Tanzania Meteorological Agency

Anthrax, caused byBacillus anthracis(BA), is a prominent neglected zoonosis with major impacts on human, livestock, and wildlife health. Despite this, limited genomic investigation at the One Health interface constrains current understanding of BA transmission and of the ecological and host factors shaping its diversity and population structure. This includes the possibility of host-specific BA lineages, given that anthrax outbreaks often disproportionally affect individual species. This study characterises the genomic diversity of BA in an endemic area, the Ngorongoro Conservation Area (NCA), in northern Tanzania. We analysed 213 BA genomes from livestock, wildlife and humans from cultured isolates combined with a culture-free targeted capture (TC) approach. NCA sequences formed a distinct genetic cluster compared with those from surrounding areas, and we observed surprisingly high levels of strain diversity within apparent epidemiological clusters, as well as within single animals, though strain diversity was lowest at the within host scale. We found limited evidence for seasonal clustering of cases as well as for BA lineages clustering by host species. This indicates that disproportional impacts on certain species during outbreaks are more likely driven by host ecology factors or, hypothetically, by accessory parts of the bacterial genome not represented in our data. TC-derived data significantly expanded the range of host species and geographic locations for genomic analysis, demonstrating the value of this approach. Although TC data may contain artefactual variation, shared SNP profiles between isolate- and TC-derived genomes gave confidence in its use for genotyping. Our analysis demonstrates unexpectedly high BA strain diversity and limited population structure in this endemic area across a range of spatial scales, including within-host. It further highlights the need for high density sampling and adaptable sequencing strategies to generate adequate BA genomic datasets that can enable informative molecular epidemiological studies of anthrax at the One Health interface.Author SummaryAnthrax continues to threaten the health of people, livestock, and wildlife in many parts of the world, yet we still know surprisingly little about how this disease spreads in nature. One major gap is understanding why some species are affected more strongly during outbreaks, despite assumed equal susceptibility. To investigate this, we studied the genomic diversity of the anthrax-causing bacteriumBacillus anthracisin a large conservation area in northern Tanzania. We combined two ways of generating genetic data: traditional laboratory culture and a culture-free method that allowed us to recover bacterial DNA from a wider range of samples.By analysing a uniquely large and species-diverse dataset of over 200 bacterial genomes from people, livestock and wildlife, we found that bacteria from the study area formed a clearly defined group compared to those from surrounding regions. We also discovered unexpectedly high diversity of strains, not only across the landscape but even within single animals. Despite this diversity, we saw little evidence that certain bacterial lineages are tied to specific host species. Our results suggest that the behaviour and ecology of different animals, rather than host-adapted lineages of the bacterium, likely explain why some species are more affected than others.

Feature detection is one of the hot topics in the weather radar research community. This study employed a convective–stratiform classification algorithm to detect features in polarimetric radar variables and Quantitative Precipitation Estimation (QPE) retrieval during a heavy precipitation event in Crossville, Tennessee, during warm-season convection. Analysis of polarimetric radar variables revealed that strong updrafts, mixed-phase precipitation, and large hailstones in the radar resolution volume during the event were driven by the existence of supercell thunderstorms. The results of feature detection highlight that the regions with convective–stratiform cores and strong–faint features in the reflectivity field are similar to those in the rainfall field, demonstrating how the algorithm more effectively detects features in both fields. The results of the estimates, accounting for uncertainty during feature detection, indicate that an offset of +2 dB overestimated convective features in the northeast in both the reflectivity and rainfall fields, while an offset of −2 dB underestimated convective features in the northwest part of both fields. The results highlight that convective cores cover a small area with high rainfall exceeding 50 mmh−1, while stratiform cores cover a larger area with greater horizontal homogeneity and lower rainfall intensity. These findings are significant for nowcasting weather, numerical models, hydrological applications, and enhancing climatological computations.

