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Donahue K, Kimball JS, Du J, Bunt F, Colliander A, Moghaddam M, Johnson J, Kim Y, Rawlins MA. Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations. Front Big Data 2023; 6:1243559. [PMID: 38045095 PMCID: PMC10690831 DOI: 10.3389/fdata.2023.1243559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 10/31/2023] [Indexed: 12/05/2023] Open
Abstract
Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0-5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016-2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.
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Affiliation(s)
- Kellen Donahue
- Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, United States
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - John S. Kimball
- Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, United States
| | - Jinyang Du
- Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, United States
| | - Fredrick Bunt
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Andreas Colliander
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
| | - Mahta Moghaddam
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States
| | - Jesse Johnson
- Department of Computer Science, University of Montana, Missoula, MT, United States
| | - Youngwook Kim
- Department of Biology, College of Science, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Michael A. Rawlins
- Department of Earth, Geographic, and Climate Sciences, University of Massachusetts, Amherst, MA, United States
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Dong J, Lei F, Crow WT. Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat Commun 2022; 13:336. [PMID: 35039501 PMCID: PMC8764074 DOI: 10.1038/s41467-021-27938-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 12/22/2021] [Indexed: 11/09/2022] Open
Abstract
Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment exhibit a well-known summertime warm bias in mid-latitude land regions - most notably in the central contiguous United States (CUS). The dominant source of this bias is still under debate. Using validated datasets and both coupled and off-line modeling, we find that the CUS summertime warm bias is driven by the incorrect partitioning of evapotranspiration (ET) into its canopy transpiration and soil evaporation components. Specifically, CMIP6 ESMs do not effectively use available rootzone soil moisture for summertime transpiration and instead rely excessively on shallow soil and canopy-intercepted water storage to supply ET. As such, expected summertime precipitation deficits in CUS induce a negative ET bias into CMIP6 ESMs and a corresponding positive temperature bias via local land-atmosphere coupling. This tendency potentially biases CMIP6 projections of regional water stress and summertime air temperature variability under elevated CO2 conditions.
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Affiliation(s)
- Jianzhi Dong
- USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA.
- Institute of Surface-Earth System Science, Tianjin University, Tianjin, China.
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Fangni Lei
- Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
| | - Wade T Crow
- USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA.
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Huang X, Ding A. Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models. Sci Bull (Beijing) 2021; 66:1917-1924. [PMID: 36654401 DOI: 10.1016/j.scib.2021.05.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/13/2021] [Accepted: 03/19/2021] [Indexed: 02/03/2023]
Abstract
Weather prediction is essential to the daily life of human beings. Current numerical weather prediction models such as the Global Forecast System (GFS) are still subject to substantial forecast biases and rarely consider the impact of atmospheric aerosol, despite the consensus that aerosol is one of the most important sources of uncertainty in the climate system. Here we demonstrate that atmospheric aerosol is one of the important drivers biasing daily temperature prediction. By comparing observations and the GFS prediction, we find that the monthly-averaged bias in the 24-h temperature forecast varies between ± 1.5 °C in regions influenced by atmospheric aerosol. The biases depend on the properties of aerosol, the underlying land surface, and aerosol-cloud interactions over oceans. It is also revealed that forecast errors are rapidly magnified over time in regions featuring high aerosol loadings. Our study provides direct "observational" evidence of aerosol's impacts on daily weather forecast, and bridges the gaps between the weather forecast and climate science regarding the understanding of the impact of atmospheric aerosol.
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Affiliation(s)
- Xin Huang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; Jiangsu Provincial Collaborative Innovation Center of Climate Change, Nanjing 210023, China
| | - Aijun Ding
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; Jiangsu Provincial Collaborative Innovation Center of Climate Change, Nanjing 210023, China.
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Sherwood SC, Webb MJ, Annan JD, Armour KC, Forster PM, Hargreaves JC, Hegerl G, Klein SA, Marvel KD, Rohling EJ, Watanabe M, Andrews T, Braconnot P, Bretherton CS, Foster GL, Hausfather Z, von der Heydt AS, Knutti R, Mauritsen T, Norris JR, Proistosescu C, Rugenstein M, Schmidt GA, Tokarska KB, Zelinka MD. An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence. REVIEWS OF GEOPHYSICS (WASHINGTON, D.C. : 1985) 2020; 58:e2019RG000678. [PMID: 33015673 PMCID: PMC7524012 DOI: 10.1029/2019rg000678] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 04/22/2020] [Accepted: 06/24/2020] [Indexed: 05/10/2023]
Abstract
We assess evidence relevant to Earth's equilibrium climate sensitivity per doubling of atmospheric CO2, characterized by an effective sensitivity S. This evidence includes feedback process understanding, the historical climate record, and the paleoclimate record. An S value lower than 2 K is difficult to reconcile with any of the three lines of evidence. The amount of cooling during the Last Glacial Maximum provides strong evidence against values of S greater than 4.5 K. Other lines of evidence in combination also show that this is relatively unlikely. We use a Bayesian approach to produce a probability density function (PDF) for S given all the evidence, including tests of robustness to difficult-to-quantify uncertainties and different priors. The 66% range is 2.6-3.9 K for our Baseline calculation and remains within 2.3-4.5 K under the robustness tests; corresponding 5-95% ranges are 2.3-4.7 K, bounded by 2.0-5.7 K (although such high-confidence ranges should be regarded more cautiously). This indicates a stronger constraint on S than reported in past assessments, by lifting the low end of the range. This narrowing occurs because the three lines of evidence agree and are judged to be largely independent and because of greater confidence in understanding feedback processes and in combining evidence. We identify promising avenues for further narrowing the range in S, in particular using comprehensive models and process understanding to address limitations in the traditional forcing-feedback paradigm for interpreting past changes.
