Montane cloud forests are characterized by forests which are frequently immersed in clouds or fog. Previous studies suggest that over complex topography the impact of global warming on montane cloud forests could be more serious. It is necessary to understand the orographic effects on fog formation processes to further discuss the impact of climate change. In this study, we validate that the ceilometer is not only reliable to detect fog occurrence but also provides more information about low-level cloud base evolutions. In a fog event on Jan. 7th, 2016, the low-level cloud base lowering is observed before the fog formation, which is also associated with the valley winds at Xitou valley of Taiwan. To understand the orographic effects on moisture convergence and the processes of fog formation, we performed a series of idealized numerical experiments by TaiwanVVM. The simulations demonstrate that the valley winds induced by the heating difference on topography converge the low-level moisture and initiate the convection in the valley. The convection limited by capping inversion keeps the moisture in the valley.
Meanwhile, the formation of the low-level clouds by convections also reduce the insolation heating on the valley surface. The moistening in the valley results in the low-level cloud base lowering, which and the declining of surface temperature both promote
the fog formation. By prescribing four different synoptic inversion strength in the simulations, we also found that the duration of the fog is controlled by the capping inversion strength. The results show that under weak inversion strength the moisture convergence by orographic effects could be transported upward into the free atmosphere so that the fog duration in the valley is reduced. We conclude that orographic effects on low-level moisture convergence are the essential processes to supply moisture in the Xitou valley, and the capping inversion promotes the fog formation by limiting the development of convections and confine moisture in the valley (Fig. 16).
The TaiwanVVM framework applied in this study allows us to use initial conditions as representatives of synoptic forcing to simulate the boundary layer eddy development and evaluate its interactions with complex orography. To assess the potential impact of global warming to local orographic fog at Xitou, we need to apply the current climate and future projection scenarios as the synoptic forcing to our framework. With enough semi-realistic experiments, the local responses of the current climate could be evaluated by the statistical characteristics of simulation results. The potential impact of the future climate
can therefore be examined through the changes of the local upslope fog. The following work will focus on applying the semi-realistic simulation strategy to understand the interaction between the orographic effects and future climate change scenarios.
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Reference
1. Bruijnzeel, L. A., 2001: Hydrology of tropical montane cloud forests: A Reassessment. Land Use and Water Resources Research, 1, 1.1.
2. Central Weather Bureau, 2018: Climate Monitoring 2017 Annual Report, Central Weather Bureau, Taipei, Taiwan, 43 pp., Chinese, available at:
https://www.cwb.gov.tw/V7/service/notice/download/publish_20181011155441.pd f.
3. Chen, F., and Coauthors, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. Journal of Geophysical Research:
Atmospheres, 101, 7251–7268, doi:10.1029/95JD02165.
4. Chien, M.-H., and C.-M. Wu, 2016: Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM):
VVM PARTIAL STEP. Journal of Advances in Modeling Earth Systems, 8, 212–
223, doi:10.1002/2015MS000514.
5. Clyne, J., and M. Rast, 2005: A prototype discovery environment for analyzing and visualizing terascale turbulent fluid flow simulations. R.F. Erbacher, J.C. Roberts, M.T. Grohn, and K. Borner, Eds., Electronic Imaging 2005, San Jose, CA, 284 http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.586032.
6. Clyne, J., P. Mininni, A. Norton, and M. Rast, 2007: Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New J. Phys., 9, 301–301, doi:10.1088/1367-2630/9/8/301.
7. Dawson, T. E., 1998: Fog in the California redwood forest: ecosystem inputs and use by plants. Oecologia, 117, 476–485, doi:10.1007/s004420050683.
8. De Wekker, S. F. J., and M. Kossmann, 2015: Convective Boundary Layer Heights Over Mountainous Terrain—A Review of Concepts. Frontiers in Earth Science, 3, doi:10.3389/feart.2015.00077.
9. Deardorff, J. W., 1972: Parameterization of the Planetary Boundary layer for Use in General Circulation Models. Mon. Wea. Rev., 100, 93–106, doi:10.1175/1520-0493(1972)100<0093:POTPBL>2.3.CO;2.
10. Gultepe, I., and Coauthors, 2007: Fog Research: A Review of Past Achievements and Future Perspectives. Pure appl. geophys., 164, 1121–1159, doi:10.1007/s00024-007-0211-x.
11. Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W.
23
D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, doi:10.1029/2008JD009944.
12. Jung, J.-H., and A. Arakawa, 2008: A Three-Dimensional Anelastic Model Based on the Vorticity Equation. Mon. Wea. Rev., 136, 276–294, doi:10.1175/2007MWR2095.1.
