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2. Increase in gross primary production of boreal forests balanced out by increase in ecosystem respiration

Author

Pulliainen, Jouni, Aurela, Mika, Aalto, Tuula, Böttcher, Kristin, Cohen, Juval, Derksen, Chris, Heimann, Martin, Helbig, Manuel, Kolari, Pasi, Kontu, Anna, Krasnova, Alisa, Launiainen, Samuli, Lemmetyinen, Juha, Lindqvist, Hannakaisa, Lindroth, Anders, Lohila, Annalea, Luojus, Kari, Mammarella, Ivan, Markkanen, Tiina, Nevala, Elma, Noe, Steffen, Peichl, Matthias, Pumpanen, Jukka, Rautiainen, Kimmo, Salminen, Miia, Sonnentag, Oliver, Takala, Matias, Thum, Tea, Vesala, Timo, and Vestin, Patrik

Published
2024
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5. Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment.

Author

Montpetit, Benoit, King, Joshua, Meloche, Julien, Derksen, Chris, Siqueira, Paul, Adam, J. Max, Toose, Peter, Brady, Mike, Wendleder, Anna, Vionnet, Vincent, and Leroux, Nicolas R.

Subjects
WATER management, GEOMETRICAL optics, BACKSCATTERING, GEOMETRIC surfaces, RADIATIVE transfer, SNOW accumulation
Abstract

Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Early snowmelt significantly enhances boreal springtime carbon uptake.

Author

Pulliainen, Jouni, Aurela, Mika, Laurila, Tuomas, Aalto, Tuula, Takala, Matias, Salminen, Miia, Kulmala, Markku, Barr, Alan, Heimann, Martin, Lindroth, Anders, Laaksonen, Ari, Derksen, Chris, Mäkelä, Annikki, Markkanen, Tiina, Lemmetyinen, Juha, Susiluoto, Jouni, Dengel, Sigrid, Mammarella, Ivan, Tuovinen, Juha-Pekka, and Vesala, Timo

Subjects
carbon uptake, earth observation, snowmelt
Abstract

We determine the annual timing of spring recovery from space-borne microwave radiometer observations across northern hemisphere boreal evergreen forests for 1979-2014. We find a trend of advanced spring recovery of carbon uptake for this period, with a total average shift of 8.1 d (2.3 d/decade). We use this trend to estimate the corresponding changes in gross primary production (GPP) by applying in situ carbon flux observations. Micrometeorological CO2 measurements at four sites in northern Europe and North America indicate that such an advance in spring recovery would have increased the January-June GPP sum by 29 g⋅C⋅m-2 [8.4 g⋅C⋅m-2 (3.7%)/decade]. We find this sensitivity of the measured springtime GPP to the spring recovery to be in accordance with the corresponding sensitivity derived from simulations with a land ecosystem model coupled to a global circulation model. The model-predicted increase in springtime cumulative GPP was 0.035 Pg/decade [15.5 g⋅C⋅m-2 (6.8%)/decade] for Eurasian forests and 0.017 Pg/decade for forests in North America [9.8 g⋅C⋅m-2 (4.4%)/decade]. This change in the springtime sum of GPP related to the timing of spring snowmelt is quantified here for boreal evergreen forests.

Published
2017

7. Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment

Author

Montpetit, Benoit, primary, King, Joshua, additional, Meloche, Julien, additional, Derksen, Chris, additional, Siqueira, Paul, additional, Adam, J. Max, additional, Toose, Peter, additional, Brady, Mike, additional, Wendleder, Anna, additional, Vionnet, Vincent, additional, and Leroux, Nicolas R., additional

Published
2024
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14. Exploring the decision-making process in model development: focus on the Arctic snowpack

Author

Menard, Cecile B., primary, Rasmus, Sirpa, additional, Merkouriadi, Ioanna, additional, Balsamo, Gianpaolo, additional, Bartsch, Annett, additional, Derksen, Chris, additional, Domine, Florent, additional, Dumont, Marie, additional, Ehrich, Dorothee, additional, Essery, Richard, additional, Forbes, Bruce C., additional, Krinner, Gerhard, additional, Lawrence, David, additional, Liston, Glen, additional, Matthes, Heidrun, additional, Rutter, Nick, additional, Sandells, Melody, additional, Schneebeli, Martin, additional, and Stark, Sari, additional

Published
2024
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15. Supplementary material to "Exploring the decision-making process in model development: focus on the Arctic snowpack"

Author

Menard, Cecile B., primary, Rasmus, Sirpa, additional, Merkouriadi, Ioanna, additional, Balsamo, Gianpaolo, additional, Bartsch, Annett, additional, Derksen, Chris, additional, Domine, Florent, additional, Dumont, Marie, additional, Ehrich, Dorothee, additional, Essery, Richard, additional, Forbes, Bruce C., additional, Krinner, Gerhard, additional, Lawrence, David, additional, Liston, Glen, additional, Matthes, Heidrun, additional, Rutter, Nick, additional, Sandells, Melody, additional, Schneebeli, Martin, additional, and Stark, Sari, additional

Published
2024
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20. GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

Author

Luojus, Kari, Pulliainen, Jouni, Takala, Matias, Lemmetyinen, Juha, Mortimer, Colleen, Derksen, Chris, Mudryk, Lawrence, Moisander, Mikko, Hiltunen, Mwaba, Smolander, Tuomo, Ikonen, Jaakko, Cohen, Juval, Salminen, Miia, Norberg, Johannes, Veijola, Katriina, and Venäläinen, Pinja

Published
2021
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21. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018

Author

Pulliainen, Jouni, Luojus, Kari, Derksen, Chris, Mudryk, Lawrence, Lemmetyinen, Juha, Salminen, Miia, and Ikonen, Jaakko

Subjects
Northern Hemisphere -- Environmental aspects -- History, Snowpack -- Analysis -- Statistics -- Forecasts and trends -- Environmental aspects, Surface-ice melting -- Forecasts and trends -- Analysis -- Statistics -- Environmental aspects, Global warming -- Forecasts and trends -- Environmental aspects -- Statistics -- Analysis, Market trend/market analysis, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover.sup.1-3. These changes in snow cover affect Earth's climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere.sup.4-6. In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking.sup.7-9. Here we use the new GlobSnow 3.0 dataset to show that the 1980-2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 [plus or minus] 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40° N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433-3,380 gigatonnes (mean 2,867) to 2,846-3,062 gigatonnes (mean 2,938)--a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia; both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth's energy, water and carbon budgets. Applying a bias correction to a state-of-the-art dataset covering non-alpine regions of the Northern Hemisphere and to three other datasets yields a more constrained quantification of snow mass in March from 1980 to 2018., Author(s): Jouni Pulliainen [sup.1] , Kari Luojus [sup.1] , Chris Derksen [sup.2] , Lawrence Mudryk [sup.2] , Juha Lemmetyinen [sup.1] , Miia Salminen [sup.1] , Jaakko Ikonen [sup.1] , Matias [...]

Published
2020
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23. Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment.

Author

Montpetit, Benoit, King, Joshua, Meloche, Julien, Derksen, Chris, Siqueira, Paul, Adam, J. Max, Toose, Peter, Brady, Mike, Wendleder, Anna, Vionnet, Vincent, and Leroux, Nicolas R.

Subjects
SNOW accumulation, RADIATIVE transfer, WATER management, SNOW cover, TUNDRAS, GEOMETRICAL optics, BACKSCATTERING, GEOMETRIC surfaces
Abstract

Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (mss soil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (ε soil = 2.47) to X-Band (ε soil = 2.61), to Ku-Band (ε soil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale. [ABSTRACT FROM AUTHOR]

Published
2024
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24. A simple snow temperature index model exposes discrepancies between reanalysis snow water equivalent products.

Author

Chereque, Aleksandra Elias, Kushner, Paul J., Mudryk, Lawrence, Derksen, Chris, and Mortimer, Colleen

Subjects
SNOW cover, PYTHON programming language, TEMPERATURE
Abstract

Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from each of three reanalyses. B-TIM offline snow performs comparably to or better than online (coupled land-atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site.

Author

Dutch, Victoria R., Rutter, Nick, Wake, Leanne, Sonnentag, Oliver, Hould Gosselin, Gabriel, Sandells, Melody, Derksen, Chris, Walker, Branden, Meyer, Gesa, Essery, Richard, Kelly, Richard, Marsh, Phillip, Boike, Julia, and Detto, Matteo

Subjects
TUNDRAS, SNOW cover, SOIL respiration, SOIL temperature, SEASONS, COLD (Temperature)
Abstract

Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm-2d-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Benchmarking of SWE products based on outcomes of the SnowPEx+ Intercomparison Project.

Author

Mudryk, Lawrence, Mortimer, Colleen, Derksen, Chris, Chereque, Aleksandra Elias, and Kushner, Paul

Subjects
ATMOSPHERIC models, INDIVIDUAL differences, BENCHMARKING (Management), PRECIPITATION gauges, MOUNTAIN soils, SAMPLE size (Statistics), CLIMATOLOGY
Abstract

We assess and rank 23 gridded snow water equivalent (SWE) products by implementing a novel evaluation strategy using a new suite of reference data from two cross-validated sources and a series of product inter-comparisons. The new reference data combines in situ measurements from both snow courses and airborne gamma measurements. Compared to previous evaluations of gridded products, we have substantially increased the spatial coverage and sample size across North America, and we are able to evaluate product performance across both mountain and non-mountain regions. The evaluation strategy we use ranks overall relative product performance while still accounting for individual differences in ability to represent SWE climatology, variability, and trends. Assessing these gridded products fills an important gap in the literature since individual gridded products are frequently chosen without prior justification as the basis for evaluating land surface and climate model outputs, along with other climate applications. The top performing products across the range of tests performed are ERA5-Land followed by the Crocus snow model. Our evaluation indicates that accurate representation of hemispheric SWE varies tremendously across the range of products. While most products are able to represent SWE reasonably well across Northern Hemisphere non-mountainous regions, the ability to accurately represent SWE in mountain regions and to accurately represent historical trends are much more variable. Finally, we demonstrate that for the ensemble of products evaluated here, attempts to assimilate surface snow observations and/or satellite measurements lead to a deleterious influence on regional snow mass trends, which is an important consideration for how such gridded products are produced and applied in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Use of multiple reference data sources to cross validate gridded snow water equivalent products over North America.

Author

Mortimer, Colleen, Mudryk, Lawrence, Cho, Eunsang, Derksen, Chris, Brady, Mike, and Vuyvich, Carrie

Subjects
STANDARD deviations, REFERENCE sources
Abstract

We use snow course and airborne gamma data available over North America to compare the validation of gridded snow water equivalent (SWE) products when evaluated with one reference dataset versus the other. We assess product performance across both non-mountainous and mountainous regions, determining the sensitivity of relative product rankings and absolute performance measures. In non-mountainous areas, product performance is insensitive to the choice of SWE reference dataset (snow course or airborne gamma): the validation statistics (bias, unbiased root mean squared error, correlation) are consistent with one another. In mountainous areas, the choice of reference dataset has little impact on relative product ranking but a large impact on assessed error magnitudes (bias and unbiased root mean squared error). Further analysis indicates the agreement in non-mountainous regions occurs because the reference SWE estimates themselves agree up to spatial scales of at least 50 km, comparable to the grid spacing of most available SWE products. In mountain areas, there is poor agreement between the reference datasets even at short distances (< 5 km). We determine that differences in assessed error magnitudes result primarily from the range of SWE magnitudes sampled by each method, although their respective spatiotemporal distribution and elevation differences between the reference measurements and grid centroids also play a role. We use this understanding to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Exploring the decision-making process in model development: focus on the Arctic snowpack.

Author

Menard, Cecile B., Rasmus, Sirpa, Merkouriadi, Ioanna, Balsamo, Gianpaolo, Bartsch, Annett, Derksen, Chris, Domine, Florent, Dumont, Marie, Ehrich, Dorothee, Essery, Richard, Forbes, Bruce C., Krinner, Gerhard, Lawrence, David, Liston, Glen, Matthes, Heidrun, Rutter, Nick, Sandells, Melody, Schneebeli, Martin, and Stark, Sari

Subjects
DECISION making, TUNDRAS, RESEARCH personnel, PERMAFROST, PHYSICS, VAPORS, SEA ice
Abstract

The Arctic poses many challenges to Earth System and snow physics models, which are unable to simulate crucial Arctic snowpack processes, such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost and the global carbon budget. Recognizing that models are shaped by human choices, eighteen Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack, but reflected the broader research context. Inadequate and insufficient resources partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring led to diverging opinions about whether existing models are "good enough" and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations that currently impede progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Introduction

