The radiation budget in a regional climate model

Kothe, Steffen; Dobler, Andreas; Beck, Alexander; Ahrens, Bodo

doi:10.1007/s00382-009-0733-2

Cited by 45 publications

(44 citation statements)

References 101 publications

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“…These two variables are key to explain the spread, which is reasonable given that the schemes changed are those more closely related with clouds and radiation. Other studies evaluating RCMs radiative fluxes (Kothe and Ahrens 2010;Kothe et al 2011;Pessacg et al 2013;Samuelsson et al 2011) found similar relationships among the biases of the different variables, and similar error magnitudes (or even larger, in the case of tropical regions in (Pessacg et al 2013)). They also found cases in which biases compensate each other to result in small errors in some variables for the wrong reasons.…”

Section: Discussionmentioning

confidence: 80%

An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation

2015

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Regional Climate Models (RCMs) are widely used tools to add detail to the coarse resolution of global simulations. However, these are known to be affected by biases. Usually, published model evaluations use a reduced number of variables, frequently precipitation and temperature. Due to the complexity of the models, this may not be enough to assess their physical realism (e.g. to enable a fair comparison when weighting ensemble members). Furthermore, looking at only a few variables makes difficult to trace model errors. Thus, in many previous studies, these biases are described but their underlying causes and mechanisms are often left unknown. In this work the ability of a multi-physics ensemble in reproducing the observed climatologies of many variables over Europe is analysed. These are temperature, precipitation, cloud cover, radiative fluxes and total soil moisture content. It is found that, during winter, the model suffers a significant cold bias over snow covered regions. This is shown to be related with a poor representation of the snow-atmosphere interaction, and is amplified by an albedo feedback. It is shown how two members of the ensemble are able to alleviate this bias, but by generating a too large cloud cover. During summer, a large sensitivity to the cumulus parameterization is found, related to large differences in the cloud cover and short wave radiation flux. Results also show that small errors in one variable are sometimes a result of error compensation, so the high dimensionality of the model evaluation problem cannot be disregarded.

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Section: Discussionmentioning

confidence: 80%

An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation

2015

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“…A detailed documentation of the LM (Doms and Schättler, 1999) is available at http://www.cosmo-model.org. More information on the model setup and results of regional climate simulations over Europe and South Asia are given in Ahrens (2008, 2010) and Kothe et al (2010).…”

Section: Scientific Methods Appliedmentioning

confidence: 99%

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Dobler

Sharma

et al. 2011

Adv. Sci. Res.

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Abstract. Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB).This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe.Different climate change scenarios were downscaled for the time period 1960-2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks.

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“…Moreover, CP does not directly consider gaseous absorption. The model has increasingly been used to assess cirrus cloud radiative effects (Kothe et al, 2011;Kienast-Sjögren et al, 2016;Burgeois et al, 2016) from lidar measurements, owing to its relative simplicity and lower computational burden compared with a model like FLG.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Fu–Liou–Gu and Corti–Peter model performance evaluation for radiative retrievals from cirrus clouds

et al. 2017

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Abstract. We compare, for the first time, the performance of a simplified atmospheric radiative transfer algorithm package, the Corti-Peter (CP) model, versus the more complex Fu-Liou-Gu (FLG) model, for resolving top-of-theatmosphere radiative forcing characteristics from singlelayer cirrus clouds obtained from the NASA Micro-Pulse Lidar Network database in 2010 and 2011 at Singapore and in Greenbelt, Maryland, USA, in 2012. Specifically, CP simplifies calculation of both clear-sky longwave and shortwave radiation through regression analysis applied to radiative calculations, which contributes significantly to differences between the two. The results of the intercomparison show that differences in annual net top-of-the-atmosphere (TOA) cloud radiative forcing can reach 65 %. This is particularly true when land surface temperatures are warmer than 288 K, where the CP regression analysis becomes less accurate. CP proves useful for first-order estimates of TOA cirrus cloud forcing, but may not be suitable for quantitative accuracy, including the absolute sign of cirrus cloud daytime TOA forcing that can readily oscillate around zero globally.

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The radiation budget in a regional climate model

Cited by 45 publications

References 101 publications

An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation

An RCM multi-physics ensemble over Europe: multi-variable evaluation to avoid error compensation

Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

Technical note: Fu–Liou–Gu and Corti–Peter model performance evaluation for radiative retrievals from cirrus clouds

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