Improving Land-surface representation can enhance convection and rainfall prediction over the Indian Monsoon Region Dev Niyogi climate@purdue.edu niyogi@gmail.com Landsurface.org Also visiting Professor IIT Bombay (2015-2017), Visitor IITM Pune (2017) Additional collaborations with IIT BBS (UC Mohanty), IISc (PP Mujumdar), SAC (Chandra Kishtawal), JNU (AP Dimri) Work based on NSF CAREER, Monsoon Mission.
Paul Schmid and Dev Niyogi, 2017. "Modeling Urban Precipitation Modification by Spatially Heterogeneous Aerosols.“ in press, Journal of Applied Met. And Clim (JAMC-D-16-0320)
• Model results significantly improved for range of precipitation rates/ types with scale aware Cumulus Parameterization and Aerosol Considerations. • Results sensitive to (a) microphysics option and (b) initial conditions as well as boundary layer feedback.
Land cover change due to Sources: WHRC and Newslanc.com agriculture intensification and Urbanization is the new global change underway . Land feedback is causing significant, and detectable, changes in weather and climate through http://webpages.scu.edu/ftp/jready/family_urbanizationand modernization.html temperature /rainfall
Land effects are expected to be largest over India Monsoon region POPULATION DENSITY MAP FROM FAO
Land Atmosphere Coupling globally dominant over Indian Region (GLACE study; Koster et al. Science)
Why Does Land Surface matter? Does land surface / heterogeneity really matter or is it a subgrid feature? (Hydrological studies suggest e.g. 10% of grid cases detectable change in response)
Land Surface: Bottom Boundary Where it all happens (land modeler’s perspective) Courtesy Mike Ek (NOAA)
Slide courtesy: Dennis Baldocchi, UCB
Land Surface Model Development • moving beyond energy and water flux source to the atmosphere • focus on a more process-based approaches • can now produce detailed surface information – Vegetation temperature, Soil layer temperature and moisture – Snow depth and water, Vegetation, including crop growth – Upper soil – aquifer interactions • important for forecasts of all time scales. Why? – Memory – significant sources exist in soil (water and energy), snow and vegetation Our goal is to improve seasonal forecasts through the inclusion of a better land model physics representation for the atmospheric model.
Figure from A. Pitman (2003)
Noah-MP: a community land model
• Computational power and process understanding finer grid spacings more realism In land representation • Corollary- Finer the grid spacing, more would be the importance (and impact) of realistic land representation.
Model Uncertainty: Land Surface Model Structure Noah LSM in NOAA Eta, NAM, GFS, CFS, MM5 and WRF Models and the LDAS/GLDAS (Pan and Mahrt 1987, Chen and Dudhia 2001, Ek et al., 2003) Noah-MP LSM in WRF and NOAA CFS (Yang et al., 2011; Niu et al., 2011, Barlage et al. 2014)) “Reality” Noah Noah-MP Multiple surface temperatures and distinct canopy T can (x,y,z) Single surface temperature T leaf (x,y,z) T leaf T can T skin T bc (x,y,z) T bc T g T g (x,y) 15
Detecting land feedbacks from observations and model studies…..
Urbanization and land use change leads to regional temperature changes (warming= Urban Heat Island) Average Temperatures in July for Urban & Rural Areas 90 85 80 75 70 65 La Porte Jul 01 Avg Temp Midway Jul 01 Avg Temp 60 5 10 15 20 25 30
ATLANTA UHI http://earthobservatory.nasa.gov/Newsroom/N ewImages/images.php3?img_id=17489 Kishtawal, Niyogi, Pielke, Shepherd, IJOC 2010
Observed urbanization and agriculture impacts over Asia region 0.05 C/ decade ‘ observed ’ 0.34C cooling during growing season due to agricultural ‘ green revolution ’ warming impact of urbanization over China (Liming Zhou et al. in India (Roy et al. 2007 JGR) 2004 PNAS) Scales of landscape interactions– particularly agriculture and urbanization – are becoming significant in affecting climate via feedbacks and more importantly possibly detectable teleconnections
Land feedbacks have an even more profound impact on rainfall (and associated precursors) Pielke Sr., R.A., G. Marland, R.A. Betts, T.N. Chase, J.L. Eastman, J.O. Niles, D. Niyogi , and S. Running, 2002, The influence of land-use change and landscape dynamics on the climate system: Relevance to climate change policy beyond the radiative effect of greenhouse gases. Phil. Trans. Royal Soc. (London) A. 360 , 1705-1719.
