microbes improve carbon cycle models

Microorganisms are key drivers of the global carbon cycle both on the land and in the ocean. Through a diverse array of metabolic strategies, microbes decompose organic and recycle organic carbon. Just as we respire a fraction of the carbon we consume as CO2, so do microbes. Globally, vast quantities of organic carbon are funneled through many billions of microbes every year: to be decomposed, recycled, and respired into the atmosphere. In spite of this, microbial activity has historically been ignored in attempts to model the global carbon cycle and predict climate change-related feedbacks. Instead, our models rely on untested assumptions that microbes and their carbon cycle activities will respond in uniform, predictable manners to increases in temperature, and as such, can essentially be ignored.

A recent paper in Nature Geoscience paper challenges this assumption by explicitly integrating microbial physiology into a new model of the soil carbon cycle. Compared with traditional models, the new model more accurately matches current observations of carbon stocks and fluxes across ecosystems. Regarding future carbon cycling in a warming world, the model produces several widely different scenarios that vary due to the potential response of microbes to rising temperatures. In short, if microbes respond negatively to warming by decreasing their “growth efficiency”; that is, if warming slows down their growth rates, no additional carbon is released to the atmosphere as the soil warms. But if microbes are able to adapt to higher temperatures and maintain their current growth efficiencies, higher temperatures will accelerate carbon decomposition rates and lead to potentially huge additional losses of carbon dioxide from soils.

The integration of biological mechanisms into earth system models is an important step forward in our ability to forecast future climates. Future research that empirically measures microbial community responses to long-term warming is desperately needed in order to accurately model and predict this potentially huge feedback to the global carbon cycle.




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