VII at the 95% confidence level in bold. a) Cross-model standard deviation of SWA associated with clear-sky absorption (SWA𝜇aprpclr). Regions are defined where the difference is statistically insignificant at the 95% confidence level using the t-test. a) Change in the average global energy budget due to increased western water vapor absorption.
Introduction
- Motivation
- Inter-Tropical Converge Zone and tropical SST pattern
- Mean state biases in tropical climate pattern
- Extratropics-to-tropics teleconnection and Energetics Framework
- Research objectives and outline
For example, the cold Northern Hemisphere (NH) in the Last Glacial Maximum (LGM) – the period 25,000 years ago when extensive glaciation existed over the NH land – involved the southward displaced ITCZ (Koutavas & Lynch-Stieglitz , 2004). The results are included in the paper published in Journal of Climate (Kim et al., 2022).
Decompositions of the changes in vertical energy fluxes
Radiative kernel technique
The kernel technique has been widely used to quantify the contributions from different feedback processes with reasonable errors (Block & Mauritsen, 2013; Chung & . Soden, 2015; Fläschner et al., 2016; Shell et al., 2008) . We extend the approximate partial radiative perturbation (APRP) method (Taylor et al., 2007) to decompose the SW fluxes in such cases.
Approximate Partial Radiative Perturbation method
Decomposition of change in latent heat flux
Hypothesis for the inter-model spread in tropical precipitation pattern
- Background
- Data and methodology
- Results
- Characterizing inter-model spread in zonal and annual mean tropical precipitation
- Application of the Energetics Framework for the symmetric precipitation spread
- Summary and discussion
A smaller 𝐴𝑚𝑝(AET0) is associated with a smaller seasonal amplitude of the ITCZ position (Figure 3.4a), leading to an equatorward contraction of the annual mean normalized tropical precipitation, reflected as a larger SYM1-PC. The monthly variation of the hemispheric contrast in each term (Figure 3.6a) is regressed to SYM1-PC (Figure 3.6b). Inter-model regression map of SWABSclr on SYM1-PC (shaded) and the multi-model mean climatology of SWABSclr with a contour interval of 10 W/m2 (colored contour) for (b) May-June-July and (c) November- December January.
Scatter plot between the annual and global mean SWAclr and (a) the seasonal amplitude of hemispheric asymmetry in SWAclr, that is, Amp(⟨SWAclr⟩) and (b) SYM1-PC. The regression map of the annual mean SFC to SYM1-PC (colored in Figure 3.10b) reveals a large inter-model diversity of SFC congruent with SYM1-PC over the equatorial Pacific and Atlantic Oceans. Indeed, a significant, albeit weak, correlation is found between the global average SWAclrand SYM1-PC (Figure 3.8b).
Dependency of tropical mean climate pattern on the water vapor shortwave
Background
SWA uncertainty has mainly been addressed in the context of future projections at the global level. Despite the large spread of the climatological SWA between models, its effect on the mean climate state has not been much discussed in the literature. However, the global mean SWA increases by only ~1-3 W m-2, which is significantly smaller than the inter-model SWA range of 10 W m-2 over the CMIP ensemble.
The small increase in SWA driven by improved parameterizations suggests that more dramatic changes are needed to capture the range of intermodel SWA propagation in the mean climate state. In this study, we set out to diagnose the causes of the inter-model spread in global mean SWA using pre-industrial simulations of the CMIP5 and CMIP6 models (Eyring et al., 2016; Taylor et al., 2012). Finally, we use the results of our experiments to understand the propagation of the CMIP intermodel preindustrial mean climate, focusing on the SST pattern in the tropical Pacific.
Data and methodology
- Modification of radiative parameterization
- Diagnostic equation for sea surface temperature changes
To understand changes in the SST pattern, the mixed ocean layer energy budget equation has been reformulated to approximately quantify the relative contributions of each surface flux component to the regional SST changes (Xie et al., 2010; Zhang & Li, 2014) . Therefore, δLHF can be decomposed into the components due to the change of surface temperature (δTs) and others (F. Jia & Wu, 2013), and then replaced by Eq. 4.4) where the top bar indicates the reference climatology and 𝛽 = 𝐿v. ∂Ts ) δTs = δLHF − (𝛽LHF̅̅̅̅̅)δTs (see equation 4.4) can then be reformulated into a diagnostic equation for the SST changes.
Ts indicates the rate of change of LHF in response to unit SST increase for each model network, representing the climatological efficiency of evaporative damping. This evaporative damping efficiency is shown to dominate the spatial structure of the net surface flux sensitivity to unit SST change over the tropics ( Zhang & Li 2014 ). Therefore, between the different components of the surface flux in Eq. 4.3), only δLHF is expressed as a function of δTs in order to use the evaporative damping efficiency as the denominator in Eq. 4.5) avoiding complexity from other SST-dependent terms of δLWsfc and δSHF.
Results
- Inter-model spread of atmospheric shortwave absorption in CMIP
- Overarching response to altering water vapor shortwave absorptivity
- Reduction in global-mean precipitation
- La Niña-like cooling over the tropical Pacific
Since the gas absorption dominates over the ocean but coincides with the aerosol absorption over the deserts, high correlations over the ocean indicate that the uncertainty of gas absorption contributes most to the inter-model spread in global mean SWA. a) The inter-model standard deviation of the SWA associated with the clear sky absorptivity (SWA𝜇aprpclr). The inter-model spread of gaseous SWA should largely derive from the differences in water vapor absorption, which accounts for almost 72 % of the total gaseous SWA (Kiehl and Trenberth 1997). The differences can further be attributed to 1) the water vapor distribution – the amount and the spatial pattern – and 2) radiation parameterization of the water vapor SWA – how much SW is absorbed by a given amount of water vapor. The SW forcing due to the increase in water vapor SW absorptivity δSW𝑘 (k120-k60 in FOM) at the (a) atmospheric column, (b) surface and (c) TOA, whose sign conventions are (a) inward and (b, c) downward positive. d) The change in zonally averaged atmospheric SW heating rate.
