Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year

Rinke, Annette; Dethloff, Klaus; Cassano, J.; Christensen, Jens Hesselbjerg; Curry, J. A.; Du, Ping; Girard, E.; Haugen, Jan Erik; Jacob, Daniela; Jones, C.; Køltzow, Morten; Laprise, René; Lynch, A. H.; Pfeifer, Susanne; Serreze, M.; Shaw, M. J.; Tjernström, Michael; Wyser, Klaus; Žagar, Mark

doi:10.1007/s00382-005-0095-3

Cited by 73 publications

(71 citation statements)

References 34 publications

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“…Hinkelman et al (1999) find that the Eta Model has more inaccurately predicted clouds than correctly predicted clouds (based primarily on cloud height), as evaluated with observations at the Atmospheric Radiation Measurement (ARM) site in Oklahoma. In the Arctic, the greatest disagreement between eight different regional climate models was found in the predicted cloud cover, which led to errors in the surface radiation fluxes and 2-m temperature (Rinke et al 2006) as evaluated with data from the SHEBA project. Hines et al (2004) find similar errors in predicting Antarctic clouds within the NCAR climate models, especially noting that the clouds over the interior of the continent are too optically thick.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Fogt

Bromwich

2008

Weather and Forecasting

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“…The cloud cover also showed a large scatter between models due to the diversity of cloud modelling assumptions. Additionally, large internal variability for each RCM across the Arctic region (Rinke et al 2004) leads to even more scatter between results (Rinke et al 2006). Due to internal variability, each individual RCM simulation represents only one realization of the spectrum of plausible solutions.…”

Section: Introductionmentioning

confidence: 99%

Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

et al. 2017

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most notable positive effect over the Arctic is a reduction of the T2m bias over the North Pacific Ocean and the North Atlantic Ocean in all seasons. Future projections using this method are compared with the results obtained with the traditional 2-step dynamical downscaling (CGCM-RCM) to assess the impact of correcting systematic biases of SST upon future-climate projections. The future projections are mostly similar for the two methods, except for precipitation.

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“…Jones et al 1995;Christensen et al 1997;Machenhauer et al 1998;Frei et al 2003;Rinke et al 2006;Jacob et al 2007), or under the idealised Big-Brother Experiment (e.g. Denis et al 2002bDenis et al , 2003Antic et al 2004;Dimitrijevic and Laprise 2005).…”

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confidence: 99%

Can added value be expected in RCM-simulated large scales?

Diaconescu

Laprise

2013

Clim Dyn

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Nested Limited-Area Models require driving data to define their lateral boundary conditions (LBC). The optimal choice of domain size and the repercussions of LBC errors on Regional Climate Model (RCM) simulations are important issues in dynamical downscaling work. The main objective of this paper is to investigate the effect of domain size, particularly on the larger scales, and to question whether an RCM, when run over very large domains, can actually improve the large scales compared to those of the driving data. This study is performed with a detailed atmospheric model in its global and regional configurations, using the ''Imperfect Big-Brother'' (IBB) protocol. The ERA-Interim reanalyses and five global simulations are used to drive RCM simulations for five winter seasons, on four domain sizes centred over the North American continent. Three variables are investigated: precipitation, specific humidity and zonal wind component. The results following the IBB protocol show that, when an RCM is driven by perfect LBC, its skill at reproducing the large scales decreases with increasing the domain of integration, but the errors remain small even for very large domains. On the other hand, when driven by LBC that contain errors, RCMs can bring some reduction of errors in large scales when very large domains are used. The improvement is found especially in the amplitude of patterns of both the stationary and the intra-seasonal transient components. When large errors are present in the LBC, however, these are only partly corrected by the RCM.Although results showed that an RCM can have some skill at improving imperfect large scales supplied as driving LBC, the main added value of an RCM is provided by its small scales and its skill to simulate extreme events, particularly for precipitation. Under the IBB protocol all RCM simulations were fairly skilful at reproducing small scales statistics, although the skill decreased with increasing LBC errors. Coarse-resolution model simulations have difficulties in simulating heavy precipitation events, and as a result their precipitation distributions are systematically shifted toward smaller intensity. Under the IBB protocol, all RCM simulations have distributions very similar to the reference field, being little affected by LBC errors, and no significant differences were found between the small scales statistics and the precipitation distributions obtained over different RCM domains.

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Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year

Cited by 73 publications

References 34 publications

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Atmospheric Moisture and Cloud Cover Characteristics Forecast by AMPS*

Dynamical downscaling with the fifth-generation Canadian regional climate model (CRCM5) over the CORDEX Arctic domain: effect of large-scale spectral nudging and of empirical correction of sea-surface temperature

Can added value be expected in RCM-simulated large scales?

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