A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments

A multivariate, multisite daily weather generator is presented for use in decision-centric vulnerability assessments under climate change. The tool is envisioned to be useful for a wide range of socioeconomic and biophysical systems sensitive to different aspects of climate variability and change. The proposed stochastic model has several components, including (1) a wavelet decomposition coupled to an autoregressive model to account for structured, low-frequency climate oscillations, (2) a Markov chain and k-nearest-neighbor (KNN) resampling scheme to simulate spatially distributed, multivariate weather variables over a region, and (3) a quantile mapping procedure to enforce long-term distributional shifts in weather variables that result from prescribed climate changes. The Markov chain is used to better represent wet and dry spell statistics, while the KNN bootstrap resampler preserves the covariance structure between the weather variables and across space. The wavelet-based autoregressive model is applied to annual climate over the region and used to modulate the Markov chain and KNN resampling, embedding appropriate low-frequency structure within the daily weather generation process. Parameters can be altered in any of the components of the proposed model to enable the generation of realistic time series of climate variables that exhibit changes to both lower-order and higher-order statistics at long-term (interannual), mid-term (seasonal), and short-term (daily) timescales. The tool can be coupled with impact models in a bottom-up risk assessment to efficiently and exhaustively explore the potential climate changes under which a system is most vulnerable. An application of the weather generator is presented for the Connecticut River basin to demonstrate the tool's ability to generate a wide range of possible climate sequences over an extensive spatial domain.