Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-147-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-147-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2023
Research article |
| 08 Feb 2023
| 08 Feb 2023
Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System
Department of Environmental
Science, University of California Berkeley, Policy, and Management, Berkeley, CA, USA
Computational Sciences and Engineering Division, Oak Ridge National
Laboratory, Oak Ridge, TN, USA
Lauren Andrews
NASA Goddard Space Flight Center, Global Modeling & Assimilation
Office, Greenbelt, MD, USA
Rolf Reichle
NASA Goddard Space Flight Center, Global Modeling & Assimilation
Office, Greenbelt, MD, USA
Andrea Molod
NASA Goddard Space Flight Center, Global Modeling & Assimilation
Office, Greenbelt, MD, USA
Jongmin Park
Department of Environmental Engineering, Korea National University of
Transportation, Chungju, Republic of Korea
formerly at: Goddard Earth Sciences Technology and Research (GESTAR II), University of Maryland, Baltimore, MD, USA
Sophie Ruehr
Department of Environmental
Science, University of California Berkeley, Policy, and Management, Berkeley, CA, USA
Manuela Girotto
Department of Environmental
Science, University of California Berkeley, Policy, and Management, Berkeley, CA, USA
Related authors
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
Short summary
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
Hydrol. Earth Syst. Sci., 29, 3435–3445, https://doi.org/10.5194/hess-29-3435-2025, https://doi.org/10.5194/hess-29-3435-2025, 2025
Short summary
Short summary
To better utilize a given set of predictions, identifying “forecasts of opportunity” is valuable as this helps anticipate when prediction skill will be higher. This study shows that when strong land–atmosphere (L–A) coupling is detected 3–4 weeks into a forecast, the surface air temperature prediction skill at this lead time increases across the Midwest and northern Great Plains. Regions experiencing strong L–A coupling exhibit warm and dry anomalies, enhancing predictions of abnormally warm events.
Alamgir Hossan, Andreas Colliander, Nicole-Jeanne Schlegel, Joel Harper, Lauren Andrews, Jana Kolassa, Julie Z. Miller, and Richard Cullather
EGUsphere, https://doi.org/10.5194/egusphere-2025-2681, https://doi.org/10.5194/egusphere-2025-2681, 2025
Short summary
Short summary
Microwave L-band radiometry offers a promising tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.
Xin Huang, Qing He, Naota Hanasaki, Rolf H. Reichle, and Taikan Oki
EGUsphere, https://doi.org/10.5194/egusphere-2025-2004, https://doi.org/10.5194/egusphere-2025-2004, 2025
Preprint archived
Short summary
Short summary
This study demonstrates a new method using SMAP soil moisture products to identify irrigation effects, tested to be valid in an example region in California's Central Valley and showed great potential for application in arid/ semi-arid regions. The approach offers a simple, straightforward approach to monitoring irrigation signals without additional in-situ data or model tuning, providing a useful tool to extract irrigation water use data in observation-scarce regions.
Abdullah A. Fahad, Andrea Molod, Krzysztof Wargan, Dimitris Menemenlis, Patrick Heimbach, Atanas Trayanov, Ehud Strobach, and Lawrence Coy
EGUsphere, https://doi.org/10.21203/rs.3.rs-1892797/v2, https://doi.org/10.21203/rs.3.rs-1892797/v2, 2025
Short summary
Short summary
This study used a 1-degree GEOS-MITgcm coupled GCM to analyze the Northern Hemisphere (NH) stratospheric temperature response to external forcing. Results show the NH polar stratospheric temperature increased from 1992 to 2000, contrary to the expectation of stratospheric cooling with rising CO2. However, from 2000 to 2020, the temperature decreased. The study concluded that changes in CO2 and Ozone drive the meridional eddy transport of heat, dictating polar stratospheric temperature behavior.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
EGUsphere, https://doi.org/10.5194/egusphere-2025-423, https://doi.org/10.5194/egusphere-2025-423, 2025
Short summary
Short summary
Reliable SWE and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we suggest a Long Short-Term Memory neural network for operational data assimilation in snow hydrology. Once trained, it cuts computation by 70 % versus an EnKF, with a slight RMSE increase (+6 mm SWE, +6 cm snow depth). This work advances deep learning in snow hydrology, offering an efficient, scalable, and low-cost modeling framework.
Ci Song, Daniel McCoy, Andrea Molod, and Donifan Barahona
EGUsphere, https://doi.org/10.5194/egusphere-2024-4108, https://doi.org/10.5194/egusphere-2024-4108, 2025
Short summary
Short summary
The uncertainty in how clouds respond to aerosols limits our ability to predict future warming. This study uses a global reanalysis data, GiOcean, which includes a detailed treatment of cloud microphysics to represent interactions between aerosols and clouds. We evaluate the response of warm clouds to aerosols in GiOcean by comparing variables important for cloud properties from GiOcean with available spaceborne remote sensing observations.
Fatemeh Zakeri, Gregoire Mariethoz, and Manuela Girotto
EGUsphere, https://doi.org/10.5194/egusphere-2024-1943, https://doi.org/10.5194/egusphere-2024-1943, 2024
Short summary
Short summary
This study introduces a method for estimating High-Resolution Snow Water Equivalent (HR-SWE) using Low-Resolution Climate Data (LR-CD). By applying a data-driven approach, we utilize historical weather patterns from LR-CD to estimate HR-SWE maps. Our approach uses statistical relationships between LR-CD and HR-SWE data to provide HR-SWE estimates for dates when HR-SWE data is unavailable. This method improves water resource management and climate impact assessments in regions with limited data.
