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Brian Medeiros, Clara Deser, Robert A. Tomas, and Jennifer E. Kay

Abstract

Recent work indicates that climate models have a positive bias in the strength of the wintertime low-level temperature inversion over the high-latitude Northern Hemisphere. It has been argued this bias leads to underestimates of the Arctic’s surface temperature response to anthropogenic forcing. Here the bias in inversion strength is revisited. The spatial distribution of low-level stability is found to be bimodal in climate models and observational reanalysis products, with low-level inversions represented by a stable primary mode over the interior Arctic Ocean and adjacent continents, and a secondary unstable mode over the Atlantic Ocean. Averaging over these differing conditions is detrimental to understanding the origins of the inversion strength bias. While nearly all of the 21 models examined overestimate the area-average inversion strength, conditionally sampling the two modes shows about half the models are biased because of the relative partitioning of the modes and half because of biases within the stable mode.

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Elizabeth A. Maroon, Jennifer E. Kay, and Kristopher B. Karnauskas

ABSTRACT

Many modeling studies have shown that the Atlantic meridional overturning circulation (AMOC) will weaken under increased greenhouse gas forcing, but the influence of AMOC internal variability on climate change in the context of a large initial condition ensemble has received less attention. Here, the Community Earth System Model Large Ensemble (CESM LE) is used to separate the AMOC-forced response from AMOC internal variability, and then assess their joint influence on surface warming. Similar to other models, the CESM LE projects a weakening AMOC in response to increased greenhouse gas forcing caused by freshening and decreased buoyancy fluxes in the North Atlantic. Yet if this forced response is removed using the ensemble mean, there is a positive relationship between global surface warming and AMOC strength. In other words, when the AMOC strengthens relative to the ensemble mean (i.e., weakens less), global surface warming increases relative to the ensemble mean response. This unforced surface warming occurs in northern Eurasia and in the Nordic and Barents Seas near the sea ice edge. Comparison of CESM simulations with and without a dynamic ocean shows that the unforced surface warming in the Nordic and Barents Seas results from both ocean and atmospheric circulation variability. In contrast, this comparison suggests that AMOC-associated Eurasian warming results from atmospheric circulation variability alone. In sum, the AMOC-forced response and AMOC internal variability have distinct relationships with surface temperature. Forced AMOC weakening decreases with surface warming, while unforced AMOC strengthening leads to surface warming.

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Alexander V. Matus, Tristan S. L’Ecuyer, Jennifer E. Kay, Cecile Hannay, and Jean-Francois Lamarque

Abstract

Observational benchmarks of global and regional aerosol direct radiative effects, over all surfaces and all sky conditions, are generated using CloudSat’s new multisensor radiative fluxes and heating rates product. Improving upon previous techniques, the approach leverages the capability of CloudSat and CALIPSO to retrieve vertically resolved estimates of cloud and aerosol properties required for complete and accurate assessment of aerosol direct effects under all conditions. The global annually averaged aerosol direct radiative effect is estimated to be −1.9 W m−2 with an uncertainty range of ±0.6 W m−2, which is in better agreement with previously published estimates from global models than previous satellite-based estimates. Detailed comparisons against a fully coupled simulation of the Community Earth System Model, however, reveal that this agreement on the global annual mean masks large regional discrepancies between modeled and observed estimates of aerosol direct effects. A series of regional analyses demonstrate that, in addition to previously documented biases in simulated aerosol distributions, the magnitude and sign of these discrepancies are often related to model biases in the geographic and seasonal distribution of clouds. A low bias in stratocumulus cloud cover over the southeastern Pacific, for example, leads to an overestimate of the radiative effects of marine aerosols in the region. Likewise, errors in the seasonal cycle of low clouds in the southeastern Atlantic distort the radiative effects of biomass burning aerosols from southern Africa. These findings indicate that accurate assessment of aerosol direct effects requires models to correctly represent not only the source, strength, and optical properties of aerosols, but their relative proximity to clouds as well.

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Vineel Yettella, Jeffrey B. Weiss, Jennifer E. Kay, and Angeline G. Pendergrass

Abstract

Climate variability and its response to increasing greenhouse gases are important considerations for impacts and adaptation. Modeling studies commonly assess projected changes in variability in terms of changes in the variance of climate variables. Despite the distant and impactful covariations that climate variables can exhibit, the covariance response has received much less attention. Here, a novel ensemble framework is developed that facilitates a unified assessment of the response of the regional variances and covariances of a climate variable to imposed external forcings and their time of emergence from an unforced climate state.

Illustrating the framework, the response of variability and covariability of land and ocean temperatures is assessed in the Community Earth System Model Large Ensemble under historical and RCP8.5 forcing. The results reveal that land temperature variance emerges from its preindustrial state in the 1950s and, by the end of the twenty-first century, grows to 1.5 times its preindustrial level. Demonstrating the importance of covariances for variability projections, the covariance between land and ocean temperature is considerably enhanced by 2100, reaching 1.4 times its preindustrial estimate. The framework is also applied to assess changes in monthly temperature variability associated with the Arctic region and the Northern Hemisphere midlatitudes. Consistent with previous studies and coinciding with sea ice loss, Arctic temperature variance decreases in most months, emerging from its preindustrial state in the late twentieth century. Overall, these results demonstrate the utility of the framework in enabling a comprehensive assessment of variability and its response to external climate forcings.

