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ADDRESSING QUANTITATIVE REASONING AND ANALYTICAL WRITING SKILLS IMPROVEMENT USING GLOBAL AND LOCAL DATA SETS IN AN INTRODUCTORY GLOBAL CLIMATE CHANGE COURSE NIEMITZ, JEFFREY W., Department of Geology, Dickinson College, P.O. Box 1773, Carlisle, PA 17013, [email protected] EXERCISE I: UNDERSTANDING WEATHER AND CLIMATE ABSTRACT # 63108 The data collected is typical for most newspapers i.e. max. , min., and average temp for the day, the normal max., min. and average temps for that day, the extreme temps for the day, and the precipitation for the data and record rainfall for the day. They are informed they will need to do this for the first class of the semester. During the first class we talk about the difference between weather and climate. They are then asked to find the climate data for the same week of days from any other year in the climate record for their hometown or nearby city. This requires them to search the Web for historical climate data for their town. OBJECTIVES: 1) To start collecting and analyzing weather data; 2) to begin searching the Web for the required climate data; 3) to learn to graphically present all data subsets using EXCEL; 4) to begin to understand basic statistics such as maximum, minimum and averages, and the concept of standard deviation; 5) the difficulty of predicting weather even 24 hours in advance; and 6) the difference between weather and climate in terms of time and meteorological variability. EXAMPLE: WEATHER DATA FOR HARRISBURG, PA (JANUARY 15-22, 2000) Day Max Temp Min Temp Mean Norm X Last yr hi Last yr lo Record hi Record lo pred hi 15-Jan 33 18 26 28 25 13 67 -3 16-Jan 54 27 41 28 32 17 62 -4 42 17-Jan 24 12 18 28 40 15 65 -6 26 18-Jan 19 7 13 28 52 25 66 -6 25 19-Jan 39 17 28 28 44 32 66 14 32 20-Jan 31 26 29 28 47 31 68 -16 32 21-Jan 19 12 16 28 42 29 64 -22 25 22-Jan 23 7 15 28 39 27 64 -9 26 24 13 18 22 16 0 14 MDTppt Norm ppt 0.97 1.26 0.97 1.35 0.97 1.44 0.97 1.53 0.97 1.62 0.98 1.71 1.16 1.8 1.16 1.89 Precipitation Max-Min-Predicted Temperatures 60 2 0.4 1951 1959 Norm Max Temp 50 0.3 1.4 pred lo 40 30 20 30 20 1893 1982 -10 1994 0.6 0.4 10 1994 B C 0 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan Plot B shows the standard deviation of temperatures for one week Including the extreme range. Note that several days of extreme lows and highs were set in one year Plot C shows the differences between predicted and actual high and low temperatures. Students note the difficulty in even short-term forecasts Harrisburg Max T -Dec. 1999, 2000 Mean Temperatures Plot D (precipitation) shows that in the long-term record it has rained every day and in the year-to-date record Harrisburg was behind and in fact in a prolonged drought Harrisburg Avg. T - Dec. 1999, 2000 60 avg 1999 min 2000 max 2000 min 1999 40 avg 2000 50 avg norm min norm max norm 35 0.05 0 0 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan 50 max 1999 0.1 Right Scale D Harrisburg Min T - Dec. 1999, 2000 80 70 PPT 0.2 -30 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan 40 0.15 Ppt to date 1936 1994 -20 45 0.2 Norm ppt 1964 Plot A shows a typical data set for one week in January. Students would recognize that the max., min., average, and range of temperatures varies even over a short time period 1 0.8 0 0 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan 0.25 1.2 10 A Left Scale pred hi Record hi Record lo 1994 10 0.35 1.6 Min Temp Daily Norm 40 T (oF) 1.8 1967 50 Daily 30 1951 60 Min Temp 40 1990 T (oF) 50 1990 1990 Inches Max Temp 1937 Inches 70 25 50 Temp (F) 30 Temp (F) 60 Temp (F) T (oF) Over the last two decades the sciences at Dickinson College have reformed their curricula from a traditionally separated lecture and laboratory to an integrated active learning experience where by inductive reasoning students learn fundamental scientific principles and concepts. In Geology, we use topics of broad geologic interest (e.g., History of Life, Plate Tectonics, Oceanography) as a context for giving students practice in honing basic life skills specifically writing and quantitative