Detecting Trends in Ocean Acidification with Open Data
Hello, World!
My name is Nils, welcome to my website! Here you can find information about my professional practice, ocean acidification, and for the willing, lots of R code. Enjoy!
The Problem
Since the start of the industrial revolution our carbon dioxide output has increased dramatically. The oceans absorb a large portion of the atmospheric CO2 and therefore reduce the greenhouse effect. The benefits are obvious but today we know that there are major downsides. The CO2 absorbed by the oceans leads to changes in the seawater chemistry, some with major implications for marine organisms.
A visual representation of the carbonate system with its buffer reactions
Calcium carbonate shells from pteropods were placed in seawater with a pH of 7.8 (the projected pH for 2100). The top row shows the shells before the experiment, the bottom row shows the shells after 45 days of exposure.
In the last century, a decline in ocean pH from 8.2 to 8.1 has been documented. Since the pH scale is logarithmic , this decline represents a 30 % increase in acidity. This has dramatic impacts on shell building organisms. A lower pH translates into increased rates of dissolution of calcium carbonate and a lower amount of free carbonate ions in the water. While the CO2 cycle in the oceans is understood fairly well, there are many knowledge gaps in the implications for marine organisms.
Source: Introduction to Oceanography (Webb). (2019, September 16). Retrieved July 2, 2021, from https://geo.libretexts.org/@go/page/4449
The Project
The goal of my professional practice was to study patterns of ocean acidification (OA). We focused our study on the Portuguese Margin, since no ocean acidification data has been published for that area. As basis of the analysis, we used data from the open access database “The Global Ocean Data Analysis Project (GLODAP)”. Additionally, we included data from “The International Council for the Exploration of the Sea (ICES)”, and data from older oceanographic cruises in the Portuguese Margin. The data from the latter two sources did not include many of the carbon system variables that we needed. Therefore, we employed a neural network that computes carbon data from oxygen and nutrient measurements. Through this process we were able to “rescue” old data and use it to fill knowledge gaps. At the same time, we tried to test the effectiveness of this reconstruction approach by comparing our computed trends with already published trends that do not rely on the application of neural networks. After performing the preliminary analysis we moved towards computing trends of pH, xcCO3, and anthropogenic carbon, to study temporal patterns of these variables and to try to understand how the ocean chemistry changes in the framework of global climate change.