The Dependence of Mean Climate State on Shortwave Absorption by Water Vapor

a. Modification of radiative parameterization

We employ the CESM 1.2.2 (Gent et al. 2011), with the finite-volume Community Atmosphere Model version 4 (CAM4) atmosphere (Neale et al. 2013) and Community Land Model 4 (CLM4) land (Lawrence et al. 2011) at nominal 2° horizontal resolutions and 26 vertical layers in the atmosphere. The ocean component is Parallel Ocean Program version 2 (POP2) (Smith et al. 2010) and the sea ice model is Community Ice Code version 4 (CICE4) (Hunke and Lipscomb 2008), each with nominal 1° horizontal resolutions, and with 60 vertical layers for the ocean.

In CAM4, the SWA by water vapor is parameterized with Community Atmosphere Model Radiative Transfer (CAM-RT) scheme following Collins et al. (2006). In CAM-RT, the contribution of water vapor to SWA is proportional to the water vapor optical depth (τ_h2o) given by τ_h2o = uk_i, where u is the effective water vapor path, representing the mass of water vapor, k is the absorption coefficient that parameterizes the degree of SWA per unit water vapor path, and i denotes an index for seven spectral bands covering the wavenumbers between 1000 and 18 000 cm⁻¹. The parameter k that represents the water vapor SW absorptivity primarily controls the performance of parameterization.

We alter the SWA by water vapor by multiplying the water vapor SW absorptivity (k) by the factors of 0.6, 0.8, 1.0, and 1.2 for all seven spectral bands—these experiments are respectively named k60, k80, k100, and k120. The multiplication factors are chosen in such a way that the global-mean SWA of our two extreme experiments approximately covers the range of the intermodel spread in CMIP5/6 (Fig. 1). All experiments are run with the preindustrial boundary conditions for 150 years on the fully atmosphere–ocean coupled model (which we refer to as FOM, for full ocean model). We use the last 50-yr average for the FOM analysis, when the global-mean surface temperature and net top-of-atmosphere (TOA) radiation show small temporal fluctuations (not shown). The experiments are also performed with the SSTs fixed to 1870–99 climatology of the observational dataset from Hurrell et al. (2008) (referred to as FSST), which are integrated for 30 years including the 20-yr spinup period.

Global-mean atmospheric shortwave absorption (SWA) for clear-sky and all-sky fluxes, averaged for the last 100 years of the preindustrial simulation in 34 CMIP5 (green circles) and 43 CMIP6 (blue circles) models, with the respective multimodel mean in open circles. The CMIP models used in this study are summarized in Table S1. The correlation and regression coefficients of CMIP models are inserted at the lower right corner. The correlation coefficient is statistically different from zero at the 95% confidence level using the t test. Asterisks denote the CESM FOM results with a varying water vapor shortwave absorptivity.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Global-mean atmospheric shortwave absorption (SWA) for clear-sky and all-sky fluxes, averaged for the last 100 years of the preindustrial simulation in 34 CMIP5 (green circles) and 43 CMIP6 (blue circles) models, with the respective multimodel mean in open circles. The CMIP models used in this study are summarized in Table S1. The correlation and regression coefficients of CMIP models are inserted at the lower right corner. The correlation coefficient is statistically different from zero at the 95% confidence level using the t test. Asterisks denote the CESM FOM results with a varying water vapor shortwave absorptivity.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Global-mean atmospheric shortwave absorption (SWA) for clear-sky and all-sky fluxes, averaged for the last 100 years of the preindustrial simulation in 34 CMIP5 (green circles) and 43 CMIP6 (blue circles) models, with the respective multimodel mean in open circles. The CMIP models used in this study are summarized in Table S1. The correlation and regression coefficients of CMIP models are inserted at the lower right corner. The correlation coefficient is statistically different from zero at the 95% confidence level using the t test. Asterisks denote the CESM FOM results with a varying water vapor shortwave absorptivity.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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b. Decomposition of the change in radiative fluxes

The variables that determine radiative fluxes in climate models include temperature, gas concentration, surface albedo, cloud, and aerosol. Soden and Held (2006) proposed a radiative kernel technique, which approximately quantifies the contribution of each climate variable to radiative flux changes. Using the CESM-CAM5 radiative kernel data from Pendergrass et al. (2018), the LW changes at TOA and surface (with the sign convention that downward is positive) are decomposed with a small error (Fig. S1 in the online supplemental material), following the procedure in Soden et al. (2008). By contrast, the SW changes cannot be decomposed using the radiative kernel alone in our experiments because the SW radiative parameterization is modified by the water vapor SW absorptivity. Instead, we develop an extension of the approximate partial radiative perturbation (APRP) method (Taylor et al. 2007) and combine it with the radiative kernel technique to isolate the effect of water vapor SW absorptivity.

In the APRP method, a one-layer radiation model with seven radiative properties approximates the interactions of SW fluxes within a single climate state. These seven properties are atmospheric absorption and reflection, and surface albedo, each of which is separately estimated for clear and overcast skies, and a cloud area fraction. By fitting the seven radiative properties to the total cloud area fraction and SW fluxes from standard model output, the interactions that result in SW fluxes within each climate state can be captured with reasonable accuracy. Comparison can be made between two climate states by making perturbations to radiative properties to attribute the net TOA SW responses to the various combinations of radiative property changes (see the appendix for details). APRP allows us to reasonably separate the TOA SW responses to the radiative effects of changes in surface albedo, cloud, and non-cloud atmospheric constituents, which are poorly captured with the kernel technique due to nonlinear interactions that are nonetheless straightforward in the APRP framework (Yoshimori et al. 2011; Hwang et al. 2013; Frey et al. 2017; Zelinka et al. 2020). Furthermore, it relies on variables that are commonly archived for climate model simulations.

