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Journal of Climate
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The DOI for this manuscript is doi: 10.1175/JCLI-D-15-0358.1
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If you would like to cite this EOR in a separate work, please use the following full
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Kay, J., C. Wall, V. Yettella, B. Medeiros, C. Hannay, P. Caldwell, and C. Bitz,
2016: Global climate impacts of fixing the Southern Ocean shortwave radiation
bias in the Community Earth System Model (CESM). J. Climate.
doi:10.1175/JCLI-D-15-0358.1, in press.
$PHULFDQ0HWHRURORJLFDO6RFLHW\
AMERICAN
METEOROLOGICAL
SOCIETY
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Global climate impacts of fixing the Southern Ocean shortwave radiation bias in the
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Community Earth System Model (CESM)
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Jennifer E. Kay1, Casey Wall2, Vineel Yettella1, Brian Medeiros3, Cecile Hannay3, Peter
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Caldwell4, and Cecilia Bitz2,
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1Cooperative Institute for Research in Environmental Sciences (CIRES) and Department of
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Atmospheric and Oceanic Sciences (ATOC), University of Colorado at Boulder
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2 Department of Atmospheric Sciences, University of Washington - Seattle
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3 Climate and Global Dynamics, National Center for Atmospheric Research
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4 Livermore National Lab, Department of Energy
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Corresponding Author:
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Jennifer E. Kay, Jennifer.E.Kay@colorado.edu,
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CIRES/ATOC, University of Colorado Boulder
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216 UCB, Boulder, CO 80309
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Phone: 303-492-6289
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Journal of Climate
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Revised March 17, 2016
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Submitted May 18, 2015
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Manuscript (non-LaTeX) Click here to download Manuscript (non-LaTeX)
Kay_JClim_SouthernOceanASR_revised_March17,2016_text,fig
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ABSTRACT:
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A large, long-standing, and pervasive climate model bias is excessive absorbed shortwave
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radiation (ASR) over the mid-latitude oceans, especially the Southern Ocean. This study
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investigates both the underlying mechanisms for and climate impacts of this bias within the
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Community Earth System Model with the Community Atmosphere Model version 5 (CESM-
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CAM5). Excessive Southern Ocean ASR in CESM-CAM5 results in part because low-level
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clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-
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only run, an observationally motivated modification to the shallow convection detrainment
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increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces
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the Southern Ocean ASR bias. Tuning to maintain global energy balance enables reduction
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of a compensating tropical ASR bias. In the resulting pre-industrial fully coupled run with a
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brighter Southern Ocean and dimmer Tropics, the Southern Ocean cools and the Tropics
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warm. As a result of the enhanced meridional temperature gradient, poleward heat
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transport increases in both hemispheres (especially the Southern Hemisphere) and the
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Southern Hemisphere atmospheric jet strengthens. Because Northward cross-equatorial
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heat transport reductions occur primarily in the ocean (80%) not the atmosphere (20%), a
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proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and
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cooling with northward shifts in tropical precipitation has little impact. In summary,
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observationally motivated supercooled liquid water increases in shallow convective clouds
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enable large reductions in long-standing climate model shortwave radiation biases. Of
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relevance to both model bias reduction and climate dynamics, quantifying the influence of
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Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat
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transport.
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1. Motivation
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Excessive absorbed shortwave radiation (ASR) over the mid-latitude oceans is a
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ubiquitous, large, and long-standing bias in both climate models and reanalyses (Trenberth
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and Fasullo, 2010; Hwang and Frierson 2013). ASR biases are largest over the Southern
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Ocean, where local differences between satellite-observed and modeled ASR often reach
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10s of Wm-2. In many models, insufficient cloud optical depth explains the excessive mid-
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latitude ocean ASR. Simply put, the model clouds are not bright enough. Studies using
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cyclone compositing have shown that climate model ASR biases are largest in the post cold-
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front regions of mid-latitude cyclones where low-topped shallow convective clouds are the
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dominant cloud type (Bodas Salcedo et al. 2014; Williams et al. 2013; Bodas Salcedo et al.
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2012).
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Recognizing that large model radiation errors may have a profound influence on the
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simulated climate, this study has two goals. First, we aim to use observations to motivate
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improvements in climate model physics and reduce mid-latitude ocean ASR bias. Second,
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we aim to assess the impacts of reduced mid-latitude ocean ASR bias on global climate. To
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achieve these goals, we employ a widely used global coupled climate model: the
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Community Earth System Model with Community Atmosphere Model version 5 (CESM-
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CAM5) (Hurrell et al. 2013). Like most climate models of its class, CESM-CAM5 has
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excessive mid-latitude ocean ASR due to dim clouds that do not scatter enough incoming
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sunlight back to space (Kay et al. 2012a).
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Accomplishing the first goal of this study requires identifying and removing a
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deficiency in the model equations used to predict mid-latitude ocean cloud properties.
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Satellite and in-situ observations show ubiquitous supercooled cloud liquid in low-level
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clouds over the mid-latitude oceans, especially the Southern Ocean (e.g., Hu et al. 2010;
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Morrison et al. 2011; Huang et al. 2012; Cesana and Chepfer 2013; Chubb et al. 2013;
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Bodas-Salcedo et al. 2015). Even close to the Antarctic continent during winter, total
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glaciation in low clouds over the Southern Ocean is rare (e.g., Morrison et al. 2011; Huang
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et al. 2012). Supercooled cloud liquid exerts a strong control on cloud radiative effects in
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both models (e.g., Kay et al. 2014; Forbes and Ahlgrimm 2014) and observations (e.g.,
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Shupe et al. 2004; Bodas-Salcedo et al. 2015).
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Numerical weather and climate models struggle to reproduce observations of
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ubiquitous supercooled liquid-containing clouds in the extratropical atmosphere (e.g.,
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Cesana and Chepfer, 2013; Forbes and Ahlgrimm 2014; Gettelman et al. 2015). The
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processes that produce and remove supercooled cloud liquid are not explicitly resolved in
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these models because they occur at a scale that is much smaller than the model resolution.
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But beyond this scale challenge, it is important to realize that the ubiquitous presence of
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supercooled liquid is in itself surprising, and not fully understood (Morrison et al. 2012). It
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is well known that when ice and supercooled liquid co-exist, the ice grows at the expense of
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the liquid by the Wegener-Bergeron-Findeisen mechanism (Wegener 1911; Bergeron
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1935; Findeisen 1939). Indeed, the turbulent and microphysical processes that enable
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supercooled liquid to persist in the atmosphere despite the Wegener-Bergeron-Findeisen
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mechanism are complex, coupled, and enigmatic.
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Motivated by observations of ubiquitous supercooled liquid in mid-latitude oceanic
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clouds and by knowing that cloud liquid water content exerts a strong control on cloud
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radiative properties, we propose a hypothesis: mid-latitude ocean clouds are not bright
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enough in climate models because the modeled clouds contain insufficient amounts of
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supercooled liquid water. To test this hypothesis, we change the model physics in CESM-
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CAM5 to allow more supercooled liquid water in shallow convective clouds. In support of
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our hypothesis, increasing supercooled liquid in shallow convective clouds makes those
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clouds brighter and substantially reduces the excessive mid-latitude oceanic ASR bias,
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especially over the Southern Ocean.
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Having introduced our first goal, we next introduce and motivate our second goal:
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documenting the influence of mid-latitude ocean ASR bias reduction on the global climate
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system. Given the large magnitude of the ASR biases we aim to reduce, we expect bias
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reduction will produce changes in global energy budgets and in atmospheric and oceanic
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circulation. In particular, we investigate the possibility that brightening mid-latitude clouds
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will lead to a tropical precipitation response via an atmospheric teleconnection. As
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described in recent review papers by Chiang and Friedman (2012) and Schneider et al.
