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Whirlwinds and visible dust devils occur over semi-arid ecosystems and entrain particles from the ground surface. Fires produce abundant charcoal across savannahs and the resulting blackened surfaces create a large albedo contrast. Whirlwinds have been observed associated with active fires; yet, there are few published observations on post-fire landscapes. Spatiotemporal patterns of whirlwinds have been documented for a limited number of regions and have not been made for the ecosystems of eastern Africa. From field-based sightings in the Serengeti National Park, Tanzania, we report on whirlwinds over burned savannah patches that entrained large quantities of charcoal to produce black coloured charcoal devils that lofted charcoal into the atmosphere. Two occurrences of charcoal devils were sighted and photographed, one each in the Western Corridor (Bunda District) and Lamai (Serengeti District), Mara Region. The observations were compared with regional scale meteorological data and remote sensing satellite imagery and albedo estimates of the land cover conditions. Although direct meteorological or particulate matter measurements were not made, the observations show that both charcoal devils differed in colour, funnel shape, height, and savannah land cover types (different woody to grass fuel canopies), and thus different charcoal morphologies. Charcoal laden whirlwinds require further study and characterization to analyse the contribution to local-scale redistribution of matter and regional-to-global fluxes of terrestrially derived atmospheric particulates. Future research focusing on the spatiotemporal patterns of whirlwinds over burned patches of savannah, the formation, duration and dissipation mechanisms, and characterisation of the entrained material would contribute to our understanding of the phenomena. The redistribution of organic and clastic material would contribute to understanding of detrital fluxes to depositional environments, such as lakes, wetlands, and snow. Keywords: Atmospheric boundary layer; Convection; Detritus; Dust devils; Fires; Particulate matter.

Radiometric dating methods are essential for developing geochronologies to study Late Quaternary environmental change and 210Pb dating is commonly used to produce age-depth models from recent (within 150 years) sediments and other geoarchives. The past two centuries are marked by rapid environmental socio-ecological changes frequently attributed to anthropogenic land-use activities, modified biogeochemical cycles, and climate change. Consequently, historical reconstructions over this recent time interval have high societal value because analyses of these datasets provide understanding of the consequences of environmental modifications, critical ecosystem thresholds, and to define desirable ranges of variation for management, restoration, and conservation. For this information to be used more broadly, for example to support land management decisions or to contribute data to regional analyses of ecosystem change, authors must report all of the useful age-depth model information. However, at present there are no guidelines for researchers on what information should be reported to ensure 210Pb data are fully disclosed, reproducible, and reusable; leading to a plethora of reporting styles, including inadequate reporting that reduces potential reusability and shortening the data lifecycle. For example, 64% of the publications in a literature review of 210Pb dated geoarchives did not include any presentation of age uncertainty estimates in modeled calendar ages used in age-depth models. Insufficient reporting of methods and results used in 210Pb dating geoarchives severely hampers reproducibility and data reusability, especially in analyses that make use of databased palaeoenvironmental data. Reproducibility of data is fundamental to further analyses of the number of palaeoenvironmental data and the spatial coverage of published geoarchives sites. We suggest, and justify, a set of minimum reporting guidelines for metadata and data reporting for 210Pb dates, including an IEDA (Interdisciplinary Earth Data Alliance), LiPD (Linked Paleo Data) and generic format data presentation templates, to contribute to improvements in data archiving standards and to facilitate the data requirements of researchers analyzing datasets of several palaeoenvironmental study sites. We analyse practices of methods, results and first order interpretation of 210Pb data and make recommendations to authors on effective data reporting and archiving to maximize the value of datasets. We provide empirical evidence from publications and practitioners to support our suggested reporting guidelines. These guidelines increase the scientific value of 210Pb by expanding its relevance in the data lifecycle. Improving quality and fidelity of environmental datasets broadens interdisciplinary use, lengthens the potential lifecycle of data products, and achieves requirements applicable for evidenced-based policy support.

