Diurnal Temperature Cycle Research Articles

Regional Soil Moisture Biases and Their Influence on WRF Model Temperature Forecasts over the Intermountain West

Abstract Operational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in situ soil moisture observations. Bias correction of these soil moisture analyses using data from 42 North American Soil Moisture Database stations throughout the Intermountain West reduces both the afternoon and early morning bias errors and improves forecasts of upper-level temperature and stability. These results illustrate that the assimilation of in situ and remotely sensed soil moisture observations, including those from the recently launched NASA Soil Moisture Active Passive (SMAP) mission, have the potential to greatly improve land surface analyses and near-surface temperature forecasts over arid regions.

Annual Climatology of the Diurnal Cycle on the Canadian Prairies

We show the annual climatology of the diurnal cycle, stratified by opaque cloud, using the full hourly resolution of the Canadian Prairie data. The opaque cloud field itself has distinct cold and warm season diurnal climatologies; with a near-sunrise peak of cloud in the cold season and an early afternoon peak in the warm season. There are two primary climate states on the Canadian Prairies, separated by the freezing point of water, because a reflective surface snow cover acts as a climate switch. Both cold and warm season climatologies can be seen in the transition months of November, March and April with a large difference in mean temperature. In the cold season with snow, the diurnal ranges of temperature and relative humidity increase quasi-linearly with decreasing cloud, and increase from December to March with increased solar forcing. The warm season months, April to September, show a homogeneous coupling to the cloud cover, and a diurnal cycle of temperature and humidity that depends only on net longwave. Our improved representation of the diurnal cycle shows that the warm season coupling between diurnal temperature range and net longwave is weakly quadratic through the origin, rather than the linear coupling shown in earlier papers. We calculate the conceptually important 24-h imbalances of temperature and relative humidity (and other thermodynamic variables) as a function of opaque cloud cover. In the warm season under nearly clear skies, there is a warming of +2oC and a drying of -6% over the 24-h cycle, which is about 12% of their diurnal ranges. We summarize results on conserved variable diagrams and explore the impact of surface windspeed on the diurnal cycle in the cold and warm seasons. In all months, the fall in minimum temperature is reduced with increasing windspeed, which reduces the diurnal temperature range. In July and August, there is an increase of afternoon maximum temperature and humidity at low windspeeds, and a corresponding rise in equivalent potential temperature of 4.4K that appears coupled to increased precipitation. However overcast skies are associated with the major rain events and higher windspeeds.

The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set

Abstract. Here, the authors describe the construction of a forcing data set for land surface models (including both physical and biogeochemical models; LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison study, called GTMIP, which is a part of the Japanese-funded Arctic Climate Change Research Project. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries (i.e. without missing data), four observational sites across the pan-Arctic region (Fairbanks, Tiksi, Yakutsk, and Kevo) were selected to construct a blended data set using both global reanalysis and observational data. Marked improvements were found in the diurnal cycles of surface air temperature and humidity, wind speed, and precipitation. The data sets and participation in GTMIP are open to the scientific community (doi:10.17592/001.2015093001).

Impact of climate change on the timing of strawberry phenological processes in the Baltic States

Climate change has been shown to impact aspects of agriculture and phenology. This study aims to quantify changes in the timing of garden strawberry blooms and harvests in the Baltic States using Regional Climate Models (RCMs). First, parameters for a strawberry phenology model based on the growing degree day (GDD) methodology were determined. Growing degree days were calculated using a modified sine wave method that estimates the diurnal temperature cycle from the daily maximum and minimum temperature. Model parameters include the base temperature and the required cumulative GDD sum, estimated from phenological and meteorological observations in Latvia for the years 2010-2013 via iterative calibration. Then an ensemble of bias-corrected RCM results (ENSEMBLES project) was used as input to the phenological model to estimate the timing of strawberry phenological processes for the years 1951-2099. The results clearly show that strawberry phenological processes can be expected to occur earlier in the future, with a significant change in regional patterns. Differences between coastal and inland regions are expected to decrease over time. The uncertainty of the results was estimated using the RCM ensemble spread, with northern coastal locations showing the largest spread.

