Beyond the hockey stick: Climate lessons from the Common Era

Mann, Michael

doi:10.1073/pnas.2112797118

Cited by 28 publications

(24 citation statements)

References 77 publications

(121 reference statements)

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“…ECS derived from past cold climate states is not the same as ECS of the present-day and future warm climate, which is thought to be higher than the cold climate ECS (Caballero and Huber, 2013;Shaffer et al, 2016;Schneider et al, 2019) due to non-linear feedback related to ice, permafrost and clouds. Mann (2021) (2020) estimate low ECS based on paleoclimatic evidence from the last glacial maximum and mid-Pliocene warm period, Bjordal et al (2020), on the other hand, show that high ECS is possible due to transition to higher Southern Ocean cloud phase change feedback with warming climate. One way in which high sensitivity models may be better in their representation of cloud types, while still being biased too high in their climate sensitivity is due to the 'too few, too bright' problem (Kuma et al, 2020;Nam et al, 2012;Klein et al, 2013;Wall et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases

Kuma

Bender

Schuddeboom

et al. 2022

Preprint

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Abstract. Uncertainty in cloud feedback in climate models is a major limitation in projections of future climate. Therefore, to ensure the accuracy of climate models, evaluation and improvement of cloud simulation is essential. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations, and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low-resolution (approx. 1°×1°) daily mean top of atmosphere shortwave and longwave radiation fields, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System. We train this network on a satellite top of atmosphere radiation observed by the Clouds and the Earth’s Radiant Energy System (CERES), and apply it on the Climate Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) historical and abrupt-4xCO2 experiment model output and the ECMWF Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare these with satellite observations, link biases in cloud type occurrence derived from the neural network to change with respect to GMST to climate sensitivity, and compare our cloud types with an existing cloud regime classification based on the Moderate Resolution Imaging Spectroradiometer (MODIS) and International Satellite Cloud Climatology Project (ISCCP) satellite data. We show that there is a significant negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity and transient climate response (Bayes factor 22 and 17, respectively). This indicates that models with a better representation of the cloud types globally have higher climate sensitivity. Factoring in results from other studies, there are two possible explanations: either high climate sensitivity models are plausible, contrary to combined assessments of climate sensitivity by previous review studies, or the accuracy of representation of present-day clouds in models is negatively correlated with the accuracy of representation of future projected clouds.

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Section: Discussionmentioning

confidence: 99%

Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases

Kuma

Bender

Schuddeboom

et al. 2022

Preprint

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“…The AMO had originally been named because of its characterization as an approximately 50-to 80-year cycle in which SST in the North Atlantic Ocean undergoes multidecadal-scale variability of approximately 0.4 C °between the extreme phases after removing the global trend (Enfield et al, 2001). The AMO is now referred to as the Atlantic Multidecadal Variability (AMV) as recent evidence (Mann, 2021) suggests that it may not be a true oscillation. Paleoclimate reconstructions of the AMV reveal multi-decadal variability before the instrumental period, yet land- (Gray et al, 2004) and ocean-based reconstructions are misaligned in phasing and timing (Poore et al, 2009;Kilbourne et al, 2014), and the Loop Current in the Gulf of Mexico plays an important but complicating role (DeLong et al, 2014), leaving continued unanswered questions as to the AMO being a true oscillation that extends back in time.…”

Section: Overview Of the Primary Oceanatmosphere Teleconnectionsmentioning

confidence: 99%

“…Interestingly, between 1995 and 2008, the Atlantic coast of the United States was one of the few places in the North Atlantic in which SSTs were not anomalously warm (Alexander et al, 2014). Debate continues as to the extent to which the AMV is driven by processes internal to the atmosphere-ocean system (Ting et al, 2009;Han et al, 2016;Deser and Phillips, 2021) related closely to the AMOC (Fang et al, 2021), natural external variability such as volcanic forcing (Wang et al, 2017;Mann, 2021), or anthropogenic external aerosol forcing (Booth et al, 2012). Several studies suggest that some combination therein appears likely (Ting et al, 2014;Qin et al, 2020), with the NAO also potentially playing a role in the relative contributions (Watanabe and Tatebe, 2019).…”

Section: Overview Of the Primary Oceanatmosphere Teleconnectionsmentioning

confidence: 99%

Impacts of ocean-atmosphere teleconnection patterns on the south-central United States

et al. 2022

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Recent research has linked the climate variability associated with ocean-atmosphere teleconnections to impacts rippling throughout environmental, economic, and social systems. This research reviews recent literature through 2021 in which we identify linkages among the major modes of climate variability, in the form of ocean-atmosphere teleconnections, and the impacts to temperature and precipitation of the South-Central United States (SCUSA), consisting of Arkansas, Louisiana, New Mexico, Oklahoma, and Texas. The SCUSA is an important areal focus for this analysis because it straddles the ecotone between humid and arid climates in the United States and has a growing population, diverse ecosystems, robust agricultural and other economic sectors including the potential for substantial wind and solar energy generation. Whereas a need exists to understand atmospheric variability due to the cascading impacts through ecological and social systems, our understanding is complicated by the positioning of the SCUSA between subtropical and extratropical circulation features and the influence of the Pacific and Atlantic Oceans, and the adjacent Gulf of Mexico. The Southern Oscillation (SO), Pacific-North American (PNA) pattern, North Atlantic Oscillation (NAO) and the related Arctic Oscillation (AO), Atlantic Multidecadal Oscillation/Atlantic Multidecadal Variability (AMO/AMV), and Pacific Decadal Oscillation/Pacific Decadal Variability (PDO/PDV) have been shown to be important modulators of temperature and precipitation variables at the monthly, seasonal, and interannual scales, and the intraseasonal Madden-Julian Oscillation (MJO) in the SCUSA. By reviewing these teleconnection impacts in the region alongside updated seasonal correlation maps, this research provides more accessible and comparable results for interdisciplinary use on climate impacts beyond the atmospheric-environmental sciences.

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“…Millennial temperatures reconstructed from climate proxies provide crucial historical insights into the temporal and spatial variability of the Earth’s climate, as well as benchmarks for quantifying the recent warming and evaluating the realism of climate model simulations 1 – 4 . Global and Northern Hemisphere temperature reconstructions now depict high coherence with simulations at the multidecadal time scale, enhancing the predictability of the climate system 2 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Tropical volcanoes synchronize eastern Canada with Northern Hemisphere millennial temperature variability

et al. 2022

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Although global and Northern Hemisphere temperature reconstructions are coherent with climate model simulations over the last millennium, reconstructed temperatures tend to diverge from simulations at smaller spatial scales. Yet, it remains unclear to what extent these regional peculiarities reflect region-specific internal climate variability or inadequate proxy coverage and quality. Here, we present a high-quality, millennial-long summer temperature reconstruction for northeastern North America, based on maximum latewood density, the most temperature-sensitive tree-ring proxy. Our reconstruction shows that a large majority (31 out of 44) of the coldest extremes can be attributed to explosive volcanic eruptions, with more persistent cooling following large tropical than extratropical events. These forced climate variations synchronize regional summer temperatures with hemispheric reconstructions and simulations at the multidecadal time scale. Our study highlights that tropical volcanism is the major driver of multidecadal temperature variations across spatial scales.

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Beyond the hockey stick: Climate lessons from the Common Era

Cited by 28 publications

References 77 publications

Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases

Machine learning of cloud types shows higher climate sensitivity is associated with lower cloud biases

Impacts of ocean-atmosphere teleconnection patterns on the south-central United States

Tropical volcanoes synchronize eastern Canada with Northern Hemisphere millennial temperature variability

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