--- title: "Lab 2: Investigating Evidence for Climate Change" author: "Marguerite Mauritz for Ecosystem Ecology" date: "2 September 2020" output: html_document: theme: spacelab toc: true toc_depth: 2 number_sections: true toc_float: collapsed: false smooth_scroll: false --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, eval = TRUE) ``` # Why this matters Current climate change is affecting many aspects of the environment, with enormous socio-economic consequences [^1]. Mitigating the consequences of climate change by adaptation and reorganisation of infrastructure, agriculture, and human-health support will cost the US economy billions of dollars from now to 2090. **Doing nothing will cost 100 billions more per year** [^2]. When examining global climate change, we are concerned not only with the actual temperature, but also with the rate that the temperature changes. Very rapid changes make it more likely that species (maybe even including humans!) cannot adapt and will go extinct. A recent IPCC report concludes that to prevent catastrophic impacts on humanity, global warming must be maintained below 1.5$^\circ$C [^3]. Indeed, the most aggressive ambitions outlined in the Paris Climate Agreement aim for less than 1.5$^\circ$C of warming [^4]. For one of the most comprehensive summaries of global C dynamics and evidence for climate change, check out the IPCC AR5 Report carbon chapter in the physical science basis of climate change [^5]. # Learning Objectives * To analyze global temperature data to see if Earth’s average global temperatures are really increasing * To analyze CO2 data to see if atmospheric levels are really increasing * To correlate CO2 data with global temperature to see if there is a relationship * To explore evidence that increases in atmospheric CO2 since industrialisation are related to fossil fuel burning * To use ice core data for comparing current temperature and CO2 trends with rates of change during pre-historic periods * To interpret what these results mean for understanding current climate change * Use R to import data from excel files, prepare for analysis, graph, and analyse # Functions used: | Package | functions | |------:|:-----| | base R | lm(), colnames() | | dplyr | filter(), mutate(), inner_join() | | ggplot2 | geom_point(), geom_line(), labs(), geom_label(), geom_hline(), geom_vline(), geom_abline(), geom_segment() | # Lab Outline 1. **Activity A:** Determine current rates of air temperature and CO2 change from modern datasets 2. **Activity B:** Explore whether modern temperature and CO2 concentrations are related 3. **Activity C:** Compare current and pre-historical temperature and CO2 concentrations using data from the Vostok ice core 4. **Activity D:** Use modern atmospheric isotopic $\delta$ 13C data to examine supporting evidence that the current rise in CO2 comes from fossil fuel emissions # The Datasets: Background and Sources ## Global Temperature The mean global temperature data are from the NASA/GISS land and ocean surface-temperature product [^7] [^8]. We have two ways of analysing this data: 1. As actual temperatures. 2. As temperature anomalies. (see: What is an Anomaly?) ```{r, echo=FALSE, out.width = "400px"} knitr::include_graphics("Lab2_GlobalTempAnom_2019.png") ``` 2019 Global mean Annual Temperature Anomalies (1981-2010 baseline) from NASA/GISS [^6] ## Global CO2 Concentrations The global CO2 concentration data comes from the *Keeling Curve* [^9] data collected by the NOAA Global Monitoring Lab at Mauna Loa, HI [^10]. At 3400m above-sea-level, far away from big population centers, measurements from below the summit of Mauna Loa reflect an atmospheric signal. This dataset was started in 1958 by Dr. Charles David Keeling (1928-2005), a scientist at Scripps Institute of Oceanography. This dataset is the longest record of directly measured atmospheric CO2 and has documented the CO2 rise related to human burning of fossil fuels. Dr. Keeling was awarded the National Medal of Science by President George W. Bush in 2002 for his scientific achievenements. This is the highest award for lifetime scientific achievement that