern of the observed annual precipitation extremes over the river basin. Results show that in the near future, annual precipitation extremes will be characterized by a robust increase in annual total precipitation amount (PRCPTOT), maximum 5-day precipitation (RX5day), and heavy precipitation (R10mm). The models project a significant increase in PRCPTOT, RX5day, R10mm, and wet-day intensity (SDII) for the June–July–August (JJA) and September–October–November (SON) seasons. The results demonstrate a robust and higher magnitude increase in precipitation extremes during the SON season. Specifically, PRCPTOT, RX5day, R10mm and SDII are projected to increase by up to 46 mm, 24 mm, 1.2 days and 2.4 mm/day, respectively. Whereas during the March–April-May (MAM) season, the HighResMIP suggests that PRCPTOT, R10mm, and SDII will marginally increase over the eastern part of the Anambra-Imo River Basin. Besides, the December–January–February (DJF) season will be characterized by a marginal increase in the precipitation extremes, especially over the southern fringes of the river basin. We note that in the near future, precipitation extremes in the river basin will be characterized by more intense and less frequent precipitation extremes during the JJA and SON, potentially exacerbating flash flooding in the river basin. Hence, the results of this study may be vital for near-term socio-economic planning and policy decisions that will minimize the impact of flood disasters in the Anambra-Imo River Basin.

A 4-years of radar-based observation of bow echo over Bandung basin Indonesia

BackgroundThis study presents a 4-year (January 2019–April 2023) X-band radar network-based bow echo observation over Great Bandung Indonesia. This study provides insight into the temporal and spatial variability of bow echo distribution and presents the atmospheric condition associated with the bow echo events. Temporal analysis is categorical into monthly, seasonally, and diurnal. The analysis was performed using X-band radar network and reanalysis data (ERA5).ResultAt least 26 bow echoes were identified across the Bandung basin from X-band radar network during the study period. From this total number of bow echoes, the observation of initiation modes is primarily generated from a weakly organized cell, with few coming from the squall line. The bow echo mostly evolved from noon until afternoon. The rainy season (December–January–February) and transition season (March–April–May) is the most frequent period of bow echo occurrence, with March being the most active month. Nevertheless, this study also found bow echo occurrence in the dry season (June–July–August). For the spatial analysis, the studied area is divided into two regions representing the eastern and western part of Bandung basin. The eastern region recorded the most intense occurrences with 14 events. The movement of bow echo in this region covered a shorter distance (average distance only 4.56 km), with all initiation modes occurring inside the region. The atmospheric condition within this region has less moisture flux, with higher CAPE and slightly higher surface temperature. Meanwhile, in the western region have different characteristics with higher moisture flux, a slight effect of CAPE and CINH, with longer distance and zonal movement direction of bow echo displacement.ConclusionThese conditions indicate that local convection is the dominant mode of bow echo initiation mode in the eastern region of Bandung basin. Meanwhile, the monsoon effect influences the bow echo initiation mode in the western region. Given that the observed 4-year bow echo has different characteristics from previous studies of bow echo in mid latitudes, developing different criteria for bow echo detection in the tropics is crucial.

Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data

South Africa faces the urgency to comprehensively understand and manage its methane (CH4) emissions. The primary aim of this study is to compare CH4 concentrations between Eastern Cape and Mpumalanga regions dominated by cattle farming and coal mining industries, respectively. CH4 concentration trends were analyzed for the period 2019 to 2023 using satellite data. Trend analysis revealed significant increasing trends in CH4 concentrations in both provinces, supported by Mann–Kendall tests that rejected the null hypothesis of no trend (Eastern Cape: p-value = 8.9018e−08 and Mpumalanga: p-value = 2.4650e−10). The Eastern Cape, a leading cattle farming province, exhibited cyclical patterns and increasing CH4 concentrations, while Mpumalanga, a major coal mining province, displayed similar increasing trends with sharper concentration points. The results show seasonal variations in CH4 concentrations in the Eastern Cape and Mpumalanga provinces. High CH4 concentrations are observed in the northwestern region during the December-January–February (DJF) season, while lower concentrations are observed in the March–April-May (MAM) and June-July–August (JJA) seasons in the Eastern Cape province. In the Mpumalanga province, there is a dominance of high CH4 concentrations in southwestern regions and moderately low concentrations in the northeastern regions, observed consistently across all seasons. The study also showed an increasing CH4 concentration trend from 2019 to 2023 for both provinces. The study highlights the urgent need to address CH4 emissions from both cattle farming and coal mining activities to mitigate environmental impacts and promote sustainable development. Utilizing geographic information system (GIS) and remote sensing technologies, policymakers and stakeholders can identify and address the sources of CH4 emissions more effectively, thereby contributing to environmental conservation and sustainable resource management.

