Anthony DeAngelis
5/02/08
11:670:493:01
Spring 2008 Summary
Aspect 1:
One phase of my work this semester was to continue exploring the questions related to
changes in extreme precipitation in a warmer climate. To start off the semester, we decided to
expand an analysis of comparing changes in precipitation with changes in saturation vapor
pressure brought on by quadrupled CO2 in the atmosphere. In the fall 2007 semester, we
developed QQ-plots for individual grid boxes of the CM2.1 climate model over the United
States, where we also plotted a line corresponding to an increase in precipitation expected if
precipitation were to increase by the same factor as saturation vapor pressure. For many
locations within the United States, we qualitatively noticed that this line fell very close to the
actual precipitation values plotted in the scatter plot, where control precipitation was plotted on
the x-axis and quadrupled CO2 precipitation on the y-axis.
In expanding the analysis, we desired to quantitatively map the degree to which
precipitation changes in a warmer climate match the expected changes in saturation vapor
pressure of the atmosphere. Our method was to perform a linear regression (forced through the
origin) on the scatter plots for every grid box on our model. As precipitation changes depicted
by the QQ plots for many locations generally show little change in most precipitation events and
large increases in the largest precipitation events, performing a linear regression on all
precipitation event sizes would create a bias toward a smaller increase. Thus, we broke down the
precipitation distribution into several pieces, where the boundaries of each piece corresponded to
the following percentiles of both control and “quadrupled CO2” precipitation for every grid box:
0th to 50th, 50th to 80th, 80th to 97th, 97th to 99th, 97th to 100th, 99th to 100th, and 99.7th to 100th. We
then took the slope of the calculated linear regression, subtracted 1 from it, and divided the by
the change in average daily temperature between the control and quadrupled CO2 environments
for each grid box. These final values represent the percent change in the selected precipitation
range per degree K of warming. One hypothesis we had was that if circulation changes and the
effect of climate feedbacks are negligible in a warmer climate, we should expect this “percent
change per degree K” to be near 7% for all places on the map. We found that in the United
States, this value was indeed close to 7% nearly everywhere on our map for the most extreme
precipitation bins: 97th to 100th, 99th to 100th, and 99.7th to 100th percentiles. Indeed, the median
increase of precipitation for both land and water regions in our United States region was 6.5%
for 99.7th to 100th percentile precipitation.
It was then interesting and necessary to expand this analysis to the entire globe to see
how the results found in the United States would compare and/or contrast with those found for
the entire world. After encountering and overcoming technological difficulties in calculating and
displaying the same maps for the globe, we were able to produce results. We found that like in
the United States, the response of small precipitation events to increased CO2 was highly
spatially variable, exhibiting large increases in some places and large decreases in others. For
larger precipitation events and even for those that are extreme (such as the 99th to 100th
percentile), large spatial variability in change was seen near the equator and low latitudes. In
particular, many areas just north or south of the equator showed large decreases. In the middle
and high latitudes, however, the change in precipitation for large events was much more uniform,
ranging from approximately +5 to +10% in many areas, which was consistent with our United
States results. In the southern hemisphere mid latitudes, the variability in precipitation change
was greater than in the northern hemisphere mid-latitudes. Also, we found the median of the
percent change in precipitation per degree K for land and water points to be +6.73% for 99.7th to
100th percentile precipitation. The following figure demonstrates the response of 99th to 100th
percentile precipitation to quadrupled CO2 for the globe, based on the linear regression analysis
described above.
Figure 1:
a)
b)
Figure 1: Percent Change per degree K in 99th to 100th percentile precipitation distribution between the control and
quadrupled CO2 climates for (a) land only grid boxes, and (b) all grid boxes.
