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1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of
1. Introduction At the Canadian Centre for Climate Modelling and Analysis (CCCma), a new regional climate model, the CCCma Regional Climate Model (CanRCM4), has been developed. CanRCM4’s novelty does not arise from the method of solution in its dynamical core or the climate-based physics package it employs. Both of these are well known and currently operational for global model applications. The novelty of CanRCM4 stems from a new philosophy of coordinating the development and application of
, the glacier runoff has increased because of the ample quantities of water supplied by retreating glaciers. However, continuing loss of glacier mass will gradually reduce both glacier-fed and total runoff ( Braun et al. 2000 ; Hock 2005 ; Hagg et al. 2007 ; Zhao et al. 2013 ). This loss will eventually transform glacial–nival runoff regimes into nival–pluvial regimes, which will generate much greater interannual variability of water yield ( Malone 2010 ; Sorg et al. 2012 ). Successful models of
, the glacier runoff has increased because of the ample quantities of water supplied by retreating glaciers. However, continuing loss of glacier mass will gradually reduce both glacier-fed and total runoff ( Braun et al. 2000 ; Hock 2005 ; Hagg et al. 2007 ; Zhao et al. 2013 ). This loss will eventually transform glacial–nival runoff regimes into nival–pluvial regimes, which will generate much greater interannual variability of water yield ( Malone 2010 ; Sorg et al. 2012 ). Successful models of
1. Limitations of performance metrics One of the primary objectives of measurement or model evaluation is to quantify the uncertainty in the data. This is because the uncertainty directly determines the information content of the data (e.g., Jaynes 2003 ), and dictates our rational use of the information, be it for data assimilation, hypothesis testing, or decision-making. Further, by appropriately quantifying the uncertainty, one gains insight into the error characteristics of the measurement
1. Limitations of performance metrics One of the primary objectives of measurement or model evaluation is to quantify the uncertainty in the data. This is because the uncertainty directly determines the information content of the data (e.g., Jaynes 2003 ), and dictates our rational use of the information, be it for data assimilation, hypothesis testing, or decision-making. Further, by appropriately quantifying the uncertainty, one gains insight into the error characteristics of the measurement
, which shows characteristics of spatiotemporal red noise and also coherent propagating wave features ( Takayabu 1994 ; Wheeler and Kiladis 1999 ); and (iii) statistical physics perspectives including critical phenomena ( Peters and Neelin 2006 ; Neelin et al. 2009 ). The main aim of this paper is to show that a relatively simple model of water vapor dynamics has behavior very similar to these observational statistics. The new water vapor model proposed in this paper is a discrete version of the
, which shows characteristics of spatiotemporal red noise and also coherent propagating wave features ( Takayabu 1994 ; Wheeler and Kiladis 1999 ); and (iii) statistical physics perspectives including critical phenomena ( Peters and Neelin 2006 ; Neelin et al. 2009 ). The main aim of this paper is to show that a relatively simple model of water vapor dynamics has behavior very similar to these observational statistics. The new water vapor model proposed in this paper is a discrete version of the
1. Introduction Understanding the characteristics of water fluxes and storages across regions and globally and their future trajectories is crucial to plan and manage sustainable water use under changing climate and growing population. Various models, particularly global land surface models and global hydrological models, are used for modeling regional and global water fluxes and storages under present and future climate conditions ( Fung et al. 2011 ; Haddeland et al. 2011 ; Warszawski et al
1. Introduction Understanding the characteristics of water fluxes and storages across regions and globally and their future trajectories is crucial to plan and manage sustainable water use under changing climate and growing population. Various models, particularly global land surface models and global hydrological models, are used for modeling regional and global water fluxes and storages under present and future climate conditions ( Fung et al. 2011 ; Haddeland et al. 2011 ; Warszawski et al
