A model evaluation approach is proposed in which weather and climate prediction models are analyzed along a Pacific Ocean cross section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade winds, to the deep convection regions of the ITCZ-the Global Energy and Water Cycle Experiment Cloud System Study/Working Group on Numerical Experimentation (GCSS/ WGNE) Pacific Cross-Section Intercomparison (GPCI). The main goal of GPCI is to evaluate and help understand and improve the representation of tropical and subtropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross section from the subtropics to the tropics for the season June-July-August of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the 40-yr ECMWF Re-Analysis (ERA-40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical cross sections of cloud properties (in particular), vertical velocity, and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA-40 in the stratocumulus regions [as compared to the first International Satellite Cloud Climatology Project (ISCCP)] is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade wind Lagrangian trajectory. Histograms of cloud cover along the cross section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.
Transferability intercomparisons provide a new approach for advancing the science of modeling the water cycle and energy budget on regional to global scales by using multiple limited-area models applied to multiple domains. The water and associated energy cycles introduce exponential, episodic, and other nonlinear processes that create difficulties for observing, simulating, and predicting climate variations. The water cycle both creates and responds to spatial heterogeneities that feed back strongly on the energy budget and circulation system. These feedback processes represent some of the largest uncertainties in our ability to simulate future scenarios of Earths climate, especially scenarios that suggest warming beyond the temperature bounds of recent interglacial conditions and hence for which we have no previous observations for comparison. Water cycle processes also occur on a wide range of spatial and temporal scales, many being far too small to either be globally observed and or simulated by global climate and weather forecast models.Transferability intercomparions represent a new approach for understanding the water cycle and energy budget on regional to global scales. This new class of intercomparisons applies multiple regional climate models to a prescribed collection of domains where enhanced observations are conducted and results are archived in a coordinated manner. The primary goals of the transferability intercomparisons are to understand the complex interactions forming the water cycle and evaluate our ability to simulate these processes. The transferability framework goes
The term "climate services" is commonly used to refer to the generation of climate information, their transformation according to user needs and the subsequent use of the information in decision making processes. More generally, the concept also involves contextualization of information and knowledge. In the following a series of examples from the marine sector is described covering the generation, transformation and the use of climate information in decision making processes while contextualization is not considered. Examples comprise applications from naval architecture, offshore wind and more generally renewable energies, shipping emissions, and tidal basin water exchange and eutrophication levels. Moreover effects of climate change on coastal flood damages and the need for coastal protection are considered. Based on the analysis of these examples it is concluded that reliable climate information in data sparse regions is urgently needed, that for many applications historical climate information may be as or even more important as future long-term projections, and that the specific needs of different sectors substantially depend on their planning horizons.
[1] The cloud parameterization in the regional atmospheric model SN-REMO (Spectrally Nudged Regional Model) was validated using satellite data from the ISCCP (International Satellite Cloud Climatology Project). There is an overall good agreement between the cloudiness of SN-REMO and ISCCP in terms of temporal and spatial means. However, with further investigation a deficiency was localized regarding the simulation of cloud amount: Too many clouds are simulated. This overestimation occurs especially during the night. It is connected with a poor simulation of the cloud diurnal cycle. Clouds at low-level emissivity heights (1000-475 hPa) are causing this overestimation. The magnitude of the overall overestimation is also affected by the underestimation of simulated cloud amount at high-level emissivity heights (<475 hPa) and its diurnal variation. The overestimation of the simulated cloud amount is caused by subgrid-scale cloudiness. Since the simulation of subgrid-scale clouds in the regional model SN-REMO is described by a relative humidity parameterization, these deficiencies are connected with this parameterization.
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