Skillful Seasonal Forecasts of Land Carbon Uptake in Northern Mid‐ and High Latitudes
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R. Koster | K. Arsenault | S. Shukla | R. Reichle | J. Joiner | J. Kolassa | L. Ott | Eunjee Lee | F. Zeng | Abheera Hazra
[1] J. Joiner,et al. Satellite-based reflectances capture large fraction of variability in global gross primary production (GPP) at weekly time scales , 2020 .
[2] R. Koster,et al. Impact of a Regional U.S. Drought on Land and Atmospheric Carbon , 2020, Journal of Geophysical Research: Biogeosciences.
[3] Benjamin F. Zaitchik,et al. The NASA Hydrological Forecast System for Food and Water Security Applications , 2020 .
[4] Benjamin W. Green,et al. Current and Emerging Developments in Subseasonal to Decadal Prediction , 2020, Bulletin of the American Meteorological Society.
[5] T. Ilyina,et al. Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework , 2020, Geophysical Research Letters.
[6] Kazumi Nakada,et al. GEOS‐S2S Version 2: The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction , 2020, Journal of geophysical research. Atmospheres : JGR.
[7] E. Barnes,et al. Introduction to Special Collection: “Bridging Weather and Climate: Subseasonal‐to‐Seasonal (S2S) Prediction” , 2020, Journal of Geophysical Research: Atmospheres.
[8] K. Lindsay,et al. High predictability of terrestrial carbon fluxes from an initialized decadal prediction system , 2019, Environmental Research Letters.
[9] M. Newman,et al. A Priori Identification of Skillful Extratropical Subseasonal Forecasts , 2019, Geophysical Research Letters.
[10] Randal D. Koster,et al. Improving early warning of drought-driven food insecurity in southern Africa using operational hydrological monitoring and forecasting products , 2019, Natural Hazards and Earth System Sciences.
[11] A. Rosati,et al. Seasonal to multiannual marine ecosystem prediction with a global Earth system model , 2019, Science.
[12] Guangqian Wang,et al. Response of vegetation carbon uptake to snow-induced phenological and physiological changes across temperate China. , 2019, The Science of the total environment.
[13] Martha C. Anderson,et al. Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales. , 2018, Remote sensing of environment.
[14] R. Koster,et al. The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxes , 2018, Biogeosciences.
[15] Yao Zhang,et al. Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data , 2018, Remote. Sens..
[16] M. Chevallier,et al. Assessing the Decadal Predictability of Land and Ocean Carbon Uptake , 2018 .
[17] C. Schaaf,et al. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products , 2018 .
[18] W. Gregg,et al. Forecasting Ocean Chlorophyll in the Equatorial Pacific , 2017, Front. Mar. Sci..
[19] Bin Zhao,et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.
[20] R. Koster,et al. Land Surface Precipitation in MERRA-2 , 2017 .
[21] Michel Rixen,et al. The Subseasonal to Seasonal (S2S) Prediction Project Database , 2017 .
[22] P. Cox,et al. Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.
[23] R. Koster,et al. Hydroclimatic Controls on the Means and Variability of Vegetation Phenology and Carbon Uptake , 2014 .
[24] Francisco J. Doblas-Reyes,et al. Seasonal climate predictability and forecasting: status and prospects , 2013 .
[25] M. Lomas,et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends , 2013, Global change biology.
[26] R. Koster,et al. Rebound in Atmospheric Predictability and the Role of the Land Surface , 2012 .
[27] Bruce H. Raup,et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets , 2012, ISPRS Int. J. Geo Inf..
[28] B. Ramsay,et al. Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/hyp.6720 Enhancements to, and forthcoming developments in the Interactive Multisensor Snow and Ice Mapping System (IMS) † , 2022 .
[29] N. C. Strugnell,et al. First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .
[30] Praveen Kumar,et al. A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .
[31] B. Ramsay,et al. The interactive multisensor snow and ice mapping system , 1998 .
[32] J. Randerson,et al. Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .
[33] Holly K. Gibbs,et al. New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 , 2008 .
[34] D. Lettenmaier,et al. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .