Evaluation of the snow regime in dynamic vegetation land surface models using field measurements

E. Zakharova,M. Lomas,S. Quegan,E. Kantzas

doi:10.5194/tc-8-487-2014

Abstract

Abstract. An increasing number of studies have demonstrated significant climatic and ecological changes occurring in the northern latitudes over the past decades. As coupled Earth-system models attempt to describe and simulate the dynamics and complex feedbacks of the Arctic environment, it is important to reduce their uncertainties in short-term predictions by improving the description of both system processes and its initial state. This study focuses on snow-related variables and makes extensive use of a historical data set (1966–1996) of field snow measurements acquired across the extent of the former Soviet Union to evaluate a range of simulated snow metrics produced by several land surface models, most of them embedded in IPCC-standard climate models. We reveal model-specific failings in simulating snowpack properties such as magnitude, inter-annual variability, timings of snow water equivalent and evolution of snow density. We develop novel and model-independent methodologies that use the field snow measurements to extract the values of fresh snow density and snowpack sublimation, and exploit them to assess model outputs. By directly forcing the surface heat exchange formulation of a land surface model with field data on snow depth and snow density, we evaluate how inaccuracies in simulating snow metrics affect soil temperature, thaw depth and soil carbon decomposition. We also show how field data can be assimilated into models using optimization techniques in order to identify model defects and improve model performance.

Highlights

Data covering the last 125 years indicate no significant surface air temperature trends in the Arctic (Polyakov et al, 2002), warming is observed in recent decades (Serreze et al, 2000), and there are many associated indicators of change, such as the expansion of shrub cover (Sturm et al, 2001), decrease in Arctic sea ice extent (Stroeve et al, 2007, 2012), reduction in spring snow cover (Derksen and Brown, 2012) and warming of permafrost (Osterkamp, 2007)
We display the values derived by the methods set out in Sect. 3 and compare them with corresponding values calculated by the models
The similarities between the LPJ-WM and Sheffield Dynamic Global Vegetation Model (SDGVM) correlation maps can be attributed to their use of the same climate drivers

Summary

Introduction

Data covering the last 125 years indicate no significant surface air temperature trends in the Arctic (Polyakov et al, 2002), warming is observed in recent decades (Serreze et al, 2000), and there are many associated indicators of change, such as the expansion of shrub cover (Sturm et al, 2001), decrease in Arctic sea ice extent (Stroeve et al, 2007, 2012), reduction in spring snow cover (Derksen and Brown, 2012) and warming of permafrost (Osterkamp, 2007). Model projections suggest that Arctic surface air temperatures will increase by as much as 0.25–0.75 ◦C per decade over the 100 years (Serreze and Francis, 2006), with associated increases in precipitation (Christensen et al, 2007) Since these regions hold a third of the global terrestrial carbon (McGuire et al, 1995) and half of the global below-ground organic carbon (Tarnocai et al, 2009), most of it locked in permafrost soils, the importance of recording, monitoring and understanding the complex dynamics and feedbacks of the Arctic climate is evident. Lack of knowledge of the initial state of the system has been identified as a major cause of uncertainty in decadal projections from climate models (Cox and Stephenson, 2007)

Methods

Results

Discussion

Conclusion