Yeliz Yilmaz

• Langendijk, G.S; Aubry-Wake, C.; Osman, M.; Gulizia, C.; Attig-Bahar, F. & Behrens, E. [Show all 22 contributors for this article] (2019). Three Ways Forward to Improve Regional Information for Extreme Events: An Early Career Perspective. Frontiers in Environmental Science. ISSN 2296-665X. 7. doi: 10.3389/fenvs.2019.00006.
• Yilmaz, Yeliz A.; Sen, Omer Lutfi & Turuncoglu, Ufuk Utku (2019). Modeling the hydroclimatic effects of local land use and land cover changes on the water budget in the upper Euphrates – Tigris basin. Journal of Hydrology. ISSN 0022-1694. 576, p. 596–609. doi: 10.1016/j.jhydrol.2019.06.074.
• Yilmaz, Yeliz; Aalstad, Kristoffer & Sen, Omer L. (2019). Multiple remotely sensed lines of evidence for a depleting seasonal snowpack in the near east. Remote Sensing. ISSN 2072-4292. 11(5). doi: 10.3390/rs11050483. Full text in Research Archive

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• Yilmaz, Yeliz A. & Sen, Omer L. (2018). Detecting the effects of irrigation on regional temperature extremes. NCAR Technical Note NCAR/TN-550+PROC. ISBN 978-0-9973548-3-6. 151 p.

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• Engeland, Kolbjørn; Aas, Kjetil Schanke; Erlandsen, Helene Birkelund; Gelati, Emiliano; Huang, Shaochun & Narayanappa, Devaraju [Show all 11 contributors for this article] (2022). LATICE MIP evapotranspiration – A model intercomparison project for evapotranspiration estimates at high latitudes.
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bazilova, Varvara; Bertino, Laurent; Fiddes, Joel & Filhol, Simon [Show all 9 contributors for this article] (2021). Unmixing satellite imagery using data assimilation to map snow and albedo.
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bertino, Laurent; Fiddes, Joel; van Hove, Alouette & Pirk, Norbert [Show all 8 contributors for this article] (2021). Learning from Earth observations using data assimilation.
• Aalstad, Kristoffer; Fiddes, Joel; Martin, Leo Celestin Paul; Alonso-Gonzalez, Esteban; Yilmaz, Yeliz A. & Pirk, Norbert [Show all 7 contributors for this article] (2021). Workshop on downscaling with TopoSCALE for cryospheric applications. Show summary

  Meteorological forcing data is a crucial ingredient when modeling the past, present, and future state of the cryosphere. At the same time, cold regions (i.e. the high elevations and/or latitudes of our planet) where most of the cryosphere is found are typically remote with a very low density of in-situ observations. The few meteorological stations that do exist are typically quite unrepresentative due to the often extreme surface heterogeneity and complex terrain in these regions. This makes empirical-statistical and geostatistical downscaling techniques that rely heavily on station data somewhat impractical for cryospheric applications. On the other side of the scale, we have dynamical downscaling techniques that rely on using sophisticated regional climate models to downscale historical global reanalysis data or projections from global climate models. Although these are not as dependent on local observations in that they are able to mechanistically model the state of the atmosphere, they are prohibitively expensive to run at the decadal timescales and hillslope (1 km - 100 m) spatial scale that is often sought in cryospheric applications. In this workshop, we will provide an overview of various downscaling techniques and introduce the topography-based downscaling routine TopoSCALE as well as its many applications and downstream methods listed below. TopoSCALE was developed to provide a computationally feasible technique for generating hourly hillslope scale atmospheric forcing for cryospheric modeling from global reanalysis data or other atmospheric model outputs, without the need for in-situ data. It relies heavily on topographic parameters derived from digital elevation models to be able to scale the input atmospheric forcing to the local topography. It has been tested quite extensively in a variety of environments, including: the Swiss Alps, Svalbard, the California Sierra Nevada, and High Mountain Asia. TopoSCALE is also being used as part of ongoing permafrost reanalysis efforts in the ESA Permafrost_CCI project. The scheme can be coupled to a clustering framework (TopoSUB) which can speed up distributed simulations by orders of magnitude compared to the more traditional gridded approach to land surface modeling. TopoSCALE can also be used together with bias-correction techniques to help downscale future regional climate projections to the hillslope scale with applications to cryospheric climate impact studies. Recent and ongoing applications of TopoSCALE together with snow data assimilation have been particularly fruitful in being able to handle biases in solid precipitation, which has been one of the most challenging problems for downscaling in cryospheric applications. Importantly, TopoSCALE lets modellers shift limited computational resources away from the downscaling exercise to probing uncertainties through ensemble simulations and constraining these with Earth observations.

