A Hybrid Dynamical–Statistical Downscaling Technique. Part I: Development and Validation of the Technique

Abstract

In this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical–statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region’s warming patterns, enhancing the credibility of the final results.

Bias correction and downscaling of future RCM precipitation projections using a MOS‐Analog technique

“…However, it is worth noting that this method can produce accumulated values or frequencies over several days larger (or smaller) than the historical values and, therefore, can extrapolate nonobserved values for these types of indices. Finally, it must be noted that standard bias correction methods are not intended to correct climate trends [Maraun, 2016], and methods that deliberately constrain the climate change signals based on process understanding are rare [Collins et al, 2012;Walton et al, 2015]. In this study, the cases where the MOS-Analog method modifies the RCM climate signals (for extreme indices) could be simply explained by the limitation of the method to extrapolate unobserved values (e.g., higher extremes simulated by the RCM for a future period) and not by a merit of the method to introduce plausible changes in the regional climate change signal-which could be the case in other situations, since the MOS-Analog can potentially introduce extra regional information in the downscaling process.…”

WRF simulation of downslope wind events in coastal Santa Barbara County

“…The WRF-3 configuration combines modifications suggested in Walton et al (2015) and the experience of the NWS/LOX in operationally forecasting sundowner events using WRF. This is the configuration that was used by the NWS/LOX operational forecasts at the time of this study.…”

Towards predictive understanding of regional climate change

“…Downscaling global model projections without carefully considering model biases and internal variability adds essentially meaningless spatial detail 1 . Regional models may be useful to understand physical processes in areas of complex coastlines and orography, and may provide useful climate change impact information on the km scales relevant to climate adaptation planning 95 . We suggest, however, that the current priority is to understand and reduce GCM uncertainties on regional scales (> 100 km), which often dictate changes on finer-scales.…”