LONG-TERM CLIMATE CHANGE/VARIABILITY
Anthropogenic Change GCM Experiments
My involvement in the area of long-term climate change began with my
arrival at GFDL (since this is a topic of great interest here). I began with a
project aimed at examining the response of the stationary waves and zonally
averaged zonal component of the wind in GFDL GCM's and comparing with
atmospheric observations. Due to other demands, this work was never submitted
for publication, although some of the results are presented in
Lanzante (1992, 1993).
After this a collaboration with my GFDL colleague Keith Dixon examined the output from an ensemble of low resolution CO2 + aerosols GCM experiments. Dixon and Lanzante (1999) examines the effects of the historical (radiative) starting time (i.e. how far back in time you go to begin) vs. the influence of the initial conditions for the behavior in the 21st century. We found that the two have comparable influence. The implications are that it may not be necessary to go back too far in time, however, the use of an ensemble of experiments would be wise in order to reduce the impact of the differing (random) initial conditions.
Another collaborative project (Shine et al. 2003) involved a comparison of stratospheric temperature trends from a wide variety of models. Our LKS radiosonde temperature dataset (Lanzante et al. 2003a; Lanzante et al. 2003b) was used as one of the observational references. The results indicate that while the models share similar patterns in the vertical structure of trends, the magnitudes of the trends vary considerably more.
My most recent work (in progress) is aimed at comparing the vertical temperature structure of trends in GFDL GCM climate change experiments with our homogenized radiosonde temperature dataset (Lanzante et al. 2003a; Lanzante et al. 2003b). An extension of this work, in collaboration with Melissa Free of NOAA's Air Resources Laboratory (ARL), is aimed at comparing trends from several GCMs with those from both our homogenized radiosonde temperature dataset as well as those from a similar product produced by the British Met. Office. We find that systematic biases in the observed data have masked some of the greenhouse warming signal. Removal of some of these biases almost universally improves the agreement with climate models. As such this may have implications for detection and attribution studies.
Atmospheric Water Vapor
The relationship between temperature and water vapor has important implications
for long-term climate change due to the positive feedback of water vapor in
global warming. Earlier studies at GFDL
(Sun and
Oort, 1995);
Sun and
Held, 1996)
suggested that the relationship between temperature and water vapor is much
stronger in the GFDL GCM than in the real atmosphere. More recently, in collaboration
with GISS researchers Mike Bauer and Tony Del Genio, a project which reexamines
the conclusions of these earlier studies was completed
(Bauer et al. 2002).
We found that redoing the calculations with a more consistent treatment of the
GCM and observed data has a major impact, such that the difference between the
model and real atmosphere is not nearly as large as was earlier implied.
An earlier collaboration with my colleague Brian Soden examined variations in atmospheric water vapor using satellite and radiosonde measurements. This effort (Soden and Lanzante, 1996) was aimed at comparing the climatologies of Upper Tropospheric Humidity (UTH) from these two sources.
Radiosonde Temperatures
Some years ago a collaborative project
(Gaffen, Sargent, Habermann, and Lanzante, 2000) was completed,
yielding new insights into the influence of instrumental (i.e. artificial)
changes on the record of long-term climate variability derived from radiosonde
temperature data. This effort was led by Dian Seidel (Gaffen) of the
Air Resources Laboratory (ARL), located in Silver Spring, MD.
Dian's earlier work on constructing a comprehensive data base of
radiosonde station history information (metadata) proved quite valuable to us.
Her radiosonde
metadata is available from the ARL website.
One of the goals of this project was to use the metadata in conjunction with a statistical method developed by Ted Habermann as well as one which I developed [Lanzante (1996) manuscript] in order to identify artificial changes (discontinuities) in long time series of upper-air temperature. Discontinuities can occur when a new temperature sensor is introduced or when new recording procedures are implemented. These discontinuities are potentially severe enough to corrupt estimates of long term climate variations, especially in the stratosphere and upper troposphere.