c. Diagnostic equation for SST changes

Note that the denominator in Eq. (6) $\beta \overline{\text{LHF}}=\partial \text{LHF}/\partial T_s$ denotes the degree of LHF change in response to unit SST increase for each model grid, representing the climatological evaporative damping efficiency. This evaporative damping efficiency is shown to dominate the spatial structure of net surface flux sensitivity to unit SST change over the tropics (Zhang and Li 2014). Hence, among various surface flux components in Eq. (4), only δLHF is expressed as function of δT_s in order to use evaporative damping efficiency as denominator in Eq. (6) while avoiding the complexity from other SST-dependent terms of δLW_sfc and δSHF.

a. Intermodel spread of SWA in CMIP

To characterize the SWA intermodel spread in the state-of-the-art climate models, we calculate the global-mean SWA for all-sky and clear-sky fluxes for the preindustrial climate in 34 CMIP5 and 43 CMIP6 models (Fig. 1), where SWA is defined as the difference between net downward SW flux at the TOA and surface. The maximum range of the spread for all-sky fluxes is 10.6 W m⁻². The spread originates mostly from the clear-sky component, indicated by the large correlation coefficient of 0.90 and a regression coefficient close to one. We then use the APRP method to further investigate the causes of the intermodel spread in global-mean SWA (Fig. 2). The sum of all radiative components from APRP coincides well with the model SWA, indicating that this method can be used to explain the intermodel spread. The SWA intermodel spread originates mostly from the intermodel differences in the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$), indicated by the high correlation and the comparable range between the model SWA and ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$, for both the CMIP5 and CMIP6 ensembles.

The geographical distribution of the intermodel spread in ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ provides insight into the origin of the SWA intermodel spread (Figs. 3a,b). The clear-sky SWA occurs through atmospheric gases including water vapor, which have a relatively zonally symmetric distribution over the globe, and aerosols, with localized peaks over the deserts in northern Africa and central Asia where the aerosol optical depth is high due to natural aerosol emissions (Fiedler et al. 2019). To explore the effect of the spatial pattern of SWA on the global-mean SWA, we calculate the intermodel correlation coefficients between ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ at each grid point and its global mean (Fig. 3b). Equatorward of 60°, where most of the global-mean SWA occurs (e.g., 92.7% based on the multimodel mean of CMIP5/6 preindustrial climate), ocean regions with lower aerosol optical depth exhibit high correlation coefficients, whereas the desert regions with high natural aerosol emissions show relatively low correlations. Since the gaseous absorption dominates over the ocean but coincides with the aerosol absorption over the deserts, high correlations over the ocean indicate that the uncertainty of gaseous absorption contributes most to the intermodel spread in global-mean SWA.

The attribution of the intermodel spread of global-mean SWA to the intermodel spread in the following radiative properties: ${\text{ SWA}}_S^{\text{aprp}}$, ${\text{SWA}}_{\alpha }^{\text{aprp}}$, ${\text{SWA}}_{\text{clt}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\gamma }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$, and ${\text{SWA}}_{{\gamma }_{\text{clr}}}^{\text{aprp}}$, which respectively denote the SWA associated with the solar insolation (S), surface albedo (α), total-column cloud area fraction (clt), cloud absorptivity (μ_cld), cloud reflectivity (γ_cld), clear-sky absorptivity (μ_clr), and clear-sky reflectivity (γ_clr). The CMIP models are colored according to the global-mean SWA (circles for CMIP5 and squares for CMIP6) and the CESM FOM (asterisk) and FSST (plus sign) results are juxtaposed on the right. The multimodel mean of CMIP5/6 is treated as the reference climatology when applying the APRP method. The multimodel mean of SWA is added to the analysis. The correlation coefficients of each component to the model SWA are inserted separately for CMIP5 and CMIP6 at the bottom of the figure, with the statistically significant values at the 95% confidence level in bold.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The attribution of the intermodel spread of global-mean SWA to the intermodel spread in the following radiative properties: ${\text{ SWA}}_S^{\text{aprp}}$, ${\text{SWA}}_{\alpha }^{\text{aprp}}$, ${\text{SWA}}_{\text{clt}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\gamma }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$, and ${\text{SWA}}_{{\gamma }_{\text{clr}}}^{\text{aprp}}$, which respectively denote the SWA associated with the solar insolation (S), surface albedo (α), total-column cloud area fraction (clt), cloud absorptivity (μ_cld), cloud reflectivity (γ_cld), clear-sky absorptivity (μ_clr), and clear-sky reflectivity (γ_clr). The CMIP models are colored according to the global-mean SWA (circles for CMIP5 and squares for CMIP6) and the CESM FOM (asterisk) and FSST (plus sign) results are juxtaposed on the right. The multimodel mean of CMIP5/6 is treated as the reference climatology when applying the APRP method. The multimodel mean of SWA is added to the analysis. The correlation coefficients of each component to the model SWA are inserted separately for CMIP5 and CMIP6 at the bottom of the figure, with the statistically significant values at the 95% confidence level in bold.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The attribution of the intermodel spread of global-mean SWA to the intermodel spread in the following radiative properties: ${\text{ SWA}}_S^{\text{aprp}}$, ${\text{SWA}}_{\alpha }^{\text{aprp}}$, ${\text{SWA}}_{\text{clt}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\gamma }_{\text{cld}}}^{\text{aprp}}$, ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$, and ${\text{SWA}}_{{\gamma }_{\text{clr}}}^{\text{aprp}}$, which respectively denote the SWA associated with the solar insolation (S), surface albedo (α), total-column cloud area fraction (clt), cloud absorptivity (μ_cld), cloud reflectivity (γ_cld), clear-sky absorptivity (μ_clr), and clear-sky reflectivity (γ_clr). The CMIP models are colored according to the global-mean SWA (circles for CMIP5 and squares for CMIP6) and the CESM FOM (asterisk) and FSST (plus sign) results are juxtaposed on the right. The multimodel mean of CMIP5/6 is treated as the reference climatology when applying the APRP method. The multimodel mean of SWA is added to the analysis. The correlation coefficients of each component to the model SWA are inserted separately for CMIP5 and CMIP6 at the bottom of the figure, with the statistically significant values at the 95% confidence level in bold.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The intermodel standard deviation of the SWA associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). (b) The correlation coefficients between ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ at each model grid point and the global-mean ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$. Both the CMIP5 and CMIP6 models are used in the analysis, but considering CMIP5/6 separately yields similar results.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The intermodel standard deviation of the SWA associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). (b) The correlation coefficients between ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ at each model grid point and the global-mean ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$. Both the CMIP5 and CMIP6 models are used in the analysis, but considering CMIP5/6 separately yields similar results.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The intermodel standard deviation of the SWA associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). (b) The correlation coefficients between ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ at each model grid point and the global-mean ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$. Both the CMIP5 and CMIP6 models are used in the analysis, but considering CMIP5/6 separately yields similar results.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The intermodel spread of gaseous SWA should largely originate from the differences in water vapor absorption, which constitutes almost 72% of the total gaseous SWA (Kiehl and Trenberth 1997). The differences can be further ascribed to 1) the water vapor distribution (the amount and the spatial pattern) and 2) radiative parameterization of the water vapor SWA (how much SW is absorbed by given amount of water vapor). First, we estimate the effect of the water vapor distribution by comparing the global-mean ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$ with the global-mean ${\text{SWA}}_q^{\text{kernel}}$ using the same specific humidity radiative kernel for all models (Fig. 4a). Adopting the same radiative kernel excludes the contribution from differences in parameterization among models. The intermodel variance in ${\text{SWA}}_q^{\text{kernel}}$ is only 6.6% of that in ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$, indicating that the uncertainty in parameterizations is mostly responsible for the intermodel spread of global-mean SWA.

