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2005 Footprint of the Dynamical Amplifier of Global Warming and Attribution of Models' Uncertainties Christelle Clémence Castet
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FOOTPRINT OF THE DYNAMICAL AMPLIFIER OF GLOBAL WARMING AND ATTRIBUTION OF MODELS’ UNCERTAINTIES
By
Christelle Clémence Castet
A Thesis submitted to the Department of Meteorology In particular fulfillment of the Requirements for the degree of Master of Science
Degree Awarded: Summer Semester, 2005
The members of the Committee approve the thesis of Christelle Clémence Castet defended on June 28th 2005
______Ming Cai Professor Directing Thesis
______Kwang-Yul Kim Committee Member
______Paul H. Ruscher Committee Member
The Office of Graduate Studies has verified and approved the above named committee members.
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I would like to dedicate my work to my parents, Francis and Marie-Claire Castet, and my twin sister Carine for believing in me, their love, and support all the way from France.
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ACKNOWLEDGEMENTS
I would like to express my deepest appreciation to my major professor Dr Cai, who made this work possible and gave me endless support and guidance throughout my research. I am also thankful to my committee members, Dr Kim and Dr Rusher, for the time and help they offered me. Finally, I would like to thank everyone in my lab, especially Dr Lim for his valuable programming assistance.
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TABLE OF CONTENTS
List of Tables ...... VII List of Figures...... VIII Abstract...... X
1.0 INTRODUCTION ...... 1 1.1 Background on global warming ...... 1 1.2 Observed Warming and its impacts...... 3 1.3 State of understanding the large high latitude warming ...... 4 1.4 Warming projections ...... 5 1.5 Thesis objective and outline...... 6
2.0 THEORY ...... 8 2.1 Theory review ...... 8 2.2 Theoretical evidences ...... 9
3.0 DATA...... 13 3.1 Reanalysis fluxes...... 13 3.2 Climate models fluxes ...... 13
4.0 METHODOLOGY ...... 16 4.1 Validation of the dynamical amplifier theory ...... 16 4.1.1 Energy balance at the top of the atmosphere 4.1.2 Energy balance of the atmosphere 4.2 Assessment of models’ uncertainties...... 18
v 5.0 RESULTS ...... 20 5.1 Validation of the dynamical amplifier of global warming...... 20 5.1.1 Reanalysis 5.1.2 IPCC climate models – Ocean and Atmospheric Transport 5.1.3 IPCC climate models – Atmospheric Transport 5.2 Understanding Model’s Uncertainties...... 34 5.2.1 Model Warming Projections Versus Change in Poleward Heat Transport 5.2.2 Model Warming Projections Versus strength of the (unperturbed) models’ mean circulation
6.0 CONCLUSION...... 43 6.1 Validation of the Dynamical Amplifier Theory...... 43 6.2 Evaluation of Model’s Uncertainties...... 44 6.3 Future Work ...... 44
APPENDIX...... 46
REFERENCES...... 48
BIOGRAPHICAL SKETCH ...... 50
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LIST OF TABLES
1. Definition of the variables ...... 14
2. IPCC models IDs and available time periods ...... 15
3. Summary of the correlation, variance explained and sensitivity ...... 39
4. Summary of the correlation, variance explained and sensitivity ...... 42
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LIST OF FIGURES
1. Increase of CO2 concentration in the atmosphere. (a) Direct measurements of atmospheric CO2 concentration. (b) CO2 concentration record from Antarctic ice cores and at Mauna Loa (Houghton et al. 2001)...... 2
2. Earth’s annual global mean energy budget (after Kiehl and Trenberth 1997) ...... 2
3. ERA40 Reanalysis Atmospheric temperature change. Zonal averaged difference between the mean of august /1992-July/2002 and the mean of August/1982-July/1992...... 4
4. Time evolution of the globally averaged temperature change relative to the control run of the CMIP2 simulations (units C). (Houghton et al. 2001)...... 6
5. Physics of dynamical amplifier. From Cai (2005), personal communication...... 9
6. (a) High latitude surface warming versus the strength of the poleward heat transport in the unperturbed climate state, and (b) global surface warming. From Cai (2005), personal communication ...... 11
7. Surface Warming versus (a) the strength of the change in poleward heat transport, and (b) the strength of the mean circulation. From Cai (2005), personal communication ...... 12
8. Change in the long-time averaged zonal mean net radiation at the top of the atmosphere. The difference is taken between the August/1992- July/2002 mean and the August/1982-July/1992 mean derived from the ERA40 reanalysis ...... 21
9. Difference of the long-time averaged net radiation flux at the TOA in Wm-2 between the 2CO2 run and the control run derived from 14 IPCC AR4 model simulations (Wm-2)...... 24
10. Difference of the long-time averaged net radiation flux at the TOA in between the 2CO2 run and the control run derived from 14 IPCC AR4 model simulations. (a) Zonal average. (b) Area average difference for each model...... 29
11. IPCC models’ divergence of poleward atmospheric heat transport (Wm-2). Red is divergence, blue is convergence...... 31
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12. Area averaged divergence of atmospheric heat flux (Wm-2)...... 33
13. Temperature change at the surface (CO2 – Control run) from the year 1930 to 2000 using 3 CGCM models : a) GFDL-CM2.0, b) MIROC3.2(medres), c) MRI-CGCM2.3.2 ...... 35
14. Model warming projections versus changes in the poleward heat transport (DCI) in the same models. (a) Global average, (b) northern hemisphere average, (c) southern hemisphere average...... 37
15. Model warming projections versus changes in the poleward heat transport (DCI) in the same models. (a) Northern hemisphere high latitude average, (b) southern hemisphere high latitude average...... 38
16. Models’ warming projections versus the strength of the unperturbed model’s mean circulation (MCI). (a) Global average, (b) northern hemisphere average, (c) southern hemisphere average...... 40
17. Models’ warming projections versus the strength of the unperturbed model’s mean circulation (MCI). (a) Northern hemisphere high latitude average, (b) southern hemisphere high latitude average ...... 41
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ABSTRACT
The largest warming over the last several decades has been observed in high latitudes. Cai (2005) proposed that part of the large amplitude climate warming in high latitudes could be explained by the “dynamical amplifier” feedback. This study will first provide observational and modeling evidences to validate the dynamical amplifier theory. The second part will address the question whether part of the differences in the CGCM’s global warming projections can be explained by the dynamical amplifier theory. The theory predicts an upward trend of the net radiation surplus (deficit) in low (high) latitudes forced by anthropogenic greenhouse gases. The radiation budget at the top of the atmosphere (TOA) using the ERA40 reanalysis and climate model simulations forced by anthropogenic radiative forcings made at 14 climate centers were analyzed. The results indicate that both the radiation energy surplus in low latitudes and deficit in high latitudes at the TOA have been strengthened over the last several decades. Such an intensification of the radiation energy imbalance at the TOA is also confirmed by most of the climate model simulations. Furthermore, the analysis of the net radiation budget between the surface and the TOA confirms that the change in the TOA energy imbalance is indeed due to the upward trend in the poleward heat transport, in accordance with the dynamical amplifier theory. There is a large model-to-model variability of the intensification of the poleward heat transport among the 14 climate model simulations. It is found that about 59% of the global warming projection uncertainties, which varies from 1.5K to 4K, forced by the
2CO2 forcing can be explained by the variation of the intensification of the poleward heat transport among models. The inter-model variability of the change in the poleward heat transport explains about 66% of the warming projection uncertainties for the
x Northern Hemisphere (NH) and 54% for the Southern Hemisphere (SH). The differences in the poleward heat transport intensification also explain about 71% and 49% of the warming uncertainties in the NH and SH high latitudes. Therefore, it can be concluded that a large part of the uncertainties in the CGCM’s global warming projections can be explained by the dynamical amplifier theory.
