Tree-ring Reconstructions of Past Climate in the Southwest

Status: 
Completed
Start Date: 
2000
End Date: 
2002
CLIMAS Investigators: 
Gregg Garfin
Malcolm Hughes
Collaborators: 
Fenbiao Ni
Abstract: 

The longest instrumental records date back about 100 years at some locations in the Southwest. While these records are crucial to understanding variations in precipitation over the last 100 years, the full range of variability in southwestern climate may not have been experienced within this time frame. The extension of the record to earlier times can provide additional information on the length and severity of past droughts and can be used to provide water managers with information about the range of pre-industrial climate variability.

Tree rings provide a useful tool to help extend winter precipitation records further back in time. The growth of many Southwestern tree species can be linked directly to the total amount of precipitation that falls during the cool season, or winter between November—April. Statistical models have been developed to relate tree growth to precipitation. These relationships are then applied to periods prior to the instrumental record to reconstruct past precipitation back a thousand years.

This research used tree-growth information collected from hundreds on trees growing at 19 sites to reconstruct total cool-season precipitation (November—April) back to AD 1000 using linear regression and a neural network reconstruction methods (see Methods for more details). Individual reconstructions were developed for each of the National Climatic Data Center climate divisions in Arizona and New Mexico.

Background

Annual growth rings in trees have been used to reconstruct Southwest climate back to AD 1000.Drought is a common feature of the Southwestern United States and presents challenges for people living in the region. Ensuring an adequate water supply through prolonged dry periods requires advanced planning for the distribution and storage of water for later use and effective management of this valuable resource.

Winter precipitation is crucial to water managers, farmers, ranchers, and urban areas throughout the Southwest. Precipitation during the cool winter months usually falls from slow moving storms that enable water to percolate into the soil, to recharge aquifers, or collect in reservoirs. Moreover, snow falling during the winter months stores water until later in the season when temperatures rise and is an important part of the water budget. In contrast, summer precipitation often results from strong convective thunderstorms and is often intense—running off the land's surface before being captured for use by plants, and is subject to the high evaporation rates that accompany the Southwest's intense summer heat.

The relationship between tree growth and cool-season precipitation. The blue line shows the tree-ring growth index for El Malpais National Monument, New Mexico and the red line depicts precipitation recorded by rain gauges in New Mexico Climate Division 4. Notice that while the tree-rings do a good job of matching dry winters, they do not quite match the wet years. Above a certain threshold, precipitation is no longer limiting to tree growth. Also note the very dry conditions during the 1950s and the post-1976 wet period. Source: Tree-ring chronology developed by Dr. Henri Grissino-Mayer. Chronology data available from the International Tree-Ring Data Bank.In the Southwest region, the longest instrumental records date back about 100 years at some locations. While these records are crucial to understanding variations in precipitation over the last 100 years on annual and decadal timescales, the full range of variability in Southwestern climate may not have been experienced within this time frame.  The extension of the record to earlier times can provide additional information on the length and severity of past droughts and can be used to provide water managers with information about the range of pre-industrial climate variability.

Tree rings provide a useful tool to help extend winter precipitation records further back in time than the instrumental (rain gauge) record. The growth of many Southwestern tree species can be linked directly to the total amount of precipitation that falls during the extended winter, or cool season (November-April). Tree rings act as a surrogates for direct observations, providing information on pre-historic precipitation variability before rain gauges were in service. Statistical models can be developed to relate tree growth to precipitation during periods when instrumental records are available. These relationships are then applied to periods prior to the instrumental record to reconstruct past precipitation back a thousand years.

Research Methods

The reconstruction of cool-season (total November-April) precipitation back to AD 1000 was accomplished using stepwise linear regression and artificial neural network techniques. Linear regression relies on the linear relationship between tree-growth and precipitation, while the neural network approach can be used to model both linear and nonlinear relationships between tree-growth and precipitation.

