Confidential manuscript submitted to Geophysical Research Letters

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Spurious Late Historical-Era Warming in CESM2 Driven by Prescribed Biomass

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Burning Emissions

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J. T. Fasullo*1,2, Jean-Francois Lamarque1, Cecile Hannay1, Nan Rosenbloom1, Simone

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Tilmes1, Patricia DeRepentigny2, Alexandra Jahn2, and Clara Deser1

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National Center for Atmospheric Research, Boulder, CO, USA.

Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research,

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University of Colorado, Boulder, CO, USA.

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*

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Corresponding author: fasullo@ucar.edu

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Key Points:
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1997-2014 relative to other periods
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CMIP6 prescribed biomass burning emissions contain elevated interannual variance fromIn CESM2, the variability in emissions drives substantial warming in the Northern
Hemisphere extratropics•

Changes in radiation and clouds arising from nonlinear interactions with aerosols
underpin the simulated warmingConfidential manuscript submitted to Geophysical Research Letters

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Abstract

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A spurious increase in the interannual variability of prescribed biomass burning (BB)21

emissions in the CMIP6 forcing database during the satellite era of wildfire monitoring (1997-222014) is found to lead to warming in the northern hemisphere extratropics in simulations with the23

Community Earth System Model version 2 (CESM2). Using targeted sensitivity experiments

24with the CESM2 in which prescribed BB emissions are homogenized and variability is removed,

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we show that the warming is specifically attributable to BB variability from 40oN-70oN and26

arises from a net thinning of the cloud field and an associated increase in absorbed solar

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radiation. Our results also demonstrate the potential pitfalls of introducing discontinuities in

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climate forcing datasets when trying to incorporate novel observations.

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Plain Language Summary

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A discontinuity in the variability of prescribed biomass burning emissions between the31satellite era of wildfire monitoring (1997-2014) and both the preceding historical and future time

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periods is found to drive spurious warming in the Community Earth System Model version 2.

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The warming arises from a net thinning of the cloud field and an associated increase in absorbed

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solar radiation during periods of high variability in emissions. Evidence suggestive of similar

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effects in other climate models is also presented. The results highlight the challenges in

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evaluating models with observations, even in the modern satellite era.

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1 Introduction

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Quantifying the sensitivity of climate to external forcing is necessary in order to39

accurately project the impacts of climate change and anticipate appropriate adaptation needs.

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Many of the most recent climate model versions exhibit estimated equilibrium responses toConfidential manuscript submitted to Geophysical Research Letters41

carbon dioxide doubling that are considerably higher than previous model generations (Meehl et42

al. 2020, Zelinka et al. 2020). These most recent model versions are the result of a sustained

43

effort by climate research groups over the past decade to improve model physics, and44

specifically, their representation of clouds and feedback relevant processes (Andrews et al. 2019;45

Gettelman et al. 2019; Golaz et al. 2019; Lohmann et al. 2018; Wyser et al. 2019). This46

development process has been informed by an unprecedented data record used to guide47

improvements in the representation of clouds and cloud-aerosol interactions (Bender et al. 2019;48

Kay et al. 2016; Storelvmo et al. 2017; Tan et al. 2016) that have helped drive high sensitivities

49(Andrews et al. 2019; Gettelman et al. 2019; Lohmann et al. 2018; Tan et al. 2016; Wyser et al.

502019). An objective assessment of performance across model generations has concluded that51

many of the latest versions significantly outperform older models in reproducing observed

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feedback-relevant fields, with some of the highest sensitivity models agreeing most closely with53

observations (Fasullo, 2020). However, other recent studies aimed at evaluating simulated

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warming have cast doubt on the reliability of high sensitivity models (Flynn and Mauritsen 2020,

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McKitrick and Christy 2020, Nijsse et al. 2020, Tokarska et al. 2020).

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The use of the observational record to rule out high model sensitivities is on its own

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somewhat surprising given that the transient climate responses of recent models are only

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marginally larger than of earlier generations (Flynn and Mauritsen 2020, Meehl et al. 2020).

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Historical-era simulations rely on estimation of climate forcing, which itself introduces

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substantial uncertainty (Smith et al. 2020). Apparent inter-model contrasts, both across and61

within model generations, can thus arise from these uncertainties and the range of model

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sensitivities to them.

Confidential manuscript submitted to Geophysical Research Letters

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The focus of this work is on the climate response to regime changes in the interannual

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variability of biomass burning (BB) emissions prescribed in the most recent Coupled Model

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Intercomparison Project Version 6 (CMIP6) database (van Marle et al. 2017; BB emissions

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include sulfur dioxide, black carbon, and organic carbons). In constructing these data, observed

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interannual variability was retained during the duration of the Global Fire Emissions Database

68(GFED; van Der Werf et al. 2017), with the deleterious effect of creating such an artificial

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regime change. Here, we show that the associated increase in the variability of BB emissions

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during the GFED era leads to a net warming in the Community Earth System Model version 2

71(CESM2) via decreased mean cloud droplet number and low cloud amount. The anomalous

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warming abates in future projections as BB variability is again small. We also present evidence

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suggestive of the potential for a similar response in some CMIP6 models. In a related paper, the74

associated responses of Arctic sea ice and climate to this forcing are examined (DeRepentigny et75

al. 2021).

