Land Surface Physics

Michael Ek, and a number of collaborators!

Environmental Modeling Center (EMC) National Centers for Environmental Prediction (NCEP) College Park, MD, USA National Weather Service (NWS) National Oceanic and Atmospheric Administration (NOAA)

2nd WCRP Summer School on Climate Model Development Center for Weather Forecasts and Climate Studies Brazilian National Institute for Space Research Cachoeira Paulista – SP, Brazil 22-31 January 2018

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 1/50 Outline

• Role of Land Surface Models (LSMs) • Land-surface model requirements: physics & model parameters, atmospheric forcing, land data sets, initial land states, and land data assimilation • Land Applications for Weather & Climate • Testing and Validation • Land Models in a fully-coupled Earth System • Land-Atmosphere Interactions • Summary

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 2/50 Why Land? Predictability and Prediction

• Land states (namely soil moisture, snow, vegetation) can provide predictability in the window between deterministic Weather and Climate (Ocean‐Atmosphere) time scales.

• To have an effect,

must have: Atmosphere 1. Memory of initial (Weather) land states. 2. Sensitivity of …and good models! fluxes to land …and accurate Land analyses! states, Ocean (“Climate”)

atmosphere to Predictability Predictability fluxes. 3. Sufficient variability ~10 days ~2 months Time

Paul Dirmeyer (George Mason Univ.)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 3/50 Role of Land Models

Traditionally, from the perspective of Numerical Weather Prediction (NWP) or (coupled atmosphere- ocean-land-ice) Climate Modeling, a land-surface model provides quantities as boundary conditions: • Surface sensible heat flux • Surface latent heat flux (evapotranspiration) • Upward longwave radiation (or skin temperature and surface emissivity) • Upward (reflected) shortwave radiation (or surface albedo, including snow effects) • Surface momentum exchange • Surface water budget

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 4/50 Atmospheric Energy and Water Budgets

seasonal storage

Stephens et al 2012 USGS • We must more properly represent & close the (surface) energy budget and hydrological cycle (water budget) to provide surface boundary conditions in weather and climate models as they expand their role in more fully-coupled Earth System models. • It’s a careful “bookkeeping job” for energy and water, as well as BioGeoChem cycles. Particularly importance for climate models.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 5/50 Weather & Climate: a “Seamless Suite”

• Products and models are integrated & consistent throughout time & space, as well as across forecast application & domain. Land Static vegetation, Dynamic Dynamic e.g. climatology vegetation, ecosystems, modeling or realtime e.g. plant e.g. changing 2nd WCRP Summer School on Climate Model Development example:[email protected] observations growth land cover Cachoeira Paulista – SP, Brazil, 22-31 January 2017 6/50 Land Modeling History: NWP perspective from NCEP, but similarly ignored in climate & other weather models initially • 1960s (6-Layer Primitive Equations model): land surface ignored, aside from terrain height and surface friction effects. • 1970s (Limited-Area Fine-Mesh model): land surface ignored. • Late 1980s (Nested-Grid Model): 1st land surface model (LSM): - Single layer soil slab (Deardorff “force-restore” soil model). - No explicit vegetation treatment. - Temporally fixed surface wetness factor. - Diurnal cycleAt treatedleast it’s(and (generally)diurnal ABL) with no diurnal longer: surface radiation. - Surface albedo, surface skin temperature, surface energy balance. - Snow“Land cover (but doesn’t not depth). really matter too much • Early1990…unlesss (Global there’s MRF): Oregonan unexplained State University bias.” LSM: - Multi-Landlayer soil isn’t column just (2-layers). a boundary condition! - Explicit annual cycle of vegetation effects. - Snow pack physics (snowdepth, SWE). • Mid 1990s (Meso Eta model): Noah LSM replaces force-restore. • Mid 2000s (Global Model: GFS): Noah LSM replaces OSU LSM. • Mid 2000s (Meso Model: WRF): Unified Noah LSM with NCAR. • 2010s: “Noah-MP” with NCAR Noah model development group.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 7/50 Land Models for Weather and Climate Weather versus Climate (change) considerations: • Static vegetation, vs dynamic vegetation (growth), versus dynamic ecosystems (plant succession), and biogeochemical cycles with CO2-based photo- synthesis, different crops, C3, C4, CAM vegetation. • Longer time-scales spin-up for deeper soils and groundwater, regions with “slow” hydrological cycle (especially arid, cold regions), carbon stores. • Land-use change (observed/assimilated vs model- led), e.g. harvest, fires, expansion of urban areas. • Human influences, e.g. irrigation/reservoirs. • Bookkeeping of energy, water, other biogeochemical budgets, e.g. heat content transported by rivers.

2nd WCRP Summer School on Climate Model Development • [email protected] be a seamless weather & climate connection. Cachoeira Paulista – SP, Brazil, 22-31 January 2017 8/50 Land Models for Weather and Climate

Weather Seasonal Prediction Climate Change

NCEP-NCAR Noah Noah-MP NASA Catchment GFDL LM4

NWS Nat’l Water ECMWF TESSEL UKMO JULES NCAR CLM

NOAA/ESRL RUC UW/Princeton VIC BATS SiB • A sampling –use dicates complexity and processes simulated.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 9/50 Unified NCEP-NCAR Noah Land Model

• Four soil layers (shallower near-surface). • Numerically efficient surface energy budget. • Jarvis-Stewart “big-leaf” canopy conductance with associated veg parameters. • Canopy interception. • Direct soil evaporation. • Soil hydraulics and soil parameters. • Vegetation-reduced soil thermal conductivity. • Patchy/fractional snow cover effect on sfc fluxes. • Noah for NWP & seasonal prediction, • Snowpack density and coupled with NCEP short-range NAM, snow water equivalent. medium-range GFS, seasonal CFS, and • Freeze/thaw soil physics. HWRF, uncoupled NLDAS & GLDAS, etc.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 10/50 Land Model Requirements

• To provide proper boundary conditions, land surface model (LSM) must have: - Appropriate physics to represent land-surface processes (for relevant time/spatial scales) and associated LSM model parameters. - Required atmospheric forcing to drive LSM. -Corresponding land data sets, e.g. land use/land cover (vegetation type), soil type, surface albedo, snow cover, surface roughness, etc. - Proper initial land states, analogous to initial atmospheric conditions, though land states may carry more “memory” (e.g. especially in deep soil moisture), similar to ocean SSTs & heat content. • Land data sets & initial land states may “overlap”, that is, some land data sets (assumed) static while others “evolve”, with use of many remotely-sensed data sets/products, also for atmospheric forcing, and for land model validation.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 11/50 Land: Coupled with Atmospheric Model

Momentum

Thermodynamic

Continuity

Eq. State

Land: Source/Sink Terms

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 12/50 Land Physics: Basic Prognostic Equations

Soil Moisture (Θ):

• “Richard’s Equation”; DΘ (soil water diffusivity) and KΘ (hydraulic conductivity), are nonlinear functions of soil moisture and soil type (e.g. Cosby et al 1984, van Genuchten, 1980); FΘ is a source/sink term for precipitation/evapotranspiration.

Soil Temperature (T): • CT (thermal heat capacity) and KT (soil thermal conductivity; e.g. Johansen 1975), non-linear functions of soil/type; soil ice = fct(soil type/temp./moisture).

Canopy water (Cw): • P (precipitation) increases Cw, while Ec (canopy water evaporation) decreases Cw.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 13/50 Land Physics: Flux Boundary Conditions

Latent heat flux Sensible heat flux (Evapotranspiration) from soil surface/canopy Canopy Water Emitted Evaporation Transpiration longwave

canopy water Direct Soil Evaporation snow canopy

Snowpack &

frozen soil Soil heat flux

to canopy/soil surface physics soil •Surface fluxes balanced by net radiation (Rn) = sum of incoming and outgoing solar and terrestrial

radiation, with vegetationHydrology important in energy partition between turbulent sensible (H) and latent (LE) heat fluxes, and soil heat flux (G).

