A Data Resource for Cloud Cover Simulations Graham Nicholas Sortino E H U N I V E R S I T Y T O H F G R E D I N B U Master of Science School of Informatics University of Edinburgh 2006
Abstract This document describes the construction of a database to assist in climate analysis related to global warming. It was motivated by National Center for Atmospheric Research Atmospheric Physicist John Latham and University of Edinburgh Engineer Stephen Salter who are attempting to develop a technique to mitigate temperature increases related to global warming. The database will assist them in determining optimal locations for increasing cloud reflectivity to redirect more of the sun s energy away from the Earth, although it may be applicable to other domains as well. Results include a preliminary analysis of optimal locations suggested by this work. i
Acknowledgements I would like to thank the following people and organizations for their innumerable contributions: University of Edinburgh: Dr. James Frew Xibei Jia Carwyn Edwards International Satellite Cloud and Climatology Project: Dr. William B. Rossow Dr. Yuanchong Zhang Dr. Chris Brest Ely N. Dueñas European Centre for Medium-Range Weather Forecasts: Keith Fielding British Atmospheric Data Center: Dr. Shoaib Sufi Brian Lawrence Physical Oceanograpgy Distributed Active Archive Center: Ted Lungu Langley Atmospheric Sciences Data Center: Paul Carter National Center for Atmospheric Research: Dr. Natalie Mahowald...And last but not least my advisor Peter Buneman and co-conspirators Stephen Salter & John Latham. Please accept my most sincere thanks for your leadership, guidance and patience. ii
Declaration I declare that this thesis was composed by myself, that the work contained herein is my own except where explicitly stated otherwise in the text, and that this work has not been submitted for any other degree or professional qualification except as specified. (Graham Nicholas Sortino) iii
To Sarah Beth: Without your love and support none of this would have been possible. iv
Table of Contents List of Figures vii 1 Introduction 1 1.1 Optimal Locations........................... 2 1.2 Challenges................................ 2 1.3 Novelty................................. 3 1.4 Outputs................................. 3 1.5 Outline................................. 4 2 Background 6 2.1 The Earth Radiation Budget...................... 6 2.2 Clouds.................................. 8 2.2.1 Properties............................ 8 2.2.2 Classification.......................... 10 2.3 Latham & Salter............................. 12 2.4 Conclusion............................... 15 3 Description 16 3.1 Variables................................ 16 3.1.1 Shortwave Radiation...................... 17 3.1.2 Albedo & Droplet Concentration................ 18 3.1.3 Cloud Amounts......................... 19 3.1.4 Spray Vessel Variables..................... 19 3.1.5 Hypotheses........................... 20 3.2 Datasets................................. 22 3.2.1 International Satellite Cloud Climatology Project (ISCCP).. 23 3.2.2 European Centre for Medium-Range Weather Forecasts (ECMWF) 25 3.3 Data Integration............................. 27 v
3.4 Schema Design............................. 29 3.5 Optimal Locations Algorithm...................... 32 3.5.1 Pseudo Code.......................... 33 3.5.2 I/O Cost............................. 34 3.5.3 Example Run Through..................... 34 4 Analysis 36 4.1 Data Visualization............................ 36 4.2 White Box Tests............................. 37 4.3 Black Box Tests............................. 38 4.3.1 Surface Air Temperature.................... 39 4.3.2 Wind Speed and U & V Components............. 40 4.3.3 Mean Total Cloud Cover.................... 40 4.4 Benchmarks............................... 40 4.5 Initial Predictions............................ 46 5 Conclusion 52 5.1 Open Question & Future Work..................... 52 A User Guide 55 A.1 How to Access the Database & Run the Optimal Locations Algorithm 55 Bibliography 60 vi
List of Figures 2.1 (Not to Scale) Depiction of the Earth Radiation Budget. All units are in watts per meter squared. Adapted from [38]............. 7 2.2 Determining Optical Depth [44].................... 9 2.3 It becomes more difficult to increase albedo the greater a clouds initial optical thickness is. Graph is plotted from ISCCP Data [45]...... 10 2.4 The amount of each cloud type present in the atmosphere is not necessarily proportional to the size of their respective box. Figure adapted from [45]................................ 11 2.5 From left to right top to bottom: high (net-warmers), mid (neutral), and low level (net-coolers) clouds. Thin arrows represent shortwave radiation and thick arrows represent longwave. Images taken from [25] 12 2.6 Determination of CCN requirement [22]................. 13 2.7 Image: Salter s proposed albedo spray vessel design (artwork by: John MacNeill). Used with permission from [36]............... 14 3.1 Primary variables for calculating optimal locations........... 17 3.2 Various levels of incoming shortwave radiation measured in watts per meter squared at different times of the year. Areas with dark red experience the most incoming shortwave radiation while those in dark blue experience the least. Image provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, from their Web site at http://www.cdc.noaa.gov/[27]...................... 18 3.3 Estimation of droplet concentration given a value for optical thickness and cloud droplet radius. First determine column droplet concentration and use that to estimate droplet concentration with a presumed height of 800 meters [12]............................ 19 vii
