I've provided a pair of resources that will help you prepare our final assignment. First, refer to this summary by Carolyn Snyder on 'The value of paleoclimate research in our changing climate'. At the start of her review, Dr. Snyder poses three questions that cut to the underlying motivation for this type of research. Is paleoclimate research really useful as a means to understand future climate changes? What are the most important challenges faced by this discipline? And finally, does the fact that our climate is changing mean that we don't need to understand the dynamics exhibited by the system in the past?

Second, you will need to access material related to the National Science Foundation's main program that funds paleoclimate research. Make sure to pay attention to those questions and issues that have been identified as research priorities for P2C2 (Paleo Perspectives on Climate Change).

Program synopsis
Program solicitation

Finally, please bring your laptops to class! You'll need them to prepare your response to my final challenge.

First let me apologize for being tardy in posting the readings for our next meeting. I made a quick visit to Iowa State on Monday and forgot to upload this entry before leaving.

Two papers to read for Thursday, and although I'll take responsibility for leading discussion, I'd like each of you to bring in at least one question related to the content of each article (or its implications).

First, we'll read through a very interesting article by Matt Therrell and Makayla Trotter on native American records of weather during the 19th century. While reading this paper, please think about how these types of historical observations are similar or different to the information recorded by natural archives. Can we learn anything from people that we can't learn from trees, rocks or mud?

The second paper is a brand-new result published in Nature Geoscience last week. A very large group of investigators have tried to estimate past changes in temperature across six of the seven continents (sadly, Africa is missing) during the last two millennia. I was involved in this work at an early stage, but I haven't read the article yet. One of the authors, Darryl Kaufman from Northern Arizona University also wrote a companion piece on the article at Realclimate.org, so it may be helpful to look at that as well.

See you all Thursday!

First, I'll ask you to read one paper on natural hazards. Based on our collective interests, I suggest we review a paleo-hurricaine article by Jeff Donnelly and Jon Woodruff. This article uses a core recovered from a coastal lagoon in Puerto Rico to reconstruct the behavior of intense hurricanes during the last 5,000 years. When you're reading the article, make sure to consider the record's chronology, its association with hurricane landfall and the implications of what is often described as a 'censored' record.

Donnelly Nature 2007.pdf

At 3:15, we'll suspend our discussion to walk over to Pillsbury Hall for this week's Earth Sciences lecture. The speaker will be Dr. Kim Cobb from Georgia Tech - check out her website here. I'm not sure what Kim will discuss in her lecture, but I'd suggest reading her most recent (2013!) Science article on ENSO during the Holocene.

During the class, an extensive review of droughts has been discussed (Cook et al., 2007 - North American drought: reconstruction, causes and consequences). Drought conditions are often estimated by the Palmer Drought Severity Index (PDSI), which is based on precipitation, soil moisture and runoff, as well as wet or dry conditions from previous time periods. Poor data coverage can compromise the representation of PDSI at large spatial scales.

Tree rings are the most suitable proxy data reconstruct PDSI in North America, as they show remarkable sensibility to droughts. Tree ring data are made from tree ring diameters averaged over homogeneous areas. However, not all trees across North America can be used. Reconstruction of drought events back in time revealed that dry events of up to 20 years occurred over the last centuries. Droughts over large parts of North America appear to be caused by a La-Nina pattern triggered by warmer tropical Pacific SST's. Overall, drought reconstructions provide a mean to go back in past to see what happened to ENSO and droughts and therefore predict what will happen in the future.

Summary by Thiago dos Santos

Matthews and Briffa, The 'Little Ice Age': re-evaluation of an evolving concept

Diaz et al., Spatial and temporal characteristics of climate in medieval times revisited

Due on April 4
Assignment #2 - The IRI Data Library
List of 10 references related to paleoclimate of your study area during the Holocene.
Because I neglected to mention the list of paleoclimate references last class, you can either submit your list to me on April 4 or via email by April 11.

Due on April 25
15-minute presentations on Holocene paleoclimate

Due on May 2
A paper, not to exceed 10 pages of text plus figures and references, which reviews the climate history of a single region during the Holocene. Key questions that should be addressed include: (1) What are the most important features of the modern climate in your region? (2) What proxies are available in your region, over the time interval specified? How are they related to climate? and (3) How different were past climates from modern conditions? Why is that important?

The Data Library maintained by the International Research Institute (IRI) for Climate and Society, which is based at Columbia University's Lamont-Doherty Earth Observatory, hosts as extraordinary wide array of earth science data. It's also an extremely powerful tool for data visualization and analysis. In this exercise, you'll use the Data Library to extract individual records from a global network of climate stations. You'll also learn how to average climate data over space and time and create your own regional climate indices.

