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The longitudinal location of precipitation anomalies over the equatorial Pacific shows a distinctive feature with the westernmost location for La Niña, the easternmost location for eastern Pacific (EP) El Niño, and somewhere between for central Pacific (CP) El Niño, even though the center of the sea surface temperature anomaly (SSTA) for La Niña is located slightly east of that of CP El Niño. The mechanisms for such a precipitation diversity were investigated through idealized model simulations and moisture and moist static energy budget analyses. It is revealed that the boundary layer convergence anomalies associated with the precipitation diversity are mainly induced by underlying SSTA through the Lindzen–Nigam mechanism, that is, their longitudinal locations are mainly controlled by the meridional and zonal distributions of the ENSO SSTA. The westward shift of the precipitation anomaly center during La Niña relative to that during CP El Niño is primarily caused by the combined effects of nonlinear zonal moist enthalpy advection anomalies and the Lindzen–Nigam mechanism mentioned above. Such a zonal diversity is further enhanced by the “convection–cloud–longwave radiation” feedback, the SST-induced latent heat flux anomalies, and the advection of mean moist enthalpy by anomalous winds. This diversity in the longitudinal location of precipitation anomalies has contributions to the diversities in the longitudinal locations of anomalous Walker circulation and western North Pacific anomalous anticyclone/cyclone among the three types of ENSO.

Convectively coupled equatorial Kelvin waves (CCEKWs) are those types of equatorially trapped disturbances that propagate eastward and are among the most common intra-seasonal oscillations in the tropics. There exists two-way feedback between the inter-tropical convergence zone (ITCZ) and these equatorially trapped disturbances. Outgoing Longwave Radiation (OLR) was utilized as a proxy for deep convection. For CCEKWs, the modes are located over the West Atlantic, equatorial West Africa, and the Indian Ocean. The influence of other circulations and climate dynamics is studied for finding other drivers of climate within East Africa. The results show a positive relationship between Indian and Atlantic Oceans Sea Surface Temperatures and March-May rainfall over equatorial East Africa over the period of 1980 to 2010. This influence is driven by the Walker circulation and anomalous moisture influx enhanced by winds. Composite analysis reveals strong lower-tropospheric westerlies during the active phase of the CCKWs activities over Equatorial East Africa. The winds are in the opposite direction with the upper-tropospheric winds, which are easterlies. Singular Value Decomposition shows a strong coupling interaction between rainfall over equatorial East Africa and CCKWs. This study concludes that Kelvin waves are not the main factors that influence rainfall during the rainy season. Previous studies show that the main influencing factors are ITCZ, El-Nino Southern Oscillation (ENSO), and tropical anticyclones that borders the African continent. However, CCKWs are a significant factor during the dry seasons.

Heavy rainfall is one of the primary causes of flood during rainy season in Tanzania leading to severe socio-economic impacts. The study aimed at assessing and characterizing the variability of Heavy Rainfall Events (HREs) using Empirical Orthogonal Function (EOF), Mann-Kendal (MK) trend test, Correlation and Composite analysis methods. Based on the daily-observed precipitation and reanalysis data sets for the October to December (OND) rainfall season of 35 years (1981-2015), the spatial and temporal characteristics of HREs in Tanzania are studied. The relationship between heavy rainfall (HR) and large-scale circulation anomalies including the Indian Ocean dipole (IOD) and El Ni?o southern oscillation (ENSO) indices was assessed. The study found that, approximately 590 HREs were concentrated over northern sector and coastal belt of Tanzania. The monthly variability indicates that HREs are more pronounced in December followed by November while October being the least affected. The occurrence of HREs over the Lake Victoria, Kigoma and Tabora is largely attributed to low-level convergence of westerlies and enhanced moisture from Congo basin accompanied by a pronounced rising limb of Indian Walker circulation cell. A time-series analysis of HRE exhibits an inter-annual variation characterized by a slightly increasing trend, though the computed trends were not statistically significant at 95% confidence level. In most part of Tanzania HREs were positively correlated with both ENSO and IOD indices, underscoring the critical role of ENSO and Indian Ocean dynamic in modulating rainfall variability over the region. In general, it has been found that most of the HREs are generally triggered or amplified by large-scale circulation patterns such as ENSO and IOD.