3.4 Amounts of low-level stratocumulus, mid and high level clouds from left to right top to bottom respectively. Light blue indicates the greatest concentrations and dark red indicates the least. Image and data provided by [18]............................. 20 3.5 Wind direction and speed for May 10, 2006. Arrows indicate direction with red arrows representing the strongest winds. This image was produced from the SeaWinds instrument aboard the QuikSCAT satellite provided by NASA-PODAAC [29]................... 21 3.6 Determination of wind speed and direction [39]............ 21 3.7 Determination of cloud base height in meters [39]........... 22 3.8 ISCCP equal area grid (also known as a reduced Gaussian grid). There are 6596 cells starting at cell 1 in the lower left and going towards the right and then up [45].......................... 24 3.9 Typical ECMWF Equal angle grid for their datasets. Used with permission from[42]............................ 26 3.10 This shows the lat and lon for different cells using an ISCCP equal area grid. For a given cell number first determine its lat index. Lat is then calculated by multiplying the index by 2.5. To obtain the lon subtract the cell id from the begin cell number and multiply that by the lon interval. Table taken from ISCCP DX/D1/D2 Documentation [45]. 28 3.11 Runtime cost of integrating the 3 datasets into the database. All units are in minutes unless otherwise specified................ 29 3.12 Data is spread across the Primary and Supplementary tables, which have a 1 to 1 relationship. The look up table is used for computing optimal locations and is discussed in the next section. Note: Due to space limitations not all supplementary columns could be shown.... 30 3.13 a) Indicates estimated column, tuple, and page sizes. b) Indicates estimated database size. Note: This does not take into account the cost of indexes or other DB2 management techniques which may increase size. 31 4.1 The 12 tests preformed for each variable/temporal pair........ 38 4.2 Dataset Comparisons.......................... 39 4.3 Comparison of surface temp in degrees Kelvin for NOAA-CDC data (left) and this climate database (right).................. 41 viii
4.4 Comparison of U & V (top and bottom respectively) wind components for NOAA-CDC data on the left and this climate database on the right. 42 4.5 Comparison of mean wind speed for NOAA-CDC data (left) and this climate database (right).......................... 43 4.6 Comparison of mean total cloud cover for NOAA-CDC data (left) and this climate database (right)....................... 44 4.7 Benchmark test results.......................... 45 4.8 A definition of optimal locations..................... 46 4.9 Quarterly optimal locations predictions for 2001. Note: uncolored cells represent missing or undefined data................ 49 4.10 Full year optimal locations predictions for 2001. Note: uncolored cells represent missing or undefined data................... 50 4.11 Quarter 1 (January - March) 2001: Stratocumulus cloud concentration 50 4.12 Full year 2001: On the left high and mid level cloud amounts. On the right areas with large concentrations of low level clouds and small amounts of mid/high clouds above.................... 51 ix
Chapter 1 Introduction Global climate depends heavily upon the ability of clouds to reflect incoming solar energy away from the Earth[24]. This has prompted much research into clouds as a potential deterrent to climate change. However, determining their viability is extremely difficult because many variables are involved in sophisticated interactions. In addition, any study of these processes must examine data that is of global scope. A mass analysis such as this suggests the use of database technology. This document describes the construction of a database to assist in climate analysis related to global warming. The end result of which is what I believe to be the first multiyear climate resource capable of finding locations that exhibit desirable conditions on a global scale. It was motivated by National Center for Atmospheric Research Atmospheric Physicist John Latham and University of Edinburgh Engineer Stephen Salter who are attempting to develop a technique to mitigate temperature increases related to global warming. Their proposal calls for the use of ocean spray vessels to seed clouds with the tiny salt particles left over from evaporated sea water. This will increase cloud reflectivity and redirect more of the sun s energy back out towards space[36]. While theoretically possible [44], it is also quite controversial and its feasibility requires answering some important questions, such as: where should their vessels be used? How much should they spray? How many will be needed to counteract anthropogenic (man-made) temperature increases. The database will assist researchers such as Latham & Salter in answering their question of where theses sea vessels should be used; although, it may be applicable to additional domains as well. Early results (section 4.5) suggest new locations such as the North Sea above West- 1
Chapter 1. Introduction 2 ern Europe. In addition, they provide further evidence to support the claim made by [36, 22] that the areas off the west coasts of the Americas and Africa are also well suited. 1.1 Optimal Locations Establishing what constitutes an optimal location for using spray vessels involves consulting a number of climate variables and is highly subjective because no agreed upon definition exists. Thus it is not the goal of this project to specify exactly the types of conditions optimal locations exhibit. Instead it aims to provide researchers with the means to determine this based upon their own definitions. This optimal locations query can be defined in terms of a classic computer science maximization problem where there are a number of variables, each of which must be weighted, scored, and tallied. Coordinates with the highest total score must then be returned to the user. While the problem is simple to define, the solution is much more complex. Data which spans decades and is global in size must be fed into complex calculations. This, coupled with a significant number of variables to be considered, requires a different way of thinking. First and foremost, CPU calculations are not as important as disk accesses (I/O). This is because the amount of data involved ranges in the hundred s of gigabytes and disk accesses are three orders of magnitude more expensive then CPU calculations. Secondly, very large datasets must be indexed and organized in order to minimize I/O search cost. Given these constraints the logical choice is to utilize the relational model for data management which exceeds at minimizing disk I/O. 1.2 Challenges There are many challenges facing this project. Each involves applying a mature and well studied technology, the relational model, to a new domain. Extensive climate research must first be conducted in order to understand the relationships between the relevant variables. Once the domain of discourse is sufficiently understood, datasets encompassing the relevant variables are to be procured. In order to qualify as a viable dataset it is required to meet stringent qualifications such as: coverage of the necessary variables as well as having suitable temporal (time span) and spatial resolutions.
