Upscaling methane emission hotspots in boreal peatlands

Fabio Cresto Aleina,Victor Brovkin,Tim Brücher,Benjamin R K Runkle,Thomas Kleinen

doi:10.5194/gmd-9-915-2016

Abstract

Abstract. Upscaling the properties and effects of small-scale surface heterogeneities to larger scales is a challenging issue in land surface modeling. We developed a novel approach to upscale local methane emissions in a boreal peatland from the micro-topographic scale to the landscape scale. We based this new parameterization on the analysis of the water table pattern generated by the Hummock–Hollow model, a micro-topography resolving model for peatland hydrology. We introduce this parameterization of methane hotspots in a global model-like version of the Hummock–Hollow model that underestimates methane emissions. We tested the robustness of the parameterization by simulating methane emissions for the next century, forcing the model with three different RCP scenarios. The Hotspot parameterization, despite being calibrated for the 1976–2005 climatology, mimics the output of the micro-topography resolving model for all the simulated scenarios. The new approach bridges the scale gap of methane emissions between this version of the model and the configuration explicitly resolving micro-topography.

Highlights

The Earth’s land surface is a heterogeneous mixture of vegetation types, lakes, wetlands, and bare soil
How can models better account for the small-scale features in the large-scale climate system? Proposing a new parameterization to fill a scaling gap between local and larger scales is the main focus of this paper
We present an application of this upscaling method to the Hummock– Hollow (HH) model, where we analyze the dynamics of the area which we assume to be a hotspot for methane emissions

Summary

Introduction

The Earth’s land surface is a heterogeneous mixture of vegetation types, lakes, wetlands, and bare soil. Correct representation of such small-scale heterogeneities in climate system models is a challenge. Many recent studies have focused on different approaches to simulate local small-scale characteristics of the land surface, with climate enforcing evolution of different soil surface heterogeneities and small-scale vegetation patterns (Shur and Jorgenson, 2007; Couwenberg and Joosten, 2005; Rietkerk and van de Koppel, 2008). Recent efforts have been focused on downscaling remote sensing information to simulate subgrid surface heterogeneities (e.g., Peng et al, 2016; Stoy and Quaife, 2015), and to scale up information across scales using network techniques (Baudena et al, 2015)

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