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2 Data and Methods

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2.1 The Community Earth System Model

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Simulations from the CESM version 2 (CESM2, Danabasoglu et al. 2020) are taken from the 11-79member historical-era submission to CMIP6 (Eyring et al. 2016) and the 3-member 21st century

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submission for Shared Socioeconomic Pathway (SSP) 3-7.0 (O’Neill et al. 2016). The CESM2

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uses the Modal Aerosol Model version 4 (Liu et al. 2016) and cloud-aerosol interactions use the82

updated Morrison and Gettelman scheme (MG2; Morrison and Gettelman 2008).

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To provide context for the CESM2 simulations that are the focus of this work, we also consider

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simulations from the 40-member Community Earth System Model version 1 (CESM1) Large

Confidential manuscript submitted to Geophysical Research Letters

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Ensemble (Kay et al. 2015). These simulations provide a robust estimate of the climate response86

to forcing in the presence of internal variability without large interannual variability in BB

87emissions. The CESM1 uses the Modal Aerosol Model version 3 (MAM3, Hurrell et al. 2013)88and cloud-aerosol interactions are represented through the MG1 cloud microphysics scheme

89(Morrison and Gettelman 2015). Emissions of aerosols and aerosol precursors in CESM1 follow

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Lamarque et al. (2010). Differences between the treatment of cloud-aerosol interactions in

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CESM1 and CESM2 are complex and relate to both changes in the treatment of aerosols, clouds,

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and their microphysical interactions (Danabasoglu et al. 2020, Gettelman et al. 2019, Tilmes et93

al. 2019,). Important differences also exist in the prescribed forcings used in each model (e.g.

94Smith and Forster, 2021).

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2.2 CMIP6 Forcings and Homogenized Sensitivity Forcing.

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Experiments are conducted to quantify the climate response resulting solely from regime changes

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in CMIP6 BB emission variability associated with changing data sources in 1997 as documented

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in van Marle et al. (2017) for northern high latitudes (discussed in section 3.1). With this goal in

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mind, a revised emissions dataset is created in which interannual variability is removed only

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from the latitude band 40oN-70oN over the 1997-2014 period while the integrated amount of101

emissions is retained. This “homogenized” dataset, which largely removes regime changes in102

variability in the late 20th century, is then used to create a 20-member ensemble of simulations,103

referred to hereafter as the CESM2BB ensemble, using otherwise identical forcing and initial104

conditions as the 11-member CESM2 CMIP6 ensemble. The homogenized region is chosen to105

coincide with the regional definitions used in van Marle et al. (2017). This approach, identical in106

nature to what was used in CMIP5 (Lamarque et al. 2010), removes any sharp transition with the107

prescribed SSP BB emissions, which are based on the GFED emissions. We then conduct a 20-

Confidential manuscript submitted to Geophysical Research Letters108

member ensemble of sensitivity simulations initialized in 1990 but using the artificial set of109

emissions over the period 1997-2014, with identical initial conditions as in our original CESM2

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ensemble (see section 2.1).

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2.3 CMIP simulations

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Historical CMIP6 simulations for which at least 4 members have been provided with

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downwelling surface shortwave radiation (SWdn) through the Earth System Grid Federation

114(ESGF) have been included in our analysis. CMIP experiment output is available on the ESGF

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(https://www.earthsystemgrid.org). As upwelling shortwave radiation is not submitted by many

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centers in the CMIP3 database, and because differences between net and downwelling anomalies117

are small, SWdn is used rather than net surface shortwave radiation in evaluation of CMIP

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simulations.

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2.4 Observations

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Observed estimates of near surface (2 m) air temperature in the historical era are from the121

Berkeley Earth System (Rohde and Hausfather 2020). These data are well-suited for climate

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model evaluation as regions and times without observations are infilled to provide continuous

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fields. Radiative fluxes at top-of-atmosphere and the surface are from the Clouds and the Earths

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Radiant Energy System (CERES) database (Kato et al. 2018, Loeb et al. 2018). Tropospheric125

temperatures are from the European Center for Medium Range Weather Forecasts Reanalysis

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Version 5 (Hersbach et al. 2020). These estimates are derived from model assimilation of a broad

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database of in-situ and satellite observations during the GFED era.

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3 Results

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3.1 Simulated Late Historical-Era Climate

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The transient climate responses over recent decades in the CESM1 40-member Large Ensemble

131(Kay et al. 2015) and an 11-member ensemble of CESM2 (Danabasoglu et al. 2020) are

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examined and compared to observations in order to understand the temporal and regional

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structure of their contrasts (Figure 1). Notably, this type of comparison is analogous in many134

ways to ongoing comparisons of the CMIP5 and CMIP6 archives.

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Confidential manuscript submitted to Geophysical Research Letters

Figure 1. Historical-era evolution of large-scale climate anomalies and CMIP6 forcing includingnear surface air temperature averaged from 90oS-30oN (a) and 30oN-90oN (b), and mean
40oN-70oN surface net solar radiation (c), top-of-atmosphere net solar radiation (d), and 700
hPa air temperature (e). Prescribed BB emissions are also shown (f). The 1997 to 2014 era isshaded on all panels. Data include fields from the CESM1 (blue) and CESM2 (red) andobservations (black/magenta, see section 2.4). Anomalies are computed relative to the 192049 baseline except in (f) where they are raw values. Shaded regions around a line denote thestandard deviation ranges of annual mean anomalies, which for the CESM2 is also
approximately equal to the 3-standard error range. Time series have been smoothed with a 5year running mean, with the exception of BB emissions which are smoothed with 12-month(dark red) and 120-month (light red) running means.
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The broad evolution of temperature in the two models before 1997 is similar, with both137

ensembles depicting persistent net warming, interrupted by cooling from 1950 to 1970 in the138

northern extratropics (Figures 1a and 1b). While observations generally fall within the ensemble

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spreads of both models (Figures 1a and 1b), differences in warming exist in some eras and140

regions, as both model versions depict somewhat less warming than observed from 1975 to 1997.

