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Climate Impacts of Agriculture related Land Use Change in the US Jimmy Adegoke 1, Roger Peilke Sr. 2, Andrew M. Carleton 3 1 Dept. Of Geosciences, University.

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Presentation on theme: "Climate Impacts of Agriculture related Land Use Change in the US Jimmy Adegoke 1, Roger Peilke Sr. 2, Andrew M. Carleton 3 1 Dept. Of Geosciences, University."— Presentation transcript:

1 Climate Impacts of Agriculture related Land Use Change in the US Jimmy Adegoke 1, Roger Peilke Sr. 2, Andrew M. Carleton 3 1 Dept. Of Geosciences, University of Missouri-Kansas City 2 Dept. of Atmospheric Sciences, Colorado State University 3 Dept. of Geography, The Pennsylvania State University

2 Presentation Outline Cropland/Forest Impacts on Convective Cloud Development in the US Midwest : Empirical studies Agriculture-related land use change impacts on seasonal climate in the Central US: Modeling studies Crop-climate modeling Issues, challenges & questions

3 Focus of Land Surface-Climate Work: Emprical Studies Impacts of changes in US Midwest land cover parameters (e.g. land cover, surface roughness, zones of land cover transitions) on convective cloudiness ( Carleton et al., 2001 GRL Vol. 28, ) Sensitivity of the AVHRR derived Normalized Difference Vegetation Index (NDVI) and the Fractional Vegetation Cover (FVC) to growing season surface moisture conditions ( Adegoke and Carleton, 2002 JHM 4, ).

4 Land Use Changes in Illinois Jackson County [From Iverson & Risser, 1987] Lake County

5 Land Use/Land Cover Map of The Midwest Showing Sampling Locations of Convective Cloud Parameters …

6 GOES INFRA RED & VISIBLE IMAGES 11 JUNE :00 UTC

7 Stratification of Case Study Days: June-Aug Strong Flow / Weak Flow : 500 mb Vector Winds

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12 Average Cloud Top Brightness Temperature: MI & MO

13 Thermodynamic indices for selected weak flow days: 12Z Radiosonde Data: MI vs MO CCL (mb) CCL-EL (mb) K-Index (mb) Tc ( o C)Mean Tv ( o C) Mean T-Tv ( o C) RH o / o Water Content Michigan Missouri

14 Sensible and Latent Heating of the Atmosphere Required for Initiation of Convective Clouds vs. Bowen Ratio [Rabin et al., 1990]

15 Summary of Cloud Research Findings Analyses of Visible and IR GOES cloud data for contrasting circulation regimes indicate some cloud-land cover associations across major crop-forest boundaries. Land cover boundary zones are shown to be favored areas for enhanced cloud development under moderate mid-tropospheric (< 30 m/s) flow conditions. The boundary zones tend to behave like regions of differential vertical circulations (i.e., NCMCs)

16 Focus of Land Surface-Climate Work: Modeling Studies Improving the representation of land surface heterogeneity (land cover; soil moisture; soil type) in the Colorado State University Regional Atmospheric Modeling System (RAMS) ( Adegoke et al. 2003; Strack et al. 2003; Rozoff et al., 2003 ) Developing protocols for a more realistic description of seasonally and interannually varying vegetation cover and growth rates in regional climate models ( Adegoke et al. 2004; Eastman et al. 2001; Lixin Li et al, 2002 ).

17 Lessons Learned Realistic representation of spatial heterogeneity of land surface parameters improves model simulation of regional-scale effects of agriculture-related land use changes on climate and terrestrial biophysical processes. Key Parameters: - Land Cover - Soil Moisture - LAI - Soil Type - Soil Temperature

18 Soil Moisture and Surface Characteristics: ETA-32 Soil moisture pattern, which evolves from the continuous EDAS, is used to initialize all Eta model forecasts – Improved depiction of soil moisture leads to better simulation of the surface processes – Multiple soil layers better depicts drying and evaporation cycles

19 Soil Moisture Impacts Over High Plains, if model soil moisture is low – Forecast CAPE too low, about half the observed CAPE – Forecast of instability insufficient

20 Soil Moisture Simulation with Different Soil (a); Matching Soil (b) (a) (b) LDAS Evaluation Team: Alan Robock et al, 2004

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22 Recent Improvements in RAMS-LEAF2 1. Protocols for ingesting variable soil moisture 2. Incorporation of high-resolution land cover data (30 m) from the USGS NLCD database 3. Specification of variable soil type from the FAO soil type database 4. Protocols for ingesting NDVI and derivation of LAI from NDVI 5. RAMS-Century coupling for explicit modeling of the seasonal evolution of vegetation in the simulation of seasonal climate.

