Studying the Influence of Climate Change on Season Characteristics

Salah Basem Ajjur is currently completing his PhD, at Hamad Bin Khalifa University, in the field of sustainable management of water resources in arid areas under climate and anthropogenic impacts. A key part of his work is understanding season characteristics that are influenced by climate change and the impact this has on agriculture, ecosystems, etc.

The general location and elevation of the Arabian Peninsula countries [1].

Regarding the work in his recent publication Seventy-year disruption of season characteristics in the Arabian Peninsula [1], Ajjur says, “The region experiences numerous phenomena relevant to abrupt changes in seasons (e.g., prolongation of the drought period, precipitation and heat extremes, seasonal changes in temperature and humidity cycles). Therefore, I needed to explore the future variations, through the 21st century, in several climatic and hydrological parameters such as temperature, wind speed, precipitation, relative humidity, cloud cover, geopotential heights, etc. The standard data format used for weather and climate data is the Network Common Data Form (NetCDF). This requires analyzing large datasets of NetCDF files through different periods, which is cumbersome and time-consuming."

By analyzing historic climatological parameters obtained from the National Centers for Environmental Prediction(NCEP) /National Center for Atmospheric Research (NCAR) Reanalysis , Ajjur found that characteristics of the four seasons differ from the astronomical, conventional definition of the seasons and especially so during the last 30 years. When comparing 1990-2019 data to a 70-year analysis (1950-2019), he found additional prolongation in summer and a shortage in winter [1].

The characteristics of the defined Arabian Peninsula seasons during the five overlapping periods in addition to the 1950–2019 period [1].

Regarding how Origin helped him in this work, he states, “Origin had helped me achieve my goals and saved my time and efforts through its new features in handling climatic data… The new version of Origin (2021b) can pleasantly process NetCDF data files. Origin can import and connect multiple NetCDF files easily. It allowed me to do data slicing, averaging, time axis skipping, and inserting formulas through the importing options. The examples in the OriginLab YouTube channel are useful to show how to import aggregate daily, monthly, and seasonally mean data from NetCDF files. It also illustrate how to flip, rotate, interpolate matrix data, and perform a linear fit of several means… I used to spend a lot of time coding these features in Python and R. Moreover, Origin allowed me to duplicate the analysis several times. By changing the climatic parameter (i.e., NetCDF file) under study, the auto calculation option repeats the analysis with one click.”

Embedded Python support in Origin was also a key feature for Ajjur. He explains “Another area that drew my attention to Origin is the latest improvements in the embedded Python. These improvements help to deal with modern uses of climatic data without much of the annoying background work. This includes dealing with NetCDF files with various structures, different date formats, etc. The latest improvements in Origin programming also extend the user knowledge about novel techniques in machine learning, data processing, and artificial neural network. In several research problems, I first installed the required Python packages using a graphical user interface (GUI) in Origin. Then, I wrote my Python codes and functions and exchanged data between Python code and Origin matrices. I also used the external Python (installed on my computer) with Origin software. In this regard, Origin had helped performing the required further analyses and graphing after processing my data in the external Python.”

The mean patterns of 12 climatic parameters used in this study on the central date of winter (24 December) during the 1950–2019 period [1]

The mean patterns of 12 climatic parameters used in this study for the central date of winter (18 December) during the 1990–2019 period [1]

The mean patterns of 12 climatic parameters used in this study for the central date of summer (8 July) during the 1950–2019 period [1]

The mean patterns of 12 climatic parameters used in this study for the central date of summer (13 June) during the 1990–2019 period [1]

As for future use of Origin, Ajjur notes “ I am planning to discover the multivariate statistical approaches in Origin. Mainly, I will use the principal component analysis and cluster analysis. The principal component analysis is an approach that analyzes numerous input variables through reducing their dimensionality into principal components with minimal loss of information. This analysis is a valuable method for easing the process of large dataset analysis for climatic research. Cluster analysis is a statistical approach used for classifying dataset variables into smaller groups called clusters. The method is reliable to develop synoptic climatology. “

We are grateful to Salah Ajjur for his collaboration with us on NetCDF features during the beta stage of version 2021b, and we hope these features are useful to the broader scientific community studying climate data.

Learn more about working with NetCDF-based data in Origin.

References:

[1] Ajjur, SB and Al‐Ghamdi, SG (2021), Seventy‐year disruption of seasons characteristics in the Arabian Peninsula. International Journal of Climatology. https://doi.org/10.1002/joc.7160