How to report biogas production when monitoring small-scale digesters in field

The aim of this research was to evaluate the error originated when biogas production from field monitoring digesters, influenced by the diurnal temperature cycle, was normalized to standard conditions for pressure and temperature (273.15 K and 100 kPa) from local conditions. The biogas production data is often reported without indicating if done under local conditions, whether these conditions have been standardized and, if they have actually been standardized, the standard temperature and pressure is not indicated. In this research ambient and biogas temperature, as well as biogas production were monitored with a 30 min frequency during three consecutive days, in three different tubular digesters. Normalization was realized using the high frequency data collected as reference values, and also using daily biogas production with mean daily biogas, ambient and nearby meteorological station temperatures. The outcome of this research shows that normalization of biogas production can be obtained using daily biogas production and the daily mean ambient temperature with an overestimation by no more than 1.5%, in comparison to the normalization achieved by using high frequency data from biogas temperature and production. Using mean daily ambient temperature or mean daily biogas temperature results in the same overestimation, while using mean daily ambient temperature from a nearby airport weather station pushes the overestimation up to 2.7%. So, if ambient temperature and altitude is identified, biogas production reported in local conditions can be normalized.

A multilevel ocean mixed layer model resolving the diurnal cycle: Development and validation

AbstractThe representation of transient air‐sea interactions is critical to the prediction of the sea surface temperature diurnal cycle and daily variability. This study develops a multilevel upper ocean model to more realistically resolve these interactions. The model is based on the one‐dimensional turbulence kinetic energy closure developed by Noh et al. [2011], and incorporates new numerical techniques and improved schemes for model physics. The primary improvements include: (1) a surface momentum flux penetration scheme to better depict velocity shear in the diurnal mixed layer; (2) a solar penetration scheme to improve the penetration of visible and near‐infrared bands of solar radiation into the mixed layer ocean; (3) a scheme to resolve the cool‐skin and warm‐layer effects on sea skin temperature; (4) a vertical grid stretch scheme to achieve higher near‐surface resolution with fewer vertical levels; (5) a trapezoidal time integration scheme for flexible time steps; (6) a relaxation term of the previous daily mean difference between observed and modeled sea surface temperature. According to the numerical experiments based on the TOGA‐COARE IMET mooring buoy data and the validation by observations from the National Data Buoy Center, NOAA, the results indicate that the new upper ocean mixed layer model improves the simulation of the diurnal cycle of SST and sea skin temperature, especially in amplitude.

What Controls Seasonal Variations of the Diurnal Cycle of Sea Surface Temperature in the Eastern Tropical Indian Ocean?*

Abstract Recent in situ buoy observations revealed interesting seasonal features of the diurnal sea surface temperature cycle (DSST) in the eastern tropical Indian Ocean. Composite analysis shows that areas away from the equator exhibit stronger seasonal variations of DSST, while weaker seasonal variations appear near the equator. The most interesting characteristic is the distinctive contrast of the seasonal variations of DSST between the Bay of Bengal (BOB) and the region south of the equator (particularly around 12°S). While the range of DSST is weakest in the BOB during boreal summer, it has its largest range around 12°S in austral summer. Furthermore, BOB DSST exhibits two peaks that occur during the monsoon transitions (March–April and October), whereas DSST south of the equator shows only a single peak in its annual cycle. Using a one-dimensional, oceanic, mixed layer model, the authors examined the cause of the distinctive annual cycles of DSST north and south of the equator. Two parallel experiments were conducted at buoy sites 12°N, 90°E and 12°S, 80.5°E driven by surface forcing from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) product. The results demonstrated that, in the BOB, both surface shortwave radiation and wind stress contribute to the March maximum, whereas the wind stress alone drives the October maximum. In contrast, the seasonal variation of DSST south of the equator is primarily caused by the annual cycle of the wind stress, which is extremely weak in austral summer near the intertropical convergence zone (ITCZ). How the monsoon and ITCZ modulate the distinctive annual cycles of DSST is discussed.