(a) The scatterplot between the global-mean SWA estimated from the specific humidity radiative kernel ( ${\text{SWA}}_q^{\text{kernel}}$)and that associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). Green (blue) circles denote the CMIP5 (CMIP6) preindustrial simulations. Note that the multimodel mean of CMIP5/6 is treated as the reference climatology in the radiative kernel calculation. The multimodel mean of global-mean SWA is added in the analysis. (b) The clear-sky atmospheric SW absorptivity with respect to the column-integrated water vapor. Green (blue) solid lines indicate the CMIP5 (CMIP6) models, and the achromatic colored lines indicate the CESM experiments with FOM in solid and FSST in dashed. (c) The contribution of the intermodel spread in the linear regression slopes and y intercepts of the absorption curves to the intermodel spread of SW absorptivity at the reference water vapor amount w_ref = 37.5 kg m⁻². The achromatic colored asterisks indicate the CESM FOM experiments. The correlation coefficients of each contribution and the intermodel spread of global-mean SWA are inserted in the lower right corner. The values in the parentheses are the correlation coefficients excluding four outliers, CMIP5 GISS models (Table S1).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The scatterplot between the global-mean SWA estimated from the specific humidity radiative kernel ( ${\text{SWA}}_q^{\text{kernel}}$)and that associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). Green (blue) circles denote the CMIP5 (CMIP6) preindustrial simulations. Note that the multimodel mean of CMIP5/6 is treated as the reference climatology in the radiative kernel calculation. The multimodel mean of global-mean SWA is added in the analysis. (b) The clear-sky atmospheric SW absorptivity with respect to the column-integrated water vapor. Green (blue) solid lines indicate the CMIP5 (CMIP6) models, and the achromatic colored lines indicate the CESM experiments with FOM in solid and FSST in dashed. (c) The contribution of the intermodel spread in the linear regression slopes and y intercepts of the absorption curves to the intermodel spread of SW absorptivity at the reference water vapor amount w_ref = 37.5 kg m⁻². The achromatic colored asterisks indicate the CESM FOM experiments. The correlation coefficients of each contribution and the intermodel spread of global-mean SWA are inserted in the lower right corner. The values in the parentheses are the correlation coefficients excluding four outliers, CMIP5 GISS models (Table S1).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The scatterplot between the global-mean SWA estimated from the specific humidity radiative kernel ( ${\text{SWA}}_q^{\text{kernel}}$)and that associated with the clear-sky absorptivity ( ${\text{SWA}}_{{\mu }_{\text{clr}}}^{\text{aprp}}$). Green (blue) circles denote the CMIP5 (CMIP6) preindustrial simulations. Note that the multimodel mean of CMIP5/6 is treated as the reference climatology in the radiative kernel calculation. The multimodel mean of global-mean SWA is added in the analysis. (b) The clear-sky atmospheric SW absorptivity with respect to the column-integrated water vapor. Green (blue) solid lines indicate the CMIP5 (CMIP6) models, and the achromatic colored lines indicate the CESM experiments with FOM in solid and FSST in dashed. (c) The contribution of the intermodel spread in the linear regression slopes and y intercepts of the absorption curves to the intermodel spread of SW absorptivity at the reference water vapor amount w_ref = 37.5 kg m⁻². The achromatic colored asterisks indicate the CESM FOM experiments. The correlation coefficients of each contribution and the intermodel spread of global-mean SWA are inserted in the lower right corner. The values in the parentheses are the correlation coefficients excluding four outliers, CMIP5 GISS models (Table S1).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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To evaluate the uncertainty in parameterization of SWA by water vapor among models, the clear-sky absorptivity—clear-sky SWA divided by solar insolation—at each grid point over the tropical ocean (30°S–30°N) is averaged after sorting into corresponding bins of column-integrated water vapor amount, providing the absorption curve for each model as in DeAngelis et al. (2015) (Fig. 4b). To distinguish the contribution of intermodel differences in the slopes and y intercepts in driving the intermodel differences in the absorption curve, we calculate the linear regression line for each model: y_i = a_iw + b_i, where a_i and b_i indicate the best-fit slope and the y intercept for each model i within the range of column-integrated water vapor amount w. The contribution of different slopes to the absorption curves is estimated as ${\left(y_i\right)}_{\text{slopes}}=a_i{w}_{\text{ref}} + \overline{b}$, by using the reference water vapor amount w_ref = 37.5 kg m⁻², and the multimodel mean intercept $\overline{b}$ with the slope for each model a_i. Similarly, the contribution of different intercepts is estimated as ${\left(y_i\right)}_{\text{intercepts}}=\overline{a}{w}_{\text{ref}} +b_i$ with the multimodel mean slope $\overline{a}$ and the y intercept for each model b_i. Figure 4c indicates that the differences in absorption curves mostly originate from a large range of y intercepts, rather than slopes. This means that a different degree of absorptivity for the given amount of water vapor across models contributes most to the intermodel spread of global-mean SWA climatology. The absorptivity for a given amount of water vapor is higher in CMIP6 than CMIP5 on average, which explains the partial increase in global-mean SWA from CMIP5 to CMIP6 (Fig. 1). Note that differences in the slope of absorption curves—different degrees of absorptivity enhancement per unit water vapor increase—are important for explaining the intermodel uncertainty in SWA enhancement with global warming (DeAngelis et al. 2015). This implies that different aspects of water vapor SWA parameterization matter for the uncertainty in future projections compared to the mean climate state.

