Search

This study assesses the skill of existing indices and newly introduced sea surface temperature (SST) indices in predicting rainfall over East Africa (EA) during short rains from September to December (SOND) and long rains from March to May (MAM). The existing and newly introduced indices were selected based on grid point correlation and spatial mean correlation. West–east SST gradient over the Indian Ocean (WEIO), north–south SST gradient over the Pacific Ocean (NSPO), anomalies over the western Indian Ocean (WIO) and anomalies over the eastern Pacific Ocean (EPO) from newly developed indices, and bivariate ENSO time series (BEST), Oceanic Niño Index (ONI), Niño3.4, western Pacific gradient (WPG) and dipole index moment (DMI) from existing indices have a positive correlation with SOND rainfall at 5% (p<0.05) significance level. West–east SST gradient over the Pacific Ocean (WEPO) and SST anomalies over the southern Pacific Ocean (SPO) from new indices and Southern Oscillation Index (SOI) from existing indices have a significant negative correlation with SOND rainfall over EA. SST anomalies over central and eastern Pacific and Indian oceans significantly correlate with SOND rainfall over southern EA (SEA) and equatorial EA (EEA). Rainfall over northern EA (NEA) has a significant negative correlation with SST over the east and central Pacific oceans. Negative correlation extends northeast and southeast from North of Australia over the Pacific Ocean during MAM. WEIO, WIO, NSPO, Niño3.4, Atlantic Oscillation (AO) and WPG have shown strong and significant (p<0.05) relations at least over one subregion. The new SST and existing oceanic indices have a stronger and more significant correlation with short rains than long rains over EA, except in the northern part of EA. Correlation, RMSE and skill score of regression models revealed that new indices showed higher performance than existing indices over EEA and SEA in predicting rainfall, unlike over NEA during both seasons. [ABSTRACT FROM AUTHOR]

The use of artificial water points for wildlife in African savannah ecosystems has been widely criticised for affecting the distribution of wildlife and initiating changes in the heterogeneity of natural landscapes. We examined the spatiotemporal variations in the landscape before and after the installation of an artificial water point by integrating the analysis of vegetation and soil spectral response patterns with a supervised learning random forest model between 2002 and 2022 in Chobe Enclave, Northern Botswana. Our results revealed that the study area is characterised by animal species such as Equus quagga, Aepyceros melampus, and Loxodonta africana. The findings also showed that the main vegetation species in the study area landscape include Combretum elaeagnoides, Vachellia luederitzii, and Combretum hereroense. The artificial water point induced disturbances on a drought-vulnerable landscape which affected vegetation heterogeneity by degrading the historically dominant vegetation cover types such as Colophospermum mopane, Dichrostachys cinerea, and Cynodon dactylon. The immediate years following the artificial water point installation demonstrated the highest spectral response patterns by vegetation and soil features attributed to intense landscape disturbances due to abrupt high-density aggregation of wildlife around the water point. Landscapes were strongly homogenised in later years (2022), as shown by overly overlapping spectral patterns owing to an increase in dead plant-based material and senescent foliage due to vegetation toppling and trampling. The landscape disturbances disproportionately affected mopane-dominated woodlands compared to other vegetation species as indicated by statistically significant land cover change obtained from a random forest classification. The woodlands declined significantly (p < 0.05) within 0–0.5 km, 0.5–1 km, 1–5 km, and 5–10 km distances after the installation of the water point. The results of this study indicate that continuous nonstrategic and uninformed use of artificial water points for wildlife will trigger ecological alterations in savannah ecosystems. [ABSTRACT FROM AUTHOR]

Background: Leishmaniasis are a group of diseases caused by more than 20 species of the protozoan that are transmitted through the bite of female sand fly. The disease is endemic to 98 countries of the world. It affects most commonly the poorest of the poor and mainly males. Several research has been conducted to propose disease control strategies. Effective medical care, vector control, environmental hygiene, and personal protection are the mainstays of the current preventative and control methods. The mathematical models for the transmission dynamics of the disease studied so far did not consider the sex-biased burden of the disease into consideration. Methodology: Unlike the previous VL works, this study introduces a new deterministic sex-structured model for understanding the transmission dynamics of visceral leishmaniasis. Basic properties of the model including basic reproduction number (R0), and conditions for the existence of backward bifurcation of the model are explored. Baseline parameter values were estimated after the model was fitted to Ethiopia's VL data. Sensitivity analysis of the model was performed to identify the parameters that significantly impact the disease threshold. Numerical simulations were performed using baseline parameter values, and scenario analysis is performed by changing some of these parameters as appropriate. Conclusion: The analysis of the model shows that there is a possibility for a backward bifurcation for R0<1 , which means bringing R0 to less than unity may not be enough to eradicate the disease. Our numerical result shows that the implementation of disease-preventive strategies, as well as effectively treating the affected ones can significantly reduce the disease prevalence if applied for more proportion of the male population. Furthermore, the implementation of vector management strategies also can considerably reduce the total prevalence of the disease. However, it is demonstrated that putting more effort in treating affected reservoir animals may not have any significant effect on the overall prevalence of the disease as compared to other possible mechanisms. The numerical simulation infers that a maximum of 60% of extra preventative measures targeted to only male population considerably reduces the total prevalence of VL by 80%. It is also possible to decrease the total prevalence of VL by 69.51% when up to 50% additional infected males receive treatment with full efficacy. Moreover, applying a maximum of 15% additional effort to reduce the number of vectors, decreases the total VL prevalence by 57.71%. Therefore, in order to reduce the disease burden of visceral leishmaniasis, public health officials and concerned stakeholders need to give more emphasis to the proportion of male humans in their intervention strategies. [ABSTRACT FROM AUTHOR]

Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth. [ABSTRACT FROM AUTHOR]

Lake Tana, the largest inland water body in Ethiopia, has witnessed significant changes due to ongoing urbanization and socioeconomic activities in recent times. In this study, the two-decade recordings of moderate resolution imaging spectroradiometer (MODIS) were used to derive Forel–Ule index (FUI). The FUI, which ranges from 1 (dark-blue pristine water) to 21 (yellowish-brown polluted water), is important to fully understand the quality and trophic state of the lake in the last two decades. The analysis of FUI over a period of 22 years (2000–2021) indicates that Lake Tana is in a eutrophic state as confirmed by FUI values ranging from 11 to 17. This is in agreement with the trophic state index (TSI) estimated from MERIS diversity-II chlorophyll a (Chl _ a ) measurements for the overlapping 2003-2011 period. The categorical skill scores show that FUI-based lake water trophic state classification relative to MERIS-based TSI has a high performance. FUI has a positive correlation with TSI, (Chl _ a ), turbidity, and total suspended matter (TSM) and negative relations with Chl _ a and TSM (at the lake shoreline) and colored dissolved organic matter. The annual, interannual and seasonal spatial distribution of FUI over the lake show a marked variation. The hydro-meteorological, land-use–land-cover (LULC) related processes are found to modulate the spatiotemporal variability of water quality within the range of lower and upper extremes of the eutrophic state as revealed from the FUI composite analysis. The FUI composites were obtained for the terciles and extreme percentiles of variables representing hydro-meteorological and LULC processes. High FUI composite (poor water quality) is associated with above-normal and extremely high (85 percentile) lake bottom layer temperature, wind speed, precipitation, surface runoff, and hydrometeorological drought as captured by high negative standardized precipitation-evapotranspiration index (SPEI). In contrast, a high FUI composite is observed during below-normal and extremely low (15 percentile) lake skin temperature and evaporation. Conversely good water quality (i.e., low FUI) was observed during times of below-normal and above-normal values of the above two sets of drivers respectively. Moreover, FUI varies in response to seasonal NDVI/EVI variabilities. The relationship between water quality and its drivers is consistent with the expected physical processes under different ranges of the drivers. High wind speed, for instance, displaces algae blooms to the shoreline whereas intense precipitation and increased runoff lead to high sediment loads. Increasing lake skin temperature increases evaporation, thereby decreasing water volume and increasing insoluble nutrients, while the increasing lake bottom layer temperature increases microbial activity, thereby enhancing the phosphorus load. Moreover, during drought events, the low inflow and high temperature allow algal bloom, Chl _ a , and suspended particles to increase, whereas high vegetation leads to an increase in the non-point sources of total phosphorus and nitrogen. [ABSTRACT FROM AUTHOR]

This study aims to assess the performance of European Center for Medium‐Range Weather Forecasting (ECMWF) Reanalysis fifth generation (ERA5) in terms of the progress made over its predecessor, ERA‐Interim, over East Africa (EA) in reproducing observed rainfall. The observed rainfall used as reference rainfall are Global Precipitation Climatology Center (GPCC) and Climate Research Unit Time Series (CRU‐TS). The performance of ERA5 to its predecessor is evaluated using root‐mean‐square error (RMSE), correlation, and bias. The reductions of wet bias from ERA‐Interim to ERA5 are 16.81–6.94% from June to September (JJAS), 31.99–19.33% from September to December (SOND), and 29.24–17.69% from December to February (DJF) over most of EA relative to CRU‐TS. Similar reductions in the wet bias relative to GPCC are also noted. Spatially, notable reductions are observed over western Ethiopia, Sudan Republic, South Sudan, and Uganda. The decreasing trends revealed in the bias of seasonal ERA5 rainfall relative to CRU‐TS and GPCC are attributed to an increase in the number and quality of assimilated observations in ERA5 over its predecessors. RMSE of ERA5 rainfall relative to the two reference rainfall and correlations with the two reference rainfall are consistent with spatial and temporal features captured in the bias. Specifically, the correlation of the monthly time series of ERA5 ranges from 0.90 with CRU‐TS over equatorial EA (EEA) to 0.98 with GPCC over southern EA (SEA) in contrast to that of ERA‐Interim which ranges from 0.84 with CRU‐TS over EEA to 0.93 with CRU‐TS over SEA. However, the wet bias in ERA5 along the Great Rift Valley remained the same as in ERA‐Interim whereas spatial correlation of ERA5 with the two reference rainfall is relatively weaker than that of ERA‐Interim. [ABSTRACT FROM AUTHOR]

