Sub-challenge: Change in average temperature at surface, 500 metre depth and bottom on a grid over the past 10 years and 50 years.
Historical Arctic data from more than a few decades ago is very different to modern data due to the changes in sampling methods (e.g. the invention of the CTD and initiation of polar orbiting satellites) and differences in sample locations (from individual expeditions to full polar coverage). Archival data is increasingly being merged into digital forms so that historical conditions can be reconstructed. Older data are becoming more available as more reanalysis data sets become available. As more historical data become available in digital forms, we expect reanalyses to become available as well. For now however, the older data should be viewed with care.
This challenge is to find data showing the change in average temperature for the surface (z=0m), 500m depth and bottom over past 10 and 50 years on a grid for the entire study area. This practically gives us six different scenarios:
- Change in average temperature for the surface (z=0m) over past the 10 years on a grid for the entire study area;
- Change in average temperature for 500m depth over the past 10 years on a grid for the entire study area;
- Change in average temperature for the bottom over the past 10 years on a grid for the entire study area;
- Change in average temperature for the surface (z=0m) over the past 50 years on a grid for the entire study area;
- Change in average temperature for 500m depth over the past 50 years on a grid for the entire study area;
- Change in average temperature for the bottom over the past 50 years on a grid for the entire study area.
We began with a search for a database with data for all six scenarios. Hydrographic data are available from NOAA’s (U.S. National Oceanic and Atmospheric Administration) National Center for Environmental Information (NCEI, formerly the National Oceanographic Data Center (NODC)). The World Ocean Database (WOD) (Boyer et al, 2009) contains the world's largest collection of quality-controlled salinity and temperature profiles and these data are freely available. The World Ocean Atlas (WOA) (Locarinini et al., 2013) provides objectively analysed climatological means based on data from WOD. The challenge in the Arctic water is that there are few observations, especially in wintertime.
The term ‘grid’ was interpreted as a gridded map for optimal viewing. The data used to produce the map came from the World Ocean Atlas (WOA) in the form of climatologies, defined as the long-term average of a given variable (in this case temperature), often over longer time periods of up to 20-30 years.
Data was downloaded from WOA, but to obtain good measures of change in northern sea temperatures, these data require careful analysis. In a recently published paper (Seidov et al., 2015) the long term variability of hydrography of northern water is discussed based on data from WOD and WOA. (A high resolution regional climatology for the Arctic is available from this analysis (http://www.nodc.noaa.gov/OC5/regional_climate/arctic/)).
To provide the change in ocean average temperature maps for 10 and 50 years, the decadal means available from the WOA were used, butthe quality of these averages depends on data coverage. For a more thorough discussion see Seidov et al., 2015. Data are available from WOA for the periods 1955-64, 1965-74, 1975-84, 1985-94, 1995-2004 and 2005-2012. There is a large decadal variability in the ocean, and to calculate consistent change for the 10 and 50 year period we used the difference between two of the most recent available averages to calculate the 10 year change (Figure 1). For the 50 year period we used the difference between 2005-2012 and 1955-1965 (Figure 2).
Figure 1: Change in temperature (°C) calculated as differences between the average over 2005-2012 and average over 1995-2004 from surface (top), at 500 m (middle) and bottom (lower). Some artificial land occurs for bottom estimates due to interpolation challenges.
Figure 2: Change in temperature (°C) calculated as differences between the average over 2005-2012 and average over 1995-2004 (left panels) and 1955-1965 (right panels) from surface (top panels), at 500 m (middle panels) and bottom (lower panels). Some artificial land occurs for bottom estimates due to interpolation challenges.
NOAA also has an option to create images using the COBE-SST2 data, a monthly or long term mean of sea surface temperatures, with data between 1850 and 2016. GrADS Images can be created using the website, but the level of detail is quite low and the amount of data is so large, only limited timeframes can be selected. For example, a 10-year period is already too long. A one year period gives the image as presented in Figure 3.
Figure 3: Sea Surface temperatures according to COBE-SST2 on https://www.esrl.noaa.gov/psd/cgi-bin/GrADS.pl
The Extended Reconstructed Sea Surface Temperature (ERSST) v4 dataset was used for the Temperature time-series sub-challenge, but monthly gridded data are also available in ASCII and NetCDF format. To create an online viewer of this data, effort would need to be put into programming the data into workable formats and publishing it. The UK Met Office offers a similar dataset.
