Analyzing recent latitudinal and seasonal changes in simulated atmospheric temperatures from a global chemistry-climate model

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Analyzing Recent Latitudinal and Seasonal Changes

in Simulated Atmospheric Temperatures from a

Global Chemistry-Climate Model

by

Jordan T. Benjamin

Submitted to the Department of Earth, Atmospheric, and Planetary Sciences

in partial fulfillment of the requirements for the degree of Bachelor of Science in Earth, Atmospheric, and Planetary Sciences

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

June 2019

@

Signature redacted

A uthor ...

Department of Earth, At ospheric, and i4anetary Sciences May 17, 2019

Signature redacted

C ertified by ... Susan Solomon Thesis Supervisor

Signature redacted

Accepted by ... MASSACHUSETTS I$TITUTE -OF TEC~!~L9.

JUN 1

12019

LIBRARIES

Richard P. Binzel

Chair, Committee on Undergraduate Program

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Analyzing Recent Latitudinal and Seasonal Changes in Simulated Atmospheric Temperatures from a Global

Chemistry-Climate Model by

Jordan T. Benjamin

Submitted to the Department of Earth, Atmospheric, and Planetary Sciences on May 17, 2019, in partial fulfillment of the

requirements for the degree of

Bachelor of Science in Earth, Atmospheric, and Planetary Sciences

Abstract

Recent work by Santer et al. (2018) in Science examined the usefulness of the lati-tudinal structure and seasonal behavior of warming for fingerprinting anthropogenic climate change using satellite data and the CMIP5 multi-model ensemble over 1979-2016. They identify the first seasonal fingerprint in the northern hemisphere annual cycle and structure of warming, but do not specify what forcing agent (e.g. ozone, soot, or greenhouse gases) is responsible for causing it. We further probe this phe-nomena using 3 ensembles-of-opportunity over 1955-1979 and 1995-2024 of the Whole Atmosphere Community Climate Model version 4 (WACCM4), one of the world's few best fully coupled interactive chemistry-climate models. While our ensembles' con-struction covers limited time periods, it has the advantage of avoiding the effects of El Chich6n (1982) and Pinatubo (1991), which are difficult to capture in models and have different drivers (volcanic) than the ones of interest here.

The key findings of this research are that added greenhouse gas forcings nearly fully determine the latitudinal structure of warming and change in the amplitude of the annual cycle, that WACCM4 does a much better job than the CMIP5 multi-model ensemble of predicting the magnitude and latitudinal structure of climate change, and that tropical expansion and a poleward shift of the jet may drive the key subtropical features Santer observed. Interactive chemistry is not found to be a defining factor in representing the rate and structure of warming in CMIP5, and is certainly much less important than other details of model construction.

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List of Figures

1 Santer et al. (2018) zonal-mean satellite temperature trends . . . . . 7

2 Satellite temperature weighting functions and climatology . . . . 10

3 HiODSHiGHG ensemble-mean TAM trends . . . . 13

4 HiODSHiGHG ensemble TAM trend signal-to-noise . . . . 14

5 HiODSHiGHG ensemble TAC trend signal-to-noise . . . . 15

6 HiODSHiGHG ensemble vs CMIP5 annual-mean trend . . . . 16

7 HiODSHiGHG ensemble vs CMIP5 annual-cycle trend . . . . 17

8 HiODSHiGHG ensemble signal-to-noise . . . . 19

9 Forcing contributions to temperature trends . . . . 20

10 HiODSHiGHG ensemble vs CMIP5 annual-mean trend 1975-2010 22 11 Forcing contributions to temperature trends 1960-2010 . . . . 23

12 Seasonal cycle of TAM trend . . . . 25

13 Seasonal cycle of TAM baseline trend 1975-2010 . . . . 26

14 Global-mean TAM . . . . .. . .31 15 Global-mean TAC . . . .. . . .... -- . . . . ...32

16 HiODSHiGHG ensemble-mean temperature trends 1997-2018 . . . . 33

17 HiODSHiGHG ensemble-mean temperature trends 1995-2024 . . . . 34

18 HiODS 1960GHG enemmble-mean temperature trends . . . . 35

19 LoODSLoGHG ensemble-mean temperature trends . . . . 36

20 HiODSHiGHG annual-mean TMT trends . . . . 37

21 HiODSHiGHG ensemble TAC TMT trends . . . . 38

22 HiODSHiGHG zonal-mean temperature trends 1997-2018 . . . . 39

23 HiODSHiGHG zonal-mean temperature trends 1995-2024 . . . . 40

24 TAM amplitude trend seasonal cycle, 1997-2018 . . . . 41

25 Seasonal cycle of TAM trend 1995-2024 . . . . 42

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1. Introduction and Motivation

Santer et al. (2018) published a paper in Science on the usefulness of latitudinal structures and seasonal behavior for climate change fingerprinting using satellite data and the CMIP5 multi-model ensemble, particularly in the TMT region (upper tro-posphere) for 1979-2016. That work found that the CMIP5 models do a poor job of representing the latitudinal structure of warming and changes to the seasonal cycle in key areas in the subtropics, and (as is well known in the literature) warm the troposphere too quickly as shown in Figure 1. We seek to probe the usefulness of interactive chemistry in simulating the warming rate and structure of the upper tro-posphere, examine the relative importance of ozone depleting substance (ODS) and greenhouse gas (GHG) forcings in the upper troposphere, and determine potential mechanisms for the key feature Santer observed.

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C Corrected TMT (Annual mean) D Corrected TMT (Annual cycle)

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Individual realization -- SS v3.3 -- STAR 0.0 - AI v5.6

-MultI-model average - RSS v4.0 - STAR v4.C - JAM v6.0

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Figure 1: (a) Figure 3 from Santer et al. (2018) showing trends over 1979-2016. (b) the equivalent over 1995-2016 provided by Santer.

