Summary - Dependency of tropical mean climate pattern on the water vapor shortwave

Chapter 4. Dependency of tropical mean climate pattern on the water vapor shortwave

7. Summary

Southern Ocean cooling is first propagated into the equator by atmospheric eddies and climatological southeasterlies over the eastern Pacific, which is then amplified by the wind-evaporation-SST feedback, equatorial upwelling, and subtropical low cloud feedback. The degree of ITCZ shift – which indicates the teleconnection strength – largely depends on the subtropical low cloud feedback, supported by regional cloud locking experiments. Interestingly, the subtropical cloud feedback is underestimated in most state-of-the-art climate models, implying that the remote impact of the Southern Ocean on the anti-symmetric precipitation spread is stronger than previously thought. Indeed, the northward ITCZ shift corresponding to observational subtropical cloud feedback can reduce the anti-symmetric precipitation bias by as much as 49% of the multi-model mean bias. The seasonal and regional characteristics of tropical precipitation biases are investigated in Chapter 6. The symmetric precipitation bias and inter-model spread is dominated by the western and Central Pacific, which is persistent throughout the year. On the other hand, the anti-symmetric precipitation bias and intermodel spread is dominated by the eastern Pacific and Atlantic with the maximum strength over boreal Spring. The increase in water vapor SW absorptivity desiccates equatorial precipitation over the western and central Pacific regardless of season, while the Southern Ocean cooling effectively shifts the eastern Pacific and Atlantic ITCZ northward with pronounced responses in boreal spring.

In this thesis, we discover that the water vapor SW absorptivity affects the tropical SST pattern through the interactions with low clouds. Since the radiative transfer calculation for the SWA is now accurate at the expense of computational cost, the tropical mean state biases due to water vapor SW absorptivity have enough room to improve. The accurate parameterizations of water vapor SW absorptivity could also improve the low cloud representations because low clouds are often tuned after the other parameterizations are fixed. In addition, we reveal the teleconnection from the Southern Ocean to the tropical Pacific, of which the strength is regulated by the subtropical cloud feedback. This finding not only matters for the tropical mean state biases but also for the climate projections. The climate forcings over the Southern Ocean – such as Antarctic freshwater influx (Bronselaer et al., 2018), Antarctic ice melt (England et al., 2020), and Sothern Ocean heat uptake (Armour et al., 2016) – have exhibited similar teleconnection impact, of which magnitude would also depend on the subtropical cloud feedback. The teleconnection from the Southern Ocean delayed heat release (e.g., Kug et al., 2022) under the CO2 quadrupling scenario is now investigated.

Appendix A: Extension of APRP method

Figure A1. Schematic of one-layer radiation model used in this study. 𝑆, 𝜇, 𝛾, and 𝛼 indicate the insolation, atmospheric absorptivity, atmospheric reflectivity, and surface albedo. TOA (SFC) is abbreviation for top-of- atmosphere (surface).

For the APRP method, the one-layer radiation model is adopted to represent the interaction of SW fluxes between the atmosphere and surface (Figure A1). Of the incident solar radiation S, a fraction 𝜇 is absorbed by the atmosphere, a fraction 𝛾 is reflected by the atmosphere, and the remainder is transmitted to the surface. Of the solar radiation transmitted through the atmosphere, a fraction 𝛼 is reflected at the surface, of which a fraction 𝛾 is reflected from the atmosphere back toward the surface again on the return pass. This is repeated for infinite passes. Note that the atmospheric absorption and reflection occur simultaneously at the first downward pass motivated by Donohoe and Battisti (2011).

In this one-layer radiation model, the total reflected SW radiation at TOA (SW_↑^TOA) and total incident SW radiation toward SFC (SW_↓^SFC) can be expressed as the sum of an infinite geometric series, with 𝛼𝛾 as the common ratio. Then, the planetary albedo (A) and surface incident ratio (Q_s) are expressed as a function of radiative properties:

A(𝛼, 𝛾, 𝜇) =SW_↑^TOA

𝑆 = 𝛾 + (1 − 𝜇 − 𝛾)(1 − 𝛾)𝛼(1 + 𝛼𝛾 + 𝛼²𝛾²+ ⋯ )

= 𝛾 +(1 − 𝜇 − 𝛾)(1 − 𝛾) 1 − 𝛼𝛾 𝛼

(A1) Q_s(𝛼, 𝛾, 𝜇) =SW_↓^SFC

𝑆 = (1 − 𝜇 − 𝛾) (1 + 𝛼𝛾 + 𝛼²𝛾²+ ⋯ ) = (1 − 𝜇 − 𝛾)

1 − 𝛼𝛾

(A2)

