Yeliz Yilmaz

• Huang, Shaochun; Silantyeva, Olga; Gelati, Emiliano; Yilmaz, Yeliz A.; Engeland, Kolbjørn & Tallaksen, Lena M. (2026). Can we better constrain evapotranspiration estimates in data scarce cold regions? Application of a discharge-constrained modelling framework in Norwegian catchments. Journal of Hydrology Regional Studies. 67, p. 103850–103850. doi: 10.1016/j.ejrh.2026.103850. Full text in Research Archive Show summary

  Study region: 66 Norwegian catchments representing five hydrological regimes in Norway. Study focus: This study focuses on estimation of evapotranspiration (ET) in Norwegian catchments where ET estimates are highly uncertain due to sparse ET measurements and high uncertainty in measured precipitation in complex terrain. We propose a large-sample multi-modelling framework to benchmark the ET estimate, using three hydrological models calibrated against daily discharge using three objective functions. The ET simulations were indirectly evaluated using discharge-based criteria in the snow free period, due to lack of direct ET measurements. The ET simulations from model runs fulfilling a set of performance criteria were considered as benchmarks to evaluate and constrain five fine-resolution global ET products. New hydrological insights for the region: The results show that the three hydrological models differ in the sensitivity to objective functions and in performance for each hydrological regime. Most global products tend to overestimate ET for the Mountain regime and vary in performance for the other hydrological regimes. By excluding the poorly performing hydrological models and global ET products, the mean uncertainty in annual ET estimates of all catchments reduces from 265 mm to 73 mm. The constrained annual ET is around 332 ± 47 mm in the Atlantic regime, 320 ±71 mm in the Baltic and Transition regimes, 287 ±62 mm in the Inland regime and 223 ±54 mm in the Mountain regime.

• Whitney, K. M.; Page, J. Cranko; Dengri, A.; Hina, S.; Okachi, H. & Rabanal, V. [Show all 29 contributors for this article] (2026). Democratizing Hydroclimate Science for Just and Resilient Futures. Earth's Future. 14(8), p. 1–24. doi: 10.1029/2026ef008172. Full text in Research Archive Show summary

  Abstract Hydroclimatic extremes are intensifying, escalating risks to ecosystems, infrastructure, and vulnerable communities. Rapid technological innovation and scientific discovery offer powerful tools to address these challenges, yet a gap remains between hydroclimate knowledge production and its use in decision‐making. Bridging this divide requires democratizing hydroclimate science through expanded participation in research design and implementation, as well as in the distribution of recognition, resources, and decision power. Democratizing science can foster outcomes that are (a) more credible, through transparency and integration of decision‐maker perspectives; (b) more just, through fair distribution of research opportunities, resources, recognition, and decision power; and (c) more resilient, by strengthening the capacity of communities and institutions to adapt, learn, and recover from climate pressures. Drawing on insights from the 2024 Global Energy and Water EXchanges (GEWEX) Early Career Researcher Workshop, which gathered participants from 19 countries across disciplines, we outline pathways toward more democratic hydroclimate science grounded in diverse participation and shared leadership. We highlight three priorities: building trust through co‐production, improving the usability of hydroclimate information, and promoting responsible research and innovation in emerging technologies. Case studies illustrate how early‐ and mid‐career researchers and practitioners are advancing these priorities and modeling practices that connect innovation with equity. Achieving lasting and effective change requires research and funding institutions to recognize and support collaborative leadership, and to value relevance and accountability alongside technical excellence. Strengthening such democratic frameworks can help ensure that hydroclimate science advances more just research practices and supports the resilience of the communities it serves.

• Mužić, Iris; Hodnebrog, Øivind; Yilmaz, Yeliz A.; Berntsen, Terje Koren; Sillmann, Jana & Lawrence, David M. [Show all 7 contributors for this article] (2025). Soil moisture-Temperature coupling during extreme warm conditions in 2018 in Sweden: a case study with WRF-CTSM. Advances in Statistical Climatology, Meteorology and Oceanography (ASCMO). ISSN 2364-3579. 11(2), p. 273–292. doi: 10.5194/ascmo-11-273-2025. Full text in Research Archive Show summary

  Abstract. Soil moisture–temperature coupling (SM–T) significantly influences the frequency and amplitude of heat extremes. It describes how variations in soil moisture affect surface air temperature conditions and vice versa. This study aims to determine the spatial extent and duration of SM–T in southern and central Sweden, an area increasingly recognized as a coupling hot spot, during the extreme warm conditions between May and August 2018 (MJJA 2018). The assessment of coupling is based on a multi-correlation overlay analysis of key coupling variables: surface soil moisture, evaporative fraction, and daily maximum 2 m temperature from four different simulations of the coupled regional climate model WRF-CTSM, along with a merged gridded GLEAM-E-OBS observational–reanalysis dataset. These datasets demonstrate robust precision in representing the magnitude and variability of the key coupling variables during the MJJA 2018 compared to in situ observations, though the precise timing and duration of the coupling are challenging to reproduce at the local scale. WRF-CTSM provides a more realistic depiction of the key coupling variables and their interactions when recent CTSM advancements are incorporated. On average, across the study region and all five datasets, SM–T persisted for 22 d throughout the MJJA period. The atmospheric leg alone (involving daily evaporative fraction and maximum 2 m temperature), averaged across datasets, contributed 92 % to the regional coupling duration.