can be granted in the U.S. Today, you get to analyze this same dataset, except that you have more data that was available to Dr. Keeling and his colleagues, because your dataset extends up to current time. ## Global 13C-CO2 Isotopic Record Isotopes are like geochemical fingerprints that tell scientists which pathways have produced a biomolecule. Isotopic ratios of 12CO2 and 13CO2 in the atmosphere can be used to understand *which processes* have contributed to atmospheric CO2 changes. The CO2 that is derived from fossil fuel burning has a more negative $\delta$ 13C signature than the current atmosphere. Therefore, atmospheric changes in $\delta$ 13C, also from Mauna Loa [^11], provide supporting evidence for the influence of fossil fuel burning on CO2 concentrations. (We'll talk about isotopes more, later. If you are curious now, ask about them in the discussion boards.) ```{r, echo=FALSE, out.width = "400px"} knitr::include_graphics("Lab2_mlo.jpg") ``` Mauna Loa Observatory, HI ## The Vostock Ice Core Data When analyzing Earth’s climate, it is important to remember that Earth is 4.54 billion years old. Ice Cores from glaciers can be used to reconstruct the climate from Earth's geological past. The layers in a core are aged based on depth and different geochemical markers (like isotopes, ash layers, and electrical conductivity). The air bubbles trapped in each layer can be analysed for CO2 concentrations and the isotopic composition of ice is directly related to temperature at the time of ice formation. Together, this gives us a geologic record of atmospheric CO2 concentrations and global air temperatures [^12]. The Vostock Ice Core comes from East Antarctica, collected by a Russian, US, and French expedition in 1998. Ice from Antarctica reflects the local climate patterns and correlates well with the global climate. The Vostok core is 3623m long (that's the distance from Schuster & Mesa to Executive & Mesa!!), the deepest ice core ever recovered, and records the history of Earth's atmosphere as far back as 400 thousand years ago (400 kyr)[^13]. If that isn't amazing.... ```{r, echo=FALSE, out.width = "150px"} knitr::include_graphics("Lab2_IceCore.jpg") ``` An Antarctic Ice Core & read more about Antarctic drilling expeditions [^14][^15] # What is an Anomaly? In climate science, anomalies are frequently used to visualise and analyse trends that are difficult to detect among large variability. The anomaly is calculated as the differnce of each measurement relative to a defined baseline which should represent your reference conditions: $$anomaly = y~observed~ - average(baseline period)$$ A **negative anomaly** indicates a **cooler temperature** relative to baseline and a **positive anomaly** represents a **warmer temperature** relative to baseline. For example: Consider two made-up climate stations that are located in different geographic locations. One in the Arctic (Station 1) and one in the Tropics (Station 2). The actual temperatures (absolute measurements) will be very different because one Station 1 is in a cold region and Station 2 is in a warm region. Both Stations will also have fluctuations in temperature due to seasonality and variable weather conditions (Fig 1). When absolute differences between two locations are large, it becomes difficult to compare their behaviour because all we see is the large difference between locations. Anomalies help by normalising all the observations to the typical (baseline) temperature at the site and emphasises trends in warming or cooling. Anomalies also prevent us from making judgements based on what we individually perceive as "hot" or "cold". For example on the Arctic coast where summer temperatures are often in the 50's, a summer temperature of 70F would sound unusually cold to us here in El Paso. If we look at anomalies, we strip away that judgement and look only at the relative trends. In our completely made-up example, the baseline period is in the time 500 to 700. In Fig 1 the shaded area shows the baseline period and the red line is the average baseline temperature. It looks like Station 2 started to show a warming trend after 