100

To account for the effect of clouds, all-sky radiative fluxes at each grid point (R) are divided into the clear-sky (R_clr) and overcast-sky (R_oc) fluxes, weighted by the total-column cloud area fraction (clt) following Taylor et al. (2007):

R = (1 − clt) ∗ R_clr+ clt ∗ R_oc

(A3) Note that R_oc can be calculated from the standard model outputs R, R_clr and clt. The one-layer radiation model is applied to each of R_clr and R_oc. Accordingly, radiative properties for the single- column model are calculated for clear- and overcast- sky fluxes:

A =SW_↑^TOA

𝑆 , Q_s=SW_↓^SFC

𝑆 , 𝛼 =SW_↑^SFC SW_↓^SFC

(A4) The atmospheric absorptivity is defined as the difference between the fraction of net SW fluxes absorbed at TOA and SFC:

𝜇 = (1 − A) − Q_s(1 − 𝛼)

(A5) The atmospheric reflectivity is then derived from Eq. (A2):

γ =1 − 𝜇 − Q_s 1 − 𝛼Q_s

(A6) In Eq. (A3-A6), the radiative properties for clear-sky (𝛼_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑟, 𝛾_𝑐𝑙𝑟) and overcast-sky (𝛼_𝑜𝑐, 𝜇_𝑜𝑐, 𝛾_𝑜𝑐) are fitted to the climate model output. Radiative properties for the overcast-sky are calculated

assuming that the non-cloud atmospheric constituents in the overcast-sky absorb and reflect the same amount of SW fluxes as in the clear-sky. That is, the transmittance in overcast-sky is the product of transmittance in clear-sky and cloud, applied to 𝜇 and 𝛾 in the same way:

1 − 𝜇_𝑜𝑐 = (1 − 𝜇_𝑐𝑙𝑟) ∗ (1 − 𝜇_𝑐𝑙𝑑) 1 − 𝛾_𝑜𝑐 = (1 − 𝛾_𝑐𝑙𝑟) ∗ (1 − 𝛾_𝑐𝑙𝑑)

(A7) This results in seven radiative properties (clt, 𝛼_clr, 𝛼_oc, 𝜇_clr, 𝜇_cld, 𝛾_clr, 𝛾_cld) representing the SW radiative transfer in the climate model. Next, we extend the methodology for TOA SW decomposition in the original APRP method (Taylor et al. 2007) to the surface component in a parallel way. First, A (Eq. (A1)) and Q_s (Eq. (A2)) of the climate model become a function of the seven radiative properties:

101

A_clr(𝛼_𝑐𝑙𝑟, 𝛾_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑟) = 𝛾_𝑐𝑙𝑟+(1 − 𝜇_𝑐𝑙𝑟− 𝛾_𝑐𝑙𝑟)(1 − 𝛾_𝑐𝑙𝑟) 1 − 𝛼_𝑐𝑙𝑟𝛾_𝑐𝑙𝑟 𝛼_𝑐𝑙𝑟

(A8) A_oc(𝛼_𝑜𝑐, 𝛾_𝑐𝑙𝑑, 𝜇_𝑐𝑙𝑑, 𝛾_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑟) = 𝛾_𝑜𝑐+(1 − 𝜇_𝑜𝑐− 𝛾_𝑜𝑐)(1 − 𝛾_𝑜𝑐)

1 − 𝛼_𝑜𝑐𝛾_𝑜𝑐 𝛼_𝑜𝑐, where 𝜇_𝑜𝑐 = 1 − (1 − 𝜇_𝑐𝑙𝑟) ∗ (1 − 𝜇_𝑐𝑙𝑑), 𝛾_𝑜𝑐 = 1 − (1 − 𝛾_𝑐𝑙𝑟) ∗ (1 − 𝛾_𝑐𝑙𝑑)

(A9) 𝐴(clt, 𝛼_𝑐𝑙𝑟, 𝛼_𝑜𝑐, 𝜇_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑑, 𝛾_𝑐𝑙𝑟, 𝛾_𝑐𝑙𝑑) = (1 − clt) ∗ A_clr+ clt ∗ A_oc

(A10) Q_s,clr(𝛼_𝑐𝑙𝑟, 𝛾_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑟) =(1 − 𝜇_𝑐𝑙𝑟− 𝛾_𝑐𝑙𝑟)

1 − 𝛼_𝑐𝑙𝑟𝛾_𝑐𝑙𝑟

(A11) Q_s,oc(𝛼_𝑜𝑐, 𝛾_𝑐𝑙𝑑, 𝜇_𝑐𝑙𝑑, 𝛾_𝑐𝑙𝑟, 𝜇_𝑐𝑙𝑟) =(1 − 𝜇_𝑜𝑐− 𝛾𝑜𝑐)