Chapter 1. Introduction 3 After datasets are chosen they must be cleaned, transformed and integrated into a uniform format. A data parser must be robust, efficient, and able to handle input datasets in excess of one hundred gigabytes without failure. Finally, the schema is to be constructed, which allows optimal locations to computed as efficiently as possible. 1.3 Novelty A comprehensive literature review has strengthened my view that no attempt at answering this question has yet been made. There are a number of reasons why I believe this to be so. Firstly, the required pre-requisite knowledge in terms of both data management and climate sciences create high barriers to entry. Furthermore, it is difficult to gauge the complexity of multi-disciplinary projects because all dimensions may not be known in advance. Secondly, climate research typically looks at the relationship between one or two variables such as [13, 37, 14] or global climate models such as [2, 35], which try to predict future conditions. Not much research has actually been put into looking for locations that exhibit conditions based upon several variables. This may be due to the fact that it is a relatively new question and full knowledge of the exact microphysical processes are still in dispute [11]. It may also be due in part to the fact that global satellite datasets have only been available for a relatively short time and much work has been put into increasing their reliability and accuracy as opposed to analysis. 1.4 Outputs The primary outputs of this project are: A global climate database built in DB2 1 covering the relevant variables. An efficient & robust parser implemented in Java for integrating multiple heterogeneous datasets into a uniform format for insertion into the database. An efficient algorithm implemented as a DB2 stored procedure, which is executed on the database and used in determining optimal locations to increase cloud reflectivity. 1 DB2 is a registered trademark of IBM
Chapter 1. Introduction 4 In addition, its utility could easily be extended with the inclusion of new algorithms or components. For example, it could also be used to determine the immediate after-effects of increasing cloud reflectivity based upon a number of variables already contained within. Or complex visualization tools could be built to assist in analysis. Thus the real value here is the database itself. Therefore a strong emphasis has been put into creating a resource that can provide a solid foundation for future work. Since the output of this project is a tool to be used by others, results should not be expected to include detailed predictions of where optimal locations are located and what exactly the correct definition of optimal is. These questions will be left for the users to determine. Instead, results primarily show the tests conducted in order to build confidence in the correctness of the work produced. In other words users of this resource can be assured that any results obtained are valid. This being said, work was scheduled according to a timeframe, which allowed some preliminary predications to be made (section 4.5). However, it must be emphasized that they are just predictions the legitimacy of which requires further analysis by climate experts. 1.5 Outline The rest of this paper will proceed as follows. Chapter 2 is an introduction to the climate sciences that form the basis of this project. It discusses the Earth Radiation Budget, which is responsible for controlling the Earth s climate and the important role clouds play in regulating it. This is then tied into a deeper discussion of the research being conducted by Latham & Salter as well as the crucial role this dissertation project plays in their work. Chapter 3 discusses the methodologies used in building the resources as well as justifications of any design choices. It begins by analyzing the variables used in determining optimal locations. Using these variables some hypotheses are made to gauge where optimal locations may be found. Next data sources and integration are carefully examined. It then concludes with a description of the database schema and the optimal locations algorithm. Chapter 4 attempts to build confidence in this resource as a research quality tool. First a number of tests designed to determine its quality are examined. This is followed by an analysis of some preliminary optimal locations, which were predicted by this project.
Chapter 1. Introduction 5 Chapter 5 wraps up this dissertation with a short conclusion followed by a discussion of open questions and areas of future work. Additionally, an appendix has been provided containing more detailed information concerning the work undertaken, as well as documentation for using the database to determine optimal locations.
Chapter 2 Background Chapter 2, the content of which forms the backbone of this project, aims to give the reader a better understanding of the complex micro & macro processes involved in regulating the climate of the Earth. It starts on the macro level looking at the large scale picture of the Earth Radiation Budget examining how it controls radiation levels, which influence global climate. Next the intricate microphysical processes of clouds are analyzed, including the manner by which their reflectivity is controlled. This is then tied into Latham & Salter s work. The goal here is to gain a better understanding of how their research proposes to exploit these processes to alter cloud reflectivity and redirect more heat away from the Earth s surface. This also includes looking at some unknown components of their work including the key question this dissertation project hopes to help them answer: Where should one use their proposed technique to reflect the most energy, as efficiently as possible? 2.1 The Earth Radiation Budget The Earth Radiation Budget is a complex system, which regulates the temperature of the Earth by controlling the amounts of radiation that enter and leave the atmosphere [25]. Some of the strongest influences on the system are incoming short-wave (solar) radiation and outgoing longwave radiation. Solar radiation is produced by the sun, absorbed by the Earth and generally measured in the in the range of.4 to 5 µm wavelengths. Longwave radiation is emitted by the Earth and generally measured in the range of 5 to 200 µm wavelengths [45]. Another variable important to the regulation of the Earth Radiation Budget is the atmosphere itself, which helps control the amount of incoming shortwave and outgo- 6