In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. The input data are obtained from satellite and ground based measurements characterizing the solar‐terrestrial environment. We estimate the relative importance of 34 different parameters, including the solar flux, solar wind density, and speed the three components of interplanetary magnetic field, Lyman‐alpha, the Kp, Dst, and Polar Cap (PC) indices. The TEC measurements are taken with 15‐s cadence from an equatorial GPS station located at Bogota, Columbia (4.7110° N, 74.0721° W). The 2008–2017 data set, including the top five parameters estimated using the random forest, is used for training the machine learning models, and the 2018 data set is used for independent testing of the LSTM forecasting. The LSTM method as applied to forecast the TEC up to 5 h ahead, with 30‐min cadence. The results indicate that very good forecasts with low root mean square (RMS) error (high correlation) can be made in the near future and the RMS errors increase as we forecast further into the future. The data sources are satellite and ground based measurements characterizing the solar‐terrestrial environment. Plain Language Summary: Space weather affects satellite communications, precise military operations and can interfere with power grids on the ground. Physics‐based space weather forecasting is extremely challenging due to the complicated nature of the physical drivers which can come from the Sun, the magnetosphere, the ionosphere, and the lower atmosphere. In this research, we used data‐driven machine learning methods to forecast the ionospheric total electron content which provides the amount of ionization in the upper atmosphere and hence helps as a proxy to forecast other space weather phenomenon. Key Points: Data driven advanced machine learning methods applied to forecast the ionospheric total electron content 5 h aheadThe random forest and Long‐Short Term Memory methods are employed, the data sources are space measurements characterizing the solar‐terrestrial environmentVariable importance ranking showed that F10.7, Lyman alpha are top predictors agreeing well with the physics of ionospheric formation [ABSTRACT FROM AUTHOR]

The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO 2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO 2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO 2 , and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9 ; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested. [ABSTRACT FROM AUTHOR]

A ground-based, high-spectral-resolution Fourier transform infrared (FTIR) spectrometer has been operational in Addis Ababa, Ethiopia (9.01 ∘ N latitude, 38.76 ∘ E longitude; 2443 m altitude above sea level), since May 2009 to obtain information on column abundances and profiles of various constituents in the atmosphere. Vertical profile and column abundances of methane and nitrous oxide are derived from solar absorption measurements taken by FTIR for a period that covers May 2009 to March 2013 using the retrieval code PROFFIT (V9.5). A detailed error analysis of CH4 and N2O retrieval are performed. Averaging kernels of the target gases shows that the major contribution to the retrieved information comes from the measurement. Thus, average degrees of freedom for signals are found to be 2.1 and 3.4, from the retrieval of CH4 and N2O for the total observed FTIR spectra. Methane and nitrous oxide volume mixing ratio (VMR) profiles and column amounts retrieved from FTIR spectra are compared with data from the reduced spectral resolution Institute of Meteorology and Climate Research/Instituto de Astrofísica de Andalucía (IMK/IAA) MIPAS (Version V5R_CH4_224 and V5R_N2O_224), the Microwave Limb Sounder (MLS) (MLS v3.3 of N2O and CH4 derived from MLS v3.3 products of CO, N2O , and H2O), and the Atmospheric Infrared Sounder (AIRS) sensors on board satellites. The averaged mean relative difference between FTIR methane and the three correlative instruments MIPAS, MLS, and AIRS are 4.2 %, 5.8 %, and 5.3 % in the altitude ranges of 20 to 27 km, respectively. However, the biases below 20 km are negative, which indicates the profile of CH4 from FTIR is less than the profiles derived from correlative instruments by -4.9% , -1.8% , and -2.8%. The averaged positive bias between FTIR nitrous oxide and correlative instrument, MIPAS, in the altitude range of 20 to 27 km is 7.8 %, and a negative bias of -4% at altitudes below 20 km. An averaged positive bias of 9.3 % in the altitude range of 17 to 27 km is obtained for FTIR N2O with MLS. In all the comparisons of CH4 from FTIR with data from MIPAS, MLS, and AIRS, sensors on board satellites indicate a negative bias below 20 km and a positive bias above 20 km. The mean error between partial-column amounts of methane from MIPAS and the ground-based FTIR is -5.5% , with a standard deviation of 5 % that shows very good agreement as exhibited by relative differences between vertical profiles. Thus, the retrieved CH4 and N2O VMR and column amounts from Addis Ababa, tropical site, is found to exhibit very good agreement with all coincident satellite observations. Therefore, the bias obtained from the comparison is comparable to the precision of FTIR measurement, which allows the use of data in further scientific studies as it represents a unique environment of tropical Africa, a region poorly investigated in the past. [ABSTRACT FROM AUTHOR]