Key links
The main page for the Data Library is located at http://iridl.ldeo.columbia.edu. The Library also provides a very useful tutorial that explains how to find datasets, select subsets of data in space and time and perform basic manipulations and visualizations. It's a great resource that I consult whenever I need to solve data problems or when I've forgotten how to find the data I need. The tutorial is available at http://iridl.ldeo.columbia.edu/dochelp/Tutorial/index.html.

1. Plot a temperature record from a single station
Starting from the Data Library's main page, select the option to find datasets 'By Category'. We want to obtain surface temperature data from one of the climate stations in Minneapolis, so select the 'Atmosphere' category. This new page lists many sources of atmospheric weather and climate data, including several versions of NOAA's Global Historical Climate Network. The GHCN is made up of daily and monthly climate observations from land surface stations across the Earth that has been subjected to a rigorous set of quality assurance reviews. Choose the set labelled 'NOAA NCDC GHCN v2', which will bring up a map showing the location of all climate stations in the network. Immediately to the right of the map, you'll see a link to 'Searches'. Click that and conduct a search for stations named 'Minneapolis'. The first option produced by your search will lead to Minneapolis, Kansas - don't pick that one. Click the station ID for the second link (ID 72658000), which gives you the climate station at the Minneapolis-St. Paul Airport. The next page lists all the datasets and variables associated with that station. Choose 'adjusted' and then 'mean' to pull up the corrected mean monthly temperature record for Minneapolis.

You'll notice that the upper part of this page shows thumbnails illustrating three different visualizations: a colored map, a black and white contour map and a time series. Click on the time series to bring up the temperature record for Minneapolis, which spans January 1835 to March 2012. This perspective is not very useful because the time series is dominated by the very large temperature fluctuations between winter and summer. Let's remove the seasonal cycle by clicking the bottom button to get data 'in view'. On this new page, select the 'Filters' option towards the top and select the 'anomalies' filter. This choice will subtract the monthly mean from each data point in the temperature record. Now when you click on the time-series visualization, the seasonal cycle has been eliminates and it's a lot easier to see the long-term trend in local temperatures.

If you'd like to change the appearance of the figure, hit the 'Edit plot' button and then select 'More options'. Increase the number in the 'Plot size' box to make the data easier to see. Finally, go back and select the 'Expert Mode' option. This choice brings up a text box that you can use to enter commands directly. On the line following the existing text, enter 'T 12 boxAverage' , click 'OK' and then select the time series visualization. You've just created a record of annual (12-month) temperature anomalies for Minneapolis.

2. Create your own Nino 3.4 index
Next we'll learn how to create spatial averages over a gridded datasets so that we can make our own index of sea-surface temperature anomalies in the Niño 3.4 region. Search the IRI's dataset's by category and select the 'Air-Sea Interface' option. We're looking for NOAA's extended reconstructed global sea surface temperature (ERSST). This dataset doesn't have the fine spatial detail of more recent SST records (anomalies are averaged over a 2° x 2° grid) but because it goes back to 1854 it gives a very long-term perspective on ENSO dynamics. We'll use version3b, which was released in 2008. Instead of using the anomaly filter, just select the SST variable that has already been converted to anomalies. If you click on the colored map, you'll see that this dataset covers the entire global ocean but we only need data from the Niño 3.4 region. Go back and click on the 'Data Selection' link, which will allow you to select a subset of the complete dataset. The Niño 3.4 region extends between 5°N and 5°S and 170°W to 120°W. Enter those values for 'X' and 'Y' and then hit 'Restrict Ranges'. After that, click the 'Stop Selecting' button. If you click on the map icon now, you should see a long rectangular box that reflects your selection.

Now that we've chosen the geographic domain of our analysis, we need to create a single index of SSTs over the Niño 3.4 region. Click 'Expert Mode' to bring up the coding panel. Under the existing text, enter '[X Y] average' and click OK (include a space between the X and the Y!). Now choose the time series option to plot your Niño 3.4 index. Go back and choose the 'Tables' option - this link will allow you to view the numerical values for your index as a columnar table. If you'd like to download your index, go back again and select the "Data Files' option, which will allow you to obtain the data in whatever format you desire.

3. Provide a spatial context to the mid-1100s drought in the upper Colorado River basin
Tree-ring estimates of past drought in the upper Colorado River basin (Meko et al., 2007) suggest that the most extreme and long-lasting drought of the last several centuries occurred in the mid-1100s. This event included a 13-year stretch of below-normal river discharge between AD 1143 and 1155.