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CHAPTER ONE INTRODUCTION
1.1 Background on Global Warming
The earth’s climate has undoubtedly become warmer over the past decades. It is now accepted amongst climatologist that the global mean surface temperature warming is due to the anthropogenic increase of greenhouse gas concentrations in the atmosphere
(Risbey and Kandlikar 2002), especially carbon dioxide (CO2). Figure 1 shows that the
concentration of CO2 in the atmosphere has increased dramatically during the past century. In essence, greenhouse gases absorb the outgoing longwave radiation (OLR) emitted by the Earth’s surface preventing them from leaving the troposphere. This phenomenon is called the greenhouse effect and maintains the surface temperature of the
Earth at an average of +14°C. However, an increase in CO2 concentration changes the Earth’s radiative budget illustrated in Fig. 2 by increasing the atmospheric opacity to
OLR and inducing further surface warming. The increase in CO2 concentration is a form of radiative forcing: “the change in the net vertical irradiance (expressed in Watts per square meter) at the tropopause due to an internal change or a change in the external forcing of the climate system, such as, for example, a change in the concentration of carbon dioxide” (Houghton et al. 2001).
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Figure 1: Increase of CO2 concentration in the atmosphere. (a) Direct measurements of atmospheric CO2 concentration. (b) CO2 concentration record from Antarctic ice cores and at Mauna Loa (Houghton et al. 2001).
Figure 2: Earth’s annual global mean energy budget. (after Kiehl and Trenberth 1997).
2 1.2 Observed Warming and its Impacts
According to the IPCC 2001 report (Houghton et al. 2001), the global surface temperature has increased by 0.4 to 0.8 °C since the late 19th century and is projected to increase from 1.4 to 5.8 °C by the end of the 21st century due to fossil fuel burning and other human induced greenhouse gas release. Figure 3 shows the 20-year trend of the atmospheric temperature since 1982, and the surface temperature is noticeably becoming warmer, especially in the poles. The Arctic Climate Impact Assessment (Hassol 2004) reported that the temperature was increasing more rapidly in average over high latitudes with a warming trend that is about two times larger than the global mean temperature. For example, in Alaska and west Canada, the winter temperatures have increased between 3 and 4ºC over the past 50 years. This strong high latitude warming has become more and more alarming because many issues will arise from it. For instance, the high temperatures are causing the melting of ice and snow: the snow cover extent has decreased by 10% over the past 30 years and is projected to decline by another 10-20% in the next 70 years (Hassol 2004). This melting contributes to the rising of global sea level (10 to 20 centimeters in the past 100 years) and menaces the coastlines, people and ecosystems. The arctic vegetation and biodiversity will also be affected at many different levels such as wetland changes, vegetation shift, marine species decline (ice-living seals, polar bears, etc), forest fires frequency and insect infestations increase. It will also affect human lives by allowing more marine transport, increasing health concerns, expanding marine shipping due to the opening of new marine routes, and declining food security for local populations.
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Figure 3: ERA40 Reanalysis Atmospheric temperature change. Zonal averaged difference between the mean of august /1992-July/2002 and the mean of August/1982-July/1992.
1.3 State of Understanding the Large High Latitude Warming
The high latitude warming has been the subject of many studies that focus in local thermodynamic feedbacks. Climate feedbacks are defined as an “interaction mechanism between processes in the climate system such that the result of an initial process triggers changes in a second process that in turn influences the initial one” (Houghton et al. 2001). For instance, the ice albedo feedback, which is one of the leading theories, explains the larger high latitude warming. Indeed, ice and snow have a very high albedo, and any climate perturbation, which increases the surface temperature, causes melting, lowers the albedo, and leads to more sun light absorption. It therefore increases the melting and lowers the albedo even more. However, this feedback doesn’t explain the strong warming over regions not covered by ice. Some new studies also demonstrate that without the ice-albedo feedback the high latitude surface warming forced by an anthropogenic radiative forcing is still much larger than the low latitude surface warming
4 (Hall 2004). The evaporation feedback too plays a role in the response of the climate system to the radiative forcing. It mainly damps the warming signal in low latitudes more vigorously. The cloud feedback is still not well understood. Observed radiative flux data analysis showed no significant relationship on a global scale between the annual cycle of surface temperature and either solar reflectivity of clouds or effective cloud top height (Tsushima 2001). On the other hand model studies demonstrated that it accounted for approximately one-third of the global warming and 40% of the Arctic warming under
2CO2 forcing (Vavrus 2004). There are other local thermodynamical feedbacks, such as water vapor, that can also amplify the surface warming, however, no single feedback is known to be predominantly responsible for the global and high latitude temperature increase. The most recent theory has been advanced by Cai (2005) and is called the dynamical amplifier feedback. It suggests that the poleward heat transport is one of the most fundamental processes causing the polar amplification of temperatures. This theory will be described in the next chapter of this paper.
