The Medieval Climate Anomaly
I've uploaded two key resources in support of yesterday's discussion of the 'Medieval Climate Anomaly' (aka 'The Medieval Warm Period' aka 'The Medieval Warm Epoch'.

The first is a classic article by Hubert Lamb called 'The Early Medieval Warm Epoch and Its Sequel'. Because it was published in 1965, most of the sources used to support Lamb's argument are out of date and most of the important proxies spanning the last 2K didn't exist. Even so, as the first article to make the case for a warmer world during the 'medieval period', this paper is the starting point for nearly every discussion of the MCA.

The second is a more up-to-date summary written by Henry Diaz and several collaborators on 'Spatial and Temporal Characteristics of Climate in Medieval Times Revisited''. I used this article as the framework for yesterday's lecture, and several of the graphics we reviewed come straight out of this paper.

Finally, I'll also point you to an online post on medieval English vineyards and their interpretation as temperature proxies. The fact that the English were making wine in the early 11th century was one of the key points backing up Lamb's claim of a warmer medieval climate, but exactly what we should interpret from this evidence is somewhat controversial.

Genghis Khan
Next week (April 16), we'll be joined (via Skype) by Dr. Neil Pederson. Neil is a research scientist at the Lamont-Doherty Earth Observatory, which is affiliated with Columbia University in New York. He's primarily trained as a forest ecologist, but he is also an expert in tree rings, paleoclimatology, and natural history.

Neil was the lead author on a recent paper published by the Proceedings of the National Academy of Sciences linking climate change during the 13th century with the expansion of the Mongolia Empire. Neil has made several trips to Mongolia to develop tree-ring records for that region, and has worked closely with Mongolia scientists for nearly 15 years. Please read the article before class and come prepared to ask questions about Mongolia field work and the beautiful data he and his colleagues have extracted from some very ugly old trees.

You may also want to listen to Neil's colleague, Dr. Amy Hessl from West Virginia University, discuss this work on Minnesota Public Radio. You can check that out here.

This summary is courtesy of Jennifer Krueger.

The Little Ice Age (LIA) climate was generally colder conditions and occurred approximately between the 16th and 19th centuries. The LIA was hemisphere wide, meaning that it did not affect just Europe. There is evidence in northern Asia and it is important to note that other all regions experienced colder than normal temperatures but not in the same way. In class we discussed the LIA characteristics and causes and more specifically glacierization and climate.

Glaciers respond slowly and depend greatly on climate. Unfortunately they are non-linear causing it difficult to connect specific responses in climate. However, moraines provide us with helpful evidence as to when and to what extent glaciers have expanded and rescinded. A moraine is build-up of sediment among the margins of glaciers as they expand downward. As a glacier rescinds the sediment and debris is left behind creating a distinct landscape of a past glacier. Moraines have a potential for a high-resolution proxies however dating them can be challenging and older moraines get erased as glaciers extend. There a several different types of moraines yet lateral moraines are the main indicators of past glaciers. Tree rings can show when they were killed or tilted by a glacier. Trees growing above the lateral moraine, after the glacier stopped, can help in dating a glacier also. Other proxies available are photos, sea ice, ice cores, and moraine sediments.

Lower irradiance and volcanic eruptions are thought to be potential causes of the LIA. We now know that the sun experiences a cycle of sunspots, which produce a lower radiance causing an overall cooling of the earth. Evidence of higher intense volcanic eruptions occurred during the LIA and the sulfate aerosols released into the atmosphere condenses and reflects sunlight which reduces the amount of radiation able to get to the land surface resulting in cooler temperatures. Ice cores can be used to date volcanic eruptions and their magnitude, and these events can significantly affect climate and temperatures.

This summary is courtesy of Chris Mahr.