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 14/50 Land Physics: Model Parameters

• Surface momentum roughness dependent on vegetation/land-use type and vegetation fraction. • Stomatal control dependent on vegetation type, direct effect on transpiration. • Depth of snow (snow water equivalent) for deep snow and assumption of maximum snow albedo is a function of vegetation type. • Heat transfer through vegetation and into the soil a function of green vegetation fraction (coverage) and leaf area index (density). • Soil thermal and hydraulic processes highly dependent on soil type (vary by orders of magnitude).

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 15/50 Atmospheric Forcing to Land Model

• Atmospheric forcing from a parent atmospheric model (analysis or reanalysis) and/or from in situ or remotely-sensed observations, where precipitation is quite important for LSMs.

Precipitation Incoming Downward Air Solar Longwave Pressure

Air Wind Specific Temperature Speed Humidity

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 16/50 Atmospheric Forcing: Precipitation • Global Land Data Assimilation System (GLDAS) used in NCEP Climate Forecast System (CFS) relies on “blended” precipitation product, function of: Satellite-estimated precipitation (CMAP) (heaviest weight in tropics where gauges sparse); Surface gauge network (heaviest in mid-latitudes); Model-based Global Data Assimilation System (high latitudes where obs are scarce). • For other applications (e.g. higher-resolution LDAS over CONUS), temporally dis-aggregate gauge data using radar precip estimates (“Stage IV)”.

CMAP (merged rain Surface gauge GDAS (model) Gauge+Radar gauge & CMORPH satellite obs.) Jesse Meng (NCEP/EMC), Pingping Xie (NCEP/CPC) 2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 17/50 Land Data Sets

May change, Changes with e.g. urban, Quasi-static changing veg desertification type/land-use

Land-Use/Vegetation Soil Type Max.-Snow Albedo (1-km, IGBP-MODIS) (1-km, STATSGO-FAO) (1-km, UAz-MODIS)

Greenness may be Actual albedo may ahead of/behind better reflect current climatology land states Jan July Jan July

Green Vegetation Fraction Snow-Free Albedo (monthly, 1/8-deg, NESDIS/AVHRR) (monthly, 1-km, Boston Univ.-MODIS) • Fixed annual/monthly/weekly climatologies, or near real-time observations; some quantities may be assimilated into LSMs, e.g. soil moisture, snow, greenness as initial land states.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 18/50 Land Data Sets: Green Vegetation Fraction (GVF) and Wildfire Effects

• Use of near realtime GVF leads to better partition between surface heating & evaporation --> impacts surface energy budget, ABL evolution, clouds/convection. Current AVHRR 5- VIIRS near- • Wildfires affect weather and year climatology realtime climate systems: (1) atmos- pheric circulations, (2) aero- sols/clouds, (3) land surface states (GVF, albedo & surface temperature, etc.)  impact on sfc energy budget, etc. Consistency with “burned” & other products, e.g GVF. Weizhong Zheng and Yihua Wu, NCEP/EMC, Marco Vargas et al, NESDIS/STAR

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 19/50 Initial Land States: Soil Moisture

• Land state initial conditions are necessary for NWP & climate models, and must be consistent with land model used in a given weather or climate model, i.e. from same cycling land model. • Land states spun up in a given NWP or climate model cannot be used directly to initialize another model without rescaling because of differing land model soil moisture climatology. • In seasonal (and longer) climate simulations, land states are cycled, and some land data set quantities may be simulated (and therefore assimilated), e.g. green vegetation fraction & leaf area index, and even land-use type (evolving ecosystems).

May Soil Moisture Climatology from 30-year NCEP Climate Forecast System Reanalysis (CFSR), spun up from Noah land model coupled with CFS.

Jesse Meng (NCEP/EMC)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 20/50 Initial Land States: Snow

• In addition to soil moisture: the land model provides surface skin temperature, soil temperature, soil ice, canopy water, and snow depth & snow water equivalent.

National Ice Air Force Weather Agency Center snow cover snow cover & depth

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 21/50 Land Data Assimilation (LDA)

• Data assimilation: observations of actual system are incorpo- rated into model state of numerical model of that system. • Minimization of a "cost function” has the effect of making sure that analysis does not drift too far away from observations and forecasts, and requires observation and model error estimates. Analysis minimizes a combination of distances: Thanks, Google!