• Mužić, Iris; Hodnebrog, Øivind; Yilmaz, Yeliz A.; Berntsen, Terje Koren; Lawrence, David M. & Sobhani, Negin (2025). The Evaluation of Hydroclimatic Variables Over Nordic Fennoscandia Using WRF-CTSM. Journal of Geophysical Research (JGR): Atmospheres. ISSN 2169-897X. 130(9). doi: 10.1029/2024JD043103. Full text in Research Archive Show summary

  This study is the first to evaluate the state-of-the-art coupled land-atmosphere regional climate model WRF-CTSM. It comprises the Weather Research and Forecasting model, WRF, and the Community Terrestrial Systems Model, CTSM (using a configuration that is the same as the Community Land Model Version 5, CLM5). The evaluation is conducted over Nordic Fennoscandia (Norway, Sweden, and Finland) since there is uncertainty in climate models' representation of key hydroclimatic variables in high-latitude regions as they experience accelerated transformation in a changing climate. A 13-year WRF-CTSM simulation (2010–2022) is performed using a 10.5 km horizontal grid spacing to assess the model biases in simulating mean, minimum, and maximum 2 m temperature, precipitation, snow cover (snow depth, snow water equivalent, fractional snow-covered duration), and surface energy balance components. The analysis is based on annual, seasonal, monthly, and daily mean comparisons against openly available observational data sets, comprising regional scale gridded station-based and satellite-based data sets, as well as point scale observations from ground stations. The model shows robust agreement with the evaluation data sets across all considered variables. Furthermore, in situ scale conditions in 2 m temperature, precipitation, snow cover variables, and latent heat are captured with considerable precision. WRF-CTSM is thus considered a powerful research tool for the assessment of land-atmosphere interactions over Nordic Fennoscandia.

• Keetz, Lasse Torben; Aalstad, Kristoffer; Fisher, Rosie; Terán, Christian Poppe; Naz, Bibi S. & Pirk, Norbert [Show all 8 contributors for this article] (2025). Inferring Parameters in a Complex Land Surface Model by Combining Data Assimilation and Machine Learning. Journal of Advances in Modeling Earth Systems. 17(6). doi: 10.1029/2024MS004542. Full text in Research Archive Show summary

  Complex Land Surface Models (LSMs) rely on a plethora of parameters. These parameters and the associated process formulations are often poorly constrained, which hampers reliable predictions of ecosystem dynamics and climate feedbacks. Robust and uncertainty-aware parameter estimation with observations is complicated by, for example, the high dimensionality of the model parameter space and the computational cost of LSM simulations. Herein, we adapt a novel Bayesian data assimilation (DA) and machine learning framework termed “calibrate, emulate, sample” (CES) to infer parameters in a widely-used LSM coupled with a demographic vegetation model (CLM-FATES). First, an iterative ensemble Kalman smoother provides an initial estimate of the posterior distribution (“calibrate”). Subsequently, a machine-learning-based emulator is trained on the resulting model-observation mismatches to predict outcomes for unseen parameter combinations (“emulate”). Finally, this emulator replaces CLM-FATES simulations in an adaptive Markov Chain Monte Carlo approach enabling computationally feasible posterior sampling with enhanced uncertainty quantification (“sample”). We test our implementation with synthetic and real observations representing a boreal forest site in southern Finland. We estimate a total of six plant-functional-type-specific photosynthetic parameters by assimilating evapotranspiration (ET) and gross primary production (GPP) flux data. CES provided the best estimates of the synthetic truth parameters when compared to data-blind emulator sampling designs while all approaches reduced model-observation errors compared to a default parameter simulation (GPP: - 10% to - 30%, ET: -4% to -6%). Although errors were also consistently reduced with real data, comparing the emulator designs was less conclusive, which we mainly attribute to equifinality, structural uncertainty within CLM-FATES, and/or unknown errors in the data that are not accounted for.

• Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Show all 24 contributors for this article] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), p. 4440–4452. doi: 10.1111/gcb.16808. Full text in Research Archive Show summary

  Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

• Pirk, Norbert; Aalstad, Kristoffer; Yilmaz, Yeliz A.; Vatne, Astrid; Popp, Andrea & Horvath, Peter [Show all 12 contributors for this article] (2023). Snow-vegetation-atmosphere interactions in alpine tundra. Biogeosciences. ISSN 1726-4170. 20(11), p. 2031–2047. doi: 10.5194/bg-20-2031-2023. Full text in Research Archive Show summary

  The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow–vegetation–atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land–atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined 3 years (2019–2021) of continuous eddy covariance flux measurements of the net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a 1-month delay in melt-out date, which falls in the 92nd percentile in the distribution of yearly melt-out dates in the period 2001–2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to 1 month later than the other ecosystem types. While the summertime average normalized difference vegetation index (NDVI) was reduced considerably during the extreme-snow year 2020, it reached the same maximum as in the other years for all but one of the ecosystem types (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model, as well as by the high correlation of flux magnitudes (correlation coefficient r=0.92 for NEE and r=0.89 for ET) between both areas. The 1-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021 and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (−31 to −6 gC m−2 yr−1) to a source (34 to 20 gC m−2 yr−1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.

• 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. 7, p. 6–6. doi: 10.3389/fenvs.2019.00006. Full text in Research Archive
• 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. Full text in Research Archive Show summary

  The waters of the Euphrates and Tigris rivers have always been a vital resource in the water-food-energy nexus of the Middle East region. The currently ongoing Southeastern Anatolia Project (GAP) in Turkey aims to increase regional prosperity by optimizing the use of these waters for irrigation and hydropower. Since the beginning of the 1990s, the irrigation schemes and water management infrastructures within the scope of the GAP have caused significant land use and land cover (LULC) change in this semi-arid region. We employed a high resolution regional climate model to simulate the effects of irrigation induced LULC changes on the regional water and energy balances. For this purpose, historical simulations were conducted by using three land cover distributions which reflect the increase in irrigation and water surfaces. Our experiment reveals that water loss through evapotranspiration increases significantly with the areal expansion of irrigation. This increase is driven by the change in partitioning of the available energy at the surface between turbulent heat fluxes. On the one hand, a significant reduction in sensible heat flux causes local cooling by around 0.4 °C and 0.8 °C for the current and future irrigation conditions, respectively. On the other hand, the increase in latent heat flux enhances evapotranspiration and consequently atmospheric water vapor concentration. The moistening of a shallower boundary layer triggers the formation of convective clouds, which increases convective precipitation, most notably during the irrigation months. The enhanced water loss through evapotranspiration has potential to significantly alter the water budget of the GAP region. It seems that the water surplus of the headwaters region may not be enough to meet the water deficit of the GAP region in the future if the planned irrigation schemes are carried out to completion.

• 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. 11(5). doi: 10.3390/rs11050483. Full text in Research Archive Show summary

  The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region.

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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 9780997354836. 151 p. doi: 10.5065/D6BZ64XQ. Full text in Research Archive

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• Yilmaz, Yeliz A.; Pirk, Norbert; Tallaksen, Lena M.; Stordal, Frode; Berntsen, Terje Koren & Bryn, Anders (2026). Cold region land-atmosphere interactions. doi: https:/www.gewexevents.org/meetings/glass2026/program/abstracts/#Y. Full text in Research Archive Show summary

  Environmental change in high latitudes is progressing faster and more intensely than in many other regions of the Earth. A warmer climate has already triggered unprecedented changes in the terrestrial cryosphere, hydrosphere and ecosphere, which feed back to the atmosphere and the hydrologic cycle. Yet, many studies still rely on one-way coupling between the atmosphere and the land surface, thereby neglecting important interactions and feedback mechanisms. To advance our knowledge on climate change in northern environments, LATICE (Land–ATmosphere Interactions in Cold Environments) was established as a strategic research initiative at the University of Oslo in 2015. Since then, it has developed into a collaborative network of researchers from atmospheric and terrestrial research groups. We combine novel observations, process-based model development, and integrated Earth observation frameworks to improve our understanding of high latitude environmental processes and their representation in Earth System Models - particularly the Norwegian Earth System Model (NorESM). The current LATICE infrastructure including flux towers, drone-based measurements, and a cold climate container contributes to expanding our observational data records in cold environments complemented by regionally calibrated satellite products tailored to boreal and Arctic conditions. These coordinated in-situ, airborne, and spaceborne datasets provide important constraints on surface fluxes, snow dynamics, and vegetation processes. On the modelling side, LATICE researchers have been contributing to land surface and hydrological models to better represent cold region processes by including process parameterizations, new plant functional types, and vegetation demography. The integration of observations and models through data assimilation methods, and further exploration of hybrid approaches with machine learning algorithms support improved understanding of land-atmosphere coupling and uncertainty quantification. This presentation highlights key activities and scientific advances within the LATICE community, presents example studies, and discusses future directions for scientific collaboration with the Pan-GLASS community.

• Yılmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire; Rounce, David; Tober, Brandon & Yang, Ruitang [Show all 8 contributors for this article] (2026). Probabilistic calibration of glacier projections using data assimilation. doi: 10.5194/egusphere-egu26-14845. Full text in Research Archive Show summary

  Global glacier mass loss is accelerating, yet considerable spread in projected glacier changes remains due to model structure, forcing, and parameter uncertainty. Most global glacier projections rely on relatively simple models where calibration approaches are essential to constrain uncertainty by using scarce observations. Probabilistic calibration strategies that help with quantifying uncertainities have recently incorporated into a limited number of global glacier models. As a next step, we suggest employing Bayesian data assimilation methods with untapped potential to integrate multi source observations (in-situ, satellite, reanalysis) when calibrating global glacier model projections.Here, we present a probabilistic calibration framework for the Python Glacier Evolution Model (PyGEM) based on ensemble-based data assimilation. Two data assimilation techniques (PBS and AdaPBS) are used to calibrate mass balance parameters to constrain future projections of glacio-hydrological variables (surface mass balance and runoff) between 2015 and 2100 under four SSP climate scenarios across the Scandinavian region. In this work, we compare our calibration results with commonly used deterministic and probabilistic glacier model calibration algorithms which are already in use in PyGEM. Our probabilistic calibration framework based on data assimilation has the potential to quantify and disentangle uncertainties from climate forcing, model structure, and parameters. Better constrained and uncertainty-aware glacier models increases confidence in projections of future glacier change and their relevant impacts. This work paves the way for producing policy relevant global glacier projections and scenarios with their uncertainty estimates within the ERC-AdG GLACMASS project.

• Aalstad, Kristoffer; Alonso-González, Esteban; Fiddes, Joel; Kääb, Andreas; Pirk, Norbert & Treichler, Désirée [Show all 9 contributors for this article] (2026). Global ensemble-based mountain snow reanalysis. doi: https:/snowhydro.unirioja.es/public/files/Snowhydro_abstracts.pdf. Full text in Research Archive Show summary

  Snow is an essential climate variable that regulates the global cycles of energy, carbon, and water. However, the state of the snowpack is only partially observable by satellites due to gaps, noise, and indirect retrieval algorithms. The fusion of uncertain snow models and noisy observations through data assimilation presents a natural solution to this problem. Nonetheless, data assimilation has received relatively little attention from the snow remote sensing community despite its potential to serve as a generalized retrieval method that adds value by inferring gap-free hidden snow state variables with uncertainty quantification. We present ongoing research on applying ensemble-based data assimilation techniques to produce a global mountain snow reanalysis constrained by satellite data. These efforts are motivated by the challenge of estimating seasonal snow mass in mountainous terrain which remains a major unsolved problem in snow hydrology. Taking a step towards solving this problem, we aim to generate the first global, consistent, ensemble-based, daily, kilometer-scale reanalysis of seasonal snow mass (i.e. snow water equivalent) and fractional snow-covered area dynamics for all mountains outside Antarctica. To produce our reanalysis, we assimilate fractional snow-covered area satellite retrievals from ESA Snow_cci into a simple snow model run at a relatively high spatio-temporal resolution. Given the efficiency of our model, we are able to leverage promising iterative ensemble-based schemes while ensuring that the problem remains computationally feasible. Specifically, we build on developments hybridizing ensemble Kalman and particle methods for robust and tractable Bayesian inference. We also demonstrate how to hierarchically infer hyperparameters related to snow climatology. The new reanalysis approach is evaluated using independent spaceborne, airborne, and in-situ validation data. Finally, we benchmark the performance of our reanalysis by comparing it to other global mountain snow products. Unlike existing global snow mass products, our product is uncertainty-aware and is tailored to address a key knowledge gap concerning mountain snow mass. We highlight that, by combining observations with models, data assimilation can transform largely untapped climate data into actionable climate information on essential climate variables. The uncertainty quantification in this probabilistic climate information enables informed decision making in response to climate change and its impacts.

• Huang, Shaochun; Silantyeva, Olga; Gelati, Emiliano; Yilmaz, Yeliz A.; Engeland, Kolbjørn & Tallaksen, Lena M. (2025). Quantifying the uncertainty of evapotranspiration estimates for unregulated Norwegian catchments using multiple hydrological models and remote sensing products. doi: 10.5194/egusphere-egu25-14913. Full text in Research Archive Show summary