Context Limited studies have identified symptom clusters (SCs) and their risk factors and the relationships with inflammatory biomarkers in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Objectives In this study, we aimed to investigate SCs in patients with AECOPD and explore their influencing factors and relationships with inflammatory biomarkers. Methods Data were collected with sociodemographic and disease information questionnaires, and symptoms were measured with the revised Memorial Symptom Assessment Scale. SCs were extracted through exploratory factor analysis. Logistic regression analysis was conducted to explore the risk factors of SCs. Results A total of 151 patients were recruited. Two SCs, namely, emotional and respiratory functional SCs, were identified. Logistic regression analysis showed that individuals with high C-reactive protein level, Charlson Comorbidity Index score, and high modified Medical Research Council Dyspnea Scale score were more likely to belong to the high-severity symptom subgroup than to the low-severity symptom group in the emotional SC. The patients with a low body mass index and without or lax inhaled drug therapy exhibited highly prominent predictors of membership in the high-severity symptom group of the respiratory functional SC. Conclusion Symptoms experienced by patients with AECOPD were grouped into specific clusters. Targeted interventions should be performed based on SCs, and influencing factors and biological mechanisms should be considered when providing individualized approaches and interventions.

The paucity of reliable ground based datasets remains a major challenge over Kenya. In the advent of extreme wetness or drought events, reliable precipitation estimates for local characterization is a long overdue process. In the present study, four Satellite derived Precipitation Estimates (SPE): TMPA V7 3B42, PERSIANN-CDR, CHIRPS, and ARC2, are assessed over four homogeneous zones in Kenya with gauge based data during 1998 -2016. Results show that variations of SPE products are based on complex geomorphology of different climatic zones. All SPE products depict bimodal annual precipitation pattern with west-east gradient representing heavier to lighter precipitation events. The Monthly analysis reveal good statistical agreement with reference datasets despite underestimation of precipitation in most regions. Seasonal precipitation events show that the PERSIANN-CDR perform better along low altitude humid climate and western zones around Lake Basin while ARC2 has uniform performance as gauge stations over highlands regions. Strong positive linear relationship on annual scale is evident in most SPE products with CHIRPS, ARC2, and TMPA exhibiting relatively high correlation (r) and minimum root mean squareerror (RMSE), except for PERSIANN-CDR. Overall, the findings of this study show the potentials of SPE products for applications over study domain. The TMPA V7 and PERSIANN-CDR could be useful in understanding individual floods events. Since the CHIRPS perform relatively well over ASAL regions, it could be utilized in monitoring droughts events.

BACKGROUND The focus on non-rectal cancer causes of death in patients with rectal cancer remains superficial. The purpose of the current study was to review the causes of death in rectal cancer survivors. METHODS We used the population-based Surveillance, Epidemiology, and End Results (SEER) cancer registry data to evaluate the causes of death in rectal cancer patients. We summarized characteristics of patients after diagnosis and survival outcomes, using Kaplan–Meier analysis and multivariate Cox proportional hazard regression analysis for survival analysis. RESULTS The study included 121742 patients who were diagnosed with rectal cancer, of whom 72,007 (59.1%) died during follow-up. Most deaths occurred from rectal cancer (n =31,505, 43.80%), and the number of rectal cancer deaths decreased as more years passed after diagnosis. >10 years after diagnosis, the most common causes of death were heart disease followed by rectal cancer. The 5-year overall survival rate, rectal cancer-specific survival rate, and non-rectal cancer-specific survival rate were 32.0%, 17.3% and 56.6%, respectively. And the 10-year overall survival rate, rectal cancer-specific survival rate and non-rectal cancer-specific survival rate were 14.6%, 4.4% and 31.8%, respectively. On multivariate analysis, factors associated with heart disease-specific survival among rectal cancer survivors include white race, female, married, grade I, earlier stages, surgery and chemotherapy. CONCLUSIONS Rectal cancer is the most common cause of death after diagnosis, however, the proportion of non-rectal cancer causes increases with longer survival. Routine follow-up care for rectal cancer survivors should be given more attention than ever.