A Simplified Land Model (SLM) for use in cloud‐resolving models: Formulation and evaluation

AbstractA Simplified Land Model (SLM) that uses a minimalist set of parameters with a single‐layer vegetation and multilevel soil structure has been developed distinguishing canopy and undercanopy energy budgets. The primary motivation has been to design a land model for use in the System for Atmospheric Modeling (SAM) cloud‐resolving model to study land‐atmosphere interactions with a sufficient level of realism. SLM uses simplified expressions for the transport of heat, moisture, momentum, and radiation in soil‐vegetation system. The SLM performance has been evaluated over several land surface types using summertime tower observations of micrometeorological and biophysical data from three AmeriFlux sites, which include grassland, cropland, and deciduous‐broadleaf forest. In general, the SLM captures the observed diurnal cycle of surface energy budget and soil temperature reasonably well, although reproducing the evolution of soil moisture, especially after rain events, has been challenging. The SLM coupled to SAM has been applied to the case of summertime shallow cumulus convection over land based on the Atmospheric Radiation Measurements (ARM) Southern Great Plain (SGP) observations. The simulated surface latent and sensible heat fluxes as well as the evolution of thermodynamic profiles in convective boundary layer agree well with the estimates based on the observations. Sensitivity of atmospheric boundary layer development to the soil moisture and different land cover types has been also examined.

Extremum seeking control for efficient operation of hybrid ground source heat pump system

The Hybrid Ground Source Heat Pump (GSHP) systems combine the renewable geothermal energy and cooling tower for rejecting the cooling load, which is often adopted for high cooling demand. Model based control can be limited due to variations in ambient conditions, ground-loop heat exchanger (GHE) and equipment characteristics, cost and reliability of sensors. A self-optimizing control scheme is proposed for efficient operation of the hybrid GSHP based on Extremum Seeking Control (ESC), with feedback of the total power consumption and the control inputs of the relative flow rate of cooling tower and the water pump speed. The cooling capacity of the heat pump regulates the evaporator leaving water at 7 °C. A Modelica based dynamic simulation model is developed for a Hybrid GSHP system, with the vertical GHE model adopted from Modelica Buildings Library. The transient heat transfer is implemented with a finite volume method inside and outside the borehole. The proposed ESC scheme is evaluated under the scenarios of fixed cooling load, ramp change in the evaporator inlet water temperature, diurnal sinusoidal cycle of air wet-bulb temperature, and realistic ambient and cooling load condition. Simulation results show the proposed ESC strategy effectively achieves nearly optimal efficiency without the need for plant model.

Regional Contrasts of the Warming Rate over Land Significantly Depend on the Calculation Methods of Mean Air Temperature.

Global analyses of surface mean air temperature (Tm) are key datasets for climate change studies and provide fundamental evidences for global warming. However, the causes of regional contrasts in the warming rate revealed by such datasets, i.e., enhanced warming rates over the northern high latitudes and the “warming hole” over the central U.S., are still under debate. Here we show these regional contrasts depend on the calculation methods of Tm. Existing global analyses calculate Tm from daily minimum and maximum temperatures (T2). We found that T2 has a significant standard deviation error of 0.23 °C/decade in depicting the regional warming rate from 2000 to 2013 but can be reduced by two-thirds using Tm calculated from observations at four specific times (T4), which samples diurnal cycle of land surface air temperature more often. From 1973 to 1997, compared with T4, T2 significantly underestimated the warming rate over the central U.S. and overestimated the warming rate over the northern high latitudes. The ratio of the warming rate over China to that over the U.S. reduces from 2.3 by T2 to 1.4 by T4. This study shows that the studies of regional warming can be substantially improved by T4 instead of T2.

Diurnal temperature cycle deduced from extreme daily temperatures and impact over a surface reanalysis system

Abstract. Due to the evolution of the observation network, hourly 2 m temperature analysis performed by reanalysis systems shows temporal inhomogeneities. The observation network gap is less present for extreme daily temperature observations. In order to reduce inhomogeneities and enable a climatological use of temperature analysis, information from extreme temperatures could be useful. In this study, the diurnal temperature cycle has been reconstructed for stations which only record extreme temperatures. These new "pseudo" hourly temperature observations are then provided to the analysis system. Two methods have been used to deduce hourly temperatures from extremes and compared to real observations. The results have shown that using those new pseudo-observations as an input for two different reanalysis systems enables reducing the bias in temperature analysis.

