GRACE and AMSR‐E‐based estimates of winter season solid precipitation accumulation in the Arctic drainage region

Seo, Ki‐Weon; Ryu, Dongryeol; Kim, Baek-Min; Waliser, Duane E.; Tian, Baijun; Eom, Jooyoung

doi:10.1029/2009jd013504

Cited by 19 publications

(15 citation statements)

References 35 publications

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“…The uncertainty of snowfall is quite large in all atmospheric reanalysis products in the Polar regions [ Seo et al , 2010]. Due to its impact on formation of meltwater, we include sensitivity studies with an artificial increase and decrease of snowfall rate by 33%.…”

Section: Resultsmentioning

confidence: 99%

Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007

et al. 2012

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[1] The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2007 and 2011 and climate models did not predict this decline. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds form on Arctic sea ice during the melting season and their presence affects the heat and mass balances of the ice cover, mainly by decreasing the value of the surface albedo by up to 20%. We have developed a melt pond model suitable for forecasting the presence of melt ponds based on sea ice conditions. This model has been incorporated into the Los Alamos CICE sea ice model, the sea ice component of several IPCC climate models. Simulations for the period 1990 to 2007 are in good agreement with observed ice concentration. In comparison to simulations without ponds, the September ice volume is nearly 40% lower. Sensitivity studies within the range of uncertainty reveal that, of the parameters pertinent to the present melt pond parameterization and for our prescribed atmospheric and oceanic forcing, variations of optical properties and the amount of snowfall have the strongest impact on sea ice extent and volume. We conclude that melt ponds will play an increasingly important role in the melting of the Arctic ice cover and their incorporation in the sea ice component of Global Circulation Models is essential for accurate future sea ice forecasts.

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Section: Resultsmentioning

confidence: 99%

Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007

et al. 2012

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“…Over land the hydrologic cycle causes time-varying gravity changes, so, by combining GRACE total water storage estimates with independent data, near-surface terrestrial water stores (e.g., snow, glaciers, and soil moisture) and groundwater have been investigated [e.g., Swenson and Wahr, 2007;Niu et al, 2007;Swenson et al, 2008;Landerer et al, 2010;Gardner et al, 2013]. While GRACE has been used to estimate precipitation amount in high latitudes [Swenson, 2010;Seo et al, 2010;Boening et al, 2012;Behrangi et al, 2016], its application for estimating precipitation in cold mountainous basins has not been explored. Furthermore, recent advancements in GRACE data processing have led to the development of more accurate "mascon" solutions, with lower uncertainty and less bias due to leakage effects [e.g., Watkins et al, 2015].…”

Section: Introductionmentioning

confidence: 99%

Using GRACE to constrain precipitation amount over cold mountainous basins

Behrangi

Gardner

Reager

et al. 2017

Geophysical Research Letters

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Despite the importance for hydrology and climate‐change studies, current quantitative knowledge on the amount and distribution of precipitation in mountainous and high‐elevation regions is limited due to instrumental and retrieval shortcomings. Here by focusing on two large endorheic basins in High Mountain Asia, we show that satellite gravimetry (Gravity Recovery and Climate Experiment (GRACE)) can be used to provide an independent estimate of monthly accumulated precipitation using mass balance equation. Results showed that the GRACE‐based precipitation estimate has the highest agreement with most of the commonly used precipitation products in summer, but it deviates from them in cold months, when the other products are expected to have larger errors. It was found that most of the products capture about or less than 50% of the total precipitation estimated using GRACE in winter. Overall, Global Precipitation Climatology Project (GPCP) showed better agreement with GRACE estimate than other products. Yet on average GRACE showed ~30% more annual precipitation than GPCP in the study basins. In basins of appropriate size with an absence of dense ground measurements, as is a typical case in cold mountainous regions, we find GRACE can be a viable alternative to constrain monthly and seasonal precipitation estimates from other remotely sensed precipitation products that show large bias.

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“…Due to their global coverage and independence from surface conditions, the data represent a unique opportunity to quantify spatio-temporal variations of the Earth's water resources (Alkama et al, 2010;Werth et al, 2009). Therefore, GRACE data have been widely used to diagnose patterns of hydrological variability (Seo et al, 2010;Rodell et al, 2009;Ramillien et al, 2006;Feng et al, 2013), to validate and improve model simulations Güntner, 2008;Werth and Güntner, 2010;Chen et al, 2017;Eicker et al, 2014;Girotto et al, 2016;Schellekens et al, 2017), and to enhance our understanding of the water cycle on regional to global scales (Syed et al, 2009;Felfelani et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.

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GRACE and AMSR‐E‐based estimates of winter season solid precipitation accumulation in the Arctic drainage region

Cited by 19 publications

References 35 publications

Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007

Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007

Using GRACE to constrain precipitation amount over cold mountainous basins

Understanding terrestrial water storage variations in northern latitudes across scales

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