Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e)

Nijssen, Bart; Bowling, L. C.; Lettenmaier, Dennis P.; Clark, Douglas B.; Maayar, Mustapha El; Essery, Richard; Goers, Sven; Gusev, Yeugeniy M.; Habets, Florence; Hurk, Bart van den; Jin, Jiming; Kahan, Daniel; Lohmann, Dag; Ma, Xieyao; Mahanama, Sarith; Mocko, David M.; Насонова, О. Н.; Niu, Guo Yue; Samuelsson, Patrick; Шмакин, А. Б.; Takata, Kumiko; Verseghy, Diana; Viterbo, Pedro; Xia, Youlang; Xue, Yongkang; Yang, Zong‐Liang

doi:10.1016/s0921-8181(03)00004-3

Cited by 108 publications

(59 citation statements)

References 11 publications

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“…Note that in general there are also uncertainties associated with satellite-based estimates of SWE, especially at high values. The observed snow depth at Toolik at 2 May 2001 (Oberbauer, 2003) amounted to 0.66 m with a standard deviation of 0.09 m. Assuming a snow density in the range of 260-310 kg m −3 (e.g. Sturm and Wagner, 2010) this translates into a SWE of 172-205 ± 28 mm.…”

Section: Results -Site-specific Runsmentioning

confidence: 96%

“…In spite of their limitations (see e.g. Slater et al, 2001;Nijssen et al, 2003), LSMs offer the opportunity to study changes in the terrestrial environment in an integrated and consistent way and to explore key interactions between the different impacts (Betts, 2007). LSMs are thus not specific permafrost models, but if claims of LSMs being physically process-based models have any validity, they ought to be capable of simulating the dynamics of permafrost and frozen ground with some realism.…”

Section: R Dankers Et Al: Simulation Of Permafrost With Julesmentioning

confidence: 99%

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Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

2011

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Abstract. Land surface models (LSMs) need to be able to simulate realistically the dynamics of permafrost and frozen ground. In this paper we evaluate the performance of the LSM JULES (Joint UK Land Environment Simulator), the stand-alone version of the land surface scheme used in Hadley Centre climate models, in simulating the largescale distribution of surface permafrost. In particular we look at how well the model is able to simulate the seasonal thaw depth or active layer thickness (ALT). We performed a number of experiments driven by observation-based climate datasets. Visually there is a very good agreement between areas with permafrost in JULES and known permafrost distribution in the Northern Hemisphere, and the model captures 97 % of the area where the spatial coverage of the permafrost is at least 50 %. However, the model overestimates the total extent as it also simulates permafrost where it occurs sporadically or only in isolated patches. Consistent with this we find a cold bias in the simulated soil temperatures, especially in winter. However, when compared with observations on end-of-season thaw depth from around the Arctic, the ALT in JULES is generally too deep. Additional runs at three sites in Alaska demonstrate how uncertainties in the precipitation input affect the simulation of soil temperatures by affecting the thickness of the snowpack and therefore the thermal insulation in winter. In addition, changes in soil moisture content influence the thermodynamics of soil layers close to freezing. We also present results from three experiments in which the standard model setup was modified to improve physical realism of the simulations in permafrost regions. Extending the soil column to a depth of 60 m and adjusting the soil parameters for organic content had relatively little effect on the Correspondence to: R. Dankers (rutger.dankers@metoffice.gov.uk) simulation of permafrost and ALT. A higher vertical resolution improves the simulation of ALT, although a considerable bias still remains. Future model development in JULES should focus on a dynamic coupling of soil organic carbon content and soil thermal and hydraulic properties, as well as allowing for sub-grid variability in soil types.

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Section: Results -Site-specific Runsmentioning

confidence: 96%

Section: R Dankers Et Al: Simulation Of Permafrost With Julesmentioning

confidence: 99%

Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

2011

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“…To control the accumulation of noise and energy at the grid scale, horizontal diffusion is also applied. The land surface scheme employed in the PRECIS is Meteorological Office Surface Exchange Scheme (MOSES) that has shown good skill in land surface simulation Nijssen et al 2003). The reader is referred to Wilson et al (2005) for full details about PRECIS.…”

Section: Methodsmentioning

confidence: 99%

GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies

Ashiq

Zhao

et al. 2009

Theor Appl Climatol

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In this study, the baseline period precipitation simulation of regional climate model PRECIS is evaluated and downscaled on a monthly basis for northwestern Himalayan mountains and upper Indus plains of Pakistan. Different interpolation models in GIS environment are used to generate fine scale (250×250 m 2 ) precipitation surfaces from PRECIS precipitation data. Results show that the multivariate extension model of ordinary kriging that uses elevation as secondary data is the best model especially for monsoon months. Model results are further compared with observations from 25 meteorological stations in the study area. Modeled data show overall good correlation with observations confirming the ability of PRECIS to capture major precipitation features in the region. Results for low and erratic precipitation months, September and October, are however showing poor correlation with observations. During monsoon months (June, July, August) precipitation pattern is different from the rest of the months. It increases from south to north, but during monsoon maximum precipitation is in the southern regions of the Himalayas, and extreme northern areas receive very less precipitation. Modeled precipitation toward the end of the twenty-first century under A2 and B2 scenarios show overall decrease during winter and increase in spring and monsoon in the study area. Spatially, both scenarios show similar pattern but with varying magnitude. In monsoon, the Himalayan southern regions will have more precipitation, whereas northern areas and southern plains will face decrease in precipitation. Western and south western areas will suffer from less precipitation throughout the year except peak monsoon months. T test results also show that changes in monthly precipitation over the study area are significant except for July, August, and December. Result of this study provide reliable basis for further climate change impact studies on various resources.

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“…The principal findings (Liang et al, 1998;Lohmann et al, 1998a;Wood et al, 1998;Bowling et al, 2003a, b;Nijssen et al, 2003) were as follows. (1) LSMs that applied subgrid-scale runoff parameterizations could simulate largescale river discharges better than others.…”

Section: Model Comparisons Evaluations and The Need For Benchmarkingmentioning

confidence: 98%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land-atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land-atmosphere fluxes of carbon dioxide (CO 2 ), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "thirdgeneration" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by speciesdiverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.

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Simulation of high latitude hydrological processes in the Torne–Kalix basin: PILPS Phase 2(e)

Abstract: The Project

Cited by 108 publications

References 11 publications

Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme

GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

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