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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.

Deep learning artificial intelligence technology, which has the advantages of nonlinear mapping ability, massive information extraction ability, spatial-temporal modeling ability, and so on, provides new ideas and methods for further improving the accuracy of weather and climate extreme event prediction. A transfer learning CNN (Convolutional Neural Networks) classification model is established to classify the circulation patterns, along with the newly reconstructed dataset of regional persistent historical heavy rain events, daily rainfall data of 2474 observational stations, and the NCEP/NCAR global reanalysis data of daily geopotential height field in 1981–2018. Different from previous classifications, usually with one level variable, here, in addition to 500 hPa heights, 200 hPa zonal winds and 850 hPa meridional winds over the key areas are also considered in the model. The results show that the multi-level circulation pattern classification based on the transfer learning CNN network has a higher accuracy in the independent test than the single-level model, with the accuracy reaching 92.5% (while only 85% for the single-level model). The spatial correlation coefficient of precipitation between each typical mode and related patterns obtained by the multi-level transfer learning CNN classification is greater than that obtained by the single-level transfer learning CNN, and the variance of 500 hPa heights between each typical mode and the associated patterns is also greater than that obtained by the single-level transfer learning CNN. These results show that the performance of the classification by the multi-level transfer learning CNN model is better than that by the single-level transfer learning CNN. The study is helpful to develop circulation classifications related to large-scale weather or climate disaster events and then to provide a physical basis for further improving the forecast effect and extending the valid time of the forecast through combining the numerical model products.

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.

Like most of the countries in the Great Horn of Africa, Tanzania is prone to floods and droughts. Anomalously wet years over the region are associated with low-level convergence, the advection of moisture from both the Indian Ocean and the Congo and an ascending limb of the Walker circulation cell centered over the western Indian Ocean (WIO). During positive Indian Ocean dipole (IOD) events, the Walker circulation cell over the Indian Ocean is well defined, and more so compared with observed during El Niño years. In El Niño years, the Walker circulation cell becomes weak compared with positive IOD events, where the zonal winds strengthen, resulting in active convective activities over the WIO. A key finding is that the Walker circulation cell developing over the Indian Ocean is strongly connected to IOD activity. The weakening of the Walker circulation cell is likely the reason for the decrease in rainfall during El Niño events compared with that of positive IOD events in recent years over Tanzania during the October–December season. The variability of the Walker circulation cell over the Indian Ocean can lead to extreme weather conditions, making the full understanding of the climate system essential. This is crucial for decision making about adaptation and mitigation measures for coping with climate change and for the minimization of huge socio-economic losses associated with extreme weather events in the region.

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.

Global warming impacts leading to climate change is influencing all aspects of life and the ecosystems. Paris agreement capped the increase to a target below 2 °C and further downward trajectory to 1.5 °C above pre-industrial levels. This resolution requires nations to progressively monitor recent past and current temperature trends for accurate projections and adaptations. The present study characterizes recent temporal evolution of surface air temperature over Kenya during 1971–2010. The study utilized Climatic Research Unit reanalysis datasets for monthly minimum, maximum and mean average temperatures. Linear regression is utilized in determining statistical significance of trends, while Sequential Mann–Kendall rank test is employed in evaluating abrupt changes in temperature during the study period. Spatial analysis of mean temperature during the last four decades reveals evidence of rising temperature on annual and seasonal bases. Positive trends dominate western and central parts of Kenya, whereas insignificant positive trends are observed over northern and eastern parts of the study domain. Similarly, linear trends exhibit rising temperatures at the rate of 0.09 °C/decade, with an abrupt change in annual mean being detected in 1992. Overall, significant increase at 99% confidence level is observed during March–May season, while 95% confidence level is witnessed during October–December season.

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.

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.

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 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.

Using a linear dynamic operator and the daily National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data, the linearized part of the synoptic eddy feedback onto the low-frequency flow (SELF) associated with cold/warm events in January over China is investigated. Composite stream function differences between the cold and warming events show that the anomalous temperature events are linked closely to the teleconnection pattern which is related to North Atlantic Oscillation (NAO) and accompanied with remarkable anomalies over Ural Mountain and the areas south to Lake Baikal. The significant eddy feedback exists as the episodes occur, which tends to induce a similar circulation pattern to the anomalous low-frequency flow associated with the temperature episodes. Further, a linear SELF operator is used to estimate quantitatively the linearized part of the eddy feedback on the low-frequency flow for the temperature events. Results show that the magnitude of the stream function anomalies (SFAs) induced by eddy forcing is bigger at higher latitudes than lower latitudes. The positive (negative) SFA can be induced where eddy-vorticity fluxes converge (diverge) into the anomalous cyclonic (anticyclonic) circulation. Further analysis of differences in eddy feedback strength suggests that synoptic eddy feedback is more effective for the cold events than that for the warm events over North Atlantic, Ural Mountain, and the areas south to Lake Baikal. The SFA anomaly pattern over Ural Mountain and Lake Baikal associated with the anomalous temperature events in China will disappear without considering the eddy feedback. It suggests that the eddy feedback plays a very important role in maintaining the low-frequency flow during the January anomalous temperature events.