Linear regression is a statistical technique used to quantitatively model the relationship between tree growth and precipitation. Regression models for each climate division are constructed by using a stepwise multiple regression procedure to predict precipitation from one or more of the 19 tree-growth chronologies based upon the linear relationship observed between the data sets. The observed relationship can then be applied to predict precipitation prior to the time period when instrumental records were available. A suite of statistics are used to assess the reliability and strength of the relationship expressed in each regression model.

The neural network technique relies on developing generalized relationships between tree growth and cool-season precipitation after being trained in a self-organizing learning procedure. The neural network technique allows for the definition of complex relationships between input/output data including those that may be nonlinear. Neural network techniques rely on an iterative learning process that functions by repeatedly training the input data used in the model to minimize error. In a sense, a neural network functions like a human's brain in that it learns through repeated trials. The results of these repeated trials are stored as a system of weights that are applied to data used in the final model. The advantage of neural networks lies in their ability to learn and represent complex and often nonlinear relationships directly from the data being modeled.

The 19 tree-ring sites were selected based on several criteria. First, the sites were required to have a strong relationship with cool-season precipitation and extend through at least 1988. Second, all the individual tree-ring samples in a given site needed to be at least 500 years in length to preserve the multi-decadal precipitation signal contained within the tree-rings. The chronologies also had to have sufficient tree-ring sample replication between AD 1000 and the late 20th century in order to maintain the strength of the climate signal during earlier time periods.

Linear regression vs. neural network reconstructions.Cool-season precipitation for the period 1896-1988 for each of the NOAA climate divisions in Arizona and New Mexico were used to define the relationship between tree growth and precipitation to develop empirical models suitable for the reconstruction. The linear regression reconstructions explain between 11 and 53 percent of the variance in cool-season precipitation between 1896-1930 (with an average of 32 percent). The neural network reconstructions are similar, explaining between 13 to 64 percent of the variance over the same period (31 percent average).

Results indicated that the reconstructions developed using linear regression techniques were better recorders of dry periods while the neural network technique captured wet periods better. To exploit the best qualities of each reconstruction method, the two were combined.

Research Outcomes

CLIMAS developed a tool that allows stakeholders to access statewide reconstructions for Arizona and New Mexico, as well as individual reconstructions for each climate division.

Reconstructions of cool-season precipitation (November-April) for all climate divisions using both the linear regression and neural network procedures. The results of this work have been published by Ni et al., 2002 in The International Journal of Climatology.

Cool-season precipitation reconstruction data from this project are available for download from the NOAA Paleoclimatology Branch: http://www.ncdc.noaa.gov/paleo/pubs/ni2002/ni2002.html

Related Publications

Grissino-Mayer, H. 1996. A 2129-year reconstruction of precipitation for northwestern New Mexico, USA. In J. Dean, D. Meko, and T. Swetnam (eds.) Tree Rings, Environment, and Humanity. Radiocarbon:191-204.

Ni, F., T. Cavazos, M. Hughes, A. Comrie, and G. Funkhouser. 2002. Cool-season precipitation in the Southwestern USA since AD 1000: Comparison of linear and nonlinear techniques for reconstruction. International Journal of Climatology 22:1645-1662.

Paleoclimate Tool

The longest instrumental records date back only about 100 years in the Southwest, a time frame that likely does not capture the full range of climate variability. Extending the record to earlier times provides additional information on the length and severity of past droughts.

This tool allows users to visualize the climate variability during the past 1,000 years or so in each climate division in Arizona and New Mexico.

Map area of Climate Divisions for Arizona.Arizona climate division 1. Arizona climate division 2. Arizona climate division 3. Arizona climate division 4. Arizona climate division 5. Arizona climate division 6. Arizona climate division 7.
Map area of Climate Divisions for New Mexico.New Mexico climate division 1. New Mexico climate division 2. New Mexico climate division 3. New Mexico climate division 4. New Mexico climate division 5. New Mexico climate division 6. New Mexico climate division 7. New Mexico climate division 8.

Arizona Climate Divisions (CD)

New Mexico Climate Divisions (CD)

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