Data use, availability and gaps
Because of the wide breadth of the research question, several decisions were made to make the sub-challenge manageable. . It was decided to work with climatologies instead of single point data. This decision was made to limit the amount of work to put into the product. The climatologies offer an already averaged overview of time and space. Unfortunately, the level of detail of climatologies and precision of the end product is lower compared to using individual point measurements. However, this also solves the problem of data gaps; as the data is averaged to create the continuous data sets. When using individual data points a lot of spatial and temporal gaps will be visible.
Conclusion and lessons learned
In conclusion, there is enough data available to complete this sub-challenge. A lot of data is available, both in climatology form as in individual form. The real task in this challenge is to find the exact dataset required to answer specific questions. In the case of 10 and 50 year time spans, data needs to be available both temporally and spatially, which is not always the case. During the past 10 and 50 years, monitoring strategies have changed and priorities have shifted back and forth, creating gaps in knowledge and data. These gaps cannot be filled as we cannot go back in time to add monitoring points or change the monitoring strategy. However, we can learn from the gathered data (and the missing data) in setting up new monitoring strategies. Sea temperature is directly related to climate change and keeping track of water temperature, especially in the Arctic area, can be very useful in monitoring and evaluating the effects of climate change.
For the future, long term reliable datasets are needed to asses and predict changes in the Arctic system. It is recommended to link physical to biological parameters to understand the entire system and its ongoing change. For water temperature, the requirement of specific data needs to be clearly defined. The use of averaged data needs to be evaluated, in comparison with point data and internal energy of the water.
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- Huang, B., P. Thorne, T. Smith, W. Liu, J. Lawrimore, V. Banzon, H. Zhang, T. Peterson, and M. Menne, 2015: Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4). Journal of Climate, 29, 3119–3142, doi:10.1175/JCLI-D-15-0430.1 (link is external). http://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-15-0430.1
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- Japan Meteorological Agency, 2006: Characteristics of Global Sea Surface Temperature Analysis Data (COBE-SST) for Climate Use. Monthly Report on Climate System Separated Volume, 12, 116pp. http://ds.data.jma.go.jp/tcc/tcc/products/elnino/cobesst_doc.html
- Liu, W., B. Huang, P.W. Thorne, V.F. Banzon, H.-M. Zhang, E. Freeman, J. Lawrimore, T.C. Peterson, T.M. Smith, and S.D. Woodruff, 2014: Extended Reconstructed Sea Surface Temperature version 4 (ERSST.v4): Part II. Parametric and structural uncertainty estimations. Journal of Climate, 28, 931–951, doi:10.1175/JCLI-D-14-00007.1 (link is external). http://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-14-00007.1
- Locarnini, R. A., A. V. Mishonov, J. I. Antonov, T. P. Boyer, H. E. Garcia, O. K. Baranova, M. M. Zweng, and D. R. Johnson, 2010. World Ocean Atlas 2009, Volume 1: Temperature. S. Levitus, Ed., NOAA Atlas NESDIS 68, U.S. Government Printing Office, Washington, D.C., 184 pp. ftp://ftp.nodc.noaa.gov/pub/WOA09/DOC/woa09_vol1_text.pdf
- Seidov, D., J. I. Antonov, K. M. Arzayus, O. K. Baranova, M. Biddle, T. P. Boyer, D. R. Johnson, A. V. Mishonov, C. Paver and M. M. Zweng (2015). "Oceanography north of 60°N from World Ocean Database." Progress in Oceanography 132: 153-173. http://s3.amazonaws.com/academia.edu.documents/44353209/Oceanography_North_of_60N_from_World_Oce20160403-31999-1ds1st4.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1497532169&Signature=m4uL5SLWBtsuJulk3CfOLG1lV80%3D&response-content-disposition=inline%3B%20filename%3DOceanography_north_of_60_N_from_World_Oc.pdf
- COBE2 files and a README: https://amaterasu.ees.hokudai.ac.jp/~ism/pub/cobe-sst2 (grib format).
The COBE monitoring dataset is here: Japanese Oceanographic Data Center http://ds.data.jma.go.jp/tcc/tcc/products/elnino/cobesst/cobe-sst.html