2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School [email protected] Cachoeira Paulista15 -–19 SP, August Brazil, 2016 22-31 January 2017 22/50 Developing Land Data Assimilation Capabilities Snow and Soil Moisture Active Areas of Land DA Testing at NCEP Snow Precipitation

e.g. Snow Cover from FigureModerate 1: Snow water Res. equivalent Imaging (SWE) Figure 2: Annual average precipitation from based on Terra/MODIS and Aqua/AMSR-E. 1998 to 2009 based on TRMM satellite Spectroradiometer e.g. Global Precipitation Future observations will be provided by observations.Measurement Future observations (GPM will be) JPSS/VIIRS and (DWSS/MISMODIS. Jiarui) Dong C. Peters-Lidard et al (NASA) provided by GPM. (NCEP) Soil Moisture Terr. Water Storage Surface Water

Figure 3: NESDIS “SMOPS” daily product Figure 4: Changes in annual-average Figure 5: Current lakes and reservoirs blends soil moisture from a number of sensors, terrestrialGravity water storage Recovery (sum of groundwater, and monitored by OSTM/Jason-2. Shown are includinge.g. SMOS,Soil SMAP,Moisture ASCAT, AMSR Active-2. soil water, surface water, snow, and ice) currentSurface height variations Water relative & Oceanto 10-year Positive impact on global precip. forecasts. betweenClimate 2009 and 2010, Experiment based on GRACE average levels. Future observations Passive (SMAP) 2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School Topography (SWOT) Weizhong [email protected] (NCEP), Xiwu Zhan (NESDIS). satellite observations.(GRACE Future) observations will will be provided by SWOT. Cachoeirabe provided Paulista by15 GRACE -–19 SP, August-II. Brazil, 2016 22-31 January 2017 23/50 Remotely-sensed data sets: Land Summary (incomplete/evolving) Quantity Remotely-Sensed Assimilation Validation

Precipitation x - -

Incoming solar x - -

Downward longwave x - - Forcing

Wind, Temperature, Humidity, Pressure x - -

Albedo, emissivity monthly - - Maximum snow albedo static - - Vegetation fraction/Leaf Air Index weekly x - Vegetation type/land-use quasi-static - -

Land Data Sets Data Land Soil type static - -

Soil moisture x x x

Surface temperature (LST) x - x states

Soil temperature - - x

Snow depth, density, cover x x x Land Canopy water - - - Surface fluxes x - x BioGeoChemical Cycles (e.g. carbon) x x x Hydrology: streamflow, groundwater, water level x x x

2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School [email protected] Cachoeira Paulista15 -–19 SP, August Brazil, 2016 22-31 January 2017 24/50 Land Applications for Weather and Seasonal Climate: NOAA’s Operational Numerical Guidance Suite

Regional Waves Nearshore

Climate Forecast Ecosystem

Var WaveWatch III

- Waters EwE

Var Hurricane

- DA

System (CFS) En DA - SURGE:

3D GFS, MOM4, GFDL Sea-ice 3D SLOSH HWRF/Noah GLDAS/Noah Land, ESTOFS: Regional Bays Sea Ice Ocean (RTOFS) HYCOM ADCIRC •Great Lakes (POM) •N Gulf of Mexico NWPS: (FVCOM) Global Forecast •Columbia R.

WWIII (SELFE)

Var

-

-

System (GFS) •Chesapeake (ROMS) DA En NAM/NAM-nests

- •Tampa (ROMS) Global Spectral

D •Delaware (ROMS)

4 Hybrid

Noah Land model - NMMB VAR DA VAR

3D Noah land model Dispersion Global Ensemble Forecast HYSPLIT System (GEFS) Short-Range Ensemble Forecast 26 members 21 GFS Members Air Quality WRF-ARW & NMMB 13 members each CMAQ

North American Ensemble

Forecast System High Res Windows Rapid Refresh

VAR - WRF-ARW & NMMB DA

GEFS, Canadian Global Model 3D WRF ARW RUCLSM

North American Regional National NEMS Aerosol Space High-Res RR (HRRR)