1.4 Warming Projections
Climate models are used to study and simulate the climate and its variability; they calculate the properties of its components, their interactions and feedback processes, and their temporal evolutions (Ichikawa 2004). They all use different parametrizations for the physical processes in the climate system. As defined by the IPCC 2001 report (Houghton et al. 2001), climate projections are “a projection of the response of the climate system to emission or concentration scenarios of greenhouse gases and aerosols, or radiative forcing scenarios, often based upon simulations by climate models”. In recent years, most of the coupled general circulation models (CGCM) forced by anthropogenic radiative forcings have successfully simulated and predicted many aspects of the warming patterns. Indeed, according to their projections, the Arctic would experience the greatest warming, as seen in the observations. Nevertheless, CGCMs all have different warming projections under a same amount of anthropogenic forcing ranging from approximately 1.25 to 4°C
5 as shown in Fig. 4. Those differences are not well understood by the climate community. The influence of different parametrizations has been studied in order to determine how they affect global warming projections (e.g. cloud feedbacks (Tushima 2001)), but no single feedback has been found that explains the majority of the surface warming uncertainty.
Figure 4: Time evolution of the globally averaged temperature change relative to the control run of the CMIP2 simulations (units C). (Houghton et al. 2001).
1.5 Thesis Objectives and Outline
The first objective of this research is to validate the dynamical amplifier feedback mechanism using both reanalysis and climate model data. The second objective is to
6 apply the dynamical amplifier theory to explain part of the global warming patterns and the disparities between the climate models’ high latitude warming projection. Chapter two will present the dynamical amplifier theory and its supporting evidences from a simple model developed by Cai (2005). Chapter three will present the data, and their relevance for this study. Chapter four will discuss the methods developed to study and validate the dynamical amplifier feedback, and to explain the wide range of global warming scenarios by the dynamical amplifier theory. Chapter five will, in the first part, provide the results for the validation of the theory, and, in the second part, discuss the role of the dynamical feedback in the models’ warming projections. Finally, chapter six will give a summary of the findings and an overview of the future work necessary for a better understanding of the dynamical amplifier theory.
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CHAPTER TWO THEORY
2.1 Theory Review
Cai (2005 a, b) proposed that the dynamical amplifier mechanism may contribute to part of the rapid warming over high latitudes. Figure 5 is a sketch revealing the physics of the dynamical amplifier. The solar radiations are unevenly distributed at the surface of the earth with more radiations reaching low latitudes and therefore leading to a much warmer surface temperature in low latitudes. Due to the atmospheric/oceanic motions, heat is transported poleward (black arrows) and warms (cools) the high latitudes (low latitudes). As a result, the earth system emits more (less) longwave radiations back to space in high latitudes (low latitudes) than the solar radiations absorbed locally although the earth system as a whole is still in radiative balance. As discussed in the previous chapter, an increase in greenhouse gases (red dashed line in Fig. 5) increases the atmosphere’s opacity to the surface outgoing longwave radiations. Because the surface temperature in low latitudes is much warmer, more radiations are trapped in the low latitude atmosphere (purple arrows) given the same extra amount of greenhouse gases. However, only part of the extra amount of radiation trapped by the low latitude atmosphere is used to warm the surface below. The remaining part is transported poleward to warm the high latitude surface (red arrows). In other words, the high latitude surface receives an extra amount of radiations from two sources: (1) the radiation locally trapped by the atmosphere above (the purple arrow in high latitudes) and (2) the extra heat transported from the low latitudes by the atmosphere (red arrows) that has been intercepted locally by the low latitude atmosphere from the surface bellow (the purple arrow in low latitudes). This effectively implies that for a uniform increase of greenhouse
8 gases, the high latitude surface experiences a “greenhouse-plus” radiative forcing and the low latitude surface experiences a “greenhouse-minus” radiative forcing due to the dynamical feedbacks. For a sufficient strength of the dynamical feedback, the high latitude surface warming can be stronger than the low-latitude surface warming.
N
“Greenhouse- plus” feedback
E “Greenhouse- plus” feedback
“Greenhouse- plus” feedback S
Figure 5: Physics of dynamical amplifier. From Cai (2005), personal communication.
2.2 Theoretical Evidences
Cai used a simple 4-box coupled atmosphere-surface radiative-transportive climate model to make two global warming simulations: one includes thermodynamical feedbacks (such as the ice-albedo, evaporation, and water-vapor feedbacks) and the other includes both thermodynamical and dynamical feedbacks. Without the dynamical feedback (green curve in Fig. 6), the high latitude warming in his model is about 1.2K
9 and the dynamical amplifier (red curve in Fig. 6) amplifies the high latitude warming by 0.6K at the expense of reducing the warming in low latitudes (Fig. 6a). But the temperature reduction in low latitudes is smaller than the amplification in high latitudes. Due to non-linearity, the net dynamical feedback causes an additional global warming of 0.15K (Fig. 6b). The same simple model also depicts a positive correlation between the strength of the change in poleward heat transport, which is similar to the strength of the dynamical amplifier and the warming amount as a whole, and in high latitude. This correlation signifies that the stronger the dynamical amplifier is, the higher the surface temperature will be. Figure 7a demonstrates this result, with the x-axis being the change in poleward heat transport and the y-axis the temperature increase. Finally, the global and high latitude warming is explained by the different climatological states (strength of the mean circulation): the stronger the circulation is, the further away from radiative equilibrium the model climate state is, and the stronger the thermodynamic feedback is (Fig. 7).
10 a) 30N-90N Surface(K) warming
b) Global Surface warming (K) (K) warming Surface Global
Dynamical amplifier + Thermodynamic feedbacks
Thermodynamic feedbacks only
Figure 6: (a) High latitude surface warming versus the strength of the poleward heat transport in the unperturbed climate state, and (b) Global surface warming. From Cai (2005), personal communication.
11 a)
High latitude warming Global mean warming b)
Global mean warming
High latitude warming
Figure 7: Surface Warming versus (a) the strength of the change in poleward heat transport, and (b) the strength of the mean circulation. From Cai (2005), personal communication.
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CHAPTER THREE DATA
3.1 Reanalysis Fluxes
The European Center for Medium range Weather Forecast 40 years reanalysis ERA40 was used to calculate the net radiations at the TOA. The net shortwave and outgoing longwave radiation datasets were collected. The clear sky values of the radiation were used instead of the all-sky radiation because the parametrization of clouds introduces uncertainties in the simulation of the Earth’s radiation budget (Chevallier et al. 2001). The TOA radiation budget produced by the ERA40 reanalysis is not reliable due to the inaccurate representation of clouds; however, the clear-sky fluxes capture very well the observed climatology over much of the globe (Allan et al. 2004). The ECMWF reanalysis server provides the data and information about the ERA40 at: http://data.ecmwf.int/data/.
