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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.