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Input and output data files ofthe article "Effects of climate change on capacity expansion decisions of an electricity generation fleet in the Southeast U.S." The size of the climate and hydro dataset used as input in these simulations was beyond the capacity of the repository. To access the climate and hydro data used as input, please contact the authors.

The article of record as published may be found at https://agu.confex.com/agu/osm20/meetingapp.cgi/Paper/641925 Ocean Sciences Meeting 2020 The Arctic is one of the most challenging regions to model climate change due to its complexity, including the cryosphere, small scale processes and feedbacks controlling its amplified response to global climate change. The combination of these factors defines the need for high spatial and temporal model resolution, which is commonly not practical for most state-of-the-art global Earth system models (ESMs), including those participating in the Coupled Model Intercomparison Project Phase 6. We offer an alternative approach to improve model physics and reduce uncertainties in modeling Arctic climate using a high resolution regional climate system model for dynamical downscaling of output from ESMs. The Regional Arctic System Model (RASM) has been developed to better understand the past and present operation of the Arctic climate system and to predict its change at time scales up to decades. RASM is a coupled model, consisting of the atmosphere, ocean, sea ice, land hydrology and river routing scheme components. Its domain is pan-Arctic, with 50-km or 25-km grids for the atmosphere and land components. The ocean and sea ice components are configured at ~9.3-km or ~2.4-km grids horizontally and with 45 or 60 vertical layers. For hindcast simulations, RASM derives boundary conditions from global atmospheric reanalyses, allowing comparison with observations in place and time, which is a unique capability not available with ESMs. We will discuss improvements to RASM model physics offered by high resolution and in generation of internally consistent realistic initial conditions for Arctic climate prediction. We will also discuss the need for fine-tuning of scale aware parameterizations of sub-grid physical processes in varying model configurations. Finally, selected results will be presented to demonstrate gains of dynamical downscaling in comparison with observations and with the global reanalysis and predictions.

15th Conference on Polar Meteorology and Oceanography The Regional Arctic System Model (RASM) is a fully coupled limited-domain ice-ocean-atmosphere-land hydrology model. Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid. The ocean and sea ice components are configured on rotated sphere meshes with four configuration options: 1/12o (~9.3km) or 1/48o (~2.4km) in the horizontal space and with 45 or 60 vertical layers. As a regional climate model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which are derived either from global atmospheric reanalyses for simulations of the past to present or from Earth System models (ESMs) for climate projections. In the former case, this allow comparison of RASM results with observations in place and time, which is a unique capability not available in global ESMs. RASM has been developed and used to investigate critical processes controlling the evolution of the Arctic climate system under a diminishing sea ice cover. Several examples of key physical processes and coupling between different model components will be presented, that improve the representation of the past and present Arctic climate system. The impact of such processes and feedbacks will be discussed with regard to improving model physics and reducing biases in the representation of its initial state for prediction of Arctic climate at time scales from synoptic to intra-annual.

The article of record as published may be found at http://dx.doi.org/10.1002/2016JC012323 Accepted article The coastal streamflow flux from the Arctic drainage basin is an important driver of dynamics in the coupled ice-ocean system. Comprising more than one-third of the total freshwater flux into the Arctic Ocean, streamflow is a key component of the regional and global freshwater cycle. To better represent the coupling of the streamflow flux to the ocean, we have developed and applied the RVIC streamflow routing model within the Regional Arctic System Model (RASM). The RASM is a high-resolution regional Earth System Model whose domain includes all of the Arctic drainage basin. In this paper, we introduce the RVIC streamflow routing model, detailing its application within RASM and its advancements in terms of representing high-resolution streamflow processes. We evaluate model simulated streamflow relative to in-situ observations and demonstrate a method for improving model performance using a simple optimization procedure. We also present a new, spatially and temporally consistent, high-resolution dataset of coastal freshwater fluxes for the Arctic drainage basin and surrounding areas that is based on a fully-coupled RASM simulation and intended for use in Arctic Ocean modeling applications. This dataset is evaluated relative to other coastal streamflow datasets commonly used by the ocean modeling community. We demonstrate that the RASM-simulated streamflow flux better represents the annual cycle than existing datasets, especially in ungauged areas. Finally, we assess the impact that streamflow has on the coupled ice-ocean system, finding that the presence of streamflow leads to reduced sea surface salinity, increased sea surface temperatures and decreased sea ice thickness. United States Department of Energy (DOE) Department of Energy (DOE) Grant DE-FG02-07ER64460 Department of Energy (DOE) Grant DE-SC0006856 Department of Energy (DOE) DE-SC0005783 Department of Energy (DOE) DE-SC0005522

The article of record as published may be found at http://dx.doi.org/10.1175/JCLI-D-15-0415.1 The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2 degrees C; however, in the cold seasons there are local biases that exceed 6 degrees C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region. U.S. Department of Energy (DOE) [DE-FG02-07ER64460, DE-SC0006856, DE-SC0006178]; DOD

We present a forecast-based adaptive management framework for water supply reservoirs and evaluate the contribution of long-term inflow forecasts to reservoir operations. Our framework is developed for snow-dominated river basins that demonstrate large gaps in forecast skill between seasonal and inter-annual time horizons. We quantify and bound the contribution of seasonal and inter-annual forecast components to optimal, adaptive reservoir operation. The framework uses an Ensemble Streamflow Prediction (ESP) approach to generate retrospective, one-year-long streamflow forecasts based on the Variable Infiltration Capacity (VIC) hydrology model. We determine the optimal sequence of daily release decisions using the Model Predictive Control (MPC) optimization scheme. We then assess the forecast value by comparing system performance based on the ESP forecasts with the performances based on climatology and perfect forecasts. We distinguish among the relative contributions of the seasonal component of the forecast versus the inter-annual component by evaluating system performance based on hybrid forecasts, which are designed to isolate the two contributions. As an illustration, we first apply the forecast-based adaptive management framework to a specific case study, i.e., Oroville Reservoir in California, and we then modify the characteristics of the reservoir and the demand to demonstrate the transferability of the findings to other reservoir systems. Results from numerical experiments show that, on average, the overall ESP value in informing reservoir operation is 35% less than the perfect forecast value and the inter-annual component of the ESP forecast contributes 20–60% of the total forecast value.

This work advances a unified approach to process-based hydrologic modeling, which we term the “Structure for Unifying Multiple Modeling Alternatives (SUMMA).” The modeling framework, introduced in the companion paper, uses a general set of conservation equations with flexibility in the choice of process parameterizations (closure relationships) and spatial architecture. This second paper specifies the model equations and their spatial approximations, describes the hydrologic and biophysical process parameterizations currently supported within the framework, and illustrates how the framework can be used in conjunction with multivariate observations to identify model improvements and future research and data needs. The case studies illustrate the use of SUMMA to select among competing modeling approaches based on both observed data and theoretical considerations. Specific examples of preferable modeling approaches include the use of physiological methods to estimate stomatal resistance, careful specification of the shape of the within-canopy and below-canopy wind profile, explicitly accounting for dust concentrations within the snowpack, and explicitly representing distributed lateral flow processes. Results also demonstrate that changes in parameter values can make as much or more difference to the model predictions than changes in the process representation. This emphasizes that improvements in model fidelity require a sagacious choice of both process parameterizations and model parameters. In conclusion, we envisage that SUMMA can facilitate ongoing model development efforts, the diagnosis and correction of model structural errors, and improved characterization of model uncertainty.

This note describes the conservation equations and flux parameterizations used in the Structure for Unifying Multiple Modeling Alternatives (SUMMA). The processes considered here include radiation transfer through the vegetation canopy, within- and below-canopy turbulence, canopy interception, canopy transpiration, snow accumulation and ablation, and runoff generation.