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(2014), the central idea behind this atmospheric teleconnection is simple: a hemispheric
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asymmetry in heating or cooling shifts tropical rainfall and associated atmospheric
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circulation towards the relatively warmed hemisphere. Especially pertinent to this study,
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Hwang and Frierson (2013) invoke this atmospheric teleconnection when hypothesizing
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that fixing the Southern Ocean ASR climate model bias will preferentially cool the Southern
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Hemisphere, and shift tropical precipitation northward to ameliorate another long-
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standing climate model bias: the double Intertropical Convergence Zone (ITCZ) bias (e.g.,
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Lin 2007). We test this proposed extratropical-tropical teleconnection in a hierarchy of
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model configurations with varying degrees of atmosphere-ocean coupling. As we will show,
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dynamic ocean heat transport mutes the atmospheric teleconnection linking Southern
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Ocean cooling with northward ITCZ shifts.
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2. Model and Experiments
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a. Mid-latitude oceanic clouds in CESM-CAM5
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We use a single climate model for our numerical experiments: CESM-CAM5. All of
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our numerical experiments were done with the CESM Large Ensemble project code base
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(Kay et al. 2015), which uses CAM version 5.2. CESM-CAM5 is a state-of-the-art global
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coupled climate model that participated in Coupled Model Intercomparison Project version
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5 (CMIP5, Taylor et al. 2012). A full description of CESM-CAM5 and its capabilities can be
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found in a special collection of the J. Climate. Of particular relevance to this study are the
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representation of double-moment cloud microphysics (Morrison and Gettelman 2008) and
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shallow convection (Park and Bretherton 2009). A description of the integration of cloud
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processes in CAM5 can be found in Park et al. 2014.
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To introduce mid-latitude oceanic regions in CESM-CAM5, Figure 1a-c shows the
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atmospheric circulation and cloud distribution from 30 to 70 °S for the annual mean,
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Southern hemisphere winter (JJA), and Southern hemisphere summer (DJF). In the annual
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mean, ascent from 50-70 °S associated with the mid-latitude stormtrack leads to relatively
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large cloud fractions from the surface into the free troposphere, with the largest cloud
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fractions below 700 mb. From 30 to 50 °S, subsidence associated with the descending
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branch of the Hadley circulation leads to relatively small cloud fractions throughout the
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troposphere and relatively shallow low cloud tops. Seasonal changes in insolation shift the
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entire atmospheric circulation pattern poleward in DJF and equatorward in JJA, but the
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general nature of the circulation and corresponding cloud distributions are seasonally
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invariant.
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In addition to cloud fraction, cloud properties (e.g., phase, temperature, particle
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size) exert a strong control on cloud radiative effects. For example, Southern Ocean total
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cloud fractions within CAM5 are within 0.10 of satellite observations, yet CAM5 still has
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large Southern Ocean ASR biases indicative of cloud properties biases (Kay et al. 2012a).
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Motivated by the need to understand cloud properties and specifically cloud water content,
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Figure 1d-i shows the vertical distribution of Southern Ocean cloud water content in
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CESM-CAM5 in the annual mean, DJF, and JJA. At all times of the year, the vast majority of
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the CESM-CAM5 low-level (pressure > 700 mb) cloud water is liquid, not ice. Supercooled
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liquid dominates between 268 K and 273 K. Below 268 K, the cloud water content is a mix
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of water and ice. Observations show supercooled liquid dominance down to 253 K (Hu et
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al. 2010; Morrison et al. 2010; Cesana and Chepfer 2013; Chubb et al. 2013). Indeed,
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comparisons with satellite observations (Cesana and Chepfer 2013; O’Dell et al. 2008)
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show that CAM5 clouds have too much ice and insufficient supercooled cloud liquid over
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the Southern Ocean (Kay et al. 2016). In summary, CESM-CAM5 contains insufficient
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supercooled cloud liquid at temperatures below 268 K.
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Having identified that CESM-CAM5 clouds have insufficient supercooled cloud
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liquid, how can this cloud phase bias be “fixed”? Cloud properties in climate models are
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controlled by interactions between the grid-scale processes such as condensation and
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evaporation, and the parameterized physics most notably the cloud microphysics and
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convection. The dominant processes controlling modeled clouds can be identified using
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tendencies, namely the change in cloud mixing ratio per unit time resulting from the
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physical processes represented by the model.
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Figure 2 shows the annual mean cloud liquid tendencies in both the subsidence
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(30-50 °S) and ascent (50-70 °S) regions for CESM-CAM5 moist physical processes. Over
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both Southern Ocean regions, detrainment from the shallow convection parameterization
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(SHDLFLIQ) is the primary source of low-level cloud condensate. Once liquid cloud forms, it
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is depleted by precipitation in the parameterized microphysics (MPDW2P), grid-scale
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evaporation also known as “the macrophysics” (MACPDLIQ), and to a lesser extent by
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convective transport/mixing (entrainment of dry air) in the parameterized shallow
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convection (CMFDLIQ). The conversion of cloud liquid to precipitation (MPDW2P)
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primarily produces rain but also some snow in the ascent regime (not shown). Conversion
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of cloud liquid to ice (MPDW2I), which occurs primarily via the Wegener-Bergeron-
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Findeisen process (not shown), is a secondary sink for cloud liquid in the ascent regime
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(Figure 2b). In the ascent regime, vertical diffusion (turbulent mixing) is also a sink for
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cloud liquid produced by moist physical processes (not shown). Deep convection has a
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negligible influence on Southern Ocean cloud liquid, and thus deep convective tendencies
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are small.
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Since cloud liquid and ice water content are governed by complex prognostic
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equations involving many processes, coming up with a simple explanation for insufficient
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supercooled liquid in CAM5 at first seems hopeless. Knowing that shallow convective
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detrainment is the primary source of Southern Ocean cloud liquid (Figure 2) is extremely
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helpful in this regard. Indeed, knowing that shallow convective detrainment is the primary
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source of Southern Ocean cloud liquid (Figure 2) helps explain why most of the
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supercooled cloud liquid occurs between temperatures of 268 K and 273 K (Figure 1).
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While the cloud microphysics predicts cloud phase, the shallow convection prescribes
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cloud phase for the detrained condensate that forms cloud using a piecewise linear
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function of temperature:
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f=0 ; for T > Tice
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f= (Tice –T)/ 30 ; for 238.15 K < T < Tice (Eq. 1)
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f=1 ; for T < 238.15 K
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where f is the glaciated fraction (unitless), T is temperature (K), and Tice is a constant that
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specifies the temperature below which the shallow convection detrains ice (K). In the
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default configuration, Tice = 268 K. In other words, detrained condensate below 268 K
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contains ice, but above 268 K all detrained condensate is liquid.
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Because the shallow convection is the primary source of low-level cloud condensate
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over the mid-latitude oceans, changing Tice in Eq. 1 provides a simple lever to change the
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amount of supercooled liquid in shallow convective clouds. Motivated by observations
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showing supercooled liquid dominates at temperatures well below 268 K, we conduct
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experiments in which we increase the supercooled liquid detrained from shallow
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convection by changing Tice. Consistent with Figures 1 and 2, changing Tice from 268 K to
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253 K implies that a much larger fraction of the cloud condensate detrained by the shallow
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convection will be liquid over the Southern Ocean. We note that changing Tice has no effect
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on low-level tropical clouds whose cloud top temperatures exceed 273 K. We also note that
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Tice =253 K was the original value proposed by Park and Bretherton (2009), but it was not
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adopted as the default value during CESM-CAM5 development.
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b. CESM-CAM5 runs used for this study
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We next introduce the model runs used in this study (Table 1). To accomplish goal
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#1 – testing the hypothesis that increasing supercooled liquid in detrained condensate
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from the shallow convection reduces the excessive mid-latitude ocean ASR bias in CESM-
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CAM5 – we use atmosphere-only runs with fixed year 2000 sea ice and ocean conditions.
