A comparison of cloud and boundary layer variables in the ECMWF forecast model with observations at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp

Beesley, J. A.; Bretherton, Christopher S.; Jakob, Christian; Andreas, Edgar L.; Intrieri, J. M.; Uttal, Taneil

doi:10.1029/2000jd900079

Cited by 80 publications

(92 citation statements)

References 17 publications

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“…Despite the fact that summer near-surface temperatures can be warmer than Ϫ34°C within the 0.025-mm contour in Fig. 11b, the coldest temperature at which Beesley et al (2000) demonstrated the presence of supercooled liquid water in the Arctic, AMPS rarely produces any liquid water over the interior of the continent. Morrison et al (2003) demonstrate a similar problem in modeling the CLW in a single-column model in the Arctic.…”

Section: October 2008 F O G T a N D B R O M W I C Hmentioning

confidence: 93%

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Fogt

Bromwich

2008

Weather and Forecasting

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Antarctic Mesoscale Prediction System (AMPS) forecasts of atmospheric moisture and cloud fraction (CF) are compared with observations at McMurdo and Amundsen-Scott South Pole station (hereafter, South Pole station) in Antarctica. Overall, it is found that the model produces excessive moisture at both sites in the mid-to upper troposphere because of a weaker vertical decrease of moisture in AMPS than observed. Correlations with observations suggest AMPS does a reasonable job of capturing the low-level moisture variability at McMurdo and the upper-level moisture variability at South Pole station. The model underpredicts the cloud cover at both locations, but changes to the AMPS empirical CF algorithm remove this negative bias by more than doubling the weight given to the cloud ice path.A "pseudosatellite" product based on the microphysical quantities of cloud ice and cloud liquid water within AMPS is preliminarily evaluated against Defense Meteorological Satellite Program (DMSP) imagery during summer to examine the broader performance of cloud variability in AMPS. These comparisons reveal that the model predicts high-level cloud cover and movement with fidelity, which explains the good agreement between the modified CF algorithm and the observed CF. However, this product also demonstrates deficiencies in capturing low-level cloudiness over cold ice surfaces primarily related to insufficient supercooled liquid water produced by the microphysics scheme, which also reduces the CF correlation with observations.The results suggest that AMPS predicts the overall CF amount and high cloud variability notably well, making it a reliable tool for longer-term climate studies of these fields in Antarctica.

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Section: October 2008 F O G T a N D B R O M W I C Hmentioning

confidence: 93%

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Fogt

Bromwich

2008

Weather and Forecasting

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“…Beesley et al, 2000;Tjernström et al, 2008). Recently more advanced moist physics has made its way into stateof-the-art climate and weather forecast models .…”

Section: Cloud Physicsmentioning

confidence: 99%

“…This makes the Arctic ABL more liable to the effects of propagating internal gravity waves. Also, the common presence of mixedphase clouds in the Arctic marks a drastic difference from lower latitudes; observations of liquid water present in clouds at temperatures down to −34 • C during SHEBA (Beesley et al, 2000;Intrieri et al, 2002) demonstrated the need to develop better parameterization schemes for the ice and liquid water fractions (Gorodetskaya et al, 2008). In the past, forecast centres running global climate or NWP models have not paid enough attention to problems in physical parameterizations in the Arctic, but the situation is improving with the Arctic coming more into focus, driven by the worldwide attention to Arctic climate change and the increasing need for operational weather and marine forecasting services in the Arctic.…”

mentioning

confidence: 99%

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

et al. 2014

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Abstract. The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007-2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice-ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave-turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice-ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

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“…To date, the accurate representation of the vertical structure of the stable AABL above an ice surface still poses a serious challenge for climate modelers [5][6][7]. Model simulations of the observed summer sea-ice retreat depends on the correct simulation of the atmospheric circulation during the summer months and the sea-ice volume in the transition time from winter to spring at the beginning of the melting period [8].…”

Section: Introductionmentioning

confidence: 99%

The Spring-Time Boundary Layer in the Central Arctic Observed during PAMARCMiP 2009

et al. 2012

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Abstract:The Arctic atmospheric boundary layer (AABL) in the central Arctic was characterized by dropsonde, lidar, ice thickness and airborne in situ measurements during Above sea ice, a low AABL top, low near-surface temperatures, strong surface-based temperature inversions and an increase of moisture with altitude were observed. AABL properties and particle concentrations were modified by a frontal system, allowing vertical mixing with the free atmosphere. Above areas with many leads, the potential temperature decreased with height in the lowest 50 m and was nearly constant above, up to an altitude of 100-200 m, indicating vertical mixing. The increase of the backscatter coefficient towards the surface was high. Above sea ice with few refrozen leads, the stably stratified boundary layer extended up to 200-300 m altitude. It was characterized by low specific humidity and a smaller increase of the backscatter coefficient towards the surface.

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A comparison of cloud and boundary layer variables in the ECMWF forecast model with observations at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp

Cited by 80 publications

References 17 publications

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

The Spring-Time Boundary Layer in the Central Arctic Observed during PAMARCMiP 2009

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