Mitigating errors in surface temperature forecasts using approximate radiation updates

AbstractDue to computational expense, the radiation schemes in many weather and climate models are called infrequently in time and/or on a reduced spatial grid. The former can lead to a lag in the diurnal cycle of surface temperature, while the latter can lead to large surface temperature errors at coastal land points due to surface fluxes computed over the ocean being used where the skin temperature and surface albedo are very different. This paper describes a computationally efficient solution to these problems, in which the surface longwave and shortwave fluxes are updated every time step and grid point according to the local skin temperature and albedo. In order that energy is conserved, it is necessary to compute the change to the net flux profile consistent with the changed surface fluxes. The longwave radiation scheme has been modified to compute also the rate of change of the profile of upwelling longwave flux with respect to the value at the surface. Then at each grid point and time step, the upwelling flux and heating‐rate profiles are updated using the new value of skin temperature. The computational cost of performing approximate radiation updates in the ECMWF model is only 2% of the cost of the full radiation scheme, so increases the overall cost of the model by only of order 0.2%. Testing the new scheme by running daily 5 day forecasts over an 8 month period reveals significant improvement in 2 m temperature forecasts at coastal stations compared to observations.

High time and mass resolved PTR-TOF-MS measurements of VOCs at an urban site of India during winter: Role of anthropogenic, biomass burning, biogenic and photochemical sources

This study is based on the high mass and time-resolved measurements of seven VOCs using a PTR-TOF-MS instrument at an urban site of India during winter 2013. Daily levels of OVOCs and aromatics were in the ranges of 3.5–37ppbv and 0.85–23ppbv, respectively with OVOCs accounted for up to 80% of total measured VOCs. The impact of long-range transport from the polluted Indo-Gangetic Plain and clean Thar desert was observed during the episodes of high and low VOCs, respectively. VOCs exhibited strong diurnal variations with peaks during morning and evening hours and lowest in the afternoon. Relatively elevated aromatics during evening hours coincided with the lowest-OVOCs indicating influence of fresh vehicular emissions. Emission ratios of isoprene and OVOCs with respect to benzene followed the diurnal cycles of temperature and solar flux indicating role of biogenic and photochemical processes, respectively. Correlation study of VOCs with benzene suggests major contribution from anthropogenic and also from biogenic and secondary sources to some extent. The higher emission ratios of ∆methanol/∆acetonitrile correspond to the episodes of long-range transport from biomass burning sources located in the Indo-Gangetic Plain (IGP). In addition to the pattern of emission, the diurnal and day-to-day variations of VOCs were influenced by the local meteorological conditions and depth of planetary boundary layer (PBL).

An initial estimate of the global distribution of diurnal variation in sea surface salinity

AbstractDiurnal variations in sea surface salinity (SSS) have been observed at a few selected locations with adequate in situ instrumentation. Such variations result primarily from imbalances between surface freshwater fluxes and vertical mixing of deeper water to the surface. New observations becoming available from satellite salinity remote sensing missions could help to constrain estimates of diurnal variations in air‐sea exchange of freshwater, and provide insight into the processes governing diurnal variability of mixing processes in the upper ocean. Additionally, a better understanding of variation in near surface salinity is required to compare satellite measured SSS with in situ measurements at a few meters depth. The diurnal SSS variations should be reflected as differences between ascending and descending pass retrievals from the Aquarius and SMOS satellites; however, the diurnal signal can be masked by inadequacies of the geophysical corrections used in processing the satellite measurements. In this study, we quantify the expected range of diurnal SSS variations using a model developed for predicting diurnal sea surface temperature variations. We present estimates for the mean and variance of the global diurnal SSS cycle, contrasting it with the diurnal cycle of sea surface temperature. We find the SSS diurnal cycle can be significant throughout the tropics, with mean amplitudes of up to 0.1 psu in areas with heavy precipitation. Predicted maximum diurnal ranges approach 2 psu in select regions. Surface freshening in Aquarius salinity retrievals is shown to be larger for ascending than descending passes, consistent with the expectations from the model simulation.