1 − 𝛼_𝑜𝑐𝛾_𝑜𝑐 ,

where 𝜇_𝑜𝑐 = 1 − (1 − 𝜇_𝑐𝑙𝑟) ∗ (1 − 𝜇_𝑐𝑙𝑑), 𝛾_𝑜𝑐 = 1 − (1 − 𝛾_𝑐𝑙𝑟) ∗ (1 − 𝛾_𝑐𝑙𝑑)

(A12) Qs(clt, 𝛼_𝑐𝑙𝑟, 𝛼𝑜𝑐, 𝜇𝑐𝑙𝑟, 𝜇𝑐𝑙𝑑, 𝛾𝑐𝑙𝑟, 𝛾𝑐𝑙𝑑) = (1 − clt) ∗ Q_s,clr+ clt ∗ Qs,oc

(A13) The changes in planetary albedo (δA) and surface incident ratio (δQ_s) due to each radiative property can be estimated by Eq. (A10) and (A13). The forward and backward substitution are averaged to estimate a contribution from each radiative property, following Colman (2003):

δA_𝑡 =1

2[A(𝑡₂, 𝑜₁) − A(𝑡₁, 𝑜₁)] + 1

2[A(𝑡₂, 𝑜₂) − A(𝑡₁, 𝑜₂)],

(A14) δQ_s,𝑡=1

2[Q_s(𝑡₂, 𝑜₁) − Q_s(𝑡₁, 𝑜₁)] + 1

2[Q_s(𝑡₂, 𝑜₂) − Q_s(𝑡₁, 𝑜₂)],

(A15) where 𝑡 denotes target radiative property, 𝑜 denotes all other 6 radiative properties, and 𝛿 denotes the difference between state 1 and state 2.

Meanwhile, the net SW flux at the TOA and SFC are:

102

SW_net^TOA= 𝑆 − SW_↑^TOA= (1 − A)𝑆

(A16) SW_net^SFC = SW_↓^SFC− SW_↑^SFC= Q_s(1 − 𝛼)𝑆

(A17) And the changes in SW flux at the TOA and SFC are:

δSW_net^TOA= δ[(1 − A)𝑆] = (1 − A)δ𝑆 − 𝑆δA

(A18) δSW_net^SFC = δ[Q_s(1 − 𝛼)𝑆] = Q_s(1 − 𝛼)δ𝑆 − Q_s𝑆δ𝛼 + (1 − 𝛼)𝑆δQ_s,

(A19) where the terms without δ are the average between the two states, and the co-variance terms are ignored.

Substituting Eq. (A14) into (A18) and Eq. (A15) into (A19), SW flux responses at the TOA and SFC can be attributed to the change in each radiative property:

δSW_net^TOA = (1 − A)δ𝑆 − 𝑆[δA_𝛼_𝑐𝑙𝑟 + δA_𝛼_𝑜𝑐] ∙∙∙

−𝑆[δA_clt+ δA_𝜇_𝑐𝑙𝑑+ δA_𝛾_𝑐𝑙𝑑] − 𝑆[δA_𝛾_𝑐𝑙𝑟+ δA_𝜇_𝑐𝑙𝑟]

= δSW_𝑆^TOA+ δSW_𝛼^TOA+ δSW_cld^TOA+ δSW_ncld^TOA

(A20) δSW_net^SFC = Q_s(1 − 𝛼)δ𝑆 + {−Q_s𝑆δ𝛼 + 𝑆(1 − 𝛼)[δQ_s,𝛼_𝑐𝑙𝑟+ δQ_s,𝛼_𝑜𝑐]} ∙∙∙

+𝑆(1 − 𝛼)[δQs,clt+ δQs,𝜇_𝑐𝑙𝑑+ δQs,𝛾_𝑐𝑙𝑑] + 𝑆(1 − 𝛼)[δQs,𝜇_𝑐𝑙𝑟+ δQs,𝛾_𝑐𝑙𝑟]

= δSW_𝑆^SFC+ δSW_𝛼^SFC+ δSW_cld^SFC+ δSW_ncld^SFC

(A21) where subscripts 𝑆, 𝛼, cld, and ncld denote the contributions from the change in insolation, surface albedo, cloud properties and non-cloud atmospheric constituents.