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Particularly notable is that from 1997 to 2010, the warming in the latitude band 30o-90oN in the142

CESM2 is considerably greater than either that observed or that simulated by the CESM1, as the143

ensemble bounds (shaded) diverge from each other starkly by 2010. Ensemble mean warming in144

the CESM2 is also negligible from about 2010 to 2020 before increasing again after 2020 in145

close agreement with warming in the CESM1 (not shown). Associated with the enhanced

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northern extratropical warming rate in CESM2 is an abrupt increase in the surface net solar flux

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(SWsfc) in a narrower latitude band, from 40oN-70oN (Figure 1c), an increase that runs counter toConfidential manuscript submitted to Geophysical Research Letters148

the model’s historical-era decrease prior to 1997, which is considerably larger than reductions in

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CESM1. Note the band shown for radiation anomalies (Figure 1b-d) is narrower than that for

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surface temperature (Figure 1b) given the broad scale of the response to a relatively confined151

band of radiation anomalies. The stronger SWsfc decrease in CESM2 versus CESM1 from 1950152

to 1990 is consistent with differences generally between CMIP6 and CMIP5, which have been

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shown to be attributable to the combined influences of model sensitivity and contrasts in aerosol

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and greenhouse gas forcings (Smith and Forster 2021). An abrupt increase in TOA solar flux

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(SWtoa, Figure 1d) also occurs in 1997 and both are approximately 2 W m-2 in magnitude. The

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enhanced warming in CESM2 is evident through much of the lower troposphere, which is

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discussed in greater detail below, and illustrated in the evolution of 700 hPa temperature (Figure

1581e). The period of enhanced warming also coincides with a sudden shift in the observational

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datasets used to produce the wildfire emissions (Figure 1f), as 1997 marks the beginning of the160

GFED observations. The GFED era (1997-2014; shaded in all panels of Figure 1) is associated161with a marked increase in the variability of BB emissions from 40oN-70oN, mainly in the boreal

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Asian (BOAS) region and secondarily in the boreal North American (BONA) region (van Marle163

et al. 2017). This creates sharp discontinuities in prescribed forcing variability, both in 1997 and164

2014, before and after (not shown) which interannual variations are small. Contrasting changes

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between CESM1 and CESM2 are documented in the SI while specific mechanisms that may be

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at play in driving these changes are discussed further below.

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3.2 Results of Targeted Sensitivity Simulations

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To establish causality, and to demonstrate the importance of what might otherwise be perceived

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as small differences in clouds and radiation between CESM1 and CESM2, the CESM2 and170

CESM2BB ensembles are contrasted.

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Figure 2. Impact of variable BB emissions on the spatial structure of late historical-era changes.
Shown are CESM2 minus CESM2BB differences in 1995 to 2014 annual mean near surface
air temperature (a), vertically integrated cloud droplet number (b), low cloud amount (c), andnet surface shortwave flux (d). Regions where the difference in changes is less than theensemble standard error are stippled. Note that a scaling is applied to the low cloud andsurface SW flux fields as indicated in titles in panels (c/d).
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The effect of variability during the GFED era is estimated by differencing the CESM2 and173

CESM2BB ensemble means to resolve their geographical (Figure 2), vertical (Figure 3), and174

temporal characteristics (Figure 4). The net effect of BB variability is to warm surface air

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temperature in the Northern Hemisphere, particularly over land and north of 30oN, in excess of176

1oC in some regions (Figure 2a) while having only marginal effects in regions south of 30oN. We

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note that while the sensitivity tests used here are based on experiments that impose smoothed178

emissions from 40oN-70oN, other experiments in which emissions are smoothed globally, such as

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the CESM2 Large Ensemble (Rodgers et al. 2021), also show marginal effects in regions south

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of 30oN (not shown). Associated decreases in cloud droplet number and low cloud amount are

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also simulated (Figures 2b and 2c), particularly in the BOAS and BONA regions, where the182

largest emissions occur, with strong downstream effects over the eastern subtropical Pacific183

Ocean evident in the low cloud field (Figure 2c) and SWsfc (Figure 2d). As a result of changes in184

clouds, SWsfc is increased across much of the Northern Hemisphere north of 30oN and over185

northern extratropical land regions, contributing significantly to the strong warming evident in186

CESM2 CMIP6 simulations.

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Figure 3. Impact of adjusted BB emissions on the vertical structure of late historical-era

189changes. Shown are CESM2 minus CESM2BB differences from 1995-2014 in

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temperature (a) and relative humidity (b), and cloud amount (c). Stippled regions are

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where differences fail to exceed the ensemble standard error.