Chapter 2. Background 7 Earth Radiation Budget 1370 343 Outer Atmosphere Incoming Shortwave 237 390 237 Outgoing Longwave Figure 2.1: (Not to Scale) Depiction of the Earth Radiation Budget. All units are in watts per meter squared. Adapted from [38] ing longwave radiation. Taken altogether the atmosphere is what gives the Earth its generally mild climate. Without it the Earth would experience wild temperature variations between day and night such as those which occur on the moon where there is no atmosphere to trap solar energy [38]. The atmosphere controls radiation levels via an important property called albedo, which measures the amount of radiation reflected from a body on a scale from 0 to 100 percent. 0 indicates nothing is reflected and 100 means that all radiation is reflected. Different bodies have different albedos. For example, the albedo of water or dirt is generally low while that of clouds and ice is typically much higher [38]. Generally speaking, the Earth Radiation Budget is in equilibrium. That is, the mean annual incoming shortwave radiation flux is approximately equal to the mean annual outgoing longwave radiation flux. It is estimated that the average amount of solar radiation which reaches the outside of Earth s atmosphere is 1370 Wm 2 (watts per meter squared) and about 1 4 or 343 Wm 2 actually enters the upper portion of the Earth s atmosphere. Of this, approximately 30% is reflected back out to space by the Earth s global albedo. Thus 343 (343.3) or about 237 Wm 2 actually reaches the earth s surface. The shortwave radiation which is not reflected is absorbed mostly by the Earth but also to a smaller extent by the atmosphere. This absorbed radiation warms the Earth
Chapter 2. Background 8 and atmosphere and will eventually be emitted as longwave radiation back out towards space [25]. Taken as a whole, approximately 390 Wm 2 of mean annual flux longwave radiation is released out towards space; however, only about 237 Wm 2 of that actually leaves the atmosphere. This is due to similar factors which reflect incoming shortwave radiation. The difference between the longwave radiation which is released from the surface and that which actually leaves the atmosphere helps give the Earth its relatively moderate climate [38]. This system is modelled in figure in figure 2.1. One factor, which the above model has not taken into account is the role played by substantial increase in anthropogenic (man made) aerosols that have been released into the atmosphere during the industrial age. Taken altogether it is estimated that they contributed to an increase in longwave radiation at the surface of approximately 2.5 Wm 2. This is the basis for global warming and climate change [38]. Counter intuitively, the effect of anthropogenic (man made) aerosols is not entirely negative as they also help reduce the amount of shortwave radiation which reaches Earth and thus help to slightly counteract their warming effect. This is accomplished in two ways: by directly scattering shortwave radiation in clear air and indirectly by increasing the reflectivity of clouds [38, 37]. The prior will not be discussed much further; however, the physics involved in the latter are of consequence to the approach outlined by Latham [22] because as will be seen, aerosols whether natural or artificial, play a significant role in determining the albedo of clouds. 2.2 Clouds The general introduction to the Earth Radiation Budget leaves out a very important determinate in the climate of Earth: clouds. Thus before continuing this discussion of radiation and reflectivity it is important to have a better understanding of clouds, their properties, functions, and why they are important. 2.2.1 Properties The basic definition of a cloud is a collection of condensed drops of water either in a liquid or solid (ie. frozen) state, which form around tiny particles and are cooled at or below their dew point [30]. The tiny particles which clouds form around are called cloud condensation nuclei (CCN) and are commonly composed of dust, salt, soot, or another similar material. CCN are approximately 0.1 µm or more in diameter [22] and
Chapter 2. Background 9 τ = 2πNr 2 h N = droplet concentration in cm -3 τ = optical depth (which is directly related to albedo) r = average droplet radius in µm (typical radii are 10 µm for liquid drops and 30 µm for frozen drops) h = height from cloud top to base commonly measured in km (a typical depth is 1 km) Figure 2.2: Determining Optical Depth [44] range in concentration from 10 to 5000 per cubic centimetre. There are generally fewer per cubic centimetre (in the range of 10-100) over sea and greater numbers over land (commonly ranging from 500-1000), especially in the more industrialized northern hemisphere [44]. In fact in highly polluted areas cloud condensation nuclei levels can be as high as 5000 per cubic centimetre. This is because as was alluded to previously anthropogenic (man made) aerosols also act as a CCN [8]. An important property of clouds is their albedo (or reflectivity), which is primarily influenced by the size and concentration of the droplets that they are composed of. Droplet radius and concentration are influenced by the number of available CCN. Thus CCN determines droplet concentration and radius which determines albedo. In other words clouds with tightly packed small droplets are denser, can reflect more radiation, and thus have higher albedo s [38]. This was first theorized by Atmospheric Physicist Sean Twomey in his oft cited paper The Influence of Pollution on the Shortwave Albedo of Clouds [44] published in 1977. Twomey developed a formula to calculate the optical depth of a cloud (see figure 2.2), which can then be used to determine albedo. In his formula optical depth τ, which is a measure of the amount of radiation prevented from passing through a column of the atmosphere [3] is calculated from the inputs: droplet concentration, average droplet radius, and the height from cloud top to base. Once optical depth has been found albedo can easily be determined 1 via a direct correlation (see figure 2.3). Notice that a proportional increase in optical thickness does not necessarily result 1 A more accurate approximation of albedo can be found using the properties: single scattering albedo and the asymmetry factor. A formal discussion of which can be found in [44]. However, this project will use optical depth as it is most appropriate for this situation.