103

Appendix B: Decomposition of the change in latent heat flux

The standard bulk formula for the latent hear flux (LHF) is:

LHF = 𝐿_v𝐶_E𝜌_aWND(q_s− q_ref) = 𝐿_v𝐶_E𝜌_aWND(q_s(T_s) − RH ∙ q_s(T_s+ ΔT)),

(B1) where 𝐿_v = 2.5 × 10⁶ J ∙ Kg⁻¹ is the latent heat of vaporization, 𝐶_E is the transfer coefficient, 𝜌_a is the surface air density, WND is the surface wind speed, RH is the relative humidity at model reference level, ΔT = T_ref− T_s, T_ref is reference level temperature, T_s is surface temperature, and q_s is the saturation specific humidity at the ocean surface. From the Clausius-Clapeyron (CC) equation, q_s could be further expressed as

q_s(T) = q₀e^𝛽T,

(B2) where q₀ is a constant and β= 𝐿_v/(𝑅_vT²). Note that 𝑅_v= 461.5 J ∙ Kg⁻¹∙ K⁻¹. Substitution of Eq.

(B2) into Eq. (B1) yields:

LHF = 𝐿_v𝐶_E𝜌_aWND(1 − RHe^𝛽ΔT)q_s(T_s)

(B3) The change in LHF could be represented as below, assuming δLHF as function of four independent variables – T_s, WND, RH and ΔT:

δLHF = ∂LHF

∂T_s δT_s+∂LHF

∂W δW +∂LHF

∂RH δRH + ∂LHF

∂(ΔT)δ(ΔT),

(B4) where the partial derivative of LHF are resulted in the expressions composed of constants and base state variables:

∂LHF

∂T_s = LHF̅̅̅̅̅

qs(T̅ )_s

∂

∂T_s(q_s(T_s)) = ̅̅̅̅̅LHF

qs(T̅ )_s (𝛽q_s(T̅ )) = 𝛽LHF_s ̅̅̅̅̅,

(B5)

∂LHF

∂WND= LHF̅̅̅̅̅

WND̅̅̅̅̅̅̅

∂

∂WND(WND) = LHF̅̅̅̅̅

WND̅̅̅̅̅̅̅,

(B6)

∂LHF

∂RH = LHF̅̅̅̅̅

1 − RH̅̅̅̅e^𝛽ΔT^̅̅̅̅

∂

∂RH(1 − RHe^𝛽ΔT) = LHF̅̅̅̅̅

1 − RH̅̅̅̅e^𝛽ΔT^̅̅̅̅(−e^𝛽ΔT) = −LHF̅̅̅̅̅

e^−𝛽ΔT^̅̅̅̅− RH̅̅̅̅,

104

(B7)

∂LHF

∂(ΔT)= ̅̅̅̅̅LHF 1 − RH̅̅̅̅e^𝛽ΔT^̅̅̅̅

∂

∂(ΔT)(1 − RHe^𝛽ΔT) = LHF̅̅̅̅̅

1 − RH̅̅̅̅e^𝛽ΔT^̅̅̅̅(−𝛽RH̅̅̅̅ ∙ e^𝛽ΔT) =−𝛽LHF̅̅̅̅̅ ∙ RH̅̅̅̅

e^−𝛽ΔT^̅̅̅̅− RH̅̅̅̅, (B8) where overbar denotes the reference climatology. Eventually, substitution of Eq. (B5), (B6), (B7), (B8) into Eq. (B4) leads to the attribution of δLHF to the change in T_s, WND, RH and ΔT (F. Jia & Wu, 2013):

δLHF = (𝛽LHF̅̅̅̅̅)δT_s+ (LHF W̅

̅̅̅̅̅

) δW + ( −LHF̅̅̅̅̅

e^−𝛽ΔT^̅̅̅̅− RH̅̅̅̅) δRH + (−𝛽LHF̅̅̅̅̅ ∙ RH̅̅̅̅

e^−𝛽ΔT^̅̅̅̅− RH̅̅̅̅) δ(ΔT)

= δLHF_T_s+ δLHF_WND+ δLHF_RH+ δLHF_ΔT

(B9) Note that the sensitivity of the LHF change to each climate variables is proportional to its climatological magnitude.

105

Bibliography

Adam, O., Schneider, T., Brient, F., & Bischoff, T. (2016). Relation of the double-ITCZ bias to the atmospheric energy budget in climate models: DOUBLE-ITCZ BIAS AND ATMOSPHERIC ENERGY BUDGET. Geophysical Research Letters, 43(14), 7670–7677.

https://doi.org/10.1002/2016GL069465

Adam, O., Schneider, T., & Brient, F. (2018). Regional and seasonal variations of the double-ITCZ bias in CMIP5 models. Climate Dynamics, 51(1–2), 101–117. https://doi.org/10.1007/s00382-017- 3909-1

Adler, R. F., Huffman, G. J., CChang, G. J., Ferraro, R., Xie, P., J. Janowiak, et al. (2003). The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–

Present). Journal of Hydrometeorology, 4(6), 1147–1167. https://doi.org/10.1175/1525- 7541(2003)004<1147:TVGPCP>2.0.CO;2

