Reconstructions of eolian dust accumulation in northwest African margin sediments

Reconstructions of eolian dust accumulation in northwest African margin sediments provide important continuous records of past changes in atmospheric circulation and aridity in the region. Existing records indicate dramatic changes in North African dust emissions over the last 20 ka, but the limited spatial extent of these records and the lack of high-resolution flux data do not allow us to determine whether changes in dust deposition occurred with similar timing, magnitude and abruptness throughout northwest Africa. Here we present new records from a meridional transect of cores stretching from 31°N to 19°N along the northwest African margin. By combining grain size endmember modeling with 230Th-normalized fluxes for the first time, we are able to document spatial and temporal changes in dust deposition under the North African dust plume throughout the last 20 ka. Our results provide quantitative estimates of the magnitude of dust flux changes associated with Heinrich Stadial 1, the Younger Dryas, and the African Humid Period (AHP; ~11.7-5 ka), offering robust targets for model-based estimates of the climatic and biogeochemical impacts of past changes in North African dust emissions. Our data suggest that dust fluxes between 8 and 6 ka were a factor of ~5 lower than average fluxes during the last 2 ka. Using a simple model to estimate the effects of bioturbation on dust input signals, we find that our data are consistent with abrupt, synchronous changes in dust fluxes in all cores at the beginning and end of the AHP. The mean ages of these transitions are 11.8±0.2 ka (1Sigma) and 4.9±0.2 ka, respectively.

ERA-interim reanalysis debiased at FLUXNET sites

(preliminary) Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10% to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27% to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the Wind Speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

Snow-pit measurements on Atka Bay landfast sea ice during ANT-Land 2012/2013 season

Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter.