The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Sp…The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsular Spain across 15 years (2004–2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann–Kendall nonparametric test and the Theil–Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (p-value < 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrublands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%).more
The Global Space-based Inter-Calibration System (GSICS) routinely monitors the calibration of various channels of Earth-observing satellite instrument…The Global Space-based Inter-Calibration System (GSICS) routinely monitors the calibration of various channels of Earth-observing satellite instruments and generates GSICS Corrections, which are functions that can be applied to tie them to reference instruments. For the infrared channels of geostationary imagers GSICS algorithms are based on comparisons of collocated observations with hyperspectral reference instruments; whereas Pseudo Invariant Calibration Targets are currently used to compare the counterpart channels in the reflected solar band to multispectral reference sensors. This paper discusses how GSICS products derived from both approaches can be tied to an absolute scale using specialized satellite reference instruments with SI-traceable calibration on orbit. This would provide resilience against gaps between reference instruments and drifts in their calibration outside their overlap period and allow construction of robust and harmonized data records from multiple satellite sources to build Fundamental Climate Data Records, as well as more uniform environmental retrievals in both space and time, thus improving inter-operability.more
This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to …This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products.
The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8–13 W/m2, whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.more
Les vecteurs vents extraits à partir des images satellite sont utilisés quotidiennement dans les modèles de prévision numérique du temps afin d'amélio…Les vecteurs vents extraits à partir des images satellite sont utilisés quotidiennement dans les modèles de prévision numérique du temps afin d'améliorer la qualité des prévisions météorologiques. Ils constituent en fait la seule observation du déplacement des masses d'air disponible aux hautes latitudes et au-dessus des océans. Cet article présente une vue d'ensemble de l'extraction des vecteurs vents à partir des images satellite à Eumetsat. La première partie décrit l'algorithme utilisé pour extraire des vents à partir des satellites géostationnaires Météosat, la seconde partie décrit l'extraction de ces vecteurs vents à partir des satellites polaires en orbite basse.more
Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preco…Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preconditioning, local feedback mechanisms, oceanic heat transport and the synoptic- and large-scale atmospheric circulations. However, a robust quantitative statistical analysis of extremes of sea ice reduction is hindered by the small number of events that can be sampled in observations and numerical simulations with computationally expensive climate models. Recent studies tackled the problem of sampling climate extremes by using rare event algorithms, i.e., computational techniques developed in statistical physics to reduce the computational cost required to sample rare events in numerical simulations. Here we apply a rare event algorithm to ensemble simulations with the intermediate complexity coupled climate model PlaSim-LSG to investigate extreme negative summer pan-Arctic sea ice area anomalies under pre-industrial greenhouse gas conditions. Owing to the algorithm, we estimate return times of extremes orders of magnitude larger than feasible with direct sampling, and we compute statistically significant composite maps of dynamical quantities conditional on the occurrence of these extremes. We find that extremely low sea ice summers in PlaSim-LSG are associated with preconditioning through the winter sea ice-ocean state, with enhanced downward longwave radiation due to an anomalously moist and warm spring Arctic atmosphere and with enhanced downward sensible heat fluxes during the spring-summer transition. As a consequence of these three processes, the sea ice-albedo feedback becomes active in spring and leads to an amplification of pre-existing sea ice area anomalies during summer.more
Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and h…Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and high solar zenith angle cause missing values to the optical satellite products, especially around the polar areas. Therefore, our motivation is to develop gap filling models. For that purpose, we will apply monthly gradient boosting (GB) based models to the Arctic sea ice area of the 34 years long albedo time series of the Satellite Application Facility on Climate Monitoring (CM SAF) project. We demonstrate the ability of the GB models to accurately fill missing data using albedo monthly mean, brightness temperature, and sea ice concentration as model inputs. Monthly GB models produce the most unbiased, precise, and robust estimates when compared to alternative estimates presented, such as monthly mean albedo values or estimates from monthly linear regression (LR) models. The mean relative differences between GB based estimates and original pentad values vary from-20% to 20% with RMSE being 0.048, compared to relative differences varying from-20% to over 60% with RMSE varying from 0.054 to 0.074 between other estimates and original pentad values. Pixelwise mean differences and standard deviations (std) over the whole Arctic sea ice area show that GB based estimates are more accurate (mean differences from-0.02 to 0.02) and more precise (std from 0.02 to 0.08) than other estimates (mean differences varying between-0.05 to over 0.05, and std varying from around 0.03 to over 0.1). Also, albedo of the melting sea ice is predicted better by the GB model, with negligible mean differences, compared to the LR model. Based on these results, we show that GB method is a useful technique to fill missing data, and the brightness temperature and sea ice concentration are useful additional model input data sources.more