3.2 Climate Models Fluxes
The models’ simulations are provided by the Program for Climate Model Diagnosis and Inter-comparison (PCMDI). This group is archiving coupled ocean- atmosphere general circulation model output to support the Working Group 1 component of the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment Report. The data and detailed information are available online after registration at: http://www- pcmdi.llnl.gov/ipcc/info_for_analysts.php. Shortwave and longwave fluxes at the surface
13 and the TOA along with the sensible and latent heat fluxes at the surface were downloaded from the PCMDI data portal. The variable number, their standard name, the output variable name and units are standard for every model and summarized in Table 1.
Table 1: Definition of the variables # CF STANDARD NAME (UNITS) OUTPUT VARIABLE NAME UNITS 29 TOA incoming shortwave flux rsdt Wm-2 30 TOA outgoing shortwave flux rsut Wm-2 31 TOA outgoing longwave flux rlut Wm-2 9 Surface upward latent heat flux hfls Wm-2 10 Surface upward sensible heat flux hfss Wm-2 11 Surface downwelling longwave flux in air rlds Wm-2 12 Surface upwelling longwave flux in air rlus Wm-2 13 Surface downwelling shortwave flux in air rsds Wm-2 14 Surface upwelling shortwave flux in air rsus Wm-2 3 Near surface air temperature tas K
Fourteen climate models outputs are used in this study. They are coming from various climate centers participating in the IPCC AR4 report. Table 2 presents these
models. The GFDL-CM2.0, CGCM3.1(T47) and (T63), MIROC3.2(medres) and (hires), MRI-CGCM2.3.2, and UKMO-HadCM3 and HadGEM1 models have both surface and TOA fluxes, and the GISS-ER and EH, IPSL-CM4, GFDL-CM2.1, CNRM-CM3 and CCSM3 models only include TOA fluxes at this time.
The 1% / yr CO2 increase experiment (to doubling) and pre-industrial control experiment are compared in this study. In the first experiment (referred to as the 2CO2 experiment from this point on), the CO2 concentration is increased by 1% every year until it reaches twice its initial value after 70 years. The control experiment on the other hand
14 doesn’t include any anthropogenic or natural radiative forcing and is used to subtract any
residuals (unforced drift) from the 2CO2 simulation. The 14 models’ simulations have different available time periods summarized in Table 1; therefore, in order to assure consistency in the future calculations, a maximum
run of 200 years is used for the 2CO2 experiment and compared to the same time period in the control experiment.
Table 2: IPCC models IDs and available time periods CLIMATE CENTERS IPCC I.D. TIME PERIOD # YEARS US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory GFDL-CM2.0 71-270 200 NASA / Goddard Institute for Space Studies GISS-EH 1950-2129 180 Canadian Centre for Climate Modelling & Analysis CGCM3.1(T47) 1920-2069 150 Canadian Centre for Climate Modelling & Analysis CGCM3.1(T63) 1920-2070 150 Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) MIROC3.2(medres) 71-220 150 Institut Pierre Simon Laplace IPSL-CM4 1931-2080 150 Meteorological Research Institute MRI-CGCM2.3.2 1901-2000 100 US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory GFDL-CM2.1 101-200 100 NASA / Goddard Institute for Space Studies GISS-ER 2041-2120 80 Météo-France / Centre National de Recherches Météorologiques CNRM-CM3 2030-2079 50 National Center for Atmospheric Research CCSM3 481-509 29 Hadley Centre for Climate Prediction and Research / Met Office UKMO-HadCM3 1930-1939 10 Hadley Centre for Climate Prediction and Research / Met Office UKMO-HadGEM1 1930-1939 10 Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC) MIROC3.2(hires) 71-80 10
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CHAPTER FOUR METHODOLOGY
4.1 Validation of the Dynamical Amplifier Theory
Two methods are used to validate the dynamical amplifier of global warming. As discussed in the Theory section, the dynamical amplifier requires an increase in the poleward heat transport due to the increase in net radiation trapped in low latitudes (due to the increase in GHGs’ concentration). Therefore, the dynamical amplifier can be inferred from the change in poleward heat transport. The first method described is using the change in the energy balance at the TOA to infer the change in poleward heat transport by both the atmosphere and oceans. In essence, this enhanced poleward heat transport must lead to an increase in net radiation energy surplus (deficit) at the TOA in low- (high-) latitudes. This change in the net radiation imbalance at the TOA is regarded as the footprint of the dynamical amplifier of global warming. The second method is examining the energy balance of the atmosphere to deduce the increase in poleward heat transport due to the atmosphere only.
4.1.1 Energy Balance at the Top of the Atmosphere
The footprint of dynamical amplifier can be detected by the change of surplus (deficit) of the net radiation flux at the top of the atmosphere (TOA). As a global long- time average, the Earth has to be radiatively in balance at the top of the atmosphere (i.e. equal to zero). However, the amount of energy absorbed and emitted by the earth varies geographically: locally, there is a surplus of net radiation in low latitude and a deficit in
16 high latitude. The energy balance equation for the climate system is the following (Hartmann 1994): E ao = R F (1) t TOA ao E / t Where the time rate of change of the energy content of the climate system ( ao ) is
equal to the net incoming radiation at the TOA (RTOA) minus the divergence of the F horizontal flux in the atmosphere-ocean system ( ao ). If the long time average of the net radiations at the TOA is considered in the equation, the storage term becomes negligible and the equation becomes:
RTOA = Fao (2) This balanced equation indicates that the long time averaged (10 years or more) net radiation at the TOA is in balance with the horizontal transport. For the purpose of this
study the RTOA term can be rewritten as the difference between the time-averaged net
solar radiation at the TOA (STOA) and the Outgoing Longwave Radiation at the TOA
(OLRTOA):
[]STOA []OLRTOA = [] FAO (3) F ao represents the divergence of the horizontal heat flux by the atmosphere-ocean system. A change in this balance would therefore imply a change in the poleward heat transport. This is an efficient way to look at the dynamical feedback because after the system reaches equilibrium one would not be able to identify such a change in the net radiation flux at the TOA if the amplified warming in high latitude were caused by local thermodynamical feedbacks (e.g., ice-albedo feedback) only. However both the oceanic and atmospheric transports are taken into account in this calculation, and the oceanic transport of heat can be problematic because there are still some important limitations to the prediction the ocean’s behavior (Stone 2004). The next section will then focus on the heat transport by the atmosphere only.
