63-953 Climate and Satellite Data Analysis

Lars Kaleschke, Alexander Loew

MS Integrated Climate System Sciences

Date: 3.2.2014-7.2.2014

Place: Geom 1536c

Course objectives

The participants will learn to practically work with climate model, reanalysis, in-situ station and satellite data. Organized as a group project, the participants will further learn the principles of project management and shared software development.

Schedule

Monday

General Introduction

• Project management and documentation

• Python

• Access to data sets

• Data Visualization: Generating maps

Group work: develop a project plan and write a short technical proposal for your project.

Final report due by 15. March 2014

Obtain data and do preliminary analysis (e.g. data coverage).

Tuesday

Morning: Group presentations of project plan and preliminary analysis.

Afternoon: implementation and project work

Wednesday

Morning: Group presentations of methods and code implementations

Afternoon: Project work

Thursday

Morning: Group presentations of preliminary results

Afternoon: Project work

Friday

Morning: final presenation of results and discussion

Afternoon: evaluation and preparation of final report

Topics for group work

Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends

Kevin Cowtan and Robert Way fill the gaps of the HadCRUT temperature data set by using satellite data. Compare their new reconstruction of surface temperature data to independent in-situ observations and reanalysis data.

Cowtan and Way (2013) methods and data are freely available:

• http://onlinelibrary.wiley.com/doi/10.1002/qj.2297/abstract

• http://www-users.york.ac.uk/~kdc3/papers/coverage2013/methods.html

Surface temperatures are available from the International Arctic Buoy Programme (IABP) website:

• http://iabp.apl.washington.edu/data_satemp.html

Cowtan and Way (CW2013) reconstruction

• Review methods of Cowtan and Way (2013)
• Analyse their reconstructed dataset of surface air temperature (seasonal cycle, anomalies, trends etc.)
• Look at correlations with climate indices, e.g. ENSO, NAO.

/ProjectA

Variations in Surface Air Temperature Observations in the Arctic

• Review methods of Rigor et al. (2000)
• Analyse the surface air temperature measured by the drift buoys (seasonal cycle, anomalies, trends etc.)
• Are there significant trends over different period of times?

• Rigor, I., R. Colony, and S. Martin, 2000, Variations in Surface Air Temperature Observations in the Arctic, 1979 - 1997, J. Climate, Vol. 13, no 5, 896-914.

/ProjectB

Data intercomparison

Use the buoy measurements of surface air temperature as ground truth

• Write code to interpolate the different datasets in a common grid
• Compare CW2013, buoy and reanalysis data
• Was the data gap in the Arctic filled in reasonably?
• Are there biases or jumps in the data?

/ProjectC

HOAPS ocean flux sampling bias

• HOAPS climatology of ocean surface fluxes

• HOAPS is sampled twice a day
• What is the impact of undersampling the dirnal cycle on monthly means?
• What is the impact of sea ice gaps on monthly means?
• How do HOAPS surface flux estimates compare to literature values of global mean ocean surface fluxes?
• What is the impact of different land/sea masks and spatial grids (resolution, projections) on total mean global fluxes?

/ProjectD

TODOs

Data

• CW2013

• ERA-Interim

• NCEP

• Bouy data

• HOAPS data

Final report

Template structure:

• Abstract
• Introduction: state of the art (literature), statement of the problem
• Methods and data
• Results
• Discussion
• Conlcusion

References

• Python at KlimaCampus

• Python Scripting for Computational Science, Hans Petter Langtangen, Springer (available in the ZMAW library)

Examples from the past

How significant are observations of Arctic temperature trends?

• https://wiki.zmaw.de/lehre/Climate_and_Satellite_Data_Analysis_2013/ProjectA