Assessment of behavioural response to climate forecasts and climate change adaptation by small-holder farmers in Nambale sub-county of Busia county, Kenya

Under climate change and unpredictable rainfall variability, rainfed agriculture has often been characterised with high degree of crop failure, especially when rainfall seasons portray anomalies in rainfall onsets and cessation. This study sought to establish the level of utilisation of climate forecasts and behavioural response to such products by the smallholder farmers in Nambale, Busia, Kenya. A total of 30 subsistence farmers were interviewed. Results showed that about 4 in every 5 farmers are largely unconscious of climate change and its effects and did not adhere to the climatic forecasts, instead, farming was subjected to the year-to-year routine practices, dependent on the traditional approaches. About 60% of the farmers lacked knowledge of climate change and believed that crop failure is beyond human understanding. 100% of the farmers planted maize in the two main planting seasons of March-April-May (MAM) and August-September-October (ASO), with about 86.7% of them least practicing crop diversification or intercropping. Furthermore, climate change adaptation interventions such as planting of early maturing, drought resistant crops and crop irrigation were unlikely among the farmers. Crop performance on an experimental farm established by the Nambale Science Place-Centre for Community Engagement on Climate Risk Interpretation (NSP-CCECRI), showed potential for enhanced farm production if farming activities are adhered to timely utilisation of climate forecasts. Based on these findings, and considering limited climate change adaptation strategies, there is need for sensitising smallholder farmers in enhancing knowledge on climate change and adaptation for enhanced food security, building community climate change resilience and alleviating poverty levels.

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

AbstractFires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

Exploring temporal and spatial SST patterns and their impact in the Arabian Sea: Insights from the regional ocean modeling system

The Regional Ocean Modeling System (ROMS) is integrated across the Arabian Sea (AS) from 1992 to 2021 and covers 33°E to 80°E and 5°N to 32°N at high horizontal resolutions of 1/4°(∼25 km). The study demonstrates that the variability is controlled by a seasonal high-resolution setup, with superior performance for various seasons during the months of December-January-February (DJF), March-April-May (MAM), June-July-August-September (JJAS) and October–November (ON). The Sea Surface Temperature (SST) anomalies in the JJAS season werestudied. SST dominates the seasonal variability of the Indian summer monsoon (ISM). Our results show that the ROMS can simulate seasonal variability and its effects on the upper ocean properties over the AS. In addition, the impact of monthly variation is controlled. In recreating the spatio-temporal distribution of surface as well as subsurface hydrographic parameters such as surface and subsurface temperature, the simulation results are reasonable like observation and reanalysis.

Processes behind the decrease in Congo Basin precipitation during the rainy seasons inferred from ERA‐5 reanalysis

AbstractThe ongoing Congo Basin drying is linked to decreasing atmospheric water vapour. To comprehend the processes reducing precipitation, we analysed water and Moist Static Energy components, associated with heating sources using ERA5 reanalysis data over the period 1981–2022. The findings reveal that the precipitation deficit in the Congo Basin during March–April–May and September–October–November is primarily attributed to a decreasing trend in the vertical moisture advection anomaly induced by vertical velocity anomalies. Following this, a horizontal moisture advection anomaly induced by specific humidity anomalies plays a significant role. However, evaporation, horizontal moisture advection anomalies induced by wind anomalies, and vertical moisture advection anomalies induced by specific humidity anomalies do not appear to contribute significantly to the drying trend in the region. The horizontal propagation of MSE advection anomalies exhibits a similar pattern to that of moisture flux advection anomalies, both at different boundaries and within the basin itself. Conversely, the contribution of vertical advection of MSE anomalies remains weak. By integrating moisture and MSE budgets, we interpret the declining precipitation trend as a consequence of uplift driven by horizontal MSE advection. Furthermore, our analysis indicates a decreasing trend in the atmospheric heating source within the basin in association with reduced precipitation. This reduction could signify a decrease in moisture influx into the basin, thereby providing more explanations for the observed drying.

