Links Between Climate Feedbacks and the Large-Scale Circulation Across Idealized an 原版完整文件.docx

《Links Between Climate Feedbacks and the Large-Scale Circulation Across Idealized an 原版完整文件.docx》由会员分享，可在线阅读，更多相关《Links Between Climate Feedbacks and the Large-Scale Circulation Across Idealized an 原版完整文件.docx（150页珍藏版）》请在淘文阁 - 分享文档赚钱的网站上搜索。

1、ABSTRACTLINKS BETWEEN CLIMATE FEEDBACKS AND THE LARGE-SCALE CIRCULATION ACROSS IDEALIZED AND COMPLEX CLIMATE MODELSThe circulation response to anthropogenic forcing is typically considered in one of two distinct frameworks: One that uses radiative forcings and feedbacks to investigate the thermodyna

2、mics of the response, and another that uses circulation feedbacks and thermodynamic constraints to inves- tigate the dynamics of the response. In this thesis, I aim to help bridge the gap between these two frameworks by exploring direct links between climate feedbacks and the atmospheric circulation

3、 across ensembles of experiments from idealized and complex general circulation models (GCMs). I first demonstrate that an existing, widely-used type of idealized GCM the dynamical core model has climate feedbacks that are explicitly prescribed and determined by a single parame- ter: The thermal rel

4、axation timescale. The dynamical core model may thus help to fill gaps in the model hierarchies commonly used to study climate forcings and climate feedbacks. I then perform two experiments: One that explores the influence of prescribed feedbacks on the unperturbed, cli- matological circulation; and

5、 a second that explores their influence on the circulation response to a horizontally uniform, global warming-like forcing perturbation. The results indicate that more stabilizing climate feedbacks are associated with 1) a more vigorous climatological circulation with increased thermal diffusivity,

6、and 2) a weaker poleward displacement of the circulation in re- sponse to the global warming-like forcing. Importantly, since the most commonly-used relaxation timescale field resembles the real-world clear-sky feedback field, the uniform forcing perturbations produce realistic warming patterns, wit

7、h amplified warming in the tropical upper troposphere and polar lower troposphere. The warming pattern and circulation response disappear when the relax- ation timescale field is instead spatially uniform, demonstrating the critical role of spatially-varyingfeedback processes on shaping the response

8、 to anthropogenic forcing.ixI next explore circulation-feedback relationships in more complex GCMs using results from the most recent Coupled Model Intercomparison Projects (CMIP5 and CMIP6). Here, I estimate climate feedbacks by regressing top-of-atmosphere radiation against surface temperature for

9、 both1) an unperturbed pre-industrial control experiment and 2) a perturbed global warming experimentforced by an abrupt quadrupling of CO2 concentrations. I find that across both ensembles, the cloud component of the perturbed climate feedback is closely related to the cloud component of the unpert

10、urbed climate feedback. Critically, the relationship is much stronger in CMIP6 than CMIP5, contrasting with many previously proposed constraints on the perturbation response. The relationship also explains the slow part of the CO2 response better than the fast, transient response. In general, the st

11、rength of the relationship depends on the degree to which the spatial pattern of the response resembles ENSO-dominated internal variability, with “El Nio-like” East Pacific warming and related tropical cloud changes. This is consistent with fluctuation-dissipation theory: Regions with stronger deep

12、ocean heat exchange and weaker net feedbacks must always dominate both 1) internal fluctuations in the global energy budget, and 2) the slow part of the response to forcing perturbations. The stronger CMIP6 inter-model relationships are due to both an ampifi- cation of this mechanism and higher inte

13、r-model correlations between tropical cloud changes and extratropical cloud changes. Finally, I present emergent constraints on the slow response using a recent observational estimate of the unperturbed cloud feedback.I conclude by discussing some implications of these results. I consider how the re

14、laxation feedback framework might be further developed and reconciled with traditional climate feedbacks to provide future research opportunities with climate model hierarchies.ACKNOWLEDGEMENTSThis thesis was supported by the U.S. National Science Foundation Climate Dynamics Pro- gram. I would like

15、to thank my advisors David W. J. Thompson and Thomas Birner for teaching me so much, and for their kindness, encouragement, and patience over these past years. I would also like to thank Maria A. A. Rugenstein for her guidance and insight over our many engaging meetings, my Ph.D. committee for their

16、 help and for giving me time to complete this degree, and my research group colleagues (past and present) for all of their support. I would like to thank my grandparents, Vivianne T. Nachmias and Jacob Nachmias, former Professors at the University of Pennsylvania, for inspiring me to pursue a career

17、 in science from a young age. I would like to thank my mother, Lisa N. Davis, for her love and her many sacrifices, without which this thesis would not be possible and for which I will always be in debt. Id like to thank other friends and family for their love and support in times good and bad.TABLE