4.1.2 Energy Balance of the Atmosphere
The dynamical amplifier can be inferred from the change in atmospheric heat transport. The local energy balance of an atmospheric column of unit horizontal area can
17 be regarded as the balance between the time rate of change of the energy content of the E a atmospheric column extending from the surface to the TOA ( ), and Ra, LE, SH and t F a (Hartmann 1994): E a = R + LE + SH F (4) t a a
Where Ra is the net radiative heating of the atmospheric column, LE is the heating of the atmospheric column by latent heat release during precipitation, SH is the sensible heat F transfer from the surface to the atmosphere, and a is the horizontal divergence of energy out of the column by transport in the atmosphere. The net radiative heating of the atmosphere is the difference between the net radiative heating at the top of the
atmosphere (RTOA) and the net radiation at the ground (Rs):
Ra = RTOA-Rs (5) And because the heat storage becomes negligible when the fluxes are averaged over a long time period, the first equation becomes:
Ra + LE + SH = Fa (6) The combination of the last two equations leads to:
[]RTOA []RSurf + []LE + []SH = [] FA (7) Therefore, the dynamical amplifier is detected by the change in the horizontal divergence of energy out of the column by transport in the atmosphere.
4.2 Assessment of Models’ Uncertainties
The models’ uncertainties in predicting the global and high latitude warming under radiative forcing will be explained by the dynamical amplifier mechanism. First of all, the correlation between the change in poleward heat transport and warming projections will be computed in order to assess the role of the dynamical amplifier in warming uncertainties. Then, the global and high latitude warming will be correlated with
18 the strength of the mean circulation for each climate models. This part will demonstrate if the warming amount is dependent upon the models’ mean climate (without radiative forcing). The change in the poleward heat transport and the mean circulation will be calculated using the TOA fluxes in order to use all the 14 available models. The mean circulation index (MCI) will be computed to measure the global circulation strength, which is defined as:
2 MCI = F dxdy (8) ()ao F ao is the divergence of the horizontal heat flux by the atmosphere-ocean system (as described by equation 3) integrated over a certain domain. We will evaluate MCI over different areas such as the northern and southern hemispheres, and the global domain. F Because in the mean state of any models (and in observations), ao is positive in low latitudes and negative in high latitudes and its global mean is zero, the MCI is an objective way to measure the net heat transport strength in a coupled climate GCM simulation. We will also define DCI to objectively measure the change in the divergence of the horizontal heat flux due to anthropogenic greenhouse gases:
2 DCI = F 2 CO F 1 CO dxdy (9) ()ao () 2 ao() 2 F As shown later in Chapter 5, the difference between ao in 2CO2 and 1CO2 (control) runs is in general positive in low latitudes and negative in high latitudes for nearly all climate model simulations analyzed so far. As a result, DCI is an objective way to measure the change in the circulation due to anthropogenic greenhouses.
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CHAPTER FIVE RESULTS
5.1 Validation of the Dynamical Amplifier of Global Warming
The dynamical amplifier theory is validated by analyzing the ERA40 reanalysis and climate models simulations and looking at their poleward heat transport by both the atmosphere-ocean system and the atmosphere only. This section is divided in three parts; the first one shows the results from the reanalysis’ ocean-atmosphere transport, the second part shows the results from the models’ ocean-atmosphere transport, and the third part shows the results from the models’ atmospheric transport.
5.1.1 Reanalysis
The dynamical amplifier is here calculated from the change in the poleward heat transport by the ocean-atmosphere system. This variation in transport is reflected by the change in the balance of the net radiation flux at the TOA (see chapter 4.1.1). An increased poleward heat transport is detectable at the TOA from an increase of net radiations surplus in low latitudes and an increase of deficit in high latitudes. The change of net radiation at the TOA is calculated using the ERA40 reanalysis and by taking the difference between the August/1992-July/2002 mean and the August/1982-July/1992 mean of the net radiations. Figure 8 shows that the poleward heat transport has strengthened over the past 20 years. It was obtained after removing a global mean value of 0.48 Wm-2. The net radiations at the TOA has increased by about 0.9 Wm-2 over the equator and decreased subsequently over the poles. This change in the radiative balance at the TOA means that with a same amount of incoming solar radiations less outgoing
20 longwave radiations escape to space in low latitudes because more longwave radiations are transported toward high latitudes. This leads to a decrease in net radiation at the TOA in high latitudes. This result concurs with the dynamical amplifier theory that requires an intensification of the poleward heat transport (see Chapter 2.1).
Figure 8: Change in the long-time averaged zonal mean net radiation at the top of the atmosphere. The difference is taken between the August/1992-July/2002 mean and the August/1982-July/1992 mean derived from the ERA40 reanalysis.
21 5.1.2 IPCC Climate Models – Ocean and Atmospheric Transport
All fourteen CGCM models available from the IPCC AR4 are used in this part of the validation. The dynamical amplifier theory is evaluated in a similar way as the previous section, which is looking at the change in the poleward heat transport by the
atmosphere-ocean system. The difference between the 2CO2 run and the control run is computed in order to attribute the change in the radiative balance at the TOA to the
doubling of CO2 concentration in the atmosphere. Figure 9 displays the difference in
long-time averaged net radiation at the TOA between the 2CO2 and control run for the 14 models and time periods listed in Table 1. The results are from the 14 models marked a) to n) from the longest time period available to the shortest (see Table 2). The areas shaded in red represent an increased divergence of the poleward heat flux by the atmosphere-ocean system, and the areas shaded in blue an increased convergence of poleward heat flux. All the models present similar features; the red colors are generally located in low latitudes and the blue colors in high latitude. Therefore, the atmosphere-
ocean system transports more heat from low to high latitudes in the 2CO2 scenario. This feature is in accordance with the dynamical amplifier theory. However, it should be pointed out that some disparities are noticeable between models especially over the oceans. These inconsistencies in the heat transport are attributed to the fact that both the atmosphere and the oceans are responsible for the transport of heat in the calculations used in this section. As mentioned in the methodology section, the ocean’s parametrizations still have some uncertainties, and limits the predictability in climate systems. For instance, the IPSL-CM4 (Fig. 9f) model has a strong increase (up to 9 Wm-2) of the divergence of horizontal heat flux over the northern hemisphere high latitudes (30-90°N), especially northern Europe, North America and around Greenland. The southern high latitudes on the other hand depict the expected increase in horizontal flux convergence. This inconsistency might be due to the oceanic component of the system or the sea-ice parametrization in the northern hemisphere (Marti et al. 2005) give a detailed description of the model and its climatology). Another discrepancy with the theory is found with the CCSM3 model as seen in Fig. 9k. The low latitudes have two big areas of increased horizontal heat flux
22 convergence (over India and Indonesia, and over the Atlantic ocean). Moreover, over most of the southern high latitudes oceans, the horizontal heat flux is increasingly diverging. The ocean part of the model is known to have some biases (Large 2004), and this could therefore affect the heat fluxes. Finally, the UKMO-HadCM3 model (Fig. 9l) has large areas in the northern high latitudes of stronger heat flux divergence over Europe and North America. This problem is less predominant in the newest version of the UKMO model HadGEM1 (Fig. 9m), which includes better physics and is believed to outperform HadCM3 in representing the mean climate (Johns 2005). Some parts of the inconsistencies could also be due to the time period available at the time the models outputs were analyzed. The CCSM3 and UKMO-HadCM3 only had 29 and 10 years of complete data set.