The present study investigates the skills of CORDEX-CORE precipitation outputs in simulating Africa’s key seasonal climate features, emphasizing the added value (AV) of the dynamical downscaling approach from which they were derived. The results indicate the models’ good skills in capturing African rainfall patterns and dynamics at satellite-based observation resolutions, with up to 65.17% significant positive AV spatial coverage for the CCLM5 model and up to 55.47% significant positive AV spatial coverage for the REMO model. Unavoidable biases are however present in rainfall-abundant areas and are reflected in the AV results, but vary based on the season, the sub-area, and the Global Climate Model–Regional Climate Models (GCM-RCM) combination considered. The RCMs’ ensemble mean generally performs better than individual GCM–RCM simulations. A further analysis of the GCM–RCM model chain indicates a strong influence of the dynamical downscaling approach on the driving GCMs. However, exceptions are found in some seasons for specific RCMs’ outputs, where GCMs are influential. The findings also revealed that observational uncertainties can influence AV and contribute to a 6 to 34% difference in significant positive AV spatial coverage results. An analysis of these results suggests that the AV by CORDEX-CORE simulations over Africa depend on how well the GCM physics are integrated to those of the RCMs and how these features are accommodated in the high-resolution setting of the downscaling experiments. The deficiencies of the CORDEX-CORE simulations could be related to how well key processes are represented within the RCM models. For Africa, these results show that CORDEX-CORE products could be adequate for a wide range of high-resolution precipitation data applications.

The present study analyzed seasonal (i.e., Dec-Jan [DJF] and June – August [JJA]) temperature change for the near (2025-2054) and far future (2070-2099) under SSP245, SSP370, and SSP585 scenarios over Pakistan. The anomalies, Mann-Kendall trend tests, Sequential Mann-Kendall trend test (SQMK), and probability density frequency (PDF) analysis were used to investigate future mean temperature variations. The DJF season projected higher increase in temperature in the northern (3.8 oC, 5.1 oC and 6.5 oC), followed by central regions (3.8 oC, 4.9 oC and 6.4 oC) under SSP245, SSP370 and SSP585 scenarios, respectively. The central region is likely to record significant increase in JJA (3.0 oC, 4.4 oC and 5.4 oC) mean temperature in far future under the given SSP scenarios. Compared to historical (PDF), the far future DJF temperature changes revealed significant higher warming over northern, central and then over southern regions under most of SSP scenarios. The southern regions are projected to possible rise in far future JJA temperatures by 2.7 oC, 3.3 oC and 4.3 oC, under SSP245, SSP370 and SSP585, respectively. The PDFs for JJA further verify the highest positive abrupt shift in temperature across the central region and then southern region. The future diverse seasonal temperature changes supports further examination of the associated mechanisms and factors responsible for temperature changes to address climate change.

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June&ndash, July&ndash, August (JJA) and December&ndash, January&ndash, February (DJF) seasons for 1970&ndash, 2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models&rsquo, performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 &deg, C) was less than that of DJF (7 to 10 &deg, C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.

This work examines drought and flood events over Kenya from 1981 to 2016 using the Standardized Precipitation&ndash, Evapotranspiration Index (SPEI). The spatiotemporal analysis of dry and wet events was conducted for 3 and 12 months. Extreme drought incidences were observed in the years 1987, 2000, 2006, and 2009 for SPEI-3, whilst the SPEI-12 demonstrated the manifestation of drought during the years 2000 and 2006. The SPEI showed that the wettest periods, 1997 and 1998, coincided with the El Nino event for both time steps. SPEI-3 showed a reduction in moderate drought events, while severe and extreme cases were on the increase tendencies towards the end of the twentieth century. Conversely, SPEI-12 depicted an overall increase in severe drought occurrence over the study location with ab observed intensity of &minus, 1.54 and a cumulative frequency of 64 months during the study period. Wet events showed an upward trend in the western and central highlands, while the rest of the regions showed an increase in dry events during the study period. Moreover, moderate dry/wet events predominated, whilst extreme events occurred least frequently across all grid cells. It is apparent that the study area experienced mild extreme dry events in both categories, although moderately severe dry events dominated most parts of the study area. A high intensity and frequency of drought was noted in SPEI-3, while the least occurrences of extreme events were recorded in SPEI-12. Though drought event prevailed across the study area, there was evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at the projection of droughts over the study area based on regional climate models.