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The vertical structure of the response to BB variability is also noteworthy (Figure 3), where193

differences between CESM2 and CESM2BB are shown for temperature, relative humidity (RH),194

and cloud amount. A strong spatial coherence exists between surface (Figure 2a) and195

tropospheric warming (Figure 3a), with detectible warming extending from the surface, where196

contrasts are largest, through the depth of the troposphere and across all latitudes. South of 30oN,

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warming is stronger in the free troposphere than near the surface at many latitudes. CoherentConfidential manuscript submitted to Geophysical Research Letters

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with the tropospheric warming response is a decrease in RH spanning from 30oN-70oN, from the199

surface through 500 hPa. Interestingly a remote response is also apparent, both in the tropics

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where a southward displacement of the Intertropical Convergence Zone (ITCZ) is evident, and in

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the Southern Hemisphere where RH decreases above the boundary layer and through much of202

the mid- to upper-troposphere. Coincident with RH decreases are decreases in cloud amount203(Figure 3c), which are particularly strong below 850 hPa, that extend roughly from 20oN-70oN in

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the Northern Hemisphere and from the equator to 60oS in the Southern Hemisphere. The strong

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ITCZ changes noted in RH are also evident in cloud amount. The main features of these inter-206

model differences are statistically significant and strong physical ties are known to directly link207

the fields (e.g. RH and cloud amount), bolstering the case that they are causally linked.

Confidential manuscript submitted to Geophysical Research Letters208

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Figure 4. Similar to Figure 1 but where CESM2BB simulations (black curves) are plotted in

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place of observations, and panel (a) shows global near surface air temperature anomalies

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and panel (f) shows 40oN-70oN surface albedo anomalies. The 5-yr smoothing applied to212

the 25-yr CESM2BB simulations results in heightened noise near the endpoints,

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particularly 1990.

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The temporal evolution of radiation and temperature (Figure 4) demonstrates a significant

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reduction of differences between CESM1 and CESM2 in depicting large-scale transient changes

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during the GFED era as a result of BB homogenization. Both the global and northern

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extratropical magnitude of warming during the era (Figures 4a and 4b) are indistinguishable

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between CESM1 and CESM2BB, lying well within the ensemble spread. Regional changes in219

ensemble mean SWsfc anomalies (Figure 4c) are also very similar in the model versions, though

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CESM2 and CESM2BB are offset to be slightly lower than CESM1, likely due to differences in

221model physics and other forcing agents. At TOA, SWtoa anomalies in CESM2BB track closely

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with CESM1 (Figure 4d) and are well within the ensemble spread, while at 700 hPa, warming

223aligns closely with that of CESM1 (Figure 4e). Lastly, reductions in surface albedo during the224

GFED era (Figure 4f), which are disproportionate in CESM2, are largely consistent between

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CESM1 and CESM2BB, though the ensemble mean of CESM2BB decreases somewhat more226

than that of CESM1. Contributions to the reduction in surface albedo arise from both land and227

ocean regions (not shown) and raise the issue of changes in the cryosphere, their sensitivity to228

warming, and their role as a feedback agent that amplifies the radiative response to BB

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emissions. Aspects of feedbacks related to Arctic sea ice are addressed in a companion

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manuscript (DeRepentingy et al. 2021). Many of the disparities in GFED-era trends between

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CESM2 and CESM2BB are short-lived, as the ensemble averages align more closely in manyConfidential manuscript submitted to Geophysical Research Letters

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fields by the end of the historical era. (Figure 4). The alignment may result in part from the233

relatively small variability late in the GFED record compared to that in its early years (Figure2341f). As a consequence, the computation of trends over time periods that involve the GFED era

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are likely to have a spurious contribution from the response to variable BB forcings. While it236

remains to be explicitly quantified, the suggestion here (Figures 4a and 4b) is that these effects

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on temperature are likely to overshadow any differences in warming resulting from the238

contrasting climate sensitivities of the models (4.1oC for CESM1 and 5.3oC for CESM2), given239

the close agreement of CESM1 and CESM2BB time series.

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3.3 CMIP6 Climate Simulations

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The demonstrated sensitivity of CESM2 to prescribed BB emissions raises the associated242

question as to whether other CMIP6 climate models exhibit similar sensitivities. While it is243

beyond the scope of this work to perform additional targeted experiments with other models, it is244

possible to examine transient changes in surface downwelling solar flux (SWdn) as an indicator245

of potential BB responses. It is also of general interest to know whether there is a systematic shift

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between earlier generations of CMIP simulations and the most recent generation, CMIP6. The

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evolution of ensemble-mean SWdn anomalies (Figure S2a), relative to the 1970-1990 mean,248

indeed exhibits a significant positive average anomaly of 0.7 Wm-2 across the GFED era for249

CMIP6, while being near zero (~0.1 W m-2) for the two prior CMIP Versions (CMIP3 and250

CMIP5). The differences between these ensembles lie well outside of the standard deviations of251

annual means and further still outside of their standard errors (<0.1 Wm-2). Moreover, evidence

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for a dependency in the strength of the effect across models also exists (Figure S2b), with

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individual model ensemble mean SWdn anomalies during the GFED era being as much as +1.7

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Wm-2 in some models (on par with CESM2 at 1.6 W m-2), between 0.5 and 1.5 Wm-2 in over

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80% of models, and more than the mean CMIP3/5 anomaly of 0.1 Wm-2 in all but one model

256(CanESM5; -0.1 W m-2). There are therefore initial indications that similar effects of variability

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in BB emissions may exist in other simulations in the CMIP6 models and contribute to additional

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GFED-era warming. The prospects of strong model dependency are also raised. A key question259

is whether the contrasting changes in SWdn are driven by BB or other effects, and particularly the260

influence of anthropogenic aerosols. Further targeted experiments, similar to that performed here

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for CESM2, are thus motivated to clarify this issue. If these early indications are validated, a262

significant challenge exists for efforts to evaluate CMIP6 models, or attribute observed changes

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in the late 20th and early 21st centuries.

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4 Discussion

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A compelling motivation exists for winnowing the range of projected future climate through266

comparison of simulated trends with the observational record. Such efforts are surprisingly

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challenging however as they rely implicitly on our ability to 1) accurately diagnose external

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climate forcings over time, 2) resolve forced changes in the presence of internal variability in269

both observations and models, and 3) establish physical ties between the drivers of present-day

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and future trends. While much attention has been paid to the uncertainties in anthropogenic

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industrial aerosol emissions and their effects, it is shown here that climate forcings sometimes

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thought of as being of secondary importance, such as BB, can be important.

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Some aspects of the climate response in CESM2 remain to be fully understood, such as the274

influence of BB emission variability on aerosol burdens, size droplet distributions, and cloud275

microphysics. While BB aerosols are an absorber of solar radiation, they also interact directly276with cloud microphysical schemes. Are the microphysical interactions important, and if so, what

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role do they play? Moreover, the fact that our homogenized dataset yields a distinct climate

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response, despite retaining the same monthly mean bulk emissions as the original CMIP6

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emissions, suggests that the response must be intrinsically nonlinear. The origin of nonlinearities

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may stem in part from the effects of aerosols on clouds (Figure 30.4 of Haywood, 1996) whereby281

as the number of aerosols increases, the cloud droplet response asymptotes. This asymptotic

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behavior is suggested in our simulations: for example, despite aerosol emissions anomalies being

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only slightly negative in 1997 and significantly positive 1998 (Figure 1f), anomalies in cloudy-284

sky albedo are strongly negative in 1997 and only marginally positive in 1998 (not shown).

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There is also skewness in the CMIP6 emissions themselves as relative few years of high286

emissions are interspersed among multiple years of slightly below average emissions. A

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plausible hypothesis is therefore that asymptotic behavior in cloud droplet responses combined288with skewness in the emissions drive the anomalous net warming in CESM2-CMIP6289

simulations.

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Other potential mechanisms may also contribute to driving the nonlinear responses in CESM2.

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For example, the response of cloud amount itself may also be nonlinear due to the discrete RH

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threshold used to initiate cloud formation. Feedbacks with the cryosphere are likely to amplify

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the climate response and may themselves impart nonlinearity as snow and ice cover fraction are294

fundamentally bounded fields (i.e., 0-1). The effects of cloud and warming responses detailed

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here on Arctic sea ice have already been documented in CESM2 (DeRepentigny et al. 2020,2962021). The expectation of linearity is itself complicated by the strong dependence of both297

forcings and feedbacks on season at high latitudes, as small shifts in time and space of a forcing

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agent can significantly modulate its net radiative effect. Ultimately, additional sensitivity

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experiments will be needed for a full understanding of the mechanisms involved.

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The results of this work have broad relevance for interpreting previous efforts and guiding future301

follow-on work. Some recent studies, for example, may have conflated the climate response to302

BB with the climate feedbacks that drive climate sensitivity. This is evident for example in cases

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where spatial patterns of warming are used to attribute causality to greenhouse gases (GHG,

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Tokarska et al. 2020), as the BB response is spatially correlated with that response (Figure 2).

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Moreover, attempts to reduce the influence of uncertainty in aerosol forcing by extending306

analysis of trends into recent decades (Nijsse et al. 2020) remain vulnerable to the spurious

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effects induced by BB. Notably, the possibility exists that model physics that may render certain

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models more susceptible to the effects of BB emissions, such as those with more advanced309

representations of cloud microphysics in CMIP6 models (Andrews et al. 2019, Gettelman et al.

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2019, Golaz et al. 2019, Lohmann et al. 2018, Wyser et al. 2019, Zelinka et al. 2020). This in

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turn may provide a statistical basis for perceived links of recent warming to higher inferred

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climate sensitivity. Given this, it remains a challenge to reduce the uncertainty in future313

projections in CMIP6 through comparison with recent observed trends.

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Lastly, the findings of this work also call into question standard techniques for estimating

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internal variability from extended preindustrial simulations, which almost always lack variable316

BB emissions. Here it is shown that such emissions are an important component of the climate

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system, with the potential to significantly alter clouds, temperature, and radiation on timescales

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from seasons to (at least) decades. When forced with realistic variability in BB emissions,319

multiple positive feedbacks are likely to be triggered (e.g. clouds, cryosphere), thus increasing320

the potential range of variability. Moreover, as land model component capabilities improve,321

many models now include the ability to explicitly simulate BB emissions. This capability

322

provides the opportunity to represent BB emissions as an internal climate process. ExplicitConfidential manuscript submitted to Geophysical Research Letters323

representation of the emissions has the implicit advantage of allowing for climate state324

dependence of emissions and their feedbacks across a range of past and future time periods.

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Doing so seems particularly important given known links between wildfire and climate. Paths

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forward for further constraining climate sensitivity are less obvious. Continued scrutiny of

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models with a diverse and expanding observational record, and particularly with a focus on

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process-relevant fields (e.g. Fasullo 2020) is likely to provide useful information regarding

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model fidelity. Consideration of the climate response across paleoclimate timescales (Zhu et al.

