My recent review paper "Review of urban surface parameterizations for numerical climate models"(https://www.sciencedirect.com/science/article/pii/S2212095517300858) presents the historical development of urban climate models, uncertainties and challenges in obtaining representative urban weather and climate information.
There are wide scale urban weather/climate models and the level of detail and complexity required for a particular study is oftentimes a challenge for the climate modeling communities. Weather/climate models are required for different purposes, such as understanding the teleconnections between different scale weather/climate phenomena, for weather forecasting and climate projections, and for policy purposes. Therefore, improving the performance of climate models is one of the top priorities in the climate modeling communities. On the other hand, obtaining accurate initial and boundary information is challenging. Observations data of high spatial density and scale are important for model validation, as well. Furthermore, uncertainties due to specific parameterization components are also important to consider. For example, in urban climate models, the water, snow, urban induced clouds and impervious surface parameterizations are still at their infant stages. Determining urban roughness length for turbulence parameterizations at urban scales is also one of the areas where improvements are needed. Such urban climate modeling challenges and methods of improving the models are discussed in the paper.
The most important area worth discussing is how uncertainties are quantified in climate models and specifically the urbanized ones. Models are tuned to get the right match with obseration - known as validation by the climate modeling communities. While tuning my be valid for particlar reasoon and few paramters, it is oftentimes an abused concept by some climate modelers. A modeler should present the tuning strategy followed public to make the work transparent and legitmate. This issue is also discussed in the paper and I would humbly invite you to read it. Bon reading!
There are wide scale urban weather/climate models and the level of detail and complexity required for a particular study is oftentimes a challenge for the climate modeling communities. Weather/climate models are required for different purposes, such as understanding the teleconnections between different scale weather/climate phenomena, for weather forecasting and climate projections, and for policy purposes. Therefore, improving the performance of climate models is one of the top priorities in the climate modeling communities. On the other hand, obtaining accurate initial and boundary information is challenging. Observations data of high spatial density and scale are important for model validation, as well. Furthermore, uncertainties due to specific parameterization components are also important to consider. For example, in urban climate models, the water, snow, urban induced clouds and impervious surface parameterizations are still at their infant stages. Determining urban roughness length for turbulence parameterizations at urban scales is also one of the areas where improvements are needed. Such urban climate modeling challenges and methods of improving the models are discussed in the paper.
The most important area worth discussing is how uncertainties are quantified in climate models and specifically the urbanized ones. Models are tuned to get the right match with obseration - known as validation by the climate modeling communities. While tuning my be valid for particlar reasoon and few paramters, it is oftentimes an abused concept by some climate modelers. A modeler should present the tuning strategy followed public to make the work transparent and legitmate. This issue is also discussed in the paper and I would humbly invite you to read it. Bon reading!
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