13. Krueger, S. K., Q. Fu, K. N. Liou, and H.-N. S. Chin, 1995: Improvements of an Ice-Phase Microphysics Parameterization for Use in Numerical Simulations of Tropical Convection. Journal of Applied Meteorology, 34, 281–287, doi:10.1175/1520-0450-34.1.281.
14. Liang, Y.-L., T.-C. Lin, J.-L. Hwong, N.-H. Lin, and C.-P. Wang, 2009: Fog and Precipitation Chemistry at a Mid-land Forest in Central Taiwan. Journal of Environment Quality, 38, 627, doi:10.2134/jeq2007.0410.
15. Lin C.-A., 2015: The influence of near-surface boundary layer conditions on the advection fog. M.S. thesis, Department of Atmospheric Sciences, National Taiwan University, Taiwan, 62 pp.
16. Lu, S.-Y., K.-J., Tang, H.-Y., Ku, and H.-H., Huang, 2000: Climatic Conditions of Forested Lands of Taiwan Forestry Research Institute. Taiwan Journal of Forest Science, 15(3), 429–440.
17. Nowak, D., D. Ruffieux, J. L. Agnew, and L. Vuilleumier, 2008: Detection of Fog and Low Cloud Boundaries with Ground-Based Remote Sensing Systems. Journal of Atmospheric and Oceanic Technology, 25, 1357–1368, doi:10.1175/2007JTECHA950.1.
18. Scholl, M., W. Eugster, and R. Burkard, 2011: Understanding the role of fog in forest hydrology: stable isotopes as tools for determining input and partitioning of cloud water in montane forests. Hydrological Processes, 25, 353–366, doi:10.1002/hyp.7762.
19. Schulz, H. M., B. Thies, S.-C. Chang, and J. Bendix, 2015: Detection of ground fog in mountainous areas from MODIS day-time data using a statistical approach.
Atmospheric Measurement Techniques Discussions, 8, 12155–12201, doi:10.5194/amtd-8-12155-2015.
20. Schulz, H. M., C.-F. Li, B. Thies, S.-C. Chang, and J. Bendix, 2017: Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data. PLOS ONE, 12, e0172663, doi:10.1371/journal.pone.0172663.
21. Shutts, G. J., and M. E. B. Gray, 1994: A numerical modelling study of the geostrophic adjustment process following deep convection. Quarterly Journal of the
Royal Meteorological Society, 120, 1145–1178, doi:10.1002/qj.49712051903.
22. Still, C. J., P. N. Foster, and S. H. Schneider, 1999: Simulating the effects of climate change on tropical montane cloud forests. Nature, 398, 608.
23. Terra, R., 2004: PBL Stratiform Cloud Inhomogeneities Thermally Induced by the Orography: A Parameterization for Climate Models. J. Atmos. Sci., 61, 644–663, doi:10.1175/1520-0469(2004)061<0644:PSCITI>2.0.CO;2.
24. Vaisala Oyj Corporation, 2006: Vaisala Ceilometer CL31 Users Guide, Vaisala Oyj Corporation, Vantaa, Finland.
25. Wey, T.-H., Y.-J., Lai, C.-S., Chang, C.-W., Shen, C.-Y., Hong, Y.-N. Wand, and M.-C., Chen, 2011: Preliminary Studies on Fog Characteristics at Xitou Region of Central Taiwan, Journal of the Experimental Forest of National Taiwan University, 25:149–160, Chinese.
26. Wey, T.-H., Y.-J., Lai, M.-C., Chen, and P.-H., Lin, 2016: The Studies on the Relationship Between Mountain Valley Breeze and Upslope Fog at Xitou Region in Central Taiwan, in: Proceeding of the 7th International Conference on Fog, Fog Collection and Dew, Wroclaw, Poland, 24-29 July 2016.
27. Whiteman, C. D., 2000: Mountain Meteorology: Fundamentals and Applications.
Oxford University Press, 376 pp.
28. Wilson, A. M., and A. P. Barros, 2015: Landform controls on low level moisture convergence and the diurnal cycle of warm season orographic rainfall in the Southern Appalachians. Journal of Hydrology, 531, 475–493, doi:10.1016/j.jhydrol.2015.10.068.
29. Wilson, A. M., and A. P. Barros, 2017: Orographic Land–Atmosphere Interactions and the Diurnal Cycle of Low-Level Clouds and Fog. Journal of Hydrometeorology, 18, 1513–1533, doi:10.1175/JHM-D-16-0186.1.
30. Wu, C.-M., and A. Arakawa, 2011: Inclusion of Surface Topography into the Vector Vorticity Equation Model (VVM): Inclusion of Surface Topography into the VVM, Journal of Advances in Modeling Earth Systems, 3(2), doi:10.1029/2011MS000061.