Chapter 2. Background 10 Correlation of Albedo and Optical Thickness (TAU) 120.0% 100.0% 80.0% Albedo 60.0% 40.0% 20.0% 0.0% -50.000 0.000 50.000 100.000 150.000 200.000 250.000 300.000 350.000 400.000 Optical Thickness (TAU) Figure 2.3: It becomes more difficult to increase albedo the greater a clouds initial optical thickness is. Graph is plotted from ISCCP Data [45]. in a proportional increase in albedo. For example a very small increase in optical thickness of 0.03 (from 0.020 to 0.050) results in an increase in albedo of 7%. However, a much larger increase in optical thickness of over 260 (from 109.8 to 378.65) results in a proportionately much smaller increase of only 5.5%. Keeping in mind that CCN is a key determinate in optical thickness and albedo, this means that clouds with low amounts of CCN can have their albedo s increased a substantial amount with minimal effort while those with higher amounts require a much greater increase to raise their albedo. This fact is extremely important to Latham and Salter s work because in order to increase albedo as efficiently as possible they must target clouds with low initial CCN concentrations. The key point here is that a cloud s albedo (reflectivity) is determined by the number and concentration of its cloud condensation nuclei and those with lower levels of concentrations require less effort to raise their albedo than those with higher levels. 2.2.2 Classification Albedo is an important factor in the regulation of the Earth Radiation Budget because it helps determine how much shortwave radiation emitted from the sun is reflected back out towards space. Since clouds cover a large portion of the Earth s surface and their albedo taken as a whole is greater than or equal to the 30% global albedo they are considered by some to be a potential deterrent to anthropogenic climate change. This last point is the essential idea behind Latham and Salter s proposal [22, 21, 36]: use clouds to reflect more shortwave (solar) radiation by raising their CCN concentrations.
Chapter 2. Background 11 ISCCP Cloud Classification 50 Cloud Top Pressure (Milibars) 180 310 440 560 680 800 Cirrus Cirrostratus Deep Convection Altocummulus Altostratus Nimbostratus Cumulus Stratocummulus Stratus High - (6500 to 19000 meters) Middle - (3201 to 6500 meters) Low - (ground to 3200 meters) 1000 0 1.3 3.6 9.4 23 60 379 Cloud Optical Thickness Figure 2.4: The amount of each cloud type present in the atmosphere is not necessarily proportional to the size of their respective box. Figure adapted from [45] However, while it is true that clouds overall exert a cooling effect some do so more than others and furthermore, some such as high altitude cirrus clouds actually help warm the atmosphere [25]. Because of this it is worthwhile to briefly classify the different types of clouds into 3 main categories: high, middle, and low altitude. Each main category can then be subdivided by optical thickness. See figure 2.4 for a general classification. Low level clouds such as stratocumulus are classified as those that have an altitude of between 0 and 3200 meters [18]. Overall they exert a cooling effect because they are very thick, which gives them a greater albedo. Marine low clouds, for example, exert an annual globally-averaged net cooling effect of -15 Wm 2 [15]. In addition, because their tops are close to the ground their temperature is comparable as well thus they do not trap very much outgoing longwave radiation[25]. High clouds such as cirrus are typically very thin and thus have low albedo s. Therefore, they allow more solar radiation into the atmosphere. Additionally because they are so high their temperature is very low compared to the Earth s surface which means they work to trap outgoing longwave radiation and send it back towards the Earth. Thus their net effect is to warm the atmosphere [25]. The third type of clouds are middle level and they exist at an altitude of between 3200 and 6500 meters [18]. They can be very thick, however, unlike low level clouds whose tops have a temperature comparable to that of the Earth s surface the tops of
Chapter 2. Background 12 Figure 2.5: From left to right top to bottom: high (net-warmers), mid (neutral), and low level (net-coolers) clouds. Thin arrows represent shortwave radiation and thick arrows represent longwave. Images taken from [25] mid level cloud are much higher and thus cooler. So even though their thickness gives them a high albedo they also work to trap outgoing longwave radiation and send it back towards Earth. Therefore they are considered neutral [25]. In terms of cooling ability the distinct characteristics each cloud type exhibits suggest that certain clouds are more desirable than others. Since low-level clouds are considered the best coolers an attempt at increasing albedo should be targeted towards them. Furthermore, locations with mid and/or high level clouds above should be avoided because this could trap some of the reflected radiation. As will be discussed in the following section this knowledge was used by Latham and Salter in their proposed technique to counteract climate increases. 2.3 Latham & Salter Given what is known about the Earth Radiation Budget and the integral role clouds play in its regulation a logical question one may ask is: How can we exploit the relationship between CCN and the blocking of incoming solar radiation to cancel out the effects of
Chapter 2. Background 13 M = HNm M = CCN per unit area of the Earth s surface H = the altitude over Earth where that they will be sprayed N = the desired droplet concentration produced by the increase in CCN m = the mass of the CCN (measured in grams) Figure 2.6: Determination of CCN requirement [22]. global temperature increases? This is the question that was addressed by National Center for Atmospheric Research, Atmospheric Physicist John Latham first in 1990 [21] and then expanded upon in his 2002 paper Amelioration of Global Warming by Controlled Enhancement of the Albedo and Longevity of Low-Level Maritime Clouds [22]. It was then tackled from an engineering perspective by University of Edinburgh Engineer Stephen Salter [36] in order to address technical challenges. This section looks at their theories and proposals. In [22] Latham suggests seeding clouds with the salt derived from evaporated sea water because there is an abundant and readily available supply. He then tackles the practical questions of how much cloud condensation nuclei (CCN) would be required to combat global temperature increases and what the impediments to doing so are. Using a target droplet concentration (N) of 400 cm 3, which ranges from 2 to 8 times the typical levels in ocean air, an altitude of 0.5km and a ocean salt mass of 10 14 g he estimates that globally 10 9 kg or an increase of 10 26 droplets are required (figure 2.6). However, a complication to his estimate is the fact that if artificially introduced salt CCN is not added to the lower atmosphere at levels that exceed naturally occurring amounts it would lead to a decrease in albedo and a warming effect would occur. This is because cloud droplets form in preference to certain types of CCN and once a preference is chosen they will not form to others. Although this can be counteracted by knowing in advance the amount of CCN required to increase albedo and ensuring that each cloud is seeded with the correct amount. An interesting side effect of artificially introducing CCN into the atmosphere is that it may work to increase cloud lifetimes and decrease the amount of rain produced. This is because larger concentrations of CCN cause droplet radius to decrease and clouds with smaller droplets take longer to form drizzle[22]. The main benefit of this is that albedo per cloud is being increased for a longer period of time, which leads to a greater
