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Analyzing Cloud Base at Local and Regional Scales to Understand Tropical Montane Cloud Forest Vulnerability to Climate Change

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Analyzing Cloud Base at Local and Regional Scales to Understand Tropical Montane Cloud Forest Vulnerability to Climate Change Atmos. Chem. Phys., 17, 7245–7259, 2017 https://doi.org/10.5194/acp-17-7245-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Analyzing cloud base at local and regional scales to understand tropical montane cloud forest vulnerability to climate change Ashley E. Van Beusekom1, Grizelle González1, and Martha A. Scholl2 1USDA Forest Service International Institute of Tropical Forestry, Jardín Botánico Sur, 1201 Calle Ceiba, Río Piedras, Puerto Rico 00926, USA 2US Geological Survey National Research Program, 12201 Sunrise Valley Drive, Reston, VA 20192, USA Correspondence to: Ashley E. Van Beusekom ([email protected], [email protected]) Received: 23 December 2016 – Discussion started: 7 February 2017 Revised: 5 May 2017 – Accepted: 8 May 2017 – Published: 16 June 2017 Abstract. The degree to which cloud immersion provides indicate that climate change threats to low-elevation TMCFs water in addition to rainfall, suppresses transpiration, and are not limited to the dry season; changes in synoptic-scale sustains tropical montane cloud forests (TMCFs) during rain- weather patterns that increase frequency of drought periods less periods is not well understood. Climate and land use during the wet seasons (periods of higher cloud base) may changes represent a threat to these forests if cloud base alti- also impact ecosystem health. tude rises as a result of regional warming or deforestation. To establish a baseline for quantifying future changes in cloud base, we installed a ceilometer at 100 m altitude in the forest upwind of the TMCF that occupies an altitude range from 1 Introduction ∼ 600 m to the peaks at 1100 m in the Luquillo Mountains of eastern Puerto Rico. Airport Automated Surface Observing Mountains play a key role in collecting atmospheric moisture System (ASOS) ceilometer data, radiosonde data, and Cloud- in tropical regions (Wohl et al., 2012). Around 500 tropical Aerosol Lidar and Infrared Pathfinder Satellite Observation montane cloud forests (TMCFs) have been identified world- (CALIPSO) satellite data were obtained to investigate sea- wide on mountains with frequent cloud cover; these can be sonal cloud base dynamics, altitude of the trade-wind inver- at higher elevation (on larger mountains) which have lower sion (TWI), and typical cloud thickness for the surrounding temperatures or at lower elevation (on smaller mountains) Caribbean region. Cloud base is rarely quantified near moun- which have more rainfall (Jarvis and Mulligan, 2011). The tains, so these results represent a first look at seasonal and di- global set of TMCFs are almost all within 350 km of a coast urnal cloud base dynamics for the TMCF. From May 2013 to and topographically exposed to higher-humidity air (Jarvis August 2016, cloud base was lowest during the midsummer and Mulligan, 2011). Smaller mountains are likely to have dry season, and cloud bases were lower than the mountain- clouds at lower elevations due to slightly higher adiabatic tops as often in the winter dry season as in the wet seasons. lapse rates (more temperature loss with the same elevation The lowest cloud bases most frequently occurred at higher gain) than larger mountains, which undergo greater heating elevation than 600 m, from 740 to 964 m. The Luquillo for- of the land mass (the mass-elevation effect: Foster, 2001; est low cloud base altitudes were higher than six other sites in Jarvis and Mulligan, 2011). This effect and the higher humid- the Caribbean by ∼ 200–600 m, highlighting the importance ity near the ocean support TMCFs on small coastal moun- of site selection to measure topographic influence on cloud tains. height. Proximity to the oceanic cloud system where shallow Up to 60 % of the moisture input to TMCFs is derived cumulus clouds are seasonally invariant in altitude and cover, from cloud water interception from low clouds (Bruijnzeel along with local trade-wind orographic lifting and cloud for- et al., 2011), but this value varies greatly from forest to for- mation, may explain the dry season low clouds. The results est and within an individual forest. Cloud water has been deemed critical for the health of TMCFs, specifically for Published by Copernicus Publications on behalf of the European Geosciences Union. 