This leads us to ask, what is the impact of the SWA intermodel spread on mean climate state? We address this question by altering the water vapor SW absorptivity in CESM, as described in section 2a. The experiments capture the intermodel spread in the intercepts of the absorption curves while retaining a similar slope, roughly consistent with the behavior of the CMIP models (Fig. 4b). We next examine the effects of varying water vapor SW absorptivity on a wide range of climate patterns, including the SST, precipitation, and large-scale atmospheric circulation.

b. Overarching response in water vapor SW absorptivity experiments

To understand the effect of the increased water vapor SW absorptivity, the results from the lowest absorptivity (k60) are subtracted from the highest case (k120). Because the climate responses are generally linear to the degree of absorptivity enhancement, the results shown for k120 − k60 are similar to those for k100 − k60 and k80 − k60.

First, the direct effects of the increased absorptivity on the SW fluxes (δSW_k) are calculated following Eq. (3), which we consider to be the forcing on the climate system (Fig. 5). The atmospheric forcing follows the climatological profile since the water vapor SW absorptivity is increased in the same proportion globally, yielding a zonally uniform structure with a maximum value near the equator (Fig. 5a). As the SW radiation is absorbed by the atmosphere before reaching the surface, the increased absorptivity causes a reduction of net downward SW radiation at the surface (Fig. 5b). Similarly, as the SW radiation is absorbed before being reflected by the surface back to the TOA, the net downward TOA SW radiation increases, most notably over the regions with high surface albedo (Fig. 5c). However, when averaged over the globe, the SW forcing at TOA is considerably smaller than within the atmospheric column or at the surface.

The SW forcing due to the increase in water vapor SW absorptivity δSW_k (k120 − k60 in FOM) at the (a) atmospheric column, (b) surface, and (c) TOA, of which the sign conventions are inward positive in (a) and downward positive in (b) and (c). (d) The change in zonally averaged atmospheric SW heating rate. The black contours in (a) and (d) indicate the reference climatology (k60).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The SW forcing due to the increase in water vapor SW absorptivity δSW_k (k120 − k60 in FOM) at the (a) atmospheric column, (b) surface, and (c) TOA, of which the sign conventions are inward positive in (a) and downward positive in (b) and (c). (d) The change in zonally averaged atmospheric SW heating rate. The black contours in (a) and (d) indicate the reference climatology (k60).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The SW forcing due to the increase in water vapor SW absorptivity δSW_k (k120 − k60 in FOM) at the (a) atmospheric column, (b) surface, and (c) TOA, of which the sign conventions are inward positive in (a) and downward positive in (b) and (c). (d) The change in zonally averaged atmospheric SW heating rate. The black contours in (a) and (d) indicate the reference climatology (k60).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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As a result of the reduced SW radiation at the surface, the global-mean surface temperature decreases (Fig. 6a). The difference in the global-mean surface temperature among the FOM experiments reaches −0.28 K, which is less than half of the intermodel standard deviation of 0.63 K in the CMIP5/6 preindustrial simulations. By contrast, the increased SW absorption within the atmosphere leads to a large reduction in the global-mean precipitation as much as 0.17 mm day⁻¹. Even without any SST changes in FSST, the global-mean precipitation is reduced by up to 0.14 mm day⁻¹, comparable to the intermodel standard deviation across the CMIP5/6 ensemble. This is also comparable to the degree of precipitation intensification due to CO₂ doubling (Fläschner et al. 2016). The detailed mechanism for the precipitation response is investigated in sections 3c and 3d.