Africa is one of the most data-scarce regions as satellite observation at the Equator is limited by cloud cover and there is a very limited number of ground-based measurements. As a result, the use of simulations from models is mandatory to fill this data gap. A comparison of satellite observation with model and available in situ observations will be useful to estimate the performance of satellites in the region. In this study, GOSAT column-averaged carbon dioxide dry-air mole fraction (XCO2) is compared with the NOAA CT2016 and six flask observations over Africa using 5 years of data covering the period from May 2009 to April 2014. Ditto for OCO-2 XCO2 against NOAA CT16NRT17 and eight flask observations over Africa using 2 years of data covering the period from January 2015 to December 2016. The analysis shows that the XCO2 from GOSAT is higher than XCO2 simulated by CT2016 by 0.28±1.05 ppm, whereas OCO-2 XCO2 is lower than CT16NRT17 by 0.34±0.9 ppm on the African land mass on average. The mean correlations of 0.83±1.12 and 0.60±1.41 and average root mean square deviation (RMSD) of 2.30±1.45 and 2.57±0.89 ppm are found between the model and the respective datasets from GOSAT and OCO-2, implying the existence of a reasonably good agreement between CT and the two satellites over Africa's land region. However, significant variations were observed in some regions. For example, OCO-2 XCO2 are lower than that of CT16NRT17 by up to 3 ppm over some regions in North Africa (e.g. Egypt, Libya, and Mali), whereas it exceeds CT16NRT17 XCO2 by 2 ppm over Equatorial Africa (10 ∘ S–10 ∘ N). This regional difference is also noted in the comparison of model simulations and satellite observations with flask observations over the continent. For example, CT shows a better sensitivity in capturing flask observations over sites located in North Africa. In contrast, satellite observations have better sensitivity in capturing flask observations in lower-altitude island sites. CT2016 shows a high spatial mean of seasonal mean RMSD of 1.91 ppm during DJF with respect to GOSAT, while CT16NRT17 shows 1.75 ppm during MAM with respect to OCO-2. On the other hand, low RMSDs of 1.00 and 1.07 ppm during SON in the model XCO2 with respect to GOSAT and OCO-2 are respectively determined, indicating better agreement during autumn. The model simulation and satellite observations exhibit similar seasonal cycles of XCO2 with a small discrepancy over Southern Africa (35–10 ∘ S) and during wet seasons over all regions. [ABSTRACT FROM AUTHOR]

Earth's ionosphere is an important medium of radio wave propagation in modern times. However, the effective use of the ionosphere depends on the understanding of its spatiotemporal variability. Towards this end, a number of ground- and space-based monitoring facilities have been set up over the years. The information from these stations has also been complemented by model-based studies. However, assessment of the performance of ionospheric models in capturing observations needs to be conducted. In this work, the performance of the IRI-2016 model in simulating the total electron content (TEC) observed by a network of Global Positioning System (GPS) receivers is evaluated based on the RMSE, the bias, the mean absolute error (MAE) and skill score, the normalized mean bias factor (NMBF), the normalized mean absolute error factor (NMAEF), the correlation, and categorical metrics such as the quantile probability of detection (QPOD), the quantile categorical miss (QCM), and the quantile critical success index (QCSI). The IRI-2016 model simulations are evaluated against gridded International Global Navigation Satellite System (GNSS) Service (IGS) GPS-TEC and TEC observations at a network of GPS receiver stations during the solar minima in 2008 and solar maxima in 2013. The phases of modeled and simulated TEC time series agree strongly over most of the globe, as indicated by a high correlations during all solar activities with the exception of the polar regions. In addition, lower RMSE, MAE, and bias values are observed between the modeled and measured TEC values during the solar minima than during the solar maxima from both sets of observations. The model performance is also found to vary with season, longitude, solar zenith angle, and magnetic local time. These variations in the model skill arise from differences between seasons with respect to solar irradiance, the direction of neutral meridional winds, neutral composition, and the longitudinal dependence of tidally induced wave number four structures. Moreover, the variation in model performance as a function of solar zenith angle and magnetic local time might be linked to the accuracy of the ionospheric parameters used to characterize both the bottom- and topside ionospheres. However, when the NMBF and NMAEF are applied to the data sets from the two distinct solar activity periods, the difference in the skill of the model during the two periods decreases, suggesting that the traditional model evaluation metrics exaggerate the difference in model skill. Moreover, the performance of the model in capturing the highest ends of extreme values over the geomagnetic equator, midlatitudes, and high latitudes is poor, as noted from the decrease in the QPOD and QCSI as well as an increase in the QCM over most of the globe with an increase in the threshold percentile TEC values from 10 % to 90 % during both the solar minimum and the solar maximum periods. The performance of IRI-2016 in simulating observed low (as low as the 10th percentile) and high (higher than the 90th percentile) TEC correctly over equatorial ionization anomaly (EIA) crest regions is reasonably good given that IRI-2016 is a climatological model. However, it is worth noting that the performance of the IRI-2016 model is relatively poor in 2013 compared with 2008 at the highest ends of the TEC distribution. Therefore, this study reveals the strengths and weaknesses of the IRI-2016 model in simulating the observed TEC distribution correctly during all seasons and solar activities for the first time. [ABSTRACT FROM AUTHOR]

