Introduction to the Community Land Model Edouard Davin

Transcription

1 Introduction to the Community Land Model Edouard Davin

2 Goals Which processes are represented in the Community Land Model (CLM)? What makes CLM different from TERRA_ML? How is CLM coupled to COSMO? 2

3 Content Introduction Historical perspective on land surface modelling TERRA_ML vs CLM Ingredients of CLM Energy balance and radiative fluxes Turbulent fluxes and stomatal conductance Hydrology Subgrid-scale heterogeneity Transient land use Biogeochemical processes Vegetation dynamics Offline validation of CLM Coupling strategy 3

4 Community Earth System Model (CESM) A climate model freely available to the scientific community Info and download: CLM is the land component of CESM Figure: P. Lawrence 4

5 Community Earth System Model (CESM) A climate model freely available to the scientific community Info and download: CLM is the land component of CESM CESM has ~1.5M lines of code CLM has ~0.2M lines of code Figure: P. Lawrence 5

6 Community Land Model (CLM) More info and documentation: CLM versions: CLM3.5 coupled to COSMO as subroutine CLM4.0 coupled to COSMO using OASIS; basis for this presentation and tutorial CLM4.5 coupled to COSMO using OASIS 6

7 Where to find detailed information 7

8 What is a Land Surface Model? 8

9 What is a Land Surface Model? something that solves the surface energy, water (and carbon) balances Rn=λE+SH+G ds =P E Rs Rg dt 9

10 What is a Land Surface Model? something that solves the surface energy, water (and carbon) balances Rn=λE+SH+G ds =P E Rs Rg dt Based on first principles: conservation of energy and mass! Common to all LSMs; degree of complexity depends on the approach used to compute these fluxes 10

11 HISTORICAL PERSPECTIVE ON LAND SURFACE MODELLING 11

12 Evolution of climate models IPCC,

13 Sellers et al. (1997) classification Evolution of LSMs 1st generation LSM: bucket model No explicit treatment of vegetation Bucket model: Manabe,

14 Sellers et al. (1997) classification Evolution of LSMs 1st generation LSM: bucket model No explicit treatment of vegetation 1970 Bucket model: Manabe, nd generation LSM: Big-leaf approach Stomatal conductance BATS: Dickinson, 1984 SiB: Sellers et al.,

15 Sellers et al. (1997) classification Evolution of LSMs 1st generation LSM: bucket model No explicit treatment of vegetation 1970 Bucket model: Manabe, nd generation LSM: Big-leaf approach Stomatal conductance 3rd generation LSM: Photosynthesis Carbon cycle BATS: Dickinson, 1984 SiB: Sellers et al., 1986 SiB2: Sellers et al., 1992 LSM: Bonan,

16 Sellers et al. (1997) classification Evolution of LSMs 1st generation LSM: bucket model No explicit treatment of vegetation 1970 Bucket model: Manabe, nd generation LSM: Big-leaf approach Stomatal conductance 3rd generation LSM: Photosynthesis Carbon cycle 1980 Vegetation dynamics (biogeography) 1990 BATS: Dickinson, 1984 SiB: Sellers et al., 1986 SiB2: Sellers et al., 1992 LSM: Bonan, 1995 IBIS: Foley et al.,

17 Sellers et al. (1997) classification Evolution of LSMs 1st generation LSM: bucket model No explicit treatment of vegetation 1970 Bucket model: Manabe, nd generation LSM: Big-leaf approach Stomatal conductance 3rd generation LSM: Photosynthesis Carbon cycle BATS: Dickinson, 1984 SiB: Sellers et al., 1986 TERRA_ML Vegetation dynamics (biogeography) SiB2: Sellers et al., 1992 LSM: Bonan, 1995 IBIS: Foley et al., 1996 CLM 17

18 Vertical discretisation Evolution of LSMs no canopy layer Big-leaf approach 2-leaf approach bucket hydrology 2-layer hydrology Multi-layer hydrology Multi-layer canopy 2010 sunlit shaded Bucket model: Manabe, 1969 BATS: Dickinson, 1984 SiB: Sellers et al., 1986 CLM3: Oleson et al., 2004 CLM4.5: Oleson et al., 2013 ORCHIDEE: Ryder et al.,

