Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive

Schmidt, Gavin A.; Kelley, M.; Nazarenko, Larissa; Ruedy, R.; Russell, Gary L.; Aleinov, Igor; Bauer, Michael; Bauer, Susanne E.; Bhat, M. K.; Bleck, Rainer; Canuto, V. M.; Chen, Yonghua; Cheng, Yafang; Clune, Thomas; Genio, Anthony D. Del; Fainchtein, Rosalinda de; Faluvegi, G.; Hansen, James E.; Healy, Richard; Kiang, Nancy Y.; Koch, D.; Lacis, A. A.; LeGrande, Allegra N.; Lerner, J.; Lo, K.; Matthews, E.; Menon, Surabi; Miller, Ron; Oinas, Valdar; Oloso, Amidu; Perlwitz, J. P.; Puma, Michael J.; Putman, William M.; Rind, David; Romanou, Anastasia; Sato, Makiko; Shindell, D. T.; Sun, Shan; Syed, Rahman A.; Tausnev, N.; Tsigaridis, Kostas; Unger, Nadine; Voulgarakis, Apostolos; Yao, Mao‐Sung; Zhang, Jinlun

doi:10.1002/2013ms000265

Cited by 634 publications

(497 citation statements)

References 182 publications

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“…We also apply the ModelE2 version of the GISS climate model (43) to examine the influence of tropical Atlantic SST on US summer air temperature. ModelE2 has a horizontal resolution of 2°× 2.5°with 40 layers extending from surface to 0.1 hPa.…”

Section: Methodsmentioning

confidence: 99%

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

Shen

Mickley

2017

Proc. Natl. Acad. Sci. U.S.A.

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We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.ozone | seasonal forecast | teleconnection | sea surface temperature

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Section: Methodsmentioning

confidence: 99%

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

Shen

Mickley

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…In GISS, cloud condensate can only be either liquid or ice at a given time and location, and a likelihood for supercooled water to freeze instantaneously is dependent on temperature [Schmidt et al, 2014]. This leads to a growing chance of entirely freezing a supercooled layer with time, and does not allow a persistent mixed phase cloud to form in our experiment.…”

Section: Journal Of Advances In Modeling Earth Systems 101002/2016msmentioning

confidence: 97%

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

Pithan

Ackerman

Angevine

et al. 2016

J Adv Model Earth Syst

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Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build‐up of surface‐based temperature inversions. Many large‐scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single‐column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large‐scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed‐phase microphysics or to the interaction between micro‐ and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build‐up of surface‐based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior.

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“…However, it is increasingly recognised that this regression-based estimate can significantly underestimate the true equilibrium sensitivity. One of the more extreme examples of this is the GISS model, with the sensitivity reported as 2.1 • C in the IPCC AR5 but actually estimated as 2.7-2.9 • C by the PlioMIP contributors, based on a long simulation (Schmidt et al, 2014b). For most other models that have done this comparison, the discrepancy is somewhat smaller (Andrews et al, 2015).…”

Section: Modelling Uncertaintiesmentioning

confidence: 99%

Could the Pliocene constrain the equilibrium climate sensitivity?

Hargreaves¹

2016

Clim. Past

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Abstract. The mid-Pliocene Warm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene Model Intercomparison Project (PlioMIP) presented boundary conditions for the mPWP and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We observe a correlation in the ensemble between their tropical temperature anomalies at the mPWP and their equilibrium sensitivities. If the real world is assumed to also obey this relationship, then the reconstructed tropical temperature anomaly at the mPWP can in principle generate a constraint on the true sensitivity. Directly applying this methodology using available data yields a range for the equilibrium sensitivity of 1.9-3.7 • C, but there are considerable additional uncertainties surrounding the analysis which are not included in this estimate. We consider the extent to which these uncertainties may be better quantified and perhaps lessened in the next few years.

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Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive

Cited by 634 publications

References 182 publications

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

Could the Pliocene constrain the equilibrium climate sensitivity?

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