One of the trickiest processes to represent accurately in global climate models is how Earth gives off and absorbs heat. This phenomenon is called ground heat flux (expressed with the variable G), and it varies considerably depending on a region’s local geography and climate.
For example, the magnitude of G is smaller in a wet, densely canopied rain forest than in a desert, where temperatures plummet and soar over the course of a day. It also changes with the seasons, as verdant regions dry out or freeze in summer or winter. In a new study, Purdy et al. compare different models of G in 88 sites across the globe and identify which make the most accurate predictions at different time scales.
The team compared six models of G forced by real-world data sets, including satellite measurements of vegetation cover and temperature of Earth’s surface, with observations from FLUXNET, a network of more than 650 towers. These towers measure surface energy exchange and gas fluxes on every continent in locations as diverse as tropical and coniferous forests, croplands, wetlands, and tundra.
The authors’ analysis revealed a range of strengths and weaknesses among the models and quantified where the largest model disagreement exists seasonally and globally. Models forced by net radiation explain more day-to-day variability in G, whereas models forced by temperature resulted in lower errors. A spatial assessment shows that model disagreement is greatest during winter months at high latitudes.
The new study highlights the importance of G and points to areas ripe for model refinement. Results from the study have implications for other models that rely on G, such as those used to calculate evapotranspiration. (Journal of Geophysical Research: Biogeosciences, doi:10.1002/2016JG003591, 2016)
—Emily Underwood, Freelance Writer