Allen, M. R., & Ingram, W. J. (2002). Constraints on future changes in climate and the hydrologic cycle. Nature, 419(6903), 228–232. https://doi.org/10.1038/nature01092

Andrews, T., Gregory, J. M., & Webb, M. J. (2015). The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models. Journal of Climate, 28(4), 1630–1648. https://doi.org/10.1175/JCLI-D-14-00545.1

Arking, A. (1996). Absorption of Solar Energy in the Atmosphere: Discrepancy Between Model and Observations. Science, 273(5276), 779–782. https://doi.org/10.1126/science.273.5276.779

Armour, K. C., Marshall, J., Scott, J. R., Donohoe, A., & Newsom, E. R. (2016). Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nature Geoscience, 9(7), 549–554. https://doi.org/10.1038/ngeo2731

Bacmeister, J. T., Suarez, M. J., & Robertson, F. R. (2006). Rain Reevaporation, Boundary Layer–

Convection Interactions, and Pacific Rainfall Patterns in an AGCM. Journal of the Atmospheric Sciences, 63(12), 3383–3403. https://doi.org/10.1175/JAS3791.1

Baldwin, J. W., Atwood, A. R., Vecchi, G. A., & Battisti, D. S. (2021). Outsize Influence of Central American Orography on Global Climate. AGU Advances, 2(2).

https://doi.org/10.1029/2020AV000343

Bellucci, A., Gualdi, S., & Navarra, A. (2010). The Double-ITCZ Syndrome in Coupled General Circulation Models: The Role of Large-Scale Vertical Circulation Regimes. Journal of Climate, 23(5), 1127–1145. https://doi.org/10.1175/2009JCLI3002.1

106

Bischoff, T., & Schneider, T. (2014). Energetic Constraints on the Position of the Intertropical Convergence Zone. Journal of Climate, 27(13), 4937–4951. https://doi.org/10.1175/JCLI-D-13- 00650.1

Bjerknes, J. (1969). Atmospheric teleconnection from the equatorial Pacific. Monthly Weather Review, 97(3), 163–172.

Block, K., & Mauritsen, T. (2013). Forcing and feedback in the MPI-ESM-LR coupled model under abruptly quadrupled CO 2: FORCING AND FEEDBACK IN THE MPI-ESM-LR. Journal of Advances in Modeling Earth Systems, 5(4), 676–691. https://doi.org/10.1002/jame.20041

Boccaletti, G., Pacanowski, R. C., Philander, S. G. H., & Fedorov, A. V. (2004). The Thermal Structure of the Upper Ocean. JOURNAL OF PHYSICAL OCEANOGRAPHY, 34, 15.

Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S., Dufresne, J.-L., et al.

(2014). Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models*. Journal of Climate, 27(1), 41–56. https://doi.org/10.1175/JCLI-D-13-00169.1

Bony, S. (2005). Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophysical Research Letters, 32(20), L20806.

https://doi.org/10.1029/2005GL023851

Brient, F., & Schneider, T. (2016). Constraints on Climate Sensitivity from Space-Based Measurements of Low-Cloud Reflection. Journal of Climate, 29(16), 5821–5835.

https://doi.org/10.1175/JCLI-D-15-0897.1

Broccoli, A. J., Dahl, K. A., & Stouffer, R. J. (2006). Response of the ITCZ to Northern Hemisphere cooling: ITCZ RESPONSE TO N. HEMISPHERE COOLING. Geophysical Research Letters, 33(1), n/a-n/a. https://doi.org/10.1029/2005GL024546

Bronselaer, B., Winton, M., Griffies, S. M., Hurlin, W. J., Rodgers, K. B., Sergienko, O. V., et al.

(2018). Change in future climate due to Antarctic meltwater. Nature, 564(7734), 53–58.

https://doi.org/10.1038/s41586-018-0712-z

Burls, N. J., Muir, L., Vincent, E. M., & Fedorov, A. (2017). Extra-tropical origin of equatorial Pacific cold bias in climate models with links to cloud albedo. Climate Dynamics, 49(5–6), 2093–

2113. https://doi.org/10.1007/s00382-016-3435-6

Chiang, J. C. H., & Bitz, C. M. (2005). Influence of high latitude ice cover on the marine Intertropical Convergence Zone. Climate Dynamics, 25(5), 477–496. https://doi.org/10.1007/s00382-005-0040-5

107

Chiang, J. C. H., & Friedman, A. R. (2012). Extratropical Cooling, Interhemispheric Thermal Gradients, and Tropical Climate Change. Annual Review of Earth and Planetary Sciences, 40(1), 383–412. https://doi.org/10.1146/annurev-earth-042711-105545

Chung, E.-S., & Soden, B. J. (2015). An Assessment of Direct Radiative Forcing, Radiative

