Temperature in Germany increased to 10.88 celsius in 2023 from 10.78 celsius in 2022. This dataset includes a chart with historical data for Germany Average Temperature.

Abstract: Gridded climate time series for Germany derived through downscaling of EURO-CORDEX historical simulations and climate projections from following ensemble members (www.euro-cordex.net):: MPI-M-MPI-ESM-LR(r1)_CLMcom-CCLM4-8-17: RCPs 8.5, 4.5, 2.6 and historical (MPI_CLM) ICHEC-EC-EARTH(r12)_KNMI-RACMO22E(v1): RCP 8.5 and historical (ECE_RAC) CCCmaCanESM2_r1i1p1_CLMcomCCLM4817_v1: RCP 8.5 and historical (CA2_CLM) All time series were consistently calculated at daily resolution and a grid cell spacing of 250 × 250 meter. Historical 1950–2005 data sets and 2006–2100 RCP projections comprise of mean temperature, minimum temperature, maximum temperature, precipitation, global radiation, air pressure, wind speed, specific humidity and delineated variables (relative humidity, potential evapotranspiration, water vapor pressure). All data sets except specific humidity and surface air pressure are available twice, as downscaled but non-bias corrected EURO-CORDEX data, and as bias corrected data sets. Correction terms for empirical adjustment of downscaling results were computed according to Sachindra et al. (2014) using gridded WP-KS-KW data as observational reference (Dietrich et al. 2019). Dietrich, H., Wolf, T., Kawohl, T., Wehberg, J., Kändler, G., Mette, T., Röder, A. & Böhner, J. (2019): Temporal and spatial high-resolution climate data from 1961-2100 for the German National Forest Inventory (NFI). – Annals of Forest Science 76: 6, https://doi.org/10.1007/s13595-018-0788-5. Sachindra, D.A., Huang, F., Bartona, A. & Pereraa, B.J.C. (2014): Statistical downscaling of general circulation model outputs to precipitation – part 2: bias-correction and future projections. – Int. J. Climatol. 34: 3282–3303, https://doi.org/10.1002/joc.3915. TableOfContents: daily mean 2m-air temperature (tav); daily minimum 2m-air temperature (tmn), daily maximum 2m-air temperature (tmx); daily sum of precipitation (prz); daily sum of global radiation (sgz); daily surface air pressure (psz); daily mean 10m wind speed (wsp); daily mean specific humidity (hus); daily mean relative humidity (rhm); potential evapotranspiration (pet); daily mean water vapor pressure (vap) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate_Historlcal: 1950-01-01 00:00:00; temporalExtent_endDate_Historical: 2005-12-31 23:59:59; temporalDuration_Historical: 56; temporalDurationUnit_Historical: a; temporalExtent_startDate_RCPs: 2006-01-01 00:00:00; temporalExtent_endDate_RCPs: 2100-12-31 23:59:59; temporalDuration_RCPs: 95; temporalDurationUnit_RCPs: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none

Heating Degree Days data was reported at 5,362.530 Degrees Celsius in 2020. This records a decrease from the previous number of 5,499.780 Degrees Celsius for 2019. Heating Degree Days data is updated yearly, averaging 6,221.490 Degrees Celsius from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 7,395.710 Degrees Celsius in 1996 and a record low of 5,320.830 Degrees Celsius in 2014. Heating Degree Days data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Environmental: Climate Risk. A heating degree day (HDD) is a measurement designed to track energy use. It is the number of degrees that a day's average temperature is below 18°C (65°F). Daily degree days are accumulated to obtain annual values.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

Abstract: Climate time series for Germany derived from observations of the German Meteorological Service (Deutscher Wetterdienst / DWD) provided in daily resolution at a grid width of 250 meters for the period from 1961 to 2020 (current status February 2023). The following variables were processed: Daily total global radiation, separately for a horizontal and an inclined plane; daily total precipitation; daily mean, minimum and maximum 2m-air temperature; daily mean water vapor saturation deficit; daily mean wind speed. The temperature data sets are available in two different versions: V5 including a residual correction and V6 without. TableOfContents: Daily total global radiation at horizontal plane (grhds); daily total global radiation at inclined plane (grids); daily total precipitation (rrds); daily mean water vapor saturation deficit (sddm); daily mean 2m-air temperature (tadm); daily minimum 2m-air temperature (tadn); daily maximum 2m-air temperature; daily mean wind speed (wsdm) TechnicalInfo: dimension: 2578 columns x 3476 rows; temporalExtent_startDate: 1961-01-01 00:00:00; temporalExtent_endDate: 2020-12-31 23:59:59; temporalDuration: 60; temporalDurationUnit: a; temporalResolution: 1; temporalResolutionUnit: d; spatialResolution: 250; spatialResolutionUnit: m; horizontalResolutionXdirection: 250; horizontalResolutionXdirectionUnit: m; horizontalResolutionYdirection: 250; horizontalResolutionYdirectionUnit: m; verticalResolution: none; verticalResolutionUnit: none Methods: Spatialization of gridded climate fields is performed, merging Model Output Statistics (MOS) downscaling with surface parameterization techniques (Böhner and Antonic, 2009; Böhner and Bechtel, 2018) to account for terrain-forced fine-scale topoclimatic variations. For a comprehensive description of the methods, see Wehberg and Böhner (2023). A description of the methods used can be found in:

Quality controlled and gap-filled air temperature and atmospheric humidity dataset from the street-level weather sensor network (WSN) in Freiburg i. Br., Germany for the period 2022-09-01 to 2023-08-31 as described in:

Plein M, Feigel G, Zeeman M, Dormann C, Christen A (2025, in review): Using Extreme Gradient Boosting for gap-filling to enable year-round analysis of spatial temperature and humidity patterns in an urban weather station network in Freiburg, Germany. in review.