3302020) and in combined statistical assessments (Sherwood et al. 2020) is also likely to provide

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useful information. Ultimately, coordinated advances across this broad range of climate

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monitoring and modeling disciplines will be essential for further reducing uncertainty in333projections of the climate system’s response to external forcing.

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Acknowledgments, Samples, and Data

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This material is based upon work supported by the National Center for Atmospheric Research,336

which is a major facility sponsored by the National Science Foundation (NSF) under

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Cooperative Agreement 1852977. The CESM project is supported primarily by the NSF.

338

Computing and data storage resources, including the Cheyenne supercomputer339(doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems

340

Laboratory (CISL) at NCAR. We thank all the scientists, software engineers, and administrators341

who contributed to the development of CESM2. The efforts of Dr. Fasullo in this work were342

partially supported by NASA Award 80NSSC17K0565 and the Regional and Global Model

343

Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the344

U.S. Department of Energy’s Office of Biological and Environmental Research (BER) via

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National Science Foundation IA 1844590. The efforts of Dr. Fasullo in this work were also

346

supported in part by NSF Award AGS-1419571. We acknowledge the World Climate Research

347

Programme, which, through its Working Group on Coupled Modelling, coordinated and348

promoted CMIP6. We thank the climate modeling groups for producing and making available

349

their model output, the Earth System Grid Federation (ESGF) for archiving the data and350

providing access, and the multiple funding agencies who support CMIP6 and ESGF. The authors

351

would like to acknowledge the efforts of three anonymous reviewers in providing helpful352

comments on an earlier version of this manuscript.

353

Author contributions

354

JF initiated this study, contributed to the experiment design, analyzed results, prepared the355

figures, and first manuscript. JFL contributed to experiment design and manuscript revisions. CH

Confidential manuscript submitted to Geophysical Research Letters356

and NR produced the sensitivity simulations. PD, ST, CD, and AJ contributed to refining the357

manuscript.

358

Competing interests

359

The authors declare no competing interests.

360

Data and materials availability

361

The data supporting the conclusions of this paper can be found on the Earth System Grid

362

Federation (https://www.earthsystemgrid.org/search.html?freeText=CESM2+CMIP6+historical

363&Model=CESM2.1&Experiment=b.e21.BHIST.f09_g17.CMIP6-historical.003) where links to364

specific simulation cases lead to the atmospheric fields used in this work, (e.g. such as TREFHT

365

for reference near surface air temperature). Simulation output for sensitivity simulations is366

available on NCAR’s Digital Asset Services Hub (DASH, dash.ucar.edu) under CESM2 40-70N

367

Biomass Emissions Homogenization Sensitivity Tests (https://doi.org/10.5065/7f7c-zw94).

368

CERES EBAF Ed4.1 data used here are available at https://ceres-tool.larc.nasa.gov/ord-369

tool/jsp/EBAFTOA41Selection.jsp under the “TOA Fluxes” checkbox.

370

Confidential manuscript submitted to Geophysical Research Letters

371

References

372

Andrews, T. Andrews, M. B. Bodas‐Salcedo, A. Jones, G. S. Kuhlbrodt, T. Manners, J.... Senior,

373

C. A., 2019: Forcings, feedbacks, and climate sensitivity in HadGEM3‐GC3.1 and374

UKESM1, Journal of Advances in Modeling Earth Systems, 11, 4377-4394, doi:375

10.1029/2019MS001866.

376

Bender, F. M. Frey, L. McCoy, D. T. Grosvenor, D. P. Mohrmann, J. K., 2019: Assessment of377

aerosol–cloud–radiation correlations in satellite observations, climate models and378

reanalysis. Climate Dynamics, 52, 4371-4392, 10.1007/s00382-018-4384-z.

379

Danabasoglu, G. Lamarque, J. F. Bacmeister, J. Bailey, D. A. DuVivier, A. K. Edwards, J....

380

Hannay, C. 2020: The Community Earth System Model version 2 (CESM2). Journal of381

Advances in Modeling Earth Systems, 12, e2019MS001916, doi:382

10.1029/2019MS001916.

383

DeRepentigny, P. Jahn, A. Holland, M. M. Smith, A., 2020: Arctic Sea Ice in Two

384

Configurations of the Community Earth System Model Version 2 (CESM2) During the385

20th and 21st Centuries. J. Geophysical Research: Oceans, e2020JC016133, doi:386

10.1029/2020JC016133.

387

DeRepentigny, P. Jahn, A. Holland, J. E. Kay, J. T. Fasullo, J.F. Lamarque, S. Tilmes, C.

388

Hannay, M.J. Mills, D. A. Bailey, S. Tilmes, Barrett, A., 2021: Enhanced early 21st389

century Arctic sea ice loss due to CMIP6 biomass burning emissions, Nature Climate

390

Change (2021), in revision.

391
392

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

Confidential manuscript submitted to Geophysical Research Letters393

design and organization. Geoscientific Model Development, 9, 1937-1958, doi:394

10.5194/gmd-9-1937-2016.

395

Fasullo, J. T., 2020: Evaluating simulated climate patterns from the CMIP archives using396

satellite and reanalysis datasets using the Climate Model Assessment Tool

397

(CMATv1). Geoscientific Model Development, 13(8), 3627-3642, 10.5194/gmd-13-3627-

398

2020.