Adjustments, and Radiative Feedbacks in Coupled Ocean–Atmosphere Models*. Journal of Climate, 28(10), 4152–4170. https://doi.org/10.1175/JCLI-D-14-00436.1

Collins, W. D., Lee-Taylor, J. M., Edwards, D. P., & Francis, G. L. (2006). Effects of increased near- infrared absorption by water vapor on the climate system. Journal of Geophysical Research,

111(D18), D18109. https://doi.org/10.1029/2005JD006796

DeAngelis, A. M., Qu, X., Zelinka, M. D., & Hall, A. (2015). An observational radiative constraint on hydrologic cycle intensification. Nature, 528(7581), 249–253. https://doi.org/10.1038/nature15770 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828

Ding, H., Newman, M., Alexander, M. A., & Wittenberg, A. T. (2020). Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific. Geophysical Research Letters, 47(5).

https://doi.org/10.1029/2019GL086765

Dong, L., Leung, L. R., Lu, J., & Song, F. (2021). Double‐ITCZ as an Emergent Constraint for Future Precipitation Over Mediterranean Climate Regions in the North Hemisphere. Geophysical Research Letters, 48(3). https://doi.org/10.1029/2020GL091569

Dong, Y., Armour, K. C., Zelinka, M. D., Proistosescu, C., Battisti, D. S., Zhou, C., & Andrews, T.

(2020a). Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models. Journal of Climate, 33(18), 7755–7775. https://doi.org/10.1175/JCLI-D-19- 1011.1

Dong, Y., Armour, K. C., Zelinka, M. D., Proistosescu, C., Battisti, D. S., Zhou, C., & Andrews, T.

(2020b). Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models. Journal of Climate, 33(18), 7755–7775. https://doi.org/10.1175/JCLI-D-19- 1011.1

Donohoe, A., Atwood, A. R., & Byrne, M. P. (2019). Controls on the Width of Tropical Precipitation and Its Contraction Under Global Warming. Geophysical Research Letters, 46(16), 9958–9967.

https://doi.org/10.1029/2019GL082969

Donohoe, Aaron, & Battisti, D. S. (2011). Atmospheric and Surface Contributions to Planetary Albedo. Journal of Climate, 24(16), 4402–4418. https://doi.org/10.1175/2011JCLI3946.1

108

Donohoe, Aaron, & Battisti, D. S. (2013). The Seasonal Cycle of Atmospheric Heating and Temperature. Journal of Climate, 26(14), 4962–4980. https://doi.org/10.1175/JCLI-D-12-00713.1 Donohoe, Aaron, Marshall, J., Ferreira, D., & Mcgee, D. (2013). The Relationship between ITCZ Location and Cross-Equatorial Atmospheric Heat Transport: From the Seasonal Cycle to the Last Glacial Maximum. Journal of Climate, 26(11), 3597–3618. https://doi.org/10.1175/JCLI-D-12- 00467.1

England, M. R., Polvani, L. M., Sun, L., & Deser, C. (2020). Tropical climate responses to projected Arctic and Antarctic sea-ice loss. Nature Geoscience, 13(4), 275–281. https://doi.org/10.1038/s41561- 020-0546-9

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016).

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9- 1937-2016

Feng, R., & Poulsen, C. J. (2014). Andean elevation control on tropical Pacific climate and ENSO:

Andean uplift and tropical climate. Paleoceanography, 29(8), 795–809.

https://doi.org/10.1002/2014PA002640

Fiedler, S., Kinne, S., Huang, W. T. K., Räisänen, P., O’Donnell, D., Bellouin, N., et al. (2019).

Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity. Atmospheric Chemistry and Physics, 19(10), 6821–6841.

https://doi.org/10.5194/acp-19-6821-2019

Fläschner, D., Mauritsen, T., & Stevens, B. (2016). Understanding the Intermodel Spread in Global- Mean Hydrological Sensitivity*. Journal of Climate, 29(2), 801–817. https://doi.org/10.1175/JCLI-D- 15-0351.1

Freidenreich, S. M., & Ramaswamy, V. (1999). A new multiple-band solar radiative parameterization for general circulation models. Journal of Geophysical Research: Atmospheres, 104(D24), 31389–

31409. https://doi.org/10.1029/1999JD900456

Frey, W. R., Maroon, E. A., Pendergrass, A. G., & Kay, J. E. (2017). Do Southern Ocean Cloud Feedbacks Matter for 21st Century Warming? Geophysical Research Letters, 44(24).

https://doi.org/10.1002/2017GL076339

Geng, Y., Xie, S., Zheng., X., Long, X., Kang, S. M., Lin, X., & Song, Z. (2022). CMIP6 Intermodel Spread in Interhemispheric Asymmetry of Tropical Climate Response to Greenhouse Warming:

Extratropical Ocean Effects. JOURNAL OF CLIMATE, Published online, 1–49.

https://doi.org/10.1175/JCLI-D-21-0541.1

109

Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., et al. (2011).

