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Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change meteorological pattern found in the station data remains as a baseline reference and this enables synchronization of the history of system operations that occurred in response to historical meteorology with meteorological events in the future climate scenario. The disadvantage of this approach is that the method relies on the historical time series to order the occurrence of events and fixes the event arrival times, and as a result, differing future climate scenarios will not be truly independent datasets. In its simpler form, a seasonal or monthly factor is applied equally to each day within a season (month) of the station data to produce a scenario with the same meteorological pattern as the historical data but with adjustment to the seasonal mean values. When factors are calculated and applied to sub-ranges of the probability distribution of station data, nonlinear changes in distributions of meteorological variables are introduced to emulate changes in the intensity of extreme events. Even more sophisticated applications may utilize nonlinear or discontinuous functions to adjust the distributions of storm and inter-storm period lengths while maintaining a morphological connection to the original historical station data. The bias-correction and spatial downscaling (BCSD) method, introduced in Wood et al. (2004), combines change factors (Method 1 in Section B.1), quantile mapping (Method 3 in Section B.1), and stochastic weather generators (Method 6 in Section B.1). In this method, monthly precipitation and temperature output from a GCM are downscaled, and daily data are created by applying a stochastic weather generator to the monthly data. In this way, the BCSD method does not account for changes in the statistics of climate variability at scales less than monthly that may be projected by a GCM, and is not expected to exhibit skill at projecting statistics of daily extremes above simply assuming climatological daily variability. The BCSD method first aggregates monthly observations to the grid of the GCM. The technique uses a quantile-based mapping of the probability density functions for the monthly GCM precipitation and temperature to remove GCM biases. This same mapping is applied to the future GCM simulations. The final steps compute change factors between the GCM baseline and future simulations, interpolate the change factors to the high-resolution grid of observations, and apply the change factors to the observational climatology. Unlike the traditional use of change factors, this approach does not use the sequence of observed weather events. The advantage of this approach is that the mean and variability of a GCM may evolve in accordance with the GCM simulation, while preserving all statistical moments of the observations for the base period. The BCSD method is one of a small number of statistical downscaling methods that have been applied to output from both GCM and RCM simulations. Results indicate raw output from GCMs and RCMs produces a poor representation of hydrology for all time horizons (daily, seasonal, annual) in the Northwest. When BCSD is applied, the hydrology is reasonable, and the climate change sensitivity is larger when using RCM compared to GCM data as input to BCSD. The constructed analogue (CA) approach, described in Hidalgo et al. (2008), blends analogue techniques (Method 5 in Section B.1) and linear regression functions (Method 4 in Section B.1) to produce a larger universe of analogues. The technique is applied to daily weather patterns. The Page A-9
Water Utility Climate Alliance White Paper Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change pattern to be downscaled is constructed from a linear combination of previously observed coarsescale patterns that are most similar to the target pattern. Thus, a single pattern is not used as the analogue. Instead, multiple analogues are selected, and only the pieces that best fit the pattern to be downscaled are used. The linear regression coefficients are then applied to the high-resolution patterns that are associated with the observed coarse-scale patterns. The advantages of this method are that never-before-observed patterns are generated by patching together many observed patterns, thereby expanding the library of analogues, and daily sequences of weather are generated that reflect the GCM’s daily weather evolution. The disadvantage is that the sequencing of daily weather from the GCM is retained so that biases in GCM variance are reconstructed in the downscaled data. Page A-10
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