Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

Reichle, Rolf H.; Koster, Randal D.; Lannoy, Gabriëlle J. M. De; Forman, B. A.; Liu, Qing; Mahanama, Sarith; Toure, Ally M.

doi:10.1175/jcli-d-10-05033.1

Cited by 439 publications

(325 citation statements)

References 57 publications

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“…3). This result may occur because MERRA2 includes time-varying aerosol loadings (Balsamo et al, 2015;Reichle et al, 2011). However, the incorporation of this information does not improve the results over southeastern China (Fig.…”

Section: Resultsmentioning

confidence: 96%

On the suitability of current atmospheric reanalyses for regional warming studies over China

Zhou

Wang

2018

Atmos. Chem. Phys.

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Abstract. Reanalyses are widely used because they add value to routine observations by generating physically or dynamically consistent and spatiotemporally complete atmospheric fields. Existing studies include extensive discussions of the temporal suitability of reanalyses in studies of global change. This study adds to this existing work by investigating the suitability of reanalyses in studies of regional climate change, in which land-atmosphere interactions play a comparatively important role. In this study, surface air temperatures (T a ) from 12 current reanalysis products are investigated; in particular, the spatial patterns of trends in T a are examined using homogenized measurements of T a made at ∼ 2200 meteorological stations in China from 1979 to 2010. The results show that ∼ 80 % of the mean differences in T a between the reanalyses and the in situ observations can be attributed to the differences in elevation between the stations and the model grids. Thus, the T a climatologies display good skill, and these findings rebut previous reports of biases in T a . However, the biases in theT a trends in the reanalyses diverge spatially (standard deviation = 0.15-0.30 • C decade −1 using 1 • × 1 • grid cells). The simulated biases in the trends in T a correlate well with those of precipitation frequency, surface incident solar radiation (R s ) and atmospheric downward longwave radiation (L d ) among the reanalyses (r = −0.83, 0.80 and 0.77; p < 0.1) when the spatial patterns of these variables are considered. The biases in the trends in T a over southern China (on the order of −0.07 • C decade −1 ) are caused by biases in the trends in R s , L d and precipitation frequency on the order of 0.10, −0.08 and −0.06 • C decade −1 , respectively. The biases in the trends in T a over northern China (on the order of −0.12 • C decade −1 ) result jointly from those in L d and precipitation frequency. Therefore, improving the simulation of precipitation frequency and R s helps to maximize the signal component corresponding to regional climate. In addition, the analysis of T a observations helps represent regional warming in ERA-Interim and JRA-55. Incorporating vegetation dynamics in reanalyses and the use of accurate aerosol information, as in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), would lead to improvements in the modelling of regional warming. The use of the ensemble technique adopted in the twentieth-century atmospheric model ensemble ERA-20CM significantly narrows the uncertainties associated with regional warming in reanalyses (standard deviation = 0.15 • C decade −1 ).

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Section: Resultsmentioning

confidence: 96%

On the suitability of current atmospheric reanalyses for regional warming studies over China

Zhou

Wang

2018

Atmos. Chem. Phys.

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“…In contrast to the traditional gridded delineation, CLSM divides areas into irregularly shaped topographic catchments, which each contain a saturated fraction, a sub-saturated fraction, and a wilting fraction. These fractions evolve over time, and are used to determine fluxes and soil states within the catchment [more details can be found in Koster et al, 2000;Reichle et al, 2011;Houborg et al, 2012]. The soil profile depths were compiled from the FAO/ UNESCO Soil Map of the World (Webb et al, 1991).…”

Section: Land Surface Modelsmentioning

confidence: 99%

“…temperature, humidity, downward shortwave and longwave radiation, wind, and surface pressure). The Modern-Era Retrospective analysis for Research and Applications (MERRA) data product is a NASA atmospheric reanalysis for the satellite era using the Goddard Earth Observing System model (GEOS-5) and its associated data assimilation system (Reichle et al, 2011;Reichle, 2012). Our experiments were forced with two different MERRA versions: MERRA-Land and MERRA-2.…”

Section: Meteorological and Precipitation Datasetsmentioning

confidence: 99%

Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

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a b s t r a c tImproved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, insitu streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

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“…Many other off-line land surface data assimilation systems also don't currently assimilate satellite derived surface soil wetness or satellite derived LST. Examples of such off-line systems are; i) the North American Land Data Assimilation System (NLDAS; Xia et al 2014), ii) the ERA-Interim/Land reanalysis (Balsamo et al 2015) and iii) the Modern-Era Retrospective analysis for Research and Applications (MERRA) land system (Reichle et al 2011). Recently, the JULES land surface model has been incorporated into the NASA Land Information System (LIS; Kumar et al 2008) and it is anticipated that a future JASMIN system will use LIS for data assimilation to assimilate remotely sensed measurements of surface soil wetness and land surface temperature.…”

Section: Discussionmentioning

confidence: 99%