Paleoflood hydrology is the science of reconstructing the magnitude and frequency of large floods using geological evidence and a variety of interdisciplinary techniques. Paleofloods are events that are generally recorded outside of gaging records and can be extremely ancient. Paleoflood hydrology was developed in the 1970s as a way to understand the magnitude of extreme flooding in central Texas and has evolved considerably since then with broad scientific and social relevance. The evolution of these methods has made it possibly to overcome difficulties such as inaccuracies in estimating the ages of floods, inaccuracies in reconstructing discharges, lack of robust statistical methods for incorporating data in flood-frequency analysis, and the effects of climatic shifts. Climatic shifts are used since floods are a hydroclimatic process and their frequency may be affected by climatic variability. Flooding causes extensive damage, which removes much of the evidence of their occurrence, and can last as long as 2-6 weeks. Flood damages between 1903 and 1999 peaked with floods associated with the 1993 El Ninos, and the 25-year running mean of flood damages has risen from $.043 to $3.15 billion dollars per year! The damage including the removal of evidence can be due to erosion or simply washing material downstream. This destructive nature of floods can therefore make it a challenge to see the evidence of a flood in proxy records. The confined area of a flood (flood plain) is also a deterrent in studying paleofloods, compared to larger areas for finding evidence of temperature changes, for example. Because past floods can inform and prepare us for future floods in a given location, it is important to be able to predict and understand floods. However, floods are extremes, which makes it difficult to fully understand patterns over small interval, such as 100 years. Therefore, it is vital to find a way to build a longer record of flooding.

Paleoflood hydrology can be a challenge to study, but there are ways to do so. The most crucial step is to find real historic records, such as journals created by traders or farmers. Knowing when large floods have taken place makes it easier to be able to use proxy records, and these can include lake core samples, flood plain sediment accumulation, and tree ring records. Tree ring records may seem to be a dead end at first though, as it could be difficult to differentiate between the negative effect on the rings by both flooding and droughts. However, tree rings do tend to show a unique ring that has no dark fiber during years of large floods, and this can be proven by comparing the tree ring record to historical records. These rings can belong to modern trees, sub-fossil trees, or even trees used for the construction of buildings on flood plains. Using this method, a 350 year record of Red River flooding in Minnesota, North Dakota, and Canada was constructed. Other techniques for studying paleofloods include regime based paleoflow estimates, paleo-competence studies, paleostage estimates and bounds, and floodplain stratigraphy. Regime based paleoflow estimates use empirically derived relations to estimate the value of high-probability flow events, such as the mean annual flood. Paleocompetence studies use empirical regression or theoretical expressions to relate very large sedimentary particles to hydraulic conditions necessary for their transport or deposition. Paleostages are estimated by documenting flood-induced erosion or deposition near maximum water levels of large floods. Radiocarbon dating and mineral luminescence are useful dating methods for younger floods.

As promised, here are the two (optional) readings associated with today's lecture on paleoflood hydrology, as well as the (non-optional) reading for Friday's discussion of the 'Little Ice Age' concept. I won't assign specific questions to direct your reading of the Matthews and Briffa article, although the ideas and examples from my lecture are more likely to stick if you review it beforehand.

Paleoflood hydrology
Baker et al., 2002. The scientific and societal value of paleoflood hydrology. [PDF]
Wertz et al., 2013. Vessel anomalies in Quercus macrocarpa tree rings associated with recent floods along the Red River of the North, United States. Water Resources Research. [PDF]

The 'Little Ice Age'
Matthews and Briffa, 2005. The 'Little Ice Age': reevaluation of an evolving concept. Geografiska Annaler. [PDF]

Late Holocene references

As a reminder, I've asked each of you to compile a set of sources that you'll use to summarize climate variability during late Holocene in your study region.

Just like with the previous assignment, I'd like you to submit your list of citations (please aim for roughly 10 sources) as a hard copy. I'll review your list and suggest ways you might expand or limit your search to focus on one or more key aspects of paleoclimate in your target area.

In the syllabus, I set a deadline of April 2 for this second list. If you're not prepared to submit your list by Wednesday, I can allow an extension until Friday (April 4). In that case, I'll return my comments to you by email no later than end of business on Monday, April 7.

Drought and megadrought

On Friday we'll briefly discuss these two papers that outline how tree rings have been used to estimate past drought in North America and explain why that perspective is relevant to contemporary discussions of water scarcity in the America west.

The first article is a 2004 Science paper by Ed Cook and collaborators. This article presented the new (at the time) North American Drought Atlas, which uses tree-ring widths to estimate drought severity (as represented by the PDSI) over the last 2,000 years. Please consider these three questions when reading this article: What does the 'Drought Area Index' represent? How does the extent, severity or duration of modern droughts compare to those of earlier periods? And what do the authors suggest as potential causes (or 'forcings') of medieval drought?

The second is an opinion piece by Jon Overpeck and Bradley Udall that steps back to consider drought in the arid west, its potential connection to climate change, and its implications for water security in this part of the country. Consider these two questions: What environmental changes have been observed in the Colorado River basin and the broader American west? How do paleoclimate reconstructions from tree rings and other proxies contribute to discussions about the future of drought in this region?

