ALBERT

Description: A long-term measurement of precipitation chemistry has been carried-out in a rural area of Banizoumbou, in the Sahel (Niger), representative of the african semi-arid savanna ecosystem. A total of 305 rainfall samples, representing 90% of the total annual rainfall, were collected with an automatic wet-only rain sampler from June 1994 to September 2005. Using ionic chromatography, pH major inorganic and organic ions were analyzed. Rainwater chemistry at the site is controlled by soil dust emissions associated to a strong terrigeneous contribution represented by SO42–, Ca2+, Carbonates, K+ and Mg2+. Calcium and carbonates represent about 40% of the total ionic charge of precipitation. The second highest contribution is nitrogenous, with annual Volume Weighed Mean (VWM) NO3– and NH4+, concentrations of 11.6 and 18.1 μeq.l−1, respectively. This is thesignature of ammonia sources related to animals and NOx emissions from savannas soils rain-induced, at the beginning of the rainy season. The mean annual NH3 and NO2 air concentration are of 6 ppbv and 2.6 ppbv, respectively. The annual VWM precipitation concentration of sodium and chloride are both of 8.7 μeq.l−1 and reflects the marine signature from the monsoon humid air masses coming from the ocean. The mean pH value, calculated from the VWM of H+, is 5.64. Acidity is neutralized by mineral dust, mainly carbonates, and/or dissolved gases such NH3. High level of organic acidity with 8 μeq.l−1 and 5.2 μeq.l−1 of formate and acetate were found, respectively. The analysis of monthly Black Carbon emissions and FAPAR values show that both biogenic emission from vegetation and biomass burning sources could explain the organic acidity content of the precipitation. The interannual variability of the VWM concentrations around the mean (1994–2005) presents fluctuations between ±5% and ±30% mainly attributed to the variations of sources strength associated with rainfall spatio-temporal distribution. From 1994 to 2005, the total mean wet deposition flux in the Sahelian region is 60.1 mmol.m−2.yr−1 and fluctuates around ±25%. Finally, Banizoumbou measurements, are compared to other long-term measurements of precipitation chemistry in the wet savanna of Lamto (Côte d'Ivoire) and in the forested zone of Zoétélé (Cameroon). The total chemical loadings presents a strong negative gradient from the dry savanna to the forest (143.7, 100.2 to 86.6 μeq.l–1), associated with the gradient of terrigeneous compounds sources. The wet deposition fluxes present an opposite gradient, with 60.0 mmol.m−2.yr−1 in Banizoumbou, 108.6 mmol.m−2.yr–1 in Lamto and 162.9 mmol.m−2.yr−1 in Zoétélé, controlled by the rainfall gradient along the ecosystems transect.

Description: Long-term precipitation chemistry have been recorded in the rural area of Banizoumbou (Niger), representative of a semi-arid savanna ecosystem. A total of 305 rainfall samples ~90% of the total annual rainfall) were collected from June 1994 to September 2005. From ionic chromatography, pH major inorganic and organic ions were detected. Rainwater chemistry is controlled by soil/dust emissions associated with terrigeneous elements represented by SO42−, Ca2+, Carbonates, K+ and Mg2+. It is found that calcium and carbonates represent ~40% of the total ionic charge. The second highest contribution is nitrogenous, with annual Volume Weighed Mean (VWM) for NO3− and NH4+ concentrations of 11.6 and 18.1 μeq.l−1, respectively. This is the signature of ammonia sources from animals and NOx emissions from savannas soil-particles rain-induced. The mean annual NH3 and NO2 air concentration are of 6 ppbv and 2.6 ppbv, respectively. The annual VWM precipitation concentration of sodium and chloride are both of 8.7 μeq.l−1 which reflects the marine signature of monsoonal and humid air masses. The median pH value is of 6.05. Acidity is neutralized by mineral dust, mainly carbonates, and/or dissolved gases such NH3. High level of organic acidity with 8μeq.l−1 and 5.2 μeq.l−1 of formate and acetate were also found. The analysis of monthly Black Carbon emissions and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) values show that both biogenic emission from vegetation and biomass burning could explain the rainfall organic acidity content. The interannual variability of the VWM concentrations around the mean (1994–2005) is between ±5% and ±30% and mainly due to variations of sources strength and rainfall spatio-temporal distribution. From 1994 to 2005, the total mean wet deposition flux in the Sahelian region is of 60.1 mmol.m−2.yr−1 ±25%. Finally, Banizoumbou measurements are compared to other long-term measurements of precipitation chemistry in the wet savanna of Lamto (Côte d'Ivoire) and in the forested zone of Zoétélé (Cameroon). The total chemical loading presents a maximum in the dry savanna and a minimum in the forest (from 143.7, 100.2 to 86.6 μeq.l−1), associated with the gradient of terrigeneous sources. The wet deposition fluxes present an opposite trend, with 60.0 mmol.m−2.yr−1 in Banizoumbou, 108.6 mmol.m−2.yr−1 in Lamto and 162.9 mmol.m−2.yr−1 in Zoétélé, controlled by rainfall gradient along the ecosystems transect.

