Description: Understanding the responses of tundra systems to global change has global implications. Most tundra regions lack sustained environmental monitoring and one of the only ways to document multi-decadal change is to resample historic research sites. The International Polar Year (IPY) provided a unique opportunity for such research through the Back to the Future (BTF) project (IPY project #512). This article synthesizes the results from 13 papers within this Ambio Special Issue. Abiotic changes include glacial recession in the Altai Mountains, Russia; increased snow depth and hardness, permafrost warming, and increased growing season length in sub-arctic Sweden; drying of ponds in Greenland; increased nutrient availability in Alaskan tundra ponds, and warming at most locations studied. Biotic changes ranged from relatively minor plant community change at two sites in Greenland to moderate change in the Yukon, and to dramatic increases in shrub and tree density on Herschel Island, and in sub-arctic Sweden. The population of geese tripled at one site in northeast Greenland where biomass in non-grazed plots doubled. A model parameterized using results from a BTF study forecasts substantial declines in all snowbeds and increases in shrub tundra on Niwot Ridge, Colorado over the next century. In general, results support and provide improved capacities for validating experimental manipulation, remote sensing, and modeling studies

Description: The dataset contains model output presented in the manuscript 'A long-term dataset of climatic mass balance, snow conditions and runoff in Svalbard (1957-2018)', which is considered for publication in The Cryosphere. The data are structured in 3-D arrays containing spatially distributed and annual mean values of the variables specified below. The spatial resolution is 1x1-km. Variables included in the dataset: ----------------------------- - Climatic mass balance - Air temperature - Precipitation - Runoff - Refreezing - Pore space (down to 14 m) - Subsurface temperature (at 14 m depth) - Snow disappearance date - Snow onset date

Description: The dataset contains subsurface temperature measurements done at Lomonosovfonna, Svalbard, during April 2012 - 2016. All measurements are done at the site with coordinates: 78.8235 N, 17.432 E. The data is contained in four cells of a matlab structure containing data from installations deployed in April 2012 - cell 1, April 2013 - cell 2, April 2014 - cell 3 and April 2015 - cell 4. In 2012-2014 nine thermistor strings were installed in each year. The nine T-strings were arranged in a 3*3 square grid with a 3 m spacing between neighboring strings. In 2015 one t-string was installed. Hardware: Campbell Scientific CR10X data loggers in combination with several relay multiplexers (AM416 of AM16/32B) were used for temperature measurements. For that a reference temperature stable resistor (Rr Ohm) was connected is series with thermistors. Known excitation voltage (Ue) was supplied to the circuit and the voltage was measured (Um) at the leads of the reference resistor. The resistance of a thermistor (Rt) was then calculated as: Rt = Ue * Rr / Um - Rr. The resistance was then converted to temperature values provided by the manufacturer of thermistors. Technical information is contained in the variables: LF{N}.T.system. The raw temperature measurements along with the time stamps and depths are contained in the variables LF{N}.T.system.T_raw, LF{N}.T.system.t_raw and LF{N}.T.system.z_raw. After unpacking the data was subjected to the following post-processing steps: - delete data from sensors that were left above the snow surface - for the sensors installed in April 2013: delete data after 2013 July 12 - reset temperature values outsides of the range [-40 +10] degC to NaN - for the sensors installed in April 2015: correct values from one of the sensors by linear interpolation in time between the following time points: 2015 November 15 02:00 and 2015 November 15 14:00, 2015 December 18 15:00 and 2015 December 19 21:00 - introduce corrections to depths of sensors to match temperature distributions measured at different T-strings during the periods dominated by conductive heat exchange in the firn pack corrections are contained in the variable LF{N}.T.system.z_off and are given in meters. - delete data from sensors that are deemed erroneous. For the sensors installed in April 2012 that is: sensor 1 in T-string 9. For the sensors installed in April 2013 that is: sensors 1 and 2 in T-string 2, sensors 1-6 in T-string 3, sensors 1-6 in T-string 4, sensors 1-5 in T-string 5, sensors 1-7 in T-string 6. For the sensors installed in April 2014 that is: sensors 1 in T-string 1, sensor 1 in T-string 7, sensor 1 in T-string 9. - apply offsets for individual sensors defined as the mode during the time period, when the temperature is expected to be at 0 degC. For the sensors installed in April 2012 and 2015 that is the entire measurement period. For the sensors installed in April 2014 the periods are defined based on subjective data analysis and are different for individual sensors. For the sensors installed in April 2013 and some sensors installed in April 2014 the offsets are set to 0 degC. The applied temperature offsets are contained in the variables: LF{N}.T.system.off. The relation between the number of temperature values equal to the offset and the total number of values during the calibration time is saved in the variable LF{N}.T.system.f. After the above described post-processing steps the data was saved in the variable LF{N}.T.T (temperature values), LF{N}.T.z (depths of sensors) and LF{N}.T.t (time stamps). Data interpolated on a regular grid is contained in the variables: LF{N}.T.T_i (temperature values) and LF{N}.T.z_i (depth vectors). Data laterally averaged across all T-strings is contained in the variables: LF{N}.T.T_a (temperature values) and LF{N}.T.z_a (depth vectors). The standard deviation in interpolated temperature values belonging to the same depth but coming from different T-strings are contained in the variables LF{N}.T.T_sd.