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An overview of generalized land surface processes, land ...hydro.igsnrr.ac.cn/cywater/data/presentation/2016/CWater_ZLYang.pdf · •Increasing Frequency of Extreme Events Heat waves

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    Zong-Liang Yang () Professor, Department of Geological Sciences

    Jackson Endowed Chair, Earth System Science Director, Center for Integrated Earth System Science

    [email protected]

    http://www.geo.utexas.edu/climate

    http://www.jsg.utexas.edu/ciess

    6 August, 2016

    An overview of generalized land surface

    processes, land modeling, data

    assimilation, and their role in climate

    prediction

  • Exchange processes with the atmosphere o Momentum / Energy / Water o Trace gases/dusts/aerosols/pollutants

    Exchange processes with the ocean o Fresh water o Sediments/nutrients o Salinity

    Land-memory processes o Vegetation phenology o Snow/ice cover o Soil moisture / Groundwater Climate Variability (intraseasonal to decadal or longer)

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    Generalized Land Surface Processes

    Human activities

    Energy use Land use Water use

  • A famous quote on modeling

    Essentially, all models are wrong, but some are useful.

    George E. P. Box (18 October 1919 28 March 2013), statistician, quality control, time-series analysis, design of experiments, and Bayesian inference; "one of the great statistical minds of the 20th century".

  • BATS, SiB,

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    PILPS

    CLM

    History: Land has been an important

    component in weather and climate models

    IPCC 2007

    Bucket

  • E Clark (2015)

  • Two schools of thought in LSM development and evaluation

    Atmospheric Forcing

    Model Structure

    Augments (gw, dv, )

    Model Evaluation Pyramid

    Land Surface Model (BATS, SiB,

    CLM, Noah, VIC, )

    LSM developers consider

    1. Increasing realism in representing key processes

    2. Understanding feedbacks and interactions

    3. Maintaining synergism between LSM and other modules in the host GCM

    4. Aiming for past, present, and future climate applications & operational weather/climate predictions

    5. Generalizing parameter-zations across sites

    LSM evaluators consider

    1. Uncertainty in many subsurface parameters and other non-measurable parameters

    2. Uncertainty in atmospheric forcing and observations used for evaluation

    3. Calibration of the parameters for the augmented part only or for the entire LSM

    4. Evaluation in all dimensions

    5. Equifinality?

    LSM developers do not use automated, sophisticated

    evaluation tools.

    LSM evaluators calibrate/evaluate LSMs

    that already exist.

    How Can We Use Sophisticated Evaluation Methods To Guide LSM Development?

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    Over-parameterization?

  • An Effort to Reconcile Both 1) Gulden, L. E., E. Rosero, Z.-L. Yang, M. Rodell, C.S. Jackson, G.-Y. Niu, P. J.-F. Yeh, and J. Famiglietti,

    2007: Improving land-surface model hydrology: Is an explicit aquifer model better than a deeper soil

    profile? Geophys. Res. Lett., 34, L09402, doi:10.1029/2007GL029804.

    2) Gulden, L.E. et al., 2008: Model performance, model robustness, and model fitness scores: A new method

    for identifying good land-surface models, Geophys. Res. Lett., 35, L11404, doi:10.1029/2008GL033721.

    3) Jiang, X., G. Niu, and Z.-L. Yang, 2009, Impacts of vegetation and groundwater dynamics on warm

    season precipitation over the Central United States, J. Geophys. Res., 114, D06109,

    doi:10.1029/2008JD010756.

    4) Rosero, E., Z.-L. Yang, L. E. Gulden, G.-Y. Niu, and D. J. Gochis, 2009: Evaluating enhanced hydrological

    representations in Noah-LSM over transition zones: Implications for model development, J.