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Jason M. English, Jennifer E. Kay, Andrew Gettelman, Xiaohong Liu, Yong Wang, Yuying Zhang, and Helene Chepfer

Abstract

The Arctic radiation balance is strongly affected by clouds and surface albedo. Prior work has identified Arctic cloud liquid water path (LWP) and surface radiative flux biases in the Community Atmosphere Model, version 5 (CAM5), and reductions to these biases with improved mixed-phase ice nucleation schemes. Here, CAM5 net top-of-atmosphere (TOA) Arctic radiative flux biases are quantified along with the contributions of clouds, surface albedos, and new mixed-phase ice nucleation schemes to these biases. CAM5 net TOA all-sky shortwave (SW) and outgoing longwave radiation (OLR) fluxes are generally within 10 W m−2 of Clouds and the Earth’s Radiant Energy System Energy Balanced and Filled (CERES-EBAF) observations. However, CAM5 has compensating SW errors: Surface albedos over snow are too high while cloud amount and LWP are too low. Use of a new CAM5 Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar simulator that corrects an error in the treatment of snow crystal size confirms insufficient cloud amount in CAM5 year-round. CAM5 OLR is too low because of low surface temperature in winter, excessive atmospheric water vapor in summer, and excessive cloud heights year-round. Simulations with two new mixed-phase ice nucleation schemes—one based on an empirical fit to ice nuclei observations and one based on classical nucleation theory with prognostic ice nuclei—improve surface climate in winter by increasing cloud amount and LWP. However, net TOA and surface radiation biases remain because of increases in midlevel clouds and a persistent deficit in cloud LWP. These findings highlight challenges with evaluating and modeling Arctic cloud, radiation, and climate processes.

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Jennifer E. Kay, Casey Wall, Vineel Yettella, Brian Medeiros, Cecile Hannay, Peter Caldwell, and Cecilia Bitz

Abstract

A large, long-standing, and pervasive climate model bias is excessive absorbed shortwave radiation (ASR) over the midlatitude oceans, especially the Southern Ocean. This study investigates both the underlying mechanisms for and climate impacts of this bias within the Community Earth System Model, version 1, with the Community Atmosphere Model, version 5 [CESM1(CAM5)]. Excessive Southern Ocean ASR in CESM1(CAM5) results in part because low-level clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-only run, an observationally motivated modification to the shallow convection detrainment increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces the Southern Ocean ASR bias. Tuning to maintain global energy balance enables reduction of a compensating tropical ASR bias. In the resulting preindustrial fully coupled run with a brighter Southern Ocean and dimmer tropics, the Southern Ocean cools and the tropics warm. As a result of the enhanced meridional temperature gradient, poleward heat transport increases in both hemispheres (especially the Southern Hemisphere), and the Southern Hemisphere atmospheric jet strengthens. Because northward cross-equatorial heat transport reductions occur primarily in the ocean (80%), not the atmosphere (20%), a proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and cooling with northward shifts in tropical precipitation has little impact. In summary, observationally motivated supercooled liquid water increases in shallow convective clouds enable large reductions in long-standing climate model shortwave radiation biases. Of relevance to both model bias reduction and climate dynamics, quantifying the influence of Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat transport.

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Esther C. Brady, Bette L. Otto-Bliesner, Jennifer E. Kay, and Nan Rosenbloom

Abstract

Results are presented from the Community Climate System Model, version 4 (CCSM4), simulation of the Last Glacial Maximum (LGM) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) at the standard 1° resolution, the same resolution as the majority of the CCSM4 CMIP5 long-term simulations for the historical and future projection scenarios. The forcings and boundary conditions for this simulation follow the protocols of the Paleoclimate Modeling Intercomparison Project, version 3 (PMIP3). Two additional CCSM4 CO2 sensitivity simulations, in which the concentrations are abruptly changed at the start of the simulation to the low 185 ppm LGM concentrations (LGMCO2) and to a quadrupling of the preindustrial concentration (4×CO2), are also analyzed. For the full LGM simulation, the estimated equilibrium cooling of the global mean annual surface temperature is 5.5°C with an estimated radiative forcing of −6.2 W m−2. The radiative forcing includes the effects of the reduced LGM greenhouse gases, ice sheets, continental distribution with sea level lowered by approximately 120 m from the present, and orbital parameters, but not changes to atmospheric aerosols or vegetation biogeography. The LGM simulation has an equilibrium climate sensitivity (ECS) of 3.1(±0.3)°C, comparable to the CCSM4 4×CO2 result. The LGMCO2 simulation shows a greater ECS of 4.2°C. Other responses found at the LGM in CCSM4 include a global precipitation rate decrease at a rate of ~2% °C−1, similar to climate change simulations in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4); a strengthening of the Atlantic meridional overturning circulation (AMOC) with a shoaling of North Atlantic Deep Water and a filling of the deep basin up to sill depth with Antarctic Bottom Water; and an enhanced seasonal cycle accompanied by reduced ENSO variability in the eastern Pacific Ocean’s SSTs.