Under global warming, ENSO-driven precipitation anomalies over central eastern Pacific shift eastward because of narrowing ENSO SSTA., During El Niño events, increased precipitation occurs over the equatorial central eastern Pacific, corresponding to enhanced convective heating that modulates global climate by exciting atmospheric teleconnections. These precipitation anomalies are projected to shift and extend eastward in response to global warming. We show that this predicted change is caused by narrowing of the meridional span of the underlying El Niño–related sea surface temperature (SST) anomalies that leads to intensification of the meridional gradient of the SST anomalies, strengthening boundary-layer moisture convergence over the equatorial eastern Pacific, and enhancing local positive precipitation anomalies. The eastward shift and extension of these anomalies also intensify and extend eastward negative precipitation anomalies over the tropical western North Pacific, by strengthening equatorward advection of low mean moist enthalpy. Changes in El Niño–induced tropical precipitation anomalies suggest that, under global warming, El Niño events decay faster after their peak phase, thus shortening their duration.

This work examines drought and pluvial events based on Standardized Precipitation-Evapotranspiration Index (SPEI) over Kenya from 1981 to 2016. Spatiotemporal analysis of dry and wet events is conducted for 3 and 12-month SPEI. The drought incidences were observed during the period 1984, 1987, 2000, 2006, 2009, 2015, and 2016 for SPEI-3 whilst the SPEI-12 demonstrated the manifestation of drought during the year 2000 and 2006. SPEI clearly shows the wettest period, 1997 and 1998 coinciding with the El Nino event in both time steps. SPEI -3 shows a reduction in moderate drought events while severe and extreme cases were on increase towards the end of the twentieth century. Conversely, SPEI-12 depicts an overall severe drought occurrence over the study location with observed intensity of -1.54 and cumulative frequency of 64 months during the study period. The trend of wet events is upwards in the western and central highlands while the rest of the regions show increase in dry events during the study period. Moreover, moderate dry/wet events predominate whilst extreme events occur least frequent across all grid cells. It is apparent that the study area experiences mild extreme dry events in both categories although moderately severe dry events dominate most parts of the study area. High intensity and frequency of drought is noted in SPEI-3 while least occurrences of extreme events are recorded in SPEI-12. Although drought event prevails across the study area, there is evidence of extreme flood conditions over the recent decades.

Climate models are usually evaluated to understand how well the modeled data reproduce specific application-related features. In Africa, where multisource data quality is an issue, there is a need to assess climate data from a general perspective to motivate such specific types of assessment, but mostly to serve as a basis for data quality enhancement activities. In this study, we assessed the Rossby Centre Regional Climate Model (RCA4) over West Africa without targeting any application-specific feature, while jointly evaluating its boundary conditions and accounting for observational uncertainties. Results from this study revealed that the RCA4 signal highly modifies the boundary conditions (global climate models (GCMs) and reanalysis data), resulting in a significant reduction of their biases in the dynamically downscaled outputs. The results, with respect to the observational ensemble members, are in line with the differences between the observation datasets. Among the RCA4 simulations, the ensemble mean outperformed all individual simulations regardless of the statistical metric and the reference data used. This indicates that the RCA4 adds value to GCMs over West Africa, with no influence of observational uncertainty, and its ensemble mean reduces model-related uncertainties.

The present study proposes an assessment of the Added Value (AV) in seasonal mean temperature regional simulations by the Rossby Centre Regional Climate Atmospheric Model version 4 (RCA4) over West Africa, with emphasize on the contribution the driving Global Climate Models (GCMs) and the downscaling experiment. The results show that the AV by RCA4 varies depending on the driven GCMs, the season, and subareas of the study domain, while consistent performances are observed for specific GCMs. These results suggest that the key influencing factor of the quality of GCM driven RCA4 simulations is the consistencies between the GCMs and the RCA4 model physics. Our findings also point out that the RCA4's contribution to the AV results is influential, as it highly compensates the driven GCMs biases, although positive AV results are typically GCM dependent.

