(Table 1) Normalized difference vegetation index in control and warming plots, Abisco Research Station

Extreme winter warming events in the sub-Arctic have caused considerable vegetation damage due to rapid changes in temperature and loss of snow cover. The frequency of extreme weather is expected to increase due to climate change thereby increasing the potential for recurring vegetation damage in Arctic regions. Here we present data on vegetation recovery from one such natural event and multiple experimental simulations in the sub-Arctic using remote sensing, handheld passive proximal sensors and ground surveys. Normalized difference vegetation index (NDVI) recovered fast (2 years), from the 26% decline following one natural extreme winter warming event. Recovery was associated with declines in dead Empetrum nigrum (dominant dwarf shrub) from ground surveys. However, E. nigrum healthy leaf NDVI was also reduced (16%) following this winter warming event in experimental plots (both control and treatments), suggesting that non-obvious plant damage (i.e., physiological stress) had occurred in addition to the dead E. nigrum shoots that was considered responsible for the regional 26% NDVI decline. Plot and leaf level NDVI provided useful additional information that could not be obtained from vegetation surveys and regional remote sensing (MODIS) alone. The major damage of an extreme winter warming event appears to be relatively transitory. However, potential knock-on effects on higher trophic levels (e.g., rodents, reindeer, and bear) could be unpredictable and large. Repeated warming events year after year, which can be expected under winter climate warming, could result in damage that may take much longer to recover.

Minimum, maximum and mean annual ground temperatures in permafrost around Abisko ANS between 2008-2009

Monitoring of permafrost has been ongoing since 1978 in the Abisko area, northernmost Sweden, when measurements of active layer thickness started. In 1980, boreholes were drilled in three mires in the area to record permafrost temperatures. Recordings were made twice per year, and the last data were obtained in 2002. During the International Polar Year (2007-2008), new boreholes were drilled within the 'Back to the Future' (BTF) and 'Thermal State of Permafrost' (TSP) projects that enabled year-round temperature monitoring. Mean annual ground temperatures (MAGT) in the mires are close to 0°C, ranging from -0.16 to -0.47°C at 5 m depth. Data from the boreholes show increasing ground temperatures in the upper and lower part by 0.4 to 1°C between 1980 and 2002. At one mire, permafrost thickness has decreased from 15 m in 1980 to ca. 9 m in 2009, with an accelerating thawing trend during the last decade.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

Long-term environmental time series of continuously collected data in hydroelectric reservoirs in Brazil

Long-term environmental time series of continuously collected data are fundamental to identify and classify pulses and determine their role in aquatic systems. This paper presents a web based archive for limnological and meteorological data collected by integrated system for environmental monitoring (SIMA). The environmental parameters that are measured by SIMA are: chlorophyll-a (µg/L), water surface temperature (ºC), water column temperature by a thermistor string (ºC), turbidity (NTU), pH, dissolved oxygen concentration (mg/L), electric conductivity (µS/cm), wind speed (m/s) and direction (º), relative humidity (%), short wave radiation (W/m**2), barometric pressure (hPa). The data are collected in preprogrammed time interval (1 hour) and are transmitted by satellite in quasi-real time for any user in a range of 2500 km from the acquisition point. So far 11 hydroelectric reservoirs being monitored using the SIMA buoy. A basic statistics (mean and standard deviation) for some parameters and an example of time series were displayed. The main observed problem are divided into sensors and satellite. The sensors problems is due to the environmental characteristics of each water body. In acid waters the sensors of water quality rapidly degrade, and the collected data are invalid. Another problem is the infestation of periphyton in the sensor. SIMA buoy makes the parameters readings every hour, or 24 readings per day. However, not always received all readings because the system requires satellites passing over the buoy antenna to complete the transfer and due to the satellite constellation position, some locations inland are not met as often as necessary to complete all transmissions. This is the more often causes for lack in the time series.

Surface Ocean CO2 Atlas (SOCAT) V3

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.5 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.4 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This living data publication documents changes in the methods and data sets used in this new version of the SOCAT data collection compared with previous publications of this data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014).