Santer identified subtropical and extratropical 'lobes' in the trend in the amplitude of the annual-cycle over the 1979-2016 satellite record - a small trend of decreasing amplitude in the subtropics book-ended by larger increases in the extratropics. The

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reason for these lobes, particularly in the NH where the signal is larger and clearer, is unclear and despite being apparent in Donohoe and Battisti (2013) and Santer's work, their origin is contested. Santer presented a theory of a continental drying feedback, but this is not understood in the current literature (Randel, 2018). The WACCM ensembles used in this work do show the lobes are forced by GHGs and not other agents but provide no contribution to understanding what specific climate feed-backs (clouds, continental drying, etc) are involved. They also suggest an apparent connection to northward zonal-jet shifts, possibly indicative of tropical expansion.

2. Methods

2.1 Model

This work is based on an 'ensemble-of-opportunity', in that the simulations used were not created for this specific work but were useful nonetheless. We use the Community Earth System Model version 1 (CESM1) with the Whole Atmosphere Community Climate Model version 4 (WACCM4) as its atmospheric component, as described in Marsh et al. (2013). We have 3 free-running 10-member ensembles, described in Table 1. The first ensemble has evolving ODS and GHG forcings over 1995-2024, a period in which both forcings are high. The next fixes the GHG concentrations to

1960 levels but retains the evolving ODS and other (tropospheric ozone, soot, etc)

concentrations over 1995-2024. The final ensemble retains both the evolving ODS and GHG concentrations, but is run over 1955-1979, a period in which both forcings were low (ODS forcings emerged in the late 1970s/early 1980s and rapidly increased). These are referred to in future sections by their short names in Table 1. A thorough description of the model setup in reference to its original purpose is in (Solomon et al., 2017). We use WACCM as it one of the few best models with fully interactive chemistry, allowing high-fidelity analysis of ODS-driven temperature trends into the stratosphere where chemical/radiative forcings of ozone, ODSs, and water vapor are all relevant. GHGs in this model are C02, CH4, and N20; ODSs include CFCs and other similar chlorinated substances. Volcanic aerosols are repeating year 2000 values for 1955-1979 (LoODSLoGHG ensemble) and from 2014-2024 (the latter period

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of the HiODSHiGHG and HiODS_1960GHG runs). They are observed from 1995-2014. Our time periods and volcanic forcings have the benefit of avoiding the eruptions of El Chich6n (1982), Pinatubo (1991) and Calbuco (2015) (Solomon et al., 2017).

Forcings Short Name Time Period

Evolving ODS + other, evolving GHG HiODSHiGHG 1995-2024

Evolving ODS + other, 1960 GHG HiODS_1960GHG 1995-2024

Evolving ODS + other, evolving GHG LoODSLoGHG 1955-1979

Table 1: Descriptions of our 3, 10-member free-running WACCM ensembles.. 'Other'

includes tropospheric ozone, soot, and similar unmentioned chemical and radiative forcings.

2.2 Satellite Temperatures

We calculate synthetic satellite temperatures using Remote Sensing System's (RSS) Microwave Sounding Unit (MSU) weighting functions (Figure 2a) (Remote Sensing Systems, 2019b). The temperature layers are TLS (Temperature Lower Stratosphere, TTS (Temperature Troposphere/Stratosphere), TMT (Temperature Middle Tropo-sphere), and TLT (Temperature Lower Troposphere). In this work, everywhere we say 'TMT', we mean corrected TMT, with the impact of lower stratospheric cooling on the profile of the TMT region removed via Equation 1 (Fu and Johanson, 2005).

TMT = a TMTuncorrected + (1 - a) TLS, a = 1.1 (1)

Though a version of this correction that uses a latitudinally varying a is preferred, we follow the same method outlined in the Supplemental Information to Santer et al. (2018) and use a latitudinally-invariant form rather than the discontinuous form from the literature. This avoids discontinuity in trends in the subtropics that coincide with the key regions analyzed in this and Santer's work. The ensemble-mean satellite temperature climatologies of the HiODSHiGHG simulation are shown in Figure 2b.

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TLS 35 30 TTS 25 -20 TLS Corrected TMT 15 -0TTS 10 --

r

TLT TMT TL Land _,_ean ocean Land 0 (-4 0.00 0.03 0.06 0.09 0.12 0.15 0.18 Weighting Functions 225 235 245 255 265 275 285 (a) (b)

Figure 2: (a) The RSS MSU weighting functions (K/km), taken from https: //

en. wikipedia. org/wiki/MSU-temperature-measurements, designed from Remote Sensing

Systems (2019b). (b) The mean MSU temperatures for the defined atmospheric layers in the HiODSHiGHG ensemble.

2.3 Analysis

In this work, we calculate satellite temperature trends (OC/decade) in the

annual-mean, TAM, and annual-cycle, TAC. TAC is defined as the amplitude of the first

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HiODS_ HiGHG ensemble to show the combined ODS+other+GHG trend over 1995-2024, the LoODS_ LoGHG ensemble to display primarily a GHG trend over 1955-1979 as it mostly predates the significant rise of ODSs in the 1970s, and the HiODS_1960GHG ensemble to show the ODS + other+ warming commitment trend over 1995-2024. As we noted earlier, other forcings include tropospheric ozone and black carbon (soot) whose magnitudes have not been explored here. The warming commitment trend arises from the lag in response between the ocean and atmosphere due to their dis-parate heat capacities, and in the literature has a surface magnitude of 0.03-0.09

0_{C/decade when initialized from a ~2000 atmospheric state extending over a century}

