Research on Integrated Earth System Modeling at Global

Research on Integrated Earth System Modeling at Global and Regional Scales L. Ruby Leung Pacific Northwest National Laboratory, Richland, WA 2 nd RASM Workshop Monterey, CA, May 15 – 17, 2012 1

The needs for model coupling and new development Understand the role of biosphere-atmosphere feedbacks on droughts in the southwestern U. S. Asses the impacts of climate change in the southeastern U. S. (e. g. , hurricanes) Impacts of land-atmosphere (Amazon) and atmosphere-ocean (Atlantic) interactions on the tropical Atlantic biases Develop an integrated model to represent human -earth system interactions for modeling and analysis of climate change mitigation and adaptation, with a focus on the nexus of energy, water, and land use 2

Regional Earth System Model (RESM) A regional earth system model is being developed using WRF, CLM, and ROMS, following the flux coupling approach used in CESM Global CESM Consistent representations of land processes at global and regional scales CAM Atmospheric conditions Flux Coupler Surface fluxes POP Regional Atmosphere and ocean boundary conditions RESM WRF Integrate human systems in CLM Atmospheric conditions Flux Coupler Surface fluxes ROMS 3 Flexibility to model land processes using resolution or grid different from the atmospheric model Facilitate air-sea coupling at regional scale

Model development Developed global high resolution (0. 05 o) input data for CLM based on MODIS Implemented the VIC surface/subsurface runoff and groundwater parameterizations to CLM Tested grid based vs subbasin based approaches Developed a new river routing model for CLM for both grid based and subbasin based approaches (including global input data at 6 different resolutions) Developing a water management model for CLM Adding subgrid elevation classification in CLM Applied UQ to understand model sensitivity to hydrologic parameters Developing WRF-ROMS coupling through CPL 7 4

A 0. 05 -degree input dataset for CLM 5

Comparison of new and old CLM input data Bare soil Trees Shrubs Grass Crop 6

Introducing VIC soil hydrology to CLM Infiltration excess runoff Surface- and groundwater interactions Saturation excess runoff ARNO baseflow curve Hydraulic redistribution Interactions of water movement between the root system and soil porous media 7

Dynamic representation of surface and groundwater interactions Change of soil moisture Diffusion term Change of water table depth Liang et al. , JGR, 2003 8 Change of total soil moisture in the unsaturated zone Drainage term qs porosity ne(t) effective porosity Net water recharge to the groundwater body

Implementation of VICGROUND to CLM A runtime option activated through the namelist 9

Simulated water budget at Tonzi Ranch 10

Global testing of CLMVIC CLM 4 -SP Forcing: Qian et al. 2004 Land cover: current (i. e. , 2000) Simulation period: 1995 -2004 Resolution: standard one-degree (i. e. , 0. 9 x 1. 25) CLM-CN Forcing: CRU-NCEP Land cover: potential vegetation (pre-industrial) Simulation period: 1800 -1900 (by randomizing 19011930) Resolution: 0. 5 -degree grid 11

CLM 4 -SP: Summer LH, 1995 -2004 CLM 4 VIC – CLM 4 12 CLM 4 VIC – CLM 4, global mean

CLM 4 -CN: Summer LH CLM 4 CN CLM 4 VICCN – CLM 4 CN 13 CLM 4 VICCN – CLM 4 CN global mean, stabilized

Motivation for a new runoff routing model To provide more accurate freshwater flux to the ocean from subdaily to daily time scales To provide a linkage between the human (e. g. , surface water withdrawal, reservoir operation) and natural systems For transport of nutrients and sediments Features Consistent process representation across various scales (global, regional, local) Easy to be coupled with water management model Easy to be coupled with other fluxes 14

River Transport Model (RTM) in CLM 4. 0 Study area divided into cells Flow direction is determined by D 8 algorithm Cell-to-cell routing with a linear advection model Limitations Over-simplification of river network Over-simplification of physical processes Global constant channel velocity (0. 35 m/s) No account for sub-grid heterogeneity 15

Model for Scale-Adaptive River Transport (MOSART) Grid-based approach 16 Subbasin-based approach This hierarchical dominant river tracing method preserves the baseline high resolution hydrography (flow direction, flow length, upstream drainage area) at any coarse resolution (Wu et al. 2011) Subbasin representation preserves the natural boundaries of runoff accumulation and river system organization

