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Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high‐resolution simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic System Model (RASM) and conducted coupled land‐atmosphere modeling for Alaska and Yukon River Basin at 4‐km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and optimization of CTSM. A multi‐decadal climate data set (1990–2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational data sets, this data set well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation‐to‐precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic and energy fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations. 

Pangeo Forge is a new community-driven platform that accelerates science by providing high-level recipe frameworks alongside cloud compute infrastructure for extracting data from provider archives, transforming it into analysis-ready, cloud-optimized (ARCO) data stores, and providing a human- and machine-readable catalog for browsing and loading. In abstracting the scientific domain logic of data recipes from cloud infrastructure concerns, Pangeo Forge aims to open a door for a broader community of scientists to participate in ARCO data production. A wholly open-source platform composed of multiple modular components, Pangeo Forge presents a foundation for the practice of reproducible, cloud-native, big-data ocean, weather, and climate science without relying on proprietary or cloud-vendor-specific tooling. 

Climate data from Earth System Models are increasingly being used to study the impacts of climate change on a broad range of biogeophysical (forest fires, fisheries, etc.) and human systems (reservoir operations, urban heat waves, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”. In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of post processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guiding method selection or applicability. Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts in mind – we have begun working on a community-centered open framework for climate downscaling: scikit-downscale. We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods. In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare existing methods and how it can be extended to support the development of new downscaling methods.