Seasonal Variability of Rainfall and Thunderstorm Patterns in Kenya

This paper presents an analysis of spatial and temporal variation of rainfall and thunderstorm occurrence over Kenya from January 1987 to December 2017. The meteorological data used were obtained from the Kenya Meteorological Department (KMD) for the same period. This included the monthly thunderstorm occurrences and rainfall amounts of 26 synoptic stations across the country. The characteristics of monthly, seasonal and annual frequency results were presented on spatial maps while Time series graphs were used to display the pattern for annual cycle, seasonal variations and the inter-annual variability of rainfall amounts and thunderstorm occurrences. A well-known non-parametric statistical method Mann Kendall (MK) trend test was used to determine and compare the statistical significance of the trends. Thunderstorm frequencies over the Eastern, Central and Coast regions of the country showed a bimodal pattern with high frequencies coinciding with March-April-May (MAM) and October-November-December (OND) rainy seasons. Very few thunderstorm days were detected over June-July-August (JJA) season. The areas to the western part of the country, near Lake Victoria, had the highest thunderstorm frequencies in the country over the three seasons: MAM, JJAS and OND. The annual frequency showed a quasi-unimodal pattern. These places near Lake Victoria showed significantly increasing thunderstorm trends during the MAM and OND seasons irrespective of the rainfall trends. This shows the effects of Lake Victoria over these areas, and it acts as a continuous source of moisture for thunderstorm formation. However, most stations across the country showed a reducing trend of thunderstorm frequency during MAM and JJA seasons. The importance of these findings is that they could support various policy makers, and users of climate information, especially in the agriculture and aviation industries.

Dynamic and Thermodynamic Contributions to Late 21st Century Projected Rainfall Change in the Congo Basin: Impact of a Regional Climate Model’s Formulation

Addressing the impacts of climate change requires, first of all, understanding the mechanisms driving changes, especially at the regional scale. In particular, policymakers and other stakeholders need physically robust climate change information to drive societal responses to a changing climate. This study analyses late 21st-century (2071–2100) precipitation projections for the Congo Basin under representative concentration pathway (RCP) 8.5, using the Rossby Centre Regional Climate Model (RCM) RCA4. Specifically, we examine the impact of the RCM formulation (reduction of turbulent mixing) on future change in seasonal mean precipitation by comparing the results of the modified model version (RCA4-v4) with those of the standard version (RCA4-v1) used in CORDEX (Coordinated Regional Climate Downscaling Experiment). The two RCM versions are driven by two global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). The results show that seasonal precipitation is largely affected by modifications in the atmospheric column moisture convergence or divergence, and, in turn, associated with changes in the dynamic (ΔDY) and thermodynamic (ΔTH) components of the moisture-budget equation. Projected decreased precipitation in the dry seasons (December–January–February and June–July–August) is linked to increased moisture divergence driven by dynamic effects (changes in circulation), with most experiments showing ΔDY as the main contributor (>60%) to the total moisture budget. Overall, precipitation is projected to increase in the wet seasons (March–April–May and September–October–November), which can be attributed to both dynamic and thermodynamic effects, but with a larger thermodynamic contribution (changes in specific humidity, ΔTH > 45%), compared to the dynamic one (ΔDY > 40%). Through a comparison of the two model versions, we found that the formulation (reducing turbulent mixing) and boundary conditions (driving GCM) strongly influence precipitation projections. This result holds substantial value for ensuring the fitness of models for future projections intended for decision-makers.