(Wigley, 2005; Matthews and Weaver, 2010). The HiODS_1960GHG ensemble-mean has a TLT trend magnitude of .04-.10 'C/decade, depending on the initial year for the trend calculation. For the stated value, start the trend regression in 1997 as the initial ensemble excursion is coherent during the spin-up period over 1995-1997 and highly negative, introducing a positive warming rate bias (see Figure 14). A second method of calculating this trend is to use a baseline. The difference between mean 1955-1964 TLT baseline in the LoODSLoGHG ensemble-mean and the 1997 TLT in the HiODS_1960GHG ensemble-mean yields a warming rate of .01-.08 'C/decade (the explicit value is -0.2 but the interannual variability is large relative to the difference used to estimate the trend). The literature is not clear on the expected magnitude of our warming commitment given lower GHG commitments in 1960 than in present-day studies. Thus we cannot make deductions regarding the validity of our warming commitment hypothesis or the magnitude of the warming commitment relative to the ODS-driven, tropospheric ozone-driven, or black carbon-driven trend. trend. Thus, instead of labeling the HiODS_1960GHG ensemble response as 'ODS+Commitment', we will label it as ODS+'residual'. In the upper atmosphere (TTS/TLS), the ODS-driven response is strong due to slow 21st century recovery (in the two high ODS ensembles) of strong late century ozone depletion relative to the LoODS__LoGHG ensemble. The transient upper atmosphere forcing and surface response are certainly not uncoupled or insignificant (e.g. Calvo et al. (2015)), but the mean climate re-sponse is uncertain. We also analyze the noise and signal-to-noise ratio in TAM,C, where noise is the ensemble trend standard deviation. This helps qualify observed trends in the context of their uncertainty. Our ensembles yield low signal-to-noise (high variability) in the high latitudes, potentially due to poor representation of sea-ice (Santer et al. (2018) and supplement) and large internal-variability.

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3. Results

In our trend analysis and results, we generally to use the period 1995-2016 as it is the maximum time period of overlap with Santer et al. (2018)'s analysis over

1979-2016. However, noting the 1995-1996 spin-up period's downward trend bias in

TAM (discussed in Section 2.3 and shown in Figure 14), we provide where applicable

equivalent analysis over 1997-2018, a period of the same length, and 1995-2024, a period long enough to dilute the initial bias, in the Appendix (A.2 and onwards).

We examine the trends in TAM and TAC for all 3 ensembles over their entire time

periods. We verify in Appendix A.1 that the various forcings in our TAM and TAC

trends combine linearly and can be derived from separate model realizations, and use this assumption to decompose the HiODSHiGHG ensemble response into a GHG-driven and ODS+residual response.

3.1 Global Annual-Mean Trends

We examine the ensemble-mean TAM and TAC trends over 1995-2016 in Figure 3.

There is distinct latitudinal structure in TAM warming in the TLT and TMT

mid-latitudes that is not captured well in the CMIP5 multi-model ensemble as referenced

by Santer et al. (2018). The TMT plots are analogous to Figure 2 in Santer et al.

(2018), but for our model period 1995-2016. The trends in TAC also display strong

latitudinal structure observed by Santer, with cooling in the subtropics missed by the CMIP5 ensemble, warming in the extratropics, and disparate warming and cooling at the north and south poles respectively.

The analogous plots to Figure 3 for the HiODS HiGHG ensemble mean over

1997-2018 and 1995-2024 are similar and shown in the Appendix. The equivalent

plots over 1995-2016 for the HiODS_1960GHG ensemble mean, and 1955-1979 for the LoODSLoGHG ensemble mean are in the appendix as well.

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TLS

TTS

7

.

TMT

TLT

(a) Annual Mean

-0.45 -0.3 -0.15 0 0.15 0.3 0.45 TLS TTS TMT TLT (b) Annual Cycle -0.16 -0.08 0 0.08 0.16

Figure 3: 1995-2016 TAM,C from our HiODSHiGHG ensemble-mean.

3.1.1 Signals and Noise

We examine the signal-to-noise footprint of our trend data for the HiODSHiGHG ensemble, shown for the annual-mean in Figure 4 and annual-cycle in Figure 5. The signal is defined as the ensemble mean trend, the noise as the ensemble trend standard deviation, and the signal-to-noise as the ratio of the two. The meridional structure of the signal is different than that of the noise, but in general the areas of peak signal in the trend of TAM are located in areas of high noise.

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A similar result is derived for the trend in TAC, though there there are some locations in the mid-latitudes where the the signal-to-noise ratio is locally high. However, the forced responses in the subtropics (as we will observe in the zonal-mean) are coherent in sign and structure (unlike at the poles), despite displaying large variability. We are therefore careful to qualify our conclusions regarding the trends as follows: the

calculated magnitude of low-latitude and subtropical trends is not robust, but the

structure is well-defined and is quite robust. However, trends observed in the polar regions merit enhanced scrutiny as their structure is highly variable.

TLS TTS

1eV

TLSTL _________

7-1~'$

TTS 7 *7. TTS el TMT TMT TMT TLT TLT TLT

(a) signal (b) noise (c) signal-to-noise

-0.45 -0.3 -0.15 0 0.15 0.3 0.45 0.05 0.1 0.15 0.2 0.25 0.3 0.35 2 3 4 5 6 7

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TLS TLS TTS TTS TMT TMT TLT TLT _ U (a) signal -0.16 -0.08 0 (b) noise 0.08 0.16 0.05 0.1 0.15 0.2 0.25 0.3 0.35 TLS TTS _TMT TLT (c) signal-to-noise 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Figure 5: 1995-2016 TAC HiODS HiGHG ensemble trend signal, noise, and signal-to-noise

3.2 Zonal-Mean Trends

3.2.1 Comparison with CMIP5

We wish to analyze the zonal-mean structure of our WACCM ensembles. First, we

consider the zonal-mean structure of the HiODS HiGHG ensemble and compare it

to CMIP5 results prepared by Benjamin Santer (LLNL) analogous to the same plots over 1979-2016 in Santer et al. (2018). These are shown in Figures 6 and 7.

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TLS I Annual-mean trend 11995-2016

0.5[

0

-0.5

80 60 40 20 0 -20 -40 -60 -80

TMT I Annual-mean trend I1995-2016

0.8-0.6 -F 0.4-0.2 -0 ---.--- --0.2 80 60 40 20 0 -20 -40 -60 -80 1TLT I Annual-mean trend 1 1995-2016 0.8-0.6- % 0.4 0.2 0 -- --- - --- '--- -- --- ... ... .... -0.2 80 60 40 20 0 -20 -40 -60 -80 (a) ---- RSS v3.3 - RSS v4.0 TLS I Annual-mean trend 11995-2016 Interactive Semi-offilne Preecribed 0.5-0 -0.5 80 60 40 20 0 -20 -40 -60 -80 0.6 0.4 0.2 0 -0.2 0.8 0.6 0.4 0.2 0 -0.2 TMT I Annual-mean trend | 1995-2016 Interactive Semi-offline Prescribed 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-mean trend 1 1995-2016 Interactive Semi-offine Prescribed -8--6--40 -2-0 - - 40--- --6 -80 60 40 20 0 -20 -40 -60 -80 (b) ---- STAR v3.0 ---- UAH v5.6 - STAR v4.0 - UAH v6.0

Figure 6: (a) 1995-2016 TAM trends from the (a) HiODS HiGHG ensemble and (b)

CMIP5 multi-model ensemble. Satellite temperature legend taken from Santer et al. (2018).