reasoning. Topical courses allow significant depth in the content and thus lend themselves to using large datasets as vehicles for teaching fundamental principles and quantitative reasoning. The following discussion uses as an example our Global Climate Change introductory course. While traditional in its content (meteorology, climatology, paleoclimatology), the difference between weather and climate, the interactions between regional climate phenomena via teleconnections, and the evidence for and substantiation of long term climate change can be inferred using large datasets available for the most part on the World Wide Web. We have found that the introduction and statistical manipulation of large datasets needs to be progressive in nature. Starting with simple exercises using EXCEL as a tool give the students confidence when more complex datasets and statistical analyses are introduced. In addition we found that initially most students were unfamiliar with the basic functions of EXCEL. As time goes on we see less and less need for remedial spreadsheet instruction and can “raise the bar” with regard to the complexity of the datasets and exercise objectives. Moreover, we are finding that the students are readily translating the EXCEL and data analysis skills to other classes as we track those who take introductory classes and continue on to other electives or courses in the major. Here we present three exercises which require the acquisition and analysis of different datasets and increase in complexity over time. Each exercise requires a certain amount of dataset extraction, manipulation, and analysis as substantiation of inferences made in a 2-3 page fully formatted analytical paper. PPT 0 0 0 0 0 0.01 0.18 0 80 60 20 INTRODUCTION pred lo Means-Extreme Temperature Max-Min-Mean Temperatures T(oF) Many undergraduate students cannot adequately interpret large, complex datasets even when presented in graphical form. The need to improve our student’s quantitative reasoning and analytical writing skills has lead to the development of a series of integrated exercises in our introductory global climate change course. Global climate datasets are excellent resources for helping students improve their quantitative reasoning skills and understand of temporal and spatial interactive global processes. In an effort to provide formative assessment for student progress in both these critical skills, labs start with simple data extraction from newspapers and hand graphing and culminate in large and complex database analyses using Excel with computer graphing skills and basic statistics integrated into short written assignments. In advance of the first exercise, students gather a week’s worth of data from their hometown newspapers. Then the students find their state climatologist’s website and download the same data from the year before. They graph these data for both time periods, compare them, and turn their data and reasoned interpretations into a two-page paper. The following week a few students’ examples are highlighted to show the range of weather and climate change. By analyzing student results anonymously all learn the kinds of misinterpretations that can result and the depth of analysis that can be done even with a small dataset. Dataset size and complexity increases in subsequent labs using climate phenomena such as ENSO, monsoon intensity, and drought to explore the relationships between global climate change and local manifestations of those changes over time. Datasets come from the websites including NCDC climate, USGS stream gauge, and LTRR tree ring records. Besides learning the basic functions of Excel, students’ data analyses include regression and basic spectral analysis. Improved quantitative and written skills do translate to other courses and, hopefully, the quantitative literacy all citizens need in the 21st century. ASSIGNMENT: Collect one week of local weather data (predicted and actual temperature and precipitation) from your hometown newspaper. 