Land Surface Data Climate Data Water Global Component VAR - Weather DA WRF ARW Assimilation System Assimil. Syst. Model (NGAC) 3D RUCLSM GFS & GOCART Noah Land Surface Model Eta-32/NARR Noah-MP ENLIL Bill Lapenta, NCEP August 2016

2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School [email protected] Cachoeira Paulista15 -–19 SP, August Brazil, 2016 22-31 January 2017 25/50 Drive LSM “Offline”: Global Land Data Assimilation System (GLDAS) • Uses Noah land model forced with Climate Forecast System (CFS) atmospheric data assimilation cycle output, “blended” precipitation (gauge, satellite & model), “semi-coupled” –daily updated land states, with Snow cycled or assimilated (IMS snow cover, AFWA depth). • 30-year land climatology: energy/water budgets (below). • 30-year re-runs to test updated forcing, land data sets, physics.

Precipitation Evaporation Runoff Soil Moisture Snow Jan (top), July (bottom) Climatology from 30-year NCEP CFS Reanalysis (Precip, Evap, Runoff [mm/day]; Soil Moisture, Snow [mm]) Rongqian Yang, Jesse Meng (NCEP/EMC)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 26/50 North American LDAS Soil Moisture Monitoring Ensemble mean total column soil moisture anomaly March 2012 – December 2013

US Mid-west Drought

California Drought

Youlong Xia (NCEP/EMC)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 27/50 Land Model Testing and Validation

• Standard validation of NWP & climate models uses near-surface observations, e.g. routine weather observations of air temp., dew point and relative humidity, 10-meter wind, along with upper-air, precipitation, etc.

• To more fully validate land models/processes, surface fluxes and soil states (soil moisture, etc) also used. • Diurnal composites (e.g. monthly/seasonal) used to assess systematic model biases (averaging out transient atmospheric conditions), and suggest land physics upgrades.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 28/50 Testing & Validation: Land and related issues

Low-level biases in winds, temperature, and humidity are influenced in part by the land surface via biases in surface fluxes exchanged with the atmospheric model, with effect on boundary- layer evolution, and influence on clouds/convection/precipitation. Improving the proper diurnal/seasonal partition of surface energy budget between sensible, latent, soil heat flux, outgoing longwave radiation, and effect on water budget, requires: • Improved vegetation physics/parameters to calculate ET. • Better soil physics/properties address surface heterogeneity. • Improved snow physics (melt/freeze, densification). • Surface-layer physics, especially nighttime/stable conditions, and interaction with the surface & atmospheric boundary layer. • Remote sensing of different initial land states, e.g. near- real-time vegetation; corresponding data assimilation of these land states, e.g. snow, soil moisture, green vegetation fraction. • Improved forcing for land models, especially precipitation and downward radiation; requires enhanced downscaling techniques.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 29/50 Testing & Validation: Simple-to-More Complex Hierarchy of Model Parameterization Development

Simulators . Simulators: test submodel parameterizations at process level, e.g. radiation-only, land-only, ... Radiation . Testbed data sets to develop, drive & validate Clouds & convection submodels: observations, models, idealized, with Microphysics “benchmarks” before adopting changes. . Submodel interactions, with benchmarks. Boundary-Layer . Full columns, with benchmarks. Surface-layer . Limited-area/3-D (convection) with benchmarks.

Land . Regional & global NWP & seasonal climate, with

Sea-ice benchmarks. . More efficient model development, community

SURFACE Ocean, Waves engagement, R2O/O2R and computer usage. Interaction Column Limited-area Regional & Global tests tests

2nd WCRP Summere.g. School several on Climate WMO Model DevelopmentWCRP/GEWEX activities, [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 NCAR/NOAA Global Model Testbed (GMTB) 30/50 Testing & Validation: Model components

• Use surface fluxes (e.g. sensible/latent heat, momentum) to evaluate land-surface physics formulations and parameters, e.g. invert formulations to infer heat and momentum exchange coefficients, and canopy conductance (below) from data sets. • Action: adjust thermal, drag, canopy parameters.

model (solid, dashed lines)

observations (symbols)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 31/50 Testing & Validation: Land Model Benchmarking

