NOAA Great Lakes Environmental Research Laboratory collected the data from moored Realtime Coastal Observation Network, ReCON, Alpena Buoy (NDBC station 45162), Lake Huron, an in-situ moored station, in the Great Lakes. Observations have been collected at this location since 2005, this record contains the 2020 observations. Note, the short deployment of this buoy in 2020 is due to COVID-19 and a reduced field work season. This station is also known as NOAA National Data Buoy Center (NDBC) station Thunder Bay Buoy, Alpena, MI (45162). A temporal subset of these data are available from NDBC and the Great Lakes Observing System (GLOS) since 2012, this data accession contains the complete record of observations. The ReCON buoy provides continuous, real-time observations facilitates modification of sampling parameters in anticipation of episodic events, facilitates collection of field samples in response to episodic events, supports long term research, and contributes to sensor and system development. Parameters collected include currents and water temperature. The block of text at the beginning of each file contains information about the location and sensor used to collect data and the data headers followed by the observed data. Column 1 of the data is the timestamp, column 2 is the observed data, and column 3, where applicable, the QARTOD flag. Five QARTOD tests were run including gross range, climatological, spike, rate of change, and flat line tests. The highest value from the five tests were included under the “Qartod” column. If data were known to be invalid, that line of data was removed from the dataset.

NOAA Great Lakes Environmental Research Laboratory collected the data from moored Realtime Coastal Observation Network, ReCON, Saginaw Bay Buoy (NDBC station 45163), Lake Huron, an in-situ moored station, in the Great Lakes. Observations have been collected at this location since 2010, this record contains the 2020 observations. Note, the short deployment of this buoy in 2020 is due to COVID-19 and a reduced field work season. This station is also known as NOAA National Data Buoy Center (NDBC) station Saginaw Bay Buoy, MI (45163). A temporal subset of these data are available from NDBC and the Great Lakes Observing System (GLOS) since 2012, this data accession contains the complete record of observations. The ReCON buoy provides continuous, real-time observations facilitates modification of sampling parameters in anticipation of episodic events, facilitates collection of field samples in response to episodic events, supports long term research, and contributes to sensor and system development. Parameters collected include currents and water temperature. The block of text at the beginning of each file contains information about the location and sensor used to collect data and the data headers followed by the observed data. Column 1 of the data is the timestamp, column 2 is the observed data, and column 3, where applicable, the QARTOD flag. Five QARTOD tests were run including gross range, climatological, spike, rate of change, and flat line tests. The highest value from the five tests were included under the “Qartod” column. If data were known to be invalid, that line of data was removed from the dataset.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global merged land-ocean surface temperature analysis product that is derived from two independent analyses. The first is the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the second is a land surface air temperature (LSAT) analysis that uses the Global Historical Climatology Network - Monthly (GHCN-M) temperature database. The NOAAGlobalTemp data set contains global surface temperatures in gridded (5° × 5°) and monthly resolution time series (from 1850 to present time) data files. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. This version, v6.0, an updated version to the current operational release v5.1, is implemented by an Artificial Neural Network method to improve the surface temperature reconstruction over the land.

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is derived from two independent analyses: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1880 to the present, updated monthly, in gridded (5 degree x 5 degree) and time series formats. This data set is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 3.5.4 to version 4.0.0 include an update to the primary input dataset (ERSST) now at version 4.0.0 and GHCN-Monthly now at version 3.3.0. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a blended product from two independent analysis products: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1850 to the present. The monthly product output is in gridded (5 degree x 5 degree) and time series formats. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. Changes to the data in version 5.1 included: removing the EOT filtering; filling in data gaps over the polar regions; and extending the beginning data coverage from 1880 to 1850.

*This version has been superseded by a newer version. It is highly recommended for users to access the current version. Users should only access this superseded version for special cases, such as reproducing studies. If necessary, this version can be accessed by contacting NCEI.* The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is derived from two independent analyses: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1880 to the present, updated monthly, in gridded (5 degree x 5 degree) and time series formats. This data set is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 3.5.4 to version 4.0.0 include an update to the primary input dataset (ERSST) now at version 4.0.0 and GHCN-Monthly now at version 3.3.0. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

NOAA Great Lakes Environmental Research Laboratory collected the data from moored Realtime Coastal Observation Network, ReCON, Muskegon M45 Buoy, Lake Michigan, an in-situ moored station, in the Great Lakes. Observations have been collected at this location since 2016, this record contains the 2020 observations. Note, the short deployment of this buoy in 2020 is due to COVID-19 and a reduced field work season. The ReCON buoy provides continuous, real-time observations facilitates modification of sampling parameters in anticipation of episodic events, facilitates collection of field samples in response to episodic events, supports long term research, and contributes to sensor and system development. Parameters collected include currents and water temperature. The block of text at the beginning of each file contains information about the location and sensor used to collect data and the data headers followed by the observed data. Column 1 of the data is the timestamp, column 2 is the observed data, and column 3, where applicable, the QARTOD flag. Five QARTOD tests were run including gross range, climatological, spike, rate of change, and flat line tests. The highest value from the five tests were included under the “Qartod” column. If data were known to be invalid, that line of data was removed from the dataset.