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Affiliation(s)
- S C Sherwood
- Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes University of New South Wales Sydney Sydney New South Wales Australia
| | - M J Webb
- Met Office Hadley Centre Exeter UK
| | | | | | - P M Forster
- Priestley International Centre for Climate University of Leeds Leeds UK
| | | | - G Hegerl
- School of Geosciences University of Edinburgh Edinburgh UK
| | | | - K D Marvel
- Department of Applied Physics and Applied Math Columbia University New York NY USA
- NASA Goddard Institute for Space Studies New York NY USA
| | - E J Rohling
- Research School of Earth Sciences Australian National University Canberra ACT Australia
- Ocean and Earth Science, National Oceanography Centre University of Southampton Southampton UK
| | - M Watanabe
- Atmosphere and Ocean Research Institute The University of Tokyo Tokyo Japan
| | | | - P Braconnot
- Laboratoire des Sciences du Climat et de l'Environnement, unité mixte CEA-CNRS-UVSQ Université Paris-Saclay Gif sur Yvette France
| | | | - G L Foster
- Ocean and Earth Science, National Oceanography Centre University of Southampton Southampton UK
| | | | - A S von der Heydt
- Institute for Marine and Atmospheric Research, and Centre for Complex Systems Science Utrecht University Utrecht The Netherlands
| | - R Knutti
- Institute for Atmospheric and Climate Science Zurich Switzerland
| | - T Mauritsen
- Department of Meteorology Stockholm University Stockholm Sweden
| | - J R Norris
- Scripps Institution of Oceanography La Jolla CA USA
| | - C Proistosescu
- Department of Atmospheric Sciences and Department of Geology University of Illinois at Urbana-Champaign Urbana IL USA
| | - M Rugenstein
- Max Planck Institute for Meteorology Hamburg Germany
| | - G A Schmidt
- NASA Goddard Institute for Space Studies New York NY USA
| | - K B Tokarska
- School of Geosciences University of Edinburgh Edinburgh UK
- Institute for Atmospheric and Climate Science Zurich Switzerland
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Campbell PC, Bash JO, Herwehe JA, Gilliam RC, Li D. Impacts of tiled land cover characterization in the Model for Prediction Across Scales-Atmosphere (MPAS-A). JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2020; 125:10.1029/2019JD032093. [PMID: 33425636 PMCID: PMC7788010 DOI: 10.1029/2019jd032093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 06/19/2020] [Indexed: 05/12/2023]
Abstract
Parameterization of subgrid-scale variability of land cover characterization (LCC) is an active area of research, and can improve model performance compared to the dominant (i.e., most abundant tile) approach. The "Noah" land surface model implementation in the global Model for Predictions Across Scales-Atmosphere (MPAS-A), however, only uses the dominant LCC approach that leads to oversimplification in regions of highly heterogeneous LCC (e.g., urban/suburban settings). Thus, in this work we implement a subgrid tiled approach as an option in MPAS-A, version 6.0, and assess the impacts of tiled LCC on meteorological predictions for two gradually refining meshes (92-25 and 46-12 km) focused on the conterminous U.S for January and July 2016. Compared to the dominant approach, results show that using the tiled LCC leads to pronounced global changes in 2-m temperature (July global average change ~ -0.4 K), 2-m moisture, and 10-m wind speed for the 92-25 km mesh. The tiled LCC reduces mean biases in 2-m temperature (July U.S. average bias reduction ~ factor of 4) and specific humidity in the central and western U.S. for the 92-25 km mesh, improves the agreement of vertical profiles (e.g., temperature, humidity, and wind speed) with observed radiosondes; however, there is increased bias and error for incoming solar radiation at the surface. The inclusion of subgrid LCC has implications for reducing systematic temperature biases found in numerical weather prediction models, particularly those that employ a dominant LCC approach.
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Affiliation(s)
- Patrick C. Campbell
- National Academies/National Research Council (NRC) Fellowship Participant at National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Now at Center for Spatial Information Science and Systems/Cooperative Institute for Satellite Earth System Studies, George Mason University
- ARL/NOAA Affiliate
| | - Jesse O. Bash
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Jerold A. Herwehe
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Robert C. Gilliam
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Dan Li
- Department of Earth and Environment, Boston University, Boston, MA, USA
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