This study aims to detect the primary precursors and impact mechanisms for January surface temperature anomaly (JSTA) events in China against the background of global warming, by comparing the causes of two extreme JSTA events occurring in 2008 and 2011 with the common mechanisms inferred from all typical episodes during 1979–2008. The results show that these two extreme events exhibit atmospheric circulation patterns in the mid–high latitudes of Eurasia, with a positive anomaly center over the Ural Mountains and a negative one to the south of Lake Baikal (UMLB), which is a pattern quite similar to that for all the typical events. However, the Eurasian teleconnection patterns in the 2011 event, which are accompanied by a negative phase of the North Atlantic Oscillation, are different to those of the typical events and the 2008 event. We further find that a common anomalous signal appearing in early summer over the tropical Indian Ocean may be responsible for the following late-winter Eurasian teleconnections and the associated JSTA events in China. We show that sea surface temperature anomalies (SSTAs) in the preceding summer over the western Indian Ocean (WIO) are intimately related to the UMLB-like circulation pattern in the following January. Positive WIOSSTAs in early summer tend to induce strong UMLB-like circulation anomalies in January, which may result in anomalously or extremely cold events in China, which can also be successfully reproduced in model experiments. Our results suggest that the WIOSSTAs may be a useful precursor for predicting JSTA events in China.

Understanding future evolution of drought scenario across localized domains remains an imperative process in bid to adapt tailor suit innovative solutions to drought risks and their impacts. The present study examines drought events by characterizing the trend, intensity, severity and frequency based on Standardized Precipitation Index (SPI), over Kenya, East Africa for near future (2010–2039), mid-century (2040–2069), and late century (2070–2100). The study utilizes Multi-model mean ensemble (MME) of five selected regional climate models (RCMs). Further, the models are bias corrected based on a quantile mapping bias corrected algorithm in order to minimize possible bias for accurate projections. The changes in annual and seasonal precipitation over Kenya is examined in order to associate with changes in drought occurrence. Results demonstrate positive shift, indicating an increase in projected rainfall change during all the three timescales. Projections of possible future meteorological drought events under RCPs scenario over study locale was conducted using SPI. The results demonstrate relatively better performance of biased corrected MME derived from Rossby Centre regional climate model (RCA4) in simulating drought indices over the Kenya. The MME projections for drought duration show an increase in moderate drought incidences with fewer incidences of extreme events across the RCP4.5 and 8.5 scenarios respectively. However, the duration of occurrences varies from one region to another with most hotspots located around northeastern sides of the country. Examination of projected changes in drought frequency and severity depict an occurrence of severe to extreme drought incidences that are expected to intensify during the near future time slice while overall projections show that more wet scenarios is depicted, with fewer cases of drought expected to occur during mid and towards end of the century of projection period. The study calls for enactment of appropriate mitigation measures to cope with possible scenarios of drought risks over Kenya in the future.

This study uses the quantile mapping bias correction (QMBC) method to correct the bias in five regional climate models (RCMs) from the latest output of the Rossby Center Climate Regional Model (RCA4) over Kenya. The outputs were validated using various scalar metrics such as root-mean-square difference (RMSD), mean absolute error (MAE), and mean bias. The study found that the QMBC algorithm demonstrates varying performance among the models in the study domain. The results show that most of the models exhibit reasonable improvement after corrections at seasonal and annual timescales. Specifically, the European Community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict remarkable improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model shows little improvement across the rainfall seasons (i.e., March&ndash, May (MAM) and October&ndash, December (OND)). The projections forced with bias-corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data before performing any regional impact analysis. The corrected models may be used in projections of drought and flood extreme events over the study area.

This study assesses the performance of ten Regional Climate Model (RCMs) from the latest version of Rossby Centre of Atmospheric models (RCA4) in the simulation of precipitation over Greater Horn of Africa (GHA) from 1951–2005. The evaluation was performed against observed data from the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC). Results for mean seasonal analyses demonstrate an underestimation of March–May (MAM) and June–September (JJAS) precipitation whilst October to December (OND) precipitation is overestimated. Further assessment on the annual scale depicts underestimation of rainfall. However, the west to east gradient representing heavier to lighter precipitation and bimodal patterns of the north to south rainfall band is well captured by most models. The models fairly reproduce precipitation variability over the southeast region as compared to the northwest parts of the study domain. The mean ensemble invariably outperforms the individual RCA4 models due to its minimal probability deviance in precipitation in each zone and throughout the GHA region. The overall evaluation shows weak correspondence of the model data with observed CRU based on statistical metrics. The top five performing models are: MIROC5, CSIRO, CM5A-MR, MPI-ESM-LR, and EC-EARTH. Large variations of model performance are noted from one model to model, and from one region to the other. The ensemble mean of the outperforming RCMs reproduces the rainfall climatology over study domain with reasonable skill and the findings of this study will be a base for the study of extreme floods/droughts events in the region.