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Atmosphere-only runs have prognostic atmosphere and land, but prescribed surface ocean
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conditions and no ocean heat transport. Atmosphere-only runs are useful for quantifying
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model biases resulting from the model representation of atmospheric processes. For
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example, CESM-CAM5 has excessive Antarctic sea ice, a bias that complicates interpretation
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of ASR bias at high Southern Ocean latitudes. We completed both a “control” atmosphere-
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only CAM5 run in which the physics is identical to the released version of CESM-CAM5 and
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an “experiment” atmosphere-only CAM5 run in which we decreased Tice from its control
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value of 268 K to its experiment value of 253 K (see Eq. 1). To test our hypothesis, we
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evaluate top-of-atmosphere ASR biases by comparing model runs to satellite-observed
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radiative fluxes from version 2.8 of the Clouds and the Earth's Radiant Energy System-
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Energy Balanced and Filled (CERES-EBAF) dataset (Loeb et al. 2009).
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To accomplish goal #2 – evaluating the impact of reduced mid-latitude ocean ASR
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bias on global climate – we use fully coupled CESM-CAM5 simulations with constant 1850
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climate conditions starting with initial conditions from January 1, year 402 from the 1850
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fully coupled control run of the CESM-CAM5 Large Ensemble Project (Kay et al. 2015). Fully
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coupled models have prognostic atmosphere, ocean, land, and sea ice component models,
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and as such can predict changes in atmosphere and ocean heat transport in response to
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model physics changes like those proposed here. Like the atmosphere-only runs, we
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completed fully coupled runs with differing values of Tice. To complement the fully coupled
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runs, we completed slab ocean model (SOM) runs. SOM runs have prognostic mixed layer
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ocean and sea ice, but constant prescribed ocean heat transport. As a result, SOM runs
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come into equilibrium on the timescales of the mixed layer ocean (decades, proportional to
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the mixed layer heat capacity) as opposed to the timescales of the deep ocean (centuries+).
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We use the SOM runs to identify the importance of ocean heat transport for the model
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climate response to ASR bias reduction.
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3. Results
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a. Shortwave radiation bias reduction in atmosphere-only runs
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We begin by presenting ASR bias in present-day atmosphere-only runs. We compare
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the control with Tice=268 K (ATM2000_cnt) to the experiment with Tice=253 K
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(ATM2000_exp, Table 1). Decreasing Tice increases the cloud liquid water path (LWP) and
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decreases ASR over the mid-latitude oceans in both hemispheres where shallow convective
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clouds dominate (Figure 3a). At Southern Hemisphere mid-latitudes, oceans are the
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dominant surface type and there is an active stormtrack year round. As a result, we find
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decreasing Tice leads to the largest ASR bias reductions over the Southern Ocean. Not
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surprisingly, global maps show the largest Southern Ocean ASR reductions occur in
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Southern Hemisphere summer (DJF) (Figure 3b). In the North Atlantic and North Pacific,
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ASR reductions occur in transition seasons (MAM, SON), but not during Northern
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Hemisphere summer (JJA) when many shallow convective mid-latitude clouds have
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temperatures above 273 K and mid-latitude cyclone activity is reduced (not shown).
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Focusing on the region with the largest ASR bias and bias reduction, we next
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compare zonal mean ASR model bias in the control and the experiment over the Southern
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Ocean (Figure 4). Increasing the supercooled liquid condensate detrained from the shallow
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convection reduces the DJF (annual) Southern Ocean 30-70 °S ASR bias by 11.1 (6.5) Wm-2
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from 16.4 Wm-2 (7.3 Wm-2) to 5.3 Wm-2 (0.8 Wm-2). The largest ASR bias reductions occur
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from 50 to 70 °S where detrained ice in the control (Figure 1g-i) became detrained
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supercooled liquid in the experiment. In summary, the atmosphere-only model runs
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demonstrate that increasing the supercooled liquid in shallow convective clouds reduces
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mid-latitude ocean ASR biases, especially over the Southern Ocean.
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b. Shortwave radiation bias reduction in fully coupled model runs
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Having demonstrated that we can substantially reduce mid-latitude ocean ASR
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biases by decreasing Tice in atmosphere-only runs, we next evaluate the impacts of the
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same Tice changes in fully coupled model runs. We begin by comparing global mean energy
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imbalance in a fully coupled experiment (FC1850_exp, Tice=253 K) with a fully coupled
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control (FC1850_cnt, Tice=268 K). While the control has a small positive TOA energy
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imbalance of 0.3 Wm-2, the experiment has a larger negative TOA energy imbalance of -0.9
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Wm-2. This imbalance must be reduced to produce a stable coupled simulation. The
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negative global energy imbalance in the experiment results from strong shortwave cloud
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cooling (Table 2). Not surprisingly, the negative TOA energy imbalance in the experiment
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leads to sustained global cooling. After 29 years, the global mean temperature in the
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experiment decreases by 0.5 K and shows no sign of stopping. Worryingly, a similar
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experiment completed within a slab ocean framework experienced runaway global cooling
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of 40 K after 40 years with sea ice reaching the equator (not shown). Global cooling in this
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“snowball earth” run was enhanced by low-latitude shortwave cloud and sea ice feedbacks
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that were especially prominent after year 25 (not shown).
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Decreasing Tice has a large effect on ASR, which brings the model out of global
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energy balance. To obtain a stable fully coupled model run with the ASR bias reductions
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resulting from decreasing Tice, additional model parameter changes are required. We found
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appropriate additional model parameter changes by performing an assessment of the
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regional contributions to global mean energy balance in CESM-CAM5. Despite having large
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Southern Ocean ASR biases, the fully coupled control has a stable climate with a small
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global energy imbalance. The juxtaposition of a large Southern Ocean ASR bias and small
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global energy imbalance implies that the default version of CESM-CAM5 achieves a globally
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balanced radiation budget with large compensating biases. Indeed, zonal mean plots of ASR
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bias for the control (Figure 5a, FC1850_cnt, solid lines) show insufficient tropical ASR
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compensates for excessive mid-latitude ASR in CESM-CAM5, a common occurrence in
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climate models (Trenberth and Fasullo, 2010). An exciting opportunity for both
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extratropical and tropical bias reduction emerges from globally compensating Southern
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Ocean and tropical ASR errors. If we can combine model parameter changes to increase
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tropical ASR with Tice decreases to decrease Southern Ocean ASR, we can obtain a stable
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climate simulation with improved ASR in both the Tropics and the Mid-latitudes.
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In order to increase tropical ASR, we made two modifications to the threshold
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relative humidity for low cloud formation (rhminl). First, we increased rhminl from the
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default value 0.8925 to 0.9175. Second, unlike in the control where rhminl over the land
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was 0.1 lower than rhminl over the ocean, we specified that the land have the same rhminl
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as the ocean. Both rhminl changes should be considered “tuning” (Mauritsen et al. 2012) to
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obtain global energy balance as they were motivated by energy balance considerations and
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the desire for a stable coupled climate.
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Having described the motivation to increase detrained supercooled liquid (decrease
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Tice) and to tune the model (rhminl changes), we combine the Tice and rhminl changes to
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obtain a fully coupled “tuned experiment” (FC1850_texp, Table 1). The fully coupled tuned
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experiment has a near-zero global energy imbalance, and a stable global mean surface
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temperature that is similar to the fully coupled control (Table 2). Notably, the Tice and
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rhminl changes in the tuned experiment produce a stable coupled climate with reduced
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ASR bias over both the Southern Ocean and the Tropics (Figure 5a, Table 2). Because the
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rhminl changes primarily affect low clouds, their impact on shortwave cloud cooling was
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larger than on longwave cloud warming (Table 2). The rhminl changes increased tropical
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ASR by reducing shortwave cloud cooling in the tuned experiment when compared to the
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control, but had only a small impact on the extra-tropics (Figure 5b). Confirming the zonal
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mean picture, global maps reveal that the largest LWP increases and ASR decreases in the
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fully coupled tuned experiment are over the Southern Ocean (Figure 6). The fully coupled
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tuned experiment ASR reductions and LWP increases (Figure 6) are similar to the
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atmosphere-only experiment (Figure 3). In other words, we retained the desirable
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Southern Ocean ASR bias reduction in a fully coupled framework.