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Gijs de Boer, William Chapman, Jennifer E. Kay, Brian Medeiros, Matthew D. Shupe, Steve Vavrus, and John Walsh

Abstract

Simulation of key features of the Arctic atmosphere in the Community Climate System Model, version 4 (CCSM4) is evaluated against observational and reanalysis datasets for the present-day (1981–2005). Surface air temperature, sea level pressure, cloud cover and phase, precipitation and evaporation, the atmospheric energy budget, and lower-tropospheric stability are evaluated. Simulated surface air temperatures are found to be slightly too cold when compared with the 40-yr ECMWF Re-Analysis (ERA-40). Spatial patterns and temporal variability are well simulated. Evaluation of the sea level pressure demonstrates some large biases, most noticeably an under simulation of the Beaufort High during spring and autumn. Monthly Arctic-wide biases of up to 13 mb are reported. Cloud cover is underpredicted for all but summer months, and cloud phase is demonstrated to be different from observations. Despite low cloud cover, simulated all-sky liquid water paths are too high, while ice water path was generally too low. Precipitation is found to be excessive over much of the Arctic compared to ERA-40 and the Global Precipitation Climatology Project (GPCP) estimates. With some exceptions, evaporation is well captured by CCSM4, resulting in PE estimates that are too high. CCSM4 energy budget terms show promising agreement with estimates from several sources. The most noticeable exception to this is the top of the atmosphere (TOA) fluxes that are found to be too low while surface fluxes are found to be too high during summer months. Finally, the lower troposphere is found to be too stable when compared to ERA-40 during all times of year but particularly during spring and summer months.

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Kevin Raeder, Jeffrey L. Anderson, Nancy Collins, Timothy J. Hoar, Jennifer E. Kay, Peter H. Lauritzen, and Robert Pincus

Abstract

The Community Atmosphere Model (CAM) has been interfaced to the Data Assimilation Research Testbed (DART), a community facility for ensemble data assimilation. This provides a large set of data assimilation tools for climate model research and development. Aspects of the interface to the Community Earth System Model (CESM) software are discussed and a variety of applications are illustrated, ranging from model development to the production of long series of analyses. CAM output is compared directly to real observations from platforms ranging from radiosondes to global positioning system satellites. Such comparisons use the temporally and spatially heterogeneous analysis error estimates available from the ensemble to provide very specific forecast quality evaluations. The ability to start forecasts from analyses, which were generated by CAM on its native grid and have no foreign model bias, contributed to the detection of a code error involving Arctic sea ice and cloud cover. The potential of parameter estimation is discussed. A CAM ensemble reanalysis has been generated for more than 15 yr. Atmospheric forcings from the reanalysis were required as input to generate an ocean ensemble reanalysis that provided initial conditions for decadal prediction experiments. The software enables rapid experimentation with differing sets of observations and state variables, and the comparison of different models against identical real observations, as illustrated by a comparison of forecasts initialized by interpolated ECMWF analyses and by DART/CAM analyses.

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Jennifer E. Kay, Marika M. Holland, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Andrew Gettelman, Andrew Conley, and David Bailey

Abstract

This study uses coupled climate model experiments to identify the influence of atmospheric physics [Community Atmosphere Model, versions 4 and 5 (CAM4; CAM5)] and ocean model complexity (slab ocean, full-depth ocean) on the equilibrium Arctic climate response to an instantaneous CO2 doubling. In slab ocean model (SOM) experiments using CAM4 and CAM5, local radiative feedbacks, not atmospheric heat flux convergence, are the dominant control on the Arctic surface response to increased greenhouse gas forcing. Equilibrium Arctic surface air temperature warming and amplification are greater in the CAM5 SOM experiment than in the equivalent CAM4 SOM experiment. Larger 2 × CO2 radiative forcing, more positive Arctic surface albedo feedbacks, and less negative Arctic shortwave cloud feedbacks all contribute to greater Arctic surface warming and sea ice loss in CAM5 as compared to CAM4. When CAM4 is coupled to an active full-depth ocean model, Arctic Ocean horizontal heat flux convergence increases in response to the instantaneous CO2 doubling. Though this increased ocean northward heat transport slightly enhances Arctic sea ice extent loss, the representation of atmospheric processes (CAM4 versus CAM5) has a larger influence on the equilibrium Arctic surface climate response than the degree of ocean coupling (slab ocean versus full-depth ocean). These findings underscore that local feedbacks can be more important than northward heat transport for explaining the equilibrium Arctic surface climate response and response differences in coupled climate models. That said, the processes explaining the equilibrium climate response differences here may be different than the processes explaining intermodel spread in transient climate projections.

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