Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

doi:10.1007/s11707-018-0691-2

Front. Earth Sci.

2018, Vol. 12

Issue (4) : 672-682 https://doi.org/10.1007/s11707-018-0691-2

RESEARCH ARTICLE

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

Kazuyoshi SUZUKI¹(

), Milija ZUPANSKI²

¹. Institute of Arctic Climate and Environment Change Research (IACE), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama 236-0001, Japan
². Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523-1375, USA

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Abstract

In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere–land modeling system with two different land surface models, the Noah-MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snow-layer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

Keywords ensemble simulation land-atmosphere interaction ensemble spread vertical temperature snow prediction

Corresponding Author(s): Kazuyoshi SUZUKI

Online First Date: 15 January 2018 Issue Date: 20 November 2018

Cite this article:

Kazuyoshi SUZUKI,Milija ZUPANSKI. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models[J]. Front. Earth Sci., 2018, 12(4): 672-682.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0691-2
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I4/672

Tab.1 Schemes used in experiments for cumulus clouds, planetary boundary layer, microphysics, and longwave and shortwave radiation transfer parameterizations

Tab.2 Description of two LSMs

Fig.1 (a) Map of major vegetation categories, and (b) elevation in the model simulation domain. The regions within the white and the black lines denote the target analysis region (TAR) and the model simulation domain, respectively.

Fig.2 Initial conditions and their uncertainty values. Central time? t = 0 defines the initial time conditions. A deterministic prediction from? t = -T?to? t = +T?is calculated using the (a) Noah LSM and (b) the Noah-MP LSM, and? N+1 outputs are created (x₁,…,x_N?and? x^C), where? N?is the number of?ensembles. The initial conditions at? t = -T?are created?from an 8-month spin-up using the Noah and Noah-MP LSMs as shown in Table 2.?The initial?uncertainty at?t = 0?is defined as? p_i = x_i–x^C.?The analysis at? t = 0?is created by interpolating from the global model.

Fig.3 Spatial distribution of (a) daily precipitation and (b) snow depth on 5 March 2013. The region within the black line denotes the target analysis region (TAR).

Tab.3 Major and model vegetation categories and surface albedo

Fig.4 Maps of the initial state of surface albedo and surface temperature at 00:00 UTC on 5 March 2013 determined by the (a,c) Noah LSM and (b,d) the Noah-MP LSM within the model simulation domain. The region within the white line denotes the target analysis region (TAR).

Fig.5 Maps of the root square error (RSE) of snow depth distribution at 00:00 UTC on 6 March 2013 predicted by the (a) Noah LSM and (b) Noah-MP LSM.

Fig.6 Maps of root square error (RSE) in precipitation between observation and prediction using (a) Noah and (b) Noah-MP LSMs, and the uncertainty in precipitation for the (c) Noah and (d) Noah-MP LSMs at 00:00 UTC on 6 March 2013.

Fig.7 Maps of the root square error (RSE) of daily snow depth change between observations and predictions using (a) Noah and (b) Noah-MP LSMs, and the uncertainty in daily snow depth change for the (c) Noah and (d) Noah-MP LSMs at 00:00 UTC on 6 March 2013.

Fig.8 Latitudinally averaged (60°N–70°N) zonal cross-section of root square error (RSE) and uncertainty in temperature at each vertical level for (a, e) 500 hPa, (b, f) 850 hPa, (c, g) 2 m air temperature, and (d, h) and surface temperature.

Tab.4 Summary of prediction skill for the Noah-MP LSM, compared with Noah LSM results.

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