Hourly gap-filled values

The file "Freiburg_AWS_20220901_20230831_gap_filled_data_ta_rh_Plein_et_al.csv" contains gap-filled hourly air temperature and relative humidity time series from 41 stations of the street-level weather sensor network (WSN) in Freiburg i. Br., Germany from 1 Sep 2022 to 31 Aug 2023 with the following field descriptors:

• "datetime_UTC" the time stamp of the measured value in the format YYYY-MM-DDTHH:II:SSZ where Y = year, M = month, D = day of month, H = hour, I = minute, S = second in UTC attributing the start of the averaging interval.
• "station_id" - 6 letter code of WSN (FR for Freiburg and last 4 letters for station name, see also https://doi.org/10.5281/zenodo.12732552). The station FRTECH is not included.
• "variable" - the variable ("Ta_degC" for air temperature in ºC or "RH_percent" for relative humidity in %).
• "value" - the numeric value of the measurement.
• "data_type" - either "observed" (i.e. measured) or "imputed" (i.e. gap-filled using the Extreme Gradient Boosting method).

Annual statistics per station

The files "Freiburg_AWS_20220901_20230831_annual_statistics_per_station_Plein_et_al" (in csv and xlsx Format) contain annual summary statistics based on the gap-filled hourly air temperature and relative humidity time series of the street-level weather sensor network (WSN) in Freiburg i. Br., Germany from 1 Sep 2022 to 31 Aug 2023 and from two official DWD stations in Freiburg with the following field descriptors:

• "station_id" - 6 letter code of weather station (FR for Freiburg and last 4 letters for station name, see also https://doi.org/10.5281/zenodo.12732552). The two official DWD stations are also included (No. 01443 on the airfield and No. 13667 in the city centre).
• "station_name" - Full human-readable name of weather station.
• "latitude_degN" - Latitude of site in decimal degrees North.
• "longitude_degE" - Longitude of site in decimal degrees East.
• "elevation_masl" - Elevation of site in metres above mean sea level.
• "mean_ta_degC" - Annual average air temperature in the period 2022-09-01 to 2023-08-31 in ºC.
• "mean_rh_percent" - Annual average relative humidity in the period 2022-09-01 to 2023-08-31 in %.
• "mean_vp_kPa" - Annual average vapour pressure in the period 2022-09-01 to 2023-08-31 in kPa based on Tetens equation.
• "mean_vpd_Pa"- Annual average vapour pressure deficit in the period 2022-09-01 to 2023-08-31 in Pa based on Tetens equation.
• "sum_summer_day_per_year" - Annual number of summer days (maximum air temperature greater or equal to 25ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_hot_day_per_year" - Annual number of hot days (maximum air temperature greater or equal to 30ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_desert_day_per_year" - Annual number of desert days (maximum air temperature greater or equal to 35ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_tropical_night_per_year" - Annual number of tropical nights (minimum nocturnal air temperature greater or equal to 20ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_frost_day_per_year" - Annual number of frost days (minimum air temperature lower than 0ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_ice_day_per_year" - Annual number of ice days (maximum air temperature lower than 0ºC) in the period 2022-09-01 to 2023-08-31 in days per year.
• "sum_hottest_station_ranking_per_year" - Annual number of days this station was the station with the highest recorded air temperature in the period 2022-09-01 to 2023-08-31.
• "sum_coldest_station_ranking_per_year" - Annual number of days this station was the station with the lowest recorded air temperature in the period 2022-09-01 to 2023-08-31.

Station descriptions

Details on the stations can be found in the sensor network documentation:

• Plein M, Kersten F, Zeeman M, Christen A (2024): Street-level weather station network in Freiburg, Germany: Station documentation (1.0) Zenodo. https://doi.org/10.5281/zenodo.12732552

Code availability

The code used for imputation of missing values is documented and available here:

• Plein M, Feigel G, Zeeman M, Dormann C, Christen A (2024): Code Supporting the Publication "Using Extreme Gradient Boosting for Gap-Filling to Enable Year-Round Analysis of Spatial Temperature and Humidity Patterns in an Urban Weather Station Network in Freiburg, Germany." (1.0.0) Zenodo. https://doi.org/10.5281/zenodo.14536824

TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. The central monitoring site of the TERENO Eifel/Lower Rhine Valley Observatory is the catchment area of the River Rur. It covers a total area of 2354 km² and exhibits a distinct land use gradient: The lowland region in the northern part is characterised by urbanisation and intensive agriculture whereas the low mountain range in the southern part is sparsely populated and includes several drinking water reservoirs. Furthermore, the Eifel National Park is situated in the southern part of the Rur catchment serving as a reference site. Intensive test sites are placed along a transect across the Rur catchments in representative land cover, soil, and geologic settings. The Rollesbroich site is located in the low mountain range “Eifel” near the German-Belgium border and covers the area of the small Kieselbach catchment (40 ha) with altitudes ranging from 474 to 518 m.a.s.l.. The climate is temperate maritime with a mean annual air temperature and precipitation of 7.7 °C and 1033 mm, respectively, for the period from 1981 to 2001. The study site is highly instrumented. All components of the water balance (e.g. precipitation, evapotranspiration, runoff, soil water content) are continuously monitored using state-of-the-art instrumentation, including weighable lysimeters, runoff gauges, cosmic-ray soil moisture sensors, a wireless sensor network that monitors soil temperature, and soil moisture at 189 locations in different depths (5, 20 and 50 cm) throughout the study site. Periodically also different chamber measurements were made to access soil or plant gas exchange. Climate/Runoff/Water Quality station Rollesbroich:Runoff is measured at the catchment outlet using a gauging station equipped with a combination of a V-notch weir for low flow measurements and a Parshall flume to measure normal to high flows. Runoff data of the two weir types are combined by using V-notch values for water levels below 5 cm, Parshall flume values for water levels greater than 10 cm and the weighted mean of V-notch and Parshall flume values for water levels between 5 and 10 cm, where the water levels refer to those of the V-notch weir. Meteorological data, i.e. precipitation, air temperature, air humidity, radiation components, and wind speed, were recorded at 2 m height next to the runoff gauging station As a first quality check, time series of both gauge types were compared for consistency. In addition, both runoff time series were visually inspected for inexplicable outliers (e.g. runoff peak without preceding rainfall event) and sensor failures. Unreliable data were identified by visual inspection and appropriate flags were set. The observed parameters are listed in the keywords section. File format is NetCDF 4.0.

subject to appropriate attribution.