Celia Trunz, Kristin Poinar, Lauren C. Andrews, Matthew D. Covington, Jessica Mejia, Jason Gulley, and Victoria Siegel
The Cryosphere, 17, 5075–5094, https://doi.org/10.5194/tc-17-5075-2023, https://doi.org/10.5194/tc-17-5075-2023, 2023
Short summary
Short summary
Models simulating water pressure variations at the bottom of glaciers must use large storage parameters to produce realistic results. Whether that storage occurs englacially (in moulins) or subglacially is a matter of debate. Here, we directly simulate moulin volume to constrain the storage there. We find it is not enough. Instead, subglacial processes, including basal melt and input from upstream moulins, must be responsible for stabilizing these water pressure fluctuations.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
Short summary
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
Short summary
Short summary
Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Lauren C. Andrews, Kristin Poinar, and Celia Trunz
The Cryosphere, 16, 2421–2448, https://doi.org/10.5194/tc-16-2421-2022, https://doi.org/10.5194/tc-16-2421-2022, 2022
Short summary
Short summary
We introduce a model for moulin geometry motivated by the wide range of sizes and shapes of explored moulins. Moulins comprise 10–14 % of the Greenland englacial–subglacial hydrologic system and act as time-varying water storage reservoirs. Moulin geometry can vary approximately 10 % daily and over 100 % seasonally. Moulin shape modulates the efficiency of the subglacial system that controls ice flow and should thus be included in hydrologic models.
Ehud Strobach, Andrea Molod, Donifan Barahona, Atanas Trayanov, Dimitris Menemenlis, and Gael Forget
Geosci. Model Dev., 15, 2309–2324, https://doi.org/10.5194/gmd-15-2309-2022, https://doi.org/10.5194/gmd-15-2309-2022, 2022
Short summary
Short summary
The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
Short summary
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Kristin Poinar and Lauren C. Andrews
The Cryosphere, 15, 1455–1483, https://doi.org/10.5194/tc-15-1455-2021, https://doi.org/10.5194/tc-15-1455-2021, 2021
Short summary
Short summary
This study addresses Greenland supraglacial lake drainages. We analyze ice deformation associated with lake drainages over 18 summers to assess whether
precursorstrain-rate events consistently precede lake drainages. We find that currently available remote sensing data products cannot resolve these events, and thus we cannot predict future lake drainages. Thus, future avenues for evaluating this hypothesis will require major field-based GPS or photogrammetry efforts.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Chad A. Greene, Alex S. Gardner, and Lauren C. Andrews
The Cryosphere, 14, 4365–4378, https://doi.org/10.5194/tc-14-4365-2020, https://doi.org/10.5194/tc-14-4365-2020, 2020
Short summary
Short summary
Seasonal variability is a fundamental characteristic of any Earth surface system, but we do not fully understand which of the world's glaciers speed up and slow down on an annual cycle. Such short-timescale accelerations may offer clues about how individual glaciers will respond to longer-term changes in climate, but understanding any behavior requires an ability to observe it. We describe how to use satellite image feature tracking to determine the magnitude and timing of seasonal ice dynamics.
Cited articles
Arendt, A. A., Houser, P., Kapnick, S. B., Kargel, J. S., Kirschbaum, D.,
Kumar, S., Margulis, S. A., McDonald, K. C., Osmanoglu, B., Painter, T. H., and Raup, B. H.: NASA's High Mountain Asia Team (HiMAT):
collaborative research to study changes of the High Asia region, AGU Fall
Meeting Abstracts, Vol. 2017, C33D-1231, 2017AGUFM.C33D1231A,
2017.
Aquila, V., Baldwin, C., Mukherjee, N., Hackert, E., Li, F., Marshak, J.,
Molod, A., and Pawson, S.: Impacts of the Eruption of Mount Pinatubo on
Surface Temperatures and Precipitation Forecasts With the NASA GEOS
Subseasonal-to-Seasonal System, J. Geophys. Res.-Atmos.
126, e2021JD034830, https://doi.org/10.1029/2021JD034830, 2021.
Batbaatar, J., Gillespie, A. R., Koppes, M., Clark, D. H., Chadwick, O. A.,
Fink, D., Matmon, A., and Rupper, S.: Glacier development in continental
climate regions of central Asia, Untangling the Quaternary Period: A Legacy
of Stephen C. Porter, https://doi.org/10.1130/2020.2548(07), 2021.
Bekaert, D., Handwerger, A. L., Agram, P., and Kirschbaum, D. B.:
InSAR-based detection method for mapping and monitoring slow-moving
landslides in remote regions with steep and mountainous terrain: An
application to Nepal, Remote Sens. Environ., 249, 111983,
https://doi.org/10.1016/j.rse.2020.111983, 2020.
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File
Specification, GMAO Office Note No. 9 (Version 1.1), 73 pp.,
http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: August 2021),
2016.
Cannon, F., Carvalho, L., Jones, C., and Norris, J.: Winter westerly
disturbance dynamics and precipitation in the western Himalaya and
Karakoram: a wave-tracking approach, Theor. Appl. Climatol.,
125, 27–44, https://doi.org/10.1007/s00704-015-1489-8, 2016.
Cannon, F., Carvalho, L., Jones, C., Norris, J., Bookhagen, B., and Kiladis,
G. N.: Effects of topographic smoothing on the simulation of winter
precipitation in High Mountain Asia, J. Geophys. Res.-Atmos. 122, 1456–1474, https://doi.org/10.1002/2016JD026038, 2017.
