RESUMO
In this paper, three datasets are described. The first dataset is a complete set of GNSS-R (GNSS-R: Global Navigation Satellite System - Reflectometry) airborne data. This dataset has been generated with the data acquired with the GLObal Navigation Satellite System Reflectometry Instrument (GLORI) developed at Centre d'Etudes Spatiales de la Biosphère (CESBIO), during the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign in north-eastern Spain during the summer of 2021. It is the first time to our knowledge that a complete dataset of GNSS-R observables (reflectivity, incoherent component relative to the total scattering signal to noise ratio (SNR) for copolarized (right-right) and cross-polarized (right-left) measurements has been made available. The two other datasets are ground truth sets of measurements which have been acquired simultaneously with the flights. The in-situ measurements dataset consists in soil measurements (surface soil moisture, surface roughness, Leaf Area Index (LAI)) over 24 reference fields). The land use dataset provides a land use map (along with 385 ground truth plots) over the studied site for GLORI data evaluation. The combined datasets are particularly relevant for soil moisture and vegetation retrievals from GNSS-R observables, as well as studies for calibration and validation of bistatic empirical or physical models simulating coherent or incoherent components on agriculture sites, in the context of the preparation of future GNSS-R space missions, such as HydroGNSS, a European Space Agency mission, launch foreseen in 2024. The entire database is archived in the AERIS LIAISE database. One DOI is available for each of the 3 datasets (airborne GLORI dataset, in situ measurements dataset and land use dataset).
RESUMO
The PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave, surface air temperature and relative humidity. These results are explored here in greater detail and possible causes are investigated. We examine whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. We demonstrate that energy conservation in the observational data is not responsible for these results. We also show that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, we present evidence suggesting that the nature of this partitioning problem is likely shared among all contributing LSMs. While we do not find a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER, we do exclude multiple possible explanations and provide guidance on where future research should focus.
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The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., "idealized but realistic" forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the 20th century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60% of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone (ITCZ) before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40% of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.