  Previous studies show high uncertainty of evapotranspiration (ET) estimates for Norway, ranging from about 200 to more than 500 mm/year. It is partly due to sparce ET measurements in Norway to constrain the ET process in the hydrological modelling, and partly due to the uncertainty of precipitation (P) observations, especially in high mountainous regions with complex topography. In this study, we aim to quantify the uncertainty of ET estimates for 66 Norwegian catchments based on three hydrological models and four global remote sensing products. All the selected catchments are small (< 1000 km2), non-regulated and non-glacierized, with long-term discharge (Q) observations and runoff coefficient less than one (i.e. P>Q). These catchments differ substantially in climatic and hydrological characteristics and span the five hydrological regimes commonly used in Norway (Atlantic, Mountain, Inland, Baltic, and Transition). The three hydrological models (HBV, LISFLOOD and Shyft) are calibrated against daily discharge between 1981 and 2000 using three objective functions (KGE, KGE+LKGE, and KGE+BoxcoxKGE) and validated between 2001 and 2020 using six criteria (KGE, LKGE, BoxcoxKGE, percent Bias (PBias), and KGE &#160;and PBias in the snow free period). The latter two criteria are specifically selected to evaluate the model performance in simulating ET given the absence of direct ET measurements. The snow free period is identified for each catchment and year based on a daily fractional snow-covered area data at 500 m spatial resolution produced from a combined MODIS dataset (MOD10A1 and MYD10A1). The four global remote sensing estimates of ET are obtained from BESSV2, ETMonitor, PML_V2 and SSEBop_V2 datasets and they are available at fine spatial resolution (&#8804;0.05&#176;) and monthly time steps. The calibration and validation results show that the three hydrological models perform best using different objective functions for each hydrological regime and target variable (discharge and ET). The uncertainty ranges of annual mean ET are up to 177 mm based on all hydrological model results and remote sensing products. Using the best performing hydrological model results as the benchmark, all remote sensing products overestimate ET for the mountain regime and PML_V2 gives the best estimate for the other four hydrological regimes.

• Muzic, Iris; Hodnebrog, Øivind; Yilmaz, Yeliz A.; Berntsen, Terje Koren; Sillmann, Jana & Lawrence, David M. [Show all 8 contributors for this article] (2025). Evaluating Land-Atmosphere Interactions in the coupled WRF-CTSM model over Nordic Fennoscandia. Full text in Research Archive Show summary

  High latitude regions are experiencing rapid changes under evolving climate conditions. Advanced research tools are essential for understanding these dynamics and their associated feedback mechanisms. Here we present 1) a comprehensive evaluation of the state-of-the-art coupled regional climate model WRF–CTSM in simulating hydroclimatic variables and 2) soil moisture–temperature coupling analysis during the extreme 2018 heatwave. The WRF–CTSM model couples the Weather Research and Forecasting (WRF) model with the Community Terrestrial Systems Model (CTSM) which enables a more detailed representation of land surface processes and their interactions with the atmosphere. We conducted a high-resolution (10.5 km) 13-year simulation (2010–2022) over Nordic Fennoscandia (Norway, Sweden, and Finland) to assess the model’s performance in capturing key hydroclimatic variables such as mean, minimum, and maximum 2 m temperatures, precipitation, snow variables (snow depth, snow water equivalent, fractional snow‐covered duration), and surface energy balance components. A multi-source comparison using gridded, satellite-based, and in situ observations indicates that the WRF–CTSM model effectively captures high latitude climate conditions at different timescales. In addition, we used WRF–CTSM to investigate soil moisture–temperature coupling during the 2018 heatwave with a focus on southern and central Sweden that is considered as a coupling hotspot. A multi-correlation overlay analysis was performed using four different WRF–CTSM configurations. We employed a gridded observational dataset as a reference after validating against in situ measurements to assess the magnitude and variability of the key coupling variables. Our results show that soil moisture–temperature coupling persisted for an average of 40 days. The atmospheric leg contributes on average 88% of the coupling duration with considerable variability across datasets. Our findings highlight the capability of the WRF–CTSM model to simulate complex land–atmosphere interactions in high latitude environments

• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Mazzolini, Marco; Guillet, Gregoire; Yang, Ruitang & Hock, Regine (2025). Data assimilation for snow and glacier modeling in climate applications. Full text in Research Archive
• Yilmaz, Yeliz A.; Jansen, Arnaud; Aalstad, Kristoffer; Mazzolini, Marco & Alonso-González, Esteban (2025). Snow data assimilation for understanding snowpack and streamflow shifts in Hardangervidda. Full text in Research Archive
• Yilmaz, Yeliz A. & Huang, Shaochun (2025). MODIS-derived daily fractional snow-covered area over the Norwegian catchments. doi: 10.5281/zenodo.17600906. Full text in Research Archive Show summary

  This dataset includes the daily timeseries of MODIS-based fractional snow-covered area over the selected 114 Norwegian catchments between 2000 and 2020. Daily imagery at 500 m spatial resolution from the MODerate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua (MYD10A1 Version 61; Hall and Riggs, 2021a) and Terra (MOD10A1 Version 61; Hall and Riggs, 2021b) satellites were combined to reduced the cloud cover, and temporal gaps were filled by using a lineer interpolation approach. Daily fractional snow-covered area was retrieved following the approach of Salomonson and Appel (2006) on the normalized difference snow index (NDSI). This dataset is primarily produced for the study by Huang et al. (submitted 2025)

• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire; Rounce, David; Tober, Brandon & Yang, Ruitang [Show all 7 contributors for this article] (2025). Calibrating future glacier projections using data assimilation. doi: https:/imc2025.info/imc25/abstract/calibrating-future-glacier-projections-using-data-assimilation/. Full text in Research Archive Show summary