    Hydrometeorology, 10, 600-622. DOI:10.1175/2009JHM1029.1

    5) Rosero, E., Z.-L. Yang, T. Wagener, L. E. Gulden, S. Yatheendradas, and G.-Y. Niu, 2010: Quantifying

    parameter sensitivity, interaction and transferability in hydrologically enhanced versions of Noah-LSM over

    transition zones, J. Geophys. Res., 115, D03106, doi:10.1029/2009JD012035.

    6) Cai, X.-T., Z.-L. Yang, C. H. David, G.-Y. Niu, and M. Rodell, 2014: Hydrological evaluation of the Noah-

    MP land surface model for the Mississippi River Basin, J. Geophys. Res Atmospheres, 119 (1), 2338.

    7) Cai, X.-T., Z.-L. Yang, Y. L. Xia, M. Y. Huang, H. L. Wei, L. R. Leung, and M. B. Ek, 2014: Assessment of

    simulated water balance from Noah, Noah-MP, CLM, and VIC over CONUS using the NLDAS test bed, J.

    Geophys. Res. Atmos., 119 (24), 13,75113,770, doi:10.1002/2014JD022113.

    8) Cai, X.-T., Z.-L. Yang, J. B. Fisher, X. Zhang, M. Barlage and F. Chen, 2016: Integration of nitrogen

    dynamics into the Noah-MP land model v1.1 for climate and environmental predictions, Geosci. Model

    Dev., 9, 115, doi:10.5194/gmd-9-1-2016.

  • Hyperresolution global land surface

    modeling: Meeting a grand challenge

    for monitoring Earths terrestrial water

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  • Hyperresolution global land surface

    modeling: Meeting a grand challenge

    for monitoring Earths terrestrial water

    An opinion paper by Wood, E. F., et al., 2011, WRR, 47, W05301.

    A grand challenge to the community

    Hyperresolution: O(1 km) globally; O(100 m) continental scales

    Need for hyperresolution: global food production; water resources sustainability; flood, drought, and climate change prediction

    Six major challenges: surfacesubsurface interactions due to finescale topography and vegetation;

    landatmospheric interactions; soil moisture & evapotranspiration;

    inclusion of water quality as part of the biogeochemical cycle;

    representation of human impacts from water management;

    utilizing massively parallel computer systems in solving 109 unknowns; and

    developing the required in situ and remote sensing global data sets.

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    New Challenges Petascale [O(1015)] Computing Architectures

    Massively parallel supercomputers (104105 multi-core processors) New challenges in memory management Current codes may be ill-equipped May need significant level of recoding

    Rapid Transformation of Landscapes Land surface as a complex system Natural and managed components, and multi-scale interactions Deforestation, Reforestation, Urbanization, Agriculture and Irrigation Living organisms

    Increasing Frequency of Extreme Events Heat waves and cold waves Wild fires Floods/droughts Tornados/hurricanes

    Earth SystemSociety Interactions Integrated assessments: impacts, vulnerability, and resilience Scenario-based decision making Reality check

  • Biogenic emissions of volatile

    organic compounds (BVOCs)

    Trace gases in the air

    Important roles

    BVOCs = 90% VOCs

    Lindsey Gulden Ph.D., UT-Austin, 20052009, supported by EPA & NSF Senior Scientist, ExxonMobil

    Isoprene (C5H8 ), monoterpene (C10H16), other reactive VOCs

  • Precipitation Variability Drives Year-to-year Changes in

    Leaf Biomass and Biogenic Emissions

    Leaf area index in Texas Biogenic emissions in Texas

    Gulden, L. E., Z.-L. Yang and G.-N. Niu, 2007b, J. Geophys. Res., 112 (D14), D14103, 10.1029/2006JD008231. Gulden, L.E. and Z.-L. Yang, 2006, Atmospheric Environment, 40(8), 1464-1479.

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  • Biogenic Volatile Organic Compounds (BVOCs)

    and Secondary Organic Aerosols (SOAs)

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    Coupling of the atmosphere, biosphere, and hydrosphere through biogenic pathways; Improving our understanding of the earth system, its variability/change, and impacts

    Jiang, Yang et al. (2010), Atmospheric Environment

    Xiaoyan Jiang Ph.D., 20062010, supported by NASA Scientist, An Oil Company in Houston

  • Dust Emission

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    The atmospheric dust affects climate, environment, health, and energy (the function of solar panels).