(a) Global-mean surface temperature and precipitation in the CESM experiments with varying water vapor SW absorptivity (gray to black symbols) in FOM (asterisk) and FSST (plus sign) configurations. The multimodel mean value of the CMIP5 (CMIP6) preindustrial climate is denoted as a green (blue) circle, with the error bars indicating the one intermodel standard deviation. The difference between k120 and k60 in (b) surface temperature, (c) 500-hPa pressure velocity, and (d) precipitation due to the increased water vapor SW absorptivity in FOM. The contour in (c) indicates the reference climatology (k60), with the descending motion in solid and ascending motion in dashed (interval = 2 × 10⁻² Pa s⁻¹). The regions are hatched where the change is not statistically different from zero at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) Global-mean surface temperature and precipitation in the CESM experiments with varying water vapor SW absorptivity (gray to black symbols) in FOM (asterisk) and FSST (plus sign) configurations. The multimodel mean value of the CMIP5 (CMIP6) preindustrial climate is denoted as a green (blue) circle, with the error bars indicating the one intermodel standard deviation. The difference between k120 and k60 in (b) surface temperature, (c) 500-hPa pressure velocity, and (d) precipitation due to the increased water vapor SW absorptivity in FOM. The contour in (c) indicates the reference climatology (k60), with the descending motion in solid and ascending motion in dashed (interval = 2 × 10⁻² Pa s⁻¹). The regions are hatched where the change is not statistically different from zero at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) Global-mean surface temperature and precipitation in the CESM experiments with varying water vapor SW absorptivity (gray to black symbols) in FOM (asterisk) and FSST (plus sign) configurations. The multimodel mean value of the CMIP5 (CMIP6) preindustrial climate is denoted as a green (blue) circle, with the error bars indicating the one intermodel standard deviation. The difference between k120 and k60 in (b) surface temperature, (c) 500-hPa pressure velocity, and (d) precipitation due to the increased water vapor SW absorptivity in FOM. The contour in (c) indicates the reference climatology (k60), with the descending motion in solid and ascending motion in dashed (interval = 2 × 10⁻² Pa s⁻¹). The regions are hatched where the change is not statistically different from zero at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Despite a largely zonally uniform SW surface forcing over the ocean (Fig. 5b), the increased water vapor SW absorptivity features a clear La Niña–like cooling in the tropical Pacific, with the most pronounced cooling in the eastern basin (Fig. 6b). The resultant increase in zonal SST gradient across the tropical Pacific leads to a strengthening and westward extension of the Walker circulation (Fig. 6c), causing a drier equatorial Pacific (Fig. 6d). The feedbacks responsible for such zonal asymmetry will be examined in section 3e.

The vertical structure of the forcing, inferred from the change in the shortwave heating rate, shows a stronger heating in the upper than the lower troposphere (Fig. 5d). Associated with this enhanced atmospheric stability, the Hadley circulation weakens, even in the absence of SST changes (Fig. 7b). The Hadley circulation weakening is amplified in FOM (Fig. 7a) as the tropical SSTs are allowed to drop (Fig. 6b). In addition, the Hadley circulation expands poleward (Figs. 7a,b) associated with an increase in the static stability of the subtropics (Fig. 5d), which shifts the latitude of baroclinic instability onset poleward (Lu et al. 2007). Consequently, the midlatitude barotropic jet shifts poleward even without any SST changes (Figs. 7c,d).

(a),(b) The difference between k120 and k60 in mean meridional mass streamfunction in shading, with the reference climatology (k60) in contours. Solid denotes the clockwise circulation, and dashed denotes the counterclockwise circulation (interval = 15 × 10⁹ kg s⁻¹). (c),(d) As in (a) and (b), but for the zonally averaged zonal wind. Solid denotes westerlies, and dashed denotes easterlies (interval = 5 m s⁻¹). Results are for FOM in (a) and (c) and FSST in (b) and (d). The regions are hatched where the change is statistically insignificant at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a),(b) The difference between k120 and k60 in mean meridional mass streamfunction in shading, with the reference climatology (k60) in contours. Solid denotes the clockwise circulation, and dashed denotes the counterclockwise circulation (interval = 15 × 10⁹ kg s⁻¹). (c),(d) As in (a) and (b), but for the zonally averaged zonal wind. Solid denotes westerlies, and dashed denotes easterlies (interval = 5 m s⁻¹). Results are for FOM in (a) and (c) and FSST in (b) and (d). The regions are hatched where the change is statistically insignificant at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a),(b) The difference between k120 and k60 in mean meridional mass streamfunction in shading, with the reference climatology (k60) in contours. Solid denotes the clockwise circulation, and dashed denotes the counterclockwise circulation (interval = 15 × 10⁹ kg s⁻¹). (c),(d) As in (a) and (b), but for the zonally averaged zonal wind. Solid denotes westerlies, and dashed denotes easterlies (interval = 5 m s⁻¹). Results are for FOM in (a) and (c) and FSST in (b) and (d). The regions are hatched where the change is statistically insignificant at the 95% confidence level using a t test.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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c. Energy budget response in SWA experiments

As stated in section 1, the change in global-mean LHF is constrained by the change in atmospheric radiative cooling and SHF. Thus, one can explain a change in global-mean precipitation by examining the change in atmospheric energy budget. Figure 8a compares the global-mean changes in atmospheric energy budget: the SW forcing component (δSWA_k), the SWA change due to the radiative feedbacks (δSWA_fdbck), the change in longwave absorption within the atmosphere (δLWA), the change in sensible heat flux (δSHF), and the change in latent heat flux (δLHF). Following Eq. (2), δSWA_fdbck is separated into the contribution from changes in cloud ( $\delta {\text{SWA}}_{\text{cld}}^{\text{aprp}}$), specific humidity ( $\delta {\text{SWA}}_q^{\text{kernel}}$), and surface albedo ( $\delta {\text{SWA}}_{\alpha }^{\text{aprp}}$) as shown in Fig. 8b. Similarly, δLWA is attributed to the changes in atmospheric and surface temperature ( $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$), cloud ( $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$), and specific humidity ( $\delta {\text{LWA}}_q^{\text{kernel}}$) as shown in Fig. 8c. The detailed decompositions allow us to explain how the atmospheric energy components adjust to the atmospheric stabilization from increased SW forcing (Fig. 5d).