Chapter 2. Background 14 Figure 2.7: Image: Salter s proposed albedo spray vessel design (artwork by: John MacNeill). Used with permission from [36]. cooling effect. Latham also discusses many technical, scientific and ethical concerns regarding his proposal all of which require further study before its feasibility can properly be gauged. Chief among the scientific questions is the exact microphysics behind the regulation of cloud albedo. While most scientists are in agreement to the relationship between cloud condensation nuclei (CCN) and albedo, the exact mechanism is in dispute. For example, in [11] Atmospheric Scientist Q. Han argues that traditional methods of calculating albedo, such as the Twomey Equation (figure 2.2) may be incorrect because they assume that cloud water content stays the same as albedo is altered and this is not necessarily true. Ethically speaking, the biggest concern is the effect of raising CCN levels on a global scale. Determining this requires the use of global climate models such as [35, 2], which have only recently become feasible due to the large amount of computing resources required to run them. The technological barrier of creating a machine capable of seeding low-level marine stratocumulus clouds is speculated upon but not fully addressed by Latham. It is undertaken in greater depth by University of Edinburgh Engineer Stephen Salter in his paper Sea-Going Hardware for the Implementation of the Cloud Albedo Control Method for the Reduction of Global Warming [36]. Salter, who in 1974 developed a method to convert the motion of waves into electricity, has considerable experience in
Chapter 2. Background 15 dealing with engineering challenges on the sea. His design (see figure 2.7), the exact mechanics of which are not relevant to this project, works by sucking up sea water using specially designed vessels and spraying the remaining salt residue into the lower atmosphere. 2.4 Conclusion Given what is known about the Earth Radiation Budget it is easy to see how important clouds are in its regulation. Furthermore because we have a theoretical mechanism to control cloud albedo and we know that low-level clouds such as stratocumulus are the best net-coolers we also know what and how to target them. The important question, which remains to be answered, is: where are the optimal locations to increase the albedo of clouds located? Some of the variables necessary to answer that question such as: albedo, shortwave radiation, CCN, and droplet concentration have already been discussed. The rest will be examined in the following chapter.
Chapter 3 Description The previous chapter covered a broad over-view of the Earth Radiation Budget and why clouds are seen by some as a mechanism to exert a measure of control over it. It ended by examining a proposal for increasing cloud albedo and noting that in order to accomplish this one must know in what locations this should be done because some are considered more ideal then others. With this backdrop, Chapter 3 tackles the optimal locations query from a computer science perspective. The components of this challenge are analyzed chronologically. It begins by looking at the full set of variables used to determine optimal locations. Each is covered in sufficient depth to give the reader an understanding of how it is derived and the role it plays. Following this, the task of obtaining datasets covering the relevant variables and integrating them into the database is examined. This feeds into a description of the climate database schema and finally the optimal locations algorithm. 3.1 Variables While analyzing the variable list in figure 3.1 the first item to take note of is that we are most interested in stratocumulus clouds. Stratocumulus clouds, which have long been studied by atmospheric scientists, exhibit a number of properties that make them particularly desirable for CCN seeding. They have a low altitude, are prevalent, considered net-coolers, and have moderate to low initial albedo s, which make them ideal targets for increasing their reflectivity. Secondly, each variable can be loosely broken down into two categories: derived and original. Original variables are the simplest to use and the most accurate. They come directly from their respective data source and do not require much additional 16
Chapter 3. Description 17 Variable Name Units Data Source Type Mean Albedo For Stratocumulus Clouds Percentage from 0 to 100 ISCCP-D1 Derived Percent Stratocumulus Clouds Percentage from 0 to 100 ISCCP-D1 Original Percent Clouds above Stratocumulus Percentage from 0 to 100 ISCCP-D1 Original Estimated Droplet Concentration for Stratocumulus cm -3 ISCCP-D1 Derived Incoming Shortwave Radiation at 680 Milibars Watts per meter -2 ISCCP-FD (PRF) Original Surface Temperature Kelvin ECMWF Original Boundary Layer Height Meters ECMWF Original Direction the Wind is Blowing From 0-360 Degrees ECMWF Derived Cloud Base Height Meters ECMWF Derived Mean Wind Speed Meters per second ECMWF Derived Figure 3.1: Primary variables for calculating optimal locations. work in order to load them into the database. Their main limitation is that they do not cover all variables we are interested in. Thus derived variables, which are calculated from originals, are also used. While not as accurate as originals they do provide critical information necessary to determine optimal locations. Finally, there are a number of other variables, which are also relevant to this question; although they are not considered to be of primary importance. Therefore, they will not be used in calculating optimal locations. However, once a location is found they will need to be used as a check to determine whether it really does exhibit the types of conditions necessary for CCN seeding. Thus they are considered supplementary 1 and are also included (just not calculated) in the climate database. Each variable will now be covered in more detail. 3.1.1 Shortwave Radiation As discussed in the previous chapter incoming shortwave radiation indicates the amount of energy that is directed from the sun towards a point on Earth. This is important because in order to maximize the effectiveness of Latham and Salter s proposal only locations with large amounts of incoming solar radiation should be targeted. Interestingly, the amount of radiation which is directed towards a fixed location changes quite dramatically at different times in the year. For example, in July the largest amount of incoming radiation is directed towards the North Pole, while in January the opposite is true and if one averages values for an entire year the highest levels are at the equator. This suggests that in calculating optimal locations based upon the variable incoming 1 More information concerning them can be found in the section 3.4.