399

Flynn, C. M. Mauritsen, T., 2020: On the climate sensitivity and historical warming evolution in

400

recent coupled model ensembles, Atmos. Chem. Phys., 20, 7829–7842, doi: 10.5194/acp-

40120-7829-2020.

402

Gettelman, A., Morrison, H., 2015: Advanced two-moment bulk microphysics for global models.

403

Part I: Off-line tests and comparison with other schemes. Journal of Climate, 28, 1268-

404

1287, doi: 10.1175/JCLI-D-14-00102.1.

405

Gettelman, A. Hannay, C. Bacmeister, J.T. Neale, R. B. Pendergrass, A. G. Danabasoglu, G.... &406

Mills, M. J., 2020: High climate sensitivity in the Community Earth System Model

407

version 2 (CESM2). Geo. Res. Lett., 46, 8329-8337, doi: 10.1029/2019GL083978.

408

Golaz, J. C. Caldwell, P. M. Van Roekel, L. P. Petersen, M. R. Tang, Q. Wolfe, J. D., 2019: The409

DOE E3SM coupled model version 1: Overview and evaluation at standard resolution. J.

410

Adv. Modeling Earth Systems, 11, 2089–2129, doi: 10.1029/2018MS001603.

411

Haywood, J. 2021: Atmospheric aerosols and their role in climate change. In Climate

412

change (pp. 645-659). Elsevier, doi: 10.1016/B978-0-12-821575-3.00030-X.

Confidential manuscript submitted to Geophysical Research Letters

413

Hersbach, H, Bell, B. Berrisford, P. Hirahara, S. Horányi, A. Muñoz‐Sabater, J.... Simmons,A.

414

The ERA5 global reanalysis. Quar. J. Royal Met. Soc., 146, 1999-2049, doi:415

10.1002/qj.3803.

416

Hurrell, J. W. Holland, M. M. Gent, P. R. Ghan, S. Kay, J. E. Kushner, P. J. ... Lipscomb, W. H.,

417

2013: The community earth system model: a framework for collaborative research. Bull.

418

Amer. Met. Soc, 94, 1339-1360, doi: 10.1175/BAMS-D-12-00121.1.

419

Kato, S. Rose, F. G. Rutan, D. A. Thorsen, T. J. Loeb, N. G. Doelling, D. R.... Ham, S. H.

420

Surface irradiances of edition 4.0 clouds and the earth’s radiant energy system (CERES)

421

energy balanced and filled (EBAF) data product. J. Climate, 31, 4501-4527, doi:422

10.1175/JCLI-D-17-0523.1.

423

Kay, J. E. Deser, C. Phillips, A. Mai, A. Hannay, C. Strand, G.... Holland, M., 2015: The

424

Community Earth System Model (CESM) large ensemble project: A community resource

425

for studying climate change in the presence of internal climate variability. Bull. Amer.

426

Met. Soc, 96, 1333-1349, doi: 10.1175/BAMS-D-13-00255.1.

427

Kay, J. E. L’Ecuyer, T. Chepfer, H. Loeb, N. Morrison, N., A. Cesana, G., 2016: Recent

428

advances in Arctic cloud and climate research. Current Climate Change Reports, 2, 159-

429

169, doi: 10.1007/s40641-016-0051-9.

430

Lamarque, J. F. Bond, T. C. Eyring, V. Granier, C. Heil, A. Klimont, … van Vuuren, D. P.,

431

2010: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of432

reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10,433

7017–7039, doi: 10.5194/acp-10-7017-2010.

Confidential manuscript submitted to Geophysical Research Letters

434

Liu, X. Ma, P. L. Wang, H. Tilmes, S. Singh, B. Easter, R. C. Ghan, S. J. Rasch, P. J., 2016:

435

Description and evaluation of a new four-mode version of the Modal Aerosol Module

436(MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev.,

437

9, 505–522, doi: 10.5194/gmd-9-505-2016.

438

Loeb, N. G. Doelling, D. R. Wang, H. Su, W. Nguyen, C Corbett, J. G.... Kato, S., 2018: Clouds439

and the earth’s radiant energy system (CERES) energy balanced and filled (EBAF) top-

440

of-atmosphere (TOA) edition-4.0 data product. J. Climate, 31, 895-918, doi:441

10.1175/JCLI-D-17-0208.1.

442

Lohmann, U. Neubauer, D., 2018: The importance of mixed-phase and ice clouds for climate

443

sensitivity in the global aerosol-climate model ECHAM6-HAM2. Atmospheric

444

Chemistry and Physics, 18, 8807-8828, doi: 10.5194/acp-18-8807-2018.

445
446
447

McKitrick, R. Christy, J., 2020: Pervasive Warming Bias in CMIP6 Tropospheric Layers. Earthand Space Science, e2020EA001281, doi: 10.1029/2020EA001281.
Meehl, G.A. Senior, C. A. Eyring, V. Flato, G. Lamarque, J. F. Stouffer, R. J. ... Schlund, M.,

448

2020: Context for interpreting equilibrium climate sensitivity and transient climate449

response from the CMIP6 Earth system models. Science Advances, 6, eaba1981, doi:

450

10.1126/sciadv.aba1981.