    Mapping land-surface erodibility in dust-source regions based on geomorphology, meteorology, and remote sensing

    Sagar Prasad Parajuli, Zong-Liang Yang, and Gary Kocurek, JGR-Earth Surface, 2014

    New Land Surface Map

    Based on Google Earth Professional

    and ESRI Basemap

    Erodibility Map: (a) this study and (b): previous work

  • Modeling Watershed and Coastal

    Waters and Water Quality Integrating climate, extreme weather, land surface, river flow, biogeochemistry, and ecological models

    Yang (2011) 17

  • Noah-MP-CN: Integrating Noah-MP, SWAT, FUN

    Impacts of the N cycle on the water cycle

    (Cai et al., 2016, Geosci. Model Dev.)

    CN

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    Transpiration increases

    Soil evaporation, runoff and

    soil moisture decrease

  • Yangs group plays a major role in developing the National Water Model, which predicts flows for

    2.67 million river reaches.

  • Soil Moisture SWE, Ice, Rainfall

    Snow Cover Vegetation

    Radiation

    Remote Sensing of the Land Surface Aqua:

    MODIS,

    AMSR-E

    GRACE

    GRACE provides changes in total water storage, which are dominated by SWE at high latitudes and altitudes, but at very coarse resolution

    MODIS provides high resolution snow cover data but not SWE; AMSR-E provides SWE data but with significant errors where snow is deep or wet

    Terra:

    MODIS

    Rodell, 2011

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    Data Assimilation Research Testbed (DART)

    Multi-models, multiple datasets (tower fluxes, aircraft measurements, satellite observations, etc), multiple data assimilation schemes, fine-resolution, and long-term products, in collaboration with NCAR IMAGe

    Next-generation Land Data Assimilation

  • GPM

    Present and Future NASA Earth Science Missions

    Planned Missions

    SMAP, GRACE-FO, ICESat-

    II, JPSS, DESDynI, OCO-2

    Decadal Survey

    Recommended Missions:

    CLARREO, HyspIRI,

    ASCENDS, SWOT, GEO-

    CAPE, ACE, LIST, PATH,

    GRACE-2, SCLP, GACM, 3D-

    Winds

    Highly relevant to hydrology

    Matt Rodell

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  • The New Normal in Land Surface Modeling

    (Predicting extreme events, e.g., floods, droughts, heat waves, dusts, fires)

    (Targeting regional features, e.g., complex terrain and coastal lines)

    (Focusing on human activities, e.g., irrigation and urbanization)

    (Connecting with key policies, e.g. The Grain for Green Program)

    (Paying attention to clothing, food, shelter, and transportation)

  • Thank You!

    EPA

    DHS

    NASA

    NOAA

    NSF

    NSFC

    KAUST

    TACC

    Xitian Cai, Dr. Cedric David, Dr. Lindsey Gulden Lisa Helper Myer, Dr. Bryan Hong Dr. Xiaoyan Jiang, Dr. Marla Knebl

    Dr. Jeff Lo, Dr. Guo-Yue Niu Dr. Enrique Rosero, Dr. Hua Su, Ahmad Tavakoly,

    Zhongfeng Xu, Jiangfeng Wei, Yongfei Zhang Drs. David Allen, Gordon Bonan,

    Fei Chen, Jianli Chen, Robert Dickinson, Michael Ek, Congbin Fu, David Gochis, Alex

    Guenther, David Lawrence, Zhuguo Ma, David Maidment, Kenneth Mitchell, Keith Oleson, Roger

    Pielke Sr., Georgiy Stenchikov, Clark Wilson, Christine Wiedinmyer

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    http://www.jsg.utexas.edu/ciess