Chapter 3. Description 18 July 2005 January 2005 January to December 2005 Figure 3.2: Various levels of incoming shortwave radiation measured in watts per meter squared at different times of the year. Areas with dark red experience the most incoming shortwave radiation while those in dark blue experience the least. Image provided by the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, from their Web site at http://www.cdc.noaa.gov/[27] shortwave radiation one must take into account the varying amounts at different times of the year (See figure 3.2). This project is primarily interested in the amount of incoming shortwave radiation which is hitting the tops of stratocumulus clouds. This point is approximately at an atmospheric pressure level of 680 milibars, which corresponds to an altitude of about 3200 meters. 3.1.2 Albedo & Droplet Concentration Stratocumulus cloud albedo, which is determined in part by droplet concentration, represents the amount of radiation on a scale from 0 to 100 percent that a body can reflect. Since it is known that it is easier to increase the albedo of a cloud with a low initial value these two variables should be minimised. Albedo is easily derived from optical thickness using the graph in figure 2.3. Determination of droplet concentration is much more difficult and cannot be calculated from satellite remote sensing techniques alone [12]. Therefore, the approach outlined in [12] is used to determine column droplet concentration from optical thickness and
Chapter 3. Description 19 N c τ = 2 π r 2 (1 b)(1 2b) N c e = N h b = coefficient 0.193 r e = droplet radius (µm) τ = optical thickness (TAU) N = Column Droplet Concentration c (10 6 /cm 2 ) N = Droplet Concentration (cm -3 ) h = height assumed to be 800 meters Figure 3.3: Estimation of droplet concentration given a value for optical thickness and cloud droplet radius. First determine column droplet concentration and use that to estimate droplet concentration with a presumed height of 800 meters [12] cloud droplet radius and from there droplet concentration is estimated using an assumed cloud height from base to top of 800 meters (See figure 3.3). Estimated column droplet concentration while not an exact value does provide a good approximation of within 19% and shows a strong correlation to regional droplet concentration datasets [12]. 3.1.3 Cloud Amounts In addition to albedo and droplet concentration it is also necessary to consider the amounts of stratocumulus clouds in the atmosphere. Areas rich in stratocumulus clouds should be considered prime targets, however, the heat trapping caused by mid and high altitude clouds must be taken into account as well. Thus ideal locations are those with large concentrations of low-level stratocumulus clouds and smaller amounts of high-level clouds (see figure 3.4). 3.1.4 Spray Vessel Variables Sea wind speed, direction, surface temperature, cloud base height and boundary layer thickness are important for answering the more technical question of whether or not the spraying vessels can be used in a given location. Generally speaking, the spraying vessels should be used in moderately windy conditions in order to provide enough momentum to lift the cloud condensation nuclei particles up into the clouds. The clouds should ideally have a base height which is below the top of the boundary layer in order
Chapter 3. Description 20 Figure 3.4: Amounts of low-level stratocumulus, mid and high level clouds from left to right top to bottom respectively. Light blue indicates the greatest concentrations and dark red indicates the least. Image and data provided by [18]. to produce the strongest seeding. Wind direction is important as well because in order to minimize disturbances the vessels should not be used in locations where the seeded clouds could blow over land. See figure 3.5 for sample ocean wind data. With the exception of boundary layer height and surface temperature they are all derived using standard meteorological equations. Mean wind speed and direction are determined from the U & V directional components of wind using the equation found in figure 3.6. Cloud base height is calculated from surface temperature and dew point using the equation in figure 3.7. 3.1.5 Hypotheses Now that there is a better understanding of the full set of variables necessary to determine optimal locations it is worthwhile to make some early predictions as to where they may be located. Large amounts of incoming shortwave radiation and stratocumulus clouds are probably the most crucial in determining optimal locations. Following them, the most important variables are droplet concentration and albedo as well as the technical variables: wind speed/direction, cloud base height, etc. These variables while important will probably not play as significant a role in determining optimal locations. This is because without high levels of shortwave radiation and stratocumulus clouds increasing albedo would create almost no cooling effect.