31. Wu, C.-M., H.-C. Lin, F.-Y. Cheng, and M.-H. Chien, 2019: Implementation of the land surface processes into a vector vorticity equation model (VVM) to study its impact on afternoon thunderstorms over complex topography in Taiwan. Asia-Pacific J. Atmos. Sci., accepted.
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Tables
Table 1. The setting of the inversion strength for four idealized experiments. The differences of the potential temperature are set in between 2700 m to 2900 m of the initial profiles.
EXP Ɵ
TH03 3K
TH05 5K
TH08 8K
TH10(CTRL) 10K
Figures
Figure 1. Xitou is located on the west side of the central mountain range of Taiwan. It is a valley opens to its north. The red triangular represents the location of observations used in this study.
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Figure 2. The examples of the backscattering profile observations by Vaisala ceilometer
CL31 when detecting cloud (left) and the fog (right). The red line on the left panel shows the detected cloud base height by ceilometer. When the maximum backscattering intensity is on the ground so that the ceilometer couldn’t detect the cloud base, it will label the
‘DETECTION STATUS' of this data as 4 or more, which is taken as the fog observed on the valley surface in this study.
Figure 3. Diurnal variation of fog frequency by ceilometer cloud base detection (blue bar)
and visibility detection (yellow bar) at Xitou for January 2016. The grey shaded areas represent night time at Xitou. The frequency is defined by the ratio of the amount of data that detecting the fog by instruments and the total observation data count in that hour over the whole January (31 days). By visibility sensor, the fog is defined as the horizontal visibility less than 1 km.
nighttime daytime
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Figure 4. The synoptic surface weather map on Jan 7th, 2016 published by the Central
Weather Bureau of Taiwan. The center of Siberian High was at about 49⁰N while its anti-circulation covers almost entire East Asia. The prevailing winds around Taiwan were northeastern winds; it was about 15 knots over the east coast of Taiwan while less than 5 knots on the Xitou valley and western plain of Taiwan. The weather of the Xitou valley and western plain of Taiwan was somewhat stable without the influence of the synoptic weather system, which resulted in a weather regime mainly controlled by the local effects of complex topography.
Figure 5. The evolutions of backscatter profile (upper panel), surface temperature (red
line in the middle panel), surface water vapor mixing ratio (blue line in the middle panel), and surface wind (lower panel) on Jan 7th, 2016. The ‘-‘ and ‘x’ markers in the upper panel represent low-level cloud base height and the fog detected by the ceilometer. It shows that from the early morning to the noon, the sky of Xitou is cloudless, so that backscattering profiles show little signals and the temperature rose from around 12 ⁰C (9 am) to 20 ⁰C (12 pm). The low-level clouds formed between 12 and 1 pm and the ceilometer detected the cloud base at around 400m above the ground. The cloud base was lowering gradually to the ground resulted in fog formation at Xitou valley at 2:20 pm.
The fog sustained for about 4 hours then the backscattering profiles indicate the cloud base elevated after 6 pm. In the lower panel, the local circulation is mainly dominated by the mountain-valley system, in which the mountain winds (SSE winds) blow through the
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entire nighttime and the morning while the valley winds (NNW winds) initiated at 11 am and continued to late afternoon then turned to the mountain wind at 7 pm. The evolution of surface water vapor mixing ratio (middle panel) also shows that after the low-level clouds formation the moisture of valley surface air was still increasing until 3 pm.
Figure 6. The skew-T plot of sounding at Ishigakijima, Japan on Jan. 7th, 2016. The
inversion strength is objectively defined by the temperature inversion at 2700 m. The potential temperature difference between 2700 m to 2900 m is about 10K and the water vapor mixing ratio drop dramatically from 6 g kg-1 to less than 0.5 g kg-1. The inversion strength is set to 50 K km-1 for our experiment.
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Figure 7. The initial potential temperature (left) and water vapor mixing ratio (right)
profiles for idealized experiments. The inversion strength in four experiments is set to be 10K (red), 8K (orange), 5K(green), and 3K (blue) from 2700 m to 2900 m. The inversion strength is 50 K km-1, 40 K km-1, 25 K km-1, and 15 K km-1 respectively. The water vapor mixing ratio profiles in four experiments are set to be the same (black line on the right panel). The 50 K km-1 inversion strength potential temperature and water vapor mixing ratio profiles are idealized from the sounding at Ishigakijima, Japan on Jan. 7th, 2016 (black dash lines in both panels) so that the simulation of TH10 (CTRL) can represent the local topographic effects of our studying fog event on that day.
Figure 8. The x-z plane flow vector and the cloud water mixing ratio distribution along
the cross-section of the Xitou valley at 12 pm in experiment TH10(CTRL). It shows that the leading edge of valley winds lifted by valley bottom surface then induced convections.
The capping inversion layer on 3200m limited the convections so that the cloud water accumulated around 2000 to 3200 m.