• GEWEX/Global Land/Atmos. System Study (GLASS) project. • Evaluate performance of land-surface models in the context of pre-defined metrics for a number of flux sites worldwide. • Land models should out-perform empirical models, and persistence (for NWP models), climatology (for climate models). • Identify systematic biases for model development/validation. • Extend to 2-D eval. over regional/global domains, include water. winter summer The PALS Landmodel sUrface obs Model Benchmarking Evaluation pRoject (PLUMBER) spring autumn E – Evergreen Needleleaf H B – Evergreen Broadleaf LE E P E G MD E D G D – Deciduous Broadleaf E D BE C G M – Mixed Forest G - Grassland C – Cropland B W – Woody Savanna W W S – Savanna S B RnP – Permanent Wetlands G

PALS example: CABLE (BOM/Aust.) land model, Bondville, IL, USA (cropland), 1997-2006, avg diurnal cycles.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – MartinSP, Brazil, Best 22 -(UKMO),31 January Gab2017 Abramowitz (UNSW) et al. 32/50 Testing & Validation: Column Model Testing

• GEWEX/GLASS-GASS (Global Atmospheric System Studies) Project: Diurnal land-atmosphere coupling experiment (DICE). • DICE-1: Study the interactions between the land-surface and atmospheric boundary layer, and assess feedbacks. • DICE-Over-Ice: Study the interactions between the ice/snow- surface and atmospheric boundary layer under conditions of strong stability, and assess feedbacks. • Extend to many other regions/seasons across the globe. DICE-1 DICE-Over-Ice

Southern Great Plains, USA Dome C - Antarctica (CASES-99 Experiment) Martin Best, Adrian Lock (UKMO) et al.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 33/50 Land Models in Earth Systems In a more fully-coupled Earth System, this role involves Weather & Climate connections to: • Hydrology: soil moisture & ground water/water tables, irrigation and groundwater extraction, water quality, streamflow and river discharge to oceans, drought/flood, lakes, and reservoirs/human mgmt. • Biogeochemical cycles: application to ecosystems, both terrestrial & marine, dynamic vegetation and biomass, carbon budgets, etc. • Air Quality: interaction with boundary-layer, biogenic emissions, VOC, dust/aerosols, etc. More constraints, i.e. must close energy and water budgets, and those related to BGC cycles and air/waterGet quality. the right answers for the right reasons!

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 34/50 Tiled Land Grids and Scale Dependence • Because of land surface heterogeneity, a model grid may comprise sub-atmospheric-grid-scale land “tiles”, e.g. forest, grass, crop, water, etc, O(1-4km). • Coarser-resolution atmospheric forcing to land. • Aggregate flux input to “parent” atmospheric model.

Aggregate surface “blending” height flux • When blending height is greater than atmos- pheric boundary-layer depth, cannot simply use aggregate flux. • Land tiles may be coupled with yet- higher resolution hydrology-tiles to be connected to surface tiles with different fluxes groundwater and river-

2nd WCRP Summer School on Climate Model Developmentrouting scheme. [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 35/50 Hydrology: River-routing, Groundwater

Atmospheric Ensemble mean daily streamflow anomaly Forcing Hurricane Irene and Tropical Storm Lee, 20 August – 17 September 2011

Surface flow

Superstorm Sandy, 29 October – 04 November 2012

Saturated subsurface flow Colorado Front Range Flooding, September 2013 NWS high-resolution National Water Model Groundwater 1-km land, 250m hydro

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 36/50 Lakes “It’s not ocean, so let land modelers deal with it.”

• Thousands of lakes on scale of 1-4km not resolved by SST analysis -> greatly influence surface fluxes; explicit vs subgrid. • Freshwater lake “FLake” model (Dmitrii Mironov,DWD). - Two-layer. - Atmospheric forcing inputs. - Temperature & energy budget. - Mixed-layer and thermocline. - Snow-ice module - Specified depth/turbidity. - Used in COSMO, HIRLAM, NAM (regional), and global ECMWF, CMC, UKMO. Yihua Wu (NCEP/EMC)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 37/50 Ecosystems, Water and Air Quality

SST Likelihood of Chrysaora

Nearshore Control Freshwater “RTOFS” “RTOFS” Habitat Model Gulf of Mexico Observed Salinity Salinity • Hydro-Coastal Linkage • Sea Nettle Forecasting