Germany CE: OBI: Unfavorable Weather Situation data was reported at 10.600 % in May 2021. This records a decrease from the previous number of 26.700 % for Apr 2021. Germany CE: OBI: Unfavorable Weather Situation data is updated monthly, averaging 10.300 % from Jan 1991 (Median) to May 2021, with 365 observations. The data reached an all-time high of 88.500 % in Feb 1996 and a record low of 0.200 % in May 2020. Germany CE: OBI: Unfavorable Weather Situation data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S024: Business Survey: Construction: IFO Institute: WZ 2008.

The data consists of a series of tables containing individual identifiers; countrywide high-resolution (1 km × 1 km) minimum, mean, and maximum daily air temperature data estimated using hybrid spatiotemporal regression-based models; daily concentrations of relative humidity (RH), ozone (O3), nitrogen dioxide (NO2), particulate matter within Augsburg, Germany; leukocyte telomere length; age (year), sex (male, female), education (years), and body mass index (BMI [kg/m2]); lifestyle characteristics, including smoking status, alcohol consumption (g/day), and physical activity; medical history of the participants, including hypertension (no/yes), angina pectoris (no/yes), myocardial infarction (no/yes), and stroke (no/yes); and current medications, including antihypertensive (no/yes), lipid-lowering (no/yes), and antidiabetic medication (no/yes). The data also contains serum total cholesterol; high-density lipoprotein (HDL); low-density lipoprotein (LDL); and triglycerides. This dataset is not publicly accessible because: The data is owned by an institute other than the EPA (Helmholtz Institute). It can be accessed through the following means: The data can be accessed by contacting the corresponding author of the study Wenli Ni (wenli.ni@helmholtz-muenchen.de). Format: The data consists of a series of tables containing individual identifiers; countrywide high-resolution (1 km × 1 km) minimum, mean, and maximum daily air temperature data estimated using hybrid spatiotemporal regression-based models; daily concentrations of relative humidity (RH), ozone (O3), nitrogen dioxide (NO2), particulate matter within Augsburg, Germany; leukocyte telomere length; age (year), sex (male, female), education (years), and body mass index (BMI [kg/m2]); lifestyle characteristics, including smoking status, alcohol consumption (g/day), and physical activity; medical history of the participants, including hypertension (no/yes), angina pectoris (no/yes), myocardial infarction (no/yes), and stroke (no/yes); and current medications, including antihypertensive (no/yes), lipid-lowering (no/yes), and antidiabetic medication (no/yes). The data also contains serum total cholesterol; high-density lipoprotein (HDL); low-density lipoprotein (LDL); and triglycerides. This dataset is associated with the following publication: Ni, W., K. Wolf, S. Breitner, S. Zhang, N. Nikolaou, C. Ward-Caviness, M. Waldenberger, C. Gieger, A. Peters, and A. Schneider. Higher daily air temperature is associated with shorter leukocyte telomere length: KORA F3 and KORA F4. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 56(24): 17815-17824, (2022).