The CMIP5 models are labeled according to Eyring et al. (2013).

The WACCM HiODSHiGHG ensemble performs better than the CMIP5

multi-model ensemble in key areas outside of polar regions. The rate of TAM warming is much closer to the satellite temperature record in the TLT/TMT region, though

still slightly biased high. A key finding is that in TAC, the ensemble captures the

structure of the subtropical lobes that CMIP5 models do not, particularly in the

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TLS I Annual-cycle trend 11995-2016 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 80 60 40 20 0 -20 -40 -60 -80 TLS I Annual-cycle trend 1 1995-2016 Interactive Semi-offine Prescribed 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-cycle trend 11995-2016 0 ... ... 80.5-80 60 40 20 0 -20 -40 -60 -80 0.5 r TMT I Annual-cycle trend 11995-2016 Interactive Semi-offine Prescribed ~/1 -0.5' 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-cycle trend 11995-2016 0.6- 0.4- 0.2-80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-cycle trend 11995-2016 Interactive SenlMine Prescribed -0.2 -0.4 -0.6 80 60 40 20 0 -20 -40 -60 -80 (a) (b) ---- RSSv3.3 ---- STAR v3.0 ---- UAH v5.6 - RSS v4.0 - STAR v4.0 - UAH v6.0

Figure 7: 1995-2016 TAC trends from the (a) HiODSHiGHG ensemble and (b) CMIP5 multi-model ensemble. Satellite temperature legend taken from Santer et al. (2018). The CMIP5 models are labeled according to Eyring et al. (2013).

Northern Hemisphere. A number of the ensemble members do correctly diagnose the polar trends correctly, though the high variability precludes a definite statement regarding model fidelity relative to the satellite temperature record..

As we seek to understand the role of interactive chemistry representing the struc-ture of warming, we label the CMIP5 models in Figures 6 and 7 by their chemical

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 I- -- - -- --- -n ...

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schemes as defined in Eyring et al. (2013). The models have interactive, semi-offline, or prescribed chemistry. We find no clear benefit to having any particular chemical scheme, and even for models of the same type the inclusion of interactive chemistry does not systematically overcome differences in other parameters of the simulation construction. Thus we posit that in the lower atmosphere for the CMIP5 ensemble, differences in model construction and forcings are far more important than differ-ences in chemical schemes. The difference between interactive and non-interactive chemistry in the CESM-WACCM framework with respect to representing the satel-lite temperature record is not known or investigated here.

3.2.2 Signal-to-Noise

The zonal-mean signal-to-noise (Figure 8) summarizes Figures 4 and 5 nicely and allow us to understand the latitudinal structure they display. TAM trends have highest signal-to-noise ratio in the tropics where the noise is small (the signal is not large compared to high-latitude regions), non-coincident with the subtropical signal peaks. However, for TAC, we do observe signal-to-noise maxima in the subtropical-lobes, allowing for confidence in the model's fidelity in this key area. Note the signal-to-noise ratio maximizes at values less than 1, so this is still a highly variable system.

3.2.3 Decomposing Forcing Contributions

We decompose our model trends into an added GHG driven part and the ODS+residual (likely commitment warming) part, as described in the Analysis section, in Figure 9. The analogous plots over 1997-2018 and 1995-2024 are in the Appendix. We see the part of the HiODS_ HiGHG forcing that captures the variability in both TAM and TAC is the added GHG forcing. The ODS+residual forcing, while of significant magnitude, lacks the zonal structure of the satellite temperature record. This is an interesting result, as it implies that either ODS or more likely ocean-driven commitment warm-ing cannot be understood in the same framework as the direct GHG forcwarm-ing, as it primarily effects the global base-state temperature without significant regional/zonal variance. The GHG forcing however, which is decoupled from the ocean state by a time-lag, displays significant zonal structure, perhaps tied to the atmosphere-ocean coupling and the distribution of landmasses, that is both useful for fingerprinting the human forcing of climate change and predicting future impacts on human activity.

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TLS I Annual-mean trend noise |1995-2016

- .IOxSigIL - IO>xNUio -Signal-t Noise I \

S-0.5

I -C

80 60 40 20 0 -20 -40 -60 -80

TLS I Annual-cycletrend noise 11995-2016

- -Signad - -Noi -Sigual-to-Nviwe

-IN---1.5 -1

80 60 40 20 0 -20 -40 -60 -80

TTS I Annual-mean trend noise 11995-2016 - 10xSigial - -10xNoie -SignaI-to-Niw

-- --

-15 TTS|IAnnual-cycle trend noise|11995-2016

-.SignaI - -N6ie -Siga-t-Noih

0.5-0 .... ... ...----

---05

80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80

6 TMT I Annual-mean trend noise 11995-2016

- -I0xSiKgal - -10xNMie -Signal-t,-Noiw

0

TMT I Annual-cycle trend noise 11995-2016

6 --, --S d

tNoi-- - -

--0.5F

80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80

TLT I Annual-mean trend noise I 1995-2016 - .WxSignal - -IxNoiv -Signal-to-Noia 1.5

1

-0

-0.5--0.5

TLT j Annual-cycle trend noise 11995-2016 -... - .N. ia. --Sig...-to-Nwiw .

80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80

(a) (b)

Figure 8: 1995-2016 HiODSHiGHG ensemble signal-to-noise plots, (a) TAM, (b) TAC.

