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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
It is well known that the interactions between land surfaces and the atmosphere, and the resulting exchanges in water and energy have a tremendous affect on climate. The inadequate representation of land-atmosphere interactions is a major weakness in current climate models, and is providing the motivation for the HAPEX and ISLSCP experiments as well as the proposed Global Energy and Water Experiment (GEWEX) and the Earth Observing System (EOS) mission. The inadequate representation reflects the recognition that the well-known phys ical relationships, which are well described at small scales, result in different relationships when represented at the scales used in climate models. Understanding this transition in the mathematical relationships with increased space-time scales appears to be very difficult, and has led to different approaches; at one extreme, the famous bucket model where the land-surface is a simple one layer storage without vegetation; the other extreme may be Seller’s Simple Biosphere Model (Sib) where one big leaf covers the climate model grid. Given the heterogeneous nature of landforms, soils and vegetation within a climate model grid, the development of new land surface parameterizations, and their verification through large scale experiments is perceived to be a challenging area of research for the hydrology and meteorology communities. This book evolved from a workshop held at Princeton University to explore the status of land surface parameterizations within climate models, and how observa tional data can be used to assess these parameterizations and improve models.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
It is well known that the interactions between land surfaces and the atmosphere, and the resulting exchanges in water and energy have a tremendous affect on climate. The inadequate representation of land-atmosphere interactions is a major weakness in current climate models, and is providing the motivation for the HAPEX and ISLSCP experiments as well as the proposed Global Energy and Water Experiment (GEWEX) and the Earth Observing System (EOS) mission. The inadequate representation reflects the recognition that the well-known phys ical relationships, which are well described at small scales, result in different relationships when represented at the scales used in climate models. Understanding this transition in the mathematical relationships with increased space-time scales appears to be very difficult, and has led to different approaches; at one extreme, the famous bucket model where the land-surface is a simple one layer storage without vegetation; the other extreme may be Seller’s Simple Biosphere Model (Sib) where one big leaf covers the climate model grid. Given the heterogeneous nature of landforms, soils and vegetation within a climate model grid, the development of new land surface parameterizations, and their verification through large scale experiments is perceived to be a challenging area of research for the hydrology and meteorology communities. This book evolved from a workshop held at Princeton University to explore the status of land surface parameterizations within climate models, and how observa tional data can be used to assess these parameterizations and improve models.