Africa is one of the most data-scarce regions as satellite observation at the equator is limited by cloud cover and there are a very limited number of ground-based measurements. As a result, the use of simulations from models are mandatory to fill this data gap. A comparison of satellite observation with model and available in-situ observations will be useful to estimate the performance of satellites in the region. In this study, GOSAT XCO2 is compared with the NOAA CT2016 and six flask observations over Africa using five years of data covering the period from May 2009 to April 2014. Ditto for OCO-2 XCO2 against NOAA CT16NRT17 and eight flask observations over Africa using two years of data covering the period from January 2015 to December 2016. The analysis shows that the XCO2 from GOSAT is higher than XCO2 simulated by CT2016 by 0.28 ppm whereas OCO-2 XCO2 is lower than CT16NRT17 by 0.34 ppm on African landmass on average. The mean correlations of 0.83 and 0.60 and average RMSD of 2.30 and 2.57 ppm are found between the model and the respective datasets from GOSAT and OCO-2 implying the existence of a reasonably good agreement between CT and the two satellites over Africa's land region. However, significant variations were observed in some regions. For example, OCO-2 XCO2 are lower than that of CT16NRT17 by up to 3 ppm over some regions in North Africa (e.g., Egypt, Libya, and Mali) whereas it exceeds CT16NRT17 XCO2 by 2 ppm over Equatorial Africa (10° S-10° N). This regional difference is also noted in the comparison of model simulations and satellite observations with flask observations over the continent. For example, CT shows a better sensitivity in capturing flask observations over sites located in Northern Africa. In contrast, satellite observations have better sensitivity in capturing flask observations in lower altitude island sites. CT2016 shows a high spatial mean of seasonal mean RMSD of 1.91 ppm during DJF with respect to GOSAT while CT16NRT17 shows 1.75 ppm during MAM with respect to OCO-2. On the other hand, low RMSD of 1.00 and 1.07 ppm during SON in the model XCO2 with respect to GOSAT and OCO-2 are determined respectively indicating better agreement during autumn. The model simulation and satellite observations exhibit similar seasonal cycles of XCO2 with a small discrepancy over Southern Africa and during wet seasons over all regions. [ABSTRACT FROM AUTHOR]

Uncertainties of tropical methane concentrations, retrieved from spectra recorded by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), MIPAS version V5R_CH4_220 are large. We explore the relation of these uncertainties with water vapour variability. We further show that these uncertainties have been reduced in MIPAS version V5R_CH4_224. Coincident measurements of CH4 by MIPAS, ground based FTIR and CH4 derived from EOS MLS coincident measurements of atmospheric water vapour (H2O), carbon monoxide (CO) and nitrous oxide (N2O) are used to estimate the standard uncertainty of MIPAS CH4 220, MIPAS CH4 224 and natural variability of H2O. Different methods such as bias evaluation, differential method and correlation coefficient are employed to explore the latitudinal variations of standard uncertainty of MIPAS CH4 220 and natural variability of water vapour as well as its reduction on MIPAS CH4 224. The averaged bias between MIPAS CH4 220 and ground-based FTIR measurements are −12.3 %, 8.4 % and 1.2 % for tropics, mid-latitudes and high latitudes, respectively. The standard deviations of the differences for these latitudinal bands are 5.9 %, 4.8 % and 4.7 %. More-over, the correlation coefficient between MIPAS CH4 220 and MIPAS V5R_N2O_220 is 0.32 in the upper troposphere and lower stratosphere over tropics and larger than the mod-est value 0.5 in mid and high latitudes. The poor correlation between MIPAS CH4 220 and MIPAS N2O 220 over tropics can indicate the large uncertainty of MIPAS CH4 220 over tropics that is related to water variability. Similarly, mean relative difference between MIPAS CH4 224 and ground-based FTIR measurements are 3.9 %, −2.6 % and −2.7 % in altitude 15–21 km and the average estimated uncertainty of MIPAS CH4 224 methane were obtained 2.4 %, 1.4 % and 5.1 % in altitude ranges of 15 to 27 km for tropics, mid and high latitudes, respectively. The estimated measurement uncertainty of MIPAS CH4 224 is different for the three latitude bands in the northern hemisphere, reflecting the latitudinal variation of uncertainties of MIPAS methane. However, the large reduction of uncertainty in MIPAS CH4 224 as com-pared to MIPAS CH4 220 has been confirmed for the tropical measurements. The correlation coefficients between the uncertainty of MIPAS CH4 220 and the variability of water vapour in lower stratosphere are strong (0.88) on monthly temporal scales. Similar methods were used for MIPAS CH4 224. It was found that the uncertainty in methane due to the variability of water vapor has been reduced. [ABSTRACT FROM AUTHOR]

