Assessment of coupled CRCM5–FLake on the reproduction of wintertime lake-induced precipitation in the Great Lakes Basin

Baijnath-Rodino, Janine A.; Duguay, Claude R.

doi:10.1007/s00704-019-02799-8

Cited by 8 publications

(6 citation statements)

References 68 publications

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“…FLake has been implemented into the other main European NWP and regional climate models, first into COSMO (Mironov et al, 2010) then into ECMWF (Balsamo et al, 2012), Unified Model (Rooney and Bornemann, 2013), SUR-FEX surface modelling framework (Masson et al, 2013), regional climate models RCA (Samuelsson et al, 2010), HCLIM (Lindstedt et al, 2015) and REMO (Pietikäinen et al, 2018), among others. Description of lake surface state and its influence in the numerical weather and climate prediction has been validated in various ways.…”

Section: Introductionmentioning

confidence: 99%

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland

2019

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Abstract. The High Resolution Limited Area Model (HIRLAM), used for the operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated into HIRLAM. Additionally, an independent objective analysis of lake surface water temperature (LSWT) combines the short forecast of FLake to observations from the Finnish Environment Institute (SYKE). The resulting description of lake surface state – forecast FLake variables and analysed LSWT – was compared to SYKE observations of lake water temperature, freeze-up and break-up dates, and the ice thickness and snow depth for 2012–2018 over 45 lakes in Finland. During the ice-free period, the predicted LSWT corresponded to the observations with a slight overestimation, with a systematic error of +0.91 K. The colder temperatures were underrepresented and the maximum temperatures were too high. The objective analysis of LSWT was able to reduce the bias to +0.35 K. The predicted freeze-up dates corresponded well to the observed dates, mostly within the accuracy of a week. The forecast break-up dates were far too early, typically several weeks ahead of the observed dates. The growth of ice thickness after freeze-up was generally overestimated. However, practically no predicted snow appeared on lake ice. The absence of snow, presumably due to an incorrect security coefficient value, is suggested to be also the main reason for the inaccurate simulation of the lake ice melting in spring.

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Section: Introductionmentioning

confidence: 99%

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland

2019

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“…In the COSMO NWP model, snow is implicitly accounted for by modifying ice albedo using empirical data on its temperature dependence (Mironov et al, 2010). This way was applied also in a recent study over the North American Great Lakes (Baijnath-Rodino and Duguay, 2019). Semmler et al (2012) performed a detailed wintertime comparison between FLake and a more complex snow and ice thermodynamic model (HIGHTSI) on a small lake in Alaska.…”

Section: Discussion: Snow On Lake Icementioning

confidence: 99%

Updated reply to three reviewers

Rontu¹

2019

Preprint

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The High Resolution Limited Area Model (HIRLAM), used for the operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated into HIRLAM. Additionally, an independent objective analysis of lake surface water temperature (LSWT) combines the short forecast of FLake to observations from the Finnish Environment Institute (SYKE). The resulting description of lake surface state -forecast FLake variables and analysed LSWT -was compared to SYKE observations of lake water temperature, freeze-up and break-up dates, and the ice thickness and snow depth for 2012-2018 over 45 lakes in Finland. During the ice-free period, the predicted LSWT corresponded to the observations with a slight overestimation, with a systematic error of + 0.91 K. The colder temperatures were underrepresented and the maximum temperatures were too high. The objective analysis of LSWT was able to reduce the bias to +0.35 K. The predicted freeze-up dates corresponded well to the observed dates, mostly within the accuracy of a week. The forecast break-up dates were far too early, typically several weeks ahead of the observed dates. The growth of ice thickness after freeze-up was generally overestimated. However, practically no predicted snow appeared on lake ice. The absence of snow, presumably due to an incorrect security coefficient value, is suggested to be also the main reason for the inaccurate simulation of the lake ice melting in spring.

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“…Both CRCM5 configurations examined here include a coupled lake model (FLake, Mironov et al, 2010) which supports a realistic representation of lake-effect precipitation, though the one-dimensional nature of FLake ignores three-dimensional processes that may impact lake ice coverage and lake surface temperature which strongly impact lake-effect snow processes (e.g., Baijnath-Rodino & Duguay, 2019;Martynov et al, 2012). An evaluation of the CRCM5 using FLake shows a general warm bias for Great Lakes temperatures, leading to an early spring warming, a too slow autumn cooling and shorter periods of ice-coverage (Martynov et al, 2012).…”

Section: Regional Climate Model Simulationsmentioning

confidence: 99%

Changing Nature of High‐Impact Snowfall Events in Eastern North America

McCray,

Schmidt,

Paquin

et al. 2023

JGR Atmospheres

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Snowstorms cause substantial disruption in the eastern United States and Canada each winter. While reductions in annual snowfall are projected over most of this region due to anthropogenic global warming, daily snowfall extremes that have the greatest impact may not decrease in the same manner. We examine changes to two extreme snowfall metrics: the 95th percentile of daily snowfall (SF95, cm) and the number of events during which 10% of the mean annual snowfall is exceeded during a single day (TC10, events yr−1). We explore changes to these metrics in two ensembles of the fifth‐generation Canadian Regional Climate Model, including four 0.22° (≈25 km) simulations driven by different coupled general circulation models as well as the higher‐resolution (0.11°, ≈12 km) ClimEx ensemble, driven by 50 members of a large initial‐condition ensemble of one global model. We find that while mean annual snowfall is projected to decrease over our domain, SF95 is projected to remain relatively constant, suggesting that the most extreme daily snowfalls currently observed are likely to occur even in a warmer future climate. The region of the largest TC10 values exhibits a northward shift, with a larger percentage of annual snowfall occurring during a few large events along the U.S.‐Canada border. These projected changes to the nature of snowfall events may have important socioeconomic consequences in this densely populated region of North America.

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Assessment of coupled CRCM5–FLake on the reproduction of wintertime lake-induced precipitation in the Great Lakes Basin

Cited by 8 publications

References 68 publications

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland

Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland

Updated reply to three reviewers

Changing Nature of High‐Impact Snowfall Events in Eastern North America

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