(a) The change in global-mean energy budget due to the water vapor SW absorptivity increase. (b) The decomposition of δSWA_fdbck into the change due to the cloud ( $\delta {\text{SWA}}_{\text{cld}}^{\text{aprp}}$), specific humidity ( $\delta {\text{SWA}}_q^{\text{kernel}}$) and surface albedo ( $\delta {\text{SWA}}_{\alpha }^{\text{aprp}}$). (c) The decomposition of δLWA into the change due to the surface and atmospheric temperature ( $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$), cloud ( $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$), and specific humidity ( $\delta {\text{LWA}}_q^{\text{kernel}}$). Asterisks indicate FOM results and plus symbols indicate FSST results, with the bars indicating the largest response k120 − k60 for reference (closed bar for FOM and open bar for FSST).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The change in global-mean energy budget due to the water vapor SW absorptivity increase. (b) The decomposition of δSWA_fdbck into the change due to the cloud ( $\delta {\text{SWA}}_{\text{cld}}^{\text{aprp}}$), specific humidity ( $\delta {\text{SWA}}_q^{\text{kernel}}$) and surface albedo ( $\delta {\text{SWA}}_{\alpha }^{\text{aprp}}$). (c) The decomposition of δLWA into the change due to the surface and atmospheric temperature ( $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$), cloud ( $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$), and specific humidity ( $\delta {\text{LWA}}_q^{\text{kernel}}$). Asterisks indicate FOM results and plus symbols indicate FSST results, with the bars indicating the largest response k120 − k60 for reference (closed bar for FOM and open bar for FSST).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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(a) The change in global-mean energy budget due to the water vapor SW absorptivity increase. (b) The decomposition of δSWA_fdbck into the change due to the cloud ( $\delta {\text{SWA}}_{\text{cld}}^{\text{aprp}}$), specific humidity ( $\delta {\text{SWA}}_q^{\text{kernel}}$) and surface albedo ( $\delta {\text{SWA}}_{\alpha }^{\text{aprp}}$). (c) The decomposition of δLWA into the change due to the surface and atmospheric temperature ( $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$), cloud ( $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$), and specific humidity ( $\delta {\text{LWA}}_q^{\text{kernel}}$). Asterisks indicate FOM results and plus symbols indicate FSST results, with the bars indicating the largest response k120 − k60 for reference (closed bar for FOM and open bar for FSST).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The enhanced water vapor SW absorptivity produces δSWA_k by 9.33 W m⁻² (9.26 W m⁻²) in FOM (FSST) (Fig. 8a). This forcing is partly offset by δSWA_fdbck, which mainly occurs through $\delta {\text{SWA}}_{\text{cld}}^{\text{aprp}}$ that compensates 16.8% (14.8%) of the forcing in FOM (FSST) (Fig. 8b). The ${\text{SWA}}_{\text{cld}}^{\text{aprp}}$ decreases because the increased absorption by water vapor depletes the SW flux that would otherwise be absorbed by cloud water, due to an overlap between the absorption bands of water vapor and cloud water. Both $\delta {\text{SWA}}_q^{\text{kernel}}$ and $\delta {\text{SWA}}_{\alpha }^{\text{aprp}}$ are small for both FOM and FSST (Fig. 8b).

The δLWA compensates a further 19.9% (29.3%) of the forcing in FOM (FSST) (Fig. 8a). Decomposition of δLWA indicates that both $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$ and $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$ are responsible for the compensation (Fig. 8c). A reduced $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$ in FSST is associated with the tropospheric temperature increase (Fig. 9b), which enhances the LW emission toward the TOA and surface. The $\delta {\text{LWA}}_{\text{temp}}^{\text{kernel}}$ compensation is weaker with the SST adjustments in FOM (Fig. 8c) because of the temperature reduction in the lower troposphere following the SST cooling (Fig. 9a). By contrast, a reduced $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$ (Fig. 8c) is associated with not only the cloud water reduction in the upper troposphere (Figs. 9c,d), which decreases the positive LW cloud radiative effect at TOA, but also the cloud water increase in the lower troposphere, which enhances the LW emission to the surface. The resulting LW changes both at the TOA and surface hence contribute to negative $\delta {\text{LWA}}_{\text{cld}}^{\text{kernel}}$. The downward shift of cloud water from the upper to lower troposphere occurs as the increased water vapor SW absorptivity suppresses the convection, trapping the moisture within the lower troposphere, leading to a relative humidity reduction in the upper and increase in the lower troposphere (not shown). Changes in specific humidity (Figs. 9e,f) have little impact on $\delta {\text{LWA}}_q^{\text{kernel}}$ (Fig. 8c) because the specific humidity above 700 hPa causes atmospheric LW heating while the specific humidity below 700 hPa causes atmospheric LW cooling, according to the specific humidity radiative kernel (Pendergrass et al. 2018).