23 Map of U.S. High Plains Aquifer

24 Acreage of Rain fed & Irrigated Corn Farming in Nebraska ( )

25 Nebraska Irrigation Modeling Project Complex changes in the lower atmosphere (PBL) radiation budget can result from large-scale land use changes of this magnitude (e.g., vapor flux CAPE) This study was designed to evaluate the changes in the summertime surface energy budget & convective rainfall parameters due to irrigation in Nebraska using RAMS.

26 RAMS Modeling Domain Coarse Grid: 40 km ; Fine Grid:10 km; Domain Height: 20km

27 a) Kuchler Potential Vegetation b) OGE – Dry Run c) OGE + Current Irrigation – Control Run (a)(b)(c)

28 Summary of Model Results Significant inner domain area-averaged difference between the Control and Dry runs: - 36% increase in surface latent heat flux - 15% decrease in surface sensible heat flux - 28% increase in water vapor flux at 500m o C elevation in dew point temperature o C decrease in near surface temperature Greater differences observed between the Control and Natural Vegetation runs e.g., - Near ground temperature was 3.3 o C warmer & surface sensible heat 25% higher in the Natural run. [ Adegoke et al., 2003 Monthly Weather Review 131(3), ]

29 Satellite-derived Leaf Area Index Derived from AVHRR 10- day composite NDVI NDVI LAI following Sellers et al. (1996) and Nemani et al. (1996) Average JJA NDVI for Central U.S. Derived LAI for Central U.S. in dry (1988), average(1989), and wet (1993) years.

30 Comparison of LAI Forcing STRONG DIFFERENCES Magnitude of LAI Heterogeneity of LAI SOME DIFFERENCE Seasonality of LAI Default LAI in Inner (50 km) GridNDVI-derived LAI in Inner (50 km) Grid

31 Good Agreement between Model Predictions & Observations e.g. Domain-average maximum and minimum air temperature and precipitation for inner grid during 1989 (selected as an average year) for the run with NDVI- derived LAI

32 Runs Broadly Agree With Observations e.g. Distribution of maximum and minimum temperature and precipitation for inner grid for the run with NDVI-derived LAI January-March 1989June-August 1989

33 Physical Mechanism for Precipitation Increase Lower domain- averaged LAI allows more solar radiation to reach the surface, increasing CAPE Spatial variability in LAI triggers mesoscale circulations.

34 ClimRAMS Coupled with CENTURY

35 Coupling Strategy and Design Differences in spatial and temporal resolutions: RAMS: 3-D, CENTURY: 1-D time step: minute vs. day Internet Stream Socket Client/Server mechanism Both atmospheric forcings and biospheric parameters are prognostic variables

36 LAI response of CENTURY is different after harvest when run in coupled mode. The coupled model gives a response in modeled precipitation. Coupled Model Captures 2-way Feedbacks Default Coupled

37 Coupled Model Simulated Climate

38 Using AVHRR-derived LAI Using RAMS-CENTURY LAI Similar Impact of Modeled/AVHRR LAI

39 Both satellite-derived and model-calculated LAI produce a significant impact on the modeled seasonal climate In both cases, the climate is cooler and produces more precipitation relative to using RAMS default LAI The effect of heterogeneity in LAI appears to be the dominant factor in producing these differences Including realistic description of heterogeneous vegetation phenology influences the prediction of seasonal climate. Summary and Conclusions

40 Looking Ahead The challenge: Fully coupled crop-climate model capable of investigating the 2-way interactions of crop- climate system under a wide range of conditions. Must include feedbacks of crop growth on surface climate Will require much stronger cross-disciplinary interaction/collaboration between Agriculture and Atmospheric Sciences

41 Discussion Questions Crop models tend to operate at the field/plot spatial scale while climate models typically have horizontal resolutions of a few km to 100~200 km. Addressing this spatial scale disparity is not trivial. Are there additional local terrain and surface/vegetation characteristics that should to be considered in crop-climate simulations that may not currently reflected in climate models? Which crop growth parameterization issues: Century vs CERES-maize model vs General Large Area Model for annual crops (GLAM)

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