The Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP)

Elsaesser, Gregory S.; O’Dell, C.; Lebsock, Matthew; Bennartz, Ralf; Greenwald, Thomas J.; Wentz, F. J.

doi:10.1175/jcli-d-16-0902.1

Cited by 97 publications

(184 citation statements)

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“…Neither the average ECS of approximately 3 K nor its uncertainty has changed much over time. In a first model intercomparison paper by Cess et al (1989), the estimates of ECS in GCMs varied between 1.4 and 4.1 K. Estimates from the GCMs used in CMIP5 are somewhat higher with 2.1 and 4.7 K Flato et al, 2013), but the range in estimates remains similar. One of the prime contributors to this range in uncertainty are inter-model differences in the low-level shortwave cloud feedback (Yokohata et al, 2010).…”

Section: Introductionmentioning

confidence: 97%

“…There are two metrics that describe the temperature response to a doubling of CO 2 , the transient climate response (TCR) and the equilibrium climate sensitivity (ECS). TCR is estimated at the time of CO 2 doubling from atmosphere global climate model (GCM) simulations in which the CO 2 concentration increases by 1 % yr −1 and that are coupled to a dynamic ocean (e.g., Flato et al, 2013). ECS is obtained from coupled atmosphereocean simulations that are abruptly exposed to a CO 2 doubling relative to preindustrial concentrations and then run until a new equilibrium of the climate system has been established.…”

Section: Introductionmentioning

confidence: 99%

“…ECS is obtained from coupled atmosphereocean simulations that are abruptly exposed to a CO 2 doubling relative to preindustrial concentrations and then run until a new equilibrium of the climate system has been established. This requires coupling of the atmosphere GCM to a fully coupled dynamic ocean model (e.g., Gregory et al, 2004;Flato et al, 2013). As such a coupled system takes a long time to reach a new equilibrium, sometimes ECS is approximated from coupled atmosphere-mixed-layer ocean (MLO) climate models (e.g., Meehl et al, 2007;Randall et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…This term needs to be small for the decomposition into individual feedbacks to explain the vast majority of the feedback. The average λ value from the GCMs that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is 1.1 W m −2 K −1 with a 90 % uncertainty range of ±0.5 W m −2 K −1 (Flato et al, 2013). The uncertainty in λ induces the uncertainty in ECS.…”

Section: Introductionmentioning

confidence: 99%

“…One of the prime contributors to this range in uncertainty are inter-model differences in the low-level shortwave cloud feedback (Yokohata et al, 2010). While the overall cloud feedback is positive with 0.3 W m −2 K −1 , the spread in λ c is larger than that of the overall climate feedback parameter and varies by ±0.7 W m −2 K −1 between the CMIP5 models (Flato et al, 2013). This means a negative λ c cannot be ruled out and the careful conclusion from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) was that λ c is likely (with a probability of 66 %) positive.…”

Section: Introductionmentioning

confidence: 99%

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The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2

Lohmann

Neubauer

2018

Atmos. Chem. Phys.

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Abstract. How clouds change in a warmer climate remains one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that mixed-phase clouds are also important for ECS. If mixedphase clouds in the current climate contain too few supercooled cloud droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud-phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we use the newest version of the ECHAM6-HAM2 GCM to investigate the importance of mixed-phase and ice clouds for ECS.Although we also considerably underestimate the fraction of supercooled liquid water globally in the reference version of the ECHAM6-HAM2 GCM, we do not obtain increases in ECS in simulations with more supercooled liquid water in the present-day climate, different from the findings by Tan et al. (2016). We hypothesize that it is not the global supercooled liquid water fraction that matters, but only how well low-and mid-level mixed-phase clouds with cloud-top temperatures in the mixed-phase temperature range between 0 and −35 • C that are not shielded by higher-lying ice clouds are simulated. These occur most frequently in midlatitudes, in particular over the Southern Ocean where they determine the amount of absorbed shortwave radiation. In ECHAM6-HAM2 the amount of absorbed shortwave radiation over the Southern Ocean is only significantly overestimated if all clouds below 0 • C consist exclusively of ice. Only in this simulation is ECS significantly smaller than in all other simulations and the cloud optical depth feedback is the dominant cloud feedback. In all other simulations, the cloud optical depth feedback is weak and changes in cloud feedbacks associated with cloud amount and cloud-top pressure dominate the overall cloud feedback. However, apart from the simulation with only ice below 0 • C, differences in the overall cloud feedback are not translated into differences in ECS in our model. This insensitivity to the cloud feedback in our model is explained with compensating effects in the clear sky.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2

Lohmann

Neubauer

2018

Atmos. Chem. Phys.

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An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

et al. 2018

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The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9‐year record of the Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E): clear‐sky bias, cloud‐rain partition (CRP) bias, cloud‐fraction‐dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR‐E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud‐fraction‐dependent bias was found to be a combination of the CRP bias, an in‐cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m 2 , dominated by the CRP and in‐cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias‐corrected AMSR‐E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible‐infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

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