7246 A. E. Van Beusekom et al.: Analyzing cloud base at local and regional scales the abundant epiphytes which require consistent moisture in- 2 Study area put from the atmosphere (Gotsch et al., 2015). Cloud wa- ter interception (including fog) adds moisture directly to the The Luquillo Experimental Forest (LEF) is located in the soil through the process of canopy interception and fog drip northern tropics at 18.3◦ N, 65.7◦ W. The LEF area is in the (Giambelluca et al., 2011), and indirectly alters the mois- Luquillo Mountains on the eastern end of the island of Puerto ture budget through foliar uptake (Eller et al., 2013), lower- Rico; these mountains are no more than about 13 km from ing the saturation deficit of the atmosphere and suppressing the coastline (with the Atlantic Ocean to the north and east transpiration (Alvarado-Barrientos et al., 2014). For the low- and the Caribbean Sea to the south). Maximum elevation elevation TMCF of this study, it might be expected that, given is 1077 m. High rainfall causes steep, dissected slopes, and a relatively constant seasonal temperature lapse rate (within the ridges and stream valleys are covered by undeveloped 0.1 ◦C/1000 m for each season using previous study data; Van tropical forest. Weather and clouds in the Luquillo Moun- Beusekom et al., 2015), wet seasons would have higher rela- tains follow patterns typical of the Caribbean region, which is tive humidity caused by the larger amounts and spatial extent dominated by the easterly trade winds (Malkus, 1955; Odum of rain events and therefore lower clouds. Yet, previous field- and Pigeon, 1970; Taylor and Alfaro, 2005). Annual trade based studies at four different low-elevation TMCFs found winds are driven by an interplay of the North Atlantic Sub- that similar or higher absolute amounts of fog precipitation tropical High (NASH) sea level pressure system and the In- were deposited during dry season measurements than during tertropical Convergence Zone (ITCZ) position (Giannini et wet-season measurements (Cavelier and Goldstein, 1989). al., 2000). In the northern hemisphere summer, the ITCZ With their large energy inputs and fast rates of spatial and moves to its northern position in the southwest Caribbean, temporal change, tropical regions are some of the most dy- weakening the trade winds and giving way to the progres- namic atmospheric and hydrologic systems on Earth and thus sion of tropical easterly low-pressure waves, which help to may be significantly affected by climate change (Perez et al., create a Caribbean wet season from April or May through 2016; Wohl et al., 2012). Regional cloud mass has been pro- November (Gouirand et al., 2012). During winter the NASH jected to decline with continued deforestation in maritime extends westward, strengthening the trade winds and sup- climates (van der Molen et al., 2006), and warming tempera- pressing convection to create a cooler dry season from De- tures and urbanization could raise cloud base in the global cember through March (Gouirand et al., 2012). The CLLJ set of TMCFs (Foster, 2001; Still et al., 1999; Williams is a strengthening of the trade winds in June and July, sepa- et al., 2015), which could lead to some low-elevation TM- rating the rain season into early and late with a warm drier CFs losing cloud immersion. In the Caribbean, the wet sea- season called the midsummer drought (Taylor et al., 2013). sons are projected to be less wet by the end of the century Trade-wind cumuli over the ocean are found within the trade- (Karmalkar et al., 2013). Specifically, (1) shallow convec- wind moist layer, limited above by the trade-wind inversion tion over Caribbean mountains in the early rain season may (TWI) and below by the lifting condensation level (LCL; decrease if the trade winds continue to strengthen and shift Stevens, 2005). Intra-annual variation in oceanic cloud cover direction, changing sea breeze dynamics and weakening oro- is dampened as an estimated two-thirds of the cloud cover graphic lift (Comarazamy and González, 2011); and (2) the comes from annually consistent clouds near the LCL; the regional late rain season may shorten due to strengthening of other third is from seasonally changing clouds higher aloft the Caribbean low-level jet (CLLJ; Taylor et al., 2013). (Nuijens et al., 2014). A pattern of lower clouds (bases and The goal of this study was to determine cloud base al- tops) over the ocean and higher clouds over the land, with a titude and frequency of occurrence of low clouds during stronger diurnal effect of convection above land, has been ob- dry and wet seasons at a low-elevation tropical forest in served in the tropics with Cloud-Aerosol Lidar and Infrared the Luquillo Mountains, Puerto Rico, to establish a base- Pathfinder Satellite Observation (CALIPSO; Medeiros et al., line against which to quantify future change. In addition, 2010). However, with overpasses every 16 days, satellite data ceilometer, radiosonde, and satellite data for the northern do not provide the level of temporal and spatial resolution Caribbean were analyzed to determine how the surround- required to effectively study the clouds at a TMCF (Costa- ing region influences the cloud patterns over ocean and land. Surós et al., 2013). Satellite data in this study are used to Ceilometer data that are available to the research community generalize the ceilometer findings and quantify patterns of are usually collected at airports, not in mountainous areas, cloud top height and cloud thickness in the region. and furthermore do not specifically quantify cloud frequency Experiments and long-term monitoring in the Luquillo (Schulz et al., 2016).
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