19 70s 80s Biogeophysical models Biogeochemical models Biogeographical models 1st generation LSM Empirical models NPP = f(t, P) Empirical models Biome= f(t,p) MIAMI Köppen, Holdridge Diagnostic models (satellite) CASA,TURC Concept of PFT, competition Mechanistic models BIOME Bucket model 2nd generation LSM BATS, SiB, ISBA, SECHIBA TERRA_ML TEM, BIOME-BGC, CARAIB, SILVAN, CENTURY, FBM 90s 3rd generation LSM (coupling stomatal conductance-npp) SiB2, LSM, MOSES Today PFT=f(NPP) DEMETER, BIOME2/3, LPJ Coupling physics-biogeochemistry-biogeography CLM, ORCHIDEE, JULES, IBIS Adapted from N. Viovy 19

20 TERRA_ML VS CLM 20

21 TERRA_ML CLM4.0 Surface temperature and energy balance Single interface with one temperature (t_g) and bulk fluxes Distinguishes temperature and energy fluxes for canopy (tv) and ground (tg) Radiation fluxes Canopy radiative scheme 2-stream approximation of the radiative transfer equations Explicit treatment of diffuse and direct light Fluxes based on grid scale albedo and temperature. Technically in src_radiation Stomatal conductance BATS-based Empirical Jarvis-type approach Ball-Berry approach Coupling with photosynthesis 2-leaf canopy with diffuse/direct light 21

22 TERRA_ML CLM4.0 Soil hydrology Richards equation solved for multi-layer soil column Groundwater model Water table depth determined Richards equation solved for multi-layer soil column Runoff Surface runoff: Hillel, 1980 Subsurface runoff when layer is at field capacity TOPMODEL-based approach Surface runoff: saturation and infiltration excess Subsurface runoff function of water table depth Snow processes Single mass balance equation New option for multi-layer scheme? Multi-layer scheme Solid and liquid content Melt-freeze cycles Accumulation and compaction 22

23 TERRA_ML CLM4.0 Subgrid-scale heterogeneity Only accounts for partial coverage of snow. Tile approach in ICON-TERRA Tile approach Vegetated (17 PFTs), Crop, Urban, lake, glacier Surface parameters? MODIS-based (LAI, PFT distribution) IGBP Global Soil Data Task 2000 for soil texture and soil organic matter with vertical profile 23

24 TERRA_ML CLM4.0 Land use change Change in crop and pasture over time Biogeophysical and biogeochemical effects Biogeochemistry Carbon-nitrogen module Prognostic phenology BVOCs Ecosystem dynamics LPJ-based approach 24

25 Multi-layer soil structure CLM Temperature Hydrology Davin et al., Clim. Dyn.,

26 ENERGY BALANCE AND RADIATIVE FLUXES 26

27 How CLM balances energy at the surface Canopy energy balance Overall energy balance Tv Ground energy balance Tg 27

28 Ground/canopy radiative fluxes Direct solar radiation Diffuse solar radiation Longwave radiation Direct ground albedo (vis/nir) Diffuse ground albedo (vis/nir) Oleson et al., Tech. Note,

29 TURBULENT FLUXES AND STOMATAL CONDUCTANCE 29

30 Formulation of turbulent fluxes The flux is driven by the gradient of the quantity considered The role of turbulence is represented through a bulk aerodynamic resistance (inverse of bulk aerodynamic coefficient) 1 SH= (Ts Ta) ra 1 E= (qs qa) ra Ta, qa Atmospheric forcing ra Ts, qs Aerodynamic Temperature or humidity gradient resistance 30

31 How to deal with qs? 1 E= (qs qa) ra qs not easy to estimate, thus we introduce qsat(ts) instead Epot 1 E=β ( qsat ( Ts ) qa ) =βepot / ra Beta-factor to represent limited availability of moisture Surface humidity at saturation 0 β 1 31

32 How to deal with qs? 1 E= (qs qa) ra qs not easy to estimate, thus we introduce qsat(ts) instead 1 E=β ( qsat ( Ts ) qa ) =βepot / ra qa ra Or using the electrical network analogy: 1 E= (qsat (Ts ) qa ) ra+rs Surface resistance (to be discussed later) qsat rs qs 32

33 Separation of evaporation and transpiration 1 Esoil= ( qsat (Tsoil) qa) ra+rsoil soil resistance 1 Eveg= (qsat (Tveg) qa) ra+rc canopy resistance: represents the vegetation control on transpiration Figure: Sellers et al.,

34 Partitioning of evapotranspiration E = soil evaporation + interception + transpiration Half of the water flux to the atmosphere is conveyed by plants! Biological processes play a major role in controlling evapotranspiration. Dirmeyer et al.,

