Skillful Seasonal Forecasts of Land Carbon Uptake in Northern Mid‐ and High Latitudes

Here we present a first look at the Gross Primary Production (GPP) forecast skill levels achievable with a state‐of‐the‐art subseasonal‐to‐seasonal (S2S) forecast system. Using NASA's retrospective S2S ensemble forecast in conjunction with a terrestrial biosphere model, and using an independent, remote sensing‐based data set for validation, we demonstrate an ability to accurately forecast spring‐summer carbon uptake at multi‐month leads. Averaged across mid‐ and high latitudes of the Northern Hemisphere land, the GPP forecast initialized on January 1 produces statistically significant skill through summer. The skill achieved, however, is spatially variable, with some regions appearing to extract skill from accurate forecasts of snowpack removal and others extracting skill from the initialization of carbon and nitrogen states. Our results reveal some heretofore unexplored facets of climate predictability and provide a look at what might be possible with future S2S forecast systems that are fully integrated with biogeochemical cycles.

[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 .