• Gelati, Emiliano; Yilmaz, Yeliz; Bakke, Sigrid Jørgensen & Tallaksen, Lena Merete (2021). Community Land Model v5 runoff evaluation in small near-natural catchments in Fennoscandia.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Stordal, Frode & Tallaksen, Lena M. (2021). Probing Fennoscandian Snow Cover Dynamics in the Community Land Model and Climate Reanalyses during the Satellite Era.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Stordal, Frode & Tallaksen, Lena M. (2021). Benchmarking CLM5 snow cover dynamics with MODIS and reanalyses over Fennoscandia .
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Show all 30 contributors for this article] (2021). Natur i endring - samspillet mellom klima og økosystemene.
• Gelati, Emiliano; Yilmaz, Yeliz A.; Bakke, Sigrid Jørgensen & Tallaksen, Lena M. (2021). ​Community Land Model v5 runoff evaluation in small near-natural catchments in Fennoscandia.
• Mužić, Iris; Vestin, Patrik; Lindroth, Anders; Mölder, Meelis; Biermann, Tobias & Heliasz, Michal [Show all 11 contributors for this article] (2021). MSc: Changes in radiative forcing due to clear-cutting in Sweden; PhD: The role of land-atmosphere interactions on temperature variability and extremes in Fennoscandia . Full text in Research Archive
• Popp, Andrea; Hall, Caitlyn A. & Yilmaz, Yeliz A. (2020). How to Combat Bullying and Discrimination in the Geosciences. EOS. ISSN 0096-3941. 101. doi: 10.1029/2020EO151914.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). The Representation of the Fennoscandian Snow Cover Phenology in Reanalyses and CLM5 during the MODIS-era. Show summary

  Snow plays an important role in cold regions through its effect on the terrestrial exchange of energy, water, and carbon. Accurately simulating snow processes is therefore important in capturing various climate feedbacks in Earth system models. The representation of the subgrid heterogeneity of snow properties (e.g. coverage, depth, density, albedo) are, in addition to accumulation and snowmelt, major sources of uncertainty in the snow modules of land surface schemes. Using multiple data sources is essential to address these uncertainties and to evaluate overall model performance. Unlike in-situ observations, satellite remote sensing products provide unique representative information at the scale of Earth system models. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua and Terra satellites provide a continuous long-term climate record for the last two decades. In this study, two daily snow cover data sets from MODIS (MOD10A1 and MYD10A1) were used to retrieve fractional snow-covered area (fSCA) and several snow cover metrics (e.g. snow cover duration, first and last day of snow) over Fennoscandia for the 2001-2020 water years. We use these retrievals to evaluate the fSCA outputs from multiple reanalyses (ERA5-Land, ERA5, and MERRA-2) and the latest version of the Community Land Model (CLM5) which is the land component of the Community Earth System Model (CESM) and the Norwegian Earth System Model (NorESM). In order to test the accuracy of the MODIS data, we employed Sentinel-2 and Landsat 8 satellite retrievals as well as local‐scale measurements around the Finse Eco-Hydrological Observatory (Finse EcHO), a low-alpine site in central Norway. Lastly, we compared the trends in snow cover metrics with terrestrial water storage anomalies obtained from the Gravity Recovery and Climate Experiment (GRACE) to better understand the regional water cycle dynamics over this region. This study provides a useful starting point for integrating Earth observations into Earth system modeling in cold regions to help identify and constrain sources of uncertainty. Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the EMERALD (project #294948) funded by the Research Council of Norway.

• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). Fennoscandian snow cover phenology from MODIS, CLM5, and climate reanalyses.
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). Scandinavian snow cover phenology from MODIS, CLM, and reanalyses.
• Yilmaz, Yeliz; Tallaksen, Lena M. & Stordal, Frode (2020). Hydroclimatological evaluation of CLM5 simulations using multiple data sources for land-atmosphere interaction studies over Scandinavia. Show summary

  Arctic amplification leads to rapid changes in the terrestrial water and energy balances at high northern latitudes. Advances in Earth System Models (ESMs) is improving our understanding of the underlying feedback mechanisms leading to these changes. The representation of the land surface in ESMs is essential to simulate and understand changes at the global and regional scales. The latest version of the land component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5), has received substantial new implementations to help simulate the land surface processes in cold environments. At the same time, the behaviour of offline CLM5 simulations and new observational data sets have not been systematically compared over Scandinavian regions. In this study, we run the CLM5 model at relatively high resolution (0.25 degrees) over Scandinavia (including Svalbard) for 15 years between 2002 and 2016. We evaluate the water and energy budget components of CLM5 using several reanalyses and satellite-based observational data sets. In particular, we use monthly model outputs and compare with the satellite retrievals from GRACE, MODIS, AMSR2, and AMSR-E, and reanalysis data sets from ERA5, GLDAS, and MERRA-2. As an additional data source, we use the local‐scale measurements obtained from the Finse Eco-Hydrological Observatory (Finse EcHO) at 1200 m a.s.l, and the high-Arctic research site at Bayelva near Ny-Ålesund, Svalbard. Our investigation is focused on several variables including terrestrial water storage, snow water equivalent, turbulent fluxes, net radiation, and skin temperature. The results indicate that the perceived performance of the land surface model (CLM5) depends strongly on the reference observational data set. Regional discrepancies between data sets, particularly for Svalbard, prompts further investigation of the underlying sources of uncertainty. The results of this evaluation provide a valuable source of information for future studies in the region, particularly in the Land-ATmosphere Interactions in Cold Environments (LATICE) project, which focuses on cold region land surface dynamics, integrating across observational systems, laboratory experiments, field, and modeling efforts. Acknowledgement : This study is conducted under the LATICE strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.

• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Nilsen, Irene Brox & Parmentier, Frans-Jan W. [Show all 22 contributors for this article] (2020). Natur i endring - en vandreutstilling.
• Yilmaz, Yeliz; Tallaksen, Lena M. & Stordal, Frode (2019). Assessment of the water and energy balance simulations of CTSM using satellite-based observations over Scandinavia.

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