• Land sources of dust and aerosols, mixed through ABL.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 38/50 Human Influences/Water Management

Groundwater Extraction Irrigation Reservoirs

Land-cover change/deforestation Urban areas/model • Proper initial conditions (e.g. via remote sensing), and improved land model physics parameterizations.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 39/50 Land-Atmosphere Interaction • Land-atmosphere interaction and coupling strength remain weak links in current land-surface and atmospheric/earth-system prediction models. • Coupling strength affects surface fluxes, so important for weather and climate. • We need to understand many land & atmospheric processes and interactions, with proper representation in weather and climate models... • ... e.g. soil moisture/evaporative flux to the atmosphere (clouds/convection), hydrology/river- flow and connection to oceans, dust/suspension & surface chemistry interactions, plants/PAR, etc. • Coupling begins locally.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 40/50 Large-scale: Soil Moisture-Precipitation “Hot spots”

• Modeling study: Areas where soil moisture changes can affect rainfall. R. Koster* et al (2004), NASA/GSFC *author of The Swenson Code 2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School [email protected] Cachoeira Paulista15 -–19 SP, August Brazil, 2016 22-31 January 2017 41/50 Land-Atmosphere Interactions

Land-Atmosphere Feedbacks “Stand on 2 Legs”

• Terrestrial – When/where does soil moisture (vegetation, soil, snow, etc.) control the partitioning of net radiation into sensible and latent heat flux (and soil heat flux)? • Atmosphere – When/where do surface fluxes significantly affect boundary-layer growth, clouds and precipitation?

Paul Dirmeyer (George Mason Univ.), Joe Santanello (NASA/GSFC)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 42/50 Land-Atmosphere Feedback via Two Legs

• “GLACE” (Koster et al 2004) coupling strength for summer soil moisture to rainfall (“hot spots”) corresponds to regions where there are both of these factors: • High correlation between daily soil moisture and evapotranspiration during summer (from the Global Soil Moisture Wetness Project multi-model analysis, units are significance thresholds), and • High CAPE (from North American Regional Reanalysis, J/kg)

∆P  ∆SM g ∆ET g ∆P

Feedback path: Terrestrial leg Atmospheric leg Paul Dirmeyer et al (George Mason Univ.)

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 43/50 Local Land-Atmosphere Interactions above-ABL above-ABL dryness cloud cover stability incoming precipitation downward solar 7 longwave

1 entrainment wind boundary- layer growth 1 4 turbulence 3 3 5 2 relative humidity temperature 6 4 2 5 6 emitted * + moisture longwave sensible reflected canopy flux surface heat flux solar conductance temperature albedo

7 8 8 Local landsoil-atmosphere moisture soil heat flux soil temperature interactions are an importantradiation part of largesurface-scale layer & ABL land-surface processes feedbacks: terrestrial+positive energy feedback budget for C3 & 2 ndC4 WCRP JPLplants, Center Summer negativefor SchoolClimate on feedbackSciences Climate Summer Model for DevelopmentCAM School plants positive [email protected] and global water*negative cycle. feedback Cachoeira above Paulista optimal15 -–19 SP, Augusttemperature Brazil, 2016 22-31 January 2017 negative 44/50 ABL-top dry-air boundary-layer entrainment growth

direct surface-layer evaporation turb. exchange

surface energy transpiration canopy budget conductance

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 45/50 Near-Surface Interactions: Soil Moisture – Evapotranspiration Relationship

Caterina Tassone, Michael Ek (NCEP/EMC) Evaporative fraction versus 1.0

“coupling strength” from surface flux observations

from (“Fluxnet”): strong

• higher ef: - stronger land-atmosphere coupling for forests. - weaker land-atmosphere coupling for cropland and 0.5 grassland. • lower ef: strong coupling regardless of

vegetation type: due Coupling strength evergreen forest

to stronger surface deciduous forest heating and turbulence croplandcropland (larger ga, smaller gc). weak grasslandgrassland • Need to include effects of soil 0.0 hydraulics/thermodynamics. 0.0 0.5 Evap. Frac. 1.0