Model for Scale-Adaptive River Transport (MOSART) Grid-based approach Subbasin-based approach Conceptualized network Hillslope routing Sub-network routing Main channel routing 17 Hillslope routing to account for event dynamics and impacts of overland flow on soil erosion, nutrient loading, etc. Sub-network routing: scale adaptive across different resolutions to reduce scale dependence Main channel routing: explicit estimation of in-stream status (velocity, water depth, etc. )

Inputs and Parameters Daily runoff generation from UW VIC at 1/16 o resolution for the Columbia River Basin Spatial delineation and network based on Hydro. SHEDS DRT algorithm for grid-based representation 1/16, 1/8, ¼ and ½ degree resolutions (available globally) Arc. SWAT package for subbasin-based representation (average size ~109 km 2) Manning’s roughness for hillslope and channel routing set to 0. 4 and 0. 05, respectively Evaluate against monthly naturalized streamflow data at selected major stations 18

Improved streamflow simulations NS coefficient for monthly mean streamflow – grid based representation 1 Q_RTM(1/2) 0. 9 0. 8 Q_RTM(1/4) 0. 7 Q_RTM(1/8) 0. 6 Q_RTM(1/16) 0. 5 Q_MOSART(1/2) 0. 4 Q_MOSART(1/4) 0. 3 0. 2 Q_MOSART(1/8) 0. 1 Q_MOSART(1/16) 0 DALLE ICEHA PRIRA CHIEF BROWN WANET CORRA ARROW Q_VIC(1/16) NS coefficient for monthly mean streamflow – subbasin based representation 1 0. 9 0. 8 RTM(1/2) 0. 7 0. 6 MOSART_grid(1/8) 0. 5 0. 4 MOSART_subbasin 0. 3 0. 2 VIC(1/16) 0. 1 0 19 DALLE ICEHA PRIRA Large drainage area CHIEF BROWN WANET CORRA ARROW Small drainage area

Water Resource Management Model: Conceptual Design For full coupling in an earth system model: Assume no knowledge of future inflow Use generic operating rules Two components: Regulation module: extraction of water at the reservoir Storage: stores water over extended period of time Regulation: Follows monthly operating rules for flood control, environmental flow, irrigation and hydropower Constrained extraction: Daily partitioning of reservoir releases for irrigation water supply, other consumptive uses and environmental constraints. It includes the distribution across demanding units. Local surface water extraction module: extracts water at the unit Hillslope surface runoff: represent irrigation retention ponds Unit main stem if unpounded by an upstream reservoir 20

CLM-MOSART-WRM coupling Routing + reservoir model (T- Δt ) CLM (t) Loop over PFTs Aggregated demand (t- Δt) Local surface water (t- Δt) contribution to irrigation demand (t). Remaining demand? NO Irrigated fraction found YES Need irrigation NO YES Aggregated supply (t) NO Extraction from main stem if not impounded. Remaining demand? YES Updated ET, runoff, baseflow, irrigation demand Extraction from reservoir release to complement the local supply End of loop Generated runoff, Agg. irrigation demand CLM Routing model Agg. irrigation supply 21 Natural flow in each units; irrigation demand PFTs: vegetation types WRM Regulated flow; Irrigation supply at each unit

Data Preprocessing Create a “unit-reservoir dependency database”: - Local approach: independent tributaries, elevation constraint, constrained distance-based buffer - Global approach: elevation constraint and distance-based buffer Distribute the demand across the reservoir based on the dependency database and maximum storage capacity of each dependent dam 22

Downscaling CCSM Simulations WRF-CLM is being used to downscale CMIP 5 CCSM historical, RCP 4. 5, and RCP 8. 5 simulations from 1975 - 2100 23

Uncertainty quantification framework 24

Ranks of significance of input parameters over 10 Flux Tower Sites Larger sensitivity to parameters of subsurface processes 25

Effects of Barrier Layers on TC Intensification Balaguru et al. 2012 PNAS (in revision) 26

TC intensification rate is higher by 20% for TC that passes over BL than over non-BL 27