As a result of reduced SW radiation at the surface, the global mean surface temperature decreases (Figure 4.6a). Reduction in global average precipitation. a) Change in the average global energy budget due to increased western water vapor absorption. In response to increased SW water vapor absorption, SST in the eastern equatorial Pacific (5oS-5oN, 230oE-270oE) decreases while SST in the western equatorial Pacific (5oS-5oN, 120oE-160oE) changes little. Niña (Figure 4.6b).
![Table 4.1. Description of CMIP models used in this study. Models are sorted in the ascending order of global-mean SWA [W m -2 ]](https://thumb-ap.123doks.com/thumbv2/123dokinfo/10488305.0/64.892.101.779.183.1150/table-description-cmip-models-models-sorted-ascending-global.webp)
Implications for the spread among CMIP models
The inter-model spread of global mean atmospheric energy budget, with the global mean removed. We next examine whether the intermodel dispersion in the spatial pattern of equatorial Pacific SST is related to global mean SWA. Inter-model regression of equatorial (5oS-5oN) SST pattern (SST*) on the global mean SWA in (a) CMIP5 and (b) CMIP6 preindustrial simulations.
Inter-model regression of SST* induced by AMIP SW fluxes on the global mean SWA is denoted as cyan lines, where the available 19 (30) subsets of CMIP5 (CMIP6) models are used. Inter-model regression of SST* induced by each surface flux component on the zonal asymmetry in SST (∆SSTEW) in (c) CMIP5 and (d) CMIP6. We now compare the effect of SWA inter-model dispersion with the effect of other processes in creating the zonal SST asymmetry in the equatorial Pacific.
Summary and discussion
We show that the water vapor SW absorptivity experiments with the CESM model have implications for the inter-model uncertainty of the tropical Pacific SST pattern in the CMIP multi-model ensemble. The models with a higher global mean SWA simulate a more La Niña-like mean condition over the tropical Pacific. Although the uncertainties in ocean dynamics and/or cloud parameterization are the dominant cause of the inter-model distribution of east-west asymmetry in the tropical Pacific SST, the inter-model regression analysis shows a contribution from the SW cloud effect in CMIP6, which in turn arises from the distribution in the water vapor SW absorptivity.
To our knowledge, this is the first study to address the SWA impact on the spatial pattern of the tropical Pacific climate. Our experiments demonstrate that altered water vapor SW absorptivity can modulate the tropical Pacific SST pattern through changes in cloud radiative effects. It is interesting to note a substantial change in SW cloud effect, despite the direct effect of a changed water vapor SW absorptivity on the clear-sky SW fluxes.
Impact of Southern Ocean albedo on the tropical Pacific mean climate pattern
- Background
- Data and methodology
- Experimental data
- Estimation of stratocumulus cloud feedback strength
- Regional cloud locking experiment
- Results
- The teleconnection from the Southern Ocean to the tropical Pacific
- Mechanisms for the teleconnection
- Inter-model diversity and the subtropical stratocumulus cloud feedback
- Summary and Discussion
The Southern Ocean cooling causes a wedge-like, triangular cooling that extends from the southeast Pacific to the zonal band across the equatorial Pacific (Figure 5.4a, marked by the yellow triangle). The southeast Pacific cooling is further enhanced by the stratocumulus cloud feedback off the west coast of subtropical continents (Bony, 2005; Klein & Hartmann, 1993) (Figures 5.7c,d and 5.8e). The triangular cooling area in the southeast Pacific weakens by 24.7% when stratocumulus cloud feedbacks are locked west of South America (Figure 5.13b).
The feedback of the subtropical stratocumulus cloud is also closely related to the shift of the zonal mean of the ITCZ (Figure 5.14). All ETIN-MIP models underestimate the strength of the stratocumulus cloud feedback west of all major continents (Figure 5.11b,c). This suggests that the influence of Southern Ocean cooling on the tropical Pacific is underestimated in most modern climate models (Figure 5.11b,c).
Regional and seasonal characteristics of the tropical precipitation biases
Since the inter-model spread is larger than the MMM bias itself (Figure 3.1a), it is also useful to understand the regional characteristics of the inter-model spread. The close relationship between ASY1-PC and PAI over the eastern Pacific and Atlantic indicates that the EPA region contributes the most to the zonal-mean antisymmetric precipitation distribution (Figure 6.2b). The larger inter-model range and MMM bias in each region than that in the zonal mean (abscissa vs. ordinate in Figure 6.3c,d) confirms the regional dominance of the zonal-mean inter-model distribution.
Statistically significant regression coefficients based on a two-tailed student-t test with a 95% confidence interval are also bolded. a) First and second modes of the intermodel EOF for zonally averaged tropical precipitation (solid lines; TOT1, TOT2) and first mode EOF for tropical precipitation over the western and central Pacific (WCP1) and eastern Pacific and Atlantic (EPA1) ) (dashed lines ). On the other hand, in the EPA region, the negative bias and intermodel spread of PAI is dominated by a strong bias from January to May with a strong seasonal difference (Figure 6.4b). The symmetric response of precipitation to increased SW absorption is persistent in all seasons (Figure 6.4a).
Summary
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