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This study investigates the impact of enhanced horizontal resolution on simulating mean and extreme precipitation in the major river basins of southern Africa. Seven global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) are used, which are available at both high-resolution (HR) and low-resolution (LR). The models are assessed using three observational datasets from 1983–2014 during December-January–February. The performance of the models in simulating nine extreme precipitation indices, as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is quantitatively assessed using various of statistical metrics. Results show that the distributions of daily precipitation from the HR models are nearly identical to those of their LR counterparts. However, model biases are not consistent across the three observations. Most HR and LR models reasonably simulate mean precipitation, maximum consecutive dry and wet days (CDD and CWD), number of rainy days (RR1) and heavy precipitation events (R10mm and R20mm), albeit with some biases. Enhanced horizontal resolution improves the simulation of mean precipitation, CDD, CWD, RR1 and R10mm, as indicated by high spatial correlation coefficients (SCCs), low root mean square errors (RMSEs), and reduced biases in most HR models. The majority of the HighResMIP models (i.e., both LR and HR models) overestimates extreme wet days precipitation (R95p and R99p), maximum one-day precipitation (Rx1day), and simple daily intensity (SDII), with a pronounced wet bias in HR models for R95p and R99p. Most LR models outperforms HR models in simulating R95p, R99p, and SDII. By means of a Comprehensive Ranking Metrics, EC-EARTH_HR is identified as the best performing model for simulating all nine extreme precipitation indices across the basins, except for the Zambezi, where EC-EARTH_LR performs best. Our findings indicate that increased resolution can either improve or worsen performance depending on the model and basin. Therefore, no clear evidence exist that enhanced horizontal resolution under HighResMIP enhances the simulation of extreme precipitation in southern Africa. [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]

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]

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]

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]