The Greater Horn of Africa is prone to extreme climatic conditions, thus, making climate services increasingly important in supporting decision-making processes across a range of climate sensitive sectors. This study aims to provide a comprehensive review of the recent advances, gaps and challenges in the provision of climate services over the region, for each of the components of the Global Framework for Climate Services. The study explores various milestones that have been achieved toward climate service delivery. The achievements include improvement of station network coverage, and enhancing the capacity of member states to utilize various tools in data analysis and generate routine climate products. The advancement in science, and availability of High-Performance Computing has made it possible for forecast information to be provided from nowcasting to seasonal timescales. Moreover, operationalizing of the objective forecasting method for monthly and seasonal forecasts has made it possible to translate tercile forecasts for applications models. Additionally, innovative approaches to user engagement through co-production, communication channels, user-friendly interfaces, and dissemination of climate information have also been developed. Despite the significant progress that has been made in the provision of climate services, there are still many challenges and gaps that need to be overcome in order to ensure that these services are effectively meeting the needs of users. The research of the science underpinning climate variability, capacity building and stakeholder engagement, as well as improved data management and quality control processes are some of the gaps that exist over the region. Additionally, communication and dissemination of climate information, including timely warnings and risk communication, require improvement to reach diverse user groups effectively. Addressing these challenges will require strengthened partnerships, increased investment in capacity building, enhanced collaboration between the climate information producers and stakeholders, and the development of user-friendly climate products. Bridging these gaps will foster greater resilience to climate-related hazards and disasters in the Greater Horn of Africa and support sustainable development in the region.

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.

This study applies both stationary and nonstationary generalized extreme value (GEV) models to analyze annual extreme temperature patterns in four stations of Southern Highlands region of Tanzania: Iringa, Mbeya, Rukwa, and Ruvuma over a 30‐year period. Parameter estimates reveal varied distribution characteristics, with the location parameter μ ranging from 28.98 to 33.44, and shape parameter ξ indicating both bounded and heavy‐tailed distributions. These results highlight the potential for extreme temperature conditions, such as heatwaves and droughts, particularly in regions with heavy‐tailed distributions. Return level estimates show increasing temperature extremes, with 100‐year return levels reaching 33.95 °C in Ruvuma. Nonstationary models that incorporate time‐varying location and scale parameters significantly improve model fit, particularly in Mbeya, where such a model outperforms the stationary model (p value = 0.0092). Trend analyses identify significant temperature trends in Mbeya (p value = 0.0123) and Ruvuma (p value = 0.0015), emphasizing the need for adaptive climate strategies. These findings underscore the importance of accounting for nonstationarity in climate models to better understand and predict temperature extremes.

Natural and human systems are exposed and vulnerable to climate extremes, which contributes to the repercussions of climate variability and the probability of disasters. The impacts of both natural and human-caused climate variability are reflected in the reported changes in climate extremes. Particularly at the local community levels in the majority of the regions, there is currently a dearth of information regarding the distribution, dynamics, and trends of excessive temperatures among the majority of Tanzanians. Over the years 1982-2022, this study examined trends in Tanzania’s extreme temperature over the June to August season. Based on the distinction between absolute and percentile extreme temperatures, a total of eight ETCCDI climate indices were chosen. Mann-Kendall test was used to assess the presence of trends in extreme climatic indices and the Sen’s Slope was applied to compute the extent of the trends in temperature extremes. The study showed that in most regions, there is significant increase of warm days and nights while the significant decrease of cold days and nights was evident to most areas. Moreover, nighttime warming surpasses daytime warming in the study area. The study suggests that anthropogenic influences may contribute to the warming trend observed in extreme daily minimum and maximum temperatures globally, with Tanzania potentially affected, as indicated in the current research. The overall results of this study reflect patterns observed in various regions worldwide, where warm days and nights are on the rise while cold days and nights are diminishing.

The present study explored how the Indian Ocean Dipole (IOD) influences October-November-December (OND) rainfall over Tanzania in recent decade following the 2011 abrupt change. The study spans 50 years, from 1973 to 2022. Notable abrupt changes were observed in 1976 and 2011, leading us to divide our study into two periods: 1976-2010 and 2011-2022, allowing for a close investigation into the existing relationship between OND IOD and OND rainfall and their associated large-scale atmospheric circulations. It was found that the relationship between OND IOD and OND rainfall strengthened, with the correlation changed from +0.73 during 1976-2010 to +0.81 during 2011-2022. Further investigation revealed that, during 1976-2010, areas that received above- normal rainfall during positive IOD experienced below-normal during 2011- 2022 and vice versa. The same pattern relationship was observed for negative IOD. Spatial analysis demonstrates that the percentage departure of rainfall across the region mirrors the standardized rainfall anomalies. The study highlights that the changing relationship between OND IOD and OND rainfall corresponds to the east-west shift of Walker circulation, as well as the north-south shift of Hadley circulation. Analysis of sea surface temperature (SST) indicates that both positive and negative IOD events strengthened during 2011-2022 compared to 1976-2010. Close monitoring of this relationship across different timescales could be useful for updating OND rainfall seasonal forecasts in Tanzania, serving as a tool for reducing socio-economic impacts.