  Global glacier mass is changing rapidly, and projections of global glacier mass balance under changing climatic conditions are crucial for informed decision-making. Existing glacier projections use relatively simple glacier models constrained by sparse observations. In these projections, model calibration plays a key role in constraining uncertainty. The use of Bayesian data assimilation methods for calibration by integrating multiple emerging observational data sets (in-situ mass balance measurements, climate reanalyses, and satellite remote sensing) to constrain model parameters and their dynamics remains relatively unexplored. Such a probabilistic calibration strategy could enable us to quantify and disentangle uncertainties related to the glacier model, the selected climate model forcing, and internal climate variability. The Python Glacier Evolution Model (PyGEM) is one of a handful of global glacier models that allow us to simulate the evolution of the mass balance of all glaciers in the world. In this work, we adopt ensemble-based data assimilation methods to calibrate PyGEM model parameters and thus constrain future projections of glacier mass balance across Scandinavia. We compare our results with traditional glacier model calibration algorithms and the Bayesian gold standard Markov Chain Monte Carlo (MCMC) method in PyGEM for glacio-hydrological indicators (surface mass balance and runoff projections) between 2015 and 2100 with four SSP scenarios. This work serves as the kernel for a scalable glacier data assimilation framework to produce policy relevant global glacier projections and scenarios within the recently funded ERC-AdG GLACMASS project. The probabilistic calibration framework developed in this study can in principle be adapted for a wide range of cryospheric applications.

• Yilmaz, Yeliz A.; Jansen, Arnaud; Aalstad, Kristoffer; Mazzolini, Marco & Alonso-González, Esteban (2025). Investigating Snowpack and Streamflow Changes with Snow Data Assimilation in Hardangervidda. Full text in Research Archive Show summary

  Snowmelt rates have a direct influence on downstream water availability and are changing significantly in response to shifting climatic conditions. However, the specific drivers and direction of these changes require further investigation across different regions and timescales. In this study, we investigated the impact of climate change on snowmelt rates and streamflow in the Hardangervidda region of Norway. The Multiple Snow Data Assimilation System (MuSA) was used to simulate historical (2001–2020) and future (mid- and end-century) snowpack conditions by assimilating a long-term satellite-based record of fractional snow-covered area from MODIS. Using MuSA outputs under the RCP8.5 scenario, we applied the Simple Dynamical Systems approach to simulate future streamflow in the Kvenna catchment. Our results show that while meltwater volumes increase at mid-century due to higher snowmelt rates, they decline by the end of the century. Annual peak flow decreases in both future periods (by 26% and 36%, respectively) with a seasonal shift in its timing from spring/summer to autumn/winter. Seasonal total discharge increases during winter/spring, but decreases during summer. Our findings highlight substantial changes in snowmelt dynamics and streamflow patterns on the Hardangervidda plateau under future climate scenarios. The anticipated earlier and more variable melt events along with altered peak flow timing may complicate for future reservoir and water resource management.

• Aalstad, Kristoffer; Guillet, Gregoire; Yang, Ruitang; Yilmaz, Yeliz & Hock, Regine (2025). Glacier reanalysis using hierarchical data assimilation. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Guillet, Gregoire & Hock, Regine (2025). Strategies for Future Projections with PyGEM. Full text in Research Archive
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Fiddes, Joel; Guillet, Gregoire; Kääb, Andreas Max & Mazzolini, Marco [Show all 11 contributors for this article] (2025). Ensemble-based cryospheric reanalysis to infer global snow mass. Full text in Research Archive Show summary

  Snow, glaciers, and permafrost are essential climate variables (ECV) that regulate the global cycles of energy, carbon, and water. At the same time, these cryospheric ECVs are only partially observable by satellites due to gaps, noise, and indirect retrieval algorithms. Data assimilation (DA), namely the Bayesian fusion of uncertain models and noisy observations, presents a natural solution to the problem of partial observability. Nonetheless, unlike in numerical weather prediction, DA has received relatively little attention from the Earth observation (EO) community despite its potential as a generalized retrieval framework that adds value by filling gaps and inferring latent ECVs with uncertainty quantification. This contribution presents active research on applying ensemble-based DA techniques to carry out cryospheric reanalysis constrained by satellite data. Our future goals with these efforts are to generate consistent global high resolution reanalyses of seasonal snow mass (also known as snow water equivalent or simply SWE), glacier mass balance, and the thermal state of permafrost. In doing so, we seek to leverage a myriad of data streams including Earth observing satellites, global atmospheric reanalyses, airborne retrievals, and in-situ observations. Here we focus on seasonal snow since accurate global snow mass estimation, particularly in mountainous terrain, remains a major unsolved problem in snow hydrology. To help tackle this problem, we are developing a global scale ensemble-based snow reanalysis product by assimilating ESA Snow_cci fractional snow-covered area retrievals. Using a simple snow model we are able to devote considerable computational resources to generate a global snow reanalysis at daily temporal resolution and kilometric spatial resolution while still being able to afford using promising iterative ensemble-based DA schemes. For the latter, we explore recent developments in hybridizing iterative ensemble Kalman and particle methods to provide robust Bayesian posterior inference. In particular, we demonstrate how these schemes can be used as nested smoothers to hierarchically infer prior hyperparameters related to snow climatology. The new reanalysis approach is evaluated using independent spaceborne, airborne, and in-situ validation data by comparing its performance to state-of-the-art regional snow reanalyses and existing global snow mass products in the form of ERA5-Land reanalysis data and ESA Snow_cci SWE retrievals. Unlike existing global snow products, this new snow reanalysis product is uncertainty-aware, assimilates snow satellite data, and specifically targets a key knowledge gap concerning mountain snow mass. Concurrent efforts to adapt this DA framework towards the generation of global ensemble-based reanalyses for glaciers and permafrost will also be showcased to emphasize synergies across these terrestrial cryospheric reanalysis efforts. We highlight that, by combining EO with cryospheric models, ensemble-based DA can transform largely untapped climate data into actionable climate information on cryospheric ECVs. The baked-in uncertainty quantification in this probabilistic climate information empowers us to make decisions in response to climate change and its perilous impacts on the mountain cryosphere.