The Community Climate System Model Version 4. Journal of Climate, 24(19), 4973–4991.

https://doi.org/10.1175/2011JCLI4083.1

Green, B., & Marshall, J. (2017). Coupling of Trade Winds with Ocean Circulation Damps ITCZ Shifts. Journal of Climate, 30(12), 4395–4411. https://doi.org/10.1175/JCLI-D-16-0818.1 Ham, Y., Kim, J., Lee, J., Li, T., Lee, M., Son, S., & Hyun, Y. (2021). The Origin of Systematic Forecast Errors of Extreme 2020 East Asian Summer Monsoon Rainfall in GloSea5. Geophysical Research Letters, 48(16). https://doi.org/10.1029/2021GL094179

Ham, Y.-G., & Kug, J.-S. (2014). Effects of Pacific Intertropical Convergence Zone precipitation bias on ENSO phase transition. Environmental Research Letters, 9(6), 064008.

https://doi.org/10.1088/1748-9326/9/6/064008

Hawcroft, M., Haywood, J. M., Collins, M., Jones, A., Jones, A. C., & Stephens, G. (2017). Southern Ocean albedo, inter-hemispheric energy transports and the double ITCZ: global impacts of biases in a coupled model. Climate Dynamics, 48(7–8), 2279–2295. https://doi.org/10.1007/s00382-016-3205-5 Hendrickson, J. M., Terai, C. R., Pritchard, M. S., & Caldwell, P. M. (2021). Lower Tropospheric Processes: A Control on the Global Mean Precipitation Rate. Geophysical Research Letters, 48(6).

https://doi.org/10.1029/2020GL091169

Hirota, N., Takayabu, Y. N., Watanabe, M., & Kimoto, M. (2011). Precipitation Reproducibility over Tropical Oceans and Its Relationship to the Double ITCZ Problem in CMIP3 and MIROC5 Climate Models. Journal of Climate, 24(18), 4859–4873. https://doi.org/10.1175/2011JCLI4156.1

Hoskins, B. J., & Karoly, D. J. (1981). The Steady Linear Response of a Spherical Atmosphere to Thermal and Orographic Forcing. Journal of Atmospheric Sciences, 38(6), 1179–1196.

https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2

Hunke, E. C., & Lipscomb, W. H. (n.d.). CICE: the Los Alamos Sea Ice Model Documentation and Software User’s Manual Version 4., 76.

Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., & Rosinski, J. (2008). A New Sea Surface Temperature and Sea Ice Boundary Dataset for the Community Atmosphere Model. Journal of Climate, 21(19), 5145–5153. https://doi.org/10.1175/2008JCLI2292.1

Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., & Kushner, P. J. (2013). The Community Earth System Model, 22.

Hwang, Yen-Ting, Frierson, D. M. W., & Kang, S. M. (2013). Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century: AEROSOLS AND TROPICAL

110

RAIN BELT SHIFTS. Geophysical Research Letters, 40(11), 2845–2850.

https://doi.org/10.1002/grl.50502

Hwang, Yen-Ting, Xie, S.-P., Deser, C., & Kang, S. M. (2017). Connecting tropical climate change with Southern Ocean heat uptake: Tropical Climate Change and SO Heat Uptake. Geophysical Research Letters, 44(18), 9449–9457. https://doi.org/10.1002/2017GL074972

Hwang, Yen-Ting, Tseng, H.-Y., Li, K.-C., Kang, S. M., Chen, Y.-J., & Chiang, J. C. H. (2021).

Relative Roles of Energy and Momentum Fluxes in the Tropical Response to Extratropical Thermal Forcing. Journal of Climate, 34(10), 3771–3786. https://doi.org/10.1175/JCLI-D-20-0151.1

Hwang, Y.-T., & Frierson, D. M. W. (2013a). Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proceedings of the National Academy of Sciences, 110(13), 4935–4940. https://doi.org/10.1073/pnas.1213302110

Hwang, Y.-T., & Frierson, D. M. W. (2013b). Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proceedings of the National Academy of Sciences, 110(13), 4935–4940. https://doi.org/10.1073/pnas.1213302110

Hyder, P., Edwards, J. M., Allan, R. P., Hewitt, H. T., Bracegirdle, T. J., Gregory, J. M., et al. (2018).