Observational study of land-surface-cloud-atmosphere coupling on daily timescales

Our aim is to provide an observational reference for the evaluation of the surface and boundary layer parameterizations used in large-scale models using the remarkable long-term Canadian Prairie hourly dataset. First we use shortwave and longwave data from the Baseline Surface Radiation Network (BSRN) station at Bratt’s Lake, Saskatchewan, and clear sky radiative fluxes from ERA-Interim, to show the coupling between the diurnal cycle of temperature and relative humidity and effective cloud albedo and net longwave flux. Then we calibrate the nearby opaque cloud observations at Regina, Saskatchewan in terms of the BSRN radiation fluxes. We find that in the warm season, we can determine effective cloud albedo to ±0.08 from daytime opaque cloud, and net long-wave radiation to ±8 W/m2 from daily mean opaque cloud and relative humidity. This enables us to extend our analysis to the 55 years of hourly observations of opaque cloud cover, temperature, relative humidity, and daily precipitation from 11 climate stations across the Canadian Prairies. We show the land-surface-atmosphere coupling on daily timescales in summer by stratifying the Prairie data by opaque cloud, relative humidity, surface wind, day-night cloud asymmetry and monthly weighted precipitation anomalies. The multiple linear regression fits relating key diurnal climate variables, the diurnal temperature range, afternoon relative humidity and lifting condensation level, to daily mean net longwave flux, wind-speed and precipitation anomalies have R2 values between 0.61 and 0.69. These fits will be a useful guide for evaluating the fully coupled system in models.

Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area

Based on a novel bare surface soil moisture (SSM) retrieval model developed from the synergistic use of the diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR) (Leng et al. 2014. “Bare Surface Soil Moisture Retrieval from the Synergistic Use of Optical and Thermal Infrared Data”. International Journal of Remote Sensing 35: 988–1003.), this paper mainly investigated the model’s capability to estimate SSM using geostationary satellite observations over vegetated area. Results from the simulated data primarily indicated that the previous bare SSM retrieval model is capable of estimating SSM in the low vegetation cover condition with fractional vegetation cover (FVC) ranging from 0 to 0.3. In total, the simulated data from the Common Land Model (CoLM) on 151 cloud-free days at three FLUXNET sites that with different climate patterns were used to describe SSM estimates with different underlying surfaces. The results showed a strong correlation between the estimated SSM and the simulated values, with a mean Root Mean Square Error (RMSE) of 0.028 m3·m−3 and a coefficient of determination (R2) of 0.869. Moreover, diurnal cycles of LST and NSSR derived from the Meteosat Second Generation (MSG) satellite data on 59 cloud-free days were utilized to estimate SSM in the REMEDHUS soil moisture network (Spain). In particular, determination of the model coefficients synchronously using satellite observations and SSM measurements was explored in detail in the cases where meteorological data were not available. A preliminary validation was implemented to verify the MSG pixel average SSM in the REMEDHUS area with the average SSM calculated from the site measurements. The results revealed a significant R2 of 0.595 and an RMSE of 0.021 m3·m−3.

A comparative high-altitude meteorological analysis from three catchments in the Nepalese Himalaya

Meteorological studies in high-mountain environments form the basis of our understanding of catchment hydrology and glacier accumulation and melt processes, yet high-altitude (>4000 m above sea level, asl) observatories are rare. This research presents meteorological data recorded between December 2012 and November 2013 at seven stations in Nepal, ranging in elevation from 3860 to 5360 m asl. Seasonal and diurnal cycles in air temperature, vapour pressure, incoming short-wave and long-wave radiation, atmospheric transmissivity, wind speed, and precipitation are compared between sites. Solar radiation strongly affects diurnal temperature and vapour pressure cycles, but local topography and valley-scale circulations alter wind speed and precipitation cycles. The observed diurnal variability in vertical temperature gradients in all seasons highlights the importance of in situ measurements for melt modelling. The monsoon signal (progressive onset and sharp end) is visible in all data-sets, and the passage of the remnants of Typhoon Phailin in mid-October 2013 provides an interesting case study on the possible effects of such storms on glaciers in the region.