The difference between k120 and k60 in zonally averaged (a) atmospheric temperature, (c) cloud liquid water (shading) and cloud ice water (contours with positive in solid and negative in dashed; interval = 2 × 10⁻⁷), and (e) specific humidity in FOM. (b),(d),(f) As in (a), (c), and (e), but in FSST.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The difference between k120 and k60 in zonally averaged (a) atmospheric temperature, (c) cloud liquid water (shading) and cloud ice water (contours with positive in solid and negative in dashed; interval = 2 × 10⁻⁷), and (e) specific humidity in FOM. (b),(d),(f) As in (a), (c), and (e), but in FSST.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The difference between k120 and k60 in zonally averaged (a) atmospheric temperature, (c) cloud liquid water (shading) and cloud ice water (contours with positive in solid and negative in dashed; interval = 2 × 10⁻⁷), and (e) specific humidity in FOM. (b),(d),(f) As in (a), (c), and (e), but in FSST.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Of the forced SW change, δSWA_k, 12.4% (11.9%) is compensated by δSHF in FOM (FSST) (Fig. 8a), resulting from increased near-surface stability (not shown). The remaining forcing is compensated by δLHF, which commands 52.4% (43.4%) of the total compensation (Fig. 8a). We now proceed to understand the atmospheric processes that drive the evaporation reduction (equivalent to precipitation reduction) by performing an attribution analysis using the bulk formula.

d. Mechanism of evaporation reduction

The reduction in evaporation mostly occurs over the ocean (δLHF_OCN), with a minor contribution from the land (δLHF_LND) (Fig. 10). Since the evaporation over the ocean is parameterized through a bulk formula assuming a saturated surface, δLHF_OCN can be decomposed into components due to the change in surface wind speed (δLHF_WND), relative humidity at model reference level (δLHF_RH), near-surface atmospheric stability (δLHF_ΔT), and surface temperature $\delta {\text{LHF}}_{T_s}$ following Jia and Wu (2013):

$\begin{array}{l}\delta {\text{LHF}}_{\text{OCN}}=\left(\frac{\text{LHF}}{\overline{\text{WND}}}\right)\delta \text{WND}+\left(\frac{-\overline{\text{LHF}}}{e^{-\beta \overline{\Delta T}}-\overline{\text{RH}}}\right)\delta \text{RH}+\left(\frac{-\beta \overline{\text{LHF}}\overline{\text{RH}}}{e^{-\beta \overline{\Delta T}}-\overline{\text{RH}}}\right)\delta \left(\mathrm{\Delta }T\right)+\left(\beta \overline{\text{LHF}}\right)\delta T_{\mathrm{\Omega }}=\delta {\text{LHF}}_{\text{WND}}+\\ \delta {\text{LHF}}_{\text{RH}}+\delta {\text{LHF}}_{\mathrm{\Delta }T}+\delta {\text{LHF}}_{T_s},\end{array}$(7)

where the overbar indicates the reference climatology, WND the surface wind speed, RH the relative humidity at model reference level, ΔT = T_ref − T_s (with T_ref being the reference level temperature and T_s the surface temperature), and $\beta =L_{\upsilon }/R_{\upsilon }\overline{T_s^2}$ as introduced in section 2c. The similarity between the sum of all components (SUM) and the actual model response (δLHF_OCN) validates the applicability of this decomposition (Fig. 10 and Fig. S3).

Decomposition of changes in global-mean latent heat flux. Here, δLHF_LND (δLHF_OCN) indicates the contribution from land (ocean) region to the global-mean change. The δLHF_OCN is decomposed into the change due to the surface wind speed (δLHF_WND), relative humidity at model reference level (δLHF_RH), near-surface stability (δLHF_ΔT), and surface temperature ( $\delta {\text{LHF}}_{T_s}$). The summation of all components (SUM) well matches δLHF_OCN, justifying the use of the decomposition method [Eq. (7)].

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Decomposition of changes in global-mean latent heat flux. Here, δLHF_LND (δLHF_OCN) indicates the contribution from land (ocean) region to the global-mean change. The δLHF_OCN is decomposed into the change due to the surface wind speed (δLHF_WND), relative humidity at model reference level (δLHF_RH), near-surface stability (δLHF_ΔT), and surface temperature ( $\delta {\text{LHF}}_{T_s}$). The summation of all components (SUM) well matches δLHF_OCN, justifying the use of the decomposition method [Eq. (7)].

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Decomposition of changes in global-mean latent heat flux. Here, δLHF_LND (δLHF_OCN) indicates the contribution from land (ocean) region to the global-mean change. The δLHF_OCN is decomposed into the change due to the surface wind speed (δLHF_WND), relative humidity at model reference level (δLHF_RH), near-surface stability (δLHF_ΔT), and surface temperature ( $\delta {\text{LHF}}_{T_s}$). The summation of all components (SUM) well matches δLHF_OCN, justifying the use of the decomposition method [Eq. (7)].

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Three of the components, δLHF_WND, δLHF_RH, and δLHF_ΔT, are reduced by a comparable magnitude between FOM and FSST (Fig. 10). This implies that the atmospheric processes independent of SST changes are important for the variations in wind speed, relative humidity, and stability. We first examine δLHF_WND in FSST. As discussed in section 3b, the increased atmospheric SW heating leads to a weakening of the Hadley circulation even in the absence of SST changes (Fig. 7b), reducing the surface wind convergence toward the ITCZ and subsequently LHF_WND over the tropics equatorward of 30° (Fig. 11b). LHF_WND is also reduced over the extratropics in association with a poleward shift of the extratropical jet (Fig. 7d), which leads to the negative δLHF_WND in the midlatitudes and the positive δLHF_WND at high latitudes (Fig. 11b). The midlatitude δLHF_WND outweighs the high-latitude δLHF_WND because LHF is more sensitive to a unit change in wind speed at lower latitudes, as the sensitivity of the LHF change is proportional to its climatological magnitude (Xie et al. 2010). As the SSTs are allowed to change in FOM, a La Niña–like cooling intensifies the surface easterlies in the central Pacific, partially offsetting the reduced δLHF_WND over the tropics (cf. Figs. 11a,b). Hence, FOM yields a weaker reduction in LHF_WND than FSST (Fig. 10).