Chapter 3. Description 21 Figure 3.5: Wind direction and speed for May 10, 2006. Arrows indicate direction with red arrows representing the strongest winds. This image was produced from the Sea- Winds instrument aboard the QuikSCAT satellite provided by NASA-PODAAC [29]. M = + 2 ( U V ) 2 1/ 2 360 1 V α = 90 tan ( ) + 180 C U M = Wind Speed (m/s) U = Eastward wind component (m/s) V = Northward wind component (m/s) α = Direction wind is blowing from (degrees) C = 360 = 2π Angular rotation in a full circle. Used to convert between degrees and radians. Figure 3.6: Determination of wind speed and direction [39] As shown in figure 3.4, the areas with the biggest concentrations of stratocumulus clouds are those off the west coasts of the Americas and Africa. As for shortwave radiation figure 3.2 indicates that the greatest yearly concentrations are near the equator, which happen to be approximately where the 3 stratocumulus cloud concentrations are located as well. Thus all 3 of these areas will most likely be considered optimal locations (at least when comparing yearly averaged data). Although, by looking at smaller time increments other locations will likely be suggested as well. This is due to the fact that solar radiation changes its focus depending on the time of the year. One would expect to see some additional locations in the southern hemisphere around January and similar trends in the northern hemisphere in July.
Chapter 3. Description 22 H =. 122 ( T surface Tdew ) H T surface = height from surface to start of clouds (meters) = Temperature at the surface (Celsius) T = Dew point temperature (Celsius) dew Figure 3.7: Determination of cloud base height in meters [39] 3.2 Datasets In general climate variables are measured by two different methods: regional and/or satellite measurements. Examples of regional measurements include: ground based, weather balloons, and aircraft. The advantages of regional based measurements are that the temporal resolution of data can in some cases extend over hundreds of years and they are generally more cost effective; however, their spatial resolution is often limited to a small area. Additionally, combining different sets of regional based measurements often proves difficult because instruments may be calibrated in different ways with measurements recorded in different formats and at different times. Satellite measurements have only been used very recently but offer a standardized global dataset and massive amounts of data. A third hybrid approach integrates both regional and satellite data. Because this project is looking at questions which are global in scope only satellite and/or hybrid datasets are used. This section examines the issue of datasets and variables in much more detail. Each original variable is measured by a satellite instrument; however, dealing with the raw output of these instruments is a task which requires extensive domain knowledge that is far beyond the scope of this project. Thankfully there are a number of groups which are dedicated to collecting, cleaning, integrating, and documenting the data into more user friendly products. A group may create datasets which are integrated from multiple satellites/instruments or they may focus exclusively on one. For example NASA s Physical Oceanography Distributed Active Archive Center (PODAAC) manages a dataset encompassing variables for the NASA QuikSCAT satellite. Conversely, the International Satellite Cloud Climatology Project (ISCCP) D1 dataset combines measurements from 21 different satellites. In general each group tends to focus on a specific subset of variables such as clouds, ocean properties, or radiation. Additionally, they typically use their own spatial and
Chapter 3. Description 23 temporal (time) resolutions. These complications make the choice of data sources extremely important, especially where it pertains to data source compatibility. Given this a significant time investment was put into selecting data sources, which ensure not only compatibility and adequate cover of the relevant variables but include temporal and spatial resolutions expansive enough to help answer Latham and Salter s questions. The next few sub-sections discuss the selected datasets, the variables of interest to this project that they provide as well as their temporal and spatial resolutions. 3.2.1 International Satellite Cloud Climatology Project (ISCCP) The International Satellite Cloud Climatology Project (ISSCP) was founded in 1982 by the World Climate Research Program (WCRP) to look at the long-term global effects on clouds and climate [18]. They maintain a number of datasets, which are integrated from various NASA and international satellites 2. Since ISCCP datasets are created from multiple satellite measurements they provide longer-term temporal coverage that extends beyond the lifetime of a single satellite. Additionally measurements are often taken several times a day and cover a larger portion of the Earth then a single satellite. ISCCP datasets typically use equal area grids 3 with an area of approximately 280km by 280km for spatially indexing data. Equal Area grids, while at first disorientating offer the benefit of ensuring each cell has roughly the same area, whereas with equal angle grids 4 such as those typically found on wall maps and road atlases, cell area varies from very small at the poles to proportionately much larger at the equator. An additional benefit of using equal area grids is economy of storage because fewer data points must be saved to disk. Measurements are usually provided in 3-hour increments starting at 0 UTC 5 (Universal Time Code) and ending at 21 UTC for a total of 8 temporal periods per day covering both sun and twilight. For a given time and date there are up to 6596 available cells in an equal area grid. Each cell includes information concerning the type of vegetation (water, ice, desert, etc), topographic altitude, the satellite which took the measurement as well as a formula for calculating the bounding box of that cell. A picture of the ISCCP equal area grid can be found in figure 3.8. For this project two ISCCP datasets were used: ISCCP-D1 6 and ISCCP-FD (PRF). 2 ISCCP additionally maintains some regional datasets such as FIRE (First ISCCP Regional Experiment); however, they are not relevant to this project and will not be discussed. 3 Also known as reduced Gaussian grids. [17] 4 Also known as regular Gaussian grids. [17] 5 0 UTC represents 12am, 14 UTC represents 2pm, etc... 6 All D1 data were obtained from the NASA Langley Research Center Atmospheric Sciences Data