A ground based high spectral resolution Fourier transform infrared (FTIR) spectrometer has been operational at Addis Ababa (9.0° N, 38.76° E, 2443 m a.s.l.) since May 2009 to obtain information on the total column abundances and vertical distribution of various constituents in the atmosphere. The retrieval strategy and the results on information content and corresponding full error budget evaluation for methane and nitrous oxide retrievals are presented. They reveal the high quality of FTIR measurements at Addis Ababa. The FTIR products of CH4 and N2O have been compared to coincident volume mixing ratio (VMR) measurements obtained from the reduced spectral resolution (Institute of Meteorology and Climate Research) IMK/IAA MIPAS satellite instrument (Version V5R_CH4_224 and V5R_N2O_224), the Microwave Limb Sounder on board of the Aura satellite (Aura/MLS) (MLS v3.3 of N2O and CH4 derived from MLS v3.3 products of CO, N2O and H2O) and the Atmospheric Infrared Sounder (AIRS). From comparison of FTIR CH4 and IMK/IAA MIPAS V5R_CH4_224, a statistically significant bias between -4.8 and +4.6 % in altitude ranges of the upper troposphere and lower stratosphere (15-27 km) are determined. The largest negative bias in FTIR CH4 is found in the altitude range of 11-19 km with a maximum difference of -0.08 ppmv (-4.8 %) at around 15 km, a positive bias of less than 0.14 ppmv (9 %) is found in the altitude range of 21 to 27 km with a maximum value at around 27 km with respect to AIRS. On the other hand, comparison of CH4 from ground based FTIR and MLS-derived CH4 (version 3.3) indicate existence of a significant positive bias of 2.3 % to 11 % in the altitude range of 20 to 27 km and a negative bias -1.7 % at 17 km. In the case of N2O derived from FTIR and MIPAS V5R_N2O_224 comparison, a significant positive bias of less than 15 % in the altitude range 22-27 km with a maximum value at around 25 km and a negative bias of -7 % have been found at 17 km. A positive bias of less than 18.6 % in FTIR N2O for the altitude below 27 km is noted when compared to MLS v3.3 N2O. Precision of ground based FTIR CH4 and N2O in the upper troposphere and lower stratosphere over Addis Ababa are better than 7.2 % and 9 %, respectively which are comparable to the bias obtained from the comparisons. [ABSTRACT FROM AUTHOR]

In a 3.5 year long study, the long term performance of a mobile Bruker EM27/SUN spectrometer, used for greenhouse gases observations, is checked with respect to a co-located reference Bruker IFS 125HR spectrometer, which is part of the Total Carbon Column Observing Network (TCCON). We find that the EM27/SUN is stable on timescales of several years, qualifying it as an useful supplement for the existing TCCON network in remote areas. For achieving consistent performance, such an extension requires careful testing of any spectrometers involved by application of common quality assurance measures. One major aim of the COllaborative Carbon Column Observing Network (COCCON) infrastructure is to provide these services to all EM27/SUN operators. In the framework of COCCON development, the performance of an ensemble of 30 EM27/SUN spectrometers was tested and found to be very uniform, enhanced by the centralized inspection performed at the Karlsruhe Institute of Technology prior to deployment. Taking into account measured instrumental line shape parameters for each spectrometer, the resulting average bias across the ensemble in XCO2 is 0.20ppm, while it is 0.8ppb for XCH4. As indicated by the executed long-term study on one device presented here, the remaining empirical calibration factor deduced for each individual instrument can be assumed constant over time. Therefore the application of these empirical factors is expected to further improve the EM27/SUN network conformity beyond the raw residual bias reported above. [ABSTRACT FROM AUTHOR]