Seasonal Variation of Rainfall in Indonesia under Normal Conditions without ENSO and IOD Events from 1981-2021

This study explores the seasonal rainfall variation in Indonesia under normal conditions, excluding the influence of El-Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events. Monthly rainfall data is sourced from the CHIRPS dataset, and wind and atmospheric movement data are obtained from the ECMWF ERA-5 dataset. Identification of normal conditions relies on the ONI and DMI indices. Results reveal that the seasonal rainfall patterns during these normal conditions align with previous studies encompassing all atmospheric conditions. Indonesia typically witnesses one or two peaks in rainfall annually, notably in December-January-February (DJF), May-June (MJ), and March-April-May (MAM), consistent with established rainfall zone classifications in earlier research. A nuanced discrepancy emerges in the timing of the May-June peak, previously noted in June-July. The congruence in peak rainfall patterns during normal conditions, as observed in prior comprehensive research, implies a negligible influence of ENSO and IOD on the timing of seasonal rainfall peaks. Although ENSO and IOD may impact the amplitude of seasonal variation, particularly in Indonesia's eastern and western regions, they do not alter the temporal occurrence of rainfall peaks. Consequently, the timing of rainfall peaks in Indonesia predominantly reflects the combined influence of the Asian and Australian monsoons.

Integrated statistical and graphical non-parametric trend analysis of annual and seasonal rainfall in the Shire River Basin, Malawi

This study integrates purely statistical methods of Mann–Kendall (MK) and Spearman rho (SMR) with statistical-graphical methods of Onyutha trend (OT) test and innovative trend analysis (ITA) to examine annual and seasonal rainfall variations at 12 stations across the Shire River Basin (SRB) during 1976–2005. The results reveal a general decreasing trend for annual rainfall throughout the basin. At seasonal scale, the following trends were observed: an increase for the December-January–February (DJF) season, especially in the southern portion of the basin; a decrease for the March–April-May (MAM) and June-July–August (JJA) seasons; and inconclusive results for the September–October-November (SON) season. Despite nearly all time series indicating consistent trend direction as established by the four tests, the ITA identified the most significant rainfall patterns on both annual and seasonal basis. The performance abilities for the MK, SMR, and OT tests demonstrated the closest agreement at the verified significant level. In addition to the monotonic trend results obtained statistically, sub-trends are visually distinguished using the graphical features of the OT and ITA approaches. For the former, changes are seen as step jumps in the mean of the data, and for the latter, trends regarding high and low rainfall clusters are evaluated, hence offering more details regarding rainfall variability, such as the SRB’s sensitivity to both floods and droughts. Thus, the completely different aspects offered by the visually oriented methods complement the purely monotonic trend detection methods.

Atmospheric drivers of rainfall events in the Republic of Djibouti

AbstractThe Republic of Djibouti is a small country in an arid context coupled with a high variability of rainfall that generates flash floods causing severe damage to the population and infrastructure. The mechanisms controlling extreme rainfall events in this part of the Horn of Africa remain poorly understood. In this study, we document the atmospheric circulation patterns associated with such events. To that end, we use rain‐gauge data (a network of 36 stations on the period 2013–2020), satellite‐based rainfall estimates (CHIRPS, IMERG, MSWEP and RFE) and atmospheric reanalyses (ERA5), all at the daily timescale, over their common period 2001–2020. A multivariate Agglomerative Hierarchical Clustering of rainy days in Djibouti (≥10% of grid‐points exceeding 1 mm·day−1, according to all four satellite products) reveal four clusters, which differentiate from each other by the intensity and spatial extent of rainfall. These clusters show a nonhomogeneous seasonal distribution, occurring mainly in the March–April–May (MAM) and July–August–September (JAS) seasons, and more rarely in October–November–December (OND). The atmospheric circulation anomalies associated with the clusters are quite similar and highly season‐dependent. In MAM most clusters display an anomalous trough over the Red Sea, from 700 to 200 hPa. In JAS, an anomalous low over the southern Red Sea drives a thicker than normal monsoon flow at 700 hPa, while upper northerlies prevail at 200 hPa. In OND, most rainy events result from moisture advection from the Western Indian Ocean, favoured by positive phases of the Indian Ocean Dipole. Some highly unusual atmospheric circulation patterns (e.g., associated with tropical cyclones) can also result in intense rainfall events in Djibouti. These findings provide new insights on the physical causes of extreme events in the Horn of Africa, with applications to improved early warning and skill evaluation of climate models for climate change projections.