Scientific Personnel V. E. Romanovsky, S. S. Marchenko, R.R. Muskett Partner Organizations: Alaska Ecoscience, USA Alfred Wegener Institute, Germany Centre d'etudes Nordiques, Department de Geographie, Universite Laval, Quebec, Canada Danish Meteorological Institute, Denmark Institute of Earth Cryosphere, Russia Institute of Northern Engineering, UAF Interdisciplinary Centre on Climate Change and Department of Geography & Environmental Management, University of Waterloo, Canada International Arctic Research Center, UAF International Permafrost Association, USA Melinkov Permafrost Institute, Russia Moscow Institute of Geography, Russia Academy of Sciences National Center for Atmospheric Research, USA NASA Goddard Space Flight Center, USA Scenarios Network for Alaska Planning (SNAP), UAF Stokholm University, Sweden University of Delaware, USA University of New Hampshire, USA Water Environment Research Center, UAF Local Collaborators: Jorgenson, M.T., Alaska Ecoscience, AK Kholodov, A.L., Geophysical Institute, UAF Daanen, R., Institute of Northern Engineering, UAF Kanevskiy M., Institute of Northern Engineering, UAF Shur, Y., Institute of Northern Engineering, UAF Walsh, J., International Arctic Research Center, UAF Fresco, N., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF Rupp, S., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF Walter-Anthony, K., Water Environmental Research Center, UAF International Collaborators: Christensen, J., Danish Meteorological Institute, Denmark Comiso, J., NASA Goddard Space Flight Center, Oceans and Ice Branch, USA Duguay, C. R., University of Waterloo, Canada Frolking, S., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA Georgiadi, A., Moscow Institute of Geography, Russian Academy of Sciences Groisman, P., National Climatic Data Center, USA Hachem, S., Université Laval, Québec, Canada Hubberten, H.-W., Alfred Wegener Institute, Potsdam, Germany Harden Jennifer, US Geological Survey, Menlo Park, CA, USA Kattsov, V., Voeikov Main Geophysical Observatory, Russia Kuhry, P., Stockholm University, Sweden Lawrence, D., National Center for Atmospheric Research, USA Malkova, G., Institute of Earth Cryosphere, Russia Pavlova, T., Voeikov Main Geophysical Observatory, Russia Rawlins, M., University of New Hampshire, USA Rinke, A., Alfred Wegener Institute, Potsdam, Germany Romanovskii, N., Moscow State University, Russia Saito, K., Japan Agency for Marine-Earth Science Technology, Japan Shiklomanov, N., University of Delaware, USA Shiklomanov, A., University of New Hampshire, USA Shkolnik, I.M., Voeikov Main Geophysical Observatory, Russia Schirrmeister L, Alfred Wegener Institute, Potsdam, Germany Schuur A.G. Edward, University of Florida, Gainesville, FL, USA Stendel, M., Danish Meteorological Institute, Denmark Wisser, D., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA Zheleznyak, M., Melnikov Permafrost Institute, Russia Funding: NSF Grants OPP ARC-0652838 [ARC-0520578 and ARC-0632400] NASA (NNOG6M48G), Alaska EPSCoR (NSF) The State of Alaska Study Sites Permafrost Freshwater Interactions Alaska, Canada, Russia Permafrost Observatories?Thermal state of permafrost in Russia and Central Asia Permafrost Freshwater Interactions Project continues investigations began during the Thermal State of Permafrost (TSP) Project with renewed and expanded collaboration. Our efforts focus and expand on permafrost and hydrology changes through geophysical modeling and remote sensing (satellite geodesy). During TSP in cooperation with above mentioned Russian partners a large number of existing boreholes have been identified for possible measurements (candidate sites). Many of these have metadata files on the IPA coordinated GTN-P website. Additional sites will be added to the web site. New boreholes over the next several years are planned. A total of 320 boreholes, located in Russia, Kazakhstan, and Mongolia were considered from the point of view of possibility for continuous geothermal observations (see Figure). Boreholes cover all types of permafrost, from continuous to sporadic, both on the plains and in the mountains. Active (sites where regular observations were carried out recently and are intended to continue in the future), candidate (where equipment for long-term observations can be installed soon), potential (equipment for long-term observation is planned to be installed during the project) and historical (there are some existing data but now these sites are unavailable for observations for different reasons) boreholes were selected. In order to standardize all investigations within the framework of the Project the “Manual for monitoring and reporting temperature data in permafrost boreholes” was developed. It allows better standardized collection, handling and interpretation of obtained data. In the Protocol two types of observation strategies are proposed: Type 1: Long-term high-frequency (hourly to daily) continuous observations in the limited number of key boreholes, which are representative of a given regions (note: these more frequent observations are desirable to depths of 15-20 meters); Type 2: Occasional or periodical measurements in the other available and deeper boreholes (if possible annual or more frequently). As a minimum, and based primarily on cost considerations for the IPY-TSP program, the use of HOBO U12 4-External Channel Data Loggerswith temperature sensors TMC-HD are proposed. At the same time, individual participants can employ other types of loggers and/or thermal cables (chains) with similar sensor characteristics. Research Goals The goal of our research is to obtain a deeper understanding of the temporal (interannual and decadal time scales) and spatial (north to south and west to east) variability and trends in the permafrost temperatures and physical changes (such as talik and the active layer) in the North of Eurasia and Alaska to develop more reliable predictive capabilities for the projection of these changes into the 21st century. We are employing ground datasets from the global permafrost temperature networks, global positioning system sites of the International Terrestrial Reference Frame organization, together with satellite-derived datasets of physical parameters such as land-surface temperature, gravity field changes, river runoff and snow water equivalent to name a few. Our modeling efforts employ the Geophysical Institute Permafrost Models (GIPL) and Geophysical Inverse Potential Field Theory.

The first detailed reconstruction of the continental palaeoclimate evolution of the Northwest German Tertiary (Late Oligocene to Pliocene) is presented. The paleoclimate data are derived from the paleobotanical record using the coexistence approach, a method recently introduced that employs climatic requirements of the Nearest Living Relatives of a fossil flora. Twenty six megafloras (fruits and seeds, leaves, woods) from the Tertiary succession of the Lower Rhine Basin and neighboring areas are analyzed with respect to ten meteorological parameters. Additionally, two sample sets from Late Miocene to Early Pliocene sediments comprising 396 palynofloras are analyzed by the same method providing a higher temporal resolution. The temperature curves show a comparatively cooler phase in the Late Oligocene, a warm interval the Middle Miocene, and a cooling starting at 14 Ma. The cooling trend persisted until Late Pliocene with a few higher frequency temperature variations observed. From the beginning of Late Miocene to the present, the seasonality increases and climate appears to have been less stable. As indicated by the precipitation data, a Cfa climate with wet summers persisted in NW Germany from Late Oligocene to Late Pliocene. The results obtained are well in accordance with regional and global isotope curves derived from the marine record, and allow for a refined correlation of the Tertiary succession in the Lower Rhine Basin with the international standard. It is shown that the reconstructed data are largely consistent with the continental climate record for the Northern Hemisphere, as reported by various authors. Discrepancies with previous reconstructions are discussed in detail.

Ocean Station data containing Water depth and other data collected from ANTON DOHRN, ALKOR, FRIDTJOF NANSEN, GAUSS, FRIEDRICH HEINCKE, METEOR, POSEIDON, SOLEA, WALTHER HERWIG and Unknown platforms between February 26, 1972 and February 19, 1980. Data were collected by German Federal Research Center for Fisheries, Deutsches Ozeanographisches Datenzentrum and National Oceanographic Data Center. Data was submitted by the German Oceanographic Data Center. Data processed by NODC is available in C100 Ocean Station Data, C116 Bathythermograph XBT and C128 Bathythermograph MBT format. The Oceanographic Station Data (C100) format contains physical-chemical oceanographic data recorded at discrete depth levels. Most of the observations were made using multi-bottle Nansen casts or other types of water samplers. A small amount (about 5 percent) were obtained using electronic CTD (conductivity-temperature-depth) or STD (salinity-temperature-depth) recorders. The CTD/STD data were reported to NODC at depth levels equivalent to Nansen cast data, however, and have been processed and stored the same as the Nansen data. Cruise information (e.g., ship, country, institution), position, date, and time, and reported for each station. The principal measured parameters and temperature and salinity, but dissolved oxygen, phosphate, total phosphorus, silicate, nitrate, nitrite, and pH may be reported. Meteorological conditions at the time of the cast (e.g., air temperature and pressure, wind, waves) may also be reported, as well as auxiliary data such as water color (Forel-Ule scale), water transparency (Secchi disk depth), and depth to bottom. Values of density (sigma-t) sound velocity, and dynamic depth anomaly are computed from measured parameters. Each station contains the measurements taken at the observed depth levels, but also includes data values interpolated to a set of standard depth levels. The C116/C118 format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data record was comprised of pairs of temperature-depth values. Unlike the MBT Data File, in which temperature values were recorded at uniform 5 m intervals, the XBT data files contained temperature values at non-uniform depths. These depths were recorded at the minimum number of points ("inflection points") required to accurately define the temperature curve. Standard XBTs can obtain profiles to depths of either 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Prior to July 1994, XBT data were routinely processed to one of these standard types. XBT data are now processed and loaded directly in to the NODC Ocean Profile Data Base (OPDB). Historic data from these two data types were loaded into the OPDB. The C128 format is used for temperature-depth profile data obtained using the mechanical bathythermograph (MBT) instrument. The maximum depth of MBT observations is approximately 285 m. Therefore, MBT data are useful only in studying the thermal structure of the upper layers of the ocean. Cruise information, date, position, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Temperature data in this file are recorded at uniform 5 m depth intervals.