The scarcity of ground-based observations, poor global coverage and resolution of satellite observations necessitate the use of data generated from models to assess spatio-temporal variations of atmospheric CO2 concentrations in a near continuous manner in a global and regional scale. Africa is one of the most data scarce region as satellite observation at the equator is limited by cloud cover and there are very limited number of ground based measurements. As a result, use of simulations from models are mandatory to fill this data gap. However, the first step in the use of data from models requires assessment of model skill in capturing limited existing observations. Even though, the NOAA Carbon Tracker model is evaluated using TCCON and satellite observations at a global level, its performance should be assessed at a regional scale, specifically in a regions like Africa with a highly varying climatic responses and a growing local source. In this study, NOAA CT2016 CO2 is compared with the ACOS GOSAT observation over Africa using five years datasets covering the period from April 2009 to June 2014. In addition, NOAA CT2016 CO2 is compared with OCO-2 observation over Africa using two years data covering the period from January 2015 to December 2016. The results show that the XCO2 retrieved from GOSAT and OCO-2 are lower than CT2016 model simulation by 0.42 and 0.93 ppm on average respectively, which lie within the range of the errors associated with the GOSAT and OCO-2 XCO2 retrievals. The mean correlations of 0.73 and 0.6, a regional precisions of 3.49 and 3.77 ppm, and the relative accuracies of 1.22 and 1.95 ppm were found between the model and the two data sets implying the performance of the model in Africa's land regions is reasonably good despite shortage of in-situ observations over the region assimilated in the model. These differences, however, exhibit spatial and seasonal scale variations. Moreover, the model shows some weakness in capturing the whole distribution. For example, the probability of detection ranges from 0.6 to 1 and critical success index ranges from 0.4 to 1 over the continent when the analysis includes data above the 95th percentile and the whole data respectively. This shows the model misses the higher extreme ends of the CO2 distribution. Spatially, GOSAT and OCO-2 XCO2 are lower than that of CT2016 by upto 4 ppm over North Africa (10°-35° N) whereas it exceeds CT2016 XCO2 by 3 ppm over Equatorial Africa (10° S-10° N). Larger spatial mean biases of 2.11 and 1.8 ppm, 1.25 and 0.73 ppm in CT2016 XCO2 with respect to that of GOSAT and OCO-2 are observed during winter (DJF) and spring (MAM) while small biases of -0.15 and 0.21 ppm, and 0.2 and -1.14 ppm are observed during summer (JJA) and autumn (SON) respectively. The model simulation has the ability to capture seasonal cycles with a small discrepancy over the North Africa and during winter seasons over all regions. In these cases, the model overestimates the local emissions and underestimate CO2 loss. [ABSTRACT FROM AUTHOR]

Stratospheric aerosols play significant role both positively and negatively in Earths energy balance and climate change. Its main sources are particulate matters which arises from either of natural or anthropogenic activities. In the context of our country, Ethiopia, the stratospheric aerosol climatology has not been studied yet. However, Ethiopia is undergoing a boom of infrastructural development like increase of urbanization, which comes with a boom of development like building and road constructions, expansion of industries, traffic density, etc, which contributes to air pollution and influences the solar radiation budget of the earth-atmosphere system, which in turn influences the climate on the surface of the Earth by different ways. Hence, this study aimed to provide the stratospheric aerosol climatology for nearly 21 years extending from Oct., 1984 to Sept., 2005. The study was carried out by defining the stratospheric region from the temperature profile of the study area provided by Stratospheric Aerosols and Gas Experiment II (SAGEII) instrument aboard on Earths Radiation Budget Satellite (ERBS). Then, the data was filtered out over Ethiopian region at four aerosol channels and the optical depth is used as input to the Mie algorithm for aerosol size distribution (ASD) retrieval. Finally, it was observed that the spectral and vertical variation of the extinction is maximum between 17-25 km and the total column aerosol optical depth (AOD) temporal variation shows nearly steadily increasing trend with maximum variation during spring. Furthermore, from the ASD result it was observed that the maximum size distribution was in April. This paves a clue about their sources to be mechanical process on the ground and gas to particle conversion in the stratosphere with the dominant size distribution in the range of 0.452-0.525 μm radius. [ABSTRACT FROM AUTHOR]

We report on the ground-based FTIR (Fourier transform infrared) tropospheric water vapour isotopologue remote sensing data that have been recently made available via the database of NDACC (Network for the Detection of Atmospheric Composition Change; ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA/) and via doi:10.5281/zenodo.48902. Currently, data are available for 12 globally distributed stations. They have been centrally retrieved and quality-filtered in the framework of the MUSICA project (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). We explain particularities of retrieving the water vapour isotopologue state (vertical distribution of H216 O, H218 O, and HD16O) and reveal the need for a new metadata template for archiving FTIR isotopologue data. We describe the format of different data components and give recommendations for correct data usage. Data are provided as two data types. The first type is best-suited for tropospheric water vapour distribution studies disregarding different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type is needed for analysing moisture pathways by means of fH2O; δDg-pair distributions. [ABSTRACT FROM AUTHOR]

In the lower/middle troposphere, {H2O,δD} pairs are good proxies for moisture pathways; however, their observation, in particular when using remote sensing techniques, is challenging. The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) addresses this challenge by integrating the remote sensing with in situ measurement techniques. The aim is to retrieve calibrated tropospheric {H2O,δD} pairs from the middle infrared spectra measured from ground by FTIR (Fourier transform infrared) spectrometers of the NDACC (Network for the Detection of Atmospheric Composition Change) and the thermal nadir spectra measured by IASI (Infrared Atmospheric Sounding Interferometer) aboard the MetOp satellites. In this paper, we present the final MUSICA products, and discuss the characteristics and potential of the NDACC/FTIR and MetOp/IASI {H2O,δD} data pairs. First, we briefly resume the particularities of an {H2O,δD} pair retrieval. Second, we show that the remote sensing data of the final product version are absolutely calibrated with respect to H2O and δD in situ profile references measured in the subtropics, between 0 and 7 km. Third, we reveal that the {H2O,δD} pair distributions obtained from the different remote sensors are consistent and allow distinct lower/middle tropospheric moisture pathways to be identified in agreement with multi-year in situ references. Fourth, we document the possibilities of the NDACC/FTIR instruments for climatological studies (due to long-term monitoring) and of the MetOp/IASI sensors for observing diurnal signals on a quasi-global scale and with high horizontal resolution. Fifth, we discuss the risk of misinterpreting {H2O,δD} pair distributions due to incomplete processing of the remote sensing products. [ABSTRACT FROM AUTHOR]