5 4 3 2 1 0 -1 -2 6 5 4 3 2 0 - -- 2- ---1 --2 5 4 3 2 -1 -2 6 5 4 3 2 0 -1 -2 ni ... 1%- ... 6 ... - - ... 6 [

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TLS I Annual-mean trend 11995-2016 I-Combined -ODS+RsiduaI -GHG 1 0.8 0.6 0.4 0.2 -0.2 -0.4 TLS I Annual-cycle trend 11995-2016 I-Combined -ODS+Residual -GHG -0.6 80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-mean trend 11995-2016 I-Combined -ODS+Residual -GHG 0.6F 0.4 0.2 0--0.2 F 80 60 40 20 0 -20 -40 -60 -80 0 .51 _---TMT I Annual-cycle trend 11995-2016 bmed -ODS+Residual -GHG 1 -0.5 80 60 40 20 0 -20 -40 -60 -80 0.6 V 0. 0. -0. . .. ... ... .... ... .... . . .0. .4 2 TLT I Annual-cycle trend 11995-2016 I-Combined -ODS+Residuai -GHG

A

-0.6 [-80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80 RSS v3.3 - RSS v4.0 STAR v3.0 - STAR v4.0 UAH v5 6 - UAH v6.0

Figure 9: 1995-2016 zonal-mean TAM,C trends from our HiODSHiGHG ensemble. The thick line is the ensemble mean, and the shading represents the 2 standard deviation line around the ensemble mean. Satellite temperature legend taken from Santer et al. (2018)

1 0.5 0 -0.5 -1 TLT I Annual-mean trend 11995-2016 -Combined -OD+Renidua -GHG 0.8 -0.6 0.4-0.2 0

-0.2-...

.

...

I., ~ '.. ... ~ ~ ... U , k ... ... .. ... 1

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3.2.4 Trends from 1975 baseline vs CMIP5 and satellite record

To obtain longer term, higher fidelity trends and compare with the results shown in Figure la from Santer et al. (2018), we calculate trends using a baseline method. The trend is defined here as the difference in TAM,C between an initial period, taken from the LoODSLoGHG ensemble , and a final period, taken from the HiODS_1960GHG or HiODSHiGHG ensemble, divided by the duration between the mean times of the periods. In this section and Figure 10 we use a 1970-1979 LoODSLoGHG baseline with a mean year of 1975, and a 2005-2014 final period with a mean year of 2010. This is the closest we can get to the period in Santer et al. (2018). Comparing our results with Santer's figure, we observe general agreement in TAM trends with a high bias that is slightly smaller than that of the CMIP5 ensemble. The structure of the key subtropical regions of the TAM,C trend is not significantly improved, though we still do manage to capture these features in the annual cycle.

3.2.5 Decomposing forcing contributions using 1960 baseline

We use a baseline of 1955-1964 (mean year 1960) from the LoODS _ LoGHG ensemble to avoid the impact of GHG forcing in the mid-century and maintain the same baseline

GHG concentration as the HiODS_1960GHG ensemble. This allows us to calculate

both the GHG and ODS+residual impact over this long time period. We find the

GHG forcing dominant on this timescale except in the stratosphere where ODS forcing

on ozone is dominant. We again recover the key subtropical regions Santer identified, and confirm their forcing by added GHGs.

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TLS I Annual-mean trend 11975-2010 0.2 0.4 0 ---~~~ ~ ~ ~ ~ 0.2 -0.2 0--0.4 0 -0.2. -0.6 -0.8- -0.4 1 -0.6 -0.81 80 60 40 20 0 -20 -40 -60 -80 TLS I Annual-cycle trend 11975-2010 80--60. 0 -40--0- --0 ---80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-mean trend 11975-2010 0.5 0.4 0.3[ 0.2 0.1 0 -0.1 -0.2 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-cycle trend 11975-2010 0.2 0.1 0 --- . - ---0.1 -0.2 0.3 -80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-mean trend 11975-2010 0.4 0.3 0.2 0.1 0.1 0 ----...---0.1 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-cycle trend 11975-2010 JN\ -0.2 -0.3 -0.442 80 60 40 20 0 -20 -40 -60 -80 (a) (b)

Figure 10: 1975-2010 (a) TAM and (b) TAc trends.

0.7 0.6 0.5 0.4 0.3 fl) -- -o --- ft-7 0.2 -0.1

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TLS I Annual-mean trend 11960-2010 I-Combined -ODS+Rftidual -GHG I _0.3[ 0.2 0.1 -0. -0.2 80 60 40 20 0 -20 -40 -60 -80 TLS I Annual-cycle trend 11960-2010 I-Combined -ODS+Residual -GHG 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-mean trend 1 1960-2010 {-Cobined -ODS+Rsidual -GHG 0.15F 0.1 0.05 -0.05 -1---0.1 -0.15 F 80 60 40 20 0 -20 4) -60 -80 80 60 40 2( -20 -40 -60 -8( TLT I Annual-mean trend 11960-2010 I-Combined -ODS-Rsidual -GHG 0.2 0.1 -0.1 -0.2 80 60 40 20 0 -20 -40 -60 -80 (a) TLT I Annual-cycle trend 11960-2010 -Cobined -OflS+Rsm(u#J -CHG 80 60 40 20 0 -20 -40 -60 -80 (b)

Figure 11: 1960-2010 (a) TAM and (b) TAC trends calculated via the difference between the HiODSHiGHG ensemble over 2005-2014 and the LoODSLoGHG ensemble over

1955-1964 0.5 0 -0.5 0.6 0.4 0.2 0 -0.2 TMT I Annual-cycle trend 11960-2010

E-c in- .-- DS+ mi," -IIC

0.8 0.6 0.4 0.2 0 -0.2 ()6 --- 493.0-mo _ ... 4k ... ---I

...