Cavalieri, D. J., Parkinson, C. L., Gloersen, P., and Zwally, H. J.: Sea ice
concentrations from Nimbus-7SMMRandDMSP SSM/I-SSMIS Passive Microwave Data,
Version 1, 1978–2017, Boulder, Colorado USA. NASA National Snow and Ice
Data Center Distributed Active Archive Center,
https://doi.org/10.5067/8GQ8LZQVL0VL, 1996.
Christensen, M. F., Heaton, M. J., Rupper, S., Reese, C. S., and
Christensen, W. F.: Bayesian Multi-Scale Spatio-Temporal Modeling of
Precipitation in the Indus Watershed, Front. Earth Sci., 7, 210,
https://doi.org/10.3389/feart.2019.00210, 2019.
Copernicus Climate Change Service (C3S): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), [data set], https://cds.climate.copernicus.eu/cdsapp#/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview, (last access: June 2021), 2017.
Dars, G. H., Strong, C., Kochanski, A. K., Ansari, K., and Ali, S. H.: The
spatiotemporal variability of temperature and precipitation over the upper
Indus Basin: An evaluation of 15 year WRF simulations, Appl. Sci., 10,
1765, https://doi.org/10.3390/app10051765, 2020.
de Andrade, F. M., Coelho, C., and Cavalcanti, I.: Global precipitation
hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction
project models, Clim. Dynam., 52, 5451–5475,
https://doi.org/10.1007/s00382-018-4457-z, 2019.
De Lannoy, G. and Reichle, R. H.: Global assimilation of multiangle and
multipolarization SMOS brightness temperature observations into the GEOS-5
catchment land surface model for soil moisture estimation, J. Hydrometeorol., 17, 669–691, https://doi.org/10.1175/JHM-D-15-0037.1, 2016.
de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J., and
Isaksen, L.: Initialisation of land surface variables for numerical weather
prediction, Surv. Geophys., 35, 607–621,
https://doi.org/10.1007/s10712-012-9207-x, 2014.
DeFlorio, M. J., Waliser, D. E., Guan, B., Ralph, F. M., and Vitart, F.:
Global evaluation of atmospheric river subseasonal prediction skill, Clim. Dynam., 52, 3039–3060, https://doi.org/10.1007/s00382-018-4309-x, 2019.
Deoras, A., Hunt, K. M. R., and Turner, A. G.: Comparison of the Prediction
of Indian Monsoon Low Pressure Systems by Subseasonal-to-Seasonal Prediction
Models, Weather Forecast., 36, 859–877,
https://doi.org/10.1175/WAF-D-20-0081.1, 2021.
Ding, Q. and Wang, B.: Intraseasonal teleconnection between the summer
Eurasian wave train and the Indian monsoon, J. Climate, 20,
3751–3767, https://doi.org/10.1175/JCLI4221.1, 2007.
Dirmeyer, P. A., Halder, S., and Bombardi, R.: On the harvest of
predictability from land states in a global forecast model, J. Geophys. Res.-Atmos. 123, 13–111,
https://doi.org/10.1029/2018JD029103, 2018.
Dorigo, W. A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y.,Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I. Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, [data set], Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Famiglietti, J. S., Lo, M.-H., Ho, S. L. Bethune, J., Anderson, K.J., Syed,
T. H., Swenson, S. C., De Linage, C. R., and Rodell, M.: Satellites measure
recent rates of groundwater depletion in California's Central Valley,
Geophys. Res. Lett., 38, 471, https://doi.org/10.1029/2010GL046442, 2011.
Fortin, V., Abaza, M., Anctil, F. and Turcotte, R.: Why should ensemble
spread match the RMSE of the ensemble mean?, J. Hydrometeorol.,
15, 1708–1713, https://doi.org/10.1175/JHM-D-14-0008.1, 2014.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmeno, A., Bosilovich, M. G., Reichle, R., and Wargan, K.: The modern-era retrospective analysis for
research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gerlitz, L., Vorogushyn, S., and Gafurov, A.: Climate informed seasonal
forecast of water availability in Central Asia: State-of-the-art and
decision making context, Water Sec., 10, 100061,
https://doi.org/10.1016/j.wasec.2020.100061, 2020.
Getirana, A., Jung, H. C., Arsenault, K., Shukla, S., Kumar, S.,
Peters-Lidard, C., Maigari, I., and Mamane, B.: Satellite gravimetry
improves seasonal streamflow forecast initialization in Africa, Water
Resour. Res., 56, e2019WR026259, https://doi.org/10.1029/2019WR026259,
2020.
Ghatak, D., Zaitchik, B., Kumar, S., Matin, M. A., Bajracharya, D., Hain,
C., and Anderson, M.: Influence of precipitation forcing uncertainty on
hydrological simulations with the NASA South Asia land data assimilation
system, Hydrology, 5, 57, https://doi.org/10.3390/hydrology5040057, 2018.
Gibson, P. B., Waliser, D. E., Goodman, A., DeFlorio, M. J., Delle Monache,
L., and Molod, A.: Subseasonal-to-Seasonal Hindcast Skill Assessment of
Ridging Events Related to Drought Over the Western United States, J. Geophys. Res.-Atmos. 125, e2020JD033655,
https://doi.org/10.1029/2020JD033655, 2020.
Girotto, M., Margulis, S. A., and Durand, M.: Probabilistic SWE reanalysis as
a generalization of deterministic SWE reconstruction techniques,
Hydrol. Proc., 28, 3875–3895,
https://doi.org/10.1002/hyp.9887, 2014.