The Ueckeritz climate station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The agrometeorological station Ueckeritz was installed in 2013. It is located on the eastern border of a natural sink, with some bushes on the western slope of the sink. The station is equipped with sensor for measuring the following variables: AdconTR1_Temperature, AdconTR1_RelativeHumidity, AdconRainGauge_Precipitation, AdconWindspeed_Windspeed, AdconWinddirection_Winddirection, AdconBP1_BarometricPressure, KuZCMP3_PyranometerIncoming, KuZCMP3_PyranometerOutgoing, KuZCGR3_PyrgeometerIncoming, KuZCGR3_PyrgeometerOutgoing, UMSTH3_Soiltemperature005cm, UMSTH3_Soiltemperature010cm, UMSTH3_Soiltemperature020cm, UMSTH3_Soiltemperature030cm, UMSTH3_Soiltemperature050cm, UMSTH3_Soiltemperature100cm, AdconSM1_Soiltemperature015cm, AdconSM1_Soiltemperature045cm, AdconSM1_Soilmoisture010cm, AdconSM1_Soilmoisture020cm, AdconSM1_Soilmoisture030cm, AdconSM1_Soilmoisture040cm, AdconSM1_Soilmoisture050cm, AdconSM1_Soilmoisture060cm, AdconWET_LeafWetness and KuZCGR3_PyrgeometerIncoming The current version of this dataset is 2.1. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. A detailed overview on all changes is provided in the station description file. The version 1.0 is available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.277 containing the measured data and Version 2.0 contains additionally the quality flags for each measured value and extended metadata. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. Data processing was done using DMRP version: 0.5.12. Metadataprocessing was done using DMETA version: 0.3.17. The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (19812010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich FZJ, Helmholtz Centre for Environmental Research UFZ, Karlsruhe Institute of Technology KIT, Helmholtz Zentrum München - German Center for Environmental Health HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

Germany WS: Unfavourable Weather Situation data was reported at 4.700 % in Dec 2024. This records a decrease from the previous number of 11.200 % for Sep 2024. Germany WS: Unfavourable Weather Situation data is updated quarterly, averaging 5.900 % from Jun 2006 (Median) to Dec 2024, with 75 observations. The data reached an all-time high of 34.600 % in Jun 2013 and a record low of 1.300 % in Dec 2022. Germany WS: Unfavourable Weather Situation data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S045: Quarterly Business Survey: Wholesale: IFO Institute: WZ 2008.