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The climate of the fully coupled tuned experiment (FC1850_texp) quickly adjusts to
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the large radiation changes and reaches a new equilibrium climate state. Over the first 30
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years, the global ocean heat imbalance (a measure of ocean thermal disequilibrium) in
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FC1850_texp was small (0.06 Wm-2). During years 150-200 of FC1850_texp, the global
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ocean heat imbalance is an order of magnitude smaller (0.007 Wm-2) than during the first
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30 years of FC1850_texp. In addition, the global top-of-atmosphere imbalance remains
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16
small (<0.1 Wm-2) over the entire 200 years of simulation of FC1850_texp. We did not find
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a large climate-relevant transient response over 200 years of simulation. These results
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suggest that the 200-year-long FC1850_texp simulation can be used to understand the fully
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coupled climate system response to brightening the Southern Ocean and dimming the
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tropics.
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c. Climate influence of shortwave radiation bias reduction in fully coupled model runs
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To evaluate the equilibrium climate system response to the ASR bias reductions
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shown in Figure 5a, we next compare years 150-200 of the fully coupled tuned experiment
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(FC1850_texp) with the fully coupled control (FC1850_cnt). Figure 7 compares zonal mean
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air temperatures and zonal winds. Due to an enhanced meridional temperature gradient,
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the Southern Hemisphere jet strengthens in the tuned experiment as compared to the
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control. A stronger Southern Hemisphere jet is seen in all seasons, but especially in DJF
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(Figure 7d). Small poleward jet shifts are also evident in the Southern Hemisphere. The
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maximum wind speed at 850 mb, often used to identify the eddy-induced jet, shifts
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poleward by 0.1° (from -51.9 to -52.1 °S) in the annual mean and shifts poleward by 0.6°
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(from 50.9 to 51.5 °S) in DJF.
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The results shown thus far demonstrate that ASR bias reduction can alter global
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energy budgets, temperature gradients, and atmospheric circulation. These findings
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motivate an assessment of the influence of ASR bias reduction on global heat transport.
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Figure 8 shows northward heat transport in the control and tuned experiment, including a
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partitioning into the atmosphere and the ocean appropriate for equilibrium conditions (see
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Appendix of Kay et al. 2012b for methods). Consistent with enhanced meridional
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temperature gradients, poleward heat transport increases in the tuned experiment as
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compared to the control. The Southern Hemisphere poleward heat transport increase is
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more than double the Northern Hemisphere poleward heat transport increase. In addition
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to inter-hemispheric magnitude differences, there are inter-hemispheric differences in the
370
atmospheric and oceanic contributions to total heat transport change. In the Northern
371
Hemisphere, poleward heat transport increases are due entirely to the atmosphere while in
372
the Southern Hemisphere, both the ocean (dominant in the Tropics) and atmosphere
373
(dominant in the mid-latitudes) contribute to increased poleward heat transport.
374
375
We next examine the mechanisms underlying the increased poleward oceanic heat
376
transport in the Southern Hemisphere in FC1850_texp as compared to FC1850_cnt. The
377
increased Southern Hemisphere poleward ocean heat transport response emerges in the
378
first 30 years of FC1850_texp. This quick response implicates the shallow wind-driven
379
ocean circulation, not the deep thermohaline ocean circulation. Figure 9 shows the control
380
surface zonal wind stress climatology and wind stress response (FC1850_texp -
381
FC1850_cnt). Over the Southern Hemisphere sub-tropics, easterly wind stress increases,
382
especially in the Pacific basin. Figure 10 shows the meridional overturning circulation
383
climatology and response to the wind stress changes shown in Figure 9. In the top 1000
384
meters of the Southern Hemisphere subtropical ocean, there is a deepening and
385
intensification of the shallow overturning ocean circulation. The enhanced easterly winds
386
shown in Figure 9b/c increase poleward Ekman transport leading to increased poleward
387
oceanic heat transport. There is also a poleward shift in the location of upwelling in the
388
18
Southern Ocean. By comparing Figure 9b/10b based on years 1-30 of FC1850_texp and
389
Figure 9c/10c based on years 150-200 of FC1850_texp, it is clear that this shallow wind-
390
driven ocean circulation response emerged almost entirely in first 30 years and remains
391
stable over 200 years of coupled simulation. After 200 years of simulation, changes in the
392
deep ocean thermohaline circulation are small, second-order, and have a minor impact on
393
the ocean heat transport response in Figure 8 (not shown).
394
395
Table 3 compares cross-equatorial heat transport and tropical precipitation in the
396
fully coupled simulations. While both the atmosphere and the ocean contribute, the ocean
397
is responsible for 80% of the reductions in northward cross-equatorial heat transport in
398
FC1850_texp as compared to FC1850_cnt. Consistent with a small change in atmospheric
399
cross-equatorial heat transport, the tropical atmospheric circulation and precipitation
400
responses are modest. Never-the-less, the sense of the responses are consistent with
401
Hwang and Frierson (2013): ameliorating the Southern Ocean ASR bias reduces northward
402
cross-equatorial atmospheric heat transport and shifts tropical precipitation northward
403
reducing the tropical precipitation asymmetry index bias. Unlike FC1850_cnt, atmospheric
404
cross-equatorial heat transport in FC1850_texp is within the uncertainty bounds of modern
405
observationally based estimates (Loeb et al. 2015). Yet, the total and oceanic cross-
406
equatorial heat transports are underestimated in FC1850_texp when compared with
407
modern observationally based estimates. If comparison with modern observationally based
408
estimates is appropriate, FC1850_texp has less realistic total and oceanic cross-equatorial
409
heat transport than FC1850_cnt.
410
411
19
d. Influence of ocean heat transport on climate response to shortwave radiation bias
412
reduction in slab ocean model runs
413
The results shown in Figures 8-10 suggest that including dynamic ocean heat
414
transport limits a proposed atmospheric teleconnection linking Southern Ocean ASR bias
415
reduction with tropical precipitation shifts. Because slab ocean models are run with
416
prescribed ocean heat transport, they provide a useful framework to quantify the influence
417
of ocean heat transport on a modeled climate response. Leveraging the capability to
418
prescribe ocean heat transport within SOMs, we next identify the influence of ocean heat
419
transport on the climate response to ASR bias reduction. Specifically, we compare a SOM
420
experiment with prescribed ocean heat transport changes resulting from ASR bias
421
reduction (SOM1850_texp_ocnht=texp) to a SOM experiment with fixed ocean heat
422
transport (SOM1850_texp_ocnht=cnt). Both SOM experiments have ASR bias reduction
423
similar to the fully coupled experiment (Figure 5).
424
425
We begin by comparing atmospheric heat transport in the SOMs and fully coupled
426
runs (Figure 11). Like the fully coupled experiment with dynamic ocean heat transport, the
427
SOM experiment with changed ocean heat transport has small changes in cross-equatorial
428
atmospheric heat transport. In contrast, the SOM with fixed ocean heat transport has large
429
increases (0.3 Petawatts) in atmospheric cross-equatorial heat transport. When ocean heat
430
transport is fixed, ASR bias reduction produces increases in both northward cross-
431
equatorial moisture transport and southward cross-equatorial energy transport (Figure
432
11b).