Search data 'by source' and bring up the page associated with the Lamont-Doherty Earth Observatory of Columbia University. Select the 'Tree Ring Laboratory' option, followed by the 'North American Drought Atlas 2004'. The North American Drought Atlas uses a network of moisture-sensitive tree-ring records from Canada, the United States and Mexico to estimate changes in drought conditions across the continent during the past two millennia. In its primary application, the Atlas has been used to place recent dry and wet intervals within a context of long-term variability and to identify droughts that were more persistent or more severe than historical droughts. The Atlas has helped clarify the impact of drought on wildfire and ecological dynamics, provided a framework to test the stability of relationships between remote climate forcings and North American drought and served as a real-world target for climate model simulations.

The Drought Atlas contains only one variable: tree-ring estimates of the Palmer Drought Severity Index (PDSI). Choose 'Data Selection' and set the time range (T) to match the core period of the low-flow period in the Colorado River reconstruction. Click 'Restrict Ranges' and then 'Stop Selecting'. Next, use Expert Mode to add the following code: '[T] average'; then click 'OK'. Click on the colored map icon to show the geographic extent of drought conditions during the mid-1100s. Selecting the option to 'draw coasts' and hitting the 'redraw' button will make the map easier to interpret.

4. Estimate the spatial correlation between precipitation in Minneapolis and the rest of North America
We can assess the spatial heterogeneity of different aspects of the climate system by mapping the correlation between a single local record and the same parameter across a broader region. In this section, we'll examine precipitation records from the University of East Anglia's Climatic Research Unit. From the CRU's page at the Data Library, choose the dataset named 'TS3p1' and select the monthly precipitation variable. Use the data selection tool to restrict the range of the data set to the region 45° to 46°N and 92° to 93°W. At the same time, restrict the time range of the set to Jan 1901 - Dec 2002. Then, in Expert Mode, use the ' [X Y]average' command to create a spatial average and convert the record to anomalies. Next, use Expert Mode to add the following code:
SOURCES .UEA .CRU .TS2p1 .monthly .prcp
X (130W) (70W) RANGE
Y (20N) (70N) RANGE

We're almost ready to compare the single precipitation anomaly record for the area around the Twin Cities against the same data collected across central North America. We've added in a restriction for the X and Y range of the field data because the Data Library struggles to compute calculations using the entire domain (and we don't need to see the map for the entire Earth anyway).

Finally, add one last command by entering this line of code:
[T]correlate

Select the colored map thumbnail to plot the correlation between Minneapolis precipitation and precipitation across the broader region. Again, you might want to draw in the coasts to make the map easier to read.

Imagine that you had a perfect proxy for annual precipitation in the Twin Cities. Based on this map, over what region would it be reasonable for you to make inferences about past changes in precipitation?

Related resources
Cook ER, Krusic PJ (2004) The North American Drought Atlas. Lamont-Doherty Earth Observatory and the National Science Foundation.
Cook ER, Woodhouse C, Eakin CM, Meko DM and Stahle DW (2004) Long-term aridity changes in the western United States. Science 306: 1015-1018.
Global Historical Climatology Network-Monthly, National Oceanic and Atmospheric Administration, http://www.ncdc.noaa.gov/ghcnm/
Mitchell, T. D., and P. D. Jones (2005), An improved method of constructing a database of monthly climate observations and associated high‐resolution grids, Int. J. Climatol., 25, 693-712.
Smith, T.M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006). J. Climate, 21, 2283-2296
University of East Anglia Climatic Research Unit (CRU) Global 0.5° Monthly Time Series, Version 2.1 (CRU TS 2.1). http://www.cru.uea.ac.uk/~timm/grid/CRU_TS_2_1.html

Please turn in a hard copy containing the main products from each of the four sections at the beginning of next class. This exercise is intended only to introduce you to the capabilities of the IRI Data Library, so it is not necessary to include written comments describing your graphics as part of your submission.

First, we'll finish off our presentations on regional climate history with one last region (Sri, thank you again for being willing to delay your presentation). Second, Beth and Mike will lead us through a discussion of an article that uses tree rings and other natural archives to track drought across western North America. As always, the rest of us will be responsible for bringing our own questions and comments to push the discussion forward.

North American drought: Reconstructions, causes, and consequences
I know this is a long paper, so please focus your reading on Sections 1 to 6 (inclusive).

