Climate change and biodiversity in the big-data era 1. background concepts and R Duke University

Temperature trends analyzed in this vignette

What are the changes happening now and where they are leading us? This course combines key topics in climate change, biodiversity, and big data, examining scientific issues, their importance for the public at large, and how well we understand them. 89S courses focus on student discussions. In this case, discussions consider a combination of scientific literature, contemporary media, and analysis of data. Our first meeting provides logistics for the class and introduces the software package R. This vignette introduces issues in the media and courts, the promise/limitations of big data, and challenges of translating data to basic concepts like risk and cost.

Timeline for climate vignette

• day 1: Course overview, form working groups, install Rstudio, introduce R vignette

• day 2: Discussion of science and media issues on greenhouse warming; Gain experience with R using Mauna Loa C02 data

• days 3-4: in groups Introduce basics for computing in R; explore data on CO2 and climate

• day 5: Discussion of CO2 data

• day 6: in groups on vignette containing examples with NC data; can consider how local climate differs from global; how microclimate differs from ‘climate’; extreme events: drought, flood, fire, intensifying hurricanes

• day 7: in groups draft answers to questions on R vignettes

• day 8: debate litigation

Activities today

• Goals:

  1. Introduce background issues on climate change in science, the media

  2. Gain an exposure to concepts in modeling and computation, including R

• Overview of course

• Form working groups, select group coordinators for next class meeting

• Install Rstudio

• Getting started with R

For next time

Get started with R

• Work through the intro to R here

• post answers to Sakai from this unit

Readings

These papers are the basis for discussion and debate on the costs of climate change and who’s responsible for those costs.

In the media:

• New York’s Global Warming Suit Against Oil Companies Tossed (Bloomberg News). An argument similar to that made by tobacco companies does not work this time.

• Why Big Data Could Be a Big Fail (Spectrum IEEE), Jordan on potential and limitations of Big Data (misleading title).

Science resources:

• Estimating economic damage from climate change in the United States (Science). Hsiang et al. estimate the potential economic damage from severe weather related to climate change.

Discussion next time

The meeting will begin with discussion within working groups, followed by group reports from the coordinator, and a discussion in plenary. Each student will post their responses to Sakai/Resources before class. The initial meeting in groups will be used to sharpen the questions and identify points of disagreement or confusion. The coordinator will provide a brief overview for the full class, including additional information on the subject. Plenary discussion will follow. The coordinator will submit to Sakai their summary at the end of the class meeting as key bullets.

Here are the questions:

1. Is Jordan an optimist or a pessimist on the advances and prospects for ‘big data’? Why?
2. What sources of uncertainty are included in the projections from Hsiang et al?
3. Locate a state of interest to you on Hsiang et al’s figure S2, and summarize 2 big changes that are projected and why.
4. Identify two similarities and two differences between the roles of big tobacco in cancer risk and big oil in climate change.

Climate change and biodiversity in the big data era

In the 1960’s Roger Revelle warned then-president Lyndon B. Johnson that atmospheric CO2 concentrations would increase 25% by the year 2000. They did.

The basic science of greenhouse warming has been understood since the 19th century. The impacts anticipated by Revelle’s committee included melting ice caps, warming, acidifying, and rising seas, degraded fisheries, and accelerated primary productivity on land.

In January 2021, global temperatures were reported to have matched the 2016 record, a super el-Nino year, with six of the warmest years all occurring within the last decade. The 2021 data came from a global pandemic year when CO2 emissions were influenced by reductions in fossil fuel emissions from transportation. The warming trends have come with increased extremes, including droughts, fire, hurricanes, and flooding.

Human impacts on planetary health go beyond climate. Human population growth places demands on agriculture that affect habitats for plants and animals, including widespread deforestation in the tropics and contamination from pesticides. Many of the impacts are clear, while remain only partially understood.

Threats to biodiversity include interactions between stressors

The unprecedented challenges posed by global change come at a time of expanding data and ways to analyze them. Environmental insights can be gleaned from remote sensing, monitoring networks, and citizen science. The numbers of observations and variables, together with the heterogeneity of information, make these big-data problems. Our goal is better understand how we can learn about changes ahead, exploiting new data sources and ways to use them.

The readings for next time include an analysis in the scientific literature that attempts to quantify the costs of climate change and a municipality (New York City) that sought compensation for health costs from big oil.

To get us started on the analysis, we’ll introduce the package R, the standard software used to analyze data. In addition to readings, work through this vignette for next time.