Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

Takala, Matias; Luojus, Kari; Pulliainen, Jouni; Derksen, Chris; Lemmetyinen, Juha; Karna, J.-P.; Koskinen, J.; Bojkov, Bojan

doi:10.1016/j.rse.2011.08.014

Cited by 538 publications

(527 citation statements)

References 40 publications

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“…1, inset). The interaction of these climatic regimes with the complex topography of HMA -particularly the vast elevation gradients -creates a diverse set of snowfall regimes (Cannon et al, 2014;Kääb et al, 2012;Immerzeel and Bierkens, 2012;Gardner et al, 2013;Kapnick et al, 2014;Barnett et al, 2005;Dahe et al, 2006;Takala et al, 2011;Cannon et al, 2017).…”

Section: Geographic Settingmentioning

confidence: 99%

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017

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Abstract. High Mountain Asia (HMA) -encompassing the Tibetan Plateau and surrounding mountain ranges -is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications -such as agriculture, drinking-water generation, and hydropower -rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season -defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult.We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade −1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers -such as the Karakoram and Kunlun Shan -see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in ge...

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Section: Geographic Settingmentioning

confidence: 99%

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017

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“…The three SWE products tested were NASA/JAXA's AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASE-Grids (AE DySno) (Tedesco et al 2004), NSIDC's Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover (NSIDC-0321) (Brodzik et al 2007) and ESA's Globsnow SWE v1.0 (Takala et al 2011); hereafter referred to as "AMSR-E", "SSM/I" and "Globsnow". All three cover at least the northern hemisphere at 25 km spatial resolution although the standard Globsnow product masks out mountainous areas (see section 2.4 for details), a version with estimates over mountains is available, though this is unvalidated.…”

Section: Data Usedmentioning

confidence: 99%

“…In August 2010 ESA released the Globsnow v1.0 SWE and SCA products (Takala et al 2011) and these are available online (http://www.globsnow.info/). Globsnow is a fusion of EO-derived estimates with ground measurements and covers the period from 1978 to the present day.…”

Section: Globsnowmentioning

confidence: 99%

“…The most modern passive microwave radiometer available is used (SMMR 1978-1988, SSM/I 1988, AMSR-E 2002and SSM/I since October 2011. Ground measurements of snow depth are taken from the ECMWF WMO weather stations (Takala et al 2011). These are point measurements (or snow courses on the order of a few metres).…”

Section: Globsnowmentioning

confidence: 99%

“…The ground depth measurements are converted to SWE and interpolated to give a global ground map. This is assigned a variance of 250 mm 2 , based on comparison of point depth measurements to long snow courses (Takala et al 2011).…”

Section: Globsnowmentioning

confidence: 99%

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Evaluating global snow water equivalent products for testing land surface models

Hancock

Baxter

Evans³

et al. 2013

Remote Sensing of Environment

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(2013) 'Evaluating global snow water equivalent products for testing land surface models.', Remote sensing of environment., 128 . pp. 107-117. Further information on publisher's website:http://dx.doi.org/10.1016/j.rse. 2012.10.004 Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Remote sensing of environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Remote sensing of environment, 128, 2013, 10.1016/j.rse.2012.10.004 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is payed to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE > 80 -150 mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20+ days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1-6 days) although the spread was still on the order of three weeks.During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to correct or avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

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Physical properties of Arctic versus subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals

Derksen

Lemmetyinen

Toose

et al. 2014

J. Geophys. Res. Atmos.

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Two unique observational data sets are used to evaluate the ability of multi-layer snow emission models to simulate passive microwave brightness temperatures (T B ) in high latitude, observation sparse, snow-covered environments. Data were utilized from a coordinated series of 18 sites measured across the subarctic Northwest Territories and Nunavut, Canada in April 2007 during a 1000 km segment of a 4200 km snowmobile traverse from Fairbanks, Alaska to Baker Lake, Nunavut (~64°N). In April 2011, a network of 22 high Arctic sites was sampled across a 60 × 60 km study area on the Fosheim Peninsula, Ellesmere Island (~80°N). In comparison to sites across the subarctic, high Arctic snow was more spatially variable, thinner (site averages between 15 and 25 cm versus 30 to 40 cm), colder (À25°C versus À10°C), composed of fewer layers, had a proportionally higher fraction of wind slabs (storing 57% of the snow water equivalent (SWE) versus 15%), with these slabs comparatively denser (often exceeding 450 g/cm 3 , compared to 350 g/cm 3 in the subarctic). The physical snow measurements were used as inputs to snow emission model simulations.The radiometric difference between simulations of "typical" arctic and subarctic snow reached 30 K at 37 GHz. Sensitivity analysis showed that this T B difference could be partitioned between the effects of physical temperature (~5 K between À25°C and À10°C), wind slab density (~5 K between 0.40and 0.35 g/cm 3 ), and vertical depth hoar fraction (~20 K between 70% and 30% vertical fraction of total snow depth). Model simulations at the satellite scale (625 km 2 ) were produced using the observational spread for snow depth and snow stratigraphy. The range of T B from simulations with varied stratigraphy extended unrealistically far below the magnitude of satellite measured T B , illustrating that the snow depth first guess is very important for SWE retrieval schemes that are based on forward emission model simulations.

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Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements

Cited by 538 publications

References 40 publications

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

Evaluating global snow water equivalent products for testing land surface models

Physical properties of Arctic versus subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals

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