• Silantyeva, Olga; Ahlbäck, Malin; Yilmaz, Yeliz A.; Tallaksen, Lena M.; Lappegard, Gaute & Erlandsen, Helene B. [Show all 11 contributors for this article] (2024). SnowSub: Assessing snow sublimation for hydropower production in Norway. Project results at Snow Bridges workshop. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 9 contributors for this article] (2024). Leveraging Earth observations to model snow cover dynamics over Scandinavia. Full text in Research Archive
• Tallaksen, Lena M.; Stordal, Frode; Pirk, Norbert & Yilmaz, Yeliz A. (2024). Land-Atmosphere Interactions at High Northern Latitudes. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 9 contributors for this article] (2024). Using Earth observations to model snow cover over Scandinavia. Full text in Research Archive
• Treichler, Désirée; Filhol, Simon; Mazzolini, Marco; Yilmaz, Yeliz A. & Silantyeva, Olga (2024). UiO’s snow field sites, measurements, remotely sensed datasets, and climate container. Full text in Research Archive
• Aalstad, Kristoffer; Hock, Regine; Mazzolini, Marco; Pirk, Norbert; Treichler, Désirée & Westermann, Sebastian [Show all 9 contributors for this article] (2024). Ensemble-Based Reanalysis of the Terrestrial Cryosphere. Full text in Research Archive Show summary

  The terrestrial cryosphere in the form of glaciers, permafrost, and seasonal snow, plays a vital role in the climate system by regulating the global cycles of energy, carbon, and water. As a result, these components of the cryosphere are considered essential climate variables (ECVs) by the Global Climate Observing System. At the same time, these ECVs are only partially observable by Earth observing satellites due to gaps, noise, and indirect retrieval algorithms. The statistical fusion of uncertain models and noisy observations referred to as data assimilation in geoscience represents a natural solution to this problem of partial observability. Nonetheless, beyond the confines of weather prediction and atmospheric reanalyses, data assimilation has received relatively little attention in broader Earth system science despite its potential for adding value to observations by filling gaps and inferring latent ECVs with uncertainty quantification. In this contribution, we present ongoing efforts in several projects applying ensemble-based (probabilistic) data assimilation techniques to carry out long term and large scale reanalyses of the terrestrial cryosphere. Our lofty goals in these projects are to generate global high resolution multi-decadal ensemble-based reanalyses of glacier mass balance, the thermal state of permafrost, and seasonal snow mass. In doing so, we are leveraging a plethora of emerging data from Earth observing satellites, global atmospheric reanalyses, and in-situ observations. Herein, we will focus mainly on the general implementation of the novel ensemble-based methods that we are using to solve the reanalysis (or reconstruction) problem as opposed to the structure of the specific cryospheric models and data that they are applied to. Considerable emphasis is placed on how these methods can be deployed at unprecedented spatio-temporal scales while retaining uncertainty quantification that is crucial in guiding decisions. Looking further ahead, we will highlight the challenges that remain in pushing the boundaries of terrestrial reanalysis efforts towards the hillslope (100 m) scale and making use of the largely untapped and continuously growing stream of Earth observation data.

• Engeland, Kolbjørn; Erlandsen, Helene B.; Gelati, Emiliano; Huang, Shaochun; Narayanappa, Devaraju & Pirk, Norbert [Show all 10 contributors for this article] (2023). Intercomparison of local evapotranspiration estimates at high latitudes. Full text in Research Archive
• Bjerke, Jarle W.; Tallaksen, Lena M.; Stordal, Frode & Yilmaz, Yeliz A. (2023). EMERALD Open Science Day - Summary of Group Discussions. Full text in Research Archive Show summary

  Group discussions, key questions: How to move from single discipline to multidisciplinary studies? Which processes are missing in terms of plant-climate interactions for improving models?