1. Introduction Soil thermal conductivity λ is a primary property related to soil heat flux ( Bristow 2002 ). For a given type of soil, its magnitude depends largely on the soil volumetric water content θ , porosity θ s , bulk density ρ b , texture, and mineral composition ( de Vries 1963 ; Campbell 1985 ). Several λ models depending on these soil parameters have been presented. Kersten (1949) proposed an empirical λ model with only one input parameter, soil bulk density. The model
1. Introduction Soil thermal conductivity λ is a primary property related to soil heat flux ( Bristow 2002 ). For a given type of soil, its magnitude depends largely on the soil volumetric water content θ , porosity θ s , bulk density ρ b , texture, and mineral composition ( de Vries 1963 ; Campbell 1985 ). Several λ models depending on these soil parameters have been presented. Kersten (1949) proposed an empirical λ model with only one input parameter, soil bulk density. The model
1. Introduction Recent results show that ensemble-variational hybrids provide theoretical ( Bishop and Satterfield 2013 ) and practical ( Kuhl et al. 2013 ; Buehner et al. 2010a ; Zhang and Zhang 2012 ; Buehner et al. 2010b ; Kleist and Ide 2015 ) benefits for four-dimensional data assimilation. Most operational implementations of the four-dimensional hybrid system ( Kuhl et al. 2013 ; Lorenc et al. 2015 ) use the tangent linear model (TLM) and its adjoint (conjugate transpose) to
1. Introduction Recent results show that ensemble-variational hybrids provide theoretical ( Bishop and Satterfield 2013 ) and practical ( Kuhl et al. 2013 ; Buehner et al. 2010a ; Zhang and Zhang 2012 ; Buehner et al. 2010b ; Kleist and Ide 2015 ) benefits for four-dimensional data assimilation. Most operational implementations of the four-dimensional hybrid system ( Kuhl et al. 2013 ; Lorenc et al. 2015 ) use the tangent linear model (TLM) and its adjoint (conjugate transpose) to
1. Introduction Land surface models (LSMs) are utilized by general circulation models (GCMs) to represent land surface processes, primarily for the purpose of modeling land–atmosphere interactions as represented by energy and water fluxes across the land–atmosphere interface. When coupled to river transport models (RTMs), LSMs can also represent the transport of water from land back to the ocean. Accurate modeling of soil moisture is a prerequisite for a good representation of land
1. Introduction Land surface models (LSMs) are utilized by general circulation models (GCMs) to represent land surface processes, primarily for the purpose of modeling land–atmosphere interactions as represented by energy and water fluxes across the land–atmosphere interface. When coupled to river transport models (RTMs), LSMs can also represent the transport of water from land back to the ocean. Accurate modeling of soil moisture is a prerequisite for a good representation of land
1. Introduction Over the past two decades, land surface models (LSMs) have evolved from oversimplified schemes, which described the surface boundary conditions for global circulation models (GCMs), to complex models that can be used alone or as part of GCMs to investigate the biogeochemical, hydrological, and energy cycles at the earth’s surface ( Pitman 2003 ; Flato et al. 2013 ). The increasing complexities of LSMs and their broadening applications for climate change studies have warranted a
1. Introduction Over the past two decades, land surface models (LSMs) have evolved from oversimplified schemes, which described the surface boundary conditions for global circulation models (GCMs), to complex models that can be used alone or as part of GCMs to investigate the biogeochemical, hydrological, and energy cycles at the earth’s surface ( Pitman 2003 ; Flato et al. 2013 ). The increasing complexities of LSMs and their broadening applications for climate change studies have warranted a
. 2009 ). Also, intrinsic variability due to meso- and submesoscale activity can lead to cross-shore eddy fluxes ( Capet et al. 2008 ; Dong et al. 2009 ; Romero et al. 2013 ; Uchiyama et al. 2014 , hereinafter U14 ). Recent circulation modeling studies have simulated cross-shelf exchange. For example, Romero et al. (2013) applied the Regional Ocean Modeling System (ROMS) to characterize horizontal relative dispersion as a function of coastal geometry, bathymetry, and eddy kinetic energy in the
. 2009 ). Also, intrinsic variability due to meso- and submesoscale activity can lead to cross-shore eddy fluxes ( Capet et al. 2008 ; Dong et al. 2009 ; Romero et al. 2013 ; Uchiyama et al. 2014 , hereinafter U14 ). Recent circulation modeling studies have simulated cross-shelf exchange. For example, Romero et al. (2013) applied the Regional Ocean Modeling System (ROMS) to characterize horizontal relative dispersion as a function of coastal geometry, bathymetry, and eddy kinetic energy in the
