Event name: ‘Global surface ocean acidification indicators from 1750 to 2100 – A state-of-the-art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification
Event time and place: 3:30 PM EDT Thursday, September 7th 2023
Dr. Liqing Jiang, a chemical oceanographer and task leader of the Ocean Carbon and Acidification Data System (OCADS), begins his lecture describing the sources of anthropogenic carbon dioxide (CO2) and the ocean’s role within the carbon cycle, including how the oceans control atmospheric CO2 concentrations, which in turn affect the Earth’s climate. However, due to the increased anthropogenic CO2, there have been rising concerns on the imbalance between the sinks and the sources of the anthropogenic CO2 emissions, specifically throughout the ocean. Due to this increase, there have been significant increases in ocean acidification. Dr. Liqing Jiang then discusses the common OA (ocean acidification) indicators from 1750 to 2100 including acidity, total alkalinity, dissolved inorganic carbon, and more. He then follows by explaining the relationship between these indicators with CO2 and temperature, representing the data through various models. Afterwards, Liqing goes into different types of models of the indicators and proceeds to organize all of these indicators into similar graphs to model each of the trajectories of various models to show the accuracy of each of these models in relation to one another. To finalize his lecture, Liqing linked his and his team’s research paper talking about the global surface ocean acidification indicators from 1750 to 2100, the data presented earlier in his lecture, and another research paper that excellently explained the different trends of acidification throughout the years.
Overall, I was very intrigued by the final graphs of the different models and how similar or different they were from one another. I completely agree with what was presented by Dr. Liqing Jiang because it is extremely significant to compare pieces of data with other researchers in order to see commonalities and differences. This can make and break how accurate a datapoint is. This process should be a common process with collecting data because if false data were to be released, especially with important issues like climate change, it could affect the environment or change people's lives. Throughout Liqing’s graphs, many of the indicators had very similar trajectories, overlapping with one another. However, for the graph of the total alkalinity content, there are disparities between the different models. This could mean that the research done to calculate the total alkalinity may be inaccurate, and that there should ultimately be more research done on it. By comparing the different data taken by models, we can see which models were most accurate in collecting the data and which ones need more work on. We can see that CNRM-ESM2 is the most outlier points compared to CanESM5 and EC-Earth3, which are both fairly accurate with a multitude of different models. By using this comparison, researchers can work on the models that are repeatedly producing outlier data, in order to prevent the spread of false information. Because in this case the issue being talked about is ocean acidification, the information must be accurate. Ocean acidification is a rising issue occuring throughout the world and scientists are constantly trying to find answers to prevent increase in overall effects of acidification and acidification itself. This environmental issue can become detrimental to ecosystems residing within the Oceans, including marine organisms with silicon shells, slowly dissolving away the shells. I did not notice any failures with Dr. Liqing's argument, and 100% agree with everything he had presented throughout his lecture. Overall, the lecture was very intriguing to sit through and I thought that Dr. Liqing did an amazing job on the research he and his team had researched. Starting with the introduction to ocean acidification and the importance of presenting data with graphs and models to comparing these models, everything was presented in a very understandable manner, so that anyone that went to this lecture could understand what Dr. Liqing was presenting.