Author

Boyer, Tim, primary, Bartow-Gillies, Ellen, additional, Abida, A., additional, Ades, Melanie, additional, Adler, Robert, additional, Adusumilli, Susheel, additional, Agyakwah, W., additional, Ahmasuk, Brandon, additional, Aldeco, Laura S., additional, Alexe, Mihai, additional, Alfaro, Eric J., additional, Allan, Richard P., additional, Allgood, Adam, additional, Alves, Lincoln. M., additional, Amador, Jorge A., additional, Anderson, John, additional, Andrade, B., additional, Anneville, Orlane, additional, Aono, Yasuyuki, additional, Arguez, Anthony, additional, Arosio, Carlo, additional, Atkinson, C., additional, Augustine, John A., additional, Avalos, Grinia, additional, Azorin-Molina, Cesar, additional, Backensto, Stacia A., additional, Bader, Stephan, additional, Baez, Julian, additional, Baiman, Rebecca, additional, Ballinger, Thomas J., additional, Banwell, Alison F., additional, Bardin, M. Yu, additional, Barichivich, Jonathan, additional, Barnes, John E., additional, Barreira, Sandra, additional, Beadling, Rebecca L., additional, Beck, Hylke E., additional, Becker, Emily J., additional, Bekele, E., additional, Bellido, Guillem Martín, additional, Bellouin, Nicolas, additional, Benedetti, Angela, additional, Benestad, Rasmus, additional, Berne, Christine, additional, Berner, Logan. T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bhuiyan, A. E., additional, Bigalke, Siiri, additional, Biló, Tiago, additional, Bissolli, Peter, additional, Bjerke Jarle, W., additional, Blagrave, Kevin, additional, Blake, Eric S., additional, Blenkinsop, Stephen, additional, Blunden, Jessica, additional, Bochníček, Oliver, additional, Bock, Olivier, additional, Bodenstein, Barbara, additional, Bodin, Xavier, additional, Bosilovich, Michael, additional, Boucher, Olivier, additional, Bozkurt, Deniz, additional, Brettschneider, Brian, additional, Bringas, Francis G., additional, Bringas, Francis, additional, Buechler, Dennis, additional, Buehler, Stefan A., additional, Bukunt, Brandon, additional, Calderón, Blanca, additional, Camargo, Suzana J., additional, Campbell, Jayaka, additional, Campos, Diego, additional, Carrea, Laura, additional, Carter, Brendan R., additional, Cetinić, Ivona, additional, Chambers, Don P., additional, Chan, Duo, additional, Chandler, Elise, additional, Chang, Kai-Lan, additional, Chen, Hua, additional, Chen, Lin, additional, Cheng, Lijing, additional, Cheng, Vincent Y. S., additional, Chomiak, Leah, additional, Christiansen, Hanne H., additional, Christy, John R., additional, Chung, Eui-Seok, additional, Ciasto, Laura M., additional, Clarke, Leonardo, additional, Clem, Kyle R., additional, Clingan, Scott, additional, Coelho, Caio A.S., additional, Cohen, Judah L., additional, Coldewey-Egbers, Melanie, additional, Colwell, Steve, additional, Cooper, Owen R., additional, Cornes, Richard C., additional, Correa, Kris, additional, Costa, Felipe, additional, Covey, Curt, additional, Coy, Lawrence, additional, Créatux, Jean-François, additional, Crhova, Lenka, additional, Crimmins, Theresa, additional, Cronin, Meghan F., additional, Cropper, Thomas, additional, Crotwell, Molly, additional, Culpepper, Joshua, additional, Cunha, Ana P., additional, Cusicanqui, Diego, additional, Datta, Rajashree T., additional, Davis, Sean M., additional, De Bock, Veerle, additional, de Jeu, Richard A. M., additional, De Laat, Jos, additional, Decharme, Bertrand, additional, Degenstein, Doug, additional, Delaloye, Reynald, additional, Demircan, Mesut, additional, Derksen, Chris, additional, Deus, Ricardo, additional, Dhurmea, K. R., additional, Diamond, Howard J., additional, Dirkse, S., additional, Divine, Dmitry, additional, Dokulil, Martin T., additional, Donat, Markus G., additional, Dong, Shenfu, additional, Dorigo, Wouter A., additional, Drost Jensen, Caroline, additional, Druckenmiller, Matthew L., additional, Drumond, Paula, additional, du Plessis, Marcel, additional, Dugan, Hilary A., additional, Dulamsuren, Dashkhuu, additional, Dunmire, Devon, additional, Dunn, Robert J. H., additional, Durre, Imke, additional, Dusek, Robert, additional, Dutton, Geoff, additional, Duveiller, Gregory, additional, Ekici, Mithat, additional, Elias Chereque, Alesksandra, additional, ElKharrim, M., additional, Epstein, Howard E., additional, Espinoza, Jhan-Carlo, additional, Estilow, Thomas W., additional, Estrella, Nicole, additional, Fauchereau, Nicolas, additional, Fausto, Robert S., additional, Feely, Richard A., additional, Fenimore, Chris, additional, Fereday, David, additional, Fettweis, Xavier, additional, Fioletov, vitali E., additional, Flemming, Johannes, additional, Fogarty, Chris, additional, Fogt, Ryan L., additional, Forbes, Bruce C., additional, Foster, Michael J., additional, Franz, Bryan A., additional, Freeman, Natalie M., additional, Fricker, Helen A., additional, Frith, Stacey M., additional, Froidevaux, Lucien, additional, Frost, Gerald V. (JJ), additional, Fuhrman, Steven, additional, Füllekrug, Martin, additional, Ganter, Catherine, additional, Gao, Meng, additional, Gardner, Alex S., additional, Garforth, Judith, additional, Garg, Jay, additional, Gerland, Sebastian, additional, Gibbes, Badin, additional, Gille, Sarah T., additional, Gilson, John, additional, Gleason, Karin, additional, Gobron, Nadine, additional, Goetz, Scott J., additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Goodman, Steven, additional, Goto, Atsushi, additional, Grooß, Jens-Uwe, additional, Gruber, Alexander, additional, Gu, Guojun, additional, Guard, Charles “Chip” P., additional, Hagos, S., additional, Hahn, Sebastian, additional, Haimberger, Leopold, additional, Hall, Bradley D., additional, Hamlington, Benjamin D., additional, Hanna, Edward, additional, Hanssen-Bauer, Inger, additional, Harnos, Daniel S., additional, Harris, Ian, additional, He, Qiong, additional, Heim, Richard R., additional, Hellström, Sverker, additional, Hemming, Deborah L., additional, Hendricks, Stefan, additional, Hicks, J., additional, Hidalgo, Hugo G., additional, Hirschi, Martin, additional, Ho, Shu-peng (Ben), additional, Hobbs, W., additional, Holmes, Robert M., additional, Holzworth, Robert, additional, Hrbáček, Filip, additional, Hu, Guojie, additional, Hu, Zeng-Zhen, additional, Huang, Boyin, additional, Huang, Hongjie, additional, Hurst, Dale F., additional, Ialongo, Iolanda, additional, Inness, Antje, additional, Isaksen, Ketil, additional, Ishii, Masayoshi, additional, Jadra, Gerardo, additional, Jevrejeva, Svetlana, additional, John, Viju O., additional, Johns, W., additional, Johnsen, Bjørn, additional, Johnson, Bryan, additional, Johnson, Gregory C., additional, Jones, Philip D., additional, Jones, Timothy, additional, Josey, Simon A., additional, Jumaux, G., additional, Junod, Robert, additional, Kääb, Andreas, additional, Kabidi, K., additional, Kaiser, Johannes W., additional, Kaler, Robb S.A., additional, Kaleschke, Lars, additional, Kaufmann, Viktor, additional, Kazemi, Amin Fazl, additional, Keller, Linda M., additional, Kellerer-Pirklbauer, Andreas, additional, Kendon, Mike, additional, Kennedy, John, additional, Kent, Elizabeth C., additional, Kerr, Kenneth, additional, Khan, Valentina, additional, Khiem, Mai Van, additional, Kidd, Richard, additional, Kim, Mi Ju, additional, Kim, Seong-Joong, additional, Kipling, Zak, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, Koppa, Akash, additional, Korshunova, Natalia N., additional, Kraemer, Benjamin M., additional, Kramarova, Natalya A., additional, Kruger, A. C., additional, Kruger, Andries, additional, Kumar, Arun, additional, L’Heureux, Michelle, additional, La Fuente, Sofia, additional, Laas, Alo, additional, Labe, Zachary M., additional, Lader, Rick, additional, Lakatos, Mónika, additional, Lakkala, Kaisa, additional, Lam, Hoang Phuc, additional, Lan, Xin, additional, Landschützer, Peter, additional, Landsea, Chris W., additional, Lang, Timothy, additional, Lankhorst, Matthias, additional, Lantz, Kathleen O., additional, Lara, Mark J., additional, Lavado-Casimiro, Waldo, additional, Lavers, David A., additional, Lazzara, Matthew A., additional, Leblanc, Thierry, additional, Lee, Tsz-Cheung, additional, Leibensperger, Eric M., additional, Lennard, Chris, additional, Leuliette, Eric, additional, Leung, Kinson H. Y., additional, Lieser, Jan L., additional, Likso, Tanja, additional, Lin, I-I., additional, Lindsey, Jackie, additional, Liu, Yakun, additional, Locarnini, Ricardo, additional, Loeb, Norman G., additional, Loomis, Bryant D., additional, Lorrey, Andrew M., additional, Loyola, Diego, additional, Lu, Rui, additional, Lumpkin, Rick, additional, Luo, Jing-Jia, additional, Luojus, Kari, additional, Lyman, John M., additional, Maberly, Stephen C., additional, Macander, Matthew J., additional, MacFerrin, Michael, additional, MacGilchrist, Graeme A., additional, MacLennan, Michelle L., additional, Madelon, Remi, additional, Magee, Andrew D., additional, Magnin, Florence, additional, Mamen, Jostein, additional, Mankoff, Ken D., additional, Manney, Gloria L., additional, Marcinonienė, Izolda, additional, Marengo, Jose A., additional, Marjan, Mohammadi, additional, Martínez, Ana E., additional, Massom, Robert A., additional, Matsuzaki, Shin-Ichiro, additional, May, Linda, additional, Mayer, Michael, additional, Mazloff, Matthew R., additional, McAfee, Stephanie A., additional, McBride, C., additional, McCabe, Matthew F., additional, McClelland, James W., additional, McPhaden, Michael J., additional, Mcvicar, Tim R., additional, Mears, Carl A., additional, Meier, Walter N., additional, Mekonnen, A., additional, Menzel, Annette, additional, Merchant, Christopher J., additional, Merrifield, Mark A., additional, Meyer, Michael F., additional, Meyers, Tristan, additional, Mikolajczyk, David E., additional, Miller, John B., additional, Miralles, Diego G., additional, Misevicius, Noelia, additional, Mishonov, Alexey, additional, Mitchum, Gary T., additional, Moat, Ben I., additional, Moesinger, Leander, additional, Moise, Aurel, additional, Molina-Carpio, Jorge, additional, Monet, Ghislaine, additional, Montzka, Stephan A., additional, Moon, Twila A., additional, Moore, G. W. K., additional, Mora, Natali, additional, Morán, Johnny, additional, Morehen, Claire, additional, Morice, Colin, additional, Mostafa, A. E., additional, Mote, Thomas L., additional, Mrekaj, Ivan, additional, Mudryk, Lawrence, additional, Mühle, Jens, additional, Müller, Rolf, additional, Nance, David, additional, Nash, Eric R., additional, Nerem, R. Steven, additional, Newman, Paul A., additional, Nicolas, Julien P., additional, Nieto, Juan J., additional, Noetzli, Jeannette, additional, Noll, Ben, additional, Norton, Taylor, additional, Nyland, Kelsey E., additional, O’Keefe, John, additional, Ochwat, Naomi, additional, Oikawa, Yoshinori, additional, Okunaka, Yuka, additional, Osborn, Timothy J., additional, Overland, James E., additional, Park, Taejin, additional, Parrington, Mark, additional, Parrish, Julia K., additional, Pasch, Richard J., additional, Pascual Ramírez, Reynaldo, additional, Pellet, Cécile, additional, Pelto, Mauri S., additional, Perčec Tadić, Melita, additional, Perovich, Donald K., additional, Petersen, Guðrún Nína, additional, Petersen, Kyle, additional, Petropavlovskikh, Irina, additional, Petty, Alek, additional, Pezza, Alexandre B., additional, Pezzi, Luciano P., additional, Phillips, Coda, additional, Phoenix, Gareth K., additional, Pierson, Don, additional, Pinto, Izidine, additional, Pires, Vanda, additional, Pitts, Michael, additional, Po-Chedley, Stephen, additional, Pogliotti, Paolo, additional, Poinar, Kristin, additional, Polvani, Lorenzo, additional, Preimesberger, Wolfgang, additional, Price, Colin, additional, Pulkkanen, Merja, additional, Purkey, Sarah G., additional, Qiu, Bo, additional, Quisbert, Kenny, additional, Quispe, Willy R., additional, Rajeevan, M., additional, Ramos, Andrea M., additional, Randel, William J., additional, Rantanen, Mika, additional, Raphael, Marilyn N., additional, Reagan, James, additional, Recalde, Cristina, additional, Reid, Phillip, additional, Rémy, Samuel, additional, Reyes Kohler, Alejandra J., additional, Ricciardulli, Lucrezia, additional, Richardson, Andrew D., additional, Ricker, Robert, additional, Robinson, David A., additional, Robjhon, M., additional, Rocha, Willy, additional, Rodell, Matthew, additional, Rodriguez Guisado, Esteban, additional, Rodriguez-Fernandez, Nemesio, additional, Romanovsky, Vladimir E., additional, Ronchail, Josyane, additional, Rosencrans, Matthew, additional, Rosenlof, Karen H., additional, Rösner, Benjamin, additional, Rösner, Henrieke, additional, Rozanov, Alexei, additional, Rozkošný, Jozef, additional, Rubek, Frans, additional, Rusanovskaya, Olga O., additional, Rutishauser, This, additional, Sabeerali, C. T., additional, Salinas, Roberto, additional, Sánchez-Lugo, Ahira, additional, Santee, Michelle L., additional, Santini, Marcelo, additional, Sato, Katsunari, additional, Sawaengphokhai, Parnchai, additional, Sayouri, A., additional, Scambos, Theodore, additional, Schenzinger, Verena, additional, Schimanke, Semjon, additional, Schlegel, Robert W., additional, Schmid, Claudia, additional, Schmid, Martin, additional, Schneider, Udo, additional, Schreck, Carl J., additional, Schultz, Cristina, additional, _, _, additional, Segele, Z. T., additional, Sensoy, Serhat, additional, Serbin, Shawn P., additional, Serreze, Mark C., additional, Setiawan, Amsari Mudzakir, additional, Sezaki, Fumi, additional, Sharma, Sapna, additional, Sharp, Jonathan D., additional, Sheffield, Gay, additional, Shi, Jia-Rui, additional, Shi, Lei, additional, Shiklomanov, Alexander I., additional, Shiklomanov, Nikolay I., additional, Shimaraeva, Svetlana V., additional, Shukla, R., additional, Siegel, David A., additional, Silow, Eugene A., additional, Sima, F., additional, Simmons, Adrian J., additional, Smeed, David A., additional, Smith, Adam, additional, Smith, Matthew M., additional, Smith, Sharon L., additional, Soden, Brian J., additional, Sofieva, Viktoria, additional, Souza, Everaldo, additional, Sparks, Tim H., additional, Spence-Hemmings, Jacqueline, additional, Spencer, Robert G. M., additional, Spillane, Sandra, additional, Sreejith, O. P., additional, Srivastava, A. K., additional, Stackhouse, Paul W., additional, Stammerjohn, Sharon, additional, Stauffer, Ryan, additional, Steinbrecht, Wolfgang, additional, Steiner, Andrea K., additional, Stella, Jose L., additional, Stephenson, Tannecia S., additional, Stradiotti, Pietro, additional, Strahan, Susan E., additional, Streletskiy, Dmitry A., additional, Surendran, Divya E., additional, Suslova, Anya, additional, Svendby, Tove, additional, Sweet, William, additional, Takahashi, Kiyotoshi, additional, Takemura, Kazuto, additional, Tank, Suzanne E., additional, Taylor, Michael A., additional, Tedesco, Marco, additional, Thackeray, Stephen J., additional, Thiaw, W. M., additional, Thibert, Emmanuel, additional, Thoman, Richard L., additional, Thompson, Andrew F., additional, Thompson, Philip R., additional, Tian-Kunze, Xiangshan, additional, Timmermans, Mary-Louise, additional, Timofeyev, Maxim A., additional, Tobin, Skie, additional, Tømmervik, Hans, additional, Tourpali, Kleareti, additional, Trescilo, Lidia, additional, Tretiakov, Mikhail, additional, Trewin, Blair C., additional, Triñanes, Joaquin A., additional, Trotman, Adrian, additional, Truchelut, Ryan E., additional, Trusel, Luke D., additional, Tye, Mari R., additional, van der A, Ronald, additional, van der Schalie, Robin, additional, van der Schrier, Gerard, additional, Van Hemert, Caroline, additional, Van Meerbeeck, Cedric J., additional, van vliet, Arnold J.H., additional, Vazife, Ahad, additional, Verburg, Piet, additional, Vernier, Jean-Paul, additional, Vimont, Isaac J., additional, Virts, Katrina, additional, Vivero, Sebastián, additional, Volkov, Denis L., additional, Vömel, Holger, additional, Vose, Russell S., additional, Walker, Donald (Skip) A., additional, Walsh, John E., additional, Wang, Bin, additional, Wang, Hui, additional, Wang, Muyin, additional, Wang, Ray H. J., additional, Wang, Xinyue, additional, Wanninkhof, Rik, additional, Warnock, Taran, additional, Weber, Mark, additional, Webster, Melinda, additional, Wehrlé, Adrian, additional, Wen, Caihong, additional, Westberry, Toby K., additional, Widlansky, Matthew J., additional, Wiese, David N., additional, Wild, Jeannette D., additional, Wille, Jonathan D., additional, Willems, An, additional, Willett, Kate M., additional, Williams, Earle, additional, Willis, J., additional, Wong, Takmeng, additional, Wood, Kimberly M., additional, Woolway, Richard Iestyn, additional, Xie, Ping-Ping, additional, Yang, Daqing, additional, Yin, Xungang, additional, Yin, Ziqi, additional, Zeng, Zhenzhong, additional, Zhang, Huai-min, additional, Zhang, Li, additional, Zhang, Peiqun, additional, Zhao, Lin, additional, Zhou, Xinjia, additional, Zhu, Zhiwei, additional, Ziemke, Jerry R., additional, Ziese, Markus, additional, Zolkos, Scott, additional, Zotta, Ruxandra M., additional, Zou, Cheng-Zhi, additional, Allen, Jessicca, additional, Camper, Amy V., additional, Haley, Bridgette O., additional, Hammer, Gregory, additional, Love-Brotak, S. Elizabeth, additional, Ohlmann, Laura, additional, Noguchi, Lukas, additional, Riddle, Deborah B., additional, and Veasey, Sara W., additional

Published
2023
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30. The Arctic

Author

Moon, T. A., primary, Thoman, R., additional, Druckenmiller, M. L., additional, Ahmasuk, Brandon, additional, Backensto, Stacia A., additional, Ballinger, Thomas J., additional, Benestad, Rasmus, additional, Berner, Logan. T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bigalke, Siiri, additional, BjerkeJarle, W., additional, Brettschneider, Brian, additional, Christiansen, Hanne H., additional, Cohen, Judah L., additional, Decharme, Bertrand, additional, Derksen, Chris, additional, Divine, Dmitry, additional, Drost, Jensen, additional, Druckenmiller, Matthew L., additional, EliasChereque, Alesksandra, additional, Epstein, Howard E., additional, Fausto, Robert S., additional, Fettweis, Xavier, additional, Fioletov, Vitali E., additional, Forbes, Bruce C., additional, Frost, Gerald V., additional, Gerland, Sebastian, additional, Goetz, Scott J., additional, Grooß, Jens-Uwe, additional, Hanna, Edward, additional, Hanssen-Bauer, Inger, additional, Hendricks, Stefan, additional, Holmes, Robert M., additional, Ialongo, Iolanda, additional, Isaksen, Ketil, additional, Johnsen, Bjørn, additional, Jones, Timothy, additional, Kaler, Robb S.A., additional, Kaleschke, Lars, additional, Kim, Seong-Joong, additional, Labe, Zachary M., additional, Lader, Rick, additional, Lakkala, Kaisa, additional, Lara, Mark J., additional, Lindsey, Jackie, additional, Loomis, Bryant D., additional, Luojus, Kari, additional, Macander, Matthew J., additional, Mamen, Jostein, additional, Mankoff, Ken D., additional, Manney, Gloria L., additional, McAfee, Stephanie A., additional, McClelland, James W., additional, Meier, Walter N., additional, Moon, Twila A., additional, Moore, G. W. K., additional, Mote, Thomas L., additional, Mudryk, Lawrence, additional, Müller, Rolf, additional, Nyland, Kelsey E., additional, Overland, James E., additional, Parrish, Julia K., additional, Perovich, Donald K., additional, Petersen, Guðrún Nína, additional, Petty, Alek, additional, Phoenix, Gareth K., additional, Poinar, Kristin, additional, Rantanen, Mika, additional, Ricker, Robert, additional, Romanovsky, Vladimir E., additional, Serbin, Shawn P., additional, Serreze, Mark C., additional, Sheffield, Gay, additional, Shiklomanov, Alexander I., additional, Shiklomanov, Nikolay I., additional, Smith, Sharon L., additional, Spencer, Robert G. M., additional, Streletskiy, Dmitry A., additional, Suslova, Anya, additional, Svendby, Tove, additional, Tank, Suzanne E., additional, Tedesco, Marco, additional, Thoman, Richard L., additional, Tian-Kunze, Xiangshan, additional, Timmermans, Mary-Louise, additional, Tømmervik, Hans, additional, Tretiakov, Mikhail, additional, Walker, Donald A., additional, Walsh, John E., additional, Wang, Muyin, additional, Webster, Melinda, additional, Wehrlé, Adrian, additional, Yang, Dedi, additional, Zolkos, Scott, additional, Allen, Jessicca, additional, Camper, Amy V., additional, Haley, Bridgette O., additional, Hammer, Gregory, additional, Love-Brotak, S., additional, Ohlmann, Laura, additional, Noguchi, Lukas, additional, Riddle, Deborah B., additional, and Veasey, Sara W., additional

Published
2023
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31. Monitoring Eco-Hydrological Spring Onset Over Alaska and Northern Canada With Complementary Satellite Remote Sensing Data

Author

Derksen, Chris, Reichle, Rolf, Colliander, Andreas, Dunbar, R. Scott, Xu, Xiaolan, Parazoo, Nicholas, Kimball, John S, and Kim, Youngwook

Abstract

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of 3±2 (SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY 102±14) than arctic tundra (DOY 124±22).

Published
2021

37. Airborne Ku- and L-band polarimetric SAR observations of seasonal snow at a field site in Ontario during Winter 2023

Author

Kelly, Richard, Thompson, Aaron, Derksen, Chris, Toose, Peter, Brown, Laura, Akhavan, Zeinab, Welch, Jeffrey, Zschenderlein, Lina, Wang, Wei, Gelinas, Alex, Roy, Alex, Langlois, Alex, Vionnet, Vincent, Montpetit, Benoit, and Silis, Arvids

Abstract

Airborne Ku- (13.5 GHz) and L-band (1.3 GHz) polarimetric synthetic aperture radar (SAR) observations were made of snow and lake ice at Ontario field sites during the 2022-2023 winter season. The airborne radar system, called CryoSAR, is a fully polarimetric SAR configured for repeat pass observations of snow and lake ice. A primary goal of this experiment is to acquire SAR observations for estimating changes in snow mass and lake ice properties using microwave backscatter and land surface models. Field campaigns at Powassan (terrestrial snow) and Haliburton Highlands (lake ice) were conducted to provide correlative ground reference data of snow and lake ice. A combination of traditional field observations of snow properties, and detailed state-of-the-art measurements of microstructure properties was made to quantify the bulk and stratigraphic characteristics of the snow and lake ice at the two sites. At Powassan, six soil moisture monitoring stations and a weather station provided observations of soil moisture/state and temperature, and snow and atmospheric variables. Drone-based lidar snow depth estimates provided snow depth maps during the season. Lake ice and atmospheric variables were measured at Haliburton Highlands. CryoSAR repeat pass overflights were conducted at both sites during the season. This presentation discusses the Ku- and L-band polarimetric SAR responses from snow and lake ice during the winter season and the impact that snow mass has on the Ku-band response. This unique experimental data set is being used to support Ku-band retrieval science from snow and lake ice for the Terrestrial Snow Mass Mission., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

39. Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Author

Dutch, Victoria R., primary, Rutter, Nick, additional, Wake, Leanne, additional, Sonnentag, Oliver, additional, Hould Gosselin, Gabriel, additional, Sandells, Melody, additional, Derksen, Chris, additional, Walker, Branden, additional, Meyer, Gesa, additional, Essery, Richard, additional, Kelly, Richard, additional, Marsh, Phillip, additional, Boike, Julia, additional, and Detto, Matteo, additional

Published
2023
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40. Supplementary material to "Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site"

Author

Dutch, Victoria R., primary, Rutter, Nick, additional, Wake, Leanne, additional, Sonnentag, Oliver, additional, Hould Gosselin, Gabriel, additional, Sandells, Melody, additional, Derksen, Chris, additional, Walker, Branden, additional, Meyer, Gesa, additional, Essery, Richard, additional, Kelly, Richard, additional, Marsh, Phillip, additional, Boike, Julia, additional, and Detto, Matteo, additional

Published
2023
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