Abstract

Most observed patterns of recent Arctic surface warming and sea ice loss lie outside of unforced internal climate variability. In contrast, human influence on related changes in outgoing longwave radiation has not been assessed. Outgoing longwave radiation captures the flow of thermal energy from the surface through the atmosphere to space, making it an essential indicator of Arctic change. Furthermore, satellites have measured pan-Arctic radiation for two decades while surface temperature observations remain spatially and temporally sparse. Here, two climate model initial-condition large ensembles and satellite observations are used to investigate when and why twenty-first-century Arctic outgoing longwave radiation changes emerge from unforced internal climate variability. Observationally, outgoing longwave radiation changes from 2001 to 2021 are within the range of unforced internal variability for all months except October. The model-predicted timing of Arctic longwave radiation emergence varies throughout the year. Specifically, fall emergence occurs a decade earlier than spring emergence. These large emergence timing differences result from seasonally dependent sea ice loss and surface warming. The atmosphere and clouds then widen these seasonal differences by delaying emergence more in the spring and winter than in the fall. Finally, comparison of the two ensembles shows that more sea ice and a more transparent atmosphere during the melt season led to an earlier emergence of forced longwave radiation changes. Overall, these findings demonstrate that attributing changes in Arctic outgoing longwave radiation to human influence requires understanding the seasonality of both forced change and internal climate variability.

Abstract

Do changes in ocean heat transport (OHT) that occur with CO₂ forcing, impact climate sensitivity in Earth system models? Changes in OHT with warming are ubiquitous in model experiments: when forced with CO₂, such models exhibit declining poleward OHT in both hemispheres at most latitudes, which can persist over multicentennial time scales. To understand how changes in OHT may impact how the climate system responds to CO₂ forcing, particularly climate sensitivity, we perform a series of Earth system model experiments in which we systematically perturb OHT (in a slab ocean, relative to its preindustrial control climatology) while simultaneously doubling atmospheric CO₂. We find that equilibrium climate sensitivity varies substantially with OHT. Specifically, there is a 0.6 K decrease in global mean surface warming for every 10% decline in poleward OHT. Radiative feedbacks from CO₂ doubling, and the warming attributable to each of them, generally become more positive (or less negative) when poleward OHT increases. Water vapor feedback differences account for approximately half the spread in climate sensitivity between experiments, while differences in the lapse rate and surface albedo feedbacks account for the rest. Prescribed changes in OHT instigate opposing changes in atmospheric energy transport and the general circulation, which explain differences in atmospheric water vapor and lapse rate between experiments. Our results show that changes in OHT modify atmospheric radiative feedbacks at all latitudes, thereby driving changes in equilibrium climate sensitivity. More broadly, they demonstrate that radiative feedbacks are not independent of the coupled (atmosphere and ocean) dynamic responses that accompany greenhouse gas forcing.

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.

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.

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.

Abstract

The processes controlling idealized warming and cooling patterns are examined in 150-yr-long fully coupled Community Earth System Model, version 1 (CESM1), experiments under abrupt CO₂ forcing. By simulation end, 2 × CO₂ global warming was 20% larger than 0.5 × CO₂ global cooling. Not only was the absolute global effective radiative forcing ∼10% larger for 2 × CO₂ than for 0.5 × CO₂, global feedbacks were also less negative for 2 × CO₂ than for 0.5 × CO₂. Specifically, more positive shortwave cloud feedbacks led to more 2 × CO₂ global warming than 0.5 × CO₂ global cooling. Over high-latitude oceans, differences between 2 × CO₂ warming and 0.5 × CO₂ cooling were amplified by familiar linked positive surface albedo and lapse rate feedbacks associated with sea ice change. At low latitudes, 2 × CO₂ warming exceeded 0.5 × CO₂ cooling almost everywhere. Tropical Pacific cloud feedbacks amplified the following: 1) more fast warming than fast cooling in the west, and 2) slow pattern differences between 2 × CO₂ warming and 0.5 × CO₂ cooling in the east. Motivated to quantify cloud influence, a companion suite of experiments was run without cloud radiative feedbacks. Disabling cloud radiative feedbacks reduced the effective radiative forcing and surface temperature responses for both 2 × CO₂ and 0.5 × CO₂. Notably, 20% more global warming than global cooling occurred regardless of whether cloud feedbacks were enabled or disabled. This surprising consistency resulted from the cloud influence on non-cloud feedbacks and circulation. With the exception of the tropical Pacific, disabling cloud feedbacks did little to change surface temperature response patterns including the large high-latitude responses driven by non-cloud feedbacks. The findings provide new insights into the regional processes controlling the response to greenhouse gas forcing, especially for clouds.

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.

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.

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.

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.