Minimum Temperatures (Tmin), Maximum Temperatures (Tmax), precipitation (PPT) are random and stochastic nature climatic variables. Daily time series datasets of these climatic variables contains long-term or secular trends and movements, various cyclic movements, seasonal movements and irregular or random movements. To quantifying climatic changes we have to analyze these time series and to extract reliable long-term net trends. Available indigenous statistical procedures return some unusual results.
To find / detect trends in climatic parameters (minimum and maximum temperature, and precipitation, some suitable methodology is desired. Normally, it includes data screening, filling missing values, data, analyzing, trends determinants and trends reliability and significance etc.
Help is solicited from research fellows to work for this methodology.
Hello, I don't know if any of the people in this list will be able to help you as experts in the topic. Thanks https://climateandhealthforum.eventbrite.com.au/
Very much thankful for the answers and the links for further understanding.
All we need is to know the best form of database that we need to handle in terms of time scale and averaging procedure. I would suggest taking a look at my following two publications:
1. Modeling rainfall variability over urban areas: a case study for Kuwait.
2. Modeling pan evaporation for kuwait by multiple linear regression.
Kenneth I agree with your point, trends will constantly change depending on the time series. My approach to the question is rather than saying 'what trend does my data show?' is to say 'what is the smallest trend i can detect with my data set? ' This is not a simple problem and people can can spend years preparing a data set for trend analysis, i know of quite a few projects where they are now going back through the radiance record and homogenizing/inter calibrating the records to before running their retrievals. This is to help avoid the issue you bought up over ozone, NASA's initial treatment of ozone at the poles meant the ozone hole was considered bad data by the L2 processor and flagged accordingly. But as i said the issue is not trivial, and most of the time we can only 'update' trend estimates. Some of the processes out lined in Weatherhead as well as testing for stability (on a decadal scale), accounting for over/under sampling, uncertainties all needed to be characterised and there is still the decoupling of other climate signatures from time series to deal with. Not a simple issue and one that will continue to task us for many years to come.
I think we are agree that really need a 'climate quality' data set when looking at trends. I think there are other issues regarding the actual measurements used with regard to the end user's understanding. In my work I use nadir infrared sounders to look at profiles of water vapour. These profiles are usually described on set levels where as the actual measurement itself is really a series of overlapping partial columns that represent layers of the atmosphere. I've seen people compare these measurements to model/reanalysis outputs on interpolated level grids with no regard of to the geometry/sensitivity of the measurement which actually can give drastically different answers. However when we use any measurement to analyse trends there should be a theoretical trend model describing what we expect, and the trend we observe should be used to test that hypothesis and our understanding.
I agree with Kenneth's point. Calendar and seasonal year differences, in both temperature and precipitation seem to act randomly. Trends will constantly change depending on the time series.
Kenneth I agree with your point, many high-latitude values were simply omitted; deemed "incomplete". This is not a simple problem and the trends will constantly change depending on the time series.
This two papers should help to answer your question:
http://www.homogenisation.org/files/private/WG1/Bibliography/Applications/Applications%20%28A-B%29/begert_etal.pdf
http://www.unige.ch/climate/Publications/Beniston/JB01.pdf
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