Finally, in the last half of the class, a short set of exercises will help introduce you to the Data Library maintained by the International Research Institute for Climate and Society, which is based at Columbia University's Lamont-Doherty Earth Observatory.The library hosts as extraordinary wide array of earth science data, and is also an extremely powerful tool for data visualization and analysis. I'll post the step-by-step guide once we get closer to Thursday, but if you want to check it out ahead of time, start here.

1. Jones and Briffa, 1992, Global surface air temperature variations during the twentieth century: Part 1, spatial, temporal and seasonal details

2. Mann et al., 1998, Global-scale temperature patterns and climate forcing over the past six centuries

3. Osborn and Briffa, 2006, The Spatial Extent of 20th-Century Warmth in the Context of the Past 1200 Years

Each paper demonstrated a unique process of critical analyses, cross-references, multi-proxy networks, and statistical methods. With a clear view of past records, we can continue to record accurate world-wide temperatures.

Kathleen Wendt

Trend: relative to your frame of reference a long term progressive continuous change

Step: A distinct difference between point A and point B that is discrete or abrupt

Oscillations: periodic variations around a static or slowly changing point; (sin)

Fluctuations: change from the mean; aperiodic

Time series: geophysical record ordered in time

The time domain is not the only option we can use to look at climate data, we can also look at it through the frequency domain. The time domain can be broken into different sin waves with different frequencies and amplitudes. This can be shown as a graph with frequency on the x-axis and variance on the y-axis to show relative strength at a particular frequency. This leads to 3 additional terms used to describe climate data.

White: even wavelengths with no preferences
EX: atmospheric data like precipitation

Red: long wavelengths with slow changes
EX: hydrological data because has 'memory'

Blue: short wavelength with fast changes
EX: few (ENSO has the bluest signal)

Sarah Appleton

Next week we'll slip into a lower gear and discuss how the climate system changes over longer timescales (decades to several decades). We'll start with a paper introducing us to the Pacific Decadal Oscillation, which is similar to ENSO in many ways but has its own distinctive flavor. We'll then read a short (but technical) paper on the physical dynamics of the PDO, along with a proxy-based estimate of this system during the last thousand years.

Sarah and Grace will lead our discussion while the rest of us will be responsible for contribution our own questions and comments about this set of articles.

Mantua and Hare, 2002, The Pacific Decadal Oscillation

Newman et al., 2003, ENSO-forced variability of the Pacific Decadal Oscillation

MacDonald and Case, 2005, Variations in the Pacific Decadal Oscillation over the past millennium

However, the process is more difficult than simply measuring and counting tree rings. Tree rings can be microscopic and hard to see, missing, and/or structures can be misidentified as rings. To compensate from this many trees are compared to get a more accurate history. In addition, not all tree are sensitive to climate and sensitive trees act as the best indicators of climate. Once tree ring widths are measured, the fact that age influences tree ring width must be accounted for by applying mathematical models. This data can also be compared to instrumental data to determine the accuracy. To obtain tree ring records from around the globe, visit the International Tree-Ring Database. Overall, tree rings are a very useful proxy in the analysis of climate.

Kenzie Kelly

As a reminder, all of us will be responsible for reading all three papers and bringing a list of related questions (either about the main ideas of the paper, technical questions or terminology).

Jones and Briffa, 1992, Global surface air temperature variations during the twentieth century: Part 1, spatial, temporal and seasonal details

Mann et al., 1998, Global-scale temperature patterns and climate forcing over the past six centuries

Osborn and Briffa, 2006, The Spatial Extent of 20th-Century Warmth in the Context of the Past 1200 Years

In the second half of class, we'll talk about how paleoclimatologists use statistics to describe their data and to make inferences about the past behavior of the climate system. To prepare for that discussion, please read 'Timescales of change by Pat Bartlein.

Because we'll only spend one class on statistical methods, we'll focus on understanding the terms used commonly in climate science and try to develop a 'survival guide' that will help us interpret some of these tools.

Due February 21

I'll give a brief introduction to the Reanalysis in class, but we'll spend most of our time getting to know how the tool works. We'll help each other get familiar with making maps and conducting basic analysis, and then I'll ask you to work through a short exercise to be completed by our following class. Please bring your own laptop to class next week.

To get ready, please read through this article by Kalnay et al., which lays out the need for the reanalysis and explains (in great detail) how it was produced. I realize this paper is very long and pretty technical but skimming through it should help you get ready for our hands-on exercise next week.

Kalnay et al., 1996, The NCEP/NCAR 40-Year Reanalysis Project.pdf

If you'd like to poke around with the Reanalysis in advance, here it is:
http://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml