A developed seamless extreme heat validation approach (Ford et al. 2018) is applied to three Subseasonl Experiment’s (SubX’s) medium-range forecast models, which arethe U.S. National Oceanic and Atmospheric Administration’s Earth System Research Laboratory FIM-iHYCOM (ESRL), the U.S. National Aeronautics and Space Administration’s Earth System Research Laboratory’s Goddard Earth Observing System Atmosphere-Ocean General Circulation Model, Version 5 (GMAO), and the U.S. National Centers for Environmental Prediction’s Global Ensemble Forecast System, version 11 (GEFS), for evaluating their heat wave predictability. Moreover, two land surface initializations, green vegetation fraction (GVF) and heat fluxes (LE/H), of each model are evaluated for understanding the interaction between heat wave predictability and the inconsistencies in the terrestrial segment of land-atmosphere feedbacks. The validation approach shows the overestimated autocorrelation of maximum temperature heat waves causing (1) the lowest reliability and overestimation of heat waves hindcasts, (2) lower heat wave hindcast skill of ensemble mean, and (3) higher discrimination between heat wave hindcast and observations of each ensemble member over lead times for all three models. Both ESRL and GEFS present the relationship between GVF and heat wave hindcast is positive, but negative relationship is shown on the GMAO. In addition, both ESRL and GEFS modelsunderestimate latent heat flux, but overestimate sensible heat flux in the Midwest. Therefore, for both ESRL and GEFS models, the relationship between heat wave and sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and latent heat flux (or evapotranspiration). In contrast, the GMAO model overestimates both latent and sensible heat fluxes in the Midwest. Therefore, for the GMAO model, the relationship between heat wave and latent/sensible heat fluxes (or GVF) is positive, and negative for the relationship between heat wave and evapotranspiration.

Distributed models of the land surface are essential for global climate models because of the importance of land-atmosphere exchanges of water, energy, momentum. They are also used for high resolution hydrologic simulation because of the need to capture non-linear responses to spatially variable inputs. Continued improvements to these models, and the data which they use, is especially important given ongoing changes in climate and land cover. In hydrologic models, important aspects are sometimes neglected due to the need to simplify the models for operational simulation. For example, operational flash flood models do not consider the role of snow and are often lumped (i.e. do not discretize a watershed into multiple units, and so do not fully consider the effect of intense, localized rainstorms). To address this deficiency, an overland flow model is coupled with a subsurface flow model to create a distributed flash flood forecasting system that can simulate flash floods that involve rain on snow. The model is intended for operational use, and there are extensive algorithms to incorporate high-resolution hydrometeorologic data, to assist in the calibration of the models, and to run the model in real time. A second study, which is designed to improve snow simulation in forested environments, demonstrates the importance of explicitly representing a near canopy environment in snow models, instead of only representing open and canopy covered areas (i.e. with % canopy fraction), as is often done. Our modeling, which uses canopy structure information from Aerial Laser Survey Mapping at 1 meter resolution, suggests that areas near trees have more net snow water input than surrounding areas because of the lack of snow interception, shading by the trees, and the effects of wind. In addition, the greatest discrepancy between our model simulations that explicitly represent forest structure and those that do not occur in areas with more canopy edges. In addition, two value-added Land Cover products (land cover type and maximum green vegetation fraction; MGVF) are developed and evaluated. The new products are good successors to current generation land cover products that are used in global models (many of which rely on 20 year old AVHRR land cover data from a single year) because they are based on 10 years of recent MODIS data. There is substantial spurious interannual variability in the MODIS land cover type data, and the MGVF product can vary substantially from year to year depending on climate conditions, suggesting the importance of using climatologies for land cover data. The new land cover type climatology also agrees better with validation sites, and the MGVF climatology is more consistent with other measures of vegetation (e.g. Leaf Area Index) than the older land cover data.