23
run and the control and the control run 2
CO )
T63 (
GISS-EH CGCM3.1 ) )
b d
). -2
)
T47 (
GFDL-CM2.0 CGCM3.1 ) ) a c run derived from 14 IPCC AR4 model simulations (Wm model simulations AR4 IPCC 14 from run derived Figure 9: Difference of the long-time averaged net radiation flux at the TOA between the 2 TOA between the at the radiation flux averaged net long-time Difference the Figure of 9:
24
IPSL-CM4 GFDL-CM2.1 ) )
f h
)
medres (
MIROC3.2 MRI-CGCM2.3.2 ) e g)
- Continued. Figure 9
25
CNRM-CM3 UKMO-HadCM3 )
l j)
GISS-ER CCSM3 ) ) k i Figure 9 - Continued. Figure 9
26
MIROC3.2
)
n
UKMO-HadGEM1
) m
- Continued. Figure 9
27 Because of the difficulty to confidently conclude how the circulation is globally changing by looking at separate maps, a zonal and area average of the net radiation change at the TOA using the 14 climate models is performed (Figs. 10 a and b). The zonal average of each model has been normalized by the global average of the change in circulation index (DCI) (calculated as described in chapter 4.2, equation 9). As seen in Fig. 10a, the low latitudes (30°S-30°N) are generally experiencing an increased divergence of horizontal heat flux and the high latitudes (say 30°-90°) an increased convergence as expected from the theory. This means that under radiative forcing the models’ poleward heat transport by the ocean-atmosphere system is strengthening. However, many models also exhibit a net radiation increase at the TOA around 70 degrees latitude in the southern hemisphere. In other words, the horizontal heat flux convergence is not enhanced. Because the southern hemisphere is dominated by oceans, this phenomenon might be the effect of the uncertainties in modeling the ocean heat transport. This is why the next section is concentrating on the computation of the poleward heat transport by the atmosphere only. Figure 10b is summarizing the findings by displaying the area average of the change in net radiations at the TOA. The global mean value of each model (comprised between 0.6 and 1.81 Wm-2) has been removed before the calculation of the area mean because the models had not reached equilibrium. The 14 models are listed on the upper part of the figure and the three colors correspond to the different area averages. The light blue is an average over the southern hemisphere high latitude (90°S-30°S), the red is over low latitudes (30°S-30°N), and the dark blue over the northern hemisphere high latitude (30°N-90°N). As expected the majority of the CGCM models validate the dynamical amplifier theory by showing a net radiation increase over low latitudes and decrease over high latitudes with the exception of the IPSL-CM4, CCSM3 and UKMO-HadCM3 (models discussed earlier in this section because of their local discrepancies with the theory.
28 a)
b)
Figure 10: Difference of the long-time averaged net radiation flux at the TOA in Wm-2 between the 2CO2 run and the control run derived from 14 IPCC AR4 model simulations. (a) Zonal average. (b) Area average difference for each model.
29 5.1.3 IPCC Climate Models – Atmospheric Transport
The change in the poleward heat transport due to radiative forcings is now calculated using the method described in chapter 4.1.2 by inferring the heat transport from both the TOA and surface energy fluxes, in order to remove the impacts from the ocean heat transport. Figure 11 displays the results from the 8 models available ranked from the longest to the shortest time period available (see Table 2). They represent the difference in horizontal divergence of energy out of the column by transport in the atmosphere between the 2CO2 and control run. Therefore the red colors express an increase in divergence and the blue colors an increase in convergence of heat flux. The results using atmospheric heat flux calculations are noisier than the previous method because the net radiations at the surface are involved in this computation. Despite this issue, all the models seem to globally agree with the theory with an enhanced atmospheric divergence in low latitudes and convergence in high latitudes.
30
)
) medres ( T47 ). Red is divergence, blue is blue is divergence, is Red ). ( -2
MIROC3.2 CGCM3.1
) ) b d
)
T63 (
GFDL-CM2.0 CGCM3.1 ) ) a c Figure 11: IPCC models’ divergence of poleward atmospheric heat transport (Wm heat transport atmospheric of poleward divergence models’ IPCC Figure 11: convergence.
31
)
hires (
UKMO-HadCM3 MIROC3.2 ) ) f h
Continued.
–
11
UKMO-HadGEM1 MRI-CGCM2.3.2 ure ) g e g) Fi
32 The behavior of the poleward heat transport by the atmosphere is more easily identifiable when its area average is computed. The areas are computed from 90°S to 30°S (light blue), 30°S to 30°N (red), and 30°N to 90°N (dark blue). The areas used to compute the averages are different from Fig. 10 because they offer better results. The global mean has been taken out before the area mean calculation. All 8 IPCC AR4
models show a stronger poleward heat transport in the 2CO2 simulations. Even the UKMO-HadCM3, one of the three models in Fig. 10 not showing an intensification of energy deficit over the northern hemisphere extratropics, is now in agreement with the theory.
Figure 12: Area averaged divergence of atmospheric heat flux (Wm-2).
33 This concludes the validation of the dynamical amplifier theory, which has been verified by the reanalysis and the 14 IPCC AR4 models by computing the poleward heat transport by the ocean-atmosphere system, and the atmosphere only. Therefore, the dynamical amplifier mechanism can now be used to quantify and understand the model- to-model variation in projected warmings. The second and last part of the paper will be to understand the disparities between climate models’ warming projections.