Stress-hardening by environmental priming could increase the odds for corals to resist ocean warming. Natural environmental fluctuations, such as those observed on offshore reefs in the Andaman Sea, provide an ideal natural environment to study these effects. Here, internal waves (IW) generate short cold-water pulses that peak from January to June and are absent from August to November. Additionally, only western shores of islands are exposed to this stress-hardening stimulus of IWs, while eastern shores remain sheltered. Therefore, this study examined (1) whether exposed corals were more heat stress resistant than their sheltered conspecifics and (2) whether this trait would persist during the season of stimulus absence. We exemplify that thermal regimes featuring cold-temperature pulses successfully induced thermal stress-hardening in corals. Corals from the IW-sheltered shore responded strongly to heat stress irrespective of the season, while stress responses of IW-exposed corals were either undetectable (during stimulus presence) or very weak (during stimulus absence). However, this demonstrates the relevance of stimulus re-occurrence in maintaining heat resistance. Furthermore, priming stimuli do not need to exceed certain upper thermal thresholds to be effective and we argue that cooling pulses represent a safer stress-hardening regimen potentially implemented in conservation strategies since it avoids warming-stress accumulation. Methods Study sites and coral collection Study sites were located at Racha Island in the Andaman Sea off the coast of Thailand, both at 15 m water depth (Figure 1 A-B). A reef on the western shore was chosen (7.595530°N, 98.354320°E, Figure 1 B) where internal wave (IW) forcing as a potential stress-hardening stimulus induced environmental variability through frequent upwelling of deep, cool, and nutrient rich water onto the shelf (Schmidt et al., 2016; Wall et al., 2012). A reef on the eastern shore, sheltered from the stimulus of IWs, was chosen to represent a low variability reef (7.598910°N, 98.373100°E, Figure 1 B). Temperature fluctuations were monitored in situ as a proxy for IW impact and environmental variability. Temperature loggers (HOBO Pendant Temperature/Light 8K Data Logger, Onset, USA) were deployed at the study sites one month before heat stress assays were performed. At each study site, visually healthy coral colonies of Pocillopora sp. and Porites sp. were permanently tagged to assess their thermal resistance levels during the two seasons (n = 8 to n = 18, Figure 1 C, Table S1). These two coral species are cosmopolitan reef-builders in Thailand and within the entire Indo-Pacific region (Brown & Phongsuwan, 2012; Jain et al., 2023; Schmidt et al., 2012). Coral fragments were collected at the end of April 2018, during the season of highest IW intensity, and at the end of October (Porites sp.) and November (Pocillopora sp.), during the seasonal absence of the IW stimulus. Two fragments (Porites sp.: ø ~ 6 cm; Pocillopora sp.: length ~ 5 cm) per colony were collected using a chisel and a hammer (Table S1). Short-term heat stress assays Collected fragments were instantly transported to the Phuket Marine Biological Center (Phuket, Thailand) where they were maintained in two 500 L flow-through tanks with a flow rate of 2.8 ± 1.31 L/min until the start of each heat stress assay. Another 500 L source tank constantly supplied both flow-through tanks with 5 μm-filtered seawater from the reef adjacent to the research center. Its temperature was held at constant 29.43 ± 0.32 °C using a temperature-controlling device including a chiller and a heater (Titanium Heater 100 W, Schego, Germany; Temperature Switch TS 125, HTRONIC, Germany; Aqua Medic Titan 1500 Chiller, Germany). LED lights (135 W, Hydra Fiftytwo HD LED, Aqua Illumination, USA) mimicked the average light conditions of the sampling sites (Text S1). For each heat stress assay (Figure 1 D), two 40 L experimental tanks were set up inside each of the 500 L flow-through tanks that were used as temperature-controlling water baths (Table S2). The seawater of all four experimental tanks was supplied by daily, manual 50% water changes from the source tank. Each experimental tank was equipped with a temperature-controlling device, one heater, air supply, a small current pump and a temperature logger (Temperature Switch TS 125, HTRONIC, Germany; Titanium Heater 100 W, Schego, Germany; Koralia nano 900 L/h, Hydor, Italy; HOBO Pendant Temperature/Light 8K Data Logger, Onset, USA). Two coral fragments per coral colony were randomly distributed among the four tanks “34 °C” (n = 2) and “29 °C” (n = 2), resulting in one fragment per colony per treatment. The 34 °C-treatment was established over the course of one day by ramping temperatures from 29 °C to 34 °C for 4 h, holding at 34 °C for 5 h or 6 h (Pocillopora sp.) or for 6 h or 7 h (Porites sp.), and decreasing temperatures to 29 °C within 4 h. After the heat exposure, corals were maintained at ambient temperatures of 29 °C for 10 h until the next day. While Pocillopora sp. fragments were subjected to the short-term heat exposure once, resulting in a 24 h experiment, Porites sp. corals were exposed to the treatment over two consecutive days resulting in a duration of 72 h (Figure 1 D). Coral stress response variables We measured two variables that assessed the thermal stress response of each fragment before and after each heat stress assay (timepoints (1) and (2) in Figure 1 D). Tissue coloration, a proxy for microalgal symbiont cell density in coral tissues and therefore an indicator of holobiont health and coral bleaching severity, was assessed using a “bleaching score”. The coloration of each individual fragment was visually categorized on the scale from 1 (bleached, pale tissues) to 6 (healthy, dark tissues) using a coral bleaching chart (Siebeck et al., 2006). A minimum and maximum score was recorded per fragment and averaged. Photosynthetic efficiency of microalgal symbionts was determined by measuring effective quantum efficiency (yield Φ PSII = (Fm’ – F) / Fm’ = ΔF / Fm’, (Genty et al., 1989) of electron transport using a pulse amplitude-modulated fluorometer (Diving-PAM, Walz, Germany). Statistical analyses ∆-values of each measured thermal stress response variable (end – start of each experimental part) were calculated to reflect their change over time. Based on these ∆-values, effect sizes were estimated using dabestR v0.2.3 6 (Ho et al., 2019). Effects of the high temperature treatment (“Heat” vs. “Ambient”) were compared between the sites of origin (“IW exposed shore” and “IW sheltered shore”) and between the seasons (“Season of IW stimulus presence” and “Season of IW stimulus absence”). Statistical significance was tested in R (R Core Team, 2013) using linear mixed effect models (nlme v4 3.1-148 and lme4 v1.1-23 package). Where applicable, coral colony genotype was used as a random factor. References Brown, B., & Phongsuwan, N. (2012). Delayed mortality in bleached massive corals on intertidal reef flats around Phuket, Andaman Sea, Thailand. Phuket Marine Biological Center Research Bulletin, 48(April 2010), 43–48. Wall, M., Schmidt, G. M., Janjang, P., Khokiattiwong, S., & Richter, C. (2012). Differential Impact of Monsoon and Large Amplitude Internal Waves on Coral Reef Development in the Andaman Sea. PloS One, 7(11), e50207. https://doi.org/10.1371/journal.pone.0050207 Schmidt, G. M., Wall, M., Taylor, M., Jantzen, C., & Richter, C. (2016). Large-amplitude internal waves sustain coral health during thermal stress. Coral Reefs , 1–13. https://doi.org/10.1007/s00338-016-1450-z Siebeck, U. E., Marshall, N. J., Klüter, A., & Hoegh-Guldberg, O. (2006). Monitoring coral bleaching using a colour reference card. Coral Reefs , 25(3), 453–460. https://doi.org/10.1007/s00338-006-0123-8 Ho, J., Tumkaya, T., Aryal, S., Choi, H., & Claridge-Chang, A. (2019). Moving beyond P values: data analysis with estimation graphics. Nature Methods, 16(7), 565–566. https://doi.org/10.1038/s41592-019-0470-3 Genty, B., Briantais, J.-M., & Baker, N. R. (1989). The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochimica et Biophysica Acta (BBA) - General Subjects, 990(1), 87–92. https://doi.org/10.1016/S0304-4165(89)80016-9 Jain, T., Buapet, P., Ying, L., & Yucharoen, M. (2023). Differing Responses of Three Scleractinian Corals from Phuket Coast in the Andaman Sea to Experimental Warming and Hypoxia. Journal of Marine Science and Engineering, 11(2), 403. https://doi.org/10.3390/jmse11020403