The difference between k120 and k60 in (a),(b) the latent heat flux due to the change in surface wind speed δLHF_WND (shading) and surface wind (vectors) and (c),(d) the latent heat flux due to the change in reference-level relative humidity δLHF_RH (shading) and the planetary boundary layer (PBL) height (negative in solid and positive in dashed; interval = 15 m). The PBL height is derived from the inversion strength and the near-surface wind speed based on the bulk Richardson method. The pattern correlation coefficient between δLHF_RH and the PBL height response is inserted in the title. Results are for FOM in (a) and (c) and FSST in (b) and (d).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The difference between k120 and k60 in (a),(b) the latent heat flux due to the change in surface wind speed δLHF_WND (shading) and surface wind (vectors) and (c),(d) the latent heat flux due to the change in reference-level relative humidity δLHF_RH (shading) and the planetary boundary layer (PBL) height (negative in solid and positive in dashed; interval = 15 m). The PBL height is derived from the inversion strength and the near-surface wind speed based on the bulk Richardson method. The pattern correlation coefficient between δLHF_RH and the PBL height response is inserted in the title. Results are for FOM in (a) and (c) and FSST in (b) and (d).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The difference between k120 and k60 in (a),(b) the latent heat flux due to the change in surface wind speed δLHF_WND (shading) and surface wind (vectors) and (c),(d) the latent heat flux due to the change in reference-level relative humidity δLHF_RH (shading) and the planetary boundary layer (PBL) height (negative in solid and positive in dashed; interval = 15 m). The PBL height is derived from the inversion strength and the near-surface wind speed based on the bulk Richardson method. The pattern correlation coefficient between δLHF_RH and the PBL height response is inserted in the title. Results are for FOM in (a) and (c) and FSST in (b) and (d).

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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Next, δLHF_RH is strongly correlated with the changes in planetary boundary layer (PBL) height (r = 0.81) regardless of the SST configuration (Figs. 11c,d). Enhanced heating aloft stabilizes the atmosphere over the ocean, as discussed in section 3b, resulting in a shallower PBL. The contracted PBL raises the relative humidity at the model reference level, thereby reducing LHF_RH. Finally, the negative δLHF_ΔT is associated with an increase in the near-surface atmospheric stability due to the larger heating in the atmosphere than at the surface, for both FOM and FSST. Thus, the evaporation is largely reduced by atmospheric processes, leaving 24.4% of reduction by $\delta {\text{LHF}}_{T_s}$ in FOM (Fig. 10). The SST cooling decreases saturation vapor pressure at the ocean surface, amplifying the evaporation reduction in FOM compared to FSST.

Then, how does reduction of evaporation act to compensate for increased SWA aloft? The evaporation reduction near the surface curtails the moisture supply to the free atmosphere, effectively reducing the convective heating within the atmospheric column. Indeed, the atmospheric temperature tendency due to moist processes decreases significantly in the free atmosphere, compensating the increase in atmospheric SW heating most strongly among all physics terms in the atmospheric temperature tendency equation (not shown). This result indicates the strong coupling between evaporation, convection, and atmospheric SW heating, as described by the theoretical framework in Takahashi (2009a).

e. Tropical Pacific SST pattern response in SWA experiments

In response to the increased water vapor SW absorptivity, the SST in the eastern equatorial Pacific (5°S–5°N, 230°–270°E) decreases while the SST in the western equatorial Pacific (5°S–5°N, 120°–160°E) changes little, resembling a La Niña–like cooling pattern (Fig. 6b). The eastern Pacific cooling response is significant at the 95% confidence level and is comparable to one intermodel standard deviation calculated from the CMIP5/6 preindustrial simulations (not shown). To understand the SST pattern formation, we decompose the SST changes in the western and eastern equatorial Pacific into contributions related to surface flux variations using Eq. (6) (Fig. 12). Note that only the relevant terms are shown for simplicity.

The decomposition of the SST responses in the western (120–160°E) and eastern (230°–270°E) equatorial (5°S–5°N) Pacific to the components related to surface flux responses in (a) FOM and (b) FSST. Left (right) bar of each pair corresponds to the western (eastern) equatorial Pacific, marked by black rectangles in Fig. 6b. The δT is the actual model response, and SUM is the summation of all components. The detailed description for each component is provided in section 3e.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The decomposition of the SST responses in the western (120–160°E) and eastern (230°–270°E) equatorial (5°S–5°N) Pacific to the components related to surface flux responses in (a) FOM and (b) FSST. Left (right) bar of each pair corresponds to the western (eastern) equatorial Pacific, marked by black rectangles in Fig. 6b. The δT is the actual model response, and SUM is the summation of all components. The detailed description for each component is provided in section 3e.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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The decomposition of the SST responses in the western (120–160°E) and eastern (230°–270°E) equatorial (5°S–5°N) Pacific to the components related to surface flux responses in (a) FOM and (b) FSST. Left (right) bar of each pair corresponds to the western (eastern) equatorial Pacific, marked by black rectangles in Fig. 6b. The δT is the actual model response, and SUM is the summation of all components. The detailed description for each component is provided in section 3e.

Citation: Journal of Climate 35, 7; 10.1175/JCLI-D-21-0417.1

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