433
434
20
If cross-equatorial atmospheric heat and moisture transport change (Figure 11b),
435
we expect a large tropical atmospheric circulation, moisture convergence, and precipitation
436
response. Indeed, Figures 12 and 13 confirm the importance of cross-equatorial
437
atmospheric heat transport for shifting the ITCZ. When ocean heat transport is fixed, large
438
cross-equatorial atmospheric heat transport changes occur. As a result, the tropical
439
atmospheric response to reduced ASR includes reduced subsidence and increased
440
precipitation north of the equator, and enhanced subsidence and decreased precipitation
441
south of the equator (Figure 12a, Figure 13). The tropical precipitation asymmetry index
442
in SOM1850_tex_ocnht=cnt is 0.33, which is much larger than in FC1850_texp (0.15) or in
443
modern-day observations (0.20) (Table 3). On the other hand, when ocean heat transport
444
changes are predicted or prescribed, a muted tropical atmospheric circulation and
445
precipitation response occurs (Figure 12b/c, Figure 13). These results confirm that with
446
dynamic ocean heat transport, tropical precipitation is less affected by Southern Ocean
447
cooling. In the climate model experiments with dynamic or prescribed ocean circulation
448
changes, Southern Ocean ASR bias reduction leads to small ITCZ northward shifts because
449
the ocean, not the atmosphere, dominates the cross-equatorial heat transport response. To
450
summarize these results, Figure 14 contains a schematic contrasting the climate impacts of
451
ASR bias reduction with and without dynamic ocean heat transport.
452
453
454
455
456
21
4. Discussion
457
Our results demonstrate that shallow convective cloud phase exerts a strong control
458
on global energy balance. Specifically, increasing supercooled liquid in shallow convective
459
clouds to better match observations enables large reductions in long-standing shortwave
460
radiation biases in a state-of-the-art climate model (CESM-CAM5). The climate impacts of
461
these shortwave radiation bias reductions are profound. A cooler brighter Southern Ocean
462
and warmer dimmer Tropics leads to increased poleward heat transport, especially in the
463
Southern Hemisphere. In response to stronger meridional temperature gradients, the
464
Southern Hemisphere atmospheric jet increases in strength, and shifts slightly poleward as
465
proposed by Ceppi et al. (2012). This jet strengthening and small poleward jet shift is
466
consistent with a more poleward and realistic jet location in CESM-CAM5 (Kay et al. 2014)
467
when compared to other models of its class (Barnes and Polvani 2013). Unfortunately, the
468
westerly winds over the Southern Ocean are already too strong in CESM-CAM5, due in part
469
to a cold upper troposphere bias present in many models without a resolved stratospheric
470
circulation (e.g., Charlton-Perez et al. 2013). Though “fixing” the Southern Ocean shortwave
471
radiation bias is an important step forward and enables new science, it is not a panacea.
472
473
Yet, perhaps the most fundamental outcome of this study is a null result with
474
relevance to climate dynamics. In our fully coupled tuned experiment, ITCZ shifts are small
475
in response to a preferentially cooled Southern Hemisphere. Cooling the Southern Ocean
476
has little impact on tropical precipitation because cross-equatorial heat transport changes
477
occur primarily in the ocean (80% of the response), not the atmosphere (20% of the
478
response). For this reason, quantifying the influence of Southern Ocean cooling on global
479
22
climate requires the use of a model with dynamic ocean heat transport. Similarly, Deser et
480
al. (2015) emphasize the importance of dynamic ocean heat transport and atmosphere-
481
ocean coupling in producing the equatorially symmetric pattern of response to Arctic sea
482
ice loss. More broadly, our results raise a critical question for the climate dynamics of
483
extratropical-tropical teleconnections: Given inter-hemispheric temperature differences,
484
when does the ocean, not the atmosphere, accomplish the required cross-equatorial heat
485
transport? A dynamic ocean has been neglected in many (but not all) previous studies that
486
analyzed atmospheric teleconnections and ITCZ shifts (e.g., Seo et al. 2014; Hwang and
487
Frierson 2013 supplementary materials; Frierson and Hwang 2012, some but not all
488
references in Chiang and Friedman 2012). An important role for the ocean in cross-
489
equatorial heat transport is certainly possible. For example, recent work has emphasized
490
the importance of oceanic cross-equatorial heat transport in setting the mean position of
491
the ITCZ (Frierson et al. 2013; Fuckar et al. 2013; Marshall et al. 2014). Slab ocean models
492
are useful for isolating the role of dynamic ocean heat transport, but their use in
493
quantifying global teleconnections should be justified via comparison with fully coupled
494
model simulations.
495
496
While we have reduced a long-standing climate model bias, documented the climate
497
response, and highlighted the role of dynamic ocean circulation in global teleconnections,
498
important additional work remains. First of all, are our results robust in other models?
499
Recent work using an independent fully coupled climate model (HadGEM2-ES)
500
corroborates our finding that tropical rainfall is insensitive to southern hemispheric
501
cooling (Matt Hawcroft, personal communication). Second, this study focuses on mean
502
23
state 1850 climate, but more realistic shallow convective cloud phase in climate models
503
also has implications for transient simulations and cloud-climate feedbacks. While the
504
global mean cloud-climate feedback in response to increased greenhouse gases is likely
505
positive (Boucher et al. 2013), negative Southern Ocean cloud feedbacks are present in
506
most climate models (Zelinka et al. 2013) including CESM-CAM5 (Kay et al. 2014). These
507
negative cloud feedbacks are a consequence of optical depth increases (Zelinka et al. 2013),
508
with a large contribution from clouds changing in phase from ice to liquid (Tsushima et al.
509
2006, Kay et al. 2014) and a small contribution from poleward jet shifts (Kay et al. 2014,
510
Grise and Polvani 2014, Ceppi and Hartmann 2015). Yet, if cloud phase is diagnosed purely
511
as a function of temperature in climate models (e.g., as it is within the shallow convection
512
parameterization within CESM-CAM5 see Eq. 1), warming automatically converts ice to
513
liquid and increases cloud optical depth. Two obvious follow-on questions emerge. First,
514
could having more realistic liquid-dominated Southern Ocean clouds change the sign of the
515
Southern Ocean cloud-climate feedback from negative to positive in CESM-CAM5? We
516
expect the negative cloud feedback to be sensitive to both the amount of ice present in the
517
mean state and its susceptibility to warming (McCoy et al. 2015). Second, how realistic are
518
parameterizations that predict phase change purely a function of temperature? The
519
amount of liquid and ice in clouds in the real atmosphere depends on many factors beyond
520
temperature (Morrison et al. 2012). Prognostic microphysical processes that predict cloud
521
phase are increasingly common in models, but are still often neglected in parameterized
522
convection (e.g., Park et al. 2014, Forbes and Ahgrimm 2014).
523
524
525
24
526
527
5. Summary
528
We reduced large, long-standing, and pervasive shortwave radiation model biases
529
over the Southern Ocean and the Tropics in a state-of-the-art global coupled climate model
530
(CESM-CAM5). Bias reductions resulted from increasing the supercooled liquid water in
531
shallow convective clouds to better match observations and tuning the relative humidity
532
threshold for low clouds to obtain global energy balance. The climate impacts of the
533
reduced shortwave radiation biases included: a cooled Southern Ocean, a warmed Tropics,
534
increased poleward heat transport (especially in the Southern Hemisphere), and a stronger
535
Southern Hemisphere atmospheric jet. Cooling the Southern Ocean had a negligible
536
influence on tropical circulation and rainfall in our fully coupled model experiments. We
537
found a weak extratropical-tropical atmospheric teleconnection in our fully coupled runs
538
because Southern Ocean cooling increased cross-equatorial heat transport primarily in the
539
ocean, not the atmosphere. More broadly, this work demonstrates the importance of using
540
a global fully coupled model with dynamic ocean heat transport when quantifying the
541
global climate impacts of inter-hemispheric temperature gradients. Future work will
542
investigate the influence of more realistic cloud phase on transient climate simulations
543
including especially Southern Ocean shortwave cloud-climate feedbacks. Many important
544
climate questions remain for the Southern Ocean.