Description: Geophysical time series often feature missing data or data acquired at irregular times. Procedures are needed to either resample these series at systematic time intervals or to generate reasonable estimates at specified times in order to meet specific user requirements or to facilitate subsequent analyses. Interpolation methods have long been used to address this problem, taking into account the fact that available measurements also include errors of measurement or uncertainties. This paper inspects some of the currently used approaches to fill gaps and smooth time series (smoothing splines, Singular Spectrum Analysis and Lomb-Scargle) by comparing their performance in either reconstructing the original record or in minimizing the Mean Absolute Error (MAE), Mean Bias Error (MBE), chi-squared test statistics and autocorrelation of residuals between the underlying model and the available data, using both artificially-generated series or well-known publicly available records. Some methods make no assumption on the type of variability in the data while others hypothesize the presence of at least some dominant frequencies. It will be seen that each method exhibits advantages and drawbacks, and that the choice of an approach largely depends on the properties of the underlying time series and the objective of the research.

Description: Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the mechanisms controlling ecological and hydrogical activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams either separately or simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurement, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The closest agreement is found for each observed data stream when only the same data stream is assimilated. The mean uncertainty reduction (relative to the prior) over all parameters is 16.1% for the simultaneous assimilation of LHF and FAPAR, 9.2% for assimilating LHF only, and 7.8% for assimilating FAPAR only. Furthermore, the set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction is found for a parameter describing maximum plant-available soil moisture for all three cases. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.

Description: Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ−1 to 2.96 g CMJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

Description: The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2 the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty is reflecting uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR provided by the MERIS sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial biosphere model BETHY. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation for two different set-ups: first at site-scale, where MERIS FAPAR observations at a range of sites are used as simultaneous constraints, and second at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. On both scales the assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.

Description: A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The goal of this study is to identify the current state of carbon observations and needs for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion (by several orders of magnitude) of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over remote areas such as the southern oceans, tropical forests and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in-situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases inter-operable, and on the calibration of each component of the system to agreed-upon international scales.

Description: We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are partly corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.

Description: Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.

Description: The terrestrial biosphere is currently a strong sink for anthropogenic CO2 emissions. Through the radiative properties of CO2, the strength of this sink has a direct influence on the radiative budget of the global climate system. The accurate assessment of this sink and its evolution under a changing climate is, hence, paramount for any efficient management strategies of the terrestrial carbon sink to avoid dangerous climate change. Unfortunately, simulations of carbon and water fluxes with terrestrial biosphere models exhibit large uncertainties. A considerable fraction of this uncertainty reflects uncertainty in the parameter values of the process formulations within the models. This paper describes the systematic calibration of the process parameters of a terrestrial biosphere model against two observational data streams: remotely sensed FAPAR (fraction of absorbed photosynthetically active radiation) provided by the MERIS (ESA's Medium Resolution Imaging Spectrometer) sensor and in situ measurements of atmospheric CO2 provided by the GLOBALVIEW flask sampling network. We use the Carbon Cycle Data Assimilation System (CCDAS) to systematically calibrate some 70 parameters of the terrestrial BETHY (Biosphere Energy Transfer Hydrology) model. The simultaneous assimilation of all observations provides parameter estimates and uncertainty ranges that are consistent with the observational information. In a subsequent step these parameter uncertainties are propagated through the model to uncertainty ranges for predicted carbon fluxes. We demonstrate the consistent assimilation at global scale, where the global MERIS FAPAR product and atmospheric CO2 are used simultaneously. The assimilation improves the match to independent observations. We quantify how MERIS data improve the accuracy of the current and future (net and gross) carbon flux estimates (within and beyond the assimilation period). We further demonstrate the use of an interactive mission benefit analysis tool built around CCDAS to support the design of future space missions. We find that, for long-term averages, the benefit of FAPAR data is most pronounced for hydrological quantities, and moderate for quantities related to carbon fluxes from ecosystems. The benefit for hydrological quantities is highest for semi-arid tropical or sub-tropical regions. Length of mission or sensor resolution is of minor importance.