451

Morrison, H. Gettelman, A., 2008: A new two-moment bulk stratiform cloud microphysics

452

scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and453

numerical tests. J. Climate, 21, 3642-3659, doi: 10.1175/2008JCLI2105.1.

Confidential manuscript submitted to Geophysical Research Letters

454

Nijsse, F. J. Cox, P. M., 2020: Williamson, M. S. An emergent constraint on Transient Climate

455

Response from simulated historical warming in CMIP6 models. Earth System Dynamics

456

Discussions, 2020, 1-14, doi: 10.5194/esd-2019-86.

457

O'Neill, B. C., Tebaldi, C., Vuuren, D. P. V., Eyring, V., Friedlingstein, P., Hurtt, G., ... &458

Sanderson, B. M. (2016). The scenario model intercomparison project (ScenarioMIP) for459

CMIP6. Geoscientific Model Development, 9(9), 3461-3482, doi: 10.5194/gmd-9-3461-

460

2016.

461

Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C.,

462

Edwards, J., Kim, J.-E., Simpson, I., Stein, K., Stuecker, M. F., Yamaguchi, R., Bodai,

463

T., Chung, E.-S., Huang, L., Kim, W., Lamarque, J.-F., Lombardozzi, D., Wieder, W. R.,

464

and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst.

465

Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2021-50, in review, 2021

466

Rohde, R. A. Hausfather, Z., 2020: The Berkeley Earth Land/Ocean Temperature Record, Earth

467
468

Syst. Sci. Data Discuss., 1-16, doi: 10.5194/essd-12-3469-2020.
Sherwood, S. Webb, M. J. Annan, J. D. Armour, K. C. Forster, P. M. Hargreaves, J. C. ...

469

Watanabe M., 2020: An assessment of Earth's climate sensitivity using multiple lines of470

evidence. Reviews of Geophysics, e2019RG000678, doi: 10.1029/2019RG000678.

471

Smith, C. J. Kramer, R. J. Myhre, G. Alterskjær, K. Collins, W. Sima, A. … Yukimoto, S.

472

Effective radiative forcing and adjustments in CMIP6 models. Atmospheric Chemistry473

and Physics, 20, 9591-9618, doi: 10.5194/acp-20-9591-2020.

Confidential manuscript submitted to Geophysical Research Letters

474

Smith, C. J., Forster, P. M. (2021). Suppressed Late‐20th Century Warming in CMIP6 Models

475

Explained by Forcing and Feedbacks. Geophysical Research Letters, 48(19), doi:476

10.1029/2021GL094948.

477

Storelvmo, T. 2017: Aerosol effects on climate via mixed-phase and ice clouds. Annual Review

478

of Earth and Planetary Sciences, 45, 199-222, doi: 10.1146/annurev-earth-060115-

479

012240.

480

Tan, I. Storelvmo, T. Zelinka, M. D., 2016: Observational constraints on mixed-phase clouds

481

imply higher climate sensitivity. Science, 352, 224-227, doi: 10.1126/science.aad5300.

482

Tilmes, S., Hodzic, A., Emmons, L. K., Mills, M. J., Gettelman, A., Kinnison, D. E., et al.

483(2019). Climate forcing and trends of organic aerosols in the Community Earth System

484

Model (CESM2). Journal of Advances in Modeling Earth Systems, 11, 4323–4351, doi:

485

10.1029/2019MS001827.

486

Tokarska, K. B. Stolpe, M. B. Sippel, S. Fischer, E. M. Smith, C. J. Lehner, F. Knutti, R., 2020:

487

Past warming trend constrains future warming in CMIP6 models. Science Advances,488

6(12), eaaz9549, doi: 10.1126/sciadv.aaz9549.

489

van Marle, M. J. E. Kloster, S. Magi, B. I. Marlon, J. R. Daniau, A. L. Field, R. D.... Knorr, W.,

490

2017: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on

491

merging satellite observations with proxies and fire models (1750–2015), Geosci. Model

492

Dev., 10, 3329–3357, doi: 10.5194/gmd-10-3329-2017.

493
494

van der Werf, G. R. Randerson, J. T. Giglio, L. van Leeuwen, T. T. Chen, Y. Rogers, B. M. Mu,
M. van Marle, M. J. E. Morton, D. C. Collatz, G. J. Yokelson, R. J. Kasibhatla, P. S.,

Confidential manuscript submitted to Geophysical Research Letters495

2017: Global fire emissions estimates during 1997–2015, Earth Syst. Sci. Data Discuss.,

496

9, 697-720, doi: 10.5194/essd-9-697-2017.

497

Wyser, K. Noije, T. V. Yang, S. Hardenberg, J. V. O'Donnell, D. Döscher, R., 2019: On the498

increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6.

499

Geoscientific Model Development Discussions, 13, 3465-3474, doi: 10.5194/gmd-13-

500

3465-2020.

501

Zelinka, M. D. Myers, T. A. McCoy, D. T. Po‐Chedley, S. Caldwell, P. M. Ceppi, P. ... Taylor,502

K. E., 2020: Causes of higher climate sensitivity in CMIP6 models. Geo. Res. Lett., 47,503

e2019GL085782, doi: 10.1029/2019GL085782.

504

Zhu, J. Poulsen, C. J. Otto-Bliesner, B. L., 2020: High climate sensitivity in CMIP6 model not

505

supported by paleoclimate. Nature Climate Change, 10, 378-379, doi: 10.1038/s41558-

506

020-0764-6.