The above results lead us to consider reasons why global extreme precipitation changes
the way it does, especially near the equator and low latitudes. Why is the response of extreme
precipitation so much greater than 7%/°K over such a large region, and why are there areas of
severe negative response elsewhere? We notice that the pattern of precipitation change for the
99th to 100th percentile is similar to the climatological regime of precipitation in some of these
areas. Thus, one idea is that circulation pattern intensification is leading to areas of greater than
7%/°K increase in extreme (99th to 100th percentile) precipitation in wet areas, and less than
7%/°K in dry areas. In other areas, such as south of Greenland, we propose that the feedback
associated with warmer temperatures, melting ice, and increased surface moisture available for
evaporation can help explain the large increase in extreme precipitation here. However, there are
other areas on the map, such as parts of the equator and southern hemisphere mid-latitudes that
show precipitation responses that cannot immediately be explained. To complicate this issue, we
notice that the r2 coefficient of correlation for the linear regression used for this analysis is very
weak, and in some cases non-existent over many parts of the equator and low latitudes. The
following figure demonstrates this for 99th to 100th percentile precipitation events.
Figure 2:
Figure 2: r2 correlation coefficient for linear regression (forced through the origin) for 99 th to 100th percentile
precipitation. Note: Some r2 values are negative because the linear regression is forced through the origin, which
violates the range of values than can be obtained if the linear regression is calculated normally.
The above figure implies that the responses in extreme precipitation near the equator, as
depicted by the maps based on a linear regression (ex. Figure 1), may not be entirely
representative of the “true” extreme precipitation responses in these regions. Thus, one avenue
for future work in this project is to explore QQ-plots of individual locations near the equator to
shed light on how extreme precipitation is actually changing, and why a linear regression of the
data is failing to sufficiently represent these changes. Results from this analysis will help us
develop a clearer picture of how extreme precipitation changes in a warmer climate, which may
lead to better understanding of the reasons for these changes.
Aspect 2:
The second aspect of my work this semester was to prepare to pass this project along to a
junior undergraduate student, Ross Alter, who will be working on this topic for his George H.
Cook Scholars project. In doing this, I was encouraged to organize my thoughts and synthesize
the technological tools associated with the project, making the transition for Ross as easy as
possible.
The first thing I did was rewrite and reorganize the most important Matlab m-files and
data files associated with the project, to provide a complete, sensible, and unambiguous library
of Matlab tools that can be utilized by Ross at the initiation of his work on the project. Second, I
developed Matlab documentation and other documents related to the details of the CM2.1
climate model, to formally put into text the theory behind the computer tools I developed, as well
as to simply provide step by step procedures for using these tools (posted at
http://envsci.rutgers.edu/~toine379/matlabdoc.html). Lastly, I met with Ross in person to
discuss many aspects of the project itself, and to show him first-hand the vital technological
aspects affiliated both with this project and with research in general.
In his work, Ross will most likely continue to explore precipitation extreme changes by
first looking further into the global results which were developed from the CM2.1 this semester.
In particular, he will likely look at histograms and QQ plots for individual locations to further
study the changes in extreme precipitation, help to answer questions regarding the failure of
using linear regression to represent precipitation changes, and attempt to discover reasons for
particular patterns in extreme precipitation change. Furthermore, he will look at other climate
models to assess the degree to which the results of the GFDL CM2.1 are also represented by
these models. In addition to producing results, he will also embark on a general quest to answer
the question “why?” for these results.
In meeting with Ross and preparing the Matlab tools for him, it was necessary for me to
organize my own thoughts about the project, as well as to think about the topic itself and the
significance of the results we have produced to this point. Preparing the Matlab tools also gave
me the opportunity to reappraise the quality of my own computer programming, organize my
programs and better prepare them for future work, and practice my ability to communicate the
computer language I have developed with other people.
Other Things I did:
1) Formulated questions about the precipitation extreme change results for the United
States. This persuaded me to establish my own thoughts, which will be useful for
graduate study.
2) Read 2 chapters combined from 2 different textbooks about the climatology and
processes of the atmospheric branch of the global hydrologic cycle. Clearly, this helped
me learn about the hydrologic cycle and how it relates to our project. In particular, I saw
the role of both evaporation and circulation in atmospheric water content.