--- ---- .... ...

zl

---

_I

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3.3 Seasonal Cycle

To understand the seasonal variance of TAM and the morphology of trends in TAC, we

examine the seasonal cycle of TAM as done in Santer et al. (2018). Over 1995-2016, as shown in Figure 12, the seasonal cycle of maximum TAM is found to coincide with the axis of maximum zonal wind. The reason for this is unclear, but perhaps hints at something related to the storm track. We examined trends in zonal-wind over this time frame, as well as over 1997-2018 and 1992-2024 and found them too incoherent to definitely suggest any relevant physical mechanism. Despite the co-location with the subtropical jet, it is unclear if the trend over this timeframe is related to tropical expansion (see Staten et al. (2018); Lucas et al. (2014); Adam et al. (2018)) given the incoherence of trends in the zonal-wind and the fact that over this short period the jet is essentially stationary relative to the coarse resolution of WACCM. According to Staten et al. (2018), expected widening in this period would have been only ~

10. Due to this problem, we propose examining trends using the baseline method to

get a stronger signal over a longer period, and find that this approach overcomes the problem and yields a coherent signal, as discussed below in section 3.3.1.

3.3.1 Seasonal-cycle trend from 1975 baseline

We show in Figure 13 the seasonal-cycle of TAM calculated via the baseline method, using a 1970-1975 baseline (mean year 1975) and a later period of 2005-2014 (mean year 2010). Using this longer period, we are able to diagnose trends in zonal-wind, and observe that the axis of maximum temperature change occurs at the center to northern edge of the subtropical jet, and underneath a meridional dipole structure in the trend of the zonal wind that implies a northward trend in jet position in

the Northern Hemisphere. The dipole structure arises from the maximum zonal

velocity in the jet drifting northwards, causing positive anomalies on its polar side and negative anomalies on its equatorward side. This is perhaps indicative of the tropical widening discussed in the previous section, and further research in this area is necessary to confirm the relevance and accuracy of this dynamical mechanism. This jet shift effect is much stronger/clearer in the Northern Hemisphere than in the Southern Hemisphere, for reasons that are not explored here. The equivalent Figure for a baseline centered at 1960 is included in the Appendix.

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TLS I Annual-mean trend seasonal-cycle 11995-2016 TTS I Annual-mean trend seasonal-cycle I1995-2016 80 80W 60 60 40 40 20 20 0 0 -20 -20 -40 -40 -80 80 J F M A M J J A S O N D J F M A M J J A S O N D

TMT I Annual-mean trend seasonal-cycle 11995-2016 TLT I Annual-mean trend seasonal-cycle 11995-2016

80 80 60 60 0 -0 20 20 -J F M A M J J A S 0 N D J F M A M J J A S 0 N D -0.45 -0.3 -0.15 0 0.15 0.3 0.45

Figure 12: The seasonal cycle of TAM trend over 1995-2016. Where shown, contours are

the zonal-wind (m/s) weighted with the synthetic satellite temperature weights, plotted

every 4 m/s. Positive zonal-winds are solid contours, negative are dotted, and the smallest contours are +2 m/s. The large dots indicate where the ensemble mean trend is calculated to be significantly different from 0.

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TLS I Annual-mean trend seasonal-cycle I1975-2010 80 60 40 260 -0 J~~~ ~ F __MJJ OND 40 60 -20 -40 -60!*EIft. -80 J F M A M J J A S O N D

TLT I Annual-mean trend seasonal-cycle 1975-2010

-40 80 J F M A M J J A S O N D (a) 80 J F M A M J J A S O N D

TMT Annual-mean trend seasonal-cycle 11975-2010

80

60

40

TLT | Annual-mean trend seasonal-cycle | 1975-2010

20< 0 30 20 0 -40 -60 -80 J F M A M J J A S O N D (b)

-0.45 -0.3 -0.15

0 0.15 0.3 0.45

Figure 13: The seasonal cycle TAM baseline trend over 1975-2010. Where shown, contours are the trend in the zonal-wind weighted with the synthetic satellite temperature weights plotted equally spaced over the range of the data to give approximately 30 contours. Positive zonal-winds are solid contours, negative are dotted.

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4. Conclusion

We have in this work examined the origin and nature of the tropospheric global warming fingerprint of human influence presented in Santer et al. (2018). We find it is primarily driven by added GHG forcing response, and not by ODS, black carbon, tropospheric ozone, or ocean-lagged temperature forcing responses. We have also demonstrated that the combined ODS and residual forcing lacks the satellite observed zonal structure response of the GHG forcing, with implications for predicting the structure of future climate change or evolution of climate mitigation scenarios. We have also determined that the GHG driven expansion of the tropics and shift in the subtropical jet apparently play a role in generating the annual-cycle fingerprint observed by Santer. In the CMIP5 multi-model ensemble, we find no significant relation between chemical scheme and the fidelity of synthetic satellite temperature trends, suggesting other aspects of model composition are much more significant.

4.1 Future Work

Future work could include using a model construction that does not contain any com-mitment warming forcing, and runs with single forcings designed to better separate our residual contributions. This could also be accomplished via the use of a run with fixed ODSs, GHG only forcing, and an assumption of linearity in the ODS response. In addition, it would be prudent to examine if models that better represent tropical expansion also do a better job of representing the trend in the annual-cycle of tro-pospheric temperature in the subtropics. Finally, one could investigate the observed wind changes in greater detail to determine their consequences for climate feedbacks (e.g., water vapor, soil moisture, cloudiness) and determine which primarily drives the observed response. The spatial component of the trends are particularly relevant, and key in on areas such as the Tibetan Plateau and North Pacific Jet region.

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Acknowledgments

The author would like to acknowledge Professor Susan Solomon for her invalu-able expertise and mentorship in conducting this research, and Kane Stone for his unending willingness to provide support regarding both the model/simulations used and the methods used in analyzing them. Additional thanks are owed to Jane Ab-bott for her constructive advice in the drafting of this manuscript. The author would also like to acknowledge Benjamin Santer (Lawrence Livermore National Labora-tory) for providing processed data and feedback. Additionally, Santer, Carl Mears (Remote Sensing Systems), and Stephen Po-Chedley (University of Washington) pro-vided data and methods for calculating satellite temperatures for comparison with satellite temperature records. Qiang Fu (University of Washington) provided instruc-tion in calculating tropical expansion. Final acknowledgements are due to Remote Sensing Systems (RSS), the Center for Satellite Applications and Research (STAR), and the University of Alabama at Huntsville (UAH) for satellite temperature datasets used in Santer's and this analysis.