Girotto, M., De Lannoy, G., Reichle, R. H., Rodell, M., Draper, C., Bhanja,
S. N., and Mukherjee, A.: Benefits and pitfalls of GRACE data assimilation:
A case study of terrestrial water storage depletion in India, Geophys. Res. Lett., 44, 4107–4115, https://doi.org/10.1002/2017GL072994, 2017.
Girotto, M., Musselman, K. N., and Essery, R.: Data assimilation improves
estimates of climate-sensitive seasonal snow, Current Climate Change Reports,
6, 81–94, https://doi.org/10.1007/s40641-020-00159-7, 2020.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged,
Single-Level, Assimilation, Single-Level Diagnostics V5.12.4, Greenbelt, MD,
USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), [data set], https://doi.org/10.5067/AP1B0BA5PD2K, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged,
Single-Level,Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD,
USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), [data set],
https://doi.org/10.5067/0JRLVL8YV2Y4, 2015b.
Global Modeling and Assimilation Office (GMAO): MERRA-2 MERRA-2 tavgM_2d_slv_Nx: 2d, Monthly mean, Time-Averaged,
Single-Level, Assimilation, Land Surface Diagnostics V5.12.4, Greenbelt, MD,
USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), [data set],
https://doi.org/10.5067/8S35XF81C28F, 2015c.
Griffies, S. M.: Elements of the modular ocean model (MOM), GFDL Ocean Group
Tech. Rep., 7, https://mom-ocean.github.io/assets/pdfs/MOM5_manual.pdf, 2020.
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, [data set], Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019.
Hatsuzuka, D. and Fujinami, H.: Effects of the South Asian monsoon
intraseasonal modes on genesis of low pressure systems over Bangladesh,
J. Climate, 30, 2481–2499, https://doi.org/10.1175/JCLI-D-16-0360.1,
2017.
Hackert, E., Kovach, R. M., Molod, A., Vernieres, G., Borovikov, A.,
Marshak, J., and Chang, Y.: Satellite sea surface salinity observations
impact on El Niño/Southern Oscillation predictions: Case studies from
the NASA GEOS seasonal forecast system, J. Geophys. Res.-Oceans, 125, e2019JC015788, https://doi.org/10.1029/2019JC015788, 2020.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 0.05Deg
CMG, Version 6, (MOD10A1), Boulder, Colorado USA, NASA National Snow and Ice
Data Center Distributed Active Archive Center,
https://doi.org/10.5067/MODIS/MOD10C1.006, 2016a.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m
SIN Grid, Version 6, (MOD10A1), Boulder, Colorado USA, NASA National Snow
and Ice Data Center Distributed Active Archive Center,
https://doi.org/10.5067/MODIS/MOD10A1.006,
2016b.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr,
K. J.: MODIS snow-cover products, Remote Sens. Environ. 83,
181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Hill, C., DeLuca, C., Suarez, M., and Da Silva, A. R.: The architecture of the
earth system modeling framework, Comput. Sci. Eng., 6, 18–28, https://doi.org/10.1109/MCISE.2004.1255817, 2004.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., and Simmons, A.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hopson, T. M.: Assessing the ensemble spread–error relationship, Mon.
Weather Rev., 142, 1125–1142, https://doi.org/10.1175/MWR-D-12-00111.1, 2014.
Hsu, L.-H., Chen, D.-R., Chiang, C.-C., Chu, J.-L., Yu, Y.-C., and Wu,
C.-C.: Simulations of the East Asian Winter Monsoon on Subseasonal to
Seasonal Time Scales Using the Model for Prediction Across Scales,
Atmosphere, 12, 865, https://doi.org/10.3390/atmos12070865, 2021.
Hunke, E. C. and Lipscomb, W. H.: The Los Alamos sea ice model documentation
and software user's manual, Version 4.0, Los Alamos National Laboratory, http://www.ccpo.odu.edu/~klinck/Reprints/PDF/cicedoc2015.pdf,
2008.
Hwang, Y. T. and Frierson, D.: Link between the double-Intertropical
Convergence Zone problem and cloud biases over the Southern Ocean,
P. Natl. Acad. Sci. USA, 110, 4935–4940,
https://doi.org/10.1073/pnas.1213302110, 2013.
Immerzeel, W. W., Van Beek, L. P., and Bierkens, M. F.: Climate change will
affect the Asian water towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., and Emmer, A.: Importance and
vulnerability of the world's water towers, Nature, 577, 364–369,
https://doi.org/10.1038/s41586-019-1822-y, 2020.
Jiang, X., Li, T., and Wang, B.: Structures and mechanisms of the northward
propagating boreal summer intraseasonal oscillation, J. Climate, 17,
1022–1039, https://doi.org/10.1175/1520-0442(2004)017<1022:SAMOTN>2.0.CO;2, 2004.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang,
Q., Van Den Dool, H., Saha, S., Mendez, M. P., Becker, E., and Peng, P.: The
North American multimodel ensemble: phase-1 seasonal-to-interannual
prediction; phase-2 toward developing intraseasonal prediction, B. Am. Meteorol. Soc., 95, 585–601,
https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A
catchment-based approach to modeling land surface processes in a general
circulation model: 1. Model structure, J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Koster, R. D., Mahanama, S. P. P., Yamada, T. J., Balsamo, G., Berg, A. A.,
Boisserie, M., Dirmeyer, P. A., Doblas-Reyes, F. J., Drewitt, G., Gordon, C. T.,
and Guo, Z.: The second phase of the global land–atmosphere coupling
experiment: soil moisture contributions to subseasonal forecast skill,
J. Hydrometeorol., 12, 805–822,
https://doi.org/10.1175/2011JHM1365.1, 2011.