35 TERRA_ML: 2nd generation; Biophysical models Stomatal behaviour represented based on empirical relations (Jarvis et al., 1976) stomatal conductance gstom = f ( PAR, W, T, δe) Light limitation Water stress Air humidity temperature Figure: Bonan,

36 Limitation of 2nd generation LSMs Vegetation explicitly represented in 2nd generation LSMs but... Stomatal conductance is calculated empirically without considering the actual process controlling stomatal functioning Maximisation of water use efficiency (photosynthesis/water) 36

37 CLM: 3rd generation; Photosynthesis model Stomatal conductance explicitly related to photosynthetic assimilation using Ball-Berry conductance model (Collatz et al. 1991): A gstom = m h s p + b cs m A cs hs p b empirical coefficient derived from observations photosynthetic assimilation CO2 concentration at the leaf surface relative humidity at the leaf surface Figure: Sellers et al., 1997 atmospheric pressure minimum value of gstom 37

38 Photosynthetic assimilation A = min( Ac,Aj,Ae ) (Farquhar et al., 1980) AC: Efficiency of the photosynthetic enzyme system (Rubisco limitation) AL: Amount of light captured by the leaf chlorophyll (Light limitation) AS: Capacity of the leaf to utilize or export the products of photosynthesis (Capacity utilization limitation) AC and As mainly depend on Vcmax (maximum rate of carboxylation) which includes the effect of water stress and nitrogen limitation 38

39 Scaling from leaf to canopy Photosynthesis and stomatal conductance calculated separately for sunlit and shaded leaves: 2-leaf model Account for vertical gradient of nitrogen in the canopy decline in foliage nitrogen (per unit area) with depth in canopy yields decline in photosynthetic capacity Bonan et al., JGR,

40 HYDROLOGY 40

41 TERRA_ML CLM4.0 Oleson et al., 2010 COSMO model doc 41

42 TERRA_ML CLM4.0 Oleson et al., 2010 COSMO model doc TOPMODEL-based River routing Water table depthdependent subsurface runoff 42

43 Soil water dynamics: Richards equation Soil moisture change over time Vertical water flux (Darcy s law) Sink term (evapotranspiration, runoff) Hydraulic conductivity dependent on water content and soil type Mineral and organic properties Vertical profile 43

44 TOPMODEL-based runoff Surface runoff Figure: D. Lawrence 44

45 TOPMODEL-based runoff Surface runoff Subsurface runoff water table depth depends on aquifer water storage Figure: D. Lawrence 45

46 Subgrid-scale topography fsat depends on soil moisture state (water table depth) and fmax fmax integrates the effect of subgrid-scale topography (input dataset) Figure: P. Lawrence 46

47 SUBGRID-SCALE HETEROGENEITY 47

48 How to deal with subgrid-scale heterogeneity? Subgrid land cover Climate model grid Figure: P. Houser 48

49 Problem with averaging surface parameters Relations between surface parameters and fluxes are non-linear Averaging surface parameters over heterogeneous terrain will yield wrong fluxes: F(x) F(x) Figure: F. Ament 49

50 Subgrid hierarchy in CLM4.5 Oleson et al.,

51 Spatially-varying input parameters Global datasets aggregated to model grid Will be found in surfdata_xxx.nc Oleson et al.,

52 PFT-specific input parameters Optical properties Leaf angle Leaf/stem reflectance Leaf/stem transmittance Will be found in pft_physiology.xxx.nc Morphological properties Leaf dimension Root distribution Photosynthetic parameters Specific leaf area Leaf carbon-to-nitrogen ratio m (slope of conductance-photosynthesis relationship) 52

53 TRANSIENT LAND USE 53

54 PFT mapping Lawrence and Chase, JGR,

55 Lawrence et al., JAMES,

56 Biogeophysical effect Lawrence et al., J. Clim.,

57 Biogeochemical effect Lawrence et al., J. Clim.,

58 BIOGEOCHEMICAL PROCESSES 58

59 The carbon cycle Anthropogenic emissions In black: preindustrial state of reservoirs and fluxes In red: anthropogenic perturbation IPCC,

60 Importance of terrestrial ecosystems Land use change: ~1/3 of cumulative anthropogenic CO2 emissions since preindustrial ~25% of cumulative anthropogenic CO2 emissions since preindustrial were absorbed by the terrestrial biosphere (land sink) How will the terrestrial carbon sink be affected by climate change? IPCC,

61 Terrestrial carbon cycle models CO2 CO2 Figure adapted from Krinner et al.,

62 Terrestrial carbon cycle models CO2 CO2 Allocation Function of temperature, moisture availability, nutrient and light limitation Example: if the plant lacks water or nitrogen it tends to grow roots Roots water light Figure: N. Viovy Figure adapted from Krinner et al.,