545
546
547
548
25
549
550
Acknowledgements: The authors wish to thank Clara Deser, Andrew Gettelman, Matthew
551
Woelfie, and Chris Bretherton for fruitful conversations related to this work, the
552
Yellowstone CESM CSL for computing resources, and the scientists and software engineers
553
that build CESM. This work was funded by start-up funds awarded to J. E. Kay by the
554
University of Colorado Cooperative Institute for Research in Environmental Sciences
555
(CIRES). P. Caldwell was supported by the Department of Energy's Office of Science
556
Biological and Environmental Research Division Global Climate Modeling Group at
557
Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. The
558
National Center for Atmospheric Research is sponsored by the National Science
559
Foundation.
560
26
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714
Wegener, A., 1911: Thermodynamik der Atmosphäre. Leipzig, 331 pp.
715
Williams, K.D., and Coauthors, 2013: The Transpose-AMIP II Experiment and its
716
application to the understanding of southern ocean cloud biases in climate models. J.
717
Climate, 26, 3258-3274, DOI: 10.1175/JCLI-D-12-00429.1.
718
Zelinka, M. D., 2013: Contributions of Different Cloud Types to Feedbacks and Rapid
719
33
Adjustments in CMIP5*. J. Climate, 26, 5007–5027. doi:
720
http://dx.doi.org/10.1175/JCLI-D-12-00555.1
721
722
723
34
Table Captions:
724
Table 1. Description of global climate model runs. All runs use the Community Earth
725
System Model with the Community Atmosphere Model version 5 (CESM-CAM5, Hurrell et
726
al. 2013) at 1-degree horizontal resolution with the finite volume dynamical core.
727
728
Table 2. Global annual averages for fully coupled model runs. Observations are from
729
CERES-EBAF v2.8 (Loeb et al. 2009) and UWisc. (O’Dell et al. 2008). Root mean square
730
differences between model runs and observations are provided in parentheses. See Table 1
731
for a description of model runs.
732
733
Table 3. Cross-equatorial Heat Transport (CHT) and Tropical Precipitation
734
Asymmetry Index. The tropical precipitation asymmetry index (TPAI) is defined following
735
Hwang and Frierson (2013) as precipitation in the Northern Hemisphere tropics (0-20 °N
736
area-averaged) minus precipitation in the Southern Hemisphere tropics (0-20 °S area
737
averaged) normalized by the tropical mean precipitation (20 °S – 20 °N area averaged). See
738
Table 1 for a description of model runs.
739
740
Figure Captions:
741
Figure 1. Atmospheric circulation, clouds, and air temperatures over the Southern
742
Ocean in the fully coupled control run (FC1850_cnt, Table 1). The left column shows
743
cloud fraction (colors) and subsidence (black lines) for the annual mean ANN (a), Southern
744
Hemisphere winter JJA (b) and Southern Hemisphere summer DJF (c). The middle column
745
shows grid-box cloud liquid mixing ratio (colors) and air temperature (black lines) for ANN
746
(d), JJA (e) DJF (f). The right column shows grid-box cloud ice mixing ratio (colors) and air
747
temperature (black lines) for ANN (g), JJA (h) and DJF (i).
748
749
Figure 2. Annual mean cloud liquid tendencies over the subsidence (30-50 °S, solid)
750
and ascent (50-70 °S, dash) regions of the Southern Ocean in the fully coupled
751
control run (FC1850_cnt, Table 1): a) vertical diffusion and all moist physics
752
tendencies, b) microphysics tendencies. Note: The total tendency due to moist physics
753
processes is the sum of MPDLIQ, MACPDLIQ, SHDLFLIQ CMFDLIQ, ZMDLIQ, and DPDLFLIQ.
754
The total tendency due to microphysical processes is the sum of MPDW2P, MPDW2I,
755
MPDW2V, QCSEDTEN, and QCSEVAP.
756
757
Figure 3. Global difference maps for atmosphere-only runs (ATM2000_exp –
758
ATM2000_cnt): a) Absorbed shortwave radiation (ASR) difference for annual mean
759
(ANN) b) as in a) but for Southern Hemisphere summer (DJF), c) ANN grid-box mean
760
cloud liquid water path (LWP), d) as in c) but for DJF. See Table 1 for model run
761
descriptions.
762
763
Figure 4. Zonal mean Absorbed Shortwave Radiation bias over the Southern Ocean in
764
atmosphere-only runs. Values in parentheses indicate Southern Ocean (30-70 ° S) bias.
765
Observations are from CERES-EBAF (Loeb et al. 2009) v2.8 for the years 2000-2013. See
766
35
Table 1 for model run descriptions. Both annual means (ANN) and Southern Hemisphere
767
summer (DJF) means are plotted.
768
769
Figure 5. Zonal global mean absorbed shortwave radiation in fully coupled runs: a)
770
bias, b) difference from control (FC1850_cnt). Observations are from CERES-EBAF v2.8
771
(Loeb et al. 2009) for the years 2000-2013. An area-weighted axis is used to show the
772
balancing of mid-latitude and tropical ASR biases and differences. See Table 1 for model
773
run description. Both annual means (ANN) and Southern Hemisphere summer (DJF) means
774
are plotted.
775
776
777
36
Figure 6. Global difference maps for fully coupled climate model runs (FC1850_texp –
778
FC1850_cnt): a) Annual mean (ANN) absorbed shortwave radiation (ASR) difference,
779
b) as in a) but for Southern Hemisphere summer (DJF), c) ANN grid-box mean cloud
780
liquid water path (LWP) difference, d) as in c) but for DJF. See Table 1 for model run
781
description. An average of years 150-200 is shown for FC1850_texp, while an average of
782
years 1-29 is shown for FC1850_cnt.
783
784
Figure 7. Global zonal vertical mean temperature and circulation in fully coupled
785
runs: a) Annual mean air temperature (black contours, FC1850_cnt) and
786
temperature response (colors, FC1850_texp - FC1850_cnt), b) as in a) but for
787
Southern Hemisphere summer (DJF), c) Annual zonal mean wind (black contours,
788
FC1850_cnt) and zonal mean wind response (colors, FC1850_texp - FC1850_cnt), d)
789
as in c) but for DJF. See Table 1 for model run descriptions. An average of years 150-200
790
is shown for FC1850_texp, while an average of years 1-29 is shown for FC1850_cnt.
791
792
Figure 8. Annual mean northward heat transport in fully coupled runs: a)
793
Climatology in fully coupled control run (FC1850_cnt, solid) and tuned experiment
794
(FC1850_texp, dash) b) Response to increased detrainment of supercooled liquid in
795
shallow convective clouds and tuning (FC1850_texp- FC1850_cnt). See Table 1 for
796
model run descriptions. An average of years 150-200 is shown for FC1850_texp, while an
797
average of years 1-29 is shown for FC1850_cnt. Heat transport calculated using methods
798
described in Kay et al. 2012b Appendix A. Total heat transport was calculated based on the
799
fact that, in steady state, horizontal heat flux convergence across a latitude band is
800
balanced by the net TOA flux poleward of that latitude. Ocean heat transport was calculated
801
inline. Atmospheric heat transport was calculated by integrating the residual between the
802
top-of-atmosphere and surface heat fluxes.
803
804
Figure 9. Surface zonal wind stress and wind stress response: a) climatology in fully
805
coupled control (FC1850_cnt, years 1-29), b) response to increased detrainment of
806
supercooled liquid in shallow convective clouds and tuning (FC1850_texp-
807
FC1850_cnt) using years 1-30 of FC1850_texp, c) as in b) but using years 150-200 of
808
FC1850_texp.