An example of the discontinuities that can occur in a long record of upper-air data is given by the time series of 700 hPa geopotential height anomaly at Veracruz, Mexico. The dots indicate points of discontinuity identified using the method of Lanzante (1996) and the horizontal lines are the means over each segment defined by these points. Another example is found in the time series of 100 hPa temperature at Valentia, Ireland. In this case the metadata indicate that all except the second point of discontinuity occur near times corresponding to major changes in the observing system.
While the Gaffen et. al (2000) project narrowed the uncertainties, we were not able to provide any remedies. Furthermore, a subsequent comparison (Free et al. 2002) of the approaches taken by several different groups demonstrates that these competing methods yield very different results, with no indication of which, if any, are on the right track. Eventually we were able to make some progress both in diagnosing the biases in the radiosonde temperature data as well as creating an improved dataset. We utilized a limited network (87) of radiosonde temperature stations with relatively long periods of record. We employed a variety of tools to help us identify the major discontinuities, applied adjustments, and then made improved estimates of temperature trends in the free atmosphere. We have also compared our adjusted temperature data set with MSU satellite temperatures and demonstrated that our adjustments enhance data quality. Two manuscripts (Lanzante et al. 2003a; Lanzante et al. 2003b) report on our findings.
Because the dataset that we created terminates in 1997, and the method to produce it is quite laborious, we expanded our team and created a new dataset that incorporates our earlier one, but is much easier to update in real time. This new product, RATPAC (Free et al. 2005) is available online as an operational climate monitoring product distributed by NCDC. However, inspite of our best efforts there is compelling evidence that we have not eliminated all of the systematic biases from our dataset (nor have others with competing datasets). Several recent studies have made this point, including one of ours (Sherwood et al. 2005)
Climate Assessment
I recently served as a Convening Lead Author in the preparation of a synthesis and assessment report
(SAP1.1) for the U.S. Climate Change Science Program (CCSP).
My chapter
presents the observed temperature trends.
The entire report
details current knowledge of
temperature trends in the lower atmosphere and their correspondence with similar trends computed from
a large collection of climate models. Preparation of this report was a very demanding exercise that
occupied the majority of my time for a period of more than two years. It included an organizing workshop
at NCDC in October 2003, another workshop hosted by the British Meteorological Office in September 2004,
and six meetings of the authors in Chicago. The result was a comprehensive report on the current state of
knowledge as well as recommendations for future activities to serve as guidance for the international
community. A major conclusion of this report was that a previously reported apparent discrepancy in the
vertical structure of climate change between observations and climate models no longer exists.
A study (Santer et al., 2005), led by Ben Santer of the Lawrence Livermore National Laboratory compared the vertical structure of warming in the tropical atmosphere in climate models with that from observations. It concluded that an inconsistency between results for short and long timescales suggests that there are residual biases in the observed data, since no such inconsistency exists in the model results.
In an unrelated study (Seidel and Lanzante, 2004), Dian Seidel and I found that some of the long-term changes in atmospheric temperature could be characterized as having occurred nonlinearly in the form of a step change. However, there is sufficient ambiguity that a linear change is also plausible.
Plans
I am just getting results from a project aimed at trying to resolve differences between competing
I also have an ongoing project with my GFDL colleague Keith Dixon aimed at demonstrating to a less technical audience how some of the manifestations of climate change vary by spatial scale. This involves examining temperature time series from climate models averaged over different domains. The basic idea is that for more localized regions climate change is not as readily obvious for some time into the future. By contrast, when temperature is averaged over large regions climate change is noticeable much sooner.
Another area of interest is the vertical temperature structure, especially the
spatial and temporal aspects of lower-tropospheric lapse rate. A few years back
I began some analyses examining GCM runs for this purpose. I haven't progressed
Steve Sherwood (Yale) has expressed an interest in examination of heat index in future climate and possibly collaborating with me on this. Steve's idea that motivates this is that there may be an upper-limit on heat stress induced by increases in temperature and humidity.
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