18、 OF CONTENTSABSTRACTiiACKNOWLEDGEMENTSivLIST OF TABLESviiLIST OF FIGURESviiiChapter 1Introduction11.1 Motivation and outline11.2 Circulation responses31.3 Climate feedbacks8Chapter 2Dynamical core: Theory142.1 Thermal relaxation timescales142.2 Coupled model comparisons162.3 Experimental design21Cha

19、pter 3Dynamical core: Experiments243.1 Unperturbed circulation243.2 Perturbed circulation31Chapter 4Coupled models: Feedbacks414.1 Context and motivation414.2 Cloud feedback constraints44Chapter 5Coupled models: Patterns525.1 Context and motivation525.2 Multi-model average patterns535.3 Inter-model

20、pattern contributions57Chapter 6Conclusions and discussion656.1 Dynamical core model656.2 Coupled models676.3 Interpretation706.4 Reconciling the frameworks736.5 Future work75Appendix AForcing-feedback metrics121A.1 Global feedback parameters121A.2 Radiative feedback kernels122A.3 Relaxation climate

21、 sensitivity124A.4 Model description125Appendix BCoupled models128B.1 Forcing-feedback metrics128B.2 Institute averaging134B.3 Institute grouping136Appendix CSoftware and data availability140LIST OF TABLESB.1 The CMIP6 models used in this thesis.129B.2 The CMIP5 models used in this thesis.130LIST OF

22、 FIGURES1.1 Summary of frameworks for assessing the climate system response to forcing pertur- bations.51.2 Idealized box model for pattern effects on the global climate feedback.112.1 Climate sensitivity and the thermal relaxation timescale.172.2 Radiative feedback kernels and the thermal relaxatio

23、n timescale.192.3 As in Figure 2.2 but showing vertically-integrated values.202.4 Dynamical core model forcing terms.223.1 Relaxation climate sensitivity and the large-scale circulation.253.2 Relaxation climate sensitivity and the extratropical circulation.273.3 Thermodynamic constraints on the extr

24、atropical circulation.303.4 Relaxation climate sensitivity and the thermodynamic response to global warming.323.5 Relaxation climate sensitivity and the large-scale circulation response to global warming. 343.6 As in Figure 3.5, but for the experiments with uniform thermal relaxation timescales.353.

25、7 Relaxation climate sensitivity and the extratropical circulation response to global warm-ing.373.8 As in Figure 3.7, but for the experiments with uniform thermal relaxation timescales.384.1 Inter-model correlation between local CMIP6 perturbed and unperturbed feedbacks.424.2 Multi-model distributi

26、ons of perturbed and unperturbed climate feedback parameters.454.3 Inter-model regressions of perturbed climate feedback parameters against unperturbed feedback parameters.474.4 As in Figure 4.3 but with the early perturbed feedbacks calculated for years 050 andlate perturbed feedbacks calculated fo

27、r years 100150.474.5 Emergent constraints on the perturbation response from observed unperturbed cloud feedbacks.505.1 Local contributions to global warming and climate feedbacks.535.2 As in Figure 5.1 but for the unperturbed and perturbed shortwave and longwave cloud feedbacks.555.3 Local contribut

28、ions to inter-model spread in global warming and global cloud feedbacks. 585.4 As in Figure 5.3 but showing local contributions to inter-model spread in global cloud feedbacks alongside local contributions to the unperturbed global cloud feedback con- straints.595.5 As in Figure 5.3 but for the shor

29、twave and longwave cloud feedbacks.615.6 As in Figure 5.3 but showing zonal averages and including the shortwave and longwavecloud feedback.625.7 Inter-model relationships between perturbed tropical and Southern Ocean feedbacks.63B.1 Forcing-feedback analysis results for CMIP5 and CMIP6 abrupt 4 CO2

30、 experiments(see also Figure B.4).132B.2 As in Figure B.1 but showing institute-averages instead of individual models (see also Figure B.6).133B.3 Comparison of abrupt 4 CO2 forcing-feedback analysis from this thesis to analysisfrom Zelinka et al. (2020).135B.4 As in Figure 4.2 but using individual

31、models instead of institute averages.138B.5 As in Figure 4.3 but using individual models instead of institute averages.138B.6 As in Figure 4.2 but using two separate groups of CMIP6 institutes.139B.7 As in Figure 4.3 but using two separate groups of CMIP6 institutes.139Chapter 1 Introduction1.1 Moti

32、vation and outlineThe physical processes that determine the temperature of Earths surface have been of scien- tific interest for centuries (Anderson et al. 2016, Lacis et al. 2010, Mudge 1997). An insulating greenhouse effect, caused by atmospheric absorption of infrared radiation, was first propose