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A. Supplemental Material

A.1 Global-Mean, annual-mean trends and the

as-sumption of linear forcing

In this section we verify that GHG, ODS, commitment warming, tropospheric ozone, black carbon, and other forced trends combine linearly across disparate base-state model realizations. We show in Figure 14 that this assumption is mostly valid, with a slight overestimation of cooling in the HiODSHiGHG ensemble trend, presumably from the nonlinear effect of the decrease in ozone loss in a GHG-cooled stratosphere (Aquila et al., 2016). Note that although the trends add linearly, one should be care-ful when extrapolating in the gap between 1960 and 1995 in the low GHG limit and

1979 and 1995 in the high GHG limit, particularly aloft (TMT/TLS) where the ozone

hole and volcanic forcings are nonlinear. Examination of the timeseries of TLT and

TLS in Remote Sensing Systems (2019a); Thompson et al. (2012) show that, as an

example, while TLT rapidly recovered from its Pinatubo induced forcing in 1991, TLS dropped discontinuously following the eruptions of El Chich6n (1982) and Pinatubo

(1991) leading to a larger negative overall trend through the interim periods than can

be extrapolated from the model realized trends.

The validity of our linear combination is slightly degraded in the TAC case (Fig-ure 15), where the relationship actually becomes more accurate as one moves aloft

(TLS/TTS) and ozone dominates the variability. At the surface, the ODS-driven

and residual, likely primarily commitment warming, trends do not map well onto the internal variability in the HiODSHiGHG ensemble, but it is unclear where the nonlinearity in the combination arises. Our linear combination consistently under-estimates the trend. However, the differences are not so large as to preclude linear combination analysis, which we proceed with in later sections.

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212.0 r TLS I Global-Mean Temperature Annual-Mean

211.5-

211.0-210.5 ' ' ' ' ' ' ' '

1950 1960 1970 1980 1990 2000 2010 2020

253.5 r TMT I Global-Mean Temperature Annual-Mean

253.0 252.5-252.0 251.5 -251.0 1950 1960 1970 1980 1990 2000 2010 2020 225.5 r TTS I

Global-Mean Temperature Annual-Mean

225.0. 2224.5 - 224.0-223.5 1950 1960 1970 1980 1990 2000 2010 2020 269.5 269.0 Y268.5 -268.0 F

TLT I Global-Mean Temperature Annual-Mean

267.5

1950 1960 1970 1980 1990 2000 2010 2020

-Low ODS, Low GHG -High ODS, High GHG

-High ODS, 1960 GHG-Combined, centered

Figure 14: TAM for our 3 ensembles. The colored, dashed lines are linear fits to the ensem-ble mean, the ensemensem-ble envelope is shaded. The black line is the sum of the LoODS _ LoGHG

and HiODS_1960GHG ensemble-mean trends, and is nearly the same as the trend over the

HiODSHiGHG simulation, except in TLS with nonlinear effects.

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TLS I Global-Mean Temoerature Annual-Cycle 9.00- 8.75- 8.50-8.25 8.00-7.25- - 7.00 - 6.75-6.50 6.25 6.00,-- ----1950 1960 1970 1980 1990 2000 2010 2020 8.00 7.75 F 7.50

6.00 r TTS I Global-Mean Temperature Annual-Cycle

5.75 5.50 7'

TMT I Global-Mean Temperature Annual-Cycle

1950 1960 1970 1980 5.25- - 5.00-4.75 4.50 1950 1960 1970 1980 1990 2000 2010 2020

TLT I Global-Mean Temperature Annual-Cycle

9.50 99.25 1990 2000 2010 2020

r~vs~,

4.

9_.00 1950 1960 1970 1980 1990 2000 2010 2020

-Low ODS, Low GHG -High ODS, High GHG

-High ODS, 1960 GHG --- Combined, centered

Figure 15: TAC for our 3 ensembles, with coloring and shading as in Figure 14.

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-A.2

Global Annual-Mean Trends

TLS TTS V2> TMT TLT

(a) Annual Mean

-0.45 -0.3 -0.15 0 0.15 0.3 0.45 TLS TMT !4fq TLT (b) Annual Cycle -0.16 -0.08 0 0.08 0.16

Figure 16: 1997-2018 satellite temperature trends from our HiODSHiGHG ensemble-mean.

r

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TLS

TTS

TMT

TLT

(a) Annual Mean

-0.45 -0.3 -0.15 0 0.15 0.3 0.45 Figure 17: mean. TTS

4i~

TMT TLT (b) Annual Cycle -0.16 -0.08 0 0.08 0.16

1995-2024 satellite temperature trends from our HiODS HiGHG

ensemble-WWI.

aw-mirp

AL

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TLS

TTS

TMT

N

TLT TLT

(a) Annual Mean

-0.45 -0.3 -0.15 0 0.15 0.3 0.45

(b) Annual Cycle

-0.16 -0.08 0 0.08 0.16

Figure 18: 1995-2016 TAM,C from our HiODS_1960GHG ensemble-mean.

TLS

TTS

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TLS

TTS TTS

TMT

TLT

(a) Annual Mean

-0.45 -0.3 -0.15 0 0.15 0.3 0.45

TMT

TET

(b) Annual Cycle

-0.16 -0.08 0 0.08 0.16

Figure 19: 1995-2016 TAM,C from our LoODS LoGHG ensemble-mean.

