Neutral modes of surface temperature and the optimal ocean thermal forcing for global cooling

Lu, Jian; Liu, Fukai; Leung, L. Ruby; Lei, Huan Yao

doi:10.1038/s41612-020-0112-6

Cited by 9 publications

(17 citation statements)

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“…provided the inverse of the matrix χ Ψ,s exists. The problem of static response is considered by Lu et al, 49 who take the N geoengineering forcings as those acting at N different gridpoints of a climate model and look for forcing fields to which the climate system is most susceptible.…”

Section: B the Geoengineering Problemmentioning

confidence: 99%

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Can we use linear response theory to assess geoengineering strategies?

Bódai

Lucarini

Lunkeit³

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Geoengineering can control only some climatic variables but not others, resulting in side-effects. We investigate in an intermediate-complexity climate model the applicability of linear response theory (LRT) to the assessment of a geoengineering method. This application of LRT is twofold. First, our objective (O1) is to assess only the best possible geoengineering scenario by looking for a suitable modulation of solar forcing that can cancel out or otherwise modulate a climate change signal that would result from a rise in carbon dioxide concentration [CO 2 ] alone. Here we consider only the cancellation of the expected global mean surface air temperature ∆ [T s ] . It is in fact a straightforward inverse problem for this solar forcing, and, considering an infinite time period, we use LRT to provide the solution in the frequency domain in closed form as f swhere the χ's are linear susceptibilities. We provide procedures suitable for numerical implementation that apply to finite time periods too. Second, to be able to utilize LRT to quantify side-effects, the response with respect to uncontrolled observables, such as regional averages T s , must be approximately linear. Therefore, our objective (O2) here is to assess the linearity of the response. We find that under geoengineering in the sense of (O1), i.e., under combined greenhouse and required solar forcing, the asymptotic response ∆ [T s ] is actually not zero. This turns out not to be due to nonlinearity of the response under geoengineering, but rather a consequence of inaccurate determination of the linear susceptibilities χ. The error is in fact due to a significant quadratic nonlinearity of the response under system identification achieved by a forced experiment. This nonlinear contribution can be easily removed, which results in much better estimates of the linear susceptibility, and, in turn, in a fivefold reduction in ∆ [T s ] under geoengineering practice. This correction dramatically improves also the agreement of the spatial patterns of the predicted linear and the true model responses. However, considering (O2), such an agreement is not perfect and is worse in the case of the precipitation patterns as opposed to surface temperature. Some evidence suggests that it could be due to a greater degree of nonlinearity in the case of precipitation.

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Section: B the Geoengineering Problemmentioning

confidence: 99%

“…TB would like to thank Jian Lu for inspiring discussions on geoengineering, and sharing their manuscript. 49 This work was part of the EU Horizon 2020 project CRESCENDO (under Grant No. 641816); the financial support is gratefully acknowledged.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Can we use linear response theory to assess geoengineering strategies?

Bódai

Lucarini

Lunkeit³

2020

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…This question may be addressed by comparing the atmospheric circulation pattern of the NM1 with that derived from a similar Green's function perturbation approach with the atmospheric component of CESM coupled to a motionless ocean slab (F. Liu, Lu, Garuba, Huang, et al, 2018;Lu et al, 2020). The fact that the slab counterpart of the NM1 (Figure S6 in Supporting Information S1, see also Figure 2e of Lu et al (2020)) exhibits similar annular mode structure as the NM1 here strongly suggests the orchestrating role of atmospheric dynamics in the wind and ocean current structures shown in Figure 3. The meridional dipole pattern in the atmosphere is a natural result of conservation of angular momentum and mass by fluid motions (e.g., Gerber et al, 2008).…”

Section: The Nm1 As the Most Excitable Response Pattern To Co 2 Forcingmentioning

confidence: 99%

“…For the Earth's climate, the surface temperature (TS) is governed by complex interplay among many different components constituting the climate and no single physical law‐based equation can fully capture its response and evolution. Nevertheless, the linear response function (LRF) approach can encompass the dominant dynamics for the TS in a single matrix, and is useful in understanding the modal behavior of the climate system and identifying a causal relationship between the forcing and response (Liu, Lu, Garuba, Huang, et al., 2018; Liu, Lu, Garuba, Leung, et al., 2018; Lu et al., 2020). To compute the LRF and its corresponding NMs, we perform a large suite of Green's function perturbation experiments with the fully coupled Community Earth System Model version 1 (CESM1) by perturbing the shortwave forcing at the top of the atmosphere from an array of cells covering the globe, one at a time.…”