30 20 40 30 40 20 Daily Norm Last yr 15 E 10 15-Jan 16-Jan 17-Jan 18-Jan 19-Jan 20-Jan 21-Jan 22-Jan G F H 10 0 20 1 8 15 Dates in December Plot E shows the students the variability of daily mean temperatures from one year to the next compared to the longterm mean 20 10 30 22 29 1 8 15 Dates in December 22 29 1 8 15 22 Dates in December 29 When comparing the months of December for 1999 and 2000, the maximum (F), minimum (G), and average (H) temperatures for 1999 are significa EXERCISE III: ENSO, CLIMATE CHANGE, AND STREAM FLOW IN THE WESTERN U.S. EXERCISE II: ANALYSIS OF EFFECTS OF LATITUDE, ATTITUDE, AND CONTINENTALITY USING HISTORICAL TEMPERATURE AND PRECIPITATION RECORDS Determine whether Southern Oscillation in the has America. an effect on temperature, precipitation and st ASSIGNMENT: Using the National Climate Data Center’s Global Historical Climatology Network find historical temperature andASSIGNMENT: precipitation data for two cities, one inthe North America and one on anysouthwest continent Pacific but North OBJECTIVES: 1) to determine if regional climate phenomena have global control of weather historically 2) to use EXCEL to do regres Your two cities should be at least 40o latitude apart. One city should be on a coastline, the other at least 500 km from the coast. OBJECTIVES: 1) A geography lesson in finding appropriate cities; 2) Finding good historical records in a dataset with 7000+ stations with records back to 1800; 3) manipulating, graphing, and analyzing a large dataset in EXCEL; 4) synthesizing the data to determine EXAMPLES: SNOHOMISH, WA and TOMBSTONE, AZ EXAMPLE: Students are given a list of weather stations for which there are associated USGS gauging station in the states of Arizona, California, Oreg Locale they choose. The students already know how to get to the NCDC site, now they must find a site with good ENSO data and explore DATA SHEET City #1 PORT TOWNSEND, WASHINGTON, USA Longitude:122.75o W Latitude: 48.1o N Elevation (meters): 0 m Distance from coast (km): 0 km # of years of record: Temp: 86 PPT: 86 Other geographically interesting facts: In rain shadow of Olympic Mts. On the Straits of Juan de Fuca City #2 BOULIA, AUSTRALIA Longitude: 139.9o E Latitude: 22.9o S Elevation (meters): 146 m Distance from coast (km): 780 KM # of years of record: Temp: 86 PPT: 86 Other geographically interesting facts: Middle of Great Australian Desert Version 1 Precipitation Data: Rainfall Data Through 1990 Version 2 Temperature Data: Regularly Updated Max/Min Temperature Data* Southern Oscillation Index (SOI) http://www.cgd.ucar.edu/cas/catalog/climind/soi.html Water Resources of the United States http://water.usgs.gov./ http://www.ncdc.noaa.gov/oa/climate/ghcn/ghcn.SELECT.html Snohomish Q vs. PPT Tombstone Q vs PPT 16000 1200 800 600 Rain-Discharge Relationship 12000 10000 8000 6000 2000 0 0 10 12 0 14 5 10 1 2 0 0 0 10 1961 Boul Temp 40 -12 YEARS SOI INDEX 19 82 19 77 19 72 19 67 19 47 19 42 19 62 30 F 2 25 20 0 15 -2 10 -4 -6 5 -8 0 YEARS PRECIPITATION (in.) 4 82 4 1915 Boul Temp 50 -10 6 19 2 20 -8 60 SNOHOMISH PPT VS SOI SOI Ppt Snohomish 12 per. Mov. Avg. (SOI) 12 per. Mov. Avg. (Ppt Snohomish) 77 6 70 YEARS 19 3 -6 -10 72 AVERAGE YRS 8 80 0 19 EXTREME YRS -4 -8 2000 67 10 90 -6 4000 19 -2 6000 62 100 14 -4 19 0 8000 47 110 -2 42 2 10000 19 120 C 0 19 37 19 37 19 42 19 47 19 52 19 57 19 62 19 67 19 72 19 77 19 82 16 12 5 4 130 Two examples of association of SOI with Precipitation, temperature, and stream discharge show relatively weak correlation For Tombstone, AZ and a relatively strong correlation for Snohomish, WA. Plot A shows good correlation of rainfall with discharge because rains tend to be monsoonal with significant runoff potential. However, in Plot B discharge and SOI do not show consistent correlation. Monsoonal rains are correlated to El Nino and La Nina events. Plot C shows small temperature range variations. Abnormally low high and low temps for a year show inconsistent correlation with La Nina events (note circled years). Rain-Discharge relations in Snohomish (plot D) is attributable to runoff and snowmelt depending on time of year. Both discharge and rainfall versus SOI show strong correlations with abnormally high rain and discharge being correlated with La Nina events and low rain and discharge correlated with El Nino events. 