Sensitivity of modeled carbon pools and fluxes to biases in reanalysis meteorology forcing data D.M. Ricciuto1, P.E. Thornton1, R.B. Cook1

, NACP site interim synthesis participants2

1 Environmental Sciences Division/Climate Change Science Institute, Oak Ridge National Laboratory 2 Representing PIs of 45 eddy covariance sites, 22 carbon cycle models and data coordination team

Introduction:The overall objective of the interim synthesis model-data comparison (MDC) is to establish a quantitative framework that allows NACP investigators to answer the question: Are the various measurement and modeling estimates of carbon fluxes at individual sites consistent with each other, and if not, why?

This effort required complete and accurate meteorological forcing data. For each site, we performed gap-filling of the forcing data provided by AmeriFlux and FLUXNET Canada using nearby flux towers, climate stations, and statistical relationships. However, regional and global modeling efforts generally use reanalysis forcing data from numerical weather prediction models. We note that there is often disagreement when a model is forced with site level data compared to when a model is forced with regional data at the same location (Raczka et al., in prep). This may be caused by differences in soil, plant functional type, forcing meteorology, or other causes. How much of this disagreement results from forcing meteorology?Methods:For selected NACP interim synthesis sites, we perform a statistical comparison of forcing and reanalysis meteorology datasets, and of carbon cycle model simulations using these different forcing datasets. Our results focus on differences in incoming shortwave radiation (SWdown), precip, air temperature (Tair), gross primary production (GPP) net ecosystem exchange (NEE), and carbon pool sizes.

Forcing data products:

Acknowledgement: This work was sponsored by the U.S. Department of Energy’s Office of Science and Environmental Research Program under contract DE-AC05-00OR22725 with Oak Ridge National Laboratory (ORNL), managed by UT-Battelle, LLC.Please contact ricciutodm@ornl.gov for further information

We selected 34 sites covering a wide range of climate regimes in North America. At each site, we compiled the gap-filled tower observed forcing and forcing from 7 reanalysis products (selecting the nearest gridcell). At 12 of these sites (yellow circles), we also performed LoTEC model runs with each forcing product.

Wavelet analysis:

Selected NACP interim synthesis sites

SW radiation biases are a strong function of cloudiness. All products overestimate radiation in cloudy conditions, with ERA-interim having the least bias. NCEP, NARR, and ERA-interim are unbiased in cloud-free conditions. The Princeton and CRU-NCEP products, although having the least overall bias, have a positive bias in cloudy conditions and negative bias in cloud-free conditions. This could have significant implications for modeling.

Following the interim synthesis protcol, LoTEC is spun up to steady state separately for each forcing product by cycling over the available years of data, typically for 2000 years. Significant differences in NEE, GPP and initial carbon pool sizes are caused by differences in the forcing datasets, most notably SWdown.

Product Spatial resolution

Temporal resolution (hr)

Reference

Tower observed

Site-level 0.5 or 1 Ricciuto et al. (in prep)

CRU-NCEP 0.5o x 0.5o 6 Viovy and Ciais

ERA-interim 1.5o x 1.5o 6 or 12 Uppala et al. (2005)

NARR 32km 3 Mesinger et al. (2004)

NCEP 1.9o x 1.9o 6 Kalnay et al. (1996)

NCEP2 1.9o x 1.9o 6 Kalnay et al. (1996)NLDAS* 1/8o x 1/8o 1 Cosgrove et al. (2003)

Princeton 1o x 1o 3 Sheffield et al. (2006)

* - NLDAS domain is limited and analysis does not include sites above 55oN

Forcing data comparisons were performed on daily averages of all products. LoTEC model runs required hourly forcing data. For the products with greater than 1 hour time resolution, forcing variables were first averaged to daily resolution, then downscaled to hourly using the shape of the tower observed diurnal cycle.

Forcing data comparison:Averages over 34 sites.Most products exhibit a strong bias in SWdown, with overall biases ranging between -5% (CRU-NCEP) and +40% (NCEP).Temperature biases are generally small. NCEP products have too much summertime precipitation. ERA-interim generally matches the site-observed synoptic variability better than other products.

SWdown

Tair

precip

Bias RMSE

SWdownobserved /SWdownpotential

SWdownobserved /SWdownpotential

ERA-interim NCEP Princeton

This is an example of a wavelet analysis for a single site (UMBS) for SWdown. This kind of analysis can show patterns of bias and coherence that would otherwise be difficult to pinpoint. Here we wee that despite the overall bias of ERA-interim, it is most coherent with the observed signal. These differences in coherence are most pronounced at multiday (synoptic) to monthly time scales.

Wavelet coherence

Wavelet residual power

Overall bias No overall bias

Bias RMSEModeling comparison:

There is a strong bias in GPP that is highly correlated with biases in SWdown. The reanalysis products with higher values of SWdown result in increased live biomass and soil carbon, resulting in an amplified diurnal and seasonal cycle of NEE. Annual NEE averages are zero for all products and sites because of the imposed steady state condition. These biases are likely to significantly impact the simulated response of the carbon cycle to future climate change.

GPP

NEE

Day of year Day of year

annual

hourly

annual

hourly