Attributes of each dataset used in the analyses. Locations of each plot are provided as *.pdf file (Additional file 2). N - Number of plots used for the analyses (we excluded plots that measured at less than eight sampling points). Year - Year of field measurements. Researcher - AB, Andrew Burt; ACS, Aida Cuni-Sanchez; AG, Alemu Gonsamo; AL, Alicia Ledo; ARM, Andrew R Marshall; BW, Beatrice Wedeux; DD, Dereje Denu; DS, Deo Shirima; HS, Hamidu Seki; JGT, Jose Gonzalez de Tanago Menaca; KC, Kim Calders; LC, Luis Cayuela; LAS, Lau Alvaro Sarmiento; MJM, Manuel J Macia; MP, Marion Pfeifer; ND, Nicolas Deere; PO, Pieter Olivier; PKEP, Petri Pellikka; PJP, Philip J Platts; RT, Rebecca Trevithick; RH, Robin Hayward; RM, Robert Marchant; TP, Timothy Paine; WW, Woodgate William. Figure S1. Example maps of human population pressure, calculated from human population density grids using a range of sigma values (σ = 5, 15, 25, 50). Colours are graduated on a log base 2 scale (light colours, low pressure; dark colours, high pressure). The maps provide scope for capturing human-driven pressures at a variety of spatial scales (Platts 2012). For example, if σ = 5 then the weight given to remote populations (relative to the local population) halves over a distance of ~4 km, nearing zero by ~15 km, whereas if σ = 25 then the weight halves over a distance of ~20 km, nearing zero by ~60 km. We imposed a maximum distance of 100 km, beyond which no pressure is exerted. Figure S2. Relationships between Annual Moisture Index (AMI) and Mean Annual Precipitation (MAP) and canopy attributes LAI, fAPAR and FCover. We fitted linear, polynomial and nonlinear (nls model 1: y ~ a + b * I(x^z); nls model 2: y~a/(1 + exp.(−(b + c*x))) models. Upper panel: polynomial models fitted to LAI ~ MAP, FCover - MAP and fAPAR - MAP relationships. The polynomial (RSS 1.464) and sigmoidal growth models (RSS 1.464) produced slightly better fits to the LAI data compared to the fits produced by the linear (RSS 1.47) and exponential (RSS 1.467) models. The polynomial model produced the best fit to the FCover (RSS 24.76) and fAPAR (RSS 0.2371) data. Lower panel: nls model 2 fitted to LAI ~ MAP, FCover - MAP and fAPAR - MAP relationships. The logistic growth model produced the best fit to the LAI data (RSS 1.347), the FCover data (RSS 22.95) and the fAPAR data (RSS 0.2191). (DOC 590 kb)

Attributes of each dataset used in the analyses. Locations of each plot are provided as *.pdf file (Additional file 2). N - Number of plots used for the analyses (we excluded plots that measured at less than eight sampling points). Year - Year of field measurements. Researcher - AB, Andrew Burt; ACS, Aida Cuni-Sanchez; AG, Alemu Gonsamo; AL, Alicia Ledo; ARM, Andrew R Marshall; BW, Beatrice Wedeux; DD, Dereje Denu; DS, Deo Shirima; HS, Hamidu Seki; JGT, Jose Gonzalez de Tanago Menaca; KC, Kim Calders; LC, Luis Cayuela; LAS, Lau Alvaro Sarmiento; MJM, Manuel J Macia; MP, Marion Pfeifer; ND, Nicolas Deere; PO, Pieter Olivier; PKEP, Petri Pellikka; PJP, Philip J Platts; RT, Rebecca Trevithick; RH, Robin Hayward; RM, Robert Marchant; TP, Timothy Paine; WW, Woodgate William. Figure S1. Example maps of human population pressure, calculated from human population density grids using a range of sigma values (σ = 5, 15, 25, 50). Colours are graduated on a log base 2 scale (light colours, low pressure; dark colours, high pressure). The maps provide scope for capturing human-driven pressures at a variety of spatial scales (Platts 2012). For example, if σ = 5 then the weight given to remote populations (relative to the local population) halves over a distance of ~4 km, nearing zero by ~15 km, whereas if σ = 25 then the weight halves over a distance of ~20 km, nearing zero by ~60 km. We imposed a maximum distance of 100 km, beyond which no pressure is exerted. Figure S2. Relationships between Annual Moisture Index (AMI) and Mean Annual Precipitation (MAP) and canopy attributes LAI, fAPAR and FCover. We fitted linear, polynomial and nonlinear (nls model 1: y ~ a + b * I(x^z); nls model 2: y~a/(1 + exp.(−(b + c*x))) models. Upper panel: polynomial models fitted to LAI ~ MAP, FCover - MAP and fAPAR - MAP relationships. The polynomial (RSS 1.464) and sigmoidal growth models (RSS 1.464) produced slightly better fits to the LAI data compared to the fits produced by the linear (RSS 1.47) and exponential (RSS 1.467) models. The polynomial model produced the best fit to the FCover (RSS 24.76) and fAPAR (RSS 0.2371) data. Lower panel: nls model 2 fitted to LAI ~ MAP, FCover - MAP and fAPAR - MAP relationships. The logistic growth model produced the best fit to the LAI data (RSS 1.347), the FCover data (RSS 22.95) and the fAPAR data (RSS 0.2191). (DOC 590 kb)

Locations of each plot. (PDF 730Â kb)

Locations of each plot. (PDF 730Â kb)