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- Author or Editor: Jo Dixon x
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Abstract
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.
Abstract
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.
As part of the U.K. contribution to the international Surface Ocean-Lower Atmosphere Study, a series of three related projects—DOGEE, SEASAW, and HiWASE—undertook experimental studies of the processes controlling the physical exchange of gases and sea spray aerosol at the sea surface. The studies share a common goal: to reduce the high degree of uncertainty in current parameterization schemes. The wide variety of measurements made during the studies, which incorporated tracer and surfactant release experiments, included direct eddy correlation fluxes, detailed wave spectra, wind history, photographic retrievals of whitecap fraction, aerosolsize spectra and composition, surfactant concentration, and bubble populations in the ocean mixed layer. Measurements were made during three cruises in the northeast Atlantic on the RRS Discovery during 2006 and 2007; a fourth campaign has been making continuous measurements on the Norwegian weather ship Polarfront since September 2006. This paper provides an overview of the three projects and some of the highlights of the measurement campaigns.
As part of the U.K. contribution to the international Surface Ocean-Lower Atmosphere Study, a series of three related projects—DOGEE, SEASAW, and HiWASE—undertook experimental studies of the processes controlling the physical exchange of gases and sea spray aerosol at the sea surface. The studies share a common goal: to reduce the high degree of uncertainty in current parameterization schemes. The wide variety of measurements made during the studies, which incorporated tracer and surfactant release experiments, included direct eddy correlation fluxes, detailed wave spectra, wind history, photographic retrievals of whitecap fraction, aerosolsize spectra and composition, surfactant concentration, and bubble populations in the ocean mixed layer. Measurements were made during three cruises in the northeast Atlantic on the RRS Discovery during 2006 and 2007; a fourth campaign has been making continuous measurements on the Norwegian weather ship Polarfront since September 2006. This paper provides an overview of the three projects and some of the highlights of the measurement campaigns.
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