Abstract: MOZAIC instrument measured enhanced ozone on two occasions in February, 1996 and 1997 at cruise altitude over North Africa. The cause and source of ozone enhancements over the region are investigated using additional reanalysis data from ERA-Interim. The ERA-Interim reprocessed GOME ozone indicated existence of enhancement as well. Both observational data revealed that the increase in ozone has wider latitudinal coverage extending from North Europe upto North Africa. The geopotential heights and zonal wind from ERA-Interim have indicated existence of planetary-scale flow that allowed meridional airmass exchanges between subtropics and higher latitudes. The presence of troughs-ridge pattern are attributable to large amplitude waves of zonal wavenumber 1–5 propagating eastward in the winter hemisphere westerly current as determined from Hayashi spectra as well as local fractional variance spectra determined from Multitaper Method-Singular Value Decomposition (MTM-SVD) spectral method. MTM-SVD is also used to understand the role of these waves on ozone enhancement and variability during the observation period in a mechanistic approach. A joint analysis of driving field, such as wind and potential vorticity (PV) for which only signals of the dominant zonal wavenumbers of prevailing planetary waves are retained, has revealed strong linkage between wave activity and ozone enhancement over the region at a temporal cycle of 5.8days. One of these features is the displacement of the polar vortex southward during the enhancements, allowing strong airmass, energy and momentum exchanges. Evidence of cutoff laws that are formed within the deep trough, characteristics of Rossby wave breaking, is also seen in the ozone horizontal distribution at different pressure levels during the events. The reconstruction of signals with the cycle of 5.8days has shown that the time and strength of enhancement depend on the circulation patterns dictated by planetary-scale flow relative to the location of observation. The positive PV anomalies upstream or at the observation region bring ozone rich airmass to the region while a negative PV anomaly upstream does the opposite. The position of the anomalies with time changes in accordance with the period of the waves involved. The snap shot of coherent variation of PV and ozone at different time during half cycle of the 5.8-day period has indicated that a region could experience positive (enhancement) or negative (depletion) ozone anomalies of different degree as the wave propagates eastward. [Copyright &y& Elsevier]

Abstract: The possible cause and sources of enhanced ozone at upper tropospheric equatorial Africa, observed by cruise Measurements of OZone by Airbus In Service Aircraft (MOZAIC) during the Northern Hemisphere winter in 1996 and 1997 on flight routes from Johannesburg to Vienna, are investigated. Two enhanced ozone events over upper tropospheric equatorial Africa are identified from MOZAIC observations on April 6, 1996 and March 27, 1997. High resolution ECMWF reanalysis GOME ozone has exhibited enhancement as well during these periods suggesting that the two events are not isolated small scale events but part of a larger scale process. As a result, the source and mechanisms of ozone increase over the region are further analysed using reanalysis data from ECMWF, outgoing long wave radiation (OLR) from NOAA and Meteosat images from NASA, International Satellite Cloud Climatology Project. Equivalent latitude computed from potential vorticity has shown that massive mid- and high-latitude stratospheric ozone rich airmass is funnelled into lower latitude troposphere through troughs extending from large amplitude planetary waves towards equator. The Space-time Fourier decomposition of meridionally averaged zonal wind has revealed that these planetary wave activities are linked to waves with zonal wavenumber 1–2, which prevail during Northern Hemisphere winter. Additional analysis to understand the mechanisms of ozone enhancement was made using Multitaper Method–Singular Value Decomposition (MTM–SVD) spectral approach. The analysis confirms that ozone enhancement over the region is dependent on the relative position of positive PV and direction of wind anomalies. The high relative humidity measured simultaneously with ozone onboard MOZAIC, Meteosat imageries and circulation during the events have shown presence of deep convection. The coherent variation of OLR and ozone found over 8-day temporal cycle determined from MTM–SVD has indicated existence of OLR negative forcing in the upper troposphere and positive forcing in the lower stratosphere. These results show coupling of PV intrusion and deep convection over continental equatorial Africa in the same manner as the climatologically preferred intrusion over mid-ocean in eastern pacific. Moreover, the results enrich previous understanding with purely observational high resolution MOZAIC and ERA-Interim datasets, and statistical method. [Copyright &y& Elsevier]