T T R

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002 003 --

7I~

004 005 006 008 00 mean -0.45 -0.3 -0.15 0 0.15 0.3 0.45

Figure 20: 1995-2016 Annual-mean HiODSHiGHG ensemble TMT trends

001

0070

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001 002 003

004 005 006

007 008 009

010 miean

-0.16 -0.08 0 0.08 0.16

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A.3 Zonal-Mean Annual-Mean Trends

TLS I Annual-mean trend 11997-2018 I-Combined -ODS+Rebidual -CHG 0.5 1 -0.8 -0.6. 0.4 0.2 TLS I Annual-cycle trend 1 1997-2018 I-Combined -ODS+Rtsidual -GHC

-4

-0.2 -0.4--0.6. -1' 80 60 40 20 0 -20 -40 -60 -80 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-mean trend 1 1997-2018 H-Combined -ODS+Rebidual -GHG 0.5 0 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-mean trend 11997-2018 F I-Cobined -ODS+ResiduaI -GHG -0 0 0 0 -0 . .--- -. .---- - - - - 0 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-cycle trend 11997-2018 .5' 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-cycle trend 11997-2018 Combined -OD+ResidWa -GHG .6 .4 .2 . ... .2 .4 -0.6 . 80 60 40 20 0 -20 -40 -60 -80 -- RSS v3.3 ---- STAR v3.0 ---- UAH v5.6 - RSS v4.0 - STAR v4.0 - UAH v6.0

Figure 22: 1997-2018 zonal-mean TAM,C trends from our HiODSHiGHG ensemble. The thick line is the ensemble mean, and the shading represents the 2 standard deviation line around the ensemble mean. Satellite temperature legend taken from Santer et al. (2018)

0.6 -0.4 0.2 0 -0.2 1 0.8F 0.6 0.4 0.2 0 -0.2 r) --- ... --- .. .. ... . ----1 -0.5|-\NC 7 _-O .- -- ' __ I

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TLS I Annual-mean trend 11995-2024 -Combined -ODS+Residuai -GHG 1 0.5 -0.5F 1 0.8 0.6 0.4 0.2 -0.2 -0.4 -1 ' ' ' ' 80 60 40 20 0 -20 -40 -60 -80 TLS I Annual-cycle trend 11995-2024 I-Combined -ODS+Residual -GHG I

7

-0.6 80 60 40 20 0 -20 -40 -60 -80 TMT I Annual-mean trend 11995-2024 I-Comined -ODS+Ridual -GHG; | 0.5r 0.6 F 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-mean trend 11995-2024 -Combined -ODS+Residuai -GHG -0.2 80 60 40 20 0 -20 -40 -60 -80 0 0 0 TMT I Annual-cycle trend 11995-2024 I-Combined -ODS+Residu al -GHG .5 80 60 40 20 0 -20 -40 -60 -80 TLT I Annual-cycle trend 11995-2024 |-Combined -ODS+RebiduWl -GHG .6-.4 .2L -0.2 -0.4 -0.6 80 60 40 20 0 -20 -40 -60 -80 RSS v3.3 - RSS v4.0 STAR v3.0 - STAR v4.0 UAH v5.6 - UAH v6.0

Figure 23: 1995-2024 zonal-mean TAM,C trends from our HiODS _HiGHG ensemble. The thick line is the ensemble mean, and the shading represents the 2 standard deviation line

around the ensemble mean. Satellite temperature legend taken from Santer et al. (2018)

0.4 0.2 0 -0.2 1 0.8 F 0.6 0.4 0.2 0

...

-0

--- ---

---

----

^

--

---

--

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---A.4

Seasonal Cycle of

TAM

trend

TLS I Annual-mean trend seasonal-cycle 11997-2018 TTS I Annual-mean trend seasonal-cycle 11997-2018

80 80 60 60 20 20 0 0 -20 -20 -401 4 -80 8 J F M A M J J A S 0 N D J F M A M J J A S 0 N D

TMT I Annual-mean trend seasonal-cycle 11997-2018 TLT | Annual-mean trend seasonal-cycle 1997-2018

80 80 60 60 0 20 20 -20 -40 -40 7 -60 -60 -80 -80 -Jay.-J F M A M J J A S O N D J F M A M J J A S O N D -0.45 -0.3 -0.15 0 0.15 0.3 0.45

Figure 24: The seasonal cycle of TAN trend over 1997-2018. Where shown, contours are

the zonal wind weighted with the synthetic satellite temperature weights, plotted every 4 m/s. Positive zonal-winds are solid contours, negative are dotted, and the smallest contours are 2 m/s. The large dots indicate where the ensemble mean trend is calculated to be

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TLS I Annual-mean trend seasonal-cycle | 1995-2024 TTS I Annual-mean trend seasonal-cycle I1995-2024 80 80 60, 60 401 40 20 20 0 0 -20 -20i j -40 -40 -60 -60 -80 -80 J F M A M J J A S O N D J F M A M J J A S O N D

TMT I Annual-mean trend seasonal-cycle 1995-2024 TLT I Annual-mean trend seasonal-cycle 11995-2024

80 -0 60 60 202 0 0--20 -20: -40. -4 J F M A M J J A S 0 N D J F M A M J J A S 0 N D -0.45 -0.3 -0.15 0 0.15 0.3 0.45

Figure 25: The seasonal cycle of TAM trend over 1995-2024. Where shown, contours are

the zonal wind weighted with the synthetic satellite temperature weights, plotted every 4

m/s. Positive zonal-winds are solid contours, negative are dotted, and the smallest contours are 2 m/s. The large dots indicate where the ensemble mean trend is calculated to be significantly different from 0.

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TLS IAnnual-mean trend seasonal-cycle 11960-2010 40> 20 0 -20 ---40 -60 -80 J F M A M J J A S O N D 80 60. 40 20 -20 -40 -6-0 -80 J F M A M J J A S 0 N D

TLT Annual-mean trend seasonal-cycle |1960-2010

80 -2C -4C -6C .8C J i M A M J J A 0 U N U (a)

TLS | Annual-mean trend seasonal-cycle 1960-2010

80 60 -40 60 -60 -80 J F M A M J J A S 0 N D

TMT Annual-mean trend seasonal-cycle 1960-2010

80 40 -20 < ~~ 207 -20 -40~ -60 -800 J F M A M J J A S O N D

TLT Annual-mean trend seasonal-cycle 1960-2010

80 200 -401 -600 -80-J F M A M J J A S 0 N D (b) -0.45 -0.3 -0.15 0 0.15 0.3 0.45

Figure 26: The seasonal cycle TAM baseline trend over 1960-2010. Where shown, contours are the trend in the zonal-wind weighted with the synthetic satellite temperature weights plotted equally paced over the range of the data to give approximately 30 contours. Positive zonal-winds are solid contours, negative are dotted.

Analyzing recent latitudinal and seasonal changes in simulated atmospheric temperatures from a global chemistry-climate model