analyze climate data, interpret models/graphs
EAS 2680 Spring 2024 Homework #5 Due: 11:59pm Friday March 29 Please submit through Gradescope Estimating the influence of the El Niño-Southern Oscillation In this homework you will use the Climate Explorer, a free online tool that allows to make simple analyses of climate data on the fly. You are a climate scientist working for a company that trades crop yield futures and thus wants to know what the weather in certain agricultural regions is going to be next winter (December-February 2024/25). All you know is that there’s a La Niña in the forecast for next winter (https://iri.columbia.edu/our-expertise/climate/forecasts/enso/current/). You are tasked with investigating the following science questions: – What is the typical influence of the El Niño-Southern Oscillation (ENSO) on winter temperature and precipitation across the United States? How strong is this influence? You are given the following specific tasks: – Calculate and plot the Nino 3.4 monthly time series of sea surface temperature (SST) anomalies, which is a measure for ENSO phase and strength Correlate your Nino 3.4 time series with spatially resolved monthly temperature and precipitation for December-February average, resulting in correlation maps that show where precipitation and temperature co-vary with ENSO And here is how to do it: – Go to https://climexp.knmi.nl/start.cgi – On the right side under “Select a field”, click “Monthly observations” – Under “SST”, click on the Hadley Center SST dataset (“1870-now: HadISST1 1° reconstruction”) – Under “Get grid points, average area or generate subset” enter the latitude and longitude coordinates to calculate the Nino3.4 index (definition: https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4oni-and-tni). Make sure to correctly enter the longitudes; check the [ i ]nfo button on the same line for details. If you’re not familiar with longitudes, degrees East, degrees West, and their sign convention, look it up or make use of office hours. The Climate Explorer takes longitudes as °E (degrees East). Select “average” and set “50%” valid points as a criterion. Then click “Make time series” – – – You should see four plots. Make sure to find the plot that shows the SST anomalies and copy it into a word doc and give it a proper caption describing the data and method (basically the analysis you just did). As a sanity check: you should be able to identify the last two big El Niño events in 1997/98 and 2015/16 in your time series. Now, on the right side under “Investigate this time series”, click “Correlate with a field” Under “Precipitation”, select the “GPCC v2020 analysis (land)” at 1°x1° spatial resolution Scroll down to “Options” and specify under “Season” that you would like to calculate the correlation map based on 3-month averages – this way you will get correlation maps for all 3-month seasons (like December-February) Click “Correlate” and wait Among all the maps, find the December-February correlation map, copy it to your word doc and give it a proper caption that describes how you arrived at the map Following the same procedure, create a correlation map for temperature, using the temperature dataset labeled “1750-now: Berkeley 1°” (under “Temperature” > “Land”) Now all that is left to do is interpret the maps and write a short report – specifically, answer these three questions: 1) Where in the U.S. is the correlation between Nino3.4 and precipitation strongest? How strong is it? 2) Where in the U.S. is the correlation between Nino3.4 and temperature strongest? How strong is it? 3) Knowing that we are likely to experience a La Niña next winter, where in the U.S. do we expect lower chances for precipitation? For a refresher on correlation and how to interpret its values: https://en.wikipedia.org/wiki/Correlation Once you have finished your report, make a PDF and submit the PDF on Gradescope. Grading rubric (total 10 pts): • Correct figures (3 pts) • Correct captions (3pts) • Right answers to three questions (3 pts) • Nicely formatted PDF (1 pt)
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