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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, solar absorption Bruker EM27/SUN spectrometer, used for greenhouse gas 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; the drift per year between the EM27/SUN and the official TCCON product is 0.02 ppmv for X CO2 and 0.9 ppbv for X CH4 , which is within the 1 σ precision of the comparison, 0.6 ppmv for X CO2 and 4.3 ppbv for X CH4. The bias between the two data sets is 3.9 ppmv for X CO2 and 13.0 ppbv for X CH4. In order to avoid sensitivity-dependent artifacts, the EM27/SUN is also compared to a truncated IFS 125HR data set derived from full-resolution TCCON interferograms. The drift is 0.02 ppmv for X CO2 and 0.2 ppbv for X CH4 per year, with 1 σ precisions of 0.4 ppmv for X CO2 and 1.4 ppbv for X CH4 , respectively. The bias between the two data sets is 0.6 ppmv for X CO2 and 0.5 ppbv for X CH4. With the presented long-term stability, the EM27/SUN qualifies as an useful supplement to the existing TCCON network in remote areas. To achieve 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 with respect to the reference EM27/SUN used in the long-term study in X CO2 is 0.20 ppmv, while it is 0.8 ppbv for X CH4. The average standard deviation of the ensemble is 0.13 ppmv for X CO2 and 0.6 ppbv for X CH4. In addition to the robust metric based on absolute differences, we calculate the standard deviation among the empirical calibration factors. The resulting 2 σ uncertainty is 0.6 ppmv for X CO2 and 2.2 ppbv for X CH4. 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 scatter among the empirical calibration factors reported above. [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]

The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 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 CO2, and incomplete representation of important processes in numerical models. In this study, we, therefore, turn to two remotely-sensed vegetation products that have 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 (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, 4 sites) over savannas in the northern and southern hemispheres. These coefficients are slightly higher than for the widely used MPI-BGC GPP products and Enhanced Vegetation Index (EVI). Similar 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, that 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 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]