809
810
Figure 10. Meridional Overturning Circulation (MOC): a) climatology in fully coupled
811
control (FC1850_cnt, years 1-29), b) response to increased detrainment of
812
supercooled liquid in shallow convective clouds and tuning (FC1850_texp-
813
FC1850_cnt) using years 1-30 of FC1850_texp, c) as in b) but using years 150-200 of
814
FC1850_texp.
815
816
817
818
819
820
821
822
37
Figure 11. Atmospheric northward heat transport response to increased
823
detrainment of supercooled liquid in shallow convective clouds and tuning: a) total,
824
b) latent and dry static energy. Responses are shown for fully coupled model
825
(FC1850_texp - FC1850_cnt), slab ocean model with prescribed ocean heat transport
826
changes (SOM1850_texp_ocnht=texp - SOM1850_cnt), and slab ocean model run with fixed
827
ocean heat transport (SOM1850_texp_ocnht=cnt -SOM1850_cnt). Years 1-20 are shown for
828
all slab ocean experiments, years 1-29 are shown for FC1850_cnt and years 150-200 are
829
used for FC1850_texp. See Table 1 for model run descriptions.
830
831
Figure 12. Annual mean subsidence climatology (black contours) and subsidence
832
response (colors): a) slab ocean model run with fixed ocean heat transport
833
(SOM1850_cnt, SOM1850_cnt-SOM1850_texp_ocnht=cnt.), b) fully coupled model
834
(FC1850_cnt, FC1850_texp-FC1850_cnt), c) slab ocean model with prescribed ocean
835
heat transport changes (SOM1850_cnt, SOM1850_cnt-SOM1850_texp_ocnht=texp).
836
Years 1-20 are shown for all slab ocean experiments, years 1-29 are shown for FC1850_cnt
837
and years 150-200 are used for FC1850_texp. See Table 1 for model run descriptions.
838
839
Figure 13. Zonal annual mean precipitation a) climatology in fully coupled control
840
(FC1850_cnt, years 1-29) and Global Precipitation Climatology Project observations
841
(1979-2009) (Adler et al. 2003), b) ANN response, c) DJF response. Response shown
842
for fully coupled run (FC1850_texp - FC1850_cnt), slab ocean model run with changes in
843
ocean heat transport (SOM1850_texp_ocnht=texp - SOM1850_cnt), and slab ocean model
844
run without changes in ocean heat transport (SOM1850_texp_ocnht=cnt - SOM1850_cnt).
845
Years 1-20 are shown for all slab ocean experiments, years 1-29 are shown for FC1850_cnt
846
and years 150-200 are used for FC1850_texp. See Table 1 for model run descriptions.
847
848
Figure 14. Schematic showing climate response to absorbed shortwave radiation
849
bias reduction (less ASR in Southern Ocean, more ASR in the Tropics): a) with fixed
850
ocean heat transport, b) with dynamic ocean heat transport. All indicated changes
851
are anomalies relative to a mean state climate.
852
853
38
854
Name
Length
(years)
Climate
Forcing
(year)
Description
ATM2000_cnt
10
2000
Atmosphere-only control with prescribed 2000
surface ocean
ATM2000_exp
10
2000
Atmosphere-only experiment (Tice =253 K) with
prescribed 2000 surface ocean
FC1850_cnt
29
1850
Fully coupled control, Initial condition January
1, year 402 of CESM-LE 1850 control run
b.e11.B1850C5CN.f09_g16.005 (Kay et al. 2015)
FC1850_exp
29
1850
Fully coupled experiment (Tice =253 K ), Initial
condition January 1, year 402 of CESM-LE 1850
control run b.e11.B1850C5CN.f09_g16.005 (Kay
et al. 2015)
FC1850_texp
200
1850
Fully coupled tuned experiment (Tice =253 K),
Initial condition January 1, year 402 of CESM-LE
1850 control run
b.e11.B1850C5CN.f09_g16.005 (Kay et al. 2015)
SOM1850_cnt
20
1850
Slab ocean model control with prescribed ocean
heat transport from FC1850_cnt, Initial
condition January 1, year 402 of CESM-LE 1850
control run b.e11.B1850C5CN.f09_g16.005 (Kay
et al. 2015)
SOM1850_texp_ocnht=texp
20
1850
Slab ocean model tuned experiment (Tice =253
K) with prescribed ocean heat transport from
FC1850_texp, Initial condition January 1, year
150 of FC1850_texp
SOM1850_texp_ocnht=cnt
20
1850
Slab ocean model tuned experiment (Tice =253
K) with prescribed ocean heat transport from
FC1850_cnt, Initial condition January 1, year
402 of CESM-LE 1850 control run
b.e11.B1850C5CN.f09_g16.005 (Kay et al. 2015)
Table 1. Description of global climate model runs. All runs use the Community Earth
855
System Model with the Community Atmosphere Model version 5 (CESM-CAM5, Hurrell et
856
al. 2013) at 1-degree horizontal resolution with the finite volume dynamical core.
857
858
39
859
Top-of-
model
Energy
Imbalance
(Wm-2)
Shortwave
Cloud
Radiative
Effect
(Wm-2)
Longwave
Cloud
Radiative
Effect
(Wm-2)
Total
Cloud
Fraction
(%)
Grid-box
Cloud
Liquid
Water Path
(g m-2)
Surface
Tempera
ture
(K)
CERES-EBAF
2000-2013
-47.2
26.0
n/a
UWisc.
1987-2000
87.2
FC1850_cnt
Yrs. 1-29
0.3
-47.7 (13.4)
22.6 (6.2)
63.1%
40.1 (60.9)
287.2
FC1850_exp
Yrs. 1-29
-0.9
-49.9 (13.0)
23.0 (5.8)
64.4%
45.4 (54.8)
286.6
FC1850_texp
Yrs. 1-29
0.0
-46.6 (10.9)
22.3 (6.1)
62.9%
44.0 (55.8)
287.5
FC1850_texp
Yrs. 150-200
0.0
-46.7 (10.9)
22.3 (6.1)
62.9%
44.0 (55.7)
287.5
Table 2. Global annual averages for fully coupled model runs. Observations are from
860
CERES-EBAF v2.8 (Loeb et al. 2009) and UWisc. (O’Dell et al. 2008). Root mean square
861
differences between model runs and observations are provided in parentheses. See Table 1
862
for a description of model runs.
863
864
865
866
Total
CHT
(PW)
Atmospheric
CHT
(PW)
Oceanic
CHT
(PW)
Tropical
Precipitation
Asymmetry Index
Observationally
Constrained
2001-2012
(Loeb et al. 2015)
0.20 ± 0.05
-0.24 ± 0.04
0.44 ± 0.07
0.20
FC1850_cnt
Yrs. 1-29
0.26
-0.18
0.44
0.11
FC1850_texp
Yrs. 1-29
0.13
-0.19
0.32
0.12
FC1850_texp
Yrs. 150-200
0.11
-0.21
0.32
0.15
Table 3. Cross-equatorial Heat Transport (CHT) and Tropical Precipitation
867
Asymmetry Index. The tropical precipitation asymmetry index (TPAI) is defined following
868
Hwang and Frierson (2013) as precipitation in the Northern Hemisphere tropics (0-20 °N
869
area-averaged) minus precipitation in the Southern Hemisphere tropics (0-20 °S area
870
averaged) normalized by the tropical mean precipitation (20 °S – 20 °N area averaged). See
871
Table 1 for a description of model runs.
872
873
40
874
Figure 1. Atmospheric circulation, clouds, and air temperatures over the Southern
875
Ocean in the fully coupled control run (FC1850_cnt, Table 1). The left column shows
876
cloud fraction (colors) and subsidence (black lines) for the annual mean ANN (a), Southern
877
Hemisphere winter JJA (b) and Southern Hemisphere summer DJF (c). The middle column
878
shows grid-box cloud liquid mixing ratio (colors) and air temperature (black lines) for ANN
879
(d), JJA (e) DJF (f). The right column shows grid-box cloud ice mixing ratio (colors) and air
880
temperature (black lines) for ANN (g), JJA (h) and DJF (i).
