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CDC (Climate Data Center)

Free access and download to of a growing selection of DWD’s climate data. Via CDC Search you will find data for direct download and interactive access to station data. The interactive mode gives graphical and tabular previews of the German station data. In addition, all data sets remain accessible from our ftp server for direct download

H2020-EU.3.5. - Societal Challenges - Climate action, Environment, Resource Efficiency and Raw Materials - (H2020-EU.3.5. - Gesellschaftliche Herausforderungen - Klimaschutz, Umwelt, Ressourceneffizienz und Rohstoffe), Constraining uncertainty of multi decadal climate projections (CONSTRAIN)

CONSTRAIN will focus research on three climate science knowledge gaps and a policy-facing knowledge gap that can be resolved over the next 4-5 years to significantly improve our understanding of how natural and human factors affect multi-decadal regional climate change. This will cement EU science as the world-leader in understanding climate sensitivity and climate variability, deliver significantly improved capability to make climate projections for the next 20-50 years, and provide up-to-date scientific evidence for international climate policy in two phases: Phase 1 will deliver a timely characterisation of physical science uncertainty and how it affects projections and committed levels of warming to the 2021 IPCC sixth assessment report; Phase 2 will deliver constrained surface temperature projections for the 2023 UNFCCC Global Stocktake. CONSTRAIN will take full advantage of climate model integrations from the sixth Climate Model Intercomparison Project (CMIP6) and will leverage existing H2020 and ERC projects. Novel CMIP6 analyses will be combined with dedicated high resolution simulations and new observations to address identified knowledge gaps on radiative forcing, cloud feedbacks and the relationship between ocean variability and atmospheric change. A fourth identified knowledge gap is the effective translation of new physical science understanding into an improved evidence base for policy decisions. CONSTRAIN will address this by developing climate model emulators that integrate and operationalise learning from across the consortium to provide new capability to assess impacts of climate change under a broad range of emission scenarios. We will focus on the expected spatially resolved decadal changes until mid-century providing robust evidence on climate sensitivity, and regional temperature, precipitation and circulation changes, thereby enabling evidence-based policy decisions that will directly benefit the EU's adaptation and mitigation strategy.

Soil- moisture and temperature from the PhytOakmeter plot DKr (Kreinitz, Germany) from 2024

As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach. The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.

openSenseMap: Sensor Box HE Humboldt-Gymnasium Berlin senseBox8

Das ist eine senseBox der Humboldt Explorers. Weitere Informationen unter: www.humboldt-explorers.de

openSenseMap: Sensor Box HE Schiller-Gymnasium senseBox3

Das ist eine senseBox der Humboldt Explorers. Weitere Informationen unter: https://www.humboldt-explorers.de/

openSenseMap: Sensor Box HE Renée-Sintenis-Grundschule senseBox8

Das ist eine senseBox der Humboldt Explorers. Weitere Informationen unter: humboldtexplorers.de

Air- and soil temperature data from PhytOakmeter plot DGRL_14 (Greifenhagen, Germany) from 2019

Soil temperature at 15cm depth and air temperature at 60cm height were collected using HOBO Pro V2 loggers, model U23-004. Two loggers were used. After data visualization, unrealistic values were removed manually for each logger, and mean temperature values were calculated at 30-minute intervals.

openSenseMap: Sensor Box HE Heinrich-Roller-Grundschule senseBox3

Das ist eine senseBox der Humboldt Explorers. Weitere Informationen unter: www.humboldt-explorers.de

Soil- moisture and temperature from the PhytOakmeter plot DGRL_14 (Greifenhagen, Germany) from 2021

As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach.The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.

Soil- moisture and temperature from the PhytOakmeter plot DGRL_14 (Greifenhagen, Germany) from 2019

As part of PhytOakmeter (www.phytoakmeter.de), time-domain transmission, soil moisture and -temperature sensors with custom-made logger systems were used to measure time series of soil state variables. The aim of these investigations was to provide data on environmental properties used in a cross-disciplinary approach.The measurement device consisted of two sensors at three different depths. The dataset contains the values of time (UTC), relative permittivity, soil moisture (in % vol) derived from permittivity and soil temperature (in °C). Determination of soil moisture was done using the formula of Topp et al. (1980). As sensors, the SMT100 soil moisture sensors with integrated temperature measurement were used. All sensors were installed within the upper 50cm below ground. The exact depths for each sensor are listed in the dataset and parameter comment.

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