We describe and evaluate version 2.1 of the Community Ice Sheet Model (CISM). CISM is a parallel, 3-D thermomechanical model, written mainly in Fortran, that solves equations for the momentum balance and the thickness and temperature evolution of ice sheets. CISM's velocity solver incorporates a hierarchy of Stokes flow approximations, including shallow-shelf, depth-integrated higher order, and 3-D higher order. CISM also includes a suite of test cases, links to third-party solver libraries, and parameterizations of physical processes such as basal sliding, iceberg calving, and sub-ice-shelf melting. The model has been verified for standard test problems, including the Ice Sheet Model Intercomparison Project for Higher-Order Models (ISMIP-HOM) experiments, and has participated in the initMIP-Greenland initialization experiment. In multimillennial simulations with modern climate forcing on a 4 km grid, CISM reaches a steady state that is broadly consistent with observed flow patterns of the Greenland ice sheet. CISM has been integrated into version 2.0 of the Community Earth System Model, where it is being used for Greenland simulations under past, present, and future climates. The code is open-source with extensive documentation and remains under active development.

Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process‐based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches by embedding DL methods into PBHMs to represent individual processes. We demonstrate that this is viable by developing DL‐based representations of turbulent heat fluxes and coupling them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one‐way coupled implementation which relies only on model inputs and a two‐way coupled implementation, which also incorporates SUMMA‐derived model states. Our results demonstrate that the DL parameterizations are able to outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two‐way coupling can simulate the long‐term latent heat flux better than the standalone benchmark and one‐way coupled configuration. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.

While there has been a boom in the availablity of climate, weather, and environmental data thanks to satellite observations, syntheses of in situ observed data sets, and the ubiquity of powerful computers, it is also still often the case that the required data to run complex environmental models is either missing, or at the wrong spatial or temporal resolution. For example, incoming shortwave radiation, longwave radiation, and humidity are often observed with varying record lengths and observation intervals. Further, even when such quantities are measured it is often at a daily timestep, while many environmental models require finer time scales for simulation. To provide the necessary data to solve the model equations in such circumstances we must be able to provide estimates for these quantities at the appropriate temporal resolution. MetSim is a Python package and standalone tool for the estimation of meteorological quantities at variable time scales that can address the issues described above. The data that MetSim can generate covers most of the variables that sophisticated environmental models may require as input, making it possible to run them in a wider variety of situations than is possible with off-the-shelf datasets. This is especially important in fields such as hydrology and ecology, where there is an increasing push towards finer scale data than is commonly available. MetSim can be used to generate spatially distributed sub-daily timeseries of incoming shortwave radiation, outgoing longwave radiation, air pressure, specific humidity, relative humidity, vapor pressure, precipitation, and air temperature given daily timeseries of minimum temperature, maximum temperature, and precipitation. Figure 1 shows an example of MetSim's transformations of these daily values into some of the available subdaily outputs. A summary of the available output variables and tunable parameters are included in the documentation.

Quantifying the behavior and performance of hydrologic models is an important aspect of understanding the underlying hydrologic systems. We argue that classical error measures do not offer a complete picture for building this understanding. This study demonstrates how the information theoretic measure known as transfer entropy can be used to quantify the active transfer of information between hydrologic processes at various timescales and facilitate further understanding of the behavior of these systems. To build a better understanding of the differences in dynamics, we compare model instances of the Structure for Unifying Multiple Modeling Alternatives (SUMMA), the Variable Infiltration Capacity (VIC) model, and the Precipitation Runoff Modeling System (PRMS) across a variety of hydrologic regimes in the Columbia River Basin in the Pacific Northwest of North America. Our results show differences in the runoff of the SUMMA instance compared to the other two models in several of our study locations. In the Snake River region, SUMMA runoff was primarily snowmelt driven, while VIC and PRMS runoff was primarily influenced by precipitation and evapotranspiration. In the Olympic mountains, evapotranspiration interacted with the other water balance variables much differently in PRMS than in VIC and SUMMA. In the Willamette River, all three models had similar process networks at the daily time scale but showed differences in information transfer at the monthly timescale. Additionally, we find that all three models have similar connectivity between evapotranspiration and soil moisture. Analyzing information transfers to runoff at daily and monthly time steps shows how processes can operate on different timescales. By comparing information transfer with correlations, we show how transfer entropy provides a complementary picture of model behavior.