700, but it's hard to tell. By looking at the anomalies calculate in Fig 2 it becomes more clear that after the baseline period, Station 1 (black line) became 2-3 degrees warmer and Station 2 (orange line) warmed by only 1 degree. The anomalies in Fig 2 also show that Station 2 (orange) has more negataive anomalies that Station 1 which means Station 2 tends to be unusually cold more often than Station 1. ```{r ggplot2, warning=FALSE, eval=TRUE, echo=FALSE, message=FALSE, out.width="50%"} library(dplyr) library(ggplot2) dat.anom2 <- data.frame(x=seq(500,1000,by=10)) %>% mutate(y=case_when(x>=500&x<700~sin(x)+rnorm(x/2), x>=700~exp(x/3000)+rnorm(x/2)), y2=case_when(x>=500&x<700~sin(x)+rnorm(x)+50, x>=700~exp(x/3000)+rnorm(x)+50)) %>% mutate(bl1=mean(y[1]:y[20]), bl2=mean(y2[1]:y2[20]))%>% mutate(anom1=y-bl1, anom2=y2-bl2) ggplot(dat.anom2)+ geom_rect(aes(xmin=500, xmax=700, ymin=-5, ymax=Inf), fill="light grey",alpha=0.05)+ geom_line(aes(x,y))+ geom_line(aes(x,y2), colour="orange")+ geom_line(aes(x,bl1),colour="red")+ geom_line(aes(x,bl2),colour="red")+ geom_vline(xintercept=c(500,700),linetype="dotted",colour="blue",size=1)+ geom_label(label="Station 1",x=520,y=5)+ geom_label(label="Station 2",x=520,y=54)+ geom_label(label="Baseline Period",x=600,y=20)+ #geom_label(label="Baseline 2",x=970,y=49)+ theme_minimal(base_size=24)+ labs(title="Fig 1: Absolute Measurement",x="Time",y="Temperature") ggplot(dat.anom2)+ geom_rect(aes(xmin=500, xmax=700, ymin=-3, ymax=Inf), fill="light grey",alpha=0.05)+ geom_line(aes(x,anom1))+ geom_line(aes(x,anom2),colour="orange")+ theme_minimal(base_size=24)+ geom_vline(xintercept=c(500,700),linetype="dotted",colour="blue",size=1)+ geom_label(label="warmer than baseline",x=552,y=4.7, colour="red")+ geom_label(label="cooler than baseline",x=550,y=-2, colour="blue")+ geom_hline(yintercept=0)+ labs(title="Fig 2: Anomalies",x="Time",y="Temperature Anomaly") ``` # Acknowledgement **This lab was developed from:** * Hage, M. (2020). Investigating Evidence for Climate Change (Project EDDIE). Project EDDIE Faculty Mentoring Network, QUBES Educational Resources. doi:10.25334/FPA6-AS71 Link: https://qubeshub.org/publications/1756/1 # References [^1]: NASA: Vital Signs of the Planet: https://climate.nasa.gov/evidence/ [^2]: Martinich, J., Crimmins, A. Climate damages and adaptation potential across diverse sectors of the United States. Nat. Clim. Chang. 9, 397–404 (2019). https://doi.org/10.1038/s41558-019-0444-6 **and (in a slightly more digestible format):** https://yaleclimateconnections.org/2019/04/climate-change-could-cost-u-s-economy-billions/ [^3]: IPCC, 2018: Summary for Policymakers. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. World Meteorological Organization, Geneva, Switzerland, 32 pp. https://www.ipcc.ch/sr15/chapter/spm/ [^4]: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement [^5]: Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao and P. Thornton, 2013: Carbon and Other Biogeochemical Cycles. In: Cli- mate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [^6]: https://www.ncdc.noaa.gov/sotc/global/201913 [^7]: https://climate.nasa.gov/vital-signs/global-temperature/ [^8]: http://www.earth-policy.org/?/data_center/C23/ [^9]: Beck, Ernst-Georg. 50 YEARS OF CONTINUOUS MEASUREMENT OF CO2 ON MAUNA LOA. Energy & Environment, vol. 19, no. 7, 2008, pp. 1017:1028. JSTOR, www.jstor.org/stable/44397321 [^10]: https://www.esrl.noaa.gov/gmd/ccgg/trends/ [^11]: C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps Institution of Oceanography, San Diego, 88 pages, 2001. https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html [^12]:http://www.antarcticglaciers.org/glaciers-and-climate/ice-cores/ice-core-basics/#SECTION_2 [^13]: https://cdiac.ess-dive.lbl.gov/trends/co2/vostok.html **Original publication:** Petit, J., Jouzel, J., Raynaud, D. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999). https://doi.org/10.1038/20859 [^14]: http://www.antarcticglaciers.org/glaciers-and-climate/ice-cores/ice-core-drilling/ [^15]: A video of ice core analysis: https://www.youtube.com/watch?time_continue=37&v=oHzADl-XID8&feature=emb_title