Landerer, F.: TELLUS_GRAC_L3_CSR_RL06_LND_v04, Ver. RL06 v04, PO.DAAC, CA, USA, [data set], https://doi.org/10.5067/TELND-3AC64, 2021.
Li, J., Yu, R., Zhou, T., and Wang, B.: Why is there an early spring cooling
shift downstream of the Tibetan Plateau?, J. Climate, 18,
4660–4668, https://doi.org/10.1175/JCLI3568.1, 2005.
Li, J., Yu, R., and Zhou, T.: Teleconnection between NAO and climate
downstream of the Tibetan Plateau, J. Climate, 21, 4680–4690,
https://doi.org/10.1175/2008JCLI2053.1, 2008.
Lim, Y. K.: The East Atlantic/West Russia (EA/WR) teleconnection in the
North Atlantic: climate impact and relation to Rossby wave propagation,
Clim. Dynam., 44, 3211–3222, https://doi.org/10.1007/s00382-014-2381-4,
2015.
Lim, Y. K., Arnold, N. P., Molod, A. M., and Pawson, S.: Seasonality in
Prediction Skill of the Madden-Julian Oscillation and Associated Dynamics in
Version 2 of NASA's GEOS-S2S Forecast System, J. Geophys. Res.-Atmos., 126, e2021JD034961,
https://doi.org/10.1029/2021JD034961, 2021.
Liu, Y. and Margulis, S. A.: Deriving Bias and uncertainty in MERRA-2
snowfall precipitation over High Mountain Asia, Front. Earth Sci.,
7, 280, https://doi.org/10.3389/feart.2019.00280, 2019.
Liu, Y., Fang, Y., and Margulis, S. A.: Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset, The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, 2021a.
Liu, Y., Fang, Y., and Margulis, S. A.: High Mountain Asia UCLA Daily Snow
Reanalysis, Version 1, Boulder, Colorado USA, NASA National Snow and Ice
Data Center Distributed Active Archive Center, [data set],
https://doi.org/10.5067/HNAUGJQXSCVU, 2021b.
Loomis, B. D., Richey, A. S., Arendt, A. A., Appana, R., Deweese, Y.,
Forman, B. A., Kumar, S. V., Sabaka, T. J., and Shean, D. E.: Water storage
trends in high mountain Asia, Front. Earth Sci., 7, 235,
https://doi.org/10.3389/feart.2019.00235, 2019a.
Loomis, B. D., Luthcke, S. B., and Sabaka, T. J.: Regularization and error
characterization of GRACE mascons, J. Geod., 93, 1381–1398,
https://doi.org/10.1007/s00190-019-01252-y, 2019b.
Maeda, M., Yasutomi, N., Yatagai, A., and National Center for Atmospheric
Research Staff (Eds): Last modified 29 July 2020, The Climate Data Guide:
APHRODITE: Asian Precipitation – Highly-Resolved Observational Data
Integration Towards Evaluation of Water Resources, https://climatedataguide.ucar.edu/climate-data/aphrodite-asian-precipitation-highly-resolved-observational-data-integration-towards, last access: December 2020.
Margulis, S. A., Liu, Y., and Baldo, E.: A joint landsat-and MODIS-based
reanalysis approach for midlatitude montane seasonal snow characterization,
Front. Earth Sci., 7, 272, https://doi.org/10.3389/feart.2019.00272, 2019.
Mariotti, A., Ruti, P. M., and Rixen, M.: Progress in subseasonal to
seasonal prediction through a joint weather and climate community effort,
npj Clim. Atmos. Sci., 1, 1–4,
https://doi.org/10.1038/s41612-018-0014-z, 2018.
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins,
D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., and Kirtman, B. P.: Windows of opportunity for skillful
forecasts subseasonal to seasonal and beyond, B. Am. Meteorol. Soc., 101, 608–625,
https://doi.org/10.1175/BAMS-D-18-0326.1, 2020.
Massoud, E. C., Purdy, A. J., Miro, M. E., and Famiglietti, J. S.: Projecting
groundwater storage changes in California's Central Valley, Sci. Rep., 8,
12917, https://doi.org/10.1038/s41598-018-31210-1, 2018.
Massoud, E. C., Liu, Z., Shaban, A., and El Hage, M.: Groundwater Depletion
Signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1
Data, Remote Sens., 13, 915, https://doi.org/10.3390/rs13050915, 2021.
Massoud, E. C., Bloom, A. A., Longo, M., Reager, J. T., Levine, P. A., and Worden, J. R.: Information content of soil hydrology in a west Amazon watershed as informed by GRACE, Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, 2022.
Maurer, J. M., Schaefer, J. M., Russell, J. B., Rupper, S., Wangdi, N.,
Putnam, A. E., and Young, N.: Seismic observations, numerical modeling, and
geomorphic analysis of a glacier lake outburst flood in the Himalayas,
Sci. Adv. 6, eaba3645, https://doi.org/10.1126/sciadv.aba3645, 2020.
Meena, N. K., Diwate, P., and Pandita, S.: Evidence of ENSO and IOD
Interplay in Continental Climatic Records from Southern Himalaya (Renuka
Lake), India, J. Geosci. Res., 7, 2455–1953, 2022.