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Figure 9. The wind fields (barbs) and water vapor mixing ratio (shaded) on the bottom
of the valley (1300 m, upper row) and the top of the valley (2000 m, lower row) at 10 am (left column) and 3 pm (right column) in experiment TH10(CTRL). The gray shaded areas and contours in the upper panels represent the topography of ridges higher than 1300 m surrounding the Xitou valley. The black (red) boxes in the upper (lower) panels show the Xitou valley area where moisture could be trapped. The upper panels show that the persistent valley winds on the surface of the valley carried moisture into the valley so that the water vapor mixing ratio increased in the lower part of the valley from 10 am (upper left panel) to 3 pm (upper right panel). The lower panels show that on the level of 2000
m, which is higher than the surrounding ridges, the moisture also increased from 10 am (lower left panel) to 3 pm (lower right panel). The wind fields also show that the depth of valley winds are shallow and only exist on the surface of the valley.
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Figure 10. The evolutions of the liquid water path (red line) and precipitation (green line
in the upper panel), liquid water profile along with cloud base height (middle panel), and surface valley wind strength (lower panel) of the valley in experiment TH10(CTRL). The continuous valley winds initiated right after sunrise and strengthened in the daytime (lower panel). The convections initiated by valley winds resulted in the condensation of the cloud liquid water (middle panel). The dense cloud liquid water accumulated right below strong inversion at the level of 3200 m at the beginning so that the cloud base height (red dashed line) and its hourly mean (black line in the middle panel) is at around 2000 m in the morning. Then with more liquid water condensed in the late afternoon, cloud base height lowered to the ground at around 6 pm. The upper panel shows that the precipitation and liquid water path increased along with the lowering of cloud base height in the late afternoon.
Figure 11. The three-dimensional flow structure over the Xitou complex terrain in
experiment TH10(CTRL). Different colors of streamlines represent separate air parcels sampled in the valley. It shows that the upslope valley winds converged into the valley surface. The air parcels uplifted by valley bottom topography and convections initiated.
The flows then twisted up while upward developing to the level of inversion. The capping inversion prohibited the further development of convections so that the air parcels flowed out the valley horizontally.
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Figure 12. The fog duration of four experiments. The experiment TH03 with the inversion
strength set to only 3 K within 200 m (15 K km-1 respectively) results in the fog duration lasting less than one hour. The fog durations are gradually increasing with the prescribing inversion strength systematically. With stronger inversion strengths as 8K and 10K, The resulting fog durations could be apparent longer than 7 hours.
Figure 13. The evolutions of liquid water path of experiment TH03, TH05, TH08, and
TH10. The liquid water path in the experiment TH03 is under 4 g m-2 in the whole simulation daytime period and shows no significant changes. On the other hand, the liquid water path increased approximately 7g m-2 in the late afternoon in both the experiments TH10 (CTRL) and TH08. The liquid water path in the experiment TH05 also gradually increased in the afternoon and the maximum value of it is in between of the results in experiment TH08 and TH03.
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Figure 14. The comparison of wind fields (barbs) and water vapor mixing ratio (shaded)
on the bottom of valley (1300m, upper row) and the top of valley (2000m, lower row) at 10 am (left column) and 3 pm (right column) between experiment TH03 (left), TH08 (middle), and TH10 (right) at 3 pm. The gray shaded areas and contours in the upper panels represent the topography of ridges higher than 1300 m surrounding the Xitou valley. The black (red) boxes in the upper (lower) panels show the Xitou valley area where moisture could be trapped. The water vapor mixing ratio of experiment TH03 is the driest case whether on the bottom (upper panels) or top (lower panels) of the valley while the wind fields on the bottom of the valley (upper panels) suggest that the valley winds in the experiment TH03 is comparable with experiment TH08 and TH10 (CTRL).
Figure 15. The potential temperature (left), water vapor mixing ratio (middle), and liquid
water mixing ratio (right) profiles of the boundary layer at 11 am of experiment TH03 (blue), TH05 (green), TH08(orange), and TH10 (red). The potential temperature profile of experiment TH03 shows that the prescribe inversion had been smeared so that convections could develop up to higher than 3000 m and more moisture is carried upward to a more upper atmosphere. On the contrast, the potential temperature profiles of the other experiments show that the inversion strengths are strong enough to limit convections so that the condensed liquid water only exists below the inversion layer and the water vapor suddenly dropped above the inversion level.
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Figure 16. A schematic of the upslope fog formation processes at Xitou valley. The
upslope winds and the convections moisten the valley boundary layer so that the low-level clouds form and consequently reduce the insolation. With the capping inversion to confine the moisture in the valley, the accumulation of moisture keeps lowering the cloud base to the ground become the fog.