We present a detailed palaeoclimate analysis of the Middle Miocene (uppermost Badenian-lowermost Sarmatian) Schrotzburg locality in S Germany, based on the fossil macro- and micro-flora, using four different methods for the estimation of palaeoclimate parameters: the coexistence approach (CA), leaf margin analysis (LMA), the Climate-Leaf Analysis Multivariate Program (CLAMP), as well as a recently developed multivariate leaf physiognomic approach based on an European calibration dataset (ELPA). Considering results of all methods used, the following palaeoclimate estimates seem to be most likely: mean annual temperature ~15-16°C (MAT), coldest month mean temperature ~7°C (CMMT), warmest month mean temperature between 25 and 26°C, and mean annual precipiation ~1,300 mm, although CMMT values may have been colder as indicated by the disappearance of the crocodile Diplocynodon and the temperature thresholds derived from modern alligators. For most palaeoclimatic parameters, estimates derived by CLAMP significantly differ from those derived by most other methods. With respect to the consistency of the results obtained by CA, LMA and ELPA, it is suggested that for the Schrotzburg locality CLAMP is probably less reliable than most other methods. A possible explanation may be attributed to the correlation between leaf physiognomy and climate as represented by the CLAMP calibration data set which is largely based on extant floras from N America and E Asia and which may be not suitable for application to the European Neogene. All physiognomic methods used here were affected by taphonomic biasses. Especially the number of taxa had a great influence on the reliability of the palaeoclimate estimates. Both multivariate leaf physiognomic approaches are less influenced by such biasses than the univariate LMA. In combination with previously published results from the European and Asian Neogene, our data suggest that during the Neogene in Eurasia CLAMP may produce temperature estimates, which are systematically too cold as compared to other evidence. This pattern, however, has to be further investigated using additional palaeofloras.

The Wietzow BF1 soil moisture station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The soil moisture station station Wietzow BF1 was installed in 2012. It is located next to a pylon on a field. The station is equipped with sensor for measuring the following variables: ScemeSpadeSoilMoisture_Spade_1, ScemeSpadeSoilMoisture_Spade_2, ScemeSpadeSoilMoisture_Spade_3, ScemeSpadeSoilMoisture_Spade_4, ScemeSpadeSoilMoisture_Spade_5, ScemeSpadeSoilMoisture_Spade_6 and ScemeSpadeSoilMoisture_Spade_3_Temperature The current version of this dataset is 1.1. This version includes two additional years of data (from-year to-year)and a revised version of the data flags. A detailed overview on all changes is provided in the station description file. The version 1.0 is available in the 'previous_versions' subfolder via the Data Download link. A first version of this data was provided under http://doi.org/10.5880/TERENO.GFZ.2018.078 containing the measured data and Version 2.0 contains additionally the quality flags for each measured value and extended metadata. The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process. Data processing was done using DMRP version: 0.5.12. Metadataprocessing was done using DMETA version: 0.3.17. The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (19812010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich FZJ, Helmholtz Centre for Environmental Research UFZ, Karlsruhe Institute of Technology KIT, Helmholtz Zentrum München - German Center for Environmental Health HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.

Scientific Personnel V. E. Romanovsky, S. S. Marchenko, R.R. Muskett

Partner Organizations:

Alaska Ecoscience, USA

Alfred Wegener Institute, Germany

Centre d'etudes Nordiques, Department de Geographie, Universite Laval, Quebec, Canada

Danish Meteorological Institute, Denmark

Institute of Earth Cryosphere, Russia

Institute of Northern Engineering, UAF

Interdisciplinary Centre on Climate Change and Department of Geography & Environmental Management, University of Waterloo, Canada

International Arctic Research Center, UAF

International Permafrost Association, USA

Melinkov Permafrost Institute, Russia

Moscow Institute of Geography, Russia Academy of Sciences

National Center for Atmospheric Research, USA

NASA Goddard Space Flight Center, USA

Scenarios Network for Alaska Planning (SNAP), UAF

Stokholm University, Sweden

University of Delaware, USA

University of New Hampshire, USA

Water Environment Research Center, UAF

Local Collaborators: Jorgenson, M.T., Alaska Ecoscience, AK

Kholodov, A.L., Geophysical Institute, UAF

Daanen, R., Institute of Northern Engineering, UAF

Kanevskiy M., Institute of Northern Engineering, UAF

Shur, Y., Institute of Northern Engineering, UAF

Walsh, J., International Arctic Research Center, UAF

Fresco, N., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF

Rupp, S., Scenarios Network for Alaska Planning, School of Natural Resources & Agricultural Sciences, UAF

Walter-Anthony, K., Water Environmental Research Center, UAF

International Collaborators: Christensen, J., Danish Meteorological Institute, Denmark

Comiso, J., NASA Goddard Space Flight Center, Oceans and Ice Branch, USA

Duguay, C. R., University of Waterloo, Canada

Frolking, S., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA

Georgiadi, A., Moscow Institute of Geography, Russian Academy of Sciences

Groisman, P., National Climatic Data Center, USA

Hachem, S., Université Laval, Québec, Canada

Hubberten, H.-W., Alfred Wegener Institute, Potsdam, Germany

Harden Jennifer, US Geological Survey, Menlo Park, CA, USA

Kattsov, V., Voeikov Main Geophysical Observatory, Russia

Kuhry, P., Stockholm University, Sweden

Lawrence, D., National Center for Atmospheric Research, USA

Malkova, G., Institute of Earth Cryosphere, Russia

Pavlova, T., Voeikov Main Geophysical Observatory, Russia

Rawlins, M., University of New Hampshire, USA

Rinke, A., Alfred Wegener Institute, Potsdam, Germany

Romanovskii, N., Moscow State University, Russia

Saito, K., Japan Agency for Marine-Earth Science Technology, Japan

Shiklomanov, N., University of Delaware, USA

Shiklomanov, A., University of New Hampshire, USA

Shkolnik, I.M., Voeikov Main Geophysical Observatory, Russia

Schirrmeister L, Alfred Wegener Institute, Potsdam, Germany

Schuur A.G. Edward, University of Florida, Gainesville, FL, USA

Stendel, M., Danish Meteorological Institute, Denmark

Wisser, D., Institute for the Study of Earth, Oceans and Space, University of New Hampshire, USA