33、d in the early 19th century (Fourier 1827, Pouillet 1838). In the late 19th century, this effect was attributed to the greenhouse gases carbon dioxide (CO2) and water vapor (H2O) first qualitatively by John Tyndall (Tyndall 1861), then quantitatively by Svante Arrhenius (Arrhenius 1896). Following t

34、his foundational work, throughout the early and middle 20th century, a growing body of research no- ticed the possible influence of human activities on greenhouse gases and their attendant greenhouse effects (Bolin and Eriksson 1959, Callendar 1938, Keeling 1970, Revelle and Suess 1957, Sawyer 1972)

35、. This culminated in the development of early computer models that provided rudimentary predictions of these effects (Manabe and Wetherald 1967, 1975, Schneider and Dickinson 1974, Sellers 1969), followed by an immensely impactful synthesis of these efforts commonly called “The Charney Report” (Char

36、ney et al. 1979). This report identified fossil fuel burning as respon- sible for past increases in CO2, predicted continued and substantial future increases, and estimated the surface warming associated with an eventual doubling of CO2 at approximately 3 1.5 K, marshaling an explosion of growth in

37、the field of climate science.Today, climate science encompasses a huge variety of phenomena beyond the essential warm- ing effect of greenhouse gases. However, the average surface warming originally predicted by the Charney report has remained persistently uncertain (Charney et al. 1979, Knutti et a

38、l. 2017, Roe and Baker 2007, Sherwood et al. 2020). Much of this uncertainty propagates to the circulation response to increasing CO2: The warming itself is connected to various hydrologic and energetic aspects of the circulation response (e.g., Bony et al. 2013, Chadwick et al. 2019, He and Soden14

39、12015, Held and Soden 2006, Pendergrass and Hartmann 2014, Vecchi and Soden 2007), while patterns and gradients in the warming are connected to dynamical aspects of the circulation re- sponse (e.g., Brayshaw et al. 2008, Davis and Birner 2022, Grise and Polvani 2014, 2016, Held 1993, Williamson et a

40、l. 2013, Zhang et al. 2019). Some parts of the circulation response are also dependent on opposing or time-varying thermodynamic changes, such that individual model simu- lations may exhibit significant changes while the average across several model simulations exhibits a very weak change (e.g., Cep

41、pi and Shepherd 2017, Feldl et al. 2017, Heede et al. 2020, Shaw and Voigt 2015, Shaw et al. 2016). Overall, the close relationship between temperature and circulation responses to increasing greenhouse gas concentrations underscores the critical need to understand and constrain their coupled intera

42、ctions.The warming response to greenhouse gas emissions is commonly assessed using the forcing- feedback framework (Hansen et al. 1985). Under this framework, the warming induced by increas-ing CO2 is broken down into the ratio between 1) radiative forcing perturbations, which instigateenergetic imb

43、alances at the surface and throughout the atmospheric column, and 2) radiative cli- mate feedbacks, which comprise the pathways used by the climate system to restore energetic balance and adjust to the new equilibrium state (e.g., Hansen et al. 1985, Roe 2009, Rugenstein and Armour 2021). The global

44、 average temperature change resulting from the radiative forcing and feedback processes is typically called the climate sensitivity. While the forcing-feedback frame- work is widely used to explore the energetics of the response, it is much less commonly used to explore the circulation response. Ins

45、tead, circulation changes are typically investigated by relating the circulation to the background thermodynamic state through the lens of circulation feedbacks and steady-state thermodynamic constraints (e.g., Armour et al. 2019, Butler et al. 2011, Chen et al. 2020, Davis and Birner 2019, Held 199

46、9, Schneider and Walker 2006, Zhang et al. 2021). This is important, since there is also clearly a robust two-way coupling between the circulation and the climate feedbacks themselves (e.g., Ceppi and Shepherd 2017, Lu et al. 2021, Sun et al. 2013). The aim of this thesis is to explore direct relati

47、onships between climate feedbacks, climate sensitivity, and the large-scale circulation. The rest of Chapter 1 surveys some of the existing lit-erature on the two frameworks identified above: Forcing-feedback effects on the thermodynamic response, and thermodynamic effects on the circulation respons

48、e. Chapter 2 demonstrates that the thermal relaxation coefficients used with dynamical core configurations of general circulation models (GCMs) determine the response of the model to forcing perturbations. In other words, the climate feedbacks of dynamical core models are exactly linear and can be explicitly prescribed, allowing us to study their direct influence on the large-scale circulation. Chapter 3 uses the theory from Chapter 2 to explore the influence of climate feedbacks on both 1) the unperturbed large- scale circulation and 2) the ci