Merryfield, W. J., Baehr, J., Batté, L., Becker, E. J., Butler, A. H.,
Coelho, C. A. S., Danabasoglu, G., Dirmeyer, P. A., Doblas-Reyes, F. J.,
Domeisen, D. I. V., Ferranti, L., Ilynia, T., Kumar, A., Müller, W. A.,
Rixen, M., Robertson, A. W., Smith, D. M., Takaya, Y., Tuma, M., Vitart, F.,
White, C. J., Alvarez, M. S., Ardilouze, C., Attard, H., Baggett, C.,
Balmaseda, M. A., Beraki, A. F., Bhattacharjee, P. S., Bilbao, R., de
Andrade, F. M., DeFlorio, M. J., Díaz, L. B., Ehsan, M. A.,
Fragkoulidis, G., Grainger, S., Green, B. W., Hell, M. C., Infanti, J. M.,
Isensee, K., Kataoka, T., Kirtman, B. P., Klingaman, N. P., Lee, J., Mayer,
K., McKay, R., Mecking, J. V., Miller, D. E., Neddermann, N., Justin Ng, C.
H., Ossó, A., Pankatz, K., Peatman, S., Pegion, K., Perlwitz, J.,
Recalde-Coronel, G. C., Reintges, A., Renkl, C., Solaraju-Murali, B.,
Spring, A., Stan, C., Sun, Y. Q., Tozer, C. R., Vigaud, N., Woolnough, S.,
and Yeager, S.: Current and Emerging Developments in Subseasonal to Decadal
Prediction, B. Am. Meteorol. Soc., 101,
869–896, https://doi.org/10.1175/BAMS-D-19-0037.1, 2020.
Mishra, S. K., Veselka, T. D., Prusevich, A. A., Grogan, D. S., Lammers, R.
B., Rounce, D. R., Ali, S. H., and Christian, M. H.: Differential impact of
climate change on the hydropower economics of two river basins in high
mountain Asia, Front. Environ. Sci., 8, 26,
https://doi.org/10.3389/fenvs.2020.00026, 2020.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres,
G., Borovikov A., et al.: GEOS-S2S version 2: The GMAO high-resolution
coupled model and assimilation system for seasonal prediction, J. Geophys. Res.-Atmos. 125, e2019JD031767, https://doi.org/10.1029/2019JD031767,
2020.
Nakada, K., Kovach, R., Marshak, J., and Molod, A.: S2S-2_1:
File Specification, GMAO Office Note No. 16 (Version 1.0), 78 pp., http://gmao.gsfc.nasa.gov/pubs/office_notes (last access: June 2021), 2018.
NASA GMAO GSFC GEOS-S2S Forecasts: NASA Global Modeling and Assimilation Office, 12 Feb. 2020, [data set], http://gmao.gsfc.nasa.gov/gmaoftp/gmaofcst/, last access: 1 June 2021.
Nash, D., Carvalho, L., Jones, C., and Ding, Q.: Winter and spring
atmospheric rivers in High Mountain Asia: climatology, dynamics, and
variability, Clim. Dynam., 58, 2309–2331, https://doi.org/10.1007/s00382-021-06008-z,
2021.
National Academies of Sciences, Engineering, and Medicine: Next Generation
Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts,
Washington, DC, The National Academies Press,
https://doi.org/10.17226/21873, 2016.
Parajka, J. and Blöschl, G.: The value of MODIS snow cover data in
validating and calibrating conceptual hydrologic models, J.
Hydrol., 358, 240–258, https://doi.org/10.1016/j.jhydrol.2008.06.006,
2008.
Penny, S. G., Kalnay, E., Carton, J. A., Hunt, B. R., Ide, K., Miyoshi, T.,
and Chepurin, G. A.: The local ensemble transform Kalman filter and the
running-in-place algorithm applied to a global ocean general circulation
model, Nonlin. Proc. Geophys., 20, 1031–1046,
https://doi.org/10.5194/npg-20-1031-2013, 2013.
Pielke Sr, R. A., Liston, G. E., Eastman, J. L., Lu, L., and Coughenour,
M.: Seasonal weather prediction as an initial value problem, J. Geophys. Res.-Atmos., 104, 19463–19479,
https://doi.org/10.1029/1999JD900231, 1999.
Power, K., Axelsson, J., Wangdi, N., and Zhang, Q.: Regional and Local
Impacts of the ENSO and IOD Events of 2015 and 2016 on the Indian Summer
Monsoon – A Bhutan Case Study, Atmosphere 12, 954,
https://doi.org/10.3390/atmos12080954, 2021.
Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A., and Dorigo, W.: Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record [data set], IEEE T. Geosci. Remote, 59, 2845–2862, doi:10.1109/TGRS.2020.3012896, 2021.
Reichle, R. H., Liu, Q., Koster, R. D., Draper, C. S., Mahanama, S. P., and
Partyka, G. S.: Land surface precipitation in MERRA-2, J. Climate,
30, 1643–1664, https://doi.org/10.1175/JCLI-D-16-0570.1, 2017.
Rienecker, M. M., Todling, R., Bacmeister, J., Takacs, L., Liu, H. C., Gu, W., Sienkiewicz, M., Koster, R. D., Gelaro, R., Stajner, I., and Nielsen, J. E.: The GEOS-5 data assimilation system: Documentation of versions 5.0.1
and 5.1.0, and 5.2.0 (NASA Tech. Rep.): Series on Global Modeling and Data
Assimilation NASA/TM-2008-104606, Vol. 27, 92 pp., 2008.