As always, please also come prepared with your own questions and comments about these two articles.

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 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

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 present. 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.

Finally, choose another station (perhaps one located in the target region for your Holocene climate project), and produce the same plot showing changes in temperature over its period of record.

2. Create your own Nino 3.4 index
Next we'll scale up from individual stations and create a spatial averages over a geographic region. We'll make our own index of sea-surface temperature anomalies in the Niño 3.4 region since we know what that should look like.

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 version 3b, 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 spaces between the X and the Y!). At this stage, you may also see a line of code that appears as 'T ####'. If that's the case, your selection is restricted to only a single month. Delete that line to expand the time range of your selection to include the enter period of record.

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. Estimate the spatial correlation between precipitation in Minneapolis and the rest of North America
In the previous example, we computed a spatial average of a region that we know is an important target for climate and paleoclimate research. In other cases, it make not be clear what area we should use to compute our spatial average.

One of the ways we can assess the spatial structure (or 'representative-ness') 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:


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?

4. 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. How widespread was the 12th century 'megadrought' across the western United States?

We'll investigate that question using the North American Drought Atlas, The 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.

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 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.

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,
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).

Please turn in a hard copy containing the main products from each of the four sections at the beginning of class on April 2.

This exercise is only intended 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.

This summary comes courtesy of Dan Crawford.

Our understanding of the past climate of the Earth has changed dramatically over time. The first attempt to truly understand the temperature of the past 1000 years was undertaken by Hubert Lamb in 1965 in East Anglia, England. Lamb looked at a compilation of sources such as [glacial advances] and historical records of vineyard distribution, phenology, and botany to determine how temperature had changed over time (900 AD - 1900 AD). Additionally, his analysis was the first attempt at creating a record with both instrumental data and historical evidence. Out of this work came the first mention of the Little Ice Age and the Medieval Warm Period.

Another step forward in understanding the past temperature of the earth came in 1981 through study near the Arctic treeline in northwest North America. In this effort, tree rings are used as a proxy for past temperature change. Trees in this area are especially useful for temperature reconstructions because tree growth in the Arctic is limited by summer temperature rather than moisture availability. The region is also useful for past temperature analysis due to the effect of "polar amplification," meaning that as the climate has been changing across the globe, the effect is amplified in the Arctic. Thus, recent temperature changes in the record should be easy to identify. Additionally, this analysis incorporated use of a correlation function to directly compare the proxy data to the instrumental data. At the study site, tree growth was best correlated with June/July temperature. However, at the end of the day this is still a study using a single proxy record from a single site; the geographic distribution of data is minimal.

A more recent (1998) endeavor was the first to employ a multi-proxy, annually resolved record with a wide geographic distribution. Through statistical analysis of these many records, the researchers were able to directly compare the proxy data with instrumental data. Finally, they produced a quantitative record of temperature with a defined margin of error to estimate mean temperature and its associated spatial patterns back to they year 1400. The results show a distinct "hockey stick" shape, where temperatures in the last ~100 years start to rise rapidly (the blade of the stick). When viewed in historical context back to 1400, it can be seen that this rapid rise is pretty unusual.

I hope everyone enjoyed Ben Cook's visit last class. Ben is also affiliated with Columbia University's Lamont-Doherty Earth Observatory, which is arguably the top institute anywhere devoted to the study of our planet.

Lamont just posted its call for applications to its summer internship program. The program is open to US citizens or permanent residents who have completed their junior or sophomore year in college with majors in earth science, environmental science, chemistry, biology, physics, mathematics, or engineering. The set of proposed topics includes several dedicated to paleoclimate and drought (including one sponsored by Ben's post-doc advisor Richard Seager). If you qualify and are interested, come talk to me about it.

Lamont-Doherty Summer Internship Program

Taking the Earth's temperature

In today's lecture, we discussed three studies that attempted to use proxies to understand how the Earth's temperature has changed during the (late) Holocene.

The most up-to-date summary of proxy reconstructions of global (or hemispheric) temperature was published earlier this year by the Intergovernmental Panel on Climate Change.

The latest report from the IPCC's 'Working Group 1' (which focuses on the physical science of climate change) devotes an entire chapter to paleoclimatology. I've cut out a 3-page section of that chapter that discusses global temperature changes during the last 2,000 years and posted that here.

If you'd like to read through the entire paleoclimate chapter (called 'Information from Paleoclimate Archives', you can download that yourself from the IPPC's website (direct link is here).

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