Critical Southern Ocean climate model biases traced to atmospheric model cloud errors. Nature Communications, 9(1), 3625. https://doi.org/10.1038/s41467-018-05634-2

Jia, F., & Wu, L. (2013). A Study of Response of the Equatorial Pacific SST to Doubled-CO2 Forcing in the Coupled CAM–1.5-Layer Reduced-Gravity Ocean Model. Journal of Physical Oceanography, 43(7), 1288–1300. https://doi.org/10.1175/JPO-D-12-0144.1

Jia, Y., Richards, K. J., & Annamalai, H. (2021). The impact of vertical resolution in reducing biases in sea surface temperature in a tropical Pacific Ocean model. Ocean Modelling, 157, 101722.

https://doi.org/10.1016/j.ocemod.2020.101722

Kang, S. M. (2020). Extratropical Influence on the Tropical Rainfall Distribution. Current Climate Change Reports, 6(1), 24–36. https://doi.org/10.1007/s40641-020-00154-y

Kang, S. M., Held, I. M., Frierson, D. M. W., & Zhao, M. (2008). The Response of the ITCZ to Extratropical Thermal Forcing: Idealized Slab-Ocean Experiments with a GCM. Journal of Climate, 21(14), 3521–3532. https://doi.org/10.1175/2007JCLI2146.1

Kang, S. M., Frierson, D. M. W., & Held, I. M. (2009). The Tropical Response to Extratropical Thermal Forcing in an Idealized GCM: The Importance of Radiative Feedbacks and Convective Parameterization. Journal of the Atmospheric Sciences, 66(9), 2812–2827.

https://doi.org/10.1175/2009JAS2924.1

111

Kang, S. M., Kim, B.-M., Frierson, D. M. W., Jeong, S.-J., Seo, J., & Chae, Y. (2015). Seasonal Dependence of the Effect of Arctic Greening on Tropical Precipitation. Journal of Climate, 28(15), 6086–6095. https://doi.org/10.1175/JCLI-D-15-0079.1

Kang, S. M., Park, K., Hwang, Y., & Hsiao, W. (2018). Contrasting Tropical Climate Response Pattern to Localized Thermal Forcing Over Different Ocean Basins. Geophysical Research Letters, 45(22), 12,544-12,552. https://doi.org/10.1029/2018GL080697

Kang, S. M., Shin, Y., & Xie, S.-P. (2018). Extratropical forcing and tropical rainfall distribution:

energetics framework and ocean Ekman advection. Npj Climate and Atmospheric Science, 1(1), 20172. https://doi.org/10.1038/s41612-017-0004-6

Kang, S. M., Hawcroft, M., Xiang, B., Hwang, Y.-T., Cazes, G., Codron, F., et al. (2019).

Extratropical–Tropical Interaction Model Intercomparison Project (Etin-Mip): Protocol and Initial Results. Bulletin of the American Meteorological Society, 100(12), 2589–2606.

https://doi.org/10.1175/BAMS-D-18-0301.1

Kawai, H., Koshiro, T., & Yukimoto, S. (2021). Relationship between shortwave radiation bias over the Southern Ocean and the DOUBLE‐ intertropical convergence zone problem in MRI‐ESM2.

Atmospheric Science Letters. https://doi.org/10.1002/asl.1064

Kay, J. E., Wall, C., Yettella, V., Medeiros, B., Hannay, C., Caldwell, P., & Bitz, C. (2016). Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM). Journal of Climate, 29(12), 4617–4636. https://doi.org/10.1175/JCLI-D-15- 0358.1

Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., et al. (2020).

GISS-E2.1: Configurations and Climatology. Journal of Advances in Modeling Earth Systems, 12(8), e2019MS002025. https://doi.org/10.1029/2019MS002025

Kiehl, J. T., & Trenberth, K. E. (1997). Earth’s Annual Global Mean Energy Budget. Bulletin of the American Meteorological Society, 78(2), 12.

Kiehl, J. T., Hack, J. J., Zhang, M. H., & Cess, R. D. (1995). Sensitivity of a GCM Climate to Enhanced Shortwave Cloud Absorption. Journal of Climate, 8(9), 2200–2212.

https://doi.org/10.1175/1520-0442(1995)008<2200:SOAGCT>2.0.CO;2

Kim, D., & Ramanathan, V. (2008). Solar radiation budget and radiative forcing due to aerosols and clouds. Journal of Geophysical Research, 113(D2), D02203. https://doi.org/10.1029/2007JD008434 Kim, H., Kang, S. M., Hwang, Y.-T., & Yang, Y.-M. (2015). Sensitivity of the Climate Response to the Altitude of Black Carbon in the Northern Subtropics in an Aquaplanet GCM. Journal of Climate, 28(16), 6351–6359. https://doi.org/10.1175/JCLI-D-15-0037.1

Dalam dokumen Doctoral Thesis (Halaman 120-147)