Transcript ESS632_Final_Project_Fengx - National Space Science and

Oil Palm Plantation as Biomass Energy Sources
over Maritime Southeast Asia and Their
Impacts on Regional Climate
Nan Feng
Department of Atmospheric Sciences, University of Alabama in Huntsville,
Huntsville AL, USA
[email protected]
ESS632 Final Project
30th November 2015
Outlines
Introduction & Motivation
Datasets and Methodology
Results
Conclusion
Introduction & Motivation
Oil Palm Plantation, is a basic source of income for many farmers in
Southeast Asia. It is mainly used as the world’s most consumed plant
oil, and also increasing demand as biodiesel energy.
Background: Palm oil used as biofuel energy
Palm oil is more efficient and with much
lower price than other biofuel sources
Significant changes over the last decades,
peat swamp forest to oil palm plantation
However, the rise of palm oil comes at a heavy cost. Over the past 20 years, forest
destruction for the expansion of the palm oil market has continued relatively
unchecked. Governments, such as Indonesia, have done little to stop the conversion
of forests to palm plantations.
About 62% of the world’s tropical Peatswamp forests are in SE Asia, which is one of the most
important carbon storage place.
Future expansion of palm oil plantation
• The clearing process for agriculture used land is still going, and will keep
going like this if the government and local residents has been noticed that
their climate and environment will be permanently changed.
Background: Regional Climate and Environment Change in
Southeast Asia due to Land cover and Land-use Changes
Biogeochemical Processes
Background: Regional Climate Change in Southeast Asia
due to Land cover and Land-use Changes
Biogeophysical Processes
Pielke et al., 2007
Land Use and Land Cover (LULC) change has a measureable and equal
significant impact on climate at multiple geographic scales [Pielke et
al., 2005; Mahmood et al., 2010, 2014; IPCC 2014a].
Objective:
• The intent of study is to quantitatively evaluate
anthropogenic land use change impacts on climate over
SE Asia through applications of satellite data, in-situ
observations and numerical models.
Questions to be answered:
• To what extent does the land cover transition from peat
swamp forest to oil palm plantation over southeast Asia
impact the surface energy budget, boundary layer
development, cloud formation and precipitations?
Datasets
Study Region:
Study Period:
Satellite datasets:
Model Used :
Other datasets:
SE Asia (15 S – 35 N , 75 – 155 E)
August, 2009
CRISP MODIS land product
MODIS cloud products (MOD06)
MODIS land products (MOD11A1, MOD13A2)
MTSAT cloud product
TRMM precipitation (3B42_v7)
Cloudsat product (2GEOPROF)
Weather Research Forecasting Version 3.6
modeling system (WRF-ARW)
Initialized Meteorological fields from
NCEP GFS
Temperatures, winds, and Precipitations from
NCDC ASOS Network
Datasets: CRISP MODIS LAND Product
LULC change
Area km2(%)
LULC
changes
Deforestation 1.6M (-5.5%)
Sarawak,
Replantation Malaysia
2.1M (7.0%)
•Bare/low
Deforestation
land 0.3M (0.9%)
open
(Peatswamp +
Crop/grass/w 0.8M (-2.5%)
Montane Forest)
ooden land
2 (16.5%)
1710
km
Urbanization 28.7k (0.1%)
• Replantation
(Oil Palm Plantation)
1037 km2 (9.6%)
Methodology
1. Nested grid simulations based on
Weather Research Forecasting Version
3.6 modelling system (WRFV3.6) over
the central region of the Sarawak coast
for 2000 and 2010 LULC scenarios (From
CRISP MODIS), both assuming August
2009 atmospheric conditions.
2. All of the experiments utilized in this
study use a hierachy of four nested
grids, with the outmost grid of 64km
grid spacing covering a domain that
includes a substantial portion of
Southeast Asia.
3. Atmosphere initialized every 24 hours,
but soil conditions are propagated
between cycles
Methodology: Land cover representation improved
Results
• Evaluation of the WRF Simulations
– Surface Temperature, Dew Point, and Wind
Comparisons
– Rainfall Comparisons
• Land Use Change Impacts on Diurnal Temperature
Range
• Sensible Heat (SH) and Latent Heat Flux (HFX)
• Wind speed
• Cloud Formation
• Precipitation Differences
Validation: Surface Temperature, Dew Point, and Wind Comparisons
2
Error = (Simulations− Observations)/N
𝑅𝑀𝑆𝐸 =
(𝑆𝑖𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑠 − 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠)2
𝑁
Validation: Rainfall Comparisons
The bias and RMSE for modulated simulated rainfall compared to ground-based
rainfall observations are compared above, while most points fall into ±10% error
lines. Inter-comparison of TRMM rainfall estimates and ground observations show
an RMSE of 18.11 mm and a bias of -5.0 mm.