Frequent but Predictable Droughts in East Africa Driven by a Walker Circulation Intensification

AbstractDuring and after recent La Niña events, the decline of the eastern East African (EA) March‐April‐May (MAM) rains has set the stage for life‐threatening sequential October‐November‐December (OND) and MAM droughts. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these dry seasons is an interaction between La Niña and climate change. This interaction provides important opportunities for long‐lead prediction and proactive disaster risk management, but needs exploration. Here, for the first time, we use observations, reanalyses, and climate change simulations to show that post‐1997 OND La Niña events are robust precursors of: (a) strong MAM “Western V sea surface temperature Gradients” in the Pacific, which (b) help produce large increases in moisture convergence and atmospheric heating near Indonesia, which in turn produce (c) regional shifts in moisture transports and vertical velocities, which (d) help explain the increased frequency of dry EA MAM rainy seasons. We also show that, at 20‐year time scales, increases in atmospheric heating in the Indo‐Pacific Warm Pool region are attributable to warming Western V SST, which is in turn largely attributable to climate change. As energy builds up in the oceans and atmosphere, during and after La Niña events, we see stronger heating and heat convergence over warm tropical waters near Indonesia. The result of this causal chain is that increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. These factors link EA drying to a stronger Walker Circulation and explain the predictable risks associated with recent La Niña events.

Relationship between the North Atlantic sea surface temperature and the summer extreme high temperature in the Beijing-Tianjin-Hebei region, China

Climate change influence on the economy and environment in the Beijing–Tianjin–Hebei (BTH) region. This study examined the variations of extreme high temperature days (EHDs) over the BTH region in summer during 1982 to 2021. The trend and frequency of EHDs has increased significantly during the last 40 years. The spatial and temporal variations of EHDs also indicate a significant increase by using the rotated empirical orthogonal functions (REOF) method. Furthermore, the singular value decomposition (SVD) method and correlation analysis are used to indicate the relationship between the EHDs in June–July-August (JJA) in the BTH region and North Atlantic sea surface temperature anomalies (SSTA) in the March–April-May (MAM) during 1982–2021. The result shows that there was positive relationship between the North Atlantic SSTA in MAM and the EHDs in JJA in the BTH region. Composite analysis suggests that relationship between EHDs and SSTA are connected by associated atmospheric circulation through the wave activity flux.

Predictability of the East Africa long rains through Congo zonal winds

AbstractEast Africa is highly vulnerable to extreme weather events, such as droughts and floods. Skillful seasonal forecasts exist for the October–November–December short rains, enabling informed decisions, whereas seasonal forecasts for the March–April–May (MAM) long rains have historically had low skill, limiting preparation capacity. Therefore, improved long rains prediction is a high priority and would contribute to climate change resilience in the region. Recent work has highlighted how lower‐troposphere Congo zonal winds in MAM strongly impact regional moisture fluxes and the long rains total precipitation. We therefore approach long rains predictability through the predictability of the Congo winds. We analyze a set of hindcasts from a dynamical prediction system that is able to reproduce the long rains—Congo winds relationship in its individual ensemble members. Encouragingly, in observations, the strength of MAM Congo zonal winds and East Africa rainfall show substantial correlation with the MAM Atlantic (including North Atlantic Oscillation, NAO) and Indo‐Pacific variability, suggestive of ocean influence and potential predictability. However, these features are replaced by different teleconnections in the hindcast ensemble mean fields. This is also true for NAO linkage to Congo winds, despite correct representation in individual members, and good skill in hindcasting the NAO itself. The net effect is strongly negative skill for the Congo winds. We explore statistical correction methods, including using the Congo zonal wind as an anchor index in a signal‐to‐noise calibration for the long rains. This is considered a demonstration of concept, for subsequent implementation using models with better Congo zonal wind skill. Indeed, the clear signals found in the Atlantic (including Mediterranean) and Indo‐Pacific, studied here both in observations and a dynamical prediction system, motivate evaluation of these features across other prediction systems, and offer the prospect of improved physically‐informed long rains dynamical predictions.