5.2 Understanding Model’s Uncertainties
Climate models all have different approaches to parameterize physical processes. This leads to different results for a set of prescribed conditions. Indeed, as observed in Fig. 10 and Fig. 12, all the IPCC AR4 models studied exhibit different circulation changes. As a result, the surface warming in high latitudes would also differ strongly among different models. Figure 13 shows an example of three warming scenarios under radiative forcing. It is computed by taking the difference between the 2CO2 and control experiment of the 1930 to 2000 temperature average. The three IPCC AR4 models chosen are the (a) GFDL-CM2.0, (b) MRI-CGCM2.3.2, and (c) MIROC3.2_medres. They all show different warming intensities and patterns. This section will attempt to explain the model-to-model variation of global warming simulations using the 13 models that best show the heat transport change (< 6 Wm-2) and mean circulation (> 48 Wm-2). It will first quantify the role of the dynamical amplifier in the different warming scenarios, and then assess the dependence of the temperature projection on the models’ mean circulation.
34 a)
b)
c)
Figure 13: Temperature change at the surface (CO2 – Control run) from the year 1930 to 2000 using 3 CGCM models : a) GFDL-CM2.0, b) MIROC3.2(medres), c) MRI- CGCM2.3.2
35 5.2.1 Model Warming Projections Versus Change in Poleward Heat Transport
The warming projections vary from model to model and so do the circulation changes, given a same radiative forcing scenario. Based on the dynamical amplifier theory, the larger the positive change in poleward heat transport, the larger the high latitude surface warming will be. This section will assess if the difference in changes of poleward heat transport among models can explain part of the differences in models’ warming patterns. Figure 14 is a scattered plot of the temperature (y-axis) and heat transport (x-axis) change (DCI index) for each model, and it clearly shows that the two values are positively correlated. The models’ global warming projections are affected by the strength of their dynamical amplifier feedback, which is represented by the change in poleward heat transport (Fig. 14a). About 60% of the uncertainties in global warming projection are explained in such a way, and the correlation is very high (0.77). Figures 14b and c are similar but looking at the northern and southern hemisphere separately. Once again the correlations are very high, and therefore validate the theory. In addition, the slope of the linear regression line measures the sensitivity of the projected warming to changes in the poleward heat transport, the best sensitivity being 1. Figure 14 shows that the northern hemisphere temperature change is the most sensitive to the change in poleward heat transport, with a slope equal to 0.7. The sensitivity of the projected warming is also strong on the global scale (slope equal to 0.43). In order to quantify the margin of error when calculating the slope of the regression line, a 95% confidence band was computed for the global warming projection. The confidence band is equal to 0.22 meaning that the correlation remains positive with a 95% confidence band. This not only confirms the dynamical amplifier theory but also indicates that the variation of the change in circulation from model to model is one of the most important sources for the uncertainties.
36 a)
b)
c)
Figure 14: Models’ warming projections versus changes in the poleward heat transport (DCI) in the same models. (a) Global average, (b) northern hemisphere average, (c) southern hemisphere average.
37 The correlation between the circulation change and the temperature increase under radiative forcing is even stronger over high latitudes as expected by the dynamical amplifier theory. The correlation for the northern hemisphere high latitudes (Fig. 15a) is 0.84 and explains 71% of the uncertainties. The sensitivity is also the strongest (equal to 0.9) and the 95% confidence band for the slope of the regression line is 0.36. The lower value for the southern hemisphere (Fig. 15b) might be due to the predominance of oceans. All the values are collected in Table 3.
a)
b)
Figure 15: Models’ warming projections versus changes in the poleward heat transport (DCI) in the same models. (a) Northern hemisphere high latitude average, (b) southern hemisphere high latitude average.
38 This table summarizes the correlation (red), variance explained (purple) and sensitivity (black) between the poleward heat transport strength and the temperature changes over different areas. The stronger correlation is found over the northern
hemisphere high latitudes (
Table 3: Summary of the correlation, variance explained and sensitivity.
5.2.2 Model Warming Projections Versus Strength of the (unperturbed) Models’ Mean Circulation
The uncertainties in warming projections have been successfully related to the strength of the poleward heat transport. But each model has different unperturbed climate state (no radiative forcing involved) due to the models’ biases in their circulation strength. In the IPCC AR4 models, the circulation strength varies from 48 to 58 Wm-2 and the temperature increases from 1.9 to 3.7°K. The correlation between the global warming and the models’ mean climate is 0.61 (Fig. 16a) and decreases slightly for each hemisphere. The sensitivity of the global warming projections to the mean circulation is 0.120.10 using a 95% confidence band. This means that the correlation remains positive with 95% confidence.
39 a)
b)
c)
Figure 16: Models’ warming projections versus the strength of the unperturbed model’s mean circulation (MCI). (a) Global average, (b) northern hemisphere average, (c) southern hemisphere average.
40 This figure also shows the same result for the high latitudes. The same positive correlation is present with 0.61 and 0.43 for the northern hemisphere and southern hemisphere respectively. The sensitivity of the temperature to the mean circulation is not strong (0.180.15), but about 1/3 of the uncertainties in the global warming amount are explained in the northern hemisphere high latitudes.
a)
b)
Figure 17: Models’ warming projections versus the strength of the unperturbed model’s mean circulation (MCI). (a) Northern hemisphere high latitude average, (b) southern hemisphere high latitude average.
41 Table 4 summarizes the correlation (red), variance explained (purple) and sensitivity (black) between the warming trends and the strength of the poleward heat transport in an unperturbed climate state. Once again the northern hemisphere high latitudes temperatures are the most sensitive (0.61 correlation) to circulation changes.
Table 4: Summary of the correlation, variance explained and sensitivity
42
CHAPTER SIX CONCLUSION
The findings of this research were organized in two main parts: the validation of the dynamical amplifier theory using the ERA40 reanalysis and 14 climate models from the IPCC AR4, and the use of this theory to understand the large variability in the models’ warming projections.