Results: Monthly Averaged 2 meter Diurnal Temperature Range (K)
- 0.3K
Results: Monthly Averaged Sensible Heat (Wm-2)
+1.8W/m2
Results: Monthly Averaged Latent Heat Flux (Wm-2)
-7.6 W/m2
Results: Monthly Averaged Wind Pattern (m/s)
+0.2 m/s
• Tropical deforestation leads to roughness length 
• Further results in apparent increase of low level wind speed.
• Meso-scale wind circulation are particularly sensitive to LULC
change, more obvious in the transition area.
Results: Monthly Cloud total water path (g/m2)
-12.0g/m2
• Apparently decreasing of cloud total water path due to LULC change
• Convective clouds tend to develop over tropical forested area.
• Consistent with previous studies [e.g. Takahashi et al., 2010;
Fairman et al., 2011; Nair et al., 2011]
Results: Monthly Averaged Rainfall (mm/month)
• Decrease in monthly averaged precipitations (6.7 mm/month)
• Tropical deforestation may result in up to 2.0 mm per day reduction
in rain fall over land area.
• Decreasing of Precipitation is coincide with regions of reduced CWP
Conclusion
Numerical model projects significant changes in
non radiative transfer fluxes, cloud formations,
boundary layer development and precipitation
pattern due to observed LULC changes from peat
swamp forest to oil palm plantation over the last
decade
Not only the right land classification, it’s also
important to get the parameterizations right to be
better matched with measurements (e.g.
satellite).
Conclusion
LULC change studies for all scales should be incorporated
into developing global climate model that addressing
impacts on energy balance, atmospheric circulation,
hydrological cycle.
 Palm oil is bad not because of what it is, but because of
where it is. Southeast Asian Forests are some of the most
naturally diverse and carbon rich environments on
earth….best left alone.
Until a truly sustainable market of Palm Oil (for food & fuel)
is established, the fate of Southeast Asian forests hangs in a
delicate balance.
Acknowledgements
• CRISP MODIS Land group, Singapore
• NASA Langley Research Center (MTSAT Cloud
product, Minnis et al. 2008)
• National Centers For Environmental Prediction
Global Forecast System (NECP GFS)
• MODIS team
• ARW WRF system handled by Mesoscale and
Microscale Meteorology (MMM) Laboratory of
NCAR
Questions?
Backup slides
Backup slide :Table 1. WRF model Configuration for all of the experiments
Configuration
NX  NY
X/Y (km)
NZ
T (s)
Grid 1
6141
64
56
240
Grid 2
6565
16
56
240
Grid 3
8181
4
56
240
Grid 4
105105
1
56
240
Center latitude/longitude
2.7N,111.7E
2.7N,111.7E
2.7N,111.7E
2.7N,111.7E
Dudhia scheme
Dudhia scheme
Dudhia scheme
Dudhia scheme
Radiation
parameterization
Boundary conditions
cumulus parameterization
Minutes btw cumulus
physics calls
pressure top used in the
model
surface layer
land-surface option
Microphysics
Soil levels
Vegetation patches
Kain-Fritsch
scheme
20
YSU scheme
Kain-Fritsch
New Grel
New Grel
scheme
Parameterization Parameterization
4
2
1
5000 Pa
4
1
MM5 Monin-Obukhov scheme
unified Noah land-surface model
New Thompson et al. scheme
4
4
1
1
4
1
Diurnal variations of a) Albedo b) roughness length c) soil moistures d) Leaf area
index e) Vegetation Fraction f) PBLH g) LST and 2 meter Temperature (dashed) h)
Sensible Heat i) Latent Heat for Peatswamp evergreen forest (green), deforested
lowland open (red) and Agriculture land (brown) based on model simulations
Validation: Comparisons of Cloud Liquid Water Path
Between WRF, MODIS and MTSAT
Validation: Day time cloud frequency comparisons
• Definition of
Prediction
evaluation [Fairman
et al., 2011]
Prediction (Land)
%
Accuracy
52.7%
Overestimation 12.8%
Underestimation 34.5%