881
41
882
Figure 2. Annual mean cloud liquid tendencies over the subsidence (30-50 °S, solid)
883
and ascent (50-70 °S, dash) regions of the Southern Ocean in the fully coupled
884
control run (FC1850_cnt, Table 1): a) vertical diffusion and all moist physics
885
tendencies, b) microphysics tendencies. Note: The total tendency due to moist physics
886
processes is the sum of MPDLIQ, MACPDLIQ, SHDLFLIQ CMFDLIQ, ZMDLIQ, and DPDLFLIQ.
887
The total tendency due to microphysical processes is the sum of MPDW2P, MPDW2I,
888
MPDW2V, QCSEDTEN, and QCSEVAP.
889
890
42
891
Figure 3. Global difference maps for atmosphere-only runs (ATM2000_exp –
892
ATM2000_cnt): a) Absorbed shortwave radiation (ASR) difference for annual mean
893
(ANN) b) as in a) but for Southern Hemisphere summer (DJF), c) ANN grid-box mean
894
cloud liquid water path (LWP), d) as in c) but for DJF. See Table 1 for model run
895
descriptions.
896
897
43
898
899
Figure 4. Zonal mean Absorbed Shortwave Radiation bias over the Southern Ocean in
900
atmosphere-only runs. Values in parentheses indicate Southern Ocean (30-70 ° S) bias.
901
Observations are from CERES-EBAF (Loeb et al. 2009) v2.8 for the years 2000-2013. See
902
Table 1 for model run descriptions. Both annual means (ANN) and Southern Hemisphere
903
summer (DJF) means are plotted.
904
905
44
906
Figure 5. Zonal global mean absorbed shortwave radiation in fully coupled runs: a)
907
bias, b) difference from control (FC1850_cnt). Observations are from CERES-EBAF v2.8
908
(Loeb et al. 2009) for the years 2000-2013. An area-weighted axis is used to show the
909
balancing of mid-latitude and tropical ASR biases and differences. See Table 1 for model
910
run description. Both annual means (ANN) and Southern Hemisphere summer (DJF) means
911
are plotted.
912
913
914
45
915
Figure 6. Global difference maps for fully coupled climate model runs (FC1850_texp –
916
FC1850_cnt): a) Annual mean (ANN) absorbed shortwave radiation (ASR) difference,
917
b) as in a) but for Southern Hemisphere summer (DJF), c) ANN grid-box mean cloud
918
liquid water path (LWP) difference, d) as in c) but for DJF. See Table 1 for model run
919
description. An average of years 150-200 is shown for FC1850_texp, while an average of
920
years 1-29 is shown for FC1850_cnt.
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
46
938
Figure 7. Global zonal vertical mean temperature and circulation in fully coupled
939
runs: a) Annual mean air temperature (black contours, FC1850_cnt) and
940
temperature response (colors, FC1850_texp - FC1850_cnt), b) as in a) but for
941
Southern Hemisphere summer (DJF), c) Annual zonal mean wind (black contours,
942
FC1850_cnt) and zonal mean wind response (colors, FC1850_texp - FC1850_cnt), d)
943
as in c) but for DJF. See Table 1 for model run descriptions. An average of years 150-200
944
is shown for FC1850_texp, while an average of years 1-29 is shown for FC1850_cnt.
945
946
947
948
949
47
950
Figure 8. Annual mean northward heat transport in fully coupled runs: a)
951
Climatology in fully coupled control run (FC1850_cnt, solid) and tuned experiment
952
(FC1850_texp, dash) b) Response to increased detrainment of supercooled liquid in
953
shallow convective clouds and tuning (FC1850_texp- FC1850_cnt). See Table 1 for
954
model run descriptions. An average of years 150-200 is shown for FC1850_texp, while an
955
average of years 1-29 is shown for FC1850_cnt. Heat transport calculated using methods
956
described in Kay et al. 2012b Appendix A. Total heat transport was calculated based on the
957
fact that, in steady state, horizontal heat flux convergence across a latitude band is
958
balanced by the net TOA flux poleward of that latitude. Ocean heat transport was calculated
959
inline. Atmospheric heat transport was calculated by integrating the residual between the
960
top-of-atmosphere and surface heat fluxes.
961
48
962
963
Figure 9. Surface zonal wind stress and wind stress response: a) climatology in fully
964
coupled control (FC1850_cnt, years 1-29), b) response to increased detrainment of
965
supercooled liquid in shallow convective clouds and tuning (FC1850_texp-
966
FC1850_cnt) using years 1-30 of FC1850_texp, c) as in b) but using years 150-200 of
967
FC1850_texp.
968
49
969
Figure 10. Meridional Overturning Circulation (MOC): a) climatology in fully coupled
970
control (FC1850_cnt, years 1-29), b) response to increased detrainment of
971
supercooled liquid in shallow convective clouds and tuning (FC1850_texp-
972
FC1850_cnt) using years 1-30 of FC1850_texp, c) as in b) but using years 150-200 of
973
FC1850_texp.
974
975
50
976
Figure 11. Atmospheric northward heat transport response to increased
977
detrainment of supercooled liquid in shallow convective clouds and tuning: a) total,
978
b) latent and dry static energy. Responses are shown for fully coupled model
979
(FC1850_texp - FC1850_cnt), slab ocean model with prescribed ocean heat transport
980
changes (SOM1850_texp_ocnht=texp - SOM1850_cnt), and slab ocean model run with fixed
981
ocean heat transport (SOM1850_texp_ocnht=cnt -SOM1850_cnt). Years 1-20 are shown for
982
all slab ocean experiments, years 1-29 are shown for FC1850_cnt and years 150-200 are
983
used for FC1850_texp. See Table 1 for model run descriptions.
984
51
985
Figure 12. Annual mean subsidence climatology (black contours) and subsidence
986
response (colors): a) slab ocean model run with fixed ocean heat transport
987
climatology (SOM1850_cnt) and response (SOM1850_cnt-SOM1850_texp_ocnht=cnt.),
988
b) fully coupled model climatology (FC1850_cnt) and response (FC1850_texp-
989
FC1850_cnt), c) slab ocean model with prescribed ocean heat transport changes
990
climatology (SOM1850_cnt) and response (SOM1850_cnt-
991
SOM1850_texp_ocnht=texp). Years 1-20 are shown for all slab ocean experiments, years
992
1-29 are shown for FC1850_cnt and years 150-200 are used for FC1850_texp. See Table 1
993
for model run descriptions.
994
52
995
Figure 13. Zonal annual mean precipitation a) climatology in fully coupled control
996
(FC1850_cnt, years 1-29) and Global Precipitation Climatology Project observations
997
(1979-2009) (Adler et al. 2003), b) ANN response, c) DJF response. Response shown
998
for fully coupled run (FC1850_texp - FC1850_cnt), slab ocean model run with changes in
999
ocean heat transport (SOM1850_texp_ocnht=texp - SOM1850_cnt), and slab ocean model
1000
run without changes in ocean heat transport (SOM1850_texp_ocnht=cnt - SOM1850_cnt).
1001
Years 1-20 are shown for all slab ocean experiments, years 1-29 are shown for FC1850_cnt
1002
and years 150-200 are used for FC1850_texp. See Table 1 for model run descriptions.
1003
53
1004
Figure 14. Schematic showing climate response to absorbed shortwave radiation
1005
bias reduction (less ASR in Southern Ocean, more ASR in the Tropics): a) with fixed
1006
ocean heat transport, b) with dynamic ocean heat transport. All indicated changes
1007
are anomalies relative to a mean state climate.
1008
1009
1010
1011