Physical Changes - Deforestation Feedbacks - Replace/transform natural landscape - Energy Balance Changes - Urbanization Image: D. Baldocchi - Net Radiation and - Irrigation Partitioning Changes - Boundary Layer Moisture - Harvesting changes - Intensification - Surface temperature Effects/Impacts changes - Roughness change -Basinscale -Albedo change Hydrological - Precipitation changes - CO2 changes Pielke, R A., A Pitman, D Niyogi, R (storage/emission) Mahmood, C McAlpine, et al. "Land - snow cover use/land cover changes and climate: modeling analysis and observational evidence." Wiley Interdisciplinary Reviews: Teleconnections Climate Change 2, no. 6 (2011): 828-850.
Change in Temperature and Moisture Greater trapping of Infrared radiation (Warmer air holds more moisture) Increased CAPE, Stronger thermals, Modified regional convergence Modified location, intensity, duration of Rainfall Urban modification of Surface energy and Radiation Balance
Urbanization Impacts Scale Beyond the Surface (temperature)
30 yr rainfall climatology shows the Urban – Rural impact Niyogi et al. 2011 JAMC
The enhanced convection and rainfall is simulated only when urban heterogeneity/ flux boundary exists 0300 UTC 0100 UTC 0200 UTC CONTROL NOURBAN
Diagnosis of the land heterogeneity rainfall anomaly (M. Lei and D. Niyogi, 2012– extended study by P. Schmid and D. Niyogi GRL 2013- “ RAIL ” method) • Combination of Triple Interaction Term (F123) – Thermal Properties – (Albedo) – Surface Roughness – (z 0 ) – Landscape size – (sprawl) – Flux gradients create mesoscale convergence / divergence due to land heterogeneities
Example - Cloud convection – land surface feedback • Southwest Australia, approximately 13 million hectares of native vegetation cleared for agriculture • A 750km vermin proof fence demarcates the boundary between cleared and pristine areas • 20% reduction in precip over agricultural areas • Ray et al. 2003 • US Nair, UAH
Summary from multiple studies and reviews • Land surface feedback and heterogeneity has a significant impact on the timing, location, intensity, and magnitude of regional convection and rainfall – Tremendous improvements in satellite capabilities and physical parameterizations, land models need to keep up the pace to benefit from them.
Effect of Land Surface Representation on Convection and Precipitation simulation ( Holt T., D. Niyogi , F. Chen, M. A. LeMone, K. Manning, A. L. Qureshi*, 2006, Effect of Land - Atmosphere Interactions on the IHOP 24-25 May 2002 Convection Case, Monthly Weather Review, 134, 113 – 133) 00 UTC 24 May – 12 UTC 25 May 2002 Nest 2 (4-km) LSM impact in coupled model precipitation forecast (SLAB versus Noah LSM and land data assimilation) Radar reflectivity (dbZ) valid 00 UTC 25 May 2002 24-h forecast SLAB Noah LSM Observed 2-km Mosaic 0 20 40 60 80
LSM representation impact not just significant for great plains but also for coastal regions
LULC impact important not just for calm conditions – but also important for active synoptic conditions (e.g. TS Alison 2001)
Ensemble land surface response on tropical storm rains / track Black – NHC best track observations Red – Noah LSM (dynamic soil moisture/temperature) Yellow -Simple Slab land model (constant soil moisture) a) c)
Is the land surface feedback significant for Indian monsoon region where synoptic weather patterns and oceans, are known to be important? Perspectives on the impact of LCLUC on the Indian monsoon region Hydroclimate Dev Niyogi, Subashini Subramanian, U.C. Mohanty, K. Osuri C. M. Kishtawal, Subimal Ghosh, U. S. Nair, M. Ek, and M. Rajeevan Book chapter for NASA LCLUC Volume Image Source: S. Gadgil and wikipedia
• Some perspectives/ assumptions.. • Monsoon is not a giant sea breeze with rain band • Myriad of coherent clusters lead to seasonal and interseasonal rainfall variability and amounts (and eventually trends) – E.g. thunderstorms and monsoon depressions contribute to about third to half of heavy rains – significant spatio temporal variability in actual rains that gets blended in grid analyses • Challenge is to capture these organized clusters and coherent feedbacks
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