The inter-annual variability of rainfall onset and crop replanting in East Africa (EA) was assessed using daily estimated rainfall data from climate hazard group infrared precipitation (CHIRPS Ver2.0) and monthly Sea Surface Temperature (SST) indices [Indian Ocean Dipole (IOD) and El-Niño Southern Oscillation (ENSO) at NINO3.4 region] from the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). The data covered a period of 40 years from1981 to 2020. The methods of cumulative of daily mean rainfall, percentage of onset date departure (PODD), Mann-Kendall (MK) trend test, student t-test, and correlation were applied in the analysis. The results showed that early onset with dry spell (WDS) consideration frequently occurs in Uganda between the first and second dekads of September, while late rainfall onset WDS occurs in the first and second dekads of December over central and Northern Kenya as well as in the Northeastern highlands, parts of the northern coast and unimodal regions in Tanzania. Rainfall onset with no dry spell (WnDS) portrayed an average of 10 days before the occurrence of true onset WDS, with maximum onset departure days (ODD) above 30 days across the Rift Valley area in Kenya and the Northeastern highlands in Tanzania. The high chance of minimum ODD is seen over entire Uganda and the area around Lake Victoria. However, few regions, such as Nakuru (Kenya) Gulu and Kibale (Uganda), and Gitega (Burundi), revealed a slight positive linear trend while others showed negative trend. Significant positive patterns for correlation between onset WDS and SST indices (IOD and NINO 3.4) were discovered in Northern and Northeastern Kenya, as well as areas along the Indian Ocean (over Tanzania's Northern Coast). Inter-annual relationship between onset dates WDS and How to cite this paper: Yonah, I. B.,

The amount of rainfall and its distribution in time and space is dependent on the atmospheric instability conditions, and on its moisture content. The aim of this study was to determine the atmospheric instability conditions during January to March (JFM), February to April (FMA), March to May (MAM), and October to December (OND) rainy seasons over local climate zones in Tanzania. Zone area average seasonal Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), Precipitable Water (PW) and Lifted index (Li) were calculated and analyzed. Results showed Li < 0 in JFM and FMA over whole Tanzania. During MAM and OND, Li < 0 over the Lake Zone, Western Highlands Zone and Central Zone only. CAPE ranged from 793 J/kg to 1183 J/kg during JFM, and 700 J/kg to 1080 J/kg during FMA. During MAM, CAPE ranged from 170 J/kg to 921 J/kg and from 173 J/kg to 833 J/kg during OND. Results also showed CAPE > 1000 J/kg over the Lake Zone, Western Highlands Zone, Island Zone, and Central Zone. These results show that the atmosphere was moderately unstable during the JFM and FMA and was weakly unstable during the MAM and OND. Therefore, the atmosphere is likely to be more convective during JFM and FMA seasons. Keywords: Lifted index, Convective inhibition, Precipitable water, Convective available potential energy, Atmospheric instability.

This paper provides an analysis and a description of the best practices and lessons learned in the implementation of the Global Framework for Climate Services Adaptation Program in Africa (GFCS-APA) focusing on Tanzania country’s activities. GFCS-APA was the first multi-agency initiative implemented under the Global Framework for Climate Services (GFCS) in two African countries, namely Tanzania and Malawi with funding from the Royal Government of Norway. In Tanzania, the programme was implemented in two phases from the year 2014 to 2021 in the three pilot districts of Kondoa, Longido and Kiteto located in Dodoma, Arusha and Manyara regions, respectively. The overarching goal of the programme was to enable better management of the risks caused by climate variability and change at all levels, from end-users to policy level, through development and incorporation of science-based climate information and prediction services into planning, policy and practice. The programme focused on bridging the gap between providers and users of climate information and products through development of user-driven climate services for food security, health and disaster risk reduction. This paper aimed to analyze lessons learned and best practices in the course of the implementation of the GFCS-APA phase I and II in Tanzania. A qualitative approach was employed to analyze the lessons learned and best practices, by extracting them and exploring further on their contribution to the enhancement of climate services, as well as their applicability and potentiality for scaling out in other regions within Tanzania, and in other countries. The results indicate the identified best practices and lessons learned contributed significantly in enhancing climate services, particularly in understanding, availability, accessibility, utilization, ownership and sustainability of climate services among users (farmers and pastoralists) of various gender, as well as intermediaries and decision-makers. Results also indicate the lessons learned and the documented best practices could influence the effectiveness of climate services in other areas, to address existing challenges in access, uptake and sustainability of climate services. The best practices and lessons learned could be considered for integration in the future projects or operational activities in other regions within the country and other countries, particularly in the developing world, including Sub-Saharan Africa.