Aging of secondary organic aerosol generated from the ozonolysis of α-pinene: effects of ozone, light and temperature

Abstract. A series of experiments was conducted in the CESAM (French acronym for Experimental Multiphasic Atmospheric Simulation Chamber) simulation chamber to investigate the evolution of the physical and chemical properties of secondary organic aerosols (SOAs) during different forcings. The present experiments represent a first attempt to comprehensively investigate the influence of oxidative processing, photochemistry, and diurnal temperature cycling upon SOA properties. SOAs generated from the ozonolysis of α-pinene were exposed under dry conditions (< 1% relative humidity) to (1) elevated ozone concentrations, (2) light (under controlled temperature conditions) or (3) light and heat (6 °C light-induced temperature increase), and the resultant changes in SOA optical properties (i.e. absorption and scattering), hygroscopicity and chemical composition were measured using a suite of instrumentation interfaced to the CESAM chamber. The complex refractive index (CRI) was derived from integrated nephelometer measurements of 525 nm wavelength, using Mie scattering calculations and measured number size distributions. The particle size growth factor (GF) was measured with a hygroscopic tandem differential mobility analyzer (H-TDMA). An aerosol mass spectrometer (AMS) was used for the determination of the f44 / f43 and O : C ratio of the particles bulk. No change in SOA size or chemical composition was observed during O3 and light exposure at constant temperature; in addition, GF and CRI of the SOA remained constant with forcing. On the contrary, illumination of SOAs in the absence of temperature control led to an increase in the real part of the CRI from 1.35 (±0.03) to 1.49 (±0.03), an increase of the GF from 1.04 (±0.02) to 1.14 (±0.02) and an increase of the f44 / f43 ratio from 1.73 (±0.03) to 2.23 (±0.03). The simulation of the experiments using the master chemical mechanism (MCM) and the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) shows that these changes resulted from the evaporation of semi-volatile and less oxidized SOA species induced by the relatively minor increases in temperature (~ 6 °C). These surprising results suggest that α-pinene–O3 SOA properties may be governed more by local temperature fluctuations than by oxidative processing and photochemistry.

The Effects of the Trade Winds on the Distribution of Relative Humidity and the Diurnal Air Temperature Cycle on Oceanic Tropical Islands

On many oceanic tropical islands the trade winds blow from the east and as the air passes over the island it loses moisture to rain. The two hypotheses for this study are that the western part of the island is less humid and has a larger diurnal air temperature cycle. Using data available on the Internet, the two hypotheses were assayed 7 times on 6 different islands in the Pacific and Caribbean. The islands used in this study are Puerto Rico, Hispaniola, the Big Island of Hawaii, Maui, Oahu, and Kauai. The first hypothesis was tested only on Hispaniola and is true. The second hypothesis was tested on all the islands and is true on all the islands except on the Big Island of Hawaii and Maui. Using a p-value of 0.05, these hypotheses are as predicted 5 of the 7 times, which has a p-value of 6 x 10-6. These findings should apply to thousands oceanic tropical islands where data are sparse.

The Effects of the Trade Winds on the Distribution of Relative Humidity and the Diurnal Air Temperature Cycle on Oceanic Tropical Islands

On many oceanic tropical islands the trade winds blow from the east and as the air passes over the island it loses moisture to rain. The two hypotheses for this study are that the western part of the island is less humid and has a larger diurnal air temperature cycle. Using data available on the Internet, the two hypotheses were assayed 7 times on 6 different islands in the Pacific and Caribbean. The islands used in this study are Puerto Rico, Hispaniola, the Big Island of Hawaii, Maui, Oahu, and Kauai. The first hypothesis was tested only on Hispaniola and is true. The second hypothesis was tested on all the islands and is true on all the islands except on the Big Island of Hawaii and Maui. Using a p-value of 0.05, these hypotheses are as predicted 5 of the 7 times, which has a p-value of 6 x 10-6. These findings should apply to thousands oceanic tropical islands where data are sparse.