63 Terrestrial carbon cycle models CO2 CO2 Phenology: Senescence Triggered by reduction in temperature, daylength or moisture Temperate and boreal deciduous trees shed their leaves when days become shorter and temperature falls below a critical threshold. Tropical raingreen trees shed their leaves when moisture availability becomes critical Evergreen trees have a continuous leaf turnover all over the year Figure adapted from Krinner et al.,

64 Terrestrial carbon cycle models CO2 CO2 Heterotrophic respiration Organic matter is decomposed by microbes and other microorganisms Microbial activity, and thus decomposition, increases with warmer temperatures The rate of decomposition is also a function of soil moisture: Figure: Bonan, 2002 Figure adapted from Krinner et al.,

65 Nutrient limitation Plant growth is limited by nutrient availability Nitrogen is usually the most important nutrient at mid and high latitudes (present in chlorophyll) 65

66 Coupled Carbon-Nitrogen dynamics 2 competing processes in a changing climate: CO2 fertilization effect limited by N availability: negative impact on NPP More N mineralization in warmer soil: positive impact on NPP Thornton et al.,

67 VEGETATION DYNAMICS 67

68 Classical biogeography Vegetation can be mapped as a function of climate Holdridge, 1967 Consider only climax vegetation (in equilibrium with climate) Only one-way interaction 68

69 Vegetation dynamics (CLM-CNDV) Use Plant Functional Type (PFT) instead of biomes Competition for light, water and nutrients Successional dynamics Broadleaf evergreen Broadleaf raingreen grass Sitch et al., 2003 Bonan,

70 OFFLINE VALIDATION OF CLM 70

71 71

72 Evaluation against FLUXNET sites Source: D. Lawrence 72

73 Global partitioning of evapotranspiration Source: D. Lawrence 73

74 Annual mean albedo Lawrence et al., JAMES,

75 Lawrence et al., JAMES,

76 Overestimation of GPP in the tropics Alleviated in CLM4.5 Bonan et al., JGR,

77 Same behaviour for ET Bonan et al., JGR,

78 MODIS-derived CLM4-CNDV Castillo et al., J. Clim.,

79 COUPLING STRATEGY 79

80 Variables exchanged Atmosphere Land: atmospheric temperature, U and V wind components, specific water vapour content, height of first atmospheric level, surface pressure, direct shortwave downward radiation, diffuse shortwave downward radiation, longwave downward radiation, precipitation Land Atmosphere: surface albedo, outgoing longwave radiation, latent and sensible heat fluxes, momentum fluxes 80

81 Coupling for turbulent fluxes COSMO does not use directly surface fluxes in its turbulent scheme but uses instead surface states (e.g. surface temperature) and surface transfer coefficients (e.g. TCH) Therefore, the surface fluxes from CLM passed to COSMO have to be inverted to recalculate effective transfer coefficients (and effective qv_s) that can by used by the turbulence scheme An option for surface flux boundary conditions will be implemented in the next revision of the turbulence scheme (M. Raschendorfer)? 81

82 COSMO-CLM2 Subroutine coupling : COSMO-CLM (various versions) coupled to CLM3.5 Evaluation: Davin et al., Clim. Dyn. [2011]; Davin and Seneviratne, Biogeosciences [2012]; Lorenz et al., [2012] Currently in use within various projects, but no plans to implement it in official COSMO code OASIS coupling: Uses OASIS3-MCT as external coupler Upgrade to CLM4.0/CLM4.5 Upgrade to COSMO5.0 Code will be distributed via the redmine server 82

83 Code structure COSMO Executable 1 Oasis interface libraries OASIS3-MCT Oasis interface cesm/clm4.0 Executable 2 83

84 Performance on a Cray XE6 (CSCS) Time-to-solution increases by 30% (0.44 res) to 100% (0.11 res) in the subroutine coupling approach compared to COSMO-TERRA Almost no increase in time-to-solution with OASIS3-MCT Uncoupled Wall time on rosa (Cray XE6; CSCS) for 1 year of simulation Coupled 0.44 deg. resolution COSMO: 8x16 procs CLM4: 32 5:00 ±2min 5:14 ±4min 0.11 deg. resolution COSMO: 32x24 procs CLM4: :48 ±40min 21:26 ±30min 84

85 Redmine server for code management Register online to get an account Project CCLM-CLM Version used for training course will be uploaded on the server 85