12-point moving averages help in delineating trends. 2 12000 37 1974 Boul Ppt Temp Tombstone SOI 4 14000 19 1914 Boul Ppt 140 18 JA N FE B M A R A PR M A Y JU N JU L A U G SE P O C T N O V D EC 1950 Pt Town Temp 1942 Pt Town Ppt 6 4 TOMBSTONE TEMP VS SOI SOI INDEX AVERAGE YR 1944 Pt Town Temp 0 19 37 19 42 19 47 19 52 19 57 19 62 19 67 19 72 19 77 19 82 RIGHT SCALE 7 Inches T (oF) 1874 Pt Town Ppt JA N FE B M AR AP R M AY JU N JU L AU G SE P O C T N O V D EC 30 200 YEARS 20 8 80 40 400 -10 TEMP (oF) 9 90 50 -4 -8 Inches 10 100 EXTREME YR 600 Average and Extreme Precipitation Average and Extreme Temp. Years 60 -2 -6 Plots above come from the CLIMVIS website. Students would work in EXCEL to average the max and min temperatures and with precipitation graph the monthly values for the period of record. This would be discussed in the paper in terms of relative temperature range and amount of precipitation at each site. Extreme and average years for Temp and Ppt for each city would also be compared (see plots A and B below) graphically. 70 800 E 16000 DISCHARGE (CFS) SOI INDEX 2 6 18000 1000 DISCHARGE (CFS) B 20000 TOMBSTONE SOI Q Snohomish SOI 12 per. Mov. Avg. (Q Snohomish) 12 per. Mov. Avg. (SOI) 19 1200 SOI INDEX 6 0 25 SNOHOMISH Q VS SOI TOMBSTONE Q VS SOI 4 20 PRECIPITATION (in.) PRECIPITATION (in.) Q Tombstone 15 19 57 8 57 6 19 4 19 52 2 Rain-Discharge Relationship 4000 200 0 D 52 400 Snow Melt-Discharge Relationship 19 DISCHARGE (CFS) SNOHOMISH DISCHARGE (CFS) A 1000 14000 DISCUSSION AND CONCLUSIONS: At Dickinson College we have used large datasets obtained from the World Wide Web for a variety of disciplines including climate change, environmental geology, oceanography, geochemistry, geomorphology, and others. Large data sets can be generated from long-term studies of various geologic processes for example, stream chemistry and changes in meanders of small streams. I have also used tree ring and ice core databases to do quantitative reasoning in my climate course. With each analysis a paper is required which asks the students to formulate the problem, the methodologies used to obtain and synthesize the data, and a discussion of the data analysis. In this way the students receive practice in analytical writing. By the third or fourth exercise the students have become quite good at manipulating data in EXCEL and better at synthesizing data. They learn as we see in the SOI vs. climate of the Western US exercise that positive results will not always be achieved and frequently the data are not easy to analyze. However, there is always an explanation for the data which may require a change in ones hypothesis. One other benefit we have found is that the statistical analysis skills have translated to other courses minimizing the time spent reteaching basic EXCEL techniques. There are some pitfalls to these kinds of exercises: •In the teaching of EXCEL for use as a statistical analysis tool, instructions must be very clear and detailed otherwise the students will be lost before encountering the data from which we desire them to learn about process. A primer in EXCEL will help those who are totally unfamiliar with EXCEL and be a good refresher for those who are familiar with EXCEL. •Add new statistical techniques gradually and re-use the familiar techniques. Some redundancy is good. •In crease the sophistication of the databases from week to week to challenge students. •Make sure databases used do not contain complexities for searching which would confuse introductory students. If they do and are still useful, the instructor may need to tailor the database searching as was done in Exercise III. This requires a lot of pre-lab trial and error experimentation on the part of the instructor. •The expected outcomes of exercises can be obtained more rapidly if part of the database search is done for the students ahead of time. This is particularly useful if you are using data from a dataset searched in previous labs and re-searching would be unnecessarily time consuming.