Abstract
Long‐term variability in the hydrologic cycle is poorly simulated by current generation global climate models (GCMs), partly due to known climatological biases at shorter timescales. We demonstrate that a prototype Multi‐scale Modeling Framework (MMF) provides a superior representation of the spatial and temporal structure of precipitation at diurnal timescales than a GCM. Results from empirical orthogonal function (EOF) decomposition of the boreal summer climatological composite diurnal cycle of precipitation in an MMF are compared to a GCM and satellite data from the Tropical Rainfall Measuring Mission. The eigenspectrum, principal component time series, and the spatial structure of leading EOFs in an eigenmode decomposition of the MMF composite day are a much better match to observations than the GCM. Regional deficiencies in the MMF diurnal cycle are manifest as localized anomalies in the spatial structures of the first two leading EOFs.
Highlights
[2] At a time when reliable projections about the hydrologic response to anthropogenic climate change are in demand by many stakeholders, a climate modeling framework that accurately represents the physical drivers of moist convection on multiple time scales is needed
[9] In this study we present new results demonstrating an overall improvement in the diurnal cycle of precipitation in an Multiscale Modeling Framework (MMF) relative to a conventional global climate models (GCMs), as diagnosed by empirical orthogonal function (EOF) decomposition of the climatological mean summer day
In MMFs, parameterizations of subgrid cloud processes are replaced by nested cloud-resolving model (CRM) integrations within small high resolution subdomains housed in each host GCM grid column
Summary
[2] At a time when reliable projections about the hydrologic response to anthropogenic climate change are in demand by many stakeholders, a climate modeling framework that accurately represents the physical drivers of moist convection on multiple time scales is needed. [3] A new approach to climate modeling offers dramatic improvement in simulating diurnal hydrologic processes. [9] In this study we present new results demonstrating an overall improvement in the diurnal cycle of precipitation in an MMF relative to a conventional GCM, as diagnosed by empirical orthogonal function (EOF) decomposition of the climatological mean summer day. This EOF approach was recently advocated as a benchmark test for evaluating simulated hydrologic diurnal variability against new space-borne precipitation observations [Kikuchi and Wang, 2008]. Our results indicate that the MMF passes this test the eigenstructure of the MMF’s mean summer day’s precipitation is more faithful to the observations than in conventional GCMs
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