6.1 Validation of the Dynamical Amplifier Theory
The dynamical amplifier theory, which requires an enhanced poleward heat transport in response to an anthropogenic increase in GHGs is verified by the reanalysis and models’ data. The net radiations at the TOA (representing the change in heat transport by the atmosphere-ocean system) increase in low latitudes and decrease in high latitudes for the majority of the 14 IPCC AR4 models (11 out of 14) and the reanalysis. The areas that seem to have caused disagreement with the theory are mainly located over the southern oceans. This is believed to be due to the ocean part of the heat transport, because the parametrization of oceans has still some major uncertainties. The theory was further tested by analyzing the atmospheric transport of heat by including surface fluxes in the calculations. With this method, all the available IPCC AR4 models agreed with each other, even the UKMO-hadCM3, which was previously one of the three models in disagreement with the theory. The area average specifically showed that the divergence of heat flux was increasing in low latitude transporting heat toward high latitudes where the convergence of heat was increasing.
43 6.2 Evaluation of Model’s Uncertainties
The wide range of circulation changes demonstrated above was correlated to the warming projections’ uncertainties. The large model-to model variability in global warming scenarios was in majority (59% of the uncertainties) explained by the intensification of the poleward heat transport in each model forced by 2CO2. This trend was further confirmed by looking at the correlations between those two variables in each hemisphere (0.81 and 0.73 in the northern and southern hemisphere respectively). Finally the high latitudes (30° to 90°) showed the best agreement between the warming and the strength of the poleward heat transport with a 0.84 correlation in the northern hemisphere and a sensitivity equal to 0.910.36. Therefore, it can be concluded that a large part of
the uncertainties in the CGCM’s global warming projections can be explained by the dynamical amplifier theory. To complement this result, the agreement between the mean circulation strength and the warming scenarios was analyzed. The correlations were found to be smaller than with the previous calculations but still significant especially over the northern hemisphere high latitude (0.61 correlation and 37% of the variance explained). The sensitivity was also less significant but the slope of the regression line remains positive (equal to 0.130.10 on the global scale). This means that the mean state of the climate also plays a role in the warming projections.
6.3 Future Work
In this research, the atmospheric poleward heat transport is calculated indirectly by studying the difference between the net radiation flux at the TOA and the surface. This method can give noisy results because the flux data, especially at the surface, are affected by different parametrizations such as cloud parametrization. Alternatively, the poleward
44 heat transport can be directly estimated by evaluating the meridional circulation and the atmospheric stationary and transient eddies. []T = [] []T + [] *T * + []'T' (10) Where [] []T is the heat flux due to the meridional circulation, [] *T * is the heat flux due to stationary eddies and []'T' the heat flux due to transient eddies. This can be done by using reanalysis and climate models’ daily data. Moreover, the meridional transports of momentum and moisture can be computed in order to assess if the changes in the mean circulation are reflected in the second moments of the statistics and the processes that maintains the circulation change. The examination of the long-term trend of these fluxes can also be related to the changes in frontal activities. The second part of this work will be to calculate the net radiation change at the TOA using satellite data from the Earth Radiation Budget Experiment (ERBE). These data provide more reliable estimates of the TOA fluxes than the reanalysis data. Indeed, the ERBE data are satellite observations whereas the reanalysis data are based on operational three-dimentional variational assimilation systems that are using cloud forecasts. Finally the ocean flux will be calculated as residual from the total heat transport and the atmospheric heat transport:
F = F F (11) []o []a o []a The ocean heat transport can be indeed calculated by taking the difference between the heat transport by the atmosphere-ocean system and the atmospheric heat transport. By combining the equations (3) and (7), (10) becomes:
R LE SH = F o (12) []Surf [][][] The long time trend of the three terms on the left hand side of the equation is expected to be positive in low latitude and negative in high latitude. This result would imply an enhancement of the oceanic heat transport. The ocean flux will also be calculated using the residual method, namely, taking the difference between the total fluxes derived from (3) and the direct calculation of the atmospheric heat transport (9). These calculations will be performed using the reanalysis and the climate models simulations.
45
APPENDIX MODELS’ DESCRIPTION
The 14 IPCC AR4 models are described in this appendix. Detailed reports are available at: www-pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php
GFDL-CM2: Models name: CM2.0 and CM2.1 – AOGCM Atmosphere: AM2P13 Ocean: OM3P4 Land: LM2 Sea ice: SIS
GISS: Models name: GISS ModelE-H and GISS ModelE-R Atmosphere: N/A Ocean: N/A Land: N/A Sea Ice: N/A
CGCM3.1: Model name: CGCM3.1(T47) and CGCM3.1(T63) Atmosphere: AGCM3 Ocean: MOM1.1 Land: AGCM3 Sea Ice: N/A
MIROC3.2: Model name: MIROC3.2(medres) and MIROC3.2(highres) Atmosphere: CCSR/NIES/FRCGC AGCM5.7b Ocean: COCO3.3 Land: MATSIRO Sea Ice: COCO3.3
46 IPSL-CM4: Model name: IPSL-CM4 Atmosphere: LMDZ-4 Ocean: ORCA Land: ORCHIDEE Sea Ice: LIM
MRI-CGCM2.3.2 Model name: MRI-CGCM2.3.2 Atmosphere: N/A Ocean: N/A Land: N/A Sea Ice: N/A
CNRM-CM3 Model name: CNRM-CM3 Atmosphere: ARPEGE-Climat version 3 Ocean: OPA 8.1 Land: ISBA Sea Ice: GELATO 2
CCSM3 Model name: CCSM3 Atmosphere: CAM3 Ocean: POP 1.4.3 Land: CLM3 Sea Ice: CSIM5
UKMO Model name: HadGEM1 Atmosphere: HadGAM1 Ocean: HadGOM1 Land: MOSES2 Sea Ice: N/A
UKMO Model name: HadCM3 Atmosphere: HadAM3 Ocean: N/A Land: N/A Sea Ice: N/A
47
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49
BIOGRAPHICAL SKETCH
Christelle Clémence Castet was born on August 14th 1981 in Toulouse, France. She attended Lycée Bagatelle high school in Saint Gaudens, France where she graduated in 1999. She then moved to Sophia Antipolis, France to study Environmental Sciences at the EAI Tech University for two years. She transferred to Florida Institute of Technology to pursue her interest in Meteorology and graduated with honor from a Bachelor of Science in Meteorology in May 2003. She was then accepted in Fall 2003 to pursue a M.S. degree in Meteorology at Florida State University. The first year she was a teacher assistant in synoptic meteorology lab I and II for Dr Fuelberg and Ruscher and a grader for Dr Kim and Nicholson in atmospheric physics and physical climatology. The second year she started to work under Dr Cai on the dynamical amplifier of global warming. In January and April 2005 Christelle presented the results from her research at the AMS and EGU annual meetings. She is currently a member of the AMS, EGU, COS and AWIS.
50