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 46/50 Land-Atmosphere Interactions: Soil moisture/Evaporation roleN oinrma liABLzed RH te&nd ecloudncy (Ek and developmentHoltslag, 2004) Relative humidity tendency controls cloud initiation. greatest RH tendency & -5 4 3

4 ABL cloud2 potential. -4

3 -3 2 -2 RH tendency with decreasing ef -1 available “normalized” 2 2 energy term tendency term 0 1 Non-evaporative term: 1 1 1 1 2 3 0 “normalized” RH tendency term n 0

e 4 0 5 dry-air ABL ABL -1 entrainment growth warming 0 -2 evaporative terms terms (ne) evaporative RH tendency with increasing ef

- -1 ABL-growth regime -2 (weaker above-ABL

non -3 stability and Dq small) -4 -2 -1 0 surface moistening regime -4 -3 (stronger above-ABL 0.0 0.2 0.4 0.6 0.8 1.0 stability and/or Dq large) evaporative fraction (ef) L 2nd WCRPJPL Center Summer for SchoolClimate on Sciences Climate Summer Model Development School [email protected] Cachoeira Paulista15 -–19 SP, August Brazil, 2016 22-31 January 2017 47

evaporative fraction (ef) Summary • Land models provide surface boundary conditions for weather and climate models; requires proper representation of land- atmosphere interaction. Land important in its own right. • For weather and climate modeling, land models must have valid (scale-aware) physics and associated parameters, proper atmospheric forcing, representative land data sets (in some cases near realtime/assimilated), initial & cycled land states. • Longer time scales require more processes (physics), and longer-term land data sets, e.g. snow, vegetation fraction. • Land model validation using (near-) surface observations, e.g. air temperature, relative humidity, wind, soil moisture, surface fluxes, etc… suggests model physics improvements; requires extensive data “mining” for processes-level studies. “Get the right answers for the right reasons.” • The role of land models is expanding in increasingly more fully- coupled Earth-System Models with connections between Weather & Climate and Hydrology, Ecosystems & Biogeo- chemical cycles (e.g. carbon), and Air and Water Quality.

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 48/50 Sampling of NCEP/EMC Land Team Partners ...requires broad community collaboration NCEP/EMC Land Team: Michael Ek, Jiarui Dong, Weizhong Zheng, Helin Wei, Jesse Meng, Youlong Xia, Rongqian Yang, Yihua Wu, Roshan Shrestha, Anil Kumar working with: Land Data Assimilation (LDA), LDA Systems (e.g. “NLDAS”): • NASA/GSFC: Christa Peters-Lidard, David Mocko et al. • NCEP/Climate Prediction Center: Kingtse Mo et al. • Princeton University: Eric Wood, et al. • Univ. Washington: Dennis Lettenmaier (now UCLA), et al. • Michigan State Univ./formerly Princeton: Lifeng Luo. Remotely-sensed Land Data Sets: • NESDIS/STAR land group: Ivan Csiszar, Xiwu Zhan (soil moisture), Bob Yu (Tskin, vegetation), Peter Romanov (snow), et al. Land Model (Physics) Development: • NCAR/RAL: Fei Chen, Mike Barlage, et al. • Nat’l H2O Model: Brian Cosgrove (OWP), Dave Gochis (NCAR), et al. • Univ. Ariz.: Xubin Zeng et al. • UT-Austin: Zong-Liang Yang et al. • WRF land working group and NOAA/ESRL land model development: Tanya Smirnova et al/NOAA/ESRL. GEWEX/GLASS, GASS projects: Land model benchmarking, land- atmosphere interaction experiments with many international partners. 2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 49/50 Uh oh! These surface fluxes don’t look so good. Ugh! Look at the hydrology in this thing! It’s leaking …you’re going to need everywhere! an atmospheric alignment to get the right interactions. …and its carbon emissions are way too high...

Climate modelers: Climate Models But I like it like this… I don’t want to have to recalibrate my driving variables (...what about my forecast metrics..!?) So, how much Well… at least several more will this cost?! funding cycles. Best to book it in for regular servicing. Thank you!

2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil, 22-31 January 2017 50/50