Section: Introductionmentioning

confidence: 99%

Neutral Mode Dominates the Forced Global and Regional Surface Temperature Response in the Past and Future

Liu

Leung

2022

Geophysical Research Letters

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Barring the broadest spatial features, confidence in the regional details of climate change projections is still lacking (Neelin et al., 2006), which has been a longstanding challenge to the climate community (Beniston, 2013;Mlawer et al., 1997). The projected increase in heat waves and extreme precipitation has been largely premised on the thermodynamics-regulated temperature and moisture changes (Fischer et al., 2021;Madakumbura et al., 2021;Mantua & Hare, 2002), whereas the lack of robustness in the projected regional precipitation response by climate models has been blamed on the dynamics-regulated atmospheric and oceanic circulation changes (Biasutti et al., 2018;Hall, 2014;Xie et al., 2015). Since no one lives in the global mean climate, improving the confidence in regional-scale climate change projections is critical for adaptation and mitigation planning at national and state (provincial) levels. As astutely pointed out by Xie et al. (2015), the challenge of unreliable regional climate change information cannot be addressed solely by increasing model resolution or regional downscaling; advancing understanding of the large-scale coupled dynamics between the ocean and atmospheric circulation might be foundational to narrowing the uncertainties on scales greater than 100s km.A promising way forward to address the challenge above may be through the lens of the intrinsic dynamical modes of the climate system. The rationale behind the mode approach is that the slow, large-scale evolution/ response of the Earth climate is governed by sparse dynamics , although climate is a complex, nonlinear, and high-dimensional system. Moreover, dynamical mode manifests the deterministic

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“…Once  L is constructed from the model experiments following Equation 2, its singular vectors can be used to identify the most excitable mode of the response, which exhibits the largest response to forcing and is known as the neutral vector, and its most effective forcing (Barsugli & Sardeshmukh, 2002;Goodman & Marshall, 2002;Hassanzadeh & Kuang, 2016;Marshall & Molteni, 1993;F. Liu, Lu, Garuba, Harrop, et al, 2018;Dong et al, 2019;Lu et al, 2020). The neutral vector is the right singular vector of  L associated with the smallest singular number (see detailed discussions in Goodman & Marshall, 2002) and the optimal forcing needed to produce the neutral vector is the left singular vector.…”

Section: Linear Response Functionmentioning

confidence: 99%

Linear Response Function Reveals the Most Effective Remote Forcing in Causing September Arctic Sea Ice Melting in CESM

Ding

et al. 2021

Geophysical Research Letters

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The Arctic sea ice reaches its minimum state during boreal summer and fall and is also the most susceptible to climate forcing during these seasons (e.g., Devasthale et al., 2013;Stroeve et al., 2012). Therefore, it is of both scientific and societal importance to understand the predictability of the Arctic sea ice during summer and fall seasons and its underlying physical mechanism. Several factors are found to affect the predictability skill of the September Arctic sea ice on interannual time scales including the initial states of sea ice, oceans and stratosphere in and around the Arctic in the pre-melting seasons (e.g., preceding winter and spring) (see a review paper by Guemas et al., 2014 and references therein) and atmospheric teleconnection in summer (Baxter et al., 2019;Ding et al., 2014Ding et al., , 2019. For example, Baxter et al. (2019) found a leading tropical-Arctic teleconnection during 1979-2017 summers in observations and showed that sea surface temperature (SST) cooling over the eastern central tropical Pacific ocean and resulting decreased convection likely drives a Rossby wave train toward the Arctic and results in an anomalous high pressure over northeastern Canada and Greenland that in turn plays a key role in melting the sea ice in the region. They also argued that this "Pacific-Arctic (PARC) teleconnection" pattern was responsible for the observed accelerated Arctic sea ice melting from 2007 to 2012. However, Baxter et al. (2019) also noted that a 1800-year long pre-industrial simulation of the Community Earth System Model version 1 (CESM1) can't replicate the observed PARC teleconnection and instead simulates a leading coupled pattern that links a warm western and central tropical Pacific SST to an anomalous high pressure over the Pacific side of the Arctic. The discrepancy between observations and models is not entirely clear and could possibly be due to either model limitations and/or

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Neutral modes of surface temperature and the optimal ocean thermal forcing for global cooling

Cited by 9 publications

References 50 publications

Can we use linear response theory to assess geoengineering strategies?

Can we use linear response theory to assess geoengineering strategies?

Neutral Mode Dominates the Forced Global and Regional Surface Temperature Response in the Past and Future

Linear Response Function Reveals the Most Effective Remote Forcing in Causing September Arctic Sea Ice Melting in CESM

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