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Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change 3.2 Climate Model Downscaling This section provides an overview of downscaling techniques, where uncertainties arise in these methods, and how uncertainty may be more effectively quantified and communicated for regional and local projections of relevance to water utilities. 3.2.1 What are GCM downscaling techniques? GCM downscaling techniques are methods that transform output from GCM projections into regional and local projections containing much more spatial and temporal detail that may be useful for water utilities. Essentially, GCM downscaling techniques aim to simulate the behavior of local climate processes that are absent in GCMs. This might mean, for example, transforming the broad wind patterns of storms embedded within the jet stream by adding the effects of mountainous terrain to create local variations in wind patterns. Downscaling techniques are grouped into two classes: statistical and dynamical. Statistical models use empirical relationships between large-scale and local climate conditions. They have as their foundation the principles of stochastic and statistical models, and employ techniques such as optimal data fitting, regression, and Bayesian inference. They use extensive datasets of observations and GCM output to identify robust associations between large-scale and local climate conditions. These statistical associations are then used to produce local climate projections when provided data from a GCM projection. Dynamical downscaling models are based upon mechanistic relationships such as energy and mass conservation, just as are GCMs. They are called “dynamical” by analogy with Newtonian Physics where a force produces a response at some later time. That is, the time evolution of “dynamics” of the system is predicted through the mechanistic relationships. Dynamical models develop local climate projections when provided GCM projections by simulating local mechanistic climate processes, such as sea breezes and mountain-forced circulations, that are absent from GCM simulations. Because of their focus on regional climate mechanisms, they are often called regional climate models. It should be noted, however, that some statistical models also include time-evolving (i.e., dynamic) relationships. Thus, the following sections emphasize the empirical versus mechanistic nature of these techniques when making distinctions in their application for downscaling. There are several commonalities in the way statistical and dynamical downscaling techniques are used. For example, they both rely upon skillful representation by GCMs of large-scale climate conditions, such as the position of the jet stream. For this reason, the following sections first describe the aspects that are common to all downscaling techniques before delving into their peculiarities. Page 3-27
Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change 3.2.2 What uncertainties are associated with climate model downscaling? Downscaling techniques add a layer of modeling in-between GCM projections and application of projections in water utility management strategies. This adds some level of uncertainty in regional climate change projections, the nature of which is described below. A number of water utilities have expressed concern that the uncertainty implied by the range of downscaled GCM climate change projections is too large to drive decisions on strategic capital investments (see Items 1 and 2 in Chapter 1, Section 1.1). The overview provided below points to the need for a coordinated effort to better communicate to water utilities the degrees of uncertainty that exist and how they arise from various sources, so that they may make best use of regional climate projections in accordance with their management strategy. The need for this is succinctly stated in the U.S. Climate Change Science Program Scientific Assessment Report 5.1: For decision-makers, a critical issue concerns the extent to which the various scenarios reflect the actual uncertainty of the relevant risks versus the uncertainty due to methodological approaches and biases in underlying models. The review of climate change studies undertaken by water utilities reported earlier in this document indicates that little attention has been given to communicating all sources of uncertainty in regional climate projections. This disconnect in communicating uncertainty can arise for many reasons but, in our opinion, the most fundamental reasons are the following: 1. Climate researchers and water utility planners have vastly different definitions of, and tolerances for, uncertainty. These differences may not have been explicitly recognized. 2. Climate scenarios are provided to water utility planners in an ad hoc manner rather than as a dataset carefully designed to explicitly address their needs. As a result, management approaches for addressing uncertainty cannot be applied effectively using the current sets of climate projections. Even with improvements in climate models, it is very likely that substantial uncertainty about future climate change at scales water utilities require to properly model their systems will remain. Therefore, it is necessary not only to improve climate models but, just as importantly, to develop methods by which uncertainty can be more completely articulated and thereby address the concerns that water utilities have expressed regarding uncertainty in regional climate projections (see Chapter 5 and the Means et al., 2009 report). As discussed in Section 3.1.8, uncertainty in climate projections can be divided into three categories: climate driver uncertainty, climate system uncertainty (including variability), and downscaling uncertainty. Downscaling uncertainty is introduced by the inconsistency between the climate conditions as simulated by the GCM and by the downscaling technique. This inconsistency is inherent in all forms of downscaling, whether dynamical or statistical. To Page 3-28
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