Zheleznyak, M., Melnikov Permafrost Institute, Russia

Funding: NSF Grants OPP ARC-0652838 [ARC-0520578 and ARC-0632400]

NASA (NNOG6M48G), Alaska EPSCoR (NSF)

The State of Alaska

Study Sites Permafrost Freshwater Interactions

Alaska, Canada, Russia

Permafrost Observatories?Thermal state of permafrost in Russia and Central Asia

Permafrost Freshwater Interactions Project continues investigations began during the Thermal State of Permafrost (TSP) Project with renewed and expanded collaboration. Our efforts focus and expand on permafrost and hydrology changes through geophysical modeling and remote sensing (satellite geodesy). During TSP in cooperation with above mentioned Russian partners a large number of existing boreholes have been identified for possible measurements (candidate sites). Many of these have metadata files on the IPA coordinated GTN-P website. Additional sites will be added to the web site. New boreholes over the next several years are planned. A total of 320 boreholes, located in Russia, Kazakhstan, and Mongolia were considered from the point of view of possibility for continuous geothermal observations (see Figure). Boreholes cover all types of permafrost, from continuous to sporadic, both on the plains and in the mountains. Active (sites where regular observations were carried out recently and are intended to continue in the future), candidate (where equipment for long-term observations can be installed soon), potential (equipment for long-term observation is planned to be installed during the project) and historical (there are some existing data but now these sites are unavailable for observations for different reasons) boreholes were selected. In order to standardize all investigations within the framework of the Project the “Manual for monitoring and reporting temperature data in permafrost boreholes” was developed. It allows better standardized collection, handling and interpretation of obtained data. In the Protocol two types of observation strategies are proposed: Type 1: Long-term high-frequency (hourly to daily) continuous observations in the limited number of key boreholes, which are representative of a given regions (note: these more frequent observations are desirable to depths of 15-20 meters); Type 2: Occasional or periodical measurements in the other available and deeper boreholes (if possible annual or more frequently). As a minimum, and based primarily on cost considerations for the IPY-TSP program, the use of HOBO U12 4-External Channel Data Loggerswith temperature sensors TMC-HD are proposed. At the same time, individual participants can employ other types of loggers and/or thermal cables (chains) with similar sensor characteristics.

Research Goals The goal of our research is to obtain a deeper understanding of the temporal (interannual and decadal time scales) and spatial (north to south and west to east) variability and trends in the permafrost temperatures and physical changes (such as talik and the active layer) in the North of Eurasia and Alaska to develop more reliable predictive capabilities for the projection of these changes into the 21st century. We are employing ground datasets from the global permafrost temperature networks, global positioning system sites of the International Terrestrial Reference Frame organization, together with satellite-derived datasets of physical parameters such as land-surface temperature, gravity field changes, river runoff and snow water equivalent to name a few. Our modeling efforts employ the Geophysical Institute Permafrost Models (GIPL) and Geophysical Inverse Potential Field Theory.

Techical Information: The main purpose of the ESA Data User Element (DUE) Permafrost project (2009-2012) was to define, demonstrate, and validate a permafrost monitoring information service based on operational satellite data that are processed for the pan-permafrost region North of 55 N.DUE PERMAFROST uses a suite of indicative remote sensing-derived parameters: 'Land Surface Temperature' (LST), 'Surface Soil Moisture' (SSM), 'Surface Frozen/Thawed State' (Freeze/Thaw), 'Elevation', 'Land Cover' (LC), and 'Surface Waters'. The remote sensing data products are provided from local to large spatial scales.The DUE PERMAFROST consortium has been led by the Vienna University of Technology, Austria being responsible for all parameters based on microwave remote-sensing technology (active and passive microwave sensors): Surface Soil Moisture (SSM) with weekly to monthly averages from 2007 to 2010, Freeze/Thaw and Surface Waters.The University of Waterloo (Canada) provided Land Surface Temperature Services (LST) from MODIS and ENVISAT-AATSR with weekly to monthly averages from 2000 to 2010 (varying by sensor and region).The Friedrich Schiller University (Germany) is responsible for the circum-Arctic/ boreal Land Cover products including local high resolution information and burned areas.Gamma Remote Sensing (GAMMA, Switzerland) assembled national DEM data-sets and build-up the first circum-arctic DEM dataset with a 100 m pixel resolution north of 55 N.The Alfred Wegener Institute for Polar and Marine Research (AWI, Germany) organized the exchange between the scientific stakeholders of the permafrost community and the project consortium, including the managing of the ground data used for the evaluation of products.Researchers from permafrost monitoring and from modelling groups (permafrost, climate) provided expertise and ground data. The first DUE Permafrost User Workshop was held in May 2010 in Vienna as an official side-event of the EGU. The 2nd DUE Permafrost User Workshop has been financially supported by the International Arctic Research Centre, IARC, Fairbanks (US) and took place in March 2011 in Fairbanks, Alaska. The observation strategy for all products and regions was reviewed with the participants. The final DUE Permafrost User Workshop took place at AWI Potsdam (DE) in February 2012. The first version of the full dataset has been released in the beginning of 2011, the final version in March 2012.The Full Product Set (FPS) published in PANGAEA covers dynamics of Land Surface temperature (weekly and monthly averages), Surface Soil Moisture (weekly and monthly averages including surface status), Surface Waters over five regions (summer maximum) at minimum for the time period 2007-2010. The Circum-Arctic Land Cover and Elevation are static products. The panarctic LST and SSM products are provided in geotiff and netcdf formats.