• Mužić, Iris; Hodnebrog, Øivind; Berntsen, Terje Koren; Yilmaz, Yeliz A.; Sillmann, Jana & Lawrence, David [Show all 8 contributors for this article] (2023). The relevance of coupled climate model WRF-CTSM for land-atmosphere interactions analysis. Full text in Research Archive
• Lieungh, Eva; Yilmaz, Yeliz; Tang, Hui; Keetz, Lasse Torben; Olsen, Siri Lie & Gya, Ragnhild [Show all 11 contributors for this article] (2023). Complementary field and modelling experiments: alpine vegetation under climate warming. Full text in Research Archive
• 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. Full text in Research Archive
• Yilmaz, Yeliz A. & Aas, Kjetil Schanke (2022). Land surface modeling activities in Norway. Full text in Research Archive
• Yilmaz, Yeliz A. (2022). Terrestrial Sciences at UiO (a summary). Full text in Research Archive
• Yilmaz, Yeliz A. (2022). Terrestrial Sciences at UiO. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 8 contributors for this article] (2022). Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations and reanalyses. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Pirk, Norbert & Remmers, Janneke [Show all 8 contributors for this article] (2022). Evaluating modeled snow cover dynamics over Fennoscandia using Earth observations. Full text in Research Archive Show summary

  The snow cover is an essential part of the climate system in cold regions through its effects on the terrestrial water, energy, and carbon balance. Due to the high spatiotemporal variability of snow, it is challenging to resolve snow cover dynamics in models. Thus, our ability to improve the representation of these dynamics in Earth System Models (ESMs) leans heavily on the accuracy and representativeness of the observational data sets used for model evaluation. The big picture provided by the long-term climate data record from satellites helps us to monitor changes in land cover over large areas. At the same time, rapidly developing drone and terrestrial imaging technology provides higher resolution information over specific areas. This complimentary information from spaceborne, airborne, and terrestrial Earth observations is invaluable for better understanding the complex processes in the climate system. In our work, we are currently exploiting estimates of snow-covered area from different optical sensors onboard polar orbiting satellites that are imaging the Nordic region. Drone and terrestrial images are being explored as a source of validation and calibration data for the satellite products.  Having representative snow cover maps enables us to better evaluate the terrestrial component of the Norwegian Earth System Model (NorESM), namely the Community Land Model (CLM5). With a focus on snow processes, we are conducting an analysis using satellite-based estimates of snow-covered area (MODIS, Sentinel-2, and Landsat 8), snow simulations from CLM5, snow variables from several climate reanalyses (ERA5, ERA5-Land, and MERRA-2), and in-situ data from eddy covariance stations (LATICE flux sites). Two offline CLM5 simulations are conducted with different atmospheric forcing, namely the default data set (GSWP3) and ERA5. We are investigating trends in the snow cover phenology, which we characterize using snow cover duration, first and last days of the snow cover, and consecutive snow cover days for each snow season over the last two decades. This work illuminates a path to integrate Earth observations with Earth system modeling in cold environments to both identify and constrain sources of uncertainty. Acknowledgement: This ongoing study is supported by the LATICE (Land-ATmosphere Interactions in Cold Environments) strategic research initiative funded by the University of Oslo, and the project EMERALD (294948) funded by the Research Council of Norway.

• 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. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon; Gascoin, Simon; Stordal, Frode & Tallaksen, Lena M. (2021). Fennoscandian snow cover dynamics in the MODIS era. Full text in Research Archive
• Gelati, Emiliano; Yilmaz, Yeliz; Bakke, Sigrid Jørgensen & Tallaksen, Lena Merete (2021). Community Land Model v5 runoff evaluation in small near-natural catchments in Fennoscandia. Full text in Research Archive
• 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
• 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. Full text in Research Archive 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.

• 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. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Aalstad, Kristoffer; Alonso-Gonzalez, Esteban; Bertino, Laurent; Fiddes, Joel; Hove, Alouette van & Pirk, Norbert [Show all 8 contributors for this article] (2021). Learning from Earth observations using data assimilation. Full text in Research Archive
• 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. Full text in Research Archive
• 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. Full text in Research Archive
• Yilmaz, Yeliz A.; Aalstad, Kristoffer; Filhol, Simon Vincent P; Stordal, Frode & Tallaksen, Lena M. (2020). Scandinavian snow cover phenology from MODIS, CLM, and reanalyses. Full text in Research Archive
• 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. Full text in Research Archive 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.

• 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. Full text in Research Archive
• Yilmaz, Yeliz; Tallaksen, Lena M. & Stordal, Frode (2020). Hydroclimatological evaluation of CLM5 simulations using multiple data sources for land-atmosphere interaction studies over Scandinavia. Full text in Research Archive 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.

• Yilmaz, Yeliz; Tallaksen, Lena M. & Stordal, Frode (2019). Assessment of the water and energy balance simulations of CTSM using satellite-based observations over Scandinavia. Full text in Research Archive

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