Robertson, A. W., Vitart, F., and Camargo, S. J.: Subseasonal to seasonal
prediction of weather to climate with application to tropical cyclones,
J. Geophys. Res.-Atmos. 125, e2018JD029375,
https://doi.org/10.1029/2018JD029375, 2020.
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates
of groundwater depletion in India, Nat. Cell Biol., 460, 999–1002,
https://doi.org/10.1038/nature08238, 2009.
Sang, Y. F., Singh, V. P., and Xu, K.: Evolution of IOD-ENSO relationship at
multiple time scales, Theor. Appl. Clim., 136,
1303–1309, https://doi.org/10.1007/s00704-018-2557-7, 2019.
Sarangi, C., Qian, Y., Rittger, K., Bormann, K. J., Liu, Y., Wang, H., Wan, H., Lin, G., and Painter, T. H.: Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations, Atmos. Chem. Phys., 19, 7105–7128, https://doi.org/10.5194/acp-19-7105-2019, 2019.
Sarangi, C., Qian, Y., Rittger, K., Leung, L. R., Chand, D., Bormann, K. J.,
and Painter, T. H.: Dust dominates high-altitude snow darkening and melt
over high-mountain Asia, Nat. Clim. Change, 10,
1045–1051, https://doi.org/10.1038/s41558-020-00909-3, 2020.
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., Gordon, M., and
Hermanson, L.: Skillful long-range prediction of European and North American
winters, Geophys. Res. Lett., 41, 2514–2519,
https://doi.org/10.1002/2014GL059637, 2014.
Schneider, D., Molotch, N. P., Deems, J. S., and Painter, T. H.: Analysis of
topographic controls on depletion curves derived from airborne lidar snow
depth data, Hydrol. Res., 52, 253–265,
https://doi.org/10.2166/nh.2020.267, 2021.
Senan, R., Orsolini, Y. J., Weisheimer, A., Vitart, F., Balsamo, G.,
Stockdale, T. N., Dutra, E., Doblas-Reyes, F. J., and Basang, D.: Impact of
springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian
summer monsoon in coupled seasonal forecasts, Clim. Dynam., 47,
2709–2725, https://doi.org/10.1007/s00382-016-2993-y, 2016.
Shugar, D. H., Burr, A., Haritashya, U. K., Kargel, J. S., Watson, C. S.,
Kennedy, M. C., Bevington, A. R., Betts, R. A., Harrison, S., and Strattman,
K.: Rapid worldwide growth of glacial lakes since 1990, Nat. Clim.
Change, 10, 939–945, https://doi.org/10.1038/s41558-020-0855-4, 2020.
Shukla, R. P., Kinter, J. L., and Shin, C. S.: Sub-seasonal prediction of
significant wave heights over the Western Pacific and Indian Oceans, Part
II: The impact of ENSO and MJO, Ocean Modell., 123, 1–15,
https://doi.org/10.1016/j.ocemod.2018.01.002, 2018.
Stanley, T., Kirschbaum, D. B., Pascale, S., and Kapnick, S.: Extreme
Precipitation in the Himalayan Landslide Hotspot, in: Satellite precipitation
measurement, Springer, Cham, 1087–1111,
https://doi.org/10.1007/978-3-030-35798-6_31, 2020.
Stieglitz, M., Ducharne, A., Koster, R., and Suarez, M.: The impact of
detailed snow physics on the simulation of snow cover and subsurface
thermodynamics at continental scales, J. Hydrometeorol., 2, 228–242, https://doi.org/10.1175/1525-7541(2001)002<0228:TIODSP>2.0.CO;2, 2001.
Stuecker, M. F., Timmermann, A., Jin, F. F., Chikamoto, Y., Zhang, W.,
Wittenberg, A. T., Widiasih, E., and Zhao, S.: Revisiting ENSO/Indian Ocean
dipole phase relationships, Geophys. Res. Lett., 44, 2481–2492, https://doi.org/10.1002/2016GL072308, 2017.
Suarez, M., Trayanov, A., Hill, C., Schopf, P., and Vikhliaev, Y.: MAPL: A
high-level programming paradigm to support more rapid and robust encoding of
hierarchical trees of interacting high-performance components, in:
Proceedings of the 2007 Symposium on Component and Framework Technology in
High-Performance and Scientific Computing, 11–20,
https://doi.org/10.1145/1297385.1297388, 2007.
Su, F., Duan, X., Chen, D., Hao, Z., and Cuo, L.: Evaluation of the global
climate models in the CMIP5 over the Tibetan Plateau, J. Climate, 26,
3187–3208, https://doi.org/10.1175/JCLI-D-12-00321.1, 2013.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M.
M.: GRACE measurements of mass variability in the Earth system, Science, 305,
503–505, https://doi.org/10.1126/science.1099192, 2004.
Tekeli, A. E., Akyürek, Z., Şorman, A. A., Şensoy, A., and
Şorman, A. U.: Using MODIS snow cover maps in modeling snowmelt runoff
process in the eastern part of Turkey, Remote Sens. Environ., 97, 216–230, https://doi.org/10.1016/j.rse.2005.03.013, 2005.
Tiwari, V. M., Wahr, J., and Swenson, S.: Dwindling groundwater resources in
northern India, from satellite gravity observations, Geophys. Res. Lett., 36,
18401, https://doi.org/10.1029/2009GL039401, 2009.
Toure, A. M., Reichle, R. H., Forman, B. A., Getirana, A., and De Lannoy,
G.: Assimilation of MODIS snow cover fraction observations into the NASA
catchment land surface model, Remote Sens., 10, 316,
https://doi.org/10.3390/rs10020316, 2018.
Vitart, F. and Robertson, A. W.: The sub-seasonal to seasonal prediction
project (S2S) and the prediction of extreme events, npj Clim. Atmos. Sci., 1, 1–7, https://doi.org/10.1038/s41612-018-0013-0, 2018.
Vitart, F. and Robertson, A. W.: Introduction: Why Sub-seasonal to seasonal
prediction (S2S)?, in Sub-Seasonal to Seasonal Prediction,
Elsevier, 3–15, https://doi.org/10.1016/B978-0-12-811714-9.00001-2, 2019.
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R.:
Mountains of the world, water towers for humanity: Typology, mapping, and
global significance, Water Resour. Res., 43, W07447,
https://doi.org/10.1029/2006WR005653, 2007.
Waliser, D., Weickmann, K., Dole, R., Schubert, S., Alves, O., Jones, C.,
Newman, M., Pan, H.-L., Roubicek, A., Saha, S., Smith, C., vab deb Dool, H.,
Vitart, F., Wheeler, M., and Whitaker, J.: The Experimental MJO Prediction
Project, B. Am. Meteorol. Soc., 87, 425–431, 2006.
Waliser, D., Sperber, K., Hendon, H., Kim, D., Maloney, E., Wheeler, M.,
Weickmann, K., et al.: MJO simulation diagnostics, J. Climate, 22,
11, 3006–3030, https://doi.org/10.1175/2008JCLI2731.1, 2009.
Wang, X., Liu, L., Niu, Q., Li, H., and Xu, Z.: Multiple Data Products
Reveal Long-Term Variation Characteristics of Terrestrial Water Storage and
Its Dominant Factors in Data-Scarce Alpine Regions, Remote Sens., 13, 2356, https://doi.org/10.3390/rs13122356, 2021.
White, C. J., Domeisen, D. I. V., Acharya, N., Adefisan, E. A., Anderson, M.
L., Aura, S., Balogun, A. A., Bertram, D., Bluhm, S., Brayshaw, D. J.,
Browell, J., Büeler, D., Charlton-Perez, A., Chourio, X., Christel, I.,
Coelho, C. A. S., DeFlorio, M. J., Delle Monache, L., Di Giuseppe, F.,
García-Solórzano, A. M., Gibson, P. B., Goddard, L., González
Romero, C., Graham, R. J., Graham, R. M., Grams, C. M., Halford, A., Katty
Huang, W. T., Jensen, K., Kilavi, M., Lawal, K. A., Lee, R. W., MacLeod, D.,
Manrique-Suñén, A., Martins, E. S. P. R., Maxwell, C. J.,
Merryfield, W. J., Muñoz, Á. G., Olaniyan, E., Otieno, G., Oyedepo,
J. A., Palma, L., Pechlivanidis, I. G., Pons, D., Ralph, F. M., Reis, D. S.,
Jr., Remenyi, T. A., Risbey, J. S., Robertson, D. J. C., Robertson, A. W.,
Smith, S., Soret, A., Sun, T., Todd, M. C., Tozer, C. R., Vasconcelos, F.
C., Jr., Vigo, I., Waliser, D. E., Wetterhall, F., and Wilson, R. G.:
Advances in the application and utility of subseasonal-to-seasonal
predictions, Bulletin of the American Meteorological Society (published
online ahead of print 2021), https://doi.org/10.1175/BAMS-D-20-0224.1, 2021.
Xiang, L., Wang, H., Steffen, H., Wu, P., Jia, L., Jiang, L., and Shen, Q.:
Groundwater storage changes in the Tibetan Plateau and adjacent areas
revealed from GRACE satellite gravity data, Earth Planet. Sc. Lett., 449, 228–239, https://doi.org/10.1016/j.epsl.2016.06.002, 2016.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and
Kitoh, A.: APHRODITE: Constructing a long-term daily gridded precipitation
dataset for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415,
https://doi.org/10.1175/BAMS-D-11-00122.1, 2012.
Yoon, Y., Kumar, S. V., Forman, B. A., Zaitchik, B. F., Kwon, Y., Qian, Y.,
Rupper, S., Maggioni, V., Houser, P., Kirschbaum, D., and Richey, A.: Evaluating the uncertainty of terrestrial water budget
components over High Mountain Asia, Front. Earth Sci., 7, 120,
https://doi.org/10.3389/feart.2019.00120, 2019.
Zhang, G. J., Song, X., and Wang, Y.: The double ITCZ syndrome in GCMs: A
coupled feedback problem among convection, clouds, atmospheric and ocean
circulations, Atmos. Res., 229, 255–268,
https://doi.org/10.1016/j.atmosres.2019.06.023, 2019.
Zhou, L., Murtugudde, R., Chen, D., and Tang, Y.: A Central Indian Ocean
mode and heavy precipitation during the Indian summer monsoon, J.
Climate, 30, 2055–2067, https://doi.org/10.1175/JCLI-D-16-0347.1, 2017.
Zhou, Y., Zaitchik, B. F., Kumar, S. V., Arsenault, K. R., Matin, M. A., Qamer, F. M., Zamora, R. A., and Shakya, K.: Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins, Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, 2021.
Zhou, Z. Q., Zhang, R., and Xie, S. P.: Interannual variability of summer
surface air temperature over central India: Implications for monsoon onset,
J. Climate, 32, 